See where your AI marketing actually stands.
Explore AI adoption, maturity, governance, agentic readiness, AI-search readiness and measurement readiness across modelled Netherlands and Belgium cohorts. Assess your organisation and find practical next steps.
Start with what is directly observed.
Official statistics and independent marketing research remain separate from Markaigen's modelled indices. Population definitions are shown because these figures are not interchangeable.
Netherlands enterprise AI adoption
Enterprises with 10+ persons employed using at least one AI technology in 2025. Eurostat flags the Netherlands series as a break in 2025.
Eurostat · official observed ↗Belgium enterprise AI adoption
Enterprises using at least one AI technology in 2025; large enterprises reached 76.4% in the harmonised Eurostat size table.
Statbel / Eurostat · official observed ↗Dutch marketers using AI
DDMO 2026 primary research sample: n=436 Dutch marketing professionals recruited through the GfK panel.
DDMA · independent observed ↗AI policy in marketing organisations
DDMA reports 66% with AI policy and 56% with data governance in the same 2026 primary research sample.
DDMA · independent observed ↗Enterprise marketing budget allocated to AI
Average in Gartner's n=401 2026 CMO Spend Survey; the vast majority of respondents represented organisations above US$1bn revenue.
Gartner · enterprise context ↗Mature AI readiness among surveyed CMOs
Gartner's enterprise-heavy 2026 CMO sample. Markaigen uses this only as a soft large-enterprise model constraint, not a NL/BE prevalence claim.
Gartner · independent observed ↗Compare the metrics that matter.
Choose a modelled cohort and inspect the median, quartiles, evidence anchors and provenance. These indices are evidence-constrained estimates designed to bridge public evidence to the future proprietary Markaigen Wave 1 dataset.
AI Adoption distribution
Netherlands · All industries · All sizesEvidence-constrained score distribution for the selected synthetic cohort.
Explore the benchmark roadmap.
Six modelled indices are available in the explorer today. The remaining areas describe planned research, not published findings.
Build your AI Marketing Benchmark Index.
Eight dimensions turn the benchmark from passive research into a diagnostic. The versioned weighting is transparent and versioned; change it only through a documented methodology release.
Exploring
Modelled percentileYour dimension profile
0–100 by dimensionStrongest dimensions
Priority gaps
The percentile is modelled against the balanced NL/BE synthetic v0.6 cohort. It is not a survey percentile and will be replaced by proprietary Wave 1 distributions.
Understand the evidence behind each score.
This release combines published external evidence with a reproducible model. Surveys, opt-in tool data, crawler panels and connected performance data are planned research layers; they are not inputs collected by this release.
Observed evidence v0.5
Eurostat, CBS, Statbel, DDMA, Gartner and OECD statistics stored with population definitions, dates, limitations and source IDs.
Modelled benchmark v0.6
Evidence-constrained synthetic distributions. Every output is labelled modelled and carries a source trail; 100,000 synthetic records are not respondents.
Planned: Markaigen surveys
Annual and quarterly research for adoption, skills, governance, budgets, organization and perceived value.
Planned: opt-in tool data
Aggregated opt-in scores from relevant Markaigen tools: GEO readiness, technical SEO, ROI, agent risk and more.
Planned: crawler panel
Recurring public-web datasets measuring structured data, AI crawler access, performance, disclosure and visibility.
Planned: performance data
Opt-in aggregated data from GA4, Search Console, CRM and ad platforms for referral, conversion and revenue benchmarks.
Every published benchmark must disclose
- Sample size and respondent / domain definition
- Collection period and last-updated date
- Market, industry and company-size cohort
- Data source and collection method
- Median, P25/P75 and distribution — not average alone
- Year-over-year methodology consistency
- Confidence tier and known limitations
- Benchmark version number
- Privacy / aggregation threshold
- Any sponsor involvement
Research integrity rules
v0.5 contains observed external evidence. v0.6 contains explicitly modelled estimates. Neither is described as a Markaigen survey or proprietary respondent dataset.
- Never sell an organic ranking or benchmark outcome.
- Keep sponsors separate from methodology and scoring.
- Publish corrections and version changes.
- Use a minimum aggregation threshold before exposing cohort statistics.
- Separate observed data, survey responses, external sources and modelled estimates.
- Do not publish individual company results without explicit consent.
Every number has a professional source trail.
The release package includes a machine-readable source registry and evidence master. Markaigen preserves population definitions instead of averaging incompatible studies.
Harmonised enterprise AI statistics.
2025 country, size-class and industry adoption. Dataset codes isoc_eb_ai and isoc_eb_ain2; report DOI 10.2785/9221093. Netherlands 2025 carries a break-in-series flag.
Open Eurostat source ↗Dutch micro-enterprise AI evidence.
2025 micro AI use 13.8% with a published 95% interval of 12.8–14.8; additional purpose, acquisition and barrier context.
Open CBS source ↗Belgian AI, data and cloud context.
Official Belgian enterprise release reporting 34.5% AI adoption in 2025 plus selected data-analysis and cloud indicators. Statbel data are CC BY 4.0.
Open Statbel source ↗Marketing-specific Netherlands evidence.
GfK-panel primary sample of Dutch marketing professionals: AI use, policy, capabilities, data governance and management ownership.
Open DDMA source ↗Enterprise marketing spend, readiness and automation.
Used as enterprise context and soft model constraints only. The CMO Spend Survey is heavily weighted toward very large organisations.
Open Gartner source ↗Cross-country firm AI adoption.
2025 comparative adoption and firm-size/industry gaps used as a cross-check, not as a substitute for harmonised Eurostat NL/BE data.
Open OECD source ↗Download the audit trail
Interpretation rule
Observed means the source directly measured or surveyed the published statistic. Modelled means Markaigen calculated an estimate from documented evidence. A model can be useful, but it must never be described as a respondent result.
Model version: 0.6
Simulation seed: 20261008
Help build the benchmark marketers can actually use.
Register your interest in future Markaigen benchmark research. We store your email, role, market and consent privately on Markaigen, solely to contact you about research participation. This form does not submit your assessment answers.
Benchmark methodology questions
Which values are real observations and which are modelled?
The evidence cards are direct observations from named sources. Explorer scores and percentiles are evidence-constrained modelled estimates. The page labels both classes explicitly and the downloadable evidence master preserves the underlying source definitions.
Why use median and quartiles instead of one average?
Marketing organizations vary widely by size, maturity and sector. Median plus P25/P75 makes the distribution visible and reduces the risk that a small number of extreme values distort the benchmark.
How should Markaigen protect privacy?
Use opt-in collection where required, aggregate before publishing, enforce minimum cohort sizes, avoid exposing organization-level results without consent, and document retention and deletion practices.
Can sponsors influence the benchmark?
They should not influence sample selection, scoring, methodology or results. Sponsorship can fund the research, but the relationship should be disclosed and separated from editorial and analytical control.
When should Dataset structured data be added?
Dataset structured data may be used for the observed evidence register once the downloadable dataset and methodology remain stable. The synthetic v0.6 population must still be described as modelled/synthetic, not as collected respondent data.