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

Finland's national push - from the AuroraAI programme to the AI Programme and recent government guidance - frames AI as a way to make public services faster, more personalised and more efficient, but also disruptive to frontline roles like tax assessors, Kela caseworkers and municipal call‑centre staff; studies show automated benefit checks can turn weeks of manual processing into near‑instant responses, which is great for citizens but a clear signal that reskilling is needed.

Read the Automating Society study for concrete examples and the AI Watch summary on AuroraAI for strategy context, and consider practical upskilling: Nucamp AI Essentials for Work bootcamp (15-week) registration teaches prompt writing and using AI tools so public servants can move from at‑risk tasks to supervising and improving AI systems.

Practical training plus ethical safeguards is Finland's route to capture efficiency without leaving people behind.

AI is expected to help the public sector to predict service needs, and respond in a timely manner to each citizen's needs and personal circumstances.

This analysis draws on Finland‑rooted tools and rules of thumb: Demos Helsinki's national assessment framework for non‑discriminatory AI was used to prioritise systems that touch fundamental rights (for example Kela benefit decisions or child‑welfare screening), the OECD catalogue entry clarified a lifecycle approach that turns qualitative answers into a simple risk score, and national guidance such as the Digital and Population Data Services Agency's “Using AI responsibly” helped shape organisational checks like human oversight and citizen‑facing transparency.

Criteria included potential for discriminatory outcomes, where in the AI lifecycle risks arise (design, development or deployment), the scale of citizen impact (how many people a single automated decision affects), and regulatory exposure under the EU AI Act's high‑risk categories.

The method blends a national mapping of public‑sector AI, a scored algorithmic impact self‑assessment, and practical governance checks so agencies can spot the most urgent reskilling and oversight needs before automation is scaled across services.

“Using new technologies like AI involves various social, societal, data protection and environmental risks that organisations have to anticipate and take into account.”

Verohallinto's long march from punched cards to VALMIS and GenTax means automated tax decisions are already baked into Finnish practice: MyTax logged more than 33.5 million logins in 2023 and the agency has systematically moved dozens of legacy systems into a single automated platform, reducing face‑to‑face work and standardising assessments - a huge efficiency gain that also puts routine tax‑assessor tasks squarely at risk.

The 2010s and 2020s programmes aimed to make taxation “seamless” by using pre‑filled returns and APIs, but legislation and transparency rules introduced in 2023 expanded how automated decisions must be disclosed, raising the bar for human oversight, audit trails and data security.

For assessors whose day used to mean case‑by‑case judgment, the practical pivot is clear: move from manual decision‑making to supervising models, investigating anomalies and designing control checks so automation improves fairness rather than replacing it.

Read the official account of the Tax Administration's digitalisation history and the 2023 year report for context on automation, customer volumes and the governance steps that shape where jobs will be preserved or transformed.

“We're proud to have built a cloud environment where we can actually use confidential information. That's a priority for us, especially due to the legal requirements associated with GDPR.”

Kela's move toward automation puts the most repetitive parts of social‑benefit casework squarely at risk: routine prerequisite checks, straightforward award and cancellation decisions, and register‑based updates are already handled automatically so long as the facts are “completely indisputable,” and that scale matters - Kela pays roughly €15.5 billion in benefits a year.

The agency's public policy is explicit that machine‑learning or probabilistic models do not (yet) make these automatic decisions; instead, automation speeds responses that once took weeks to something practically instantaneous, freeing staff time but also shifting job content.

Ongoing pilots and a strategic partnership with Solita show how AI could soon suggest benefit eligibility and improve triage for complex, interlinked entitlements, which means caseworkers will need new skills: auditing model outputs, translating probabilistic reasoning into clear justifications for citizens, and validating data when laws change.

For a quick primer on what Kela automates and why, read Kela's automated‑decisions overview and Solita's study of opportunities for Kela through AI.

“AI can work to find out benefit eligibility: based on available data, it could be made to suggest the benefits a particular citizen can apply for,”

Municipal customer service agents in Helsinki face a clear double shift: routine enquiries and triage that once filled busy call‑centre queues are prime candidates for automation, as the Automating Society report notes by promising that

time‑consuming queues and telephone appointments will be eliminated by personalised services and digital assistants,

but Finland's playbook also stacks demanding transparency and human‑oversight rules on top of that change.

New guidance from the Ministry of Finance encourages using generative AI to boost efficiency while stressing verification and limits on discretionary work, and national strategy documents such as the AI Watch summary for Finland underline AuroraAI's life‑events approach and the need to strengthen civil‑servant AI capacity so staff can move from answering repetitive queries to auditing model outputs, handling escalations and explaining decisions to citizens.

Practically, chatbots in public services must tell users they're interacting with automation, offer a human contact option and allow the conversation to be recorded - so customer‑service roles will increasingly combine soft skills with prompt‑auditing, bias‑spotting and privacy‑safe data handling.

That mix - less rote transaction work, more supervision, communication and data literacy - is what will preserve public trust as Helsinki scales smarter, faster citizen services (see the Ministry of Finance guidelines and the AI Watch public‑sector summary for implementation context).

Municipal social workers in Espoo who handle first‑line child‑welfare triage confront a high‑stakes balancing act familiar to agencies worldwide: predictive analytics can speed screening but also risks turning a complex family contact into a single numeric flag that determines whether a hotline call is investigated.

Guidance from HHS's assessment of predictive analytics in child welfare stresses practical checks that any Finnish municipality should insist on - data sufficiency, clear implementation strategy, rigorous model validation and vendor contract terms that protect public accountability - and Chapin Hall's overview lays out the responsible‑use checklist that helps keep models from amplifying existing inequalities.

The debate is not theoretical: examples from other jurisdictions show tools can both focus scarce resources and, if unchecked, magnify bias, so Espoo's triage teams and procurement officers need skillsets beyond social work - data literacy for assessing model accuracy, contract clauses that require transparency and independent evaluation, and stakeholder engagement to preserve trust.

For agencies thinking about automating parts of screening, practical primers on predictive analytics and local reskilling programs that teach audit‑and‑oversight skills are the safest path to harnessing efficiency without handing away human judgment; see the HHS assessment and Chapin Hall brief for concrete planning questions and the Nucamp AI Essentials for Work triage use-case syllabus for applied prompts and workflows.

“They can be transformational at helping us to identify people whose health and well‑being are at risk.”

OmaOlo - Finland's national electronic symptom checker built on Duodecim Clinical Decision Support and EBMEDS - already sings a clear tune for primary‑care triage staff: it's safe in real‑world clinics (97.6% of 877 assessments judged safe) and helps steer patients toward timely care, but it only exactly matched nurse triage about half the time, so digital tools are helpers not replacements.

The JMIR Human Factors validation study across 18 Finnish PHC centres found exact matches for all symptoms at 53.7% (and 70.9% exact match in urgent cases), with sensitivity ~62.6% and specificity ~69.2% - statistics that mean a symptom checker can cut unnecessary calls and speed responses while still leaving nuanced judgement, escalation and error‑review firmly in clinicians' hands.

For triage staff, the practical takeaway is concrete: learn to audit ESC outputs, translate probabilistic guidance into clear patient explanations, and flag edge cases where human assessment must prevail - think of OmaOlo as a digital receptionist that safely points 98 of 100 visitors to the clinic but gives the precisely correct room number only about 54 times out of 100.

Read the JMIR Human Factors validation study of the OmaOlo symptom checker for the clinical results and the PubMed record for the OmaOlo validation study for the citation and details.

Finland's playbook for managing AI risk is straightforward and practical: pair clear governance with hands‑on skilling so automation creates value without hollowing out public services.

Studies suggest generative AI could unlock roughly EUR 1.4 billion for e‑government and that about 65% of public‑sector roles can be complemented by AI, so the priority is to shift civil‑servants from repeat processing to oversight, auditing and communicating decisions.

That means three cross‑cutting moves: strengthen capacity (AuroraAI's life‑events framing and sandboxes in the AI Watch summary show how pilots and testbeds help), harden procurement and transparency rules (ethical guidelines and new national oversight measures make explainability and human fallback non‑negotiable), and invest in practical reskilling so staff can write, evaluate and troubleshoot prompts and models.

For teams that need a focused path from risk to readiness, practical training like the Nucamp AI Essentials for Work 15‑week course maps prompt‑writing and model‑audit skills onto everyday government tasks.

Taken together these steps - pilots, rules and reskilling - turn the automation question into a workforce upgrade, not a jobs cliff.

“AI is expected to help the public sector to predict service needs, and respond in a timely manner to each citizen's needs and personal circumstances.”