Top 5 Jobs in Government That Are Most at Risk from AI in Netherlands - And How to Adapt

In the Netherlands, the CBS CBS Dutch AI Monitor 2024 signals a brisk shift: 22.7% of companies with ten or more employees used at least one AI technology in 2024, with text mining at 13.5% and natural‑language generation at 12.3% - a jump that puts routine document work and customer-answering within reach of automation.

Global context from the 2025 Stanford AI Index report shows AI adoption accelerating worldwide but Dutch public optimism remains comparatively cautious (~36%), so public managers must pair responsible governance with practical reskilling.

Municipalities and national agencies should treat this as a skills and policy moment: targeted training - like Nucamp's Nucamp AI Essentials for Work, a 15‑week program teaching promptcraft and everyday AI workflows - can help civil servants move from manual processing to oversight and service design.

“We take a fundamentally different approach compared to other AI platforms. Rather than focusing on the technology itself, we concentrate on the underlying challenge: enabling business experts to automate their knowledge without getting lost in technical complexity. With Lleverage, describing the problem is all it takes to begin solving it.”

This section explains the nuts-and-bolts behind the findings: the analysis draws on the CBS Dutch AI Monitor 2024, which combines an ICT Usage in Enterprises survey (covering seven AI technologies) with web‑scraping and vacancy modelling, and on a Textkernel job‑ads dataset (Q1 2018–Q2 2024) used to estimate demand for AI skills; see the CBS report for the full methods and caveats (CBS Dutch AI Monitor 2024 full report and the report introduction CBS Dutch AI Monitor 2024 report introduction).

Key choices: company AI‑use figures come from the ICT survey (22.7% of firms with 10+ employees in 2024), AI‑producers were identified via website‑text classifiers (scraping up to 200 pages/site), and AI vacancies were modelled from >7.5 million online ads using a machine‑learning ensemble (best model: regularised logistic regression).

The ML classifier achieved high balanced accuracy (0.93) and flagged 9,430 AI ads (weighted to ~8,725 vacancies), but CBS notes ~9% false positives and some false negatives (~15%) and stresses provisional figures and methodological limits - an important caveat when translating these numbers into local workforce planning.

Municipal administrative clerks (burgerzaken) face one of the clearest near-term impacts from automation because much of their work - reading IDs, extracting names, dates and numbers from forms, and entering records - can now be handled by modern OCR and intelligent data-capture pipelines; for example, solutions like Regula Document Reader OCR for identity documents automate ID reading and can auto-fill paperwork

in seconds,

while NLP Logix Data Capture Automation for OCR and NLP layers NLP and ML on top of OCR to understand context, learn over time, and flag uncertain cases for human review.

The practical consequence is a shift in daily tasks: fewer keystrokes and more exception-handling, identity verification, and quality‑assurance work - imagine a camera-shot of a passport becoming a searchable registry record while the clerk focuses only on disputed or handwritten entries.

For Dutch municipalities that want reliability and GDPR‑safe workflows, the priority is not resistance but retooling: train clerks to audit outputs, manage edge cases, and govern the algorithms that now touch citizens' records.

Benefits caseworkers (uitkeringszaakbeheerders) are among the clearest examples where AI can both cut backlog and change the job: automating eligibility verification can turn what used to be 20‑minute phone hunts for payor details into near‑instant checks, freeing workers to focus on complex appeals and fraught, borderline cases - exactly the triage work humans do best (Thoughtful.ai automation verification review).

At the same time, experience from large programs shows the stakes: AI and analytics can reduce errors and overpayments but, if poorly governed, also embed bias or miss exceptions - SAS case study on automation reducing manual errors in benefits programs.

For Dutch municipalities the pragmatic path is clear: pilot targeted automation to handle routine verification, retrain caseworkers for exception handling and quality assurance, and publish clear accountability documents such as an accessible public FAQ and the Netherlands' algorithm register so citizens understand decision logic and appeals steps (Dutch government guide to documenting AI systems and algorithmic decision-making).

That way, automation becomes a tool that turns repetitive paperwork into auditable workflows - and lets people keep the human judgment that matters most when a benefit decision changes someone's life.

Municipal contact-centre agents at the Belastingdienst and in gemeenten are prime candidates for task automation - AI chatbots and routing systems can take routine, repeatable queries off the phone so human agents handle the fraught, exception‑heavy calls that need empathy and judgment; targeted pilots in Dutch public services have already reported measurable savings of over €1M after focused automation, showing the fiscal upside of careful rollouts (Dutch public services automation measurable savings report).

To keep public trust while streamlining service, publish a plain‑language Accessible public FAQ that explains decision logic and appeals steps and register deployed systems in the Netherlands' algorithm register so citizens see how answers are generated and where to appeal (plain-language public FAQ for algorithmic decision-making in government, Netherlands national algorithm register for government AI transparency).

The practical takeaway: automate the scripts, invest in oversight training, and free agents to resolve the single call that really matters to a citizen - one clear win for efficiency and service quality.

Tax compliance analysts at the Belastingdienst are likely to see their day-to-day work reframe rather than disappear: AI systems that detect patterns and anomalies - already being piloted by tax authorities like the IRS to target high‑risk returns - can sift millions of lines of transactions and surface the handful of cases that merit human review (IRS using AI for tax audits and targeting high‑risk returns (2025)); commercial platforms and cloud tools likewise advertise anomaly‑detection to flag outliers and speed risk scoring for auditors (Microsoft anomaly detection for tax records with AI).

The practical upside is clear: routine cross‑checks, document matching and audit‑trail assembly can be automated so human analysts focus on complex relationship‑level errors, policy interpretation and case strategy - but only if organisations build tight feedback loops and governance to prevent model drift or blind spots, a major caveat highlighted in oversight reviews.

For Dutch public managers the imperative is pragmatic: adopt AI for triage and throughput, train auditors in model validation and exception handling, and publish plain‑language documentation such as an accessible FAQ and entries in the Netherlands' algorithm register so citizens see how and why a return was flagged (Netherlands algorithm register guidance for public-sector AI use).

Even a single unexplained mismatch can light up an automated flag - making audit‑readiness, traceable documentation and human oversight the real public‑service safeguards.

Government paralegals in Dutch public legal teams are prime beneficiaries of RAG‑powered assistants - but the payoff depends on craft, not magic: when a tool first retrieves the exact statutes, cases or contract clauses and then synthesises them, routine legal research and first‑draft memoranda can be scaled without losing traceability, yet only if prompts supply clear intent, context and instructions and the retrieval sources are high quality.

Follow practical playbooks such as the Thomson Reuters guide on writing effective legal AI prompts (Intent + Context + Instruction) and its companion primer on retrieval‑augmented generation to keep answers grounded and reduce hallucinations; prompt patterns and role‑specification turn the model from a vague

“assistant”

into a junior lawyer that cites and organises, rather than guesses.

The so‑what: a paralegal who masters RAG and prompt design becomes the office's quality gate - able to produce faster drafts while preserving citations and handing up flagged uncertainties for human review, keeping legal accuracy and accountability front and centre (Thomson Reuters guide on writing effective legal AI prompts, Thomson Reuters primer on retrieval‑augmented generation in legal tech).

The Dutch playbook for public-sector AI boils down to three practical, cross-cutting moves: learn‑by‑doing with targeted pilots, pair that learning with clear human‑centric governance, and invest in wide‑reach skills training so staff can audit, explain and manage systems rather than be replaced by them.

National guidance urges pilots and shared toolkits, mandatory impact assessments for higher‑risk uses, and transparency measures so citizens can see how decisions are made - steps that should be aligned with the EU AI Act and the DPA's oversight priorities; read the Netherlands public‑sector AI strategy for the full synthesis at AI Watch.

Operationally, that means publishing a plain‑language FAQ and registering deployed systems in the Dutch algorithm register, shifting procurement to favour SME innovation, and scaling hands‑on learning via partnerships and learning communities (AIC4NL supports fellowships and practical learning paths).

For managers planning reskilling, short, work‑focused programs that teach promptcraft, oversight workflows and RAG grounding - such as Nucamp's AI Essentials for Work - turn abstract policy into day‑to-day skills that keep people in control of automated pipelines; after all, even one unexplained automated flag can trigger reputational and legal risk unless humans can trace and fix it.