How were these figures calculated?

The map covers 103 occupational groups based on the CBS Occupational Classification ROA-CBS 2014 (BRC 2014). Together these occupations represent approximately 5.8 million jobs — roughly 60% of the Dutch working population (CBS: 9.7 million in Q3 2025).

The missing 40% consists primarily of niche specialisations, undefined occupations and occupational groups that fall outside the CBS BRC 2014 top-level categories. The dataset does not claim to cover the full labour market.

The AI exposure score indicates what proportion of the core tasks of an occupation is technically automatable by current AI technology (LLMs, computer vision, robotics, agentic AI). It is a measure of technical exposure, not of actual displacement.

The vulnerability score indicates to what extent technical AI exposure also translates into real impact in the Dutch context. High exposure does not necessarily lead to high vulnerability when protective factors are present.

Factors taken into account:

• EU AI Act Art. 14: Requires human-in-the-loop for government decisions (lowers vulnerability of public sector roles)
• DBA legislation: Self-employment regulations influence the pace at which roles can be replaced
• Healthcare sector regulation: BIG registration and statutory requirements protect healthcare occupations
• Demand elasticity: When demand grows faster than AI displaces (e.g. ICT), vulnerability decreases
• Social preference: Societal preference for human contact (healthcare, education) slows adoption
• Labour market tightness: Acute shortages (healthcare, construction, installation) make displacement unlikely

The model answers the question: if current trends continue and AI automation increases, how many jobs will there be in 2030?

It combines two forces: momentum (how has the occupation developed in recent years?) and AI disruption (how much downward pressure does automation exert on this occupation?). NL vulnerability determines how much of that AI pressure actually feeds through in the Dutch context.

A damping factor of 0.7 is applied to the momentum. This is standard practice in forecasting (mean reversion): recent trends rarely persist at full force. UWV signals, for example, that ICT labour tightness is easing — confirming that growth is decelerating. The result is presented as a range: conservative (with damping) to optimistic (without damping).

• AI exposure scores are model estimates, not empirical measurements. They have been cross-validated with academic indices but have not been independently audited.
• The dataset covers approximately 60% of the working population. Niche occupations are not included.
• Historical indices are modelled on the basis of CBS EBB trends, not taken directly from CBS tables.
• The 2030 forecast is a directional analysis, not a labour market forecast. The model does not account for economic shocks, policy changes or breakthrough technologies.
• Not all contraction is AI-related. Agriculture and fisheries are declining due to policy (nitrogen regulations, quotas), not automation.
• Salaries are medians; the spread within occupational groups can be substantial.
• The 2030 forecast is presented as a range (conservative to optimistic). UWV signals that the growth rate for ICT occupations is slowing; WEF confirms the direction but not the pace. Point estimates would imply false precision.
• Demographic ageing is not included in the forecast model. Age distribution data per occupational group (CBS 85276NED) is available at occupational class level (12 groups) but not at the detail level of our 103 occupational groups. Ageing data is shown as a contextual indicator in the career scan and the insights page. In heavily ageing sectors (e.g. administrative: 24.6% aged 55+), natural attrition may soften the AI transition. This is an important nuance that the model itself does not quantify.

The AI exposure scores and forecasts have been compared with five independent sources:

• Felten, Raj & Seamans (2023) — Occupational Heterogeneity in Exposure to AI. Our top scores (translators, writers, developers) align with their highest AIOE values. Our lowest scores (construction, frontline healthcare) match their lowest.
• OECD AI Exposure Index (2023) — Confirms the pattern: knowledge work high, physical work low.
• Frey & Osborne (2017) — The Future of Employment. Nurses scored 0.009 automation probability, consistent with our 2/10.
• WEF Future of Jobs Report (2025) — Confirms that software developers are among the fastest-growing specialist roles. +6 million developers projected globally by 2030. Growth direction consistent with our data.
• UWV Labour Market Forecast (2025) — Signals that labour market tightness for ICT occupations is easing. Less tightness ≠ fewer jobs, but the growth rate is moderating. This is the rationale for the damping factor (0.7) in our forecast model and the range presentation.

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Cite as: Janssen, S.D. (2026). Dutch AI Exposure Map: 103 occupational groups analysed for AI impact. simondjanssen.nl/en/ai-map. Retrieved [date].

Methodological inspiration: Karpathy/jobs (MIT License)