Top 10 AI Prompts and Use Cases and in the Government Industry in Finland

Finland has moved fast from strategy to pilots: national plans like the AI 4.0 programme and the FCAI ecosystem are steering AI toward better public services, while the AuroraAI network prototypes “anticipatory services” that recommend help around key life events - and even the country's Bluetooth tracing app drew over a million users on day one in a nation of 5.3 million, a vivid sign of trust and uptake.

Public-sector AI in Finland prioritises privacy, ethics and explainability as it scales rule‑based automation and generative tools across healthcare, benefits and taxation; the European Commission's Finland AI Strategy report maps these policy steps and investment lines, and GovInsider's profile of AuroraAI shows how citizen-centred design is central.

For public servants and private partners looking to apply AI responsibly, practical upskilling such as Nucamp's AI Essentials for Work bootcamp can bridge the gap between strategy and the everyday prompts and tools that make anticipatory services real.

Bootcamp	Details
AI Essentials for Work	15 weeks; practical AI skills for any workplace; courses: AI at Work, Writing AI Prompts, Job-Based Practical AI Skills; early bird $3,582; syllabus: Nucamp AI Essentials for Work syllabus (15-week bootcamp)

“It makes government more “human-centric” and efficient, she says.”

Codento's hands‑on method - running free, two‑hour discovery sessions across 1.5 years with management teams from 104 Finnish organisations - turns abstract AI talk into a short, practical pipeline: confidential pre‑work to surface ideas, facilitated in‑workshop refinement with Codento experts, and clear next steps to prototype 1–2 prioritized use cases per group.

The workshops yielded 677 suggested uses, revealing a striking pattern: up to 99% of high‑benefit ideas aim to streamline operations while barely 1% target new business growth, a reminder that Finnish efficiency thinking can blind organisations to bold, growth‑oriented AI bets.

With small, senior teams (typically 3–7 people) in the room, Codento converts local knowledge into concrete concepts and implementation advice and now offers follow‑ups like the free Apps AI value‑discovery workshop to push piloting and experimentation further - exactly the kind of practical bridge public agencies need between national strategy and deployed services.

Read Codento's full findings and sign up for a workshop to see how ideas move from list to live pilot.

Metric	Value
Timeframe	1.5 years
Participating organisations	104
Total use cases collected	677
Typical participants per org	3–7 (management / operational)

“In the future, we commit to come up with at least one wild, growth-oriented artificial intelligence use case for every organization participating in the free workshop. With this, we aim to awaken the hunger for innovating the new and expand the understanding of the potential playing field of artificial intelligence.” - Janne Flink, Codento

Kela's push to automate benefit eligibility screening sits at the practical intersection of efficiency and rule‑bound complexity: the Social Insurance Institution already uses chatbots, fraud detection and analytics as part of a wider move to streamline service delivery, while benefit legislation stretches across hundreds of rules written over 30 years - a reminder that automated screening must map tightly to dense legal detail.

Changes to residence‑based entitlements since 2019 (notably abolishing the “four‑month rule” and allowing foreign workers earning at least €696.60/month to qualify) reshape who flows into Kela's eligibility pipeline, so screening logic must track law as well as data intake (Mercer report on Finland social security eligibility criteria).

Oversight bodies have already asked for transparency about which decisions are automated and who is accountable, underlining that any rollout of automated checks needs clear audit trails, human review points and published governance - as detailed in the Automating Society report on automated decision-making in Finland - because when an algorithm touches millions of euros in benefits, explainability isn't optional, it's civic insurance.

Item	Fact
Annual benefits managed	Approximately €15.5 billion (Kela)
2019 eligibility change	Abolition of the four‑month rule; foreign nationals earning ≥ €696.60/month may qualify (Mercer report on Finland social security eligibility criteria)
Oversight	Automated benefit procedures and communications have drawn legal scrutiny; authorities requested details on ADM practices (Automating Society report on automated decision-making in Finland)

Vero (the Finnish Tax Administration) sits squarely where AI for tax fraud and risk scoring can pay off: anomaly detection and network analysis can comb vast VAT, income and transaction datasets to flag suspicious refund claims, prioritise audits and speed investigative triage while preserving taxpayer service.

International playbooks - from Poland's STIR real‑time monitoring (able to freeze suspect accounts for up to 72 hours) to HMRC's Connect and Austria's PACC - show how AI shifts authorities from reactive audits to proactive prevention; a helpful roundup of these examples is available in VATCalc's briefing on how tax authorities adopt AI for fraud and efficiencies.

Practical implementations lean on anomaly detection and instant risk scoring to surface the highest‑risk cases and allocate scarce enforcement resources, and Microsoft's guidance on anomaly detection for tax records explains how models can support audits with audit trails and governance.

The memorable payoff: when models work, investigators spend less time chasing noise and more time reclaiming public money - but only if data quality, explainability and privacy protections are non‑negotiable.

“harness” AI to “strengthen fraud detection.”

Helsinki University Hospital (HUS) can sharpen day‑to‑day capacity planning by borrowing a simple insight from a recent multicentre BMC study: feature‑engineered calendar signals (day‑of‑week, yday, week) - sometimes boosted by temperature - let machine‑learning models forecast ED arrivals far better than naive methods, with top models (XGBoost and NNAR) posting MAPEs as low as about 5% across short and medium horizons; that level of accuracy turns “mystery peaks” into actionable staffing triggers so planners can schedule nurses, open beds or reorder stock before queues form.

The study also found predictable cadence - Mondays and Sundays often busiest, August and March busier while November dips - so embedding FE-enhanced forecasts into rostering and bed‑management systems directly targets overtime and wait‑time pain points.

For Finnish public hospitals, the path is practical: adopt FE pipelines, validate them on local HUS data, and pair model rollouts with workforce reskilling to translate predictions into schedules (see a primer on reskilling Finland's public sector workforce).

The study's code and data are publicly available for reproducibility and local adaptation in HUS pilots.

Metric	Detail
Study scope	11 ED datasets (multicentre)
Top algorithms	XGBoost, NNAR
MAPE range	~5.03% – 14.1% (best cases ≈5%)
Key feature‑engineered predictors	index.num, yday, week, day‑of‑week dummies, temperature
Practical benefits	Staff scheduling, bed management, reduced wait times and overtime

BMC study on FE-enhanced ED forecasting | Open-source code and data | Reskilling Finland's public sector workforce

For the Finnish Police, an “intelligence and case prioritization assistant” looks less like a Hollywood robot and more like a secure combo of knowledge graphs, anomaly detection and LLM-driven assistants that turn complex link analysis into bite-sized leads officers can act on: analysts can surface high‑risk co‑offender networks, flag suspicious transactions or prioritise homicide and sexual‑offence leads, while patrol units get near‑real‑time, human‑verifiable intelligence at the street level - briefings suggest this democratisation could give “every patrol officer the equivalent of an expert intelligence analyst at their side.” At the same time Finland's debate over wider biometric access (registers hold information on approximately four million people and proposed rules limit use to serious crimes or identity problems) and past missteps with commercial face‑search trials underline that effectiveness must be matched by tight access controls, clear legal gates and public oversight; Europol's AI and policing report flags these trade‑offs and the coming EU rules that will shape deployments.

Tampere University's mapping also shows authorities want to expand image‑recognition tools but resist public scrutiny, so any Poliisi assistant must pair operational gains with auditable processes, privacy safeguards and transparent governance to win trust.

“Integrating link analysis with large language model (LLM) capabilities is a natural progression.”

Migri is piloting a pragmatic, risk‑limited use of automation that neatly fits Finland's emphasis on legality and auditability: the agency has begun issuing automated approvals for student residence permits when applications meet all statutory checks, while reserving negative or complex cases for human review; the system also runs post‑decision monitoring by cross‑checking national registers such as Kela, the Population Information System and the National Agency for Education's Koski, and still requires identity to be verified in person by an official - a concrete safeguard that makes automation easier to trust.

See Migri's guidance on processing of applications and its document‑access rules for how confidentiality and appeals are handled, and read reporting on the trial to understand scope and timing.

Early pilots aim to standardise straightforward approvals, free specialists to focus on borderline cases, and speed up outcomes (coverage of the automatic‑approvals pilot notes the move started 19 October 2023 and outlines next steps).

For agencies planning similar workflows, the key design choices are transparent gates for automation, clear human‑verification points, auditable register checks, and a staged rollout so checks, appeals and fees remain visible to applicants.

“When the requirements laid down by law are met, a permit can be issued effectively, and it is also possible to monitor that the requirements are still met during the validity of the permit,” Migri's Chief Digital Officer, Anna Cheung, said.

Traficom can turn Finland's roads into a live, resilient nervous system by pairing IoT sensors, high‑resolution cameras and edge processors with adaptive signal control and predictive maintenance: camera and radar feeds spot congestion and incidents in seconds, smart signals reallocate green time for buses and emergency vehicles, and road‑embedded sensors and strain monitors flag wear before a patch becomes a lane closure.

“even when the vehicle is outside the range of any road weather station,”

A Finnish pilot underlines why robust cellular links matter here - cellular connectivity keeps weather and traffic feeds current cellular connectivity for traffic management so ploughs, gritters and digital signage get timely alerts through snow or remote gaps in fixed networks.

Combined with IoT‑driven predictive maintenance - vibration and surface sensors that trigger targeted repairs - this approach reduces delays, lowers emissions from idling and protects budgets by fixing problems early; throughout, anonymisation, encryption and clear governance must anchor deployments so Finnish commuters gain reliability without sacrificing privacy real‑time IoT traffic monitoring and predictive maintenance.

City of Espoo is turning municipal permitting into a model of predictable, auditable automation by aligning local practice with Finland's national digital permitting pathway: ACCORD's review highlights Finland as the only demo country with a detailed transformation pathway and notes the building‑permit workflow can already include 47 automatic checks against the Building Code, a neat, rule‑based foothold for automated review (ACCORD transformation pathways towards digital permitting processes in demo countries).

Practically, Espoo has paired that rule automation with a human‑centred touch - a new client communication document (launched 17 May 2024) lets applicants and permit officers record estimated deadlines, interim goals and any missing supporting documents in the online service, reducing surprise requests and smoothing handoffs (Espoo new method streamlines building control permit processing (17 May 2024)).

Local innovation and construction‑sector research - from 3D city modelling to Aalto's machine‑learning work on automated waste sorting in construction - shows how the city can combine deterministic checks with data‑driven tools, so permits are faster without losing transparency or legal traceability (Aalto University machine-learning automated waste sorting in construction study (PubMed)).

Item	Detail
National pathway	Finland has a detailed ACCORD transformation pathway
Automatic checks	47 checks against the Building Code (landscape review)
Espoo initiative	Client communication document launched 17 May 2024 (online status, interim goals, missing docs)

“Our client feedback has emphasised the importance of transparency and predictability in permit processing. We decided to create a separate communication document to make it as easy as possible for both the client and the permit officer to understand and monitor the progress of permit processing,” Jari Saajo, Manager of the Building Control Department, said.

For the Finnish Border Guard (Rajavartiolaitos), anomaly‑detection becomes actionable coastal awareness when sensor fusion, automated alarms and human oversight are combined: platforms like Airbus's STYRIS® stitch cameras, radars, AIS, weather sensors and RDF into a single, replayable maritime picture that flags abnormal behaviour and protects EEZs and ports (Airbus STYRIS® coastal surveillance system); all‑weather SAR imagery adds persistent, day‑and‑night coverage to spot “dark vessels” and subtle change‑of‑life patterns along shorelines (Capella Space SAR for coastline monitoring).

AI‑driven anomaly filters - from speed‑outlier detectors to behaviour‑pattern models - can cut the caseload of false leads and focus patrols where they matter, but legal, cyber and accountability trade‑offs remain central (see the practical risks and governance points in ORF's review) (AI in maritime surveillance: uses, risks and considerations).

In trials, radar‑based abnormal‑speed detectors have even reported zero false alarms, a reminder that well‑engineered detection can turn scattered signals into a clear, auditable cue for a single interception rather than dozens of wasted chases.

Item	Detail
Integrated sensors	Radars, cameras/EO, AIS, weather sensors, RDF (sensor fusion)
Operational pedigree	STYRIS®: deployed in 250+ locations; monitored >40,000 km of coastline
Anomaly detection evidence	IEEE radar study: abnormal moving‑speed detection with reported FAR = 0 in tests

"We greatly value the strong relationship we've built with Airbus over the years. We rely on their team to continuously integrate technological advancements [...]. The improved and new capabilities will enable us to meet the growing challenges of protecting our maritime borders and conducting coastal surveillance missions." - Laurent Frayssignes, Commander, Spationav Program Officer, French Navy

The Ministry of Finance can use AI to turn static annual plans into dynamic, testable budgets: advanced analytics let teams stitch historical economic indicators together, run rapid “what‑if” scenario simulations and refresh forecasts as conditions change so policy choices are stress‑tested before they reach parliament.

AI‑driven scenario planning improves revenue forecasting, highlights trade‑offs between investment and contingency reserves, and surfaces tipping points - provided strong data stewardship and human‑in‑the‑loop checks safeguard results.

Equally important are the governance pillars highlighted for public budget offices: model explainability, audit trails, bias reviews and cybersecurity so that automated recommendations remain transparent and defensible to legislators and citizens.

Practical rollouts start with high‑value pilots (revenue, social‑spend or infrastructure forecasts), clear approval gates and continuous reskilling so analysts move from routine reconciliation to strategic interpretation.

For Finnish fiscal managers, that means pairing AI‑powered, near‑real‑time forecasting with the tried‑and‑tested controls that preserve accountability - imagine a dashboard that flags a looming revenue swing days earlier, giving ministers time to weigh targeted measures rather than scramble under pressure - an operational shift from reactive firefighting to calm, evidence‑based steering of the public purse (Modernize public finance with AI‑informed budgeting and what‑if scenario planning; AI readiness and governance roadmap for state government budget offices).

“Some people think of AI as a way to do the work they do not want to do. Top performers think of AI as a way to do the work they have always wanted to do.”

Finna and Finland's national and municipal libraries can move beyond brittle keyword hits by layering semantic search and Retrieval‑Augmented Generation (RAG) to deliver truly personalised retrieval and accessibility services: semantic indexes turn documents and queries into meaning‑rich vectors so searches match intent instead of exact words (IEEE paper: Semantic Search in Digital Libraries), while RAG stitches those retrieved passages into evidence‑backed answers that feel conversational and up‑to‑date (SSRN paper: Retrieval‑Augmented Generation for Academic Library Search).

Good metadata, controlled vocabularies and multilingual indexing - core IR best practices - remain essential to preserve precision and recall, and a semantic layer offers a low‑cost, high‑volume search surface while an optional RAG layer supplies high‑value, auditable assistance for research or accessibility queries.

For Finnish libraries this means faster discovery for citizens, better support for researchers and more inclusive services for non‑Finnish speakers and readers with accessibility needs; implemented carefully, the user experience moves from “find a list” to something that feels like having an expert librarian at hand (Guide: Semantic Search and RAG - Key Differences and Use Cases).

“Now, with Retrieval-Augmented Generation, you're not just getting a smart librarian; you're getting one who grabs the book and sits down to craft a custom answer for you, blending real facts from the latest pages with their polished storytelling skills.”

As Finland moves from pilots to scaled services, the next steps are practical and tightly governed: lock AI deployments behind the new national ethics and generative‑AI guidance and the AI Act implementation (including the Act on the Supervision of Certain AI Systems), run focused sandboxes so agencies can “test-drive” models under supervision (a flight‑simulator for policy rather than a leap of faith), and prioritise low‑risk, high‑value pilots that the Implement Consulting Group shows can unlock a EUR 1.4 billion opportunity while complementing up to 65% of public‑administration roles (Implement Consulting Group report: The AI opportunity for eGovernment in Finland).

Equally vital are procurement clauses for explainability and bias audits, continuous human‑in‑the‑loop checks, and a concrete reskilling pipeline so civil servants turn recommendations into defensible decisions - practical training such as Nucamp's 15‑week AI Essentials for Work bootcamp helps translate policy into repeatable prompt‑engineering and oversight skills (Nucamp AI Essentials for Work bootcamp syllabus); for legal and regulatory grounding, see Finland's 2025 overview of AI trends and national guidelines (Artificial Intelligence 2025 – Finland trends and developments overview).

Bootcamp	Length	Early bird cost	Syllabus
AI Essentials for Work	15 weeks	$3,582	AI Essentials for Work bootcamp syllabus

“generative AI should not be integrated into workflows that require legal or discretionary judgement.”