{ "release": "Markaigen Benchmark Intelligence v0.6", "status": "evidence_plus_modelled", "evidence_cutoff": "2026-10-08", "synthetic_population_n": 100000, "simulation_seed": 20261008, "warning": "Synthetic records are modelled estimates, not respondents or real companies. Never report synthetic_n as a survey sample size.", "rows": [ { "market": "Netherlands", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 63.8, "p25": 37.8, "p75": 76.8, "synthetic_n": 50000, "histogram": [ 2153, 3658, 4150, 3150, 2916, 5656, 9263, 9207, 6186, 3661 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 33.2% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 42.4, "p25": 30.1, "p75": 54.0, "synthetic_n": 50000, "histogram": [ 1772, 3634, 6933, 10009, 11025, 9143, 5173, 1819, 451, 41 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.9, "p25": 43.1, "p75": 77.8, "synthetic_n": 50000, "histogram": [ 1226, 2656, 2825, 3854, 7062, 8119, 6342, 7042, 6658, 4216 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "Observed marketing-sector anchors: 66% AI policy; 56% data governance (DDMA 2026, n=436).", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 39.7, "p25": 27.0, "p75": 52.1, "synthetic_n": 50000, "histogram": [ 2796, 4635, 7776, 10053, 10303, 8008, 4387, 1637, 362, 43 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 42.3, "p25": 28.9, "p75": 55.0, "synthetic_n": 50000, "histogram": [ 2478, 4031, 6810, 9174, 10231, 8671, 5354, 2350, 736, 165 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 42.3, "p25": 29.3, "p75": 55.0, "synthetic_n": 50000, "histogram": [ 2214, 3949, 6927, 9469, 10304, 8686, 5267, 2342, 688, 154 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 62.3, "p25": 34.6, "p75": 75.1, "synthetic_n": 39838, "histogram": [ 1939, 3209, 3518, 2523, 2325, 4768, 7604, 7090, 4439, 2423 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 39.9, "p25": 28.0, "p75": 51.2, "synthetic_n": 39838, "histogram": [ 1683, 3416, 6237, 8664, 8902, 6765, 3210, 832, 123, 6 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 57.5, "p25": 42.0, "p75": 76.5, "synthetic_n": 39838, "histogram": [ 1098, 2280, 2367, 3215, 5858, 6547, 5043, 5454, 5035, 2941 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 37.8, "p25": 25.3, "p75": 49.8, "synthetic_n": 39838, "histogram": [ 2589, 4153, 6737, 8338, 8146, 5842, 2882, 962, 172, 17 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 40.6, "p25": 27.3, "p75": 53.2, "synthetic_n": 39838, "histogram": [ 2266, 3581, 5872, 7645, 8159, 6498, 3770, 1535, 419, 93 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 39.8, "p25": 27.2, "p75": 52.0, "synthetic_n": 39838, "histogram": [ 2103, 3652, 6171, 8138, 8260, 6460, 3483, 1239, 293, 39 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 69.1, "p25": 49.0, "p75": 81.4, "synthetic_n": 8636, "histogram": [ 195, 416, 559, 531, 510, 790, 1469, 1786, 1407, 973 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 46.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 51.0, "p25": 39.7, "p75": 61.6, "synthetic_n": 8636, "histogram": [ 86, 206, 663, 1236, 1908, 2043, 1586, 704, 189, 15 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 64.1, "p25": 47.3, "p75": 81.0, "synthetic_n": 8636, "histogram": [ 118, 342, 402, 571, 1052, 1348, 1113, 1359, 1341, 990 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 46.3, "p25": 33.9, "p75": 58.0, "synthetic_n": 8636, "histogram": [ 205, 445, 963, 1515, 1853, 1825, 1211, 491, 116, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 48.5, "p25": 35.4, "p75": 60.4, "synthetic_n": 8636, "histogram": [ 204, 424, 849, 1361, 1757, 1815, 1302, 627, 243, 54 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 50.8, "p25": 38.7, "p75": 62.3, "synthetic_n": 8636, "histogram": [ 109, 282, 711, 1243, 1801, 1930, 1422, 819, 260, 59 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 75.5, "p25": 58.2, "p75": 85.8, "synthetic_n": 1526, "histogram": [ 19, 33, 73, 96, 81, 98, 190, 331, 340, 265 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 67.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 61.4, "p25": 50.2, "p75": 71.6, "synthetic_n": 1526, "histogram": [ 3, 12, 33, 109, 215, 335, 377, 283, 139, 20 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 71.2, "p25": 53.3, "p75": 86.5, "synthetic_n": 1526, "histogram": [ 10, 34, 56, 68, 152, 224, 186, 229, 282, 285 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 54.5, "p25": 42.5, "p75": 66.3, "synthetic_n": 1526, "histogram": [ 2, 37, 76, 200, 304, 341, 294, 184, 74, 14 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 54.5, "p25": 43.3, "p75": 66.2, "synthetic_n": 1526, "histogram": [ 8, 26, 89, 168, 315, 358, 282, 188, 74, 18 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 62.0, "p25": 49.7, "p75": 73.0, "synthetic_n": 1526, "histogram": [ 2, 15, 45, 88, 243, 296, 362, 284, 135, 56 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 72.6, "p25": 54.9, "p75": 84.3, "synthetic_n": 4915, "histogram": [ 88, 165, 258, 273, 263, 421, 743, 981, 1014, 709 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 33.2% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 50.7, "p25": 38.7, "p75": 61.4, "synthetic_n": 4915, "histogram": [ 54, 167, 386, 741, 1036, 1135, 909, 371, 104, 12 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 63.6, "p25": 46.6, "p75": 81.2, "synthetic_n": 4915, "histogram": [ 80, 189, 253, 320, 631, 757, 620, 740, 776, 549 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "Observed marketing-sector anchors: 66% AI policy; 56% data governance (DDMA 2026, n=436).", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 48.2, "p25": 36.2, "p75": 60.0, "synthetic_n": 4915, "histogram": [ 110, 226, 471, 800, 1024, 1042, 799, 334, 93, 16 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 59.2, "p25": 47.1, "p75": 70.1, "synthetic_n": 4915, "histogram": [ 14, 51, 210, 441, 738, 1086, 1139, 804, 325, 107 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 50.0, "p25": 37.1, "p75": 61.8, "synthetic_n": 4915, "histogram": [ 72, 194, 442, 742, 1004, 1034, 816, 425, 149, 37 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 71.1, "p25": 52.7, "p75": 83.2, "synthetic_n": 3947, "histogram": [ 83, 139, 235, 220, 225, 361, 633, 792, 754, 505 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 48.3, "p25": 36.7, "p75": 58.8, "synthetic_n": 3947, "histogram": [ 51, 155, 368, 675, 891, 910, 646, 212, 36, 3 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 62.0, "p25": 45.3, "p75": 80.1, "synthetic_n": 3947, "histogram": [ 73, 169, 214, 281, 515, 617, 501, 581, 600, 396 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 46.2, "p25": 34.2, "p75": 58.1, "synthetic_n": 3947, "histogram": [ 106, 209, 429, 685, 849, 810, 573, 227, 54, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 57.6, "p25": 45.4, "p75": 68.2, "synthetic_n": 3947, "histogram": [ 11, 47, 192, 391, 640, 895, 901, 597, 210, 63 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 47.6, "p25": 34.8, "p75": 59.1, "synthetic_n": 3947, "histogram": [ 68, 180, 405, 679, 861, 813, 588, 262, 80, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 77.9, "p25": 62.8, "p75": 87.5, "synthetic_n": 828, "histogram": [ 5, 25, 21, 46, 34, 56, 104, 161, 209, 167 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 46.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 59.4, "p25": 49.0, "p75": 68.0, "synthetic_n": 828, "histogram": [ 3, 12, 18, 63, 131, 201, 228, 121, 47, 4 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 69.3, "p25": 51.7, "p75": 83.9, "synthetic_n": 828, "histogram": [ 7, 16, 34, 37, 100, 122, 104, 134, 155, 119 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 55.1, "p25": 43.1, "p75": 64.3, "synthetic_n": 828, "histogram": [ 4, 17, 38, 105, 152, 206, 195, 78, 28, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 64.2, "p25": 53.2, "p75": 73.8, "synthetic_n": 828, "histogram": [ 3, 4, 17, 47, 87, 168, 208, 170, 91, 33 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 58.4, "p25": 47.0, "p75": 68.9, "synthetic_n": 828, "histogram": [ 4, 14, 36, 61, 129, 200, 190, 128, 49, 17 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 83.2, "p25": 75.9, "p75": 90.4, "synthetic_n": 140, "histogram": [ 0, 1, 2, 7, 4, 4, 6, 28, 51, 37 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 67.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 68.6, "p25": 57.8, "p75": 77.2, "synthetic_n": 140, "histogram": [ 0, 0, 0, 3, 14, 24, 35, 38, 21, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 74.1, "p25": 55.2, "p75": 89.6, "synthetic_n": 140, "histogram": [ 0, 4, 5, 2, 16, 18, 15, 25, 21, 34 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 63.1, "p25": 48.9, "p75": 72.3, "synthetic_n": 140, "histogram": [ 0, 0, 4, 10, 23, 26, 31, 29, 11, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 70.8, "p25": 59.3, "p75": 80.0, "synthetic_n": 140, "histogram": [ 0, 0, 1, 3, 11, 23, 30, 37, 24, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 68.3, "p25": 57.4, "p75": 77.6, "synthetic_n": 140, "histogram": [ 0, 0, 1, 2, 14, 21, 38, 35, 20, 9 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 67.7, "p25": 46.6, "p75": 80.5, "synthetic_n": 9196, "histogram": [ 249, 475, 646, 597, 516, 911, 1596, 1818, 1397, 991 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 33.2% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 45.8, "p25": 34.1, "p75": 56.9, "synthetic_n": 9196, "histogram": [ 186, 468, 1012, 1703, 2093, 1977, 1157, 468, 116, 16 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 61.1, "p25": 45.3, "p75": 79.2, "synthetic_n": 9196, "histogram": [ 172, 419, 505, 634, 1248, 1462, 1261, 1328, 1328, 839 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "Observed marketing-sector anchors: 66% AI policy; 56% data governance (DDMA 2026, n=436).", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 43.5, "p25": 30.7, "p75": 55.0, "synthetic_n": 9196, "histogram": [ 311, 650, 1213, 1718, 2082, 1691, 1032, 398, 88, 13 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 49.3, "p25": 37.0, "p75": 60.9, "synthetic_n": 9196, "histogram": [ 135, 339, 877, 1453, 1916, 2023, 1478, 694, 237, 44 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 45.8, "p25": 32.7, "p75": 57.8, "synthetic_n": 9196, "histogram": [ 274, 522, 1092, 1581, 1969, 1801, 1174, 572, 170, 41 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 66.2, "p25": 43.3, "p75": 78.7, "synthetic_n": 7331, "histogram": [ 227, 428, 562, 483, 413, 772, 1328, 1423, 1024, 671 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 43.3, "p25": 32.1, "p75": 54.1, "synthetic_n": 7331, "histogram": [ 176, 438, 936, 1519, 1737, 1515, 747, 232, 30, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 59.6, "p25": 44.2, "p75": 78.0, "synthetic_n": 7331, "histogram": [ 151, 360, 436, 530, 1057, 1166, 1022, 995, 1027, 587 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 41.5, "p25": 29.0, "p75": 52.7, "synthetic_n": 7331, "histogram": [ 292, 592, 1070, 1458, 1694, 1249, 688, 231, 52, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 47.6, "p25": 35.2, "p75": 59.1, "synthetic_n": 7331, "histogram": [ 129, 306, 777, 1245, 1599, 1577, 1092, 446, 135, 25 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 43.2, "p25": 30.7, "p75": 55.0, "synthetic_n": 7331, "histogram": [ 256, 481, 1000, 1388, 1620, 1384, 802, 315, 70, 15 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 73.1, "p25": 56.3, "p75": 84.7, "synthetic_n": 1577, "histogram": [ 19, 46, 74, 99, 89, 122, 242, 325, 306, 255 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 46.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 54.7, "p25": 44.0, "p75": 64.3, "synthetic_n": 1577, "histogram": [ 10, 28, 74, 166, 328, 408, 331, 170, 57, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 67.1, "p25": 49.6, "p75": 81.7, "synthetic_n": 1577, "histogram": [ 18, 56, 62, 92, 174, 249, 202, 277, 252, 195 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 50.2, "p25": 38.5, "p75": 60.8, "synthetic_n": 1577, "histogram": [ 19, 54, 134, 224, 348, 374, 272, 125, 23, 4 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 55.1, "p25": 43.2, "p75": 66.5, "synthetic_n": 1577, "histogram": [ 6, 31, 92, 187, 280, 377, 321, 195, 74, 14 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 54.7, "p25": 42.3, "p75": 65.4, "synthetic_n": 1577, "histogram": [ 18, 38, 85, 185, 304, 369, 310, 189, 64, 15 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 78.5, "p25": 66.3, "p75": 88.2, "synthetic_n": 288, "histogram": [ 3, 1, 10, 15, 14, 17, 26, 70, 67, 65 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 67.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 65.5, "p25": 54.9, "p75": 74.5, "synthetic_n": 288, "histogram": [ 0, 2, 2, 18, 28, 54, 79, 66, 29, 10 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 74.2, "p25": 58.1, "p75": 86.8, "synthetic_n": 288, "histogram": [ 3, 3, 7, 12, 17, 47, 37, 56, 49, 57 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 58.3, "p25": 46.3, "p75": 67.6, "synthetic_n": 288, "histogram": [ 0, 4, 9, 36, 40, 68, 72, 42, 13, 4 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 61.8, "p25": 50.4, "p75": 72.5, "synthetic_n": 288, "histogram": [ 0, 2, 8, 21, 37, 69, 65, 53, 28, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 64.2, "p25": 51.4, "p75": 74.5, "synthetic_n": 288, "histogram": [ 0, 3, 7, 8, 45, 48, 62, 68, 36, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 62.4, "p25": 36.0, "p75": 74.8, "synthetic_n": 10075, "histogram": [ 461, 789, 879, 617, 640, 1201, 2002, 1819, 1100, 567 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 33.2% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 41.2, "p25": 29.1, "p75": 52.7, "synthetic_n": 10075, "histogram": [ 373, 769, 1529, 2072, 2231, 1768, 962, 303, 63, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.7, "p25": 42.9, "p75": 77.5, "synthetic_n": 10075, "histogram": [ 253, 563, 591, 753, 1414, 1645, 1317, 1356, 1349, 834 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "Observed marketing-sector anchors: 66% AI policy; 56% data governance (DDMA 2026, n=436).", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 38.7, "p25": 26.2, "p75": 51.0, "synthetic_n": 10075, "histogram": [ 617, 983, 1642, 2080, 2062, 1530, 808, 296, 51, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 41.7, "p25": 28.6, "p75": 53.3, "synthetic_n": 10075, "histogram": [ 457, 813, 1461, 1945, 2217, 1828, 983, 301, 64, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 40.9, "p25": 28.2, "p75": 53.5, "synthetic_n": 10075, "histogram": [ 471, 856, 1480, 1985, 2096, 1708, 969, 395, 92, 23 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.9, "p25": 32.9, "p75": 73.2, "synthetic_n": 7962, "histogram": [ 412, 696, 732, 487, 498, 1017, 1626, 1360, 771, 363 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 38.8, "p25": 27.2, "p75": 49.9, "synthetic_n": 7962, "histogram": [ 355, 723, 1351, 1779, 1765, 1281, 565, 128, 15, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 57.4, "p25": 41.6, "p75": 76.2, "synthetic_n": 7962, "histogram": [ 234, 487, 487, 611, 1171, 1300, 1033, 1051, 1007, 581 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 36.9, "p25": 24.5, "p75": 48.5, "synthetic_n": 7962, "histogram": [ 564, 872, 1403, 1708, 1622, 1094, 507, 164, 25, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 39.7, "p25": 27.2, "p75": 51.2, "synthetic_n": 7962, "histogram": [ 425, 716, 1256, 1634, 1746, 1315, 653, 188, 27, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 38.4, "p25": 26.2, "p75": 50.7, "synthetic_n": 7962, "histogram": [ 447, 792, 1326, 1672, 1631, 1254, 594, 207, 34, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 68.0, "p25": 46.7, "p75": 79.6, "synthetic_n": 1802, "histogram": [ 45, 88, 125, 111, 120, 159, 332, 387, 275, 160 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 46.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 49.8, "p25": 38.8, "p75": 60.4, "synthetic_n": 1802, "histogram": [ 16, 44, 167, 265, 418, 419, 320, 125, 24, 4 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 63.6, "p25": 47.6, "p75": 80.9, "synthetic_n": 1802, "histogram": [ 17, 70, 91, 126, 209, 293, 243, 269, 281, 203 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 45.3, "p25": 32.5, "p75": 57.3, "synthetic_n": 1802, "histogram": [ 53, 103, 222, 322, 370, 373, 244, 99, 14, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 48.2, "p25": 35.7, "p75": 58.3, "synthetic_n": 1802, "histogram": [ 31, 90, 186, 275, 395, 429, 277, 85, 30, 4 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 49.2, "p25": 38.0, "p75": 61.0, "synthetic_n": 1802, "histogram": [ 24, 61, 143, 288, 410, 389, 301, 135, 39, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 72.2, "p25": 51.1, "p75": 83.0, "synthetic_n": 311, "histogram": [ 4, 5, 22, 19, 22, 25, 44, 72, 54, 44 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 67.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 59.5, "p25": 47.8, "p75": 69.3, "synthetic_n": 311, "histogram": [ 2, 2, 11, 28, 48, 68, 77, 50, 24, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 67.5, "p25": 51.2, "p75": 85.3, "synthetic_n": 311, "histogram": [ 2, 6, 13, 16, 34, 52, 41, 36, 61, 50 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 51.6, "p25": 40.7, "p75": 63.5, "synthetic_n": 311, "histogram": [ 0, 8, 17, 50, 70, 63, 57, 33, 12, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 52.3, "p25": 42.1, "p75": 62.3, "synthetic_n": 311, "histogram": [ 1, 7, 19, 36, 76, 84, 53, 28, 7, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 59.6, "p25": 47.4, "p75": 70.2, "synthetic_n": 311, "histogram": [ 0, 3, 11, 25, 55, 65, 74, 53, 19, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 61.7, "p25": 34.2, "p75": 74.5, "synthetic_n": 8959, "histogram": [ 461, 749, 763, 581, 541, 1103, 1693, 1651, 917, 500 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 33.2% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 40.4, "p25": 28.2, "p75": 51.6, "synthetic_n": 8959, "histogram": [ 367, 743, 1420, 1854, 2033, 1498, 759, 223, 60, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 57.8, "p25": 42.3, "p75": 77.0, "synthetic_n": 8959, "histogram": [ 227, 519, 500, 733, 1302, 1453, 1102, 1303, 1145, 675 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "Observed marketing-sector anchors: 66% AI policy; 56% data governance (DDMA 2026, n=436).", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 37.4, "p25": 25.1, "p75": 49.7, "synthetic_n": 8959, "histogram": [ 589, 927, 1514, 1955, 1796, 1301, 635, 210, 30, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 37.4, "p25": 24.8, "p75": 48.9, "synthetic_n": 8959, "histogram": [ 581, 1000, 1470, 1914, 1927, 1250, 598, 186, 32, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 40.4, "p25": 27.6, "p75": 53.0, "synthetic_n": 8959, "histogram": [ 439, 786, 1377, 1793, 1807, 1489, 816, 335, 100, 17 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.3, "p25": 31.2, "p75": 72.9, "synthetic_n": 7143, "histogram": [ 411, 656, 643, 471, 423, 934, 1384, 1263, 642, 316 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 38.0, "p25": 26.4, "p75": 48.7, "synthetic_n": 7143, "histogram": [ 348, 697, 1251, 1605, 1648, 1060, 434, 83, 17, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 56.4, "p25": 41.4, "p75": 75.5, "synthetic_n": 7143, "histogram": [ 201, 437, 413, 604, 1087, 1187, 873, 1027, 856, 458 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 35.6, "p25": 23.6, "p75": 47.2, "synthetic_n": 7143, "histogram": [ 543, 827, 1303, 1630, 1400, 927, 383, 120, 9, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 35.7, "p25": 23.4, "p75": 46.9, "synthetic_n": 7143, "histogram": [ 521, 884, 1272, 1566, 1505, 906, 383, 95, 10, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 37.9, "p25": 25.5, "p75": 50.1, "synthetic_n": 7143, "histogram": [ 419, 729, 1207, 1522, 1453, 1081, 519, 168, 42, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 66.9, "p25": 45.9, "p75": 79.2, "synthetic_n": 1552, "histogram": [ 47, 86, 111, 91, 99, 154, 273, 327, 216, 148 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 46.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 49.5, "p25": 37.5, "p75": 59.9, "synthetic_n": 1552, "histogram": [ 19, 43, 163, 227, 346, 369, 259, 98, 28, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 62.3, "p25": 45.2, "p75": 80.3, "synthetic_n": 1552, "histogram": [ 24, 75, 76, 117, 191, 237, 198, 241, 227, 166 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 44.4, "p25": 31.9, "p75": 56.4, "synthetic_n": 1552, "histogram": [ 46, 92, 199, 294, 330, 309, 206, 66, 10, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 43.1, "p25": 31.2, "p75": 54.3, "synthetic_n": 1552, "histogram": [ 59, 110, 184, 307, 358, 277, 171, 70, 16, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 49.0, "p25": 36.2, "p75": 60.3, "synthetic_n": 1552, "histogram": [ 20, 55, 159, 251, 318, 354, 225, 133, 35, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 73.5, "p25": 56.8, "p75": 84.6, "synthetic_n": 264, "histogram": [ 3, 7, 9, 19, 19, 15, 36, 61, 59, 36 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 67.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 59.0, "p25": 49.2, "p75": 68.9, "synthetic_n": 264, "histogram": [ 0, 3, 6, 22, 39, 69, 66, 42, 15, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 73.6, "p25": 52.5, "p75": 87.7, "synthetic_n": 264, "histogram": [ 2, 7, 11, 12, 24, 29, 31, 35, 62, 51 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 51.7, "p25": 43.3, "p75": 63.6, "synthetic_n": 264, "histogram": [ 0, 8, 12, 31, 66, 65, 46, 24, 11, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 51.8, "p25": 40.8, "p75": 60.9, "synthetic_n": 264, "histogram": [ 1, 6, 14, 41, 64, 67, 44, 21, 6, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 61.8, "p25": 49.7, "p75": 71.1, "synthetic_n": 264, "histogram": [ 0, 2, 11, 20, 36, 54, 72, 34, 23, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 63.2, "p25": 35.4, "p75": 76.1, "synthetic_n": 4989, "histogram": [ 229, 401, 429, 330, 294, 531, 922, 931, 610, 312 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 33.2% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 41.7, "p25": 29.8, "p75": 53.0, "synthetic_n": 4989, "histogram": [ 179, 388, 703, 1049, 1122, 880, 478, 150, 39, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.2, "p25": 42.0, "p75": 77.0, "synthetic_n": 4989, "histogram": [ 136, 291, 303, 404, 708, 788, 619, 703, 613, 424 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "Observed marketing-sector anchors: 66% AI policy; 56% data governance (DDMA 2026, n=436).", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 38.9, "p25": 26.6, "p75": 51.2, "synthetic_n": 4989, "histogram": [ 300, 484, 774, 1028, 1053, 818, 359, 136, 36, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 40.3, "p25": 27.2, "p75": 52.0, "synthetic_n": 4989, "histogram": [ 264, 454, 755, 985, 1049, 921, 400, 136, 24, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 41.5, "p25": 28.5, "p75": 53.9, "synthetic_n": 4989, "histogram": [ 259, 412, 720, 941, 1060, 837, 489, 200, 61, 10 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 61.7, "p25": 32.8, "p75": 74.5, "synthetic_n": 3994, "histogram": [ 207, 348, 358, 266, 232, 452, 766, 723, 441, 201 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 39.3, "p25": 27.8, "p75": 50.4, "synthetic_n": 3994, "histogram": [ 172, 366, 639, 893, 885, 670, 304, 55, 10, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 57.1, "p25": 40.9, "p75": 75.9, "synthetic_n": 3994, "histogram": [ 113, 253, 252, 340, 569, 647, 496, 552, 468, 304 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 37.2, "p25": 25.1, "p75": 49.2, "synthetic_n": 3994, "histogram": [ 281, 436, 664, 840, 833, 608, 241, 76, 14, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 38.5, "p25": 26.1, "p75": 50.5, "synthetic_n": 3994, "histogram": [ 240, 404, 644, 836, 829, 682, 266, 81, 12, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 39.0, "p25": 26.5, "p75": 51.1, "synthetic_n": 3994, "histogram": [ 249, 385, 629, 806, 846, 609, 347, 99, 23, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 68.3, "p25": 45.0, "p75": 80.3, "synthetic_n": 836, "histogram": [ 19, 51, 61, 52, 55, 70, 134, 178, 135, 81 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 46.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 48.9, "p25": 38.5, "p75": 60.1, "synthetic_n": 836, "histogram": [ 7, 22, 61, 142, 215, 176, 140, 60, 12, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 62.3, "p25": 45.2, "p75": 80.3, "synthetic_n": 836, "histogram": [ 22, 36, 41, 56, 115, 118, 105, 129, 125, 89 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 44.9, "p25": 33.2, "p75": 56.2, "synthetic_n": 836, "histogram": [ 19, 42, 102, 166, 193, 174, 89, 38, 13, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 45.7, "p25": 33.0, "p75": 56.1, "synthetic_n": 836, "histogram": [ 23, 48, 100, 137, 182, 196, 102, 36, 11, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 49.4, "p25": 37.7, "p75": 60.3, "synthetic_n": 836, "histogram": [ 10, 27, 85, 128, 184, 188, 112, 74, 25, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 74.6, "p25": 57.5, "p75": 86.8, "synthetic_n": 159, "histogram": [ 3, 2, 10, 12, 7, 9, 22, 30, 34, 30 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 67.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 62.1, "p25": 50.2, "p75": 72.4, "synthetic_n": 159, "histogram": [ 0, 0, 3, 14, 22, 34, 34, 35, 17, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 67.7, "p25": 49.1, "p75": 85.2, "synthetic_n": 159, "histogram": [ 1, 2, 10, 8, 24, 23, 18, 22, 20, 31 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 54.7, "p25": 41.0, "p75": 66.4, "synthetic_n": 159, "histogram": [ 0, 6, 8, 22, 27, 36, 29, 22, 9, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 53.8, "p25": 43.7, "p75": 63.4, "synthetic_n": 159, "histogram": [ 1, 2, 11, 12, 38, 43, 32, 19, 1, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 58.3, "p25": 49.0, "p75": 73.6, "synthetic_n": 159, "histogram": [ 0, 0, 6, 7, 30, 40, 30, 27, 13, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 63.1, "p25": 36.3, "p75": 76.8, "synthetic_n": 2995, "histogram": [ 121, 239, 277, 180, 161, 361, 543, 526, 377, 210 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 33.2% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 42.5, "p25": 30.4, "p75": 54.0, "synthetic_n": 2995, "histogram": [ 100, 230, 398, 611, 671, 541, 293, 120, 29, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.1, "p25": 42.9, "p75": 77.8, "synthetic_n": 2995, "histogram": [ 81, 141, 164, 232, 455, 504, 346, 417, 394, 261 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "Observed marketing-sector anchors: 66% AI policy; 56% data governance (DDMA 2026, n=436).", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 39.7, "p25": 27.1, "p75": 52.2, "synthetic_n": 2995, "histogram": [ 145, 272, 484, 609, 615, 483, 260, 96, 30, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 42.2, "p25": 29.8, "p75": 54.3, "synthetic_n": 2995, "histogram": [ 123, 228, 404, 568, 648, 533, 335, 117, 33, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 42.9, "p25": 29.6, "p75": 55.2, "synthetic_n": 2995, "histogram": [ 110, 254, 400, 557, 638, 517, 323, 145, 41, 10 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 61.7, "p25": 32.4, "p75": 74.8, "synthetic_n": 2376, "histogram": [ 109, 206, 234, 142, 125, 309, 448, 410, 249, 144 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 39.5, "p25": 27.9, "p75": 50.7, "synthetic_n": 2376, "histogram": [ 95, 219, 365, 532, 540, 391, 174, 54, 5, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 56.5, "p25": 41.9, "p75": 75.9, "synthetic_n": 2376, "histogram": [ 74, 119, 140, 194, 376, 418, 261, 334, 279, 181 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 37.4, "p25": 25.7, "p75": 49.7, "synthetic_n": 2376, "histogram": [ 136, 245, 421, 518, 477, 347, 167, 53, 12, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 40.6, "p25": 28.4, "p75": 52.4, "synthetic_n": 2376, "histogram": [ 115, 209, 357, 466, 520, 401, 216, 73, 17, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 40.1, "p25": 27.4, "p75": 52.0, "synthetic_n": 2376, "histogram": [ 105, 235, 355, 488, 504, 391, 212, 71, 15, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 69.1, "p25": 48.1, "p75": 81.6, "synthetic_n": 532, "histogram": [ 11, 27, 40, 33, 34, 43, 88, 102, 103, 51 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 46.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 51.7, "p25": 41.6, "p75": 62.0, "synthetic_n": 532, "histogram": [ 5, 11, 31, 72, 121, 135, 96, 49, 11, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 64.4, "p25": 47.9, "p75": 82.6, "synthetic_n": 532, "histogram": [ 6, 21, 22, 33, 72, 76, 74, 69, 98, 61 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 47.8, "p25": 35.4, "p75": 58.3, "synthetic_n": 532, "histogram": [ 8, 26, 59, 86, 117, 117, 74, 34, 11, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 49.5, "p25": 37.8, "p75": 61.2, "synthetic_n": 532, "histogram": [ 8, 18, 40, 94, 111, 114, 98, 35, 12, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 52.2, "p25": 40.0, "p75": 63.6, "synthetic_n": 532, "histogram": [ 5, 18, 43, 67, 116, 114, 90, 53, 20, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 77.0, "p25": 55.4, "p75": 87.1, "synthetic_n": 87, "histogram": [ 1, 6, 3, 5, 2, 9, 7, 14, 25, 15 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 67.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 64.2, "p25": 53.8, "p75": 75.1, "synthetic_n": 87, "histogram": [ 0, 0, 2, 7, 10, 15, 23, 17, 13, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 75.4, "p25": 55.7, "p75": 88.4, "synthetic_n": 87, "histogram": [ 1, 1, 2, 5, 7, 10, 11, 14, 17, 19 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 56.0, "p25": 45.8, "p75": 67.5, "synthetic_n": 87, "histogram": [ 1, 1, 4, 5, 21, 19, 19, 9, 7, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 56.8, "p25": 44.0, "p75": 65.7, "synthetic_n": 87, "histogram": [ 0, 1, 7, 8, 17, 18, 21, 9, 4, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 64.0, "p25": 49.2, "p75": 75.0, "synthetic_n": 87, "histogram": [ 0, 1, 2, 2, 18, 12, 21, 21, 6, 4 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.0, "p25": 29.6, "p75": 72.7, "synthetic_n": 4047, "histogram": [ 246, 352, 426, 259, 236, 500, 811, 678, 364, 175 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 33.2% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 38.7, "p25": 26.7, "p75": 50.6, "synthetic_n": 4047, "histogram": [ 226, 385, 659, 874, 827, 661, 292, 103, 19, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 56.9, "p25": 41.4, "p75": 75.8, "synthetic_n": 4047, "histogram": [ 123, 242, 230, 342, 570, 718, 496, 536, 479, 311 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "Observed marketing-sector anchors: 66% AI policy; 56% data governance (DDMA 2026, n=436).", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 35.9, "p25": 23.1, "p75": 48.1, "synthetic_n": 4047, "histogram": [ 323, 471, 736, 869, 762, 533, 243, 87, 21, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 36.0, "p25": 23.1, "p75": 47.3, "synthetic_n": 4047, "histogram": [ 332, 472, 709, 850, 857, 510, 236, 69, 12, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 38.7, "p25": 25.8, "p75": 51.2, "synthetic_n": 4047, "histogram": [ 246, 423, 622, 859, 799, 574, 340, 133, 46, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 58.3, "p25": 26.4, "p75": 71.1, "synthetic_n": 3209, "histogram": [ 226, 317, 361, 197, 194, 402, 643, 511, 257, 101 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 36.1, "p25": 24.3, "p75": 47.8, "synthetic_n": 3209, "histogram": [ 215, 365, 583, 742, 628, 462, 169, 38, 6, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 55.3, "p25": 40.3, "p75": 74.4, "synthetic_n": 3209, "histogram": [ 111, 199, 191, 287, 479, 576, 389, 399, 363, 215 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 33.7, "p25": 21.5, "p75": 45.8, "synthetic_n": 3209, "histogram": [ 296, 416, 638, 695, 586, 372, 151, 50, 4, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 34.1, "p25": 21.2, "p75": 45.3, "synthetic_n": 3209, "histogram": [ 311, 417, 599, 680, 661, 356, 148, 32, 5, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 36.1, "p25": 23.6, "p75": 47.8, "synthetic_n": 3209, "histogram": [ 232, 387, 547, 727, 620, 408, 208, 62, 16, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 64.8, "p25": 46.0, "p75": 77.2, "synthetic_n": 701, "histogram": [ 19, 31, 54, 53, 37, 90, 142, 138, 83, 54 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 46.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 47.3, "p25": 36.9, "p75": 57.4, "synthetic_n": 701, "histogram": [ 10, 16, 71, 125, 173, 166, 92, 44, 4, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 61.6, "p25": 46.2, "p75": 79.2, "synthetic_n": 701, "histogram": [ 12, 35, 36, 50, 76, 121, 91, 116, 89, 75 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 42.4, "p25": 30.9, "p75": 53.9, "synthetic_n": 701, "histogram": [ 27, 48, 89, 149, 153, 133, 70, 22, 10, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 42.3, "p25": 30.4, "p75": 53.2, "synthetic_n": 701, "histogram": [ 19, 51, 98, 152, 161, 125, 67, 24, 4, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 47.3, "p25": 35.9, "p75": 59.1, "synthetic_n": 701, "histogram": [ 12, 31, 72, 124, 157, 140, 100, 44, 20, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 71.0, "p25": 54.6, "p75": 81.4, "synthetic_n": 137, "histogram": [ 1, 4, 11, 9, 5, 8, 26, 29, 24, 20 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 67.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 58.1, "p25": 46.5, "p75": 68.5, "synthetic_n": 137, "histogram": [ 1, 4, 5, 7, 26, 33, 31, 21, 9, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 70.7, "p25": 52.5, "p75": 86.1, "synthetic_n": 137, "histogram": [ 0, 8, 3, 5, 15, 21, 16, 21, 27, 21 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 52.3, "p25": 35.5, "p75": 65.6, "synthetic_n": 137, "histogram": [ 0, 7, 9, 25, 23, 28, 22, 15, 7, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 49.0, "p25": 39.5, "p75": 61.6, "synthetic_n": 137, "histogram": [ 2, 4, 12, 18, 35, 29, 21, 13, 3, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 60.5, "p25": 47.0, "p75": 71.0, "synthetic_n": 137, "histogram": [ 2, 5, 3, 8, 22, 26, 32, 27, 10, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 59.3, "p25": 29.0, "p75": 72.0, "synthetic_n": 4824, "histogram": [ 298, 488, 472, 313, 265, 628, 953, 803, 407, 197 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 33.2% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 37.2, "p25": 25.4, "p75": 48.9, "synthetic_n": 4824, "histogram": [ 287, 484, 826, 1105, 1012, 683, 323, 81, 21, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 56.0, "p25": 40.5, "p75": 75.4, "synthetic_n": 4824, "histogram": [ 154, 292, 279, 436, 734, 792, 581, 659, 574, 323 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "Observed marketing-sector anchors: 66% AI policy; 56% data governance (DDMA 2026, n=436).", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 34.4, "p25": 22.5, "p75": 46.6, "synthetic_n": 4824, "histogram": [ 401, 622, 942, 994, 909, 610, 251, 80, 13, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 32.2, "p25": 19.5, "p75": 44.6, "synthetic_n": 4824, "histogram": [ 572, 674, 924, 1018, 879, 520, 185, 43, 9, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 37.8, "p25": 25.1, "p75": 50.4, "synthetic_n": 4824, "histogram": [ 343, 502, 794, 1011, 931, 726, 340, 137, 29, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 57.8, "p25": 27.1, "p75": 70.8, "synthetic_n": 3876, "histogram": [ 264, 419, 393, 257, 215, 521, 776, 608, 301, 122 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 35.1, "p25": 23.3, "p75": 45.9, "synthetic_n": 3876, "histogram": [ 271, 453, 744, 919, 808, 476, 171, 30, 4, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 54.9, "p25": 39.5, "p75": 74.1, "synthetic_n": 3876, "histogram": [ 141, 256, 234, 368, 604, 636, 468, 515, 435, 219 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 32.5, "p25": 20.6, "p75": 44.6, "synthetic_n": 3876, "histogram": [ 371, 556, 809, 804, 685, 435, 172, 41, 2, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 30.5, "p25": 17.9, "p75": 42.3, "synthetic_n": 3876, "histogram": [ 514, 598, 775, 827, 659, 366, 111, 23, 3, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 35.3, "p25": 23.3, "p75": 47.5, "synthetic_n": 3876, "histogram": [ 327, 463, 702, 856, 725, 520, 213, 55, 13, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 63.3, "p25": 37.8, "p75": 75.4, "synthetic_n": 808, "histogram": [ 30, 62, 73, 46, 42, 96, 154, 168, 80, 57 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 46.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 46.3, "p25": 34.1, "p75": 57.2, "synthetic_n": 808, "histogram": [ 16, 30, 78, 176, 176, 169, 120, 37, 6, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 61.5, "p25": 45.4, "p75": 79.4, "synthetic_n": 808, "histogram": [ 12, 33, 40, 60, 115, 132, 96, 124, 114, 82 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 41.2, "p25": 29.2, "p75": 52.1, "synthetic_n": 808, "histogram": [ 29, 63, 120, 169, 190, 139, 61, 29, 7, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 38.8, "p25": 25.7, "p75": 50.0, "synthetic_n": 808, "histogram": [ 55, 72, 132, 162, 183, 129, 58, 12, 5, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 46.1, "p25": 34.6, "p75": 58.2, "synthetic_n": 808, "histogram": [ 16, 38, 88, 139, 183, 176, 94, 63, 8, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 71.2, "p25": 50.3, "p75": 83.7, "synthetic_n": 140, "histogram": [ 4, 7, 6, 10, 8, 11, 23, 27, 26, 18 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 67.6% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 57.1, "p25": 47.6, "p75": 67.0, "synthetic_n": 140, "histogram": [ 0, 1, 4, 10, 28, 38, 32, 14, 11, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 66.1, "p25": 52.7, "p75": 83.0, "synthetic_n": 140, "histogram": [ 1, 3, 5, 8, 15, 24, 17, 20, 25, 22 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 49.3, "p25": 38.7, "p75": 58.5, "synthetic_n": 140, "histogram": [ 1, 3, 13, 21, 34, 36, 18, 10, 4, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 44.9, "p25": 34.2, "p75": 52.8, "synthetic_n": 140, "histogram": [ 3, 4, 17, 29, 37, 25, 16, 8, 1, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 59.5, "p25": 45.7, "p75": 68.1, "synthetic_n": 140, "histogram": [ 0, 1, 4, 16, 23, 30, 33, 19, 8, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 64.1, "p25": 38.2, "p75": 77.3, "synthetic_n": 50000, "histogram": [ 2127, 3533, 4098, 3207, 2892, 5671, 9099, 9049, 6445, 3879 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 42.5, "p25": 30.4, "p75": 54.0, "synthetic_n": 50000, "histogram": [ 1675, 3632, 6821, 9991, 11316, 9095, 4999, 1932, 466, 73 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 59.0, "p25": 43.1, "p75": 77.9, "synthetic_n": 50000, "histogram": [ 1180, 2663, 2931, 3875, 7016, 8012, 6231, 7121, 6773, 4198 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 39.9, "p25": 27.2, "p75": 52.2, "synthetic_n": 50000, "histogram": [ 2795, 4499, 7511, 10209, 10434, 7882, 4509, 1725, 376, 60 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 42.4, "p25": 29.3, "p75": 55.1, "synthetic_n": 50000, "histogram": [ 2402, 4020, 6616, 9392, 10322, 8577, 5397, 2395, 711, 168 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 42.4, "p25": 29.5, "p75": 55.0, "synthetic_n": 50000, "histogram": [ 2125, 3738, 7037, 9515, 10372, 8658, 5322, 2326, 753, 154 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 62.5, "p25": 34.9, "p75": 75.3, "synthetic_n": 40044, "histogram": [ 1905, 3139, 3520, 2557, 2315, 4808, 7687, 7077, 4557, 2479 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 40.1, "p25": 28.4, "p75": 51.1, "synthetic_n": 40044, "histogram": [ 1608, 3382, 6192, 8690, 9262, 6810, 3087, 875, 130, 8 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 57.5, "p25": 42.0, "p75": 76.5, "synthetic_n": 40044, "histogram": [ 1048, 2294, 2457, 3247, 5908, 6523, 4962, 5574, 5120, 2911 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 38.0, "p25": 25.6, "p75": 49.9, "synthetic_n": 40044, "histogram": [ 2614, 4011, 6520, 8597, 8354, 5844, 2980, 950, 151, 23 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 40.6, "p25": 27.8, "p75": 53.0, "synthetic_n": 40044, "histogram": [ 2205, 3580, 5714, 7928, 8358, 6501, 3743, 1499, 427, 89 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 40.0, "p25": 27.4, "p75": 51.9, "synthetic_n": 40044, "histogram": [ 2028, 3457, 6345, 8180, 8468, 6516, 3487, 1271, 252, 40 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 70.5, "p25": 50.0, "p75": 83.1, "synthetic_n": 8405, "histogram": [ 212, 357, 509, 533, 487, 776, 1237, 1642, 1559, 1093 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 54.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 51.1, "p25": 39.9, "p75": 62.2, "synthetic_n": 8405, "histogram": [ 65, 242, 603, 1198, 1835, 1950, 1544, 740, 199, 29 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 64.7, "p25": 47.3, "p75": 81.7, "synthetic_n": 8405, "histogram": [ 123, 330, 418, 552, 976, 1286, 1064, 1316, 1347, 993 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 46.8, "p25": 34.3, "p75": 58.8, "synthetic_n": 8405, "histogram": [ 171, 457, 915, 1421, 1795, 1726, 1180, 564, 153, 23 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 48.9, "p25": 35.7, "p75": 61.1, "synthetic_n": 8405, "histogram": [ 184, 409, 805, 1298, 1701, 1716, 1325, 702, 210, 55 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 51.3, "p25": 39.0, "p75": 63.2, "synthetic_n": 8405, "histogram": [ 96, 269, 656, 1224, 1707, 1825, 1472, 769, 325, 62 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 76.1, "p25": 58.2, "p75": 87.2, "synthetic_n": 1551, "histogram": [ 10, 37, 69, 117, 90, 87, 175, 330, 329, 307 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 76.4% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 62.2, "p25": 51.2, "p75": 72.6, "synthetic_n": 1551, "histogram": [ 2, 8, 26, 103, 219, 335, 368, 317, 137, 36 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 72.3, "p25": 53.6, "p75": 86.6, "synthetic_n": 1551, "histogram": [ 9, 39, 56, 76, 132, 203, 205, 231, 306, 294 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 55.6, "p25": 43.2, "p75": 67.5, "synthetic_n": 1551, "histogram": [ 10, 31, 76, 191, 285, 312, 349, 211, 72, 14 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 55.5, "p25": 44.0, "p75": 66.9, "synthetic_n": 1551, "histogram": [ 13, 31, 97, 166, 263, 360, 329, 194, 74, 24 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 62.9, "p25": 51.2, "p75": 73.3, "synthetic_n": 1551, "histogram": [ 1, 12, 36, 111, 197, 317, 363, 286, 176, 52 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 72.2, "p25": 54.4, "p75": 84.3, "synthetic_n": 5033, "histogram": [ 85, 190, 303, 277, 274, 408, 773, 1019, 955, 749 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 49.6, "p25": 38.1, "p75": 60.7, "synthetic_n": 5033, "histogram": [ 55, 179, 431, 763, 1131, 1131, 826, 394, 104, 19 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 62.8, "p25": 46.2, "p75": 81.0, "synthetic_n": 5033, "histogram": [ 82, 186, 283, 334, 650, 790, 643, 729, 796, 540 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 47.4, "p25": 35.2, "p75": 59.7, "synthetic_n": 5033, "histogram": [ 114, 232, 521, 833, 1087, 1007, 769, 359, 93, 18 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 58.8, "p25": 46.8, "p75": 69.6, "synthetic_n": 5033, "histogram": [ 14, 71, 216, 461, 776, 1109, 1166, 792, 313, 115 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 50.0, "p25": 37.4, "p75": 61.6, "synthetic_n": 5033, "histogram": [ 84, 193, 461, 766, 1008, 1118, 798, 393, 173, 39 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 70.6, "p25": 52.2, "p75": 82.7, "synthetic_n": 4063, "histogram": [ 78, 174, 261, 234, 220, 355, 669, 827, 713, 532 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 47.4, "p25": 36.3, "p75": 57.9, "synthetic_n": 4063, "histogram": [ 53, 171, 407, 678, 981, 944, 572, 217, 38, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 61.2, "p25": 45.4, "p75": 79.7, "synthetic_n": 4063, "histogram": [ 76, 161, 242, 274, 551, 658, 519, 587, 610, 385 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 45.6, "p25": 33.3, "p75": 57.6, "synthetic_n": 4063, "histogram": [ 110, 214, 467, 719, 920, 793, 566, 225, 43, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 57.3, "p25": 45.0, "p75": 68.0, "synthetic_n": 4063, "histogram": [ 14, 66, 194, 418, 675, 932, 910, 572, 214, 68 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 47.7, "p25": 35.3, "p75": 59.0, "synthetic_n": 4063, "histogram": [ 80, 185, 434, 683, 855, 900, 588, 259, 67, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 78.0, "p25": 62.5, "p75": 88.0, "synthetic_n": 821, "histogram": [ 7, 13, 36, 38, 46, 50, 96, 164, 195, 176 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 54.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 59.7, "p25": 47.7, "p75": 69.2, "synthetic_n": 821, "histogram": [ 2, 8, 21, 79, 136, 171, 213, 138, 44, 9 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 69.2, "p25": 50.0, "p75": 84.6, "synthetic_n": 821, "histogram": [ 6, 21, 35, 55, 88, 113, 103, 119, 158, 123 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 54.9, "p25": 42.5, "p75": 66.0, "synthetic_n": 821, "histogram": [ 4, 17, 50, 104, 146, 188, 162, 107, 37, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 64.4, "p25": 53.9, "p75": 74.3, "synthetic_n": 821, "histogram": [ 0, 5, 19, 38, 91, 151, 223, 182, 77, 35 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 57.8, "p25": 47.1, "p75": 69.9, "synthetic_n": 821, "histogram": [ 4, 8, 24, 77, 145, 192, 167, 108, 81, 15 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 83.8, "p25": 71.3, "p75": 91.9, "synthetic_n": 149, "histogram": [ 0, 3, 6, 5, 8, 3, 8, 28, 47, 41 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 76.4% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 69.0, "p25": 59.1, "p75": 77.3, "synthetic_n": 149, "histogram": [ 0, 0, 3, 6, 14, 16, 41, 39, 22, 8 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 75.4, "p25": 56.1, "p75": 88.5, "synthetic_n": 149, "histogram": [ 0, 4, 6, 5, 11, 19, 21, 23, 28, 32 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 63.2, "p25": 50.6, "p75": 72.7, "synthetic_n": 149, "histogram": [ 0, 1, 4, 10, 21, 26, 41, 27, 13, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 69.7, "p25": 58.6, "p75": 79.3, "synthetic_n": 149, "histogram": [ 0, 0, 3, 5, 10, 26, 33, 38, 22, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 68.7, "p25": 57.7, "p75": 79.6, "synthetic_n": 149, "histogram": [ 0, 0, 3, 6, 8, 26, 43, 26, 25, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 67.8, "p25": 47.2, "p75": 80.8, "synthetic_n": 8894, "histogram": [ 262, 452, 593, 565, 498, 900, 1522, 1724, 1444, 934 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 46.3, "p25": 34.8, "p75": 57.4, "synthetic_n": 8894, "histogram": [ 146, 453, 951, 1584, 2077, 1897, 1174, 469, 123, 20 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 61.4, "p25": 45.6, "p75": 79.5, "synthetic_n": 8894, "histogram": [ 164, 369, 472, 602, 1244, 1433, 1149, 1300, 1332, 829 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 43.7, "p25": 31.4, "p75": 55.5, "synthetic_n": 8894, "histogram": [ 288, 582, 1109, 1714, 1952, 1692, 1020, 423, 95, 19 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 49.6, "p25": 37.6, "p75": 61.1, "synthetic_n": 8894, "histogram": [ 107, 376, 756, 1367, 1891, 1970, 1476, 699, 215, 37 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 45.9, "p25": 33.3, "p75": 57.7, "synthetic_n": 8894, "histogram": [ 230, 474, 1044, 1581, 1893, 1787, 1119, 548, 174, 44 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 66.0, "p25": 44.1, "p75": 79.3, "synthetic_n": 7159, "histogram": [ 243, 411, 523, 455, 415, 775, 1285, 1342, 1078, 632 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 44.1, "p25": 32.8, "p75": 54.6, "synthetic_n": 7159, "histogram": [ 140, 432, 864, 1418, 1770, 1482, 783, 226, 39, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 59.7, "p25": 44.6, "p75": 78.1, "synthetic_n": 7159, "histogram": [ 147, 317, 391, 500, 1067, 1180, 933, 1031, 1033, 560 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 41.8, "p25": 30.0, "p75": 53.3, "synthetic_n": 7159, "histogram": [ 276, 524, 981, 1490, 1594, 1293, 713, 241, 41, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 47.7, "p25": 35.9, "p75": 59.1, "synthetic_n": 7159, "histogram": [ 97, 344, 686, 1193, 1605, 1570, 1068, 446, 132, 18 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 43.6, "p25": 31.4, "p75": 54.9, "synthetic_n": 7159, "histogram": [ 223, 451, 954, 1379, 1597, 1391, 774, 309, 68, 13 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 74.4, "p25": 57.7, "p75": 85.2, "synthetic_n": 1467, "histogram": [ 18, 38, 61, 95, 73, 107, 210, 312, 319, 234 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 54.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 55.1, "p25": 44.3, "p75": 65.5, "synthetic_n": 1467, "histogram": [ 6, 20, 85, 160, 274, 359, 325, 179, 54, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 66.0, "p25": 49.2, "p75": 83.0, "synthetic_n": 1467, "histogram": [ 16, 47, 75, 91, 154, 226, 179, 229, 243, 207 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 50.4, "p25": 38.8, "p75": 61.5, "synthetic_n": 1467, "histogram": [ 11, 56, 119, 202, 326, 344, 225, 138, 38, 8 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 56.8, "p25": 44.6, "p75": 66.8, "synthetic_n": 1467, "histogram": [ 10, 29, 65, 160, 249, 347, 334, 199, 62, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 54.7, "p25": 43.1, "p75": 66.0, "synthetic_n": 1467, "histogram": [ 7, 22, 86, 184, 272, 345, 289, 177, 69, 16 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 77.2, "p25": 66.5, "p75": 90.0, "synthetic_n": 268, "histogram": [ 1, 3, 9, 15, 10, 18, 27, 70, 47, 68 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 76.4% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 64.9, "p25": 54.2, "p75": 74.5, "synthetic_n": 268, "histogram": [ 0, 1, 2, 6, 33, 56, 66, 64, 30, 10 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 75.9, "p25": 56.6, "p75": 89.5, "synthetic_n": 268, "histogram": [ 1, 5, 6, 11, 23, 27, 37, 40, 56, 62 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 61.2, "p25": 50.2, "p75": 69.6, "synthetic_n": 268, "histogram": [ 1, 2, 9, 22, 32, 55, 82, 44, 16, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 62.8, "p25": 51.9, "p75": 72.8, "synthetic_n": 268, "histogram": [ 0, 3, 5, 14, 37, 53, 74, 54, 21, 7 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 67.5, "p25": 54.0, "p75": 77.7, "synthetic_n": 268, "histogram": [ 0, 1, 4, 18, 24, 51, 56, 62, 37, 15 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 62.2, "p25": 35.4, "p75": 75.5, "synthetic_n": 9983, "histogram": [ 443, 771, 895, 674, 620, 1190, 1841, 1788, 1124, 637 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 41.4, "p25": 29.5, "p75": 52.3, "synthetic_n": 9983, "histogram": [ 371, 752, 1444, 2075, 2344, 1795, 799, 331, 61, 11 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.6, "p25": 42.9, "p75": 77.2, "synthetic_n": 9983, "histogram": [ 266, 518, 590, 797, 1386, 1646, 1267, 1396, 1334, 783 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 38.5, "p25": 26.3, "p75": 50.6, "synthetic_n": 9983, "histogram": [ 560, 978, 1622, 2161, 2056, 1457, 793, 289, 54, 13 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 41.5, "p25": 29.3, "p75": 52.9, "synthetic_n": 9983, "histogram": [ 427, 811, 1377, 2025, 2245, 1751, 947, 316, 75, 9 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 41.4, "p25": 28.8, "p75": 53.5, "synthetic_n": 9983, "histogram": [ 443, 786, 1477, 1974, 2172, 1616, 998, 367, 129, 21 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.5, "p25": 32.3, "p75": 73.4, "synthetic_n": 7955, "histogram": [ 407, 669, 760, 554, 496, 1005, 1547, 1358, 753, 406 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 38.8, "p25": 27.4, "p75": 49.4, "synthetic_n": 7955, "histogram": [ 359, 701, 1310, 1811, 1877, 1300, 451, 132, 13, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 56.9, "p25": 41.8, "p75": 75.8, "synthetic_n": 7955, "histogram": [ 239, 447, 497, 657, 1169, 1338, 1001, 1080, 1000, 527 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 36.4, "p25": 24.6, "p75": 48.1, "synthetic_n": 7955, "histogram": [ 525, 889, 1399, 1801, 1615, 1055, 502, 138, 26, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 39.9, "p25": 27.6, "p75": 50.8, "synthetic_n": 7955, "histogram": [ 400, 736, 1183, 1677, 1839, 1296, 626, 165, 31, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 38.9, "p25": 26.5, "p75": 50.3, "synthetic_n": 7955, "histogram": [ 432, 720, 1322, 1688, 1743, 1207, 623, 186, 30, 4 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 69.0, "p25": 48.1, "p75": 81.7, "synthetic_n": 1731, "histogram": [ 36, 93, 116, 99, 106, 172, 260, 352, 317, 180 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 54.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 50.0, "p25": 40.0, "p75": 60.5, "synthetic_n": 1731, "histogram": [ 12, 49, 130, 242, 426, 422, 284, 132, 29, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 64.7, "p25": 47.0, "p75": 82.2, "synthetic_n": 1731, "histogram": [ 26, 63, 86, 123, 197, 260, 229, 268, 283, 196 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 45.8, "p25": 33.7, "p75": 57.8, "synthetic_n": 1731, "histogram": [ 32, 84, 209, 320, 383, 342, 228, 113, 15, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 47.8, "p25": 35.5, "p75": 59.6, "synthetic_n": 1731, "histogram": [ 26, 69, 176, 312, 360, 372, 260, 120, 30, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 49.7, "p25": 38.6, "p75": 62.4, "synthetic_n": 1731, "histogram": [ 11, 61, 148, 262, 393, 345, 300, 135, 65, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 74.9, "p25": 56.3, "p75": 85.1, "synthetic_n": 297, "histogram": [ 0, 9, 19, 21, 18, 13, 34, 78, 54, 51 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 76.4% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 61.2, "p25": 51.4, "p75": 71.9, "synthetic_n": 297, "histogram": [ 0, 2, 4, 22, 41, 73, 64, 67, 19, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 71.7, "p25": 53.6, "p75": 87.6, "synthetic_n": 297, "histogram": [ 1, 8, 7, 17, 20, 48, 37, 48, 51, 60 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 54.7, "p25": 42.6, "p75": 67.2, "synthetic_n": 297, "histogram": [ 3, 5, 14, 40, 58, 60, 63, 38, 13, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 55.1, "p25": 44.5, "p75": 64.9, "synthetic_n": 297, "histogram": [ 1, 6, 18, 36, 46, 83, 61, 31, 14, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 61.9, "p25": 50.3, "p75": 72.6, "synthetic_n": 297, "histogram": [ 0, 5, 7, 24, 36, 64, 75, 46, 34, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 62.0, "p25": 34.3, "p75": 75.1, "synthetic_n": 8806, "histogram": [ 425, 684, 798, 571, 503, 1083, 1688, 1527, 965, 562 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 40.6, "p25": 28.7, "p75": 51.8, "synthetic_n": 8806, "histogram": [ 350, 721, 1311, 1877, 2033, 1460, 730, 252, 65, 7 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.5, "p25": 42.4, "p75": 77.3, "synthetic_n": 8806, "histogram": [ 203, 509, 522, 713, 1250, 1382, 1122, 1284, 1134, 687 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 38.1, "p25": 25.6, "p75": 50.0, "synthetic_n": 8806, "histogram": [ 588, 874, 1426, 1827, 1878, 1313, 625, 227, 43, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 37.7, "p25": 25.3, "p75": 49.4, "synthetic_n": 8806, "histogram": [ 568, 932, 1435, 1906, 1861, 1260, 602, 200, 40, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 40.5, "p25": 28.0, "p75": 52.9, "synthetic_n": 8806, "histogram": [ 410, 748, 1383, 1745, 1802, 1428, 807, 362, 98, 23 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.4, "p25": 31.4, "p75": 72.9, "synthetic_n": 7017, "histogram": [ 381, 603, 683, 444, 410, 922, 1406, 1190, 657, 321 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 38.1, "p25": 26.6, "p75": 48.7, "synthetic_n": 7017, "histogram": [ 336, 667, 1193, 1621, 1624, 1043, 423, 101, 9, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 56.9, "p25": 41.3, "p75": 76.0, "synthetic_n": 7017, "histogram": [ 181, 444, 422, 606, 1044, 1124, 895, 986, 835, 480 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 36.1, "p25": 23.7, "p75": 47.7, "synthetic_n": 7017, "histogram": [ 548, 772, 1235, 1513, 1504, 937, 384, 111, 11, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 35.7, "p25": 23.7, "p75": 47.2, "synthetic_n": 7017, "histogram": [ 524, 827, 1214, 1607, 1459, 912, 356, 103, 15, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 38.0, "p25": 26.2, "p75": 50.0, "synthetic_n": 7017, "histogram": [ 386, 696, 1242, 1490, 1444, 1036, 500, 190, 28, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 68.9, "p25": 47.5, "p75": 81.9, "synthetic_n": 1492, "histogram": [ 43, 71, 104, 97, 74, 144, 239, 288, 248, 184 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 54.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 49.3, "p25": 38.5, "p75": 60.3, "synthetic_n": 1492, "histogram": [ 14, 52, 114, 234, 355, 338, 246, 108, 29, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 64.3, "p25": 47.2, "p75": 80.7, "synthetic_n": 1492, "histogram": [ 22, 58, 83, 86, 182, 217, 191, 259, 238, 156 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 45.7, "p25": 32.3, "p75": 57.2, "synthetic_n": 1492, "histogram": [ 39, 100, 175, 267, 303, 313, 191, 77, 24, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 44.6, "p25": 31.5, "p75": 56.5, "synthetic_n": 1492, "histogram": [ 40, 96, 195, 258, 349, 276, 183, 77, 17, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 49.8, "p25": 37.5, "p75": 61.9, "synthetic_n": 1492, "histogram": [ 24, 51, 132, 237, 306, 323, 245, 128, 37, 9 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 74.6, "p25": 53.8, "p75": 87.2, "synthetic_n": 297, "histogram": [ 1, 10, 11, 30, 19, 17, 43, 49, 60, 57 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 76.4% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 58.4, "p25": 48.3, "p75": 70.0, "synthetic_n": 297, "histogram": [ 0, 2, 4, 22, 54, 79, 61, 43, 27, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 71.0, "p25": 52.1, "p75": 85.5, "synthetic_n": 297, "histogram": [ 0, 7, 17, 21, 24, 41, 36, 39, 61, 51 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 51.8, "p25": 41.4, "p75": 64.1, "synthetic_n": 297, "histogram": [ 1, 2, 16, 47, 71, 63, 50, 39, 8, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 52.4, "p25": 38.8, "p75": 62.3, "synthetic_n": 297, "histogram": [ 4, 9, 26, 41, 53, 72, 63, 20, 8, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 59.6, "p25": 49.2, "p75": 71.7, "synthetic_n": 297, "histogram": [ 0, 1, 9, 18, 52, 69, 62, 44, 33, 9 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 62.8, "p25": 34.7, "p75": 76.3, "synthetic_n": 5111, "histogram": [ 221, 419, 453, 354, 298, 593, 899, 894, 643, 337 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 41.8, "p25": 29.8, "p75": 53.4, "synthetic_n": 5111, "histogram": [ 175, 381, 738, 1050, 1139, 890, 518, 171, 45, 4 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.5, "p25": 42.3, "p75": 78.1, "synthetic_n": 5111, "histogram": [ 129, 314, 298, 403, 698, 819, 613, 693, 707, 437 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 39.0, "p25": 26.4, "p75": 51.1, "synthetic_n": 5111, "histogram": [ 294, 492, 793, 1086, 1057, 781, 423, 146, 35, 4 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 39.7, "p25": 27.3, "p75": 51.3, "synthetic_n": 5111, "histogram": [ 271, 466, 774, 1082, 1112, 797, 433, 152, 24, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 41.8, "p25": 28.7, "p75": 54.8, "synthetic_n": 5111, "histogram": [ 232, 417, 728, 980, 1061, 832, 552, 237, 63, 9 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.6, "p25": 31.6, "p75": 73.9, "synthetic_n": 4081, "histogram": [ 195, 372, 394, 291, 235, 507, 756, 703, 430, 198 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 39.2, "p25": 27.8, "p75": 50.1, "synthetic_n": 4081, "histogram": [ 170, 355, 680, 922, 921, 653, 301, 65, 14, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 56.9, "p25": 40.7, "p75": 76.1, "synthetic_n": 4081, "histogram": [ 107, 271, 258, 348, 601, 649, 487, 537, 524, 299 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 36.6, "p25": 24.4, "p75": 48.7, "synthetic_n": 4081, "histogram": [ 274, 444, 688, 916, 833, 579, 255, 79, 10, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 37.8, "p25": 25.8, "p75": 49.0, "synthetic_n": 4081, "histogram": [ 250, 416, 674, 919, 879, 567, 277, 86, 13, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 39.3, "p25": 26.4, "p75": 51.2, "synthetic_n": 4081, "histogram": [ 222, 386, 655, 846, 864, 617, 347, 119, 23, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 69.8, "p25": 48.7, "p75": 82.8, "synthetic_n": 870, "histogram": [ 25, 45, 49, 53, 54, 76, 134, 160, 163, 111 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 54.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 51.8, "p25": 40.6, "p75": 62.0, "synthetic_n": 870, "histogram": [ 5, 24, 55, 119, 193, 211, 171, 73, 16, 3 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 65.9, "p25": 48.7, "p75": 82.8, "synthetic_n": 870, "histogram": [ 21, 39, 34, 49, 82, 150, 102, 131, 152, 110 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 46.7, "p25": 34.7, "p75": 58.5, "synthetic_n": 870, "histogram": [ 18, 42, 97, 150, 198, 166, 131, 49, 18, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 46.3, "p25": 35.0, "p75": 57.7, "synthetic_n": 870, "histogram": [ 20, 49, 89, 146, 196, 195, 120, 47, 8, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 51.8, "p25": 39.4, "p75": 63.2, "synthetic_n": 870, "histogram": [ 9, 30, 71, 119, 172, 186, 161, 93, 25, 4 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 79.5, "p25": 58.5, "p75": 85.8, "synthetic_n": 160, "histogram": [ 1, 2, 10, 10, 9, 10, 9, 31, 50, 28 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 76.4% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 63.7, "p25": 50.5, "p75": 72.6, "synthetic_n": 160, "histogram": [ 0, 2, 3, 9, 25, 26, 46, 33, 15, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 71.8, "p25": 53.4, "p75": 84.7, "synthetic_n": 160, "histogram": [ 1, 4, 6, 6, 15, 20, 24, 25, 31, 28 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 55.7, "p25": 43.0, "p75": 65.7, "synthetic_n": 160, "histogram": [ 2, 6, 8, 20, 26, 36, 37, 18, 7, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 53.2, "p25": 43.7, "p75": 64.8, "synthetic_n": 160, "histogram": [ 1, 1, 11, 17, 37, 35, 36, 19, 3, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 62.1, "p25": 47.5, "p75": 71.4, "synthetic_n": 160, "histogram": [ 1, 1, 2, 15, 25, 29, 44, 25, 15, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 64.6, "p25": 43.1, "p75": 77.7, "synthetic_n": 3013, "histogram": [ 128, 192, 232, 153, 170, 371, 592, 527, 408, 240 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 42.7, "p25": 31.2, "p75": 55.0, "synthetic_n": 3013, "histogram": [ 78, 221, 383, 637, 667, 556, 317, 126, 23, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 59.4, "p25": 42.7, "p75": 78.2, "synthetic_n": 3013, "histogram": [ 72, 173, 185, 231, 399, 472, 352, 465, 413, 251 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 40.6, "p25": 27.7, "p75": 52.9, "synthetic_n": 3013, "histogram": [ 157, 256, 449, 612, 618, 484, 310, 100, 27, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 43.5, "p25": 31.0, "p75": 55.1, "synthetic_n": 3013, "histogram": [ 117, 216, 369, 564, 659, 576, 358, 118, 31, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 42.3, "p25": 30.0, "p75": 55.7, "synthetic_n": 3013, "histogram": [ 108, 221, 423, 577, 623, 540, 325, 151, 39, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 63.5, "p25": 40.0, "p75": 75.9, "synthetic_n": 2413, "histogram": [ 116, 169, 199, 118, 130, 300, 510, 429, 293, 149 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 40.6, "p25": 29.4, "p75": 51.7, "synthetic_n": 2413, "histogram": [ 77, 204, 354, 543, 548, 425, 197, 58, 7, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.3, "p25": 41.0, "p75": 77.1, "synthetic_n": 2413, "histogram": [ 64, 152, 154, 199, 322, 375, 280, 369, 318, 180 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 38.8, "p25": 26.3, "p75": 50.8, "synthetic_n": 2413, "histogram": [ 150, 225, 388, 512, 497, 360, 210, 60, 11, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 41.8, "p25": 29.2, "p75": 53.1, "synthetic_n": 2413, "histogram": [ 110, 196, 319, 475, 542, 430, 251, 73, 16, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 40.5, "p25": 28.3, "p75": 52.4, "synthetic_n": 2413, "histogram": [ 104, 203, 377, 494, 519, 402, 216, 81, 15, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 69.1, "p25": 50.0, "p75": 83.2, "synthetic_n": 503, "histogram": [ 10, 21, 28, 31, 35, 67, 68, 81, 90, 72 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 54.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 51.1, "p25": 39.2, "p75": 62.2, "synthetic_n": 503, "histogram": [ 1, 17, 26, 88, 108, 111, 95, 43, 12, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 62.8, "p25": 46.9, "p75": 80.5, "synthetic_n": 503, "histogram": [ 7, 19, 27, 30, 67, 77, 60, 86, 81, 49 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 47.6, "p25": 33.8, "p75": 58.5, "synthetic_n": 503, "histogram": [ 6, 28, 56, 94, 101, 104, 76, 28, 10, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 49.5, "p25": 37.0, "p75": 60.4, "synthetic_n": 503, "histogram": [ 6, 18, 47, 75, 107, 119, 83, 35, 12, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 51.8, "p25": 38.9, "p75": 63.1, "synthetic_n": 503, "histogram": [ 4, 18, 43, 77, 90, 120, 87, 42, 19, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 77.3, "p25": 62.9, "p75": 85.5, "synthetic_n": 97, "histogram": [ 2, 2, 5, 4, 5, 4, 14, 17, 25, 19 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 76.4% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 63.8, "p25": 52.4, "p75": 72.6, "synthetic_n": 97, "histogram": [ 0, 0, 3, 6, 11, 20, 25, 25, 4, 3 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 69.0, "p25": 53.4, "p75": 87.2, "synthetic_n": 97, "histogram": [ 1, 2, 4, 2, 10, 20, 12, 10, 14, 22 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 56.4, "p25": 45.7, "p75": 67.8, "synthetic_n": 97, "histogram": [ 1, 3, 5, 6, 20, 20, 24, 12, 6, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 56.8, "p25": 47.1, "p75": 65.2, "synthetic_n": 97, "histogram": [ 1, 2, 3, 14, 10, 27, 24, 10, 3, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 64.6, "p25": 52.3, "p75": 72.1, "synthetic_n": 97, "histogram": [ 0, 0, 3, 6, 14, 18, 22, 28, 5, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.9, "p25": 31.7, "p75": 73.3, "synthetic_n": 4031, "histogram": [ 248, 359, 340, 263, 241, 501, 794, 699, 394, 192 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 38.9, "p25": 26.6, "p75": 50.5, "synthetic_n": 4031, "histogram": [ 209, 376, 661, 870, 874, 619, 308, 88, 23, 3 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 56.5, "p25": 40.8, "p75": 75.8, "synthetic_n": 4031, "histogram": [ 114, 248, 250, 346, 625, 630, 490, 555, 475, 298 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 36.3, "p25": 23.7, "p75": 48.4, "synthetic_n": 4031, "histogram": [ 337, 449, 656, 888, 809, 533, 256, 85, 17, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 36.4, "p25": 23.6, "p75": 47.8, "synthetic_n": 4031, "histogram": [ 343, 436, 708, 868, 830, 558, 218, 64, 6, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 38.9, "p25": 26.0, "p75": 51.6, "synthetic_n": 4031, "histogram": [ 255, 376, 671, 805, 788, 611, 353, 134, 35, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 59.5, "p25": 28.8, "p75": 71.7, "synthetic_n": 3250, "histogram": [ 214, 329, 297, 201, 194, 416, 668, 543, 272, 116 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 36.7, "p25": 24.9, "p75": 47.4, "synthetic_n": 3250, "histogram": [ 197, 353, 592, 744, 700, 440, 181, 38, 5, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 55.2, "p25": 39.9, "p75": 74.4, "synthetic_n": 3250, "histogram": [ 98, 210, 210, 295, 532, 517, 384, 426, 362, 216 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 34.7, "p25": 22.2, "p75": 46.3, "synthetic_n": 3250, "histogram": [ 314, 394, 565, 750, 633, 375, 168, 49, 1, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 34.4, "p25": 22.2, "p75": 45.9, "synthetic_n": 3250, "histogram": [ 310, 382, 627, 729, 625, 410, 139, 27, 1, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 36.3, "p25": 23.7, "p75": 48.5, "synthetic_n": 3250, "histogram": [ 240, 347, 598, 680, 650, 444, 217, 61, 12, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 66.8, "p25": 42.6, "p75": 79.3, "synthetic_n": 658, "histogram": [ 31, 28, 40, 53, 39, 73, 106, 132, 99, 57 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 54.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 47.5, "p25": 36.7, "p75": 57.7, "synthetic_n": 658, "histogram": [ 10, 23, 65, 113, 160, 151, 96, 30, 8, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 62.0, "p25": 43.9, "p75": 78.6, "synthetic_n": 658, "histogram": [ 13, 35, 37, 47, 82, 99, 90, 105, 87, 63 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 42.9, "p25": 31.3, "p75": 55.4, "synthetic_n": 658, "histogram": [ 22, 51, 80, 124, 146, 135, 68, 25, 7, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 43.2, "p25": 30.9, "p75": 53.3, "synthetic_n": 658, "histogram": [ 30, 51, 70, 122, 174, 122, 64, 22, 3, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 48.3, "p25": 34.3, "p75": 59.8, "synthetic_n": 658, "histogram": [ 15, 27, 68, 114, 128, 143, 113, 39, 10, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 72.8, "p25": 57.7, "p75": 85.1, "synthetic_n": 123, "histogram": [ 3, 2, 3, 9, 8, 12, 20, 24, 23, 19 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 76.4% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 60.1, "p25": 48.8, "p75": 69.9, "synthetic_n": 123, "histogram": [ 2, 0, 4, 13, 14, 28, 31, 20, 10, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 75.0, "p25": 55.2, "p75": 85.2, "synthetic_n": 123, "histogram": [ 3, 3, 3, 4, 11, 14, 16, 24, 26, 19 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 50.6, "p25": 40.4, "p75": 63.5, "synthetic_n": 123, "histogram": [ 1, 4, 11, 14, 30, 23, 20, 11, 9, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 48.9, "p25": 38.8, "p75": 60.3, "synthetic_n": 123, "histogram": [ 3, 3, 11, 17, 31, 26, 15, 15, 2, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 61.8, "p25": 51.8, "p75": 72.9, "synthetic_n": 123, "histogram": [ 0, 2, 5, 11, 10, 24, 23, 34, 13, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.4, "p25": 30.3, "p75": 73.3, "synthetic_n": 5129, "histogram": [ 315, 466, 484, 350, 288, 625, 990, 871, 512, 228 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 37.3, "p25": 25.6, "p75": 49.3, "synthetic_n": 5129, "histogram": [ 291, 549, 902, 1135, 1051, 747, 327, 101, 22, 4 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 55.7, "p25": 40.1, "p75": 75.1, "synthetic_n": 5129, "histogram": [ 150, 346, 331, 449, 764, 840, 595, 699, 582, 373 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 35.0, "p25": 22.4, "p75": 46.8, "synthetic_n": 5129, "histogram": [ 457, 636, 935, 1088, 977, 615, 313, 96, 12, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 32.7, "p25": 20.1, "p75": 44.6, "synthetic_n": 5129, "histogram": [ 555, 712, 981, 1119, 948, 556, 197, 54, 7, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 37.3, "p25": 24.9, "p75": 49.9, "synthetic_n": 5129, "histogram": [ 363, 523, 850, 1087, 1025, 726, 370, 134, 42, 9 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 59.2, "p25": 28.4, "p75": 71.7, "synthetic_n": 4106, "histogram": [ 271, 412, 403, 260, 215, 528, 846, 685, 361, 125 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 28.8% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 35.0, "p25": 24.0, "p75": 46.7, "synthetic_n": 4106, "histogram": [ 276, 499, 792, 953, 841, 523, 179, 38, 5, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 54.6, "p25": 38.8, "p75": 74.0, "synthetic_n": 4106, "histogram": [ 136, 292, 283, 368, 622, 682, 463, 558, 438, 264 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 33.1, "p25": 21.0, "p75": 45.0, "synthetic_n": 4106, "histogram": [ 417, 549, 797, 896, 758, 452, 182, 47, 8, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 31.3, "p25": 18.7, "p75": 42.8, "synthetic_n": 4106, "histogram": [ 500, 613, 817, 910, 734, 384, 116, 27, 5, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 34.9, "p25": 22.8, "p75": 47.3, "synthetic_n": 4106, "histogram": [ 341, 469, 763, 920, 796, 519, 222, 66, 9, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 64.6, "p25": 36.1, "p75": 79.0, "synthetic_n": 863, "histogram": [ 42, 48, 75, 67, 60, 87, 124, 153, 128, 79 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 54.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 45.5, "p25": 32.5, "p75": 56.7, "synthetic_n": 863, "histogram": [ 15, 49, 107, 163, 183, 187, 114, 37, 7, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 59.1, "p25": 44.3, "p75": 77.9, "synthetic_n": 863, "histogram": [ 12, 48, 41, 71, 124, 144, 110, 119, 105, 89 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 41.1, "p25": 28.6, "p75": 53.2, "synthetic_n": 863, "histogram": [ 39, 79, 129, 160, 192, 134, 99, 27, 4, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 37.9, "p25": 25.7, "p75": 49.8, "synthetic_n": 863, "histogram": [ 52, 92, 144, 187, 175, 134, 58, 20, 1, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 46.0, "p25": 34.5, "p75": 57.5, "synthetic_n": 863, "histogram": [ 22, 52, 84, 154, 201, 171, 110, 47, 19, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 69.8, "p25": 41.4, "p75": 83.8, "synthetic_n": 160, "histogram": [ 2, 6, 6, 23, 13, 10, 20, 33, 23, 24 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official enterprise AI adoption anchor: 76.4% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 58.6, "p25": 46.1, "p75": 68.5, "synthetic_n": 160, "histogram": [ 0, 1, 3, 19, 27, 37, 34, 26, 10, 3 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 70.3, "p25": 48.5, "p75": 84.6, "synthetic_n": 160, "histogram": [ 2, 6, 7, 10, 18, 14, 22, 22, 39, 20 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 50.8, "p25": 37.0, "p75": 63.0, "synthetic_n": 160, "histogram": [ 1, 8, 9, 32, 27, 29, 32, 22, 0, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 46.4, "p25": 35.5, "p75": 57.2, "synthetic_n": 160, "histogram": [ 3, 7, 20, 22, 39, 38, 23, 7, 1, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 59.3, "p25": 47.3, "p75": 69.8, "synthetic_n": 160, "histogram": [ 0, 2, 3, 13, 28, 36, 38, 21, 14, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 63.9, "p25": 38.0, "p75": 77.1, "synthetic_n": 100000, "histogram": [ 4280, 7191, 8248, 6357, 5808, 11327, 18362, 18256, 12631, 7540 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 42.5, "p25": 30.3, "p75": 54.0, "synthetic_n": 100000, "histogram": [ 3447, 7266, 13754, 20000, 22341, 18238, 10172, 3751, 917, 114 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 59.0, "p25": 43.1, "p75": 77.8, "synthetic_n": 100000, "histogram": [ 2406, 5319, 5756, 7729, 14078, 16131, 12573, 14163, 13431, 8414 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 39.8, "p25": 27.1, "p75": 52.2, "synthetic_n": 100000, "histogram": [ 5591, 9134, 15287, 20262, 20737, 15890, 8896, 3362, 738, 103 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 42.3, "p25": 29.1, "p75": 55.0, "synthetic_n": 100000, "histogram": [ 4880, 8051, 13426, 18566, 20553, 17248, 10751, 4745, 1447, 333 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 42.3, "p25": 29.4, "p75": 55.0, "synthetic_n": 100000, "histogram": [ 4339, 7687, 13964, 18984, 20676, 17344, 10589, 4668, 1441, 308 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 62.4, "p25": 34.8, "p75": 75.2, "synthetic_n": 79882, "histogram": [ 3844, 6348, 7038, 5080, 4640, 9576, 15291, 14167, 8996, 4902 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 40.0, "p25": 28.2, "p75": 51.1, "synthetic_n": 79882, "histogram": [ 3291, 6798, 12429, 17354, 18164, 13575, 6297, 1707, 253, 14 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 57.5, "p25": 42.0, "p75": 76.5, "synthetic_n": 79882, "histogram": [ 2146, 4574, 4824, 6462, 11766, 13070, 10005, 11028, 10155, 5852 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 37.9, "p25": 25.5, "p75": 49.9, "synthetic_n": 79882, "histogram": [ 5203, 8164, 13257, 16935, 16500, 11686, 5862, 1912, 323, 40 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 40.6, "p25": 27.5, "p75": 53.1, "synthetic_n": 79882, "histogram": [ 4471, 7161, 11586, 15573, 16517, 12999, 7513, 3034, 846, 182 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 39.9, "p25": 27.3, "p75": 52.0, "synthetic_n": 79882, "histogram": [ 4131, 7109, 12516, 16318, 16728, 12976, 6970, 2510, 545, 79 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 69.7, "p25": 49.5, "p75": 82.2, "synthetic_n": 17041, "histogram": [ 407, 773, 1068, 1064, 997, 1566, 2706, 3428, 2966, 2066 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 51.0, "p25": 39.8, "p75": 61.8, "synthetic_n": 17041, "histogram": [ 151, 448, 1266, 2434, 3743, 3993, 3130, 1444, 388, 44 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 64.4, "p25": 47.3, "p75": 81.4, "synthetic_n": 17041, "histogram": [ 241, 672, 820, 1123, 2028, 2634, 2177, 2675, 2688, 1983 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 46.6, "p25": 34.1, "p75": 58.4, "synthetic_n": 17041, "histogram": [ 376, 902, 1878, 2936, 3648, 3551, 2391, 1055, 269, 35 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 48.7, "p25": 35.5, "p75": 60.8, "synthetic_n": 17041, "histogram": [ 388, 833, 1654, 2659, 3458, 3531, 2627, 1329, 453, 109 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 51.1, "p25": 38.8, "p75": 62.7, "synthetic_n": 17041, "histogram": [ 205, 551, 1367, 2467, 3508, 3755, 2894, 1588, 585, 121 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 75.7, "p25": 58.2, "p75": 86.5, "synthetic_n": 3077, "histogram": [ 29, 70, 142, 213, 171, 185, 365, 661, 669, 572 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 61.8, "p25": 50.7, "p75": 72.2, "synthetic_n": 3077, "histogram": [ 5, 20, 59, 212, 434, 670, 745, 600, 276, 56 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 71.8, "p25": 53.5, "p75": 86.5, "synthetic_n": 3077, "histogram": [ 19, 73, 112, 144, 284, 427, 391, 460, 588, 579 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 55.0, "p25": 42.8, "p75": 66.7, "synthetic_n": 3077, "histogram": [ 12, 68, 152, 391, 589, 653, 643, 395, 146, 28 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 55.0, "p25": 43.7, "p75": 66.4, "synthetic_n": 3077, "histogram": [ 21, 57, 186, 334, 578, 718, 611, 382, 148, 42 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "All industries", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 62.4, "p25": 50.5, "p75": 73.2, "synthetic_n": 3077, "histogram": [ 3, 27, 81, 199, 440, 613, 725, 570, 311, 108 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 72.4, "p25": 54.5, "p75": 84.3, "synthetic_n": 9948, "histogram": [ 173, 355, 561, 550, 537, 829, 1516, 2000, 1969, 1458 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 50.1, "p25": 38.4, "p75": 61.1, "synthetic_n": 9948, "histogram": [ 109, 346, 817, 1504, 2167, 2266, 1735, 765, 208, 31 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 63.1, "p25": 46.4, "p75": 81.1, "synthetic_n": 9948, "histogram": [ 162, 375, 536, 654, 1281, 1547, 1263, 1469, 1572, 1089 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 47.9, "p25": 35.6, "p75": 59.9, "synthetic_n": 9948, "histogram": [ 224, 458, 992, 1633, 2111, 2049, 1568, 693, 186, 34 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 59.0, "p25": 46.9, "p75": 69.8, "synthetic_n": 9948, "histogram": [ 28, 122, 426, 902, 1514, 2195, 2305, 1596, 638, 222 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 50.0, "p25": 37.2, "p75": 61.7, "synthetic_n": 9948, "histogram": [ 156, 387, 903, 1508, 2012, 2152, 1614, 818, 322, 76 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 70.9, "p25": 52.5, "p75": 83.0, "synthetic_n": 8010, "histogram": [ 161, 313, 496, 454, 445, 716, 1302, 1619, 1467, 1037 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 47.8, "p25": 36.5, "p75": 58.3, "synthetic_n": 8010, "histogram": [ 104, 326, 775, 1353, 1872, 1854, 1218, 429, 74, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 61.5, "p25": 45.3, "p75": 79.9, "synthetic_n": 8010, "histogram": [ 149, 330, 456, 555, 1066, 1275, 1020, 1168, 1210, 781 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 45.9, "p25": 33.8, "p75": 57.9, "synthetic_n": 8010, "histogram": [ 216, 423, 896, 1404, 1769, 1603, 1139, 452, 97, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 57.5, "p25": 45.2, "p75": 68.1, "synthetic_n": 8010, "histogram": [ 25, 113, 386, 809, 1315, 1827, 1811, 1169, 424, 131 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 47.6, "p25": 35.0, "p75": 59.0, "synthetic_n": 8010, "histogram": [ 148, 365, 839, 1362, 1716, 1713, 1176, 521, 147, 23 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 77.9, "p25": 62.8, "p75": 87.9, "synthetic_n": 1649, "histogram": [ 12, 38, 57, 84, 80, 106, 200, 325, 404, 343 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 59.4, "p25": 48.2, "p75": 68.7, "synthetic_n": 1649, "histogram": [ 5, 20, 39, 142, 267, 372, 441, 259, 91, 13 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 69.2, "p25": 50.9, "p75": 84.4, "synthetic_n": 1649, "histogram": [ 13, 37, 69, 92, 188, 235, 207, 253, 313, 242 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 55.0, "p25": 42.9, "p75": 65.0, "synthetic_n": 1649, "histogram": [ 8, 34, 88, 209, 298, 394, 357, 185, 65, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 64.4, "p25": 53.4, "p75": 74.0, "synthetic_n": 1649, "histogram": [ 3, 9, 36, 85, 178, 319, 431, 352, 168, 68 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 58.1, "p25": 47.0, "p75": 69.5, "synthetic_n": 1649, "histogram": [ 8, 22, 60, 138, 274, 392, 357, 236, 130, 32 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 83.2, "p25": 74.1, "p75": 90.7, "synthetic_n": 289, "histogram": [ 0, 4, 8, 12, 12, 7, 14, 56, 98, 78 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 68.8, "p25": 58.2, "p75": 77.3, "synthetic_n": 289, "histogram": [ 0, 0, 3, 9, 28, 40, 76, 77, 43, 13 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 74.6, "p25": 55.3, "p75": 88.7, "synthetic_n": 289, "histogram": [ 0, 8, 11, 7, 27, 37, 36, 48, 49, 66 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 63.2, "p25": 49.9, "p75": 72.7, "synthetic_n": 289, "histogram": [ 0, 1, 8, 20, 44, 52, 72, 56, 24, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 69.9, "p25": 58.6, "p75": 79.5, "synthetic_n": 289, "histogram": [ 0, 0, 4, 8, 21, 49, 63, 75, 46, 23 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Information & communication", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 68.6, "p25": 57.6, "p75": 78.9, "synthetic_n": 289, "histogram": [ 0, 0, 4, 8, 22, 47, 81, 61, 45, 21 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 67.8, "p25": 46.8, "p75": 80.6, "synthetic_n": 18090, "histogram": [ 511, 927, 1239, 1162, 1014, 1811, 3118, 3542, 2841, 1925 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 46.0, "p25": 34.4, "p75": 57.2, "synthetic_n": 18090, "histogram": [ 332, 921, 1963, 3287, 4170, 3874, 2331, 937, 239, 36 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 61.3, "p25": 45.4, "p75": 79.3, "synthetic_n": 18090, "histogram": [ 336, 788, 977, 1236, 2492, 2895, 2410, 2628, 2660, 1668 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 43.6, "p25": 31.1, "p75": 55.3, "synthetic_n": 18090, "histogram": [ 599, 1232, 2322, 3432, 4034, 3383, 2052, 821, 183, 32 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 49.5, "p25": 37.2, "p75": 61.0, "synthetic_n": 18090, "histogram": [ 242, 715, 1633, 2820, 3807, 3993, 2954, 1393, 452, 81 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 45.8, "p25": 33.0, "p75": 57.8, "synthetic_n": 18090, "histogram": [ 504, 996, 2136, 3162, 3862, 3588, 2293, 1120, 344, 85 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 66.1, "p25": 43.6, "p75": 79.0, "synthetic_n": 14490, "histogram": [ 470, 839, 1085, 938, 828, 1547, 2613, 2765, 2102, 1303 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 43.7, "p25": 32.4, "p75": 54.3, "synthetic_n": 14490, "histogram": [ 316, 870, 1800, 2937, 3507, 2997, 1530, 458, 69, 6 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 59.7, "p25": 44.4, "p75": 78.1, "synthetic_n": 14490, "histogram": [ 298, 677, 827, 1030, 2124, 2346, 1955, 2026, 2060, 1147 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 41.7, "p25": 29.5, "p75": 52.9, "synthetic_n": 14490, "histogram": [ 568, 1116, 2051, 2948, 3288, 2542, 1401, 472, 93, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 47.6, "p25": 35.6, "p75": 59.1, "synthetic_n": 14490, "histogram": [ 226, 650, 1463, 2438, 3204, 3147, 2160, 892, 267, 43 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 43.4, "p25": 31.0, "p75": 55.0, "synthetic_n": 14490, "histogram": [ 479, 932, 1954, 2767, 3217, 2775, 1576, 624, 138, 28 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 73.9, "p25": 57.1, "p75": 85.0, "synthetic_n": 3044, "histogram": [ 37, 84, 135, 194, 162, 229, 452, 637, 625, 489 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 54.9, "p25": 44.1, "p75": 64.9, "synthetic_n": 3044, "histogram": [ 16, 48, 159, 326, 602, 767, 656, 349, 111, 10 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 66.8, "p25": 49.5, "p75": 82.5, "synthetic_n": 3044, "histogram": [ 34, 103, 137, 183, 328, 475, 381, 506, 495, 402 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 50.3, "p25": 38.6, "p75": 61.1, "synthetic_n": 3044, "histogram": [ 30, 110, 253, 426, 674, 718, 497, 263, 61, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 56.0, "p25": 43.8, "p75": 66.7, "synthetic_n": 3044, "histogram": [ 16, 60, 157, 347, 529, 724, 655, 394, 136, 26 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 54.7, "p25": 42.5, "p75": 65.7, "synthetic_n": 3044, "histogram": [ 25, 60, 171, 369, 576, 714, 599, 366, 133, 31 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 78.0, "p25": 66.5, "p75": 89.1, "synthetic_n": 556, "histogram": [ 4, 4, 19, 30, 24, 35, 53, 140, 114, 133 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 65.0, "p25": 54.5, "p75": 74.5, "synthetic_n": 556, "histogram": [ 0, 3, 4, 24, 61, 110, 145, 130, 59, 20 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 75.2, "p25": 57.9, "p75": 88.4, "synthetic_n": 556, "histogram": [ 4, 8, 13, 23, 40, 74, 74, 96, 105, 119 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 60.0, "p25": 47.2, "p75": 68.8, "synthetic_n": 556, "histogram": [ 1, 6, 18, 58, 72, 123, 154, 86, 29, 9 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 62.4, "p25": 51.0, "p75": 72.6, "synthetic_n": 556, "histogram": [ 0, 5, 13, 35, 74, 122, 139, 107, 49, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Professional services", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 66.2, "p25": 53.2, "p75": 76.5, "synthetic_n": 556, "histogram": [ 0, 4, 11, 26, 69, 99, 118, 130, 73, 26 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 62.3, "p25": 35.6, "p75": 75.1, "synthetic_n": 20058, "histogram": [ 904, 1560, 1774, 1291, 1260, 2391, 3843, 3607, 2224, 1204 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 41.3, "p25": 29.3, "p75": 52.5, "synthetic_n": 20058, "histogram": [ 744, 1521, 2973, 4147, 4575, 3563, 1761, 634, 124, 16 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.7, "p25": 42.9, "p75": 77.3, "synthetic_n": 20058, "histogram": [ 519, 1081, 1181, 1550, 2800, 3291, 2584, 2752, 2683, 1617 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 38.6, "p25": 26.2, "p75": 50.7, "synthetic_n": 20058, "histogram": [ 1177, 1961, 3264, 4241, 4118, 2987, 1601, 585, 105, 19 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 41.6, "p25": 28.9, "p75": 53.1, "synthetic_n": 20058, "histogram": [ 884, 1624, 2838, 3970, 4462, 3579, 1930, 617, 139, 15 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 41.2, "p25": 28.5, "p75": 53.5, "synthetic_n": 20058, "histogram": [ 914, 1642, 2957, 3959, 4268, 3324, 1967, 762, 221, 44 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.7, "p25": 32.6, "p75": 73.3, "synthetic_n": 15917, "histogram": [ 819, 1365, 1492, 1041, 994, 2022, 3173, 2718, 1524, 769 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 38.8, "p25": 27.3, "p75": 49.7, "synthetic_n": 15917, "histogram": [ 714, 1424, 2661, 3590, 3642, 2581, 1016, 260, 28, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 57.2, "p25": 41.7, "p75": 76.0, "synthetic_n": 15917, "histogram": [ 473, 934, 984, 1268, 2340, 2638, 2034, 2131, 2007, 1108 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 36.6, "p25": 24.6, "p75": 48.3, "synthetic_n": 15917, "histogram": [ 1089, 1761, 2802, 3509, 3237, 2149, 1009, 302, 51, 8 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 39.8, "p25": 27.4, "p75": 51.0, "synthetic_n": 15917, "histogram": [ 825, 1452, 2439, 3311, 3585, 2611, 1279, 353, 58, 4 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 38.6, "p25": 26.3, "p75": 50.5, "synthetic_n": 15917, "histogram": [ 879, 1512, 2648, 3360, 3374, 2461, 1217, 393, 64, 9 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 68.5, "p25": 47.3, "p75": 80.7, "synthetic_n": 3533, "histogram": [ 81, 181, 241, 210, 226, 331, 592, 739, 592, 340 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 49.9, "p25": 39.2, "p75": 60.4, "synthetic_n": 3533, "histogram": [ 28, 93, 297, 507, 844, 841, 604, 257, 53, 9 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 64.2, "p25": 47.3, "p75": 81.5, "synthetic_n": 3533, "histogram": [ 43, 133, 177, 249, 406, 553, 472, 537, 564, 399 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 45.6, "p25": 33.1, "p75": 57.5, "synthetic_n": 3533, "histogram": [ 85, 187, 431, 642, 753, 715, 472, 212, 29, 7 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 48.0, "p25": 35.6, "p75": 58.9, "synthetic_n": 3533, "histogram": [ 57, 159, 362, 587, 755, 801, 537, 205, 60, 10 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 49.5, "p25": 38.3, "p75": 61.5, "synthetic_n": 3533, "histogram": [ 35, 122, 291, 550, 803, 734, 601, 270, 104, 23 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 73.7, "p25": 52.6, "p75": 84.2, "synthetic_n": 608, "histogram": [ 4, 14, 41, 40, 40, 38, 78, 150, 108, 95 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 60.2, "p25": 48.8, "p75": 70.8, "synthetic_n": 608, "histogram": [ 2, 4, 15, 50, 89, 141, 141, 117, 43, 6 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 70.2, "p25": 52.2, "p75": 86.1, "synthetic_n": 608, "histogram": [ 3, 14, 20, 33, 54, 100, 78, 84, 112, 110 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 52.6, "p25": 41.6, "p75": 65.7, "synthetic_n": 608, "histogram": [ 3, 13, 31, 90, 128, 123, 120, 71, 25, 4 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 53.5, "p25": 43.3, "p75": 63.3, "synthetic_n": 608, "histogram": [ 2, 13, 37, 72, 122, 167, 114, 59, 21, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Wholesale & retail", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 60.8, "p25": 48.8, "p75": 71.1, "synthetic_n": 608, "histogram": [ 0, 8, 18, 49, 91, 129, 149, 99, 53, 12 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 61.9, "p25": 34.3, "p75": 74.8, "synthetic_n": 17765, "histogram": [ 886, 1433, 1561, 1152, 1044, 2186, 3381, 3178, 1882, 1062 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 40.5, "p25": 28.4, "p75": 51.7, "synthetic_n": 17765, "histogram": [ 717, 1464, 2731, 3731, 4066, 2958, 1489, 475, 125, 9 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.1, "p25": 42.3, "p75": 77.2, "synthetic_n": 17765, "histogram": [ 430, 1028, 1022, 1446, 2552, 2835, 2224, 2587, 2279, 1362 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 37.8, "p25": 25.3, "p75": 49.8, "synthetic_n": 17765, "histogram": [ 1177, 1801, 2940, 3782, 3674, 2614, 1260, 437, 73, 7 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 37.5, "p25": 25.1, "p75": 49.2, "synthetic_n": 17765, "histogram": [ 1149, 1932, 2905, 3820, 3788, 2510, 1200, 386, 72, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 40.5, "p25": 27.8, "p75": 53.0, "synthetic_n": 17765, "histogram": [ 849, 1534, 2760, 3538, 3609, 2917, 1623, 697, 198, 40 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.3, "p25": 31.3, "p75": 72.9, "synthetic_n": 14160, "histogram": [ 792, 1259, 1326, 915, 833, 1856, 2790, 2453, 1299, 637 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 38.1, "p25": 26.6, "p75": 48.7, "synthetic_n": 14160, "histogram": [ 684, 1364, 2444, 3226, 3272, 2103, 857, 184, 26, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 56.7, "p25": 41.3, "p75": 75.8, "synthetic_n": 14160, "histogram": [ 382, 881, 835, 1210, 2131, 2311, 1768, 2013, 1691, 938 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 35.9, "p25": 23.6, "p75": 47.4, "synthetic_n": 14160, "histogram": [ 1091, 1599, 2538, 3143, 2904, 1864, 767, 231, 20, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 35.7, "p25": 23.6, "p75": 47.1, "synthetic_n": 14160, "histogram": [ 1045, 1711, 2486, 3173, 2964, 1818, 739, 198, 25, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 38.0, "p25": 25.9, "p75": 50.1, "synthetic_n": 14160, "histogram": [ 805, 1425, 2449, 3012, 2897, 2117, 1019, 358, 70, 8 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 67.8, "p25": 46.8, "p75": 80.7, "synthetic_n": 3044, "histogram": [ 90, 157, 215, 188, 173, 298, 512, 615, 464, 332 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 49.4, "p25": 37.8, "p75": 60.1, "synthetic_n": 3044, "histogram": [ 33, 95, 277, 461, 701, 707, 505, 206, 57, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 63.5, "p25": 46.0, "p75": 80.5, "synthetic_n": 3044, "histogram": [ 46, 133, 159, 203, 373, 454, 389, 500, 465, 322 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 45.1, "p25": 32.2, "p75": 57.0, "synthetic_n": 3044, "histogram": [ 85, 192, 374, 561, 633, 622, 397, 143, 34, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 43.9, "p25": 31.4, "p75": 55.3, "synthetic_n": 3044, "histogram": [ 99, 206, 379, 565, 707, 553, 354, 147, 33, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 49.5, "p25": 36.9, "p75": 60.9, "synthetic_n": 3044, "histogram": [ 44, 106, 291, 488, 624, 677, 470, 261, 72, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 73.7, "p25": 55.7, "p75": 85.4, "synthetic_n": 561, "histogram": [ 4, 17, 20, 49, 38, 32, 79, 110, 119, 93 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 58.7, "p25": 48.9, "p75": 69.5, "synthetic_n": 561, "histogram": [ 0, 5, 10, 44, 93, 148, 127, 85, 42, 7 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 71.9, "p25": 52.1, "p75": 86.6, "synthetic_n": 561, "histogram": [ 2, 14, 28, 33, 48, 70, 67, 74, 123, 102 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 51.8, "p25": 41.9, "p75": 63.8, "synthetic_n": 561, "histogram": [ 1, 10, 28, 78, 137, 128, 96, 63, 19, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 52.1, "p25": 39.9, "p75": 61.7, "synthetic_n": 561, "histogram": [ 5, 15, 40, 82, 117, 139, 107, 41, 14, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Manufacturing", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 61.0, "p25": 49.3, "p75": 71.4, "synthetic_n": 561, "histogram": [ 0, 3, 20, 38, 88, 123, 134, 78, 56, 21 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 63.0, "p25": 35.0, "p75": 76.2, "synthetic_n": 10100, "histogram": [ 450, 820, 882, 684, 592, 1124, 1821, 1825, 1253, 649 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 41.8, "p25": 29.8, "p75": 53.1, "synthetic_n": 10100, "histogram": [ 354, 769, 1441, 2099, 2261, 1770, 996, 321, 84, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.4, "p25": 42.1, "p75": 77.5, "synthetic_n": 10100, "histogram": [ 265, 605, 601, 807, 1406, 1607, 1232, 1396, 1320, 861 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 39.0, "p25": 26.5, "p75": 51.1, "synthetic_n": 10100, "histogram": [ 594, 976, 1567, 2114, 2110, 1599, 782, 282, 71, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 39.9, "p25": 27.3, "p75": 51.7, "synthetic_n": 10100, "histogram": [ 535, 920, 1529, 2067, 2161, 1718, 833, 288, 48, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 41.6, "p25": 28.6, "p75": 54.3, "synthetic_n": 10100, "histogram": [ 491, 829, 1448, 1921, 2121, 1669, 1041, 437, 124, 19 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 61.2, "p25": 32.2, "p75": 74.2, "synthetic_n": 8075, "histogram": [ 402, 720, 752, 557, 467, 959, 1522, 1426, 871, 399 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 39.2, "p25": 27.8, "p75": 50.2, "synthetic_n": 8075, "histogram": [ 342, 721, 1319, 1815, 1806, 1323, 605, 120, 24, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 57.0, "p25": 40.8, "p75": 76.0, "synthetic_n": 8075, "histogram": [ 220, 524, 510, 688, 1170, 1296, 983, 1089, 992, 603 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 36.9, "p25": 24.8, "p75": 48.9, "synthetic_n": 8075, "histogram": [ 555, 880, 1352, 1756, 1666, 1187, 496, 155, 24, 4 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 38.2, "p25": 26.0, "p75": 49.7, "synthetic_n": 8075, "histogram": [ 490, 820, 1318, 1755, 1708, 1249, 543, 167, 25, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 39.2, "p25": 26.4, "p75": 51.1, "synthetic_n": 8075, "histogram": [ 471, 771, 1284, 1652, 1710, 1226, 694, 218, 46, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 69.1, "p25": 47.4, "p75": 81.7, "synthetic_n": 1706, "histogram": [ 44, 96, 110, 105, 109, 146, 268, 338, 298, 192 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 50.2, "p25": 39.6, "p75": 61.0, "synthetic_n": 1706, "histogram": [ 12, 46, 116, 261, 408, 387, 311, 133, 28, 4 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 64.1, "p25": 46.8, "p75": 81.7, "synthetic_n": 1706, "histogram": [ 43, 75, 75, 105, 197, 268, 207, 260, 277, 199 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 45.7, "p25": 33.8, "p75": 57.3, "synthetic_n": 1706, "histogram": [ 37, 84, 199, 316, 391, 340, 220, 87, 31, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 46.1, "p25": 33.9, "p75": 56.9, "synthetic_n": 1706, "histogram": [ 43, 97, 189, 283, 378, 391, 222, 83, 19, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 50.3, "p25": 38.4, "p75": 61.9, "synthetic_n": 1706, "histogram": [ 19, 57, 156, 247, 356, 374, 273, 167, 50, 7 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 76.4, "p25": 57.5, "p75": 86.0, "synthetic_n": 319, "histogram": [ 4, 4, 20, 22, 16, 19, 31, 61, 84, 58 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 62.7, "p25": 50.3, "p75": 72.5, "synthetic_n": 319, "histogram": [ 0, 2, 6, 23, 47, 60, 80, 68, 32, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 68.6, "p25": 51.2, "p75": 85.1, "synthetic_n": 319, "histogram": [ 2, 6, 16, 14, 39, 43, 42, 47, 51, 59 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 55.0, "p25": 41.4, "p75": 66.0, "synthetic_n": 319, "histogram": [ 2, 12, 16, 42, 53, 72, 66, 40, 16, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 53.4, "p25": 43.6, "p75": 64.4, "synthetic_n": 319, "histogram": [ 2, 3, 22, 29, 75, 78, 68, 38, 4, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Administrative & support", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 60.3, "p25": 48.3, "p75": 71.8, "synthetic_n": 319, "histogram": [ 1, 1, 8, 22, 55, 69, 74, 52, 28, 9 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 63.9, "p25": 39.4, "p75": 77.2, "synthetic_n": 6008, "histogram": [ 249, 431, 509, 333, 331, 732, 1135, 1053, 785, 450 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 42.5, "p25": 30.8, "p75": 54.5, "synthetic_n": 6008, "histogram": [ 178, 451, 781, 1248, 1338, 1097, 610, 246, 52, 7 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 58.7, "p25": 42.8, "p75": 78.0, "synthetic_n": 6008, "histogram": [ 153, 314, 349, 463, 854, 976, 698, 882, 807, 512 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 40.2, "p25": 27.4, "p75": 52.6, "synthetic_n": 6008, "histogram": [ 302, 528, 933, 1221, 1233, 967, 570, 196, 57, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 42.9, "p25": 30.4, "p75": 54.8, "synthetic_n": 6008, "histogram": [ 240, 444, 773, 1132, 1307, 1109, 693, 235, 64, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 42.6, "p25": 29.8, "p75": 55.5, "synthetic_n": 6008, "histogram": [ 218, 475, 823, 1134, 1261, 1057, 648, 296, 80, 16 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 62.6, "p25": 35.9, "p75": 75.1, "synthetic_n": 4789, "histogram": [ 225, 375, 433, 260, 255, 609, 958, 839, 542, 293 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 40.0, "p25": 28.6, "p75": 51.2, "synthetic_n": 4789, "histogram": [ 172, 423, 719, 1075, 1088, 816, 371, 112, 12, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 57.5, "p25": 41.5, "p75": 76.6, "synthetic_n": 4789, "histogram": [ 138, 271, 294, 393, 698, 793, 541, 703, 597, 361 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 38.2, "p25": 25.9, "p75": 50.2, "synthetic_n": 4789, "histogram": [ 286, 470, 809, 1030, 974, 707, 377, 113, 23, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 41.2, "p25": 28.7, "p75": 52.7, "synthetic_n": 4789, "histogram": [ 225, 405, 676, 941, 1062, 831, 467, 146, 33, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 40.3, "p25": 27.9, "p75": 52.2, "synthetic_n": 4789, "histogram": [ 209, 438, 732, 982, 1023, 793, 428, 152, 30, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 69.1, "p25": 49.2, "p75": 82.2, "synthetic_n": 1035, "histogram": [ 21, 48, 68, 64, 69, 110, 156, 183, 193, 123 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 51.4, "p25": 40.3, "p75": 62.1, "synthetic_n": 1035, "histogram": [ 6, 28, 57, 160, 229, 246, 191, 92, 23, 3 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 64.1, "p25": 47.2, "p75": 81.2, "synthetic_n": 1035, "histogram": [ 13, 40, 49, 63, 139, 153, 134, 155, 179, 110 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 47.7, "p25": 34.5, "p75": 58.5, "synthetic_n": 1035, "histogram": [ 14, 54, 115, 180, 218, 221, 150, 62, 21, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 49.5, "p25": 37.6, "p75": 60.8, "synthetic_n": 1035, "histogram": [ 14, 36, 87, 169, 218, 233, 181, 70, 24, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 52.1, "p25": 39.3, "p75": 63.3, "synthetic_n": 1035, "histogram": [ 9, 36, 86, 144, 206, 234, 177, 95, 39, 9 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 77.1, "p25": 59.4, "p75": 86.6, "synthetic_n": 184, "histogram": [ 3, 8, 8, 9, 7, 13, 21, 31, 50, 34 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 64.2, "p25": 53.6, "p75": 73.5, "synthetic_n": 184, "histogram": [ 0, 0, 5, 13, 21, 35, 48, 42, 17, 3 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 71.3, "p25": 54.7, "p75": 87.8, "synthetic_n": 184, "histogram": [ 2, 3, 6, 7, 17, 30, 23, 24, 31, 41 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 56.2, "p25": 45.7, "p75": 67.6, "synthetic_n": 184, "histogram": [ 2, 4, 9, 11, 41, 39, 43, 21, 13, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 56.8, "p25": 44.5, "p75": 65.5, "synthetic_n": 184, "histogram": [ 1, 3, 10, 22, 27, 45, 45, 19, 7, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Real estate", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 64.3, "p25": 50.3, "p75": 73.2, "synthetic_n": 184, "histogram": [ 0, 1, 5, 8, 32, 30, 43, 49, 11, 5 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 60.5, "p25": 30.7, "p75": 73.1, "synthetic_n": 8078, "histogram": [ 494, 711, 766, 522, 477, 1001, 1605, 1377, 758, 367 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 38.8, "p25": 26.7, "p75": 50.5, "synthetic_n": 8078, "histogram": [ 435, 761, 1320, 1744, 1701, 1280, 600, 191, 42, 4 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 56.8, "p25": 41.1, "p75": 75.8, "synthetic_n": 8078, "histogram": [ 237, 490, 480, 688, 1195, 1348, 986, 1091, 954, 609 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 36.1, "p25": 23.4, "p75": 48.3, "synthetic_n": 8078, "histogram": [ 660, 920, 1392, 1757, 1571, 1066, 499, 172, 38, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 36.2, "p25": 23.3, "p75": 47.6, "synthetic_n": 8078, "histogram": [ 675, 908, 1417, 1718, 1687, 1068, 454, 133, 18, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 38.8, "p25": 25.8, "p75": 51.4, "synthetic_n": 8078, "histogram": [ 501, 799, 1293, 1664, 1587, 1185, 693, 267, 81, 8 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 58.9, "p25": 27.4, "p75": 71.4, "synthetic_n": 6459, "histogram": [ 440, 646, 658, 398, 388, 818, 1311, 1054, 529, 217 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 36.5, "p25": 24.7, "p75": 47.6, "synthetic_n": 6459, "histogram": [ 412, 718, 1175, 1486, 1328, 902, 350, 76, 11, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 55.2, "p25": 40.1, "p75": 74.4, "synthetic_n": 6459, "histogram": [ 209, 409, 401, 582, 1011, 1093, 773, 825, 725, 431 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 34.2, "p25": 21.8, "p75": 46.1, "synthetic_n": 6459, "histogram": [ 610, 810, 1203, 1445, 1219, 747, 319, 99, 5, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 34.2, "p25": 21.7, "p75": 45.6, "synthetic_n": 6459, "histogram": [ 621, 799, 1226, 1409, 1286, 766, 287, 59, 6, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 36.2, "p25": 23.6, "p75": 48.1, "synthetic_n": 6459, "histogram": [ 472, 734, 1145, 1407, 1270, 852, 425, 123, 28, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 65.9, "p25": 44.4, "p75": 78.2, "synthetic_n": 1359, "histogram": [ 50, 59, 94, 106, 76, 163, 248, 270, 182, 111 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 47.5, "p25": 36.8, "p75": 57.7, "synthetic_n": 1359, "histogram": [ 20, 39, 136, 238, 333, 317, 188, 74, 12, 2 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 61.9, "p25": 45.0, "p75": 78.8, "synthetic_n": 1359, "histogram": [ 25, 70, 73, 97, 158, 220, 181, 221, 176, 138 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 42.6, "p25": 31.1, "p75": 54.8, "synthetic_n": 1359, "histogram": [ 49, 99, 169, 273, 299, 268, 138, 47, 17, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 42.7, "p25": 30.6, "p75": 53.3, "synthetic_n": 1359, "histogram": [ 49, 102, 168, 274, 335, 247, 131, 46, 7, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 47.8, "p25": 35.2, "p75": 59.5, "synthetic_n": 1359, "histogram": [ 27, 58, 140, 238, 285, 283, 213, 83, 30, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 71.1, "p25": 56.2, "p75": 84.5, "synthetic_n": 260, "histogram": [ 4, 6, 14, 18, 13, 20, 46, 53, 47, 39 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 59.0, "p25": 47.5, "p75": 69.4, "synthetic_n": 260, "histogram": [ 3, 4, 9, 20, 40, 61, 62, 41, 19, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 72.7, "p25": 54.4, "p75": 85.7, "synthetic_n": 260, "histogram": [ 3, 11, 6, 9, 26, 35, 32, 45, 53, 40 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 51.4, "p25": 38.0, "p75": 64.2, "synthetic_n": 260, "histogram": [ 1, 11, 20, 39, 53, 51, 42, 26, 16, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 49.0, "p25": 38.9, "p75": 60.6, "synthetic_n": 260, "histogram": [ 5, 7, 23, 35, 66, 55, 36, 28, 5, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Accommodation & food", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 61.5, "p25": 49.6, "p75": 72.2, "synthetic_n": 260, "histogram": [ 2, 7, 8, 19, 32, 50, 55, 61, 23, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 59.8, "p25": 29.6, "p75": 72.7, "synthetic_n": 9953, "histogram": [ 613, 954, 956, 663, 553, 1253, 1943, 1674, 919, 425 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 37.3, "p25": 25.5, "p75": 49.1, "synthetic_n": 9953, "histogram": [ 578, 1033, 1728, 2240, 2063, 1430, 650, 182, 43, 6 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 55.9, "p25": 40.3, "p75": 75.2, "synthetic_n": 9953, "histogram": [ 304, 638, 610, 885, 1498, 1632, 1176, 1358, 1156, 696 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 34.7, "p25": 22.4, "p75": 46.7, "synthetic_n": 9953, "histogram": [ 858, 1258, 1877, 2082, 1886, 1225, 564, 176, 25, 2 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 32.5, "p25": 19.8, "p75": 44.6, "synthetic_n": 9953, "histogram": [ 1127, 1386, 1905, 2137, 1827, 1076, 382, 97, 16, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "All sizes (10+)", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 37.6, "p25": 24.9, "p75": 50.1, "synthetic_n": 9953, "histogram": [ 706, 1025, 1644, 2098, 1956, 1452, 710, 271, 71, 20 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 58.7, "p25": 27.7, "p75": 71.2, "synthetic_n": 7982, "histogram": [ 535, 831, 796, 517, 430, 1049, 1622, 1293, 662, 247 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 35.1, "p25": 23.7, "p75": 46.4, "synthetic_n": 7982, "histogram": [ 547, 952, 1536, 1872, 1649, 999, 350, 68, 9, 0 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 54.7, "p25": 39.1, "p75": 74.0, "synthetic_n": 7982, "histogram": [ 277, 548, 517, 736, 1226, 1318, 931, 1073, 873, 483 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 32.8, "p25": 20.8, "p75": 44.8, "synthetic_n": 7982, "histogram": [ 788, 1105, 1606, 1700, 1443, 887, 354, 88, 10, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 31.0, "p25": 18.4, "p75": 42.7, "synthetic_n": 7982, "histogram": [ 1014, 1211, 1592, 1737, 1393, 750, 227, 50, 8, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Small 10–49", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 35.1, "p25": 23.1, "p75": 47.3, "synthetic_n": 7982, "histogram": [ 668, 932, 1465, 1776, 1521, 1039, 435, 121, 22, 3 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 64.1, "p25": 36.7, "p75": 77.2, "synthetic_n": 1671, "histogram": [ 72, 110, 148, 113, 102, 183, 278, 321, 208, 136 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 46.0, "p25": 33.5, "p75": 56.9, "synthetic_n": 1671, "histogram": [ 31, 79, 185, 339, 359, 356, 234, 74, 13, 1 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 60.3, "p25": 44.8, "p75": 78.8, "synthetic_n": 1671, "histogram": [ 24, 81, 81, 131, 239, 276, 206, 243, 219, 171 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 41.2, "p25": 28.8, "p75": 52.6, "synthetic_n": 1671, "histogram": [ 68, 142, 249, 329, 382, 273, 160, 56, 11, 1 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 38.4, "p25": 25.7, "p75": 49.9, "synthetic_n": 1671, "histogram": [ 107, 164, 276, 349, 358, 263, 116, 32, 6, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Medium 50–249", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 46.1, "p25": 34.5, "p75": 57.7, "synthetic_n": 1671, "histogram": [ 38, 90, 172, 293, 384, 347, 204, 110, 27, 6 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "adoption", "benchmark_name": "AI Adoption Index", "icon": "A", "median": 70.6, "p25": 43.7, "p75": 83.8, "synthetic_n": 300, "histogram": [ 6, 13, 12, 33, 21, 21, 43, 60, 49, 42 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S01", "S04" ], "external_anchor": "Official anchors: Netherlands 33.2%; Belgium 34.5% (Eurostat 2025).", "copy": "How broadly and frequently AI is used across marketing work. The index is modelled from marketing-use and enterprise-adoption evidence; it is not the same statistic as the official enterprise AI-adoption rate.", "action": "Map adoption gaps to your AI Marketing Adoption Framework.", "href": "/learn/frameworks/ai-marketing-adoption-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "maturity", "benchmark_name": "AI Marketing Maturity", "icon": "M", "median": 58.0, "p25": 46.7, "p75": 67.9, "synthetic_n": 300, "histogram": [ 0, 2, 7, 29, 55, 75, 66, 40, 21, 5 ], "confidence_tier": "C — Evidence-constrained model", "source_ids": [ "S04", "S05" ], "external_anchor": "Soft enterprise anchor: 30% of Gartner CMO respondents reported mature/fully developed AI readiness (n=401; enterprise-heavy sample).", "copy": "How far strategy, operating model, use cases and leadership have progressed beyond experimentation.", "action": "Use the AI Marketing Maturity Framework to define the next stage.", "href": "/learn/frameworks/ai-marketing-maturity-framework/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "governance", "benchmark_name": "AI Governance", "icon": "G", "median": 69.1, "p25": 50.4, "p75": 83.8, "synthetic_n": 300, "histogram": [ 3, 9, 12, 18, 33, 38, 39, 42, 64, 42 ], "confidence_tier": "C/D — Mixed observed/modelled anchor", "source_ids": [ "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Policies, data governance, management ownership, human review and accountability. Netherlands is directly anchored to DDMO 2026; Belgian values are modelled.", "action": "Move from policy gaps to the AI Governance Framework and Disclosure Builder.", "href": "/learn/frameworks/ai-governance-framework-marketing/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "agents", "benchmark_name": "Agentic AI Readiness", "icon": "↯", "median": 50.2, "p25": 37.9, "p75": 61.3, "synthetic_n": 300, "histogram": [ 2, 11, 22, 53, 61, 65, 50, 32, 4, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05", "S06" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Workflow suitability, permissions, human approval, data access, monitoring and evaluation for marketing agents.", "action": "Risk-test your next workflow in the Marketing Agent Risk Sandbox.", "href": "/ai-system/marketing-agent-risk-sandbox/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "search", "benchmark_name": "AI Search Readiness", "icon": "V", "median": 45.6, "p25": 34.6, "p75": 55.2, "synthetic_n": 300, "histogram": [ 6, 11, 37, 51, 76, 63, 39, 15, 2, 0 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S01", "S04" ], "external_anchor": "No directly comparable observed cohort statistic; estimate is modelled from documented inputs.", "copy": "Preparedness to manage AI-search visibility through content, data, citations, monitoring and measurement. This is a readiness model, not a measured visibility score.", "action": "Run the GEO Citation Readiness Lab to diagnose AI-search readiness gaps.", "href": "/ai-system/geo-citation-readiness/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" }, { "market": "Netherlands + Belgium", "industry": "Construction", "company_size": "Large 250+", "benchmark_id": "roi", "benchmark_name": "AI ROI Measurement Readiness", "icon": "€", "median": 59.5, "p25": 46.7, "p75": 69.6, "synthetic_n": 300, "histogram": [ 0, 3, 7, 29, 51, 66, 71, 40, 22, 11 ], "confidence_tier": "D — Scenario model", "source_ids": [ "S05" ], "external_anchor": "Enterprise context: 15.3% average marketing-budget allocation to AI; 21.3% among mature/fully ready organisations (Gartner 2026).", "copy": "Capability to connect AI activity to cost, productivity, conversion, revenue and business outcomes. It is not an observed ROI percentage.", "action": "Quantify value with the AI Marketing ROI Calculator.", "href": "/ai-system/ai-marketing-roi-calculator/", "model_version": "0.6", "evidence_cutoff": "2026-10-08" } ] }