How AI Is Helping Government Companies in Finland Cut Costs and Improve Efficiency

AI matters for government companies in Finland because it promises concrete efficiency gains - freeing up staff time, cutting bottlenecks and making services more proactive and personalised rather than reactive.

Finland's AI Programme and subsequent reports frame AI as a lever to improve public service quality and to “predict service needs” across healthcare and benefits administration, with automation capable of turning processes that take weeks into near‑instant digital responses (AlgorithmWatch report: Automating Society - Finland).

Recent surveys argue that scaled hyperautomation in wellbeing services could unlock very large productivity gains (estimates of up to €3 billion annually) by streamlining administrative workflows and care pathways (Digital Workforce hyperautomation survey for wellbeing services).

Realising this potential depends on ethics, data governance and people: practical reskilling for public-sector teams - courses that teach promptcraft, tool use and on‑the‑job AI workflows - helps bridge the gap between pilots and reliable, citizen‑centred services (see the Nucamp AI Essentials for Work syllabus).

“The survey aimed to gather information about our organization's key personnel's understanding of work automation-related productivity opportunities. We will use the information obtained in the survey as part of our digital transformation program. Increasing automation plays a significant role in promoting the productivity of the wellbeing services county.” - Juuso Tamminen

Generative AI is already shifting the boring middle‑tier of public administration in Finland - the repetitive text work, long document searches and first‑pass drafting that tie up skilled staff - into fast, verifiable support tools that free people for judgement tasks: the Ministry of Finance guidelines on using generative AI in public administration explicitly encourage responsible use of generative AI across public administration while insisting on transparency and human oversight, and a recent study finds a roughly EUR 1.4 billion opportunity from applying generative models to administrative processes and that 65% of public‑sector roles can be meaningfully complemented by these tools (Implement Consulting Group study: AI opportunity for e‑Government in Finland).

Practical pilots show how this plays out: Hansel turned a 400‑page procurement manual into an AI assistant that helps buyers find answers without wading through appendices, and Helsinki's Copilot trials (1,000 staff across hundreds of legacy systems) proved the value of tight data rules, excluding sensitive data and disabling training‑use, while also revealing realistic limits such as hallucination and language coverage.

Sitra's legislative‑drafting pilots and other experiments make the point practical: start with low‑risk, high‑value tasks, bake in verification and procurement safeguards, and scale only when human review and data governance are rock solid - otherwise the gains evaporate under the weight of errors or privacy risk.

“By utilizing new work methods and the possibilities of AI, we can streamline operations and reduce the phases of manual work. It doesn't necessarily mean reducing staff but doing things more efficiently and smartly, allowing work time to be focused on more productive and sensible activities.” - Lasse Ahonen, Tech & Architecture Lead, Hansel

Prioritising low‑risk, high‑impact AI use cases in Finland means picking projects that deliver tangible citizen benefits quickly while keeping legal, procurement and privacy risks tightly controlled: recent analysis puts the generative‑AI productivity prize at about €1.4 billion a year and notes that up to 20% of that potential could be unlocked straight away if pilots are better coordinated and risk‑managed (Implement Consulting and Google Finland report on the €1.4 billion AI opportunity).

Practical starters include customer‑service automation and routine document work - think application triage and document verification to unclog immigration and benefits backlogs (Application triage and document verification use cases for immigration and benefits) - paired with citizen‑facing transparency measures such as Sitra's rulebook process so the public sees clear safeguards and benefits (Sitra rulebook for AI in public services).

Start small, measure accuracy and privacy outcomes, and scale only when governance, procurement flexibility and public trust are proven - this approach turns pilot wins into system‑level savings without gambling with sensitive services.

Shared digital platforms are the quiet force multipliers that make AI adoption practical across Finland's public sector: by standardising terminologies, code lists and data models the national interoperability platform turns bespoke data silos into predictable inputs for automation and AI models, and even offers a new Excel upload path for updating terminologies so teams can keep vocabularies in sync as services evolve (Finnish Interoperability Platform for public sector data).

The Suomi.fi family bundles identification, messaging, a national service catalogue and APIs that let agencies plug common building blocks into citizen journeys - an infrastructure that once replaced a 400‑page Citizen's Manual (it famously took three months for three civil servants to refresh all country links) and now supports AI‑assisted features as AuroraAI is introduced into the network (Suomi.fi national web service for citizen identification and APIs).

Complementary tooling such as the Suomi.fi Quality Tools gives teams unified metrics and real‑time feedback so AI-driven services scale without fragmenting user experience or inflating maintenance costs (Suomi.fi Quality Tools for unified service metrics).

Together these shared services lower duplication, speed pilot-to-production handovers and let agencies focus AI investment on solving citizen problems rather than rebuilding plumbing.

AuroraAI represents Finland's pivot from one‑off pilots to a people‑centred, proactive public‑service vision: designed as a decentralised network of smart, data‑based services that stitches together help around real life‑events rather than forcing citizens to navigate dozens of siloed agencies, the programme was a core action of Finland's national AI strategy and was actively developed between 2019–2023 (Finland national AI strategy report).

Managed from the Ministry of Finance, AuroraAI aimed to deliver personalised, AI‑driven support for individuals and businesses while embedding ethical safeguards, human‑centred design and trust as first principles - so adoption focused on service orchestration, transparency and clear governance instead of quick, risky automation (OECD overview of the AuroraAI initiative).

The memorable shift is structural: by treating public services as interoperable building blocks in a living network, AuroraAI promised to make government more anticipatory and convenient - if ethical frameworks, data rights and workforce skills keep pace with the technical rollout.

Infrastructure at industrial scale is what lets AI move from clever pilots to system‑level savings in Finland: the EuroHPC‑backed LUMI supercomputer in Kajaani anchors a national cluster of compute resources (alongside Mahti and Puhti and the planned Roihu), offering roughly ≈380 petaflops of sustained performance, ~11,900 AMD MI250X GPUs and multi‑petabyte storage that researchers, companies and public bodies can apply to use - often remotely - for large‑scale AI training, climate digital twins and hybrid quantum–HPC experiments (LUMI supercomputer at CSC).

LUMI's EuroHPC governance gives member states and businesses dedicated quotas, its waste heat is fed into Kajaani's district heating (40°C water boosted for homes), and VTT's quantum node access via LUMI is already opening hybrid workflows for industry - a vivid example of compute power, green engineering and access models combining to lower the cost and ramp time for public‑sector AI projects (EuroHPC Joint Undertaking).

“In Lumi, you can build everything, from the smallest scales [such as] simulating matter [and] particles, how they interact, what are their properties, to what happens in the universe at the galaxy scale.” - Katja Mankinen, CSC

Public–private programmes and targeted grants are quietly lowering the cost and risk of AI adoption across Finland by sharing early-stage expenses, training and talent pipelines: the Technology Industries of Finland's €10 million AI investment backs seed funding (covering up to 50% of project costs, max €15,000 per project), subsidised researcher hires (up to half a recruit's salary for two years), executive training and €2.8M for member‑company development projects, while Business Finland's Generative AI campaign and large calls such as the Rise to Challenge pilot steer complementary R&D and proof‑of‑concept grants that accelerate SME uptake and internationalisation (Technology Industries of Finland AI investment program details; Business Finland Generative AI campaign information).

Public funders also underwrite municipal pilots and platform work: Sitra's 2025 call funded City of Espoo and the Finnish National Agency for Education projects to embed AI in strategic decision‑making, proving that co‑funded trials and researcher placements can turn promising prototypes into operational tools without forcing agencies to carry the full upfront bill (Sitra call for proposals on data and AI in public-sector decision-making).

The net effect is pragmatic: smaller, shared bets - like covering half a researcher's salary for two years - make it realistic for government companies to pilot AI with strong governance and measurable ROI.

Finland's push to make AI a force for efficiency hinges on people as much as on platforms: the Ministry of Economic Affairs and Employment expects employment to start rising in 2026 even as long‑term unemployment remains stubbornly high, so targeted reskilling and smarter hiring are essential to avoid wasted human potential (Ministry of Economic Affairs and Employment labour market forecast).

Recent analysis shows higher participation has raised both employment and unemployment, underlining that more workers are available but many need new skills to be productive in an AI‑augmented workplace (Bank of Finland labour market analysis).

Practical responses combine selective recruitment via programmes such as Talent Boost to ease sectoral shortages, focused upskilling for mid‑career professionals, and short, work‑aligned courses that teach 'model supervision' and prompt‑centred workflows so staff supervise AI instead of doing routine drudge work (routine task replacement and the rise of model supervisors).

The payoff is concrete: when reskilling is tied to specific tasks - application triage, document verification, verification checks - AI becomes a productivity multiplier rather than a cost‑shifting risk.

Finland's path to trustworthy, efficient AI in government depends as much on rules and shared infrastructure as on models: the Ministry of Finance's project to “open up and use public data” has produced an operational framework, national API guidelines and feeds avoindata.fi - already hosting over 1,700 datasets - to make public data reusable for AI-driven services (Finland Ministry of Finance - Opening up and using public data); at the same time Finland's OGP commitment explicitly ties open data work to ethical AI by promising quality criteria, developer‑friendly interfaces and a general set of guidelines to prevent discriminatory models (Open Government Partnership - Open Data and AI Policy (FI0033)).

Practical governance must bridge EU rules and local practice: Sitra's roadmap for holistic EU data governance highlights rolebooks, rulebooks and sectoral data spaces as tools to unlock data while protecting rights and interoperability (Sitra - Towards a Holistic EU Data Governance).

Reality checks remain - ethical guidelines were delayed during COVID‑19 and experts urge sandboxing, civil‑society access to background data and active stakeholder dialogue - but the combination of open APIs, quality criteria and targeted sandboxes creates a realistic route from transparent datasets to accountable, low‑risk AI that citizens can trust.

Water and environment monitoring offer some of the clearest, measurable wins: Finnish research projects and pilots show AI trimming fieldwork and speeding warnings across thousands of lakes and nearly 400 summertime cyanobacteria sites, turning weeks of sampling into targeted actions.

Syke's long‑running AI Vesi360 programme explores forecasting, virtual assistants and image‑based citizen detection to reduce expert monitoring effort (Finnish Environment Institute AI Vesi360 water AI project), while earlier AIWaterBio work used scalable cloud and IoT tooling to recognise algal blooms with over 90% reliability and shrink manual classification time.

More recently Syke's public–private pilot with Kuva Space is testing hyperspectral satellites plus machine learning to identify species and biomass across inland and coastal waters - higher spectral detail that promises faster, cheaper targeting of restoration and safety alerts (Syke and Kuva Space hyperspectral satellite pilot for water quality monitoring); these initiatives illustrate a simple equation for government companies: better sensors + AI models = fewer routine samples, faster interventions, and lower monitoring costs.

“We're very excited about this pilot with Kuva Space because rather than just detecting the presence of algae, we can use Kuva's hyperspectral technology and AI to explore the spectral range and take a step further in identifying which algae species are present and assessing their biomass.” - Jenni Attila, Leading Researcher & Group Manager, Finnish Environment Institute

A practical roadmap for government companies in Finland turns high‑level strategy into step‑by‑step action: begin by prioritising low‑risk, high‑impact pilots (application triage, document verification and customer‑service automation) and document measurable accuracy and privacy outcomes so pilots can feed evidence into procurement decisions; use national regulatory sandboxes to test systems - explicitly supported in Finland's public‑sector AI strategy as “sandboxes” where solutions can be tried with public administrations' own personal data under strict oversight (AI Watch analysis of Finland public-sector AI strategy) - and rely on the EU AI Act framework that protects providers from administrative fines when they follow sandbox guidance while still remaining liable for damages (EU AI Act regulatory sandboxes rules and Member‑State timelines).

Parallel tracks should harden governance and procurement (reviewing Public Procurement Act implications and record‑keeping), build internal skills through targeted reskilling and EDIH/TEF collaborations, and capture compliance artefacts from sandbox tests to demonstrate conformity with the incoming supervisory regime (including the Act on the Supervision of Certain AI Systems and national guidance) (Artificial Intelligence 2025 Finland legal and governance checklist).

The memorable test of success is simple: pilots that can safely process real public data in a supervised sandbox and produce a published compliance summary are the ones ready to scale without legal or trust backslides.

Finland's next moves should be pragmatic: convert pilots into governed, measurable rollouts by prioritising low‑risk, high‑value projects, documenting accuracy and privacy outcomes, and using supervised regulatory sandboxes before scaling - the EU AI Act and Finland's national implementation set the timeline and test‑environment expectations that make this possible (Chambers Artificial Intelligence 2025 Finland practice guide).

Pair those legal steps with joined‑up operations (shared platforms, co‑funded researcher placements and public–private grants) and targeted reskilling so staff move into “model supervisor” roles rather than rote tasks; short, practical upskilling like Nucamp's Nucamp AI Essentials for Work bootcamp syllabus maps neatly to this need.

Keep collaboration channels open - Helsinki's cross‑sector forum shows that learning together reduces risk and speeds adoption - and use published compliance summaries from sandboxed pilots as the concrete signal that a service is ready to scale without eroding public trust (Interreg: Collaboration Drives Responsible AI in the Public Sector).

“With AI and its challenges, it's better to bang our heads against the wall together than alone.”