# Markaigen Benchmark Intelligence — Citation Guide

**Release:** Evidence & Modelled Edition v0.6  
**Evidence cut-off:** 8 October 2026  
**Purpose:** standardize how Markaigen cites external evidence and distinguishes it from Markaigen modelled estimates.

## Citation standard

For every observed statistic published on Markaigen, show at minimum: **publisher · title/report · reference period · population/cohort · value · direct source · access/verification date**. Where available, also show dataset code, DOI, sample size and methodological caveat.

For every modelled statistic, add: **“Modelled estimate — not directly observed” · model version · evidence cut-off · source IDs used · relevant calibration note**.

## Recommended citations

### S01 — Eurostat / European Commission
Eurostat. (2026). *The use of artificial intelligence (AI) technologies in the European Union — Key results — 2026 edition* (Product code KS-01-26-009; DOI: 10.2785/9221093). Reference period: 2025. Online datasets: `isoc_eb_ai`, `isoc_eb_ain2`. Verified 8 October 2026. https://ec.europa.eu/eurostat/web/products-statistical-reports/w/ks-01-26-009

**Facts used:** enterprise AI adoption by country and size; EU industry adoption; AI use purposes.  
**Critical note:** Netherlands 2025 values carry Eurostat's `(b)` break-in-time-series flag.

### S02 — Statistics Netherlands (CBS)
Statistics Netherlands (CBS). (2026, 16 March). *Gebruik van AI-technologie door Nederlandse microbedrijven*. Reference period: 2025. Verified 8 October 2026. https://www.cbs.nl/nl-nl/longread/rapportages/2026/gebruik-van-ai-technologie-door-nederlandse-microbedrijven?onepage=true

**Facts used:** 13.8% AI use among Dutch micro-enterprises (2–9 employed; 95% CI 12.8–14.8), plus contextual SME/large-enterprise comparisons and selected AI-use-purpose/barrier figures.  
**Critical note:** the micro-enterprise population is not interchangeable with Eurostat's harmonised 10+ enterprise population.

### S03 — Statbel
Statbel. (2025, 2 December). *Artificial intelligence is gaining ground in Belgian enterprises*. Reference period: 2025. Verified 8 October 2026. https://statbel.fgov.be/en/news/artificial-intelligence-gaining-ground-belgian-enterprises

**Facts used:** Belgian enterprise AI adoption and contextual written-language, customer-data, transaction-data and cloud indicators.  
**Critical note:** the public release exposes selected results; Markaigen does not claim access to Statbel microdata.

### S04 — DDMA
DDMA. (2026, July). *DDMO 2026: AI evolves from a marketing tool into an organisational challenge*. Primary published research sample: n=436 Dutch marketing professionals recruited through the GfK panel; an additional 89 DDMA-community respondents participated, while headline results are based on the primary sample unless otherwise stated. Verified 8 October 2026. https://ddma.nl/kennisbank/ddmo-2026-ai-evolves-from-a-marketing-tool-into-an-organisational-challenge/

**Facts used:** 70% marketing-AI use, 72% expected expansion, 66% AI policy, 56% AI-capabilities investment, 56% data governance, 38% management ownership, 66% expected fundamental work change.  
**Critical note:** marketing-professional self-report is not equivalent to official enterprise-adoption statistics.

### S05 — Gartner
Gartner. (2026, 11 May). *Gartner 2026 CMO Spend Survey Finds CMOs Allocate 15.3% of Marketing Budgets to AI, But Only 30% Are Ready to Scale AI Capabilities*. Survey: n=401 CMOs/marketing leaders; North America, UK and Europe; vast majority from organisations with annual revenue above US$1bn. Verified 8 October 2026. https://www.gartner.com/en/newsroom/press-releases/2026-05-11-gartner-2026-cmo-spend-survey-finds-cmos-allocate-15-point-3-percent-of-marketing-budgets-to-ai-but-only-30-percent-are-ready-to-scale-ai-capabilities

**Facts used:** 15.3% average marketing-budget allocation to AI; 30% mature/fully developed AI readiness; 21.3% AI allocation among mature/ready organisations; 70% AI-leader ambition and 70% process-maturity constraint.  
**Critical note:** enterprise-heavy executive sample; Markaigen uses it as context/soft calibration, not an NL/BE prevalence estimate.

### S06 — Gartner
Gartner. (2026, 11 May). *Gartner Survey Reveals Marketing Leaders Expect AI Automation of Marketing Work to Double to 36% by 2028*. Survey: n=402 CMOs; fielded August–October 2025. Verified 8 October 2026. https://www.gartner.com/en/newsroom/press-releases/2026-05-11-gartner-survey-reveals-marketing-leaders-expect-ai-automation-of-marketing-work-to-double-to-36-percent-by-2028

**Facts used:** expected AI-driven automation of marketing work from 16% in 2026 to 36% in 2028; 80% citing staff fear/anxiety as a barrier to AI experimentation.  
**Critical note:** 2028 is respondent expectation, not observed future performance.

### S07 — OECD
OECD. (2026, January). *AI use by individuals surges across the OECD as adoption by firms continues to expand*. Reference period: 2023–2025. Verified 8 October 2026. https://www.oecd.org/en/about/news/announcements/2026/01/ai-use-by-individuals-surges-across-the-oecd-as-adoption-by-firms-continues-to-expand.html

**Facts used:** 20.2% firm AI adoption in 2025, 52.0% large-firm adoption, 17.4% small-firm adoption, 57.3% ICT and 36.8% professional/scientific-service adoption.  
**Critical note:** OECD figures are comparative context; national definitions/coverage can differ from Eurostat and are not used as direct NL/BE calibration when harmonised Eurostat data exist.

## Markaigen model citation

Use the following formulation for v0.6 modelled outputs:

> Markaigen Benchmark Intelligence, Evidence & Modelled Edition v0.6. Evidence-constrained synthetic model, seed 20261008, evidence cut-off 8 October 2026. **Modelled estimate — not directly observed.** Calibration sources: [source IDs].

Never cite `synthetic_n` as a sample size. It is the number of computational synthetic records contributing to a modelled cohort.