The Complete Guide to Using AI in the Real Estate Industry in Finland in 2025

As Finland's 2025 market rebound takes shape, AI is rapidly moving from theory to competitive edge in property markets: CBRE notes GDP growth of about 1.8% and highlights “artificial‑intelligence led data centre investment” as a key support for recovery in its Finland Real Estate Market Outlook 2025 (CBRE Finland Real Estate Market Outlook 2025), while Business Finland's mapping of the Finnish AI ecosystem shows how startups, research and industry partnerships are turning AI into practical tools for faster deal screening, predictive valuations, energy‑efficient retrofits and construction schedule optimisation (Business Finland: The State of AI in Finland 2025).

With transaction volumes and investor appetite picking up in early 2025, AI is becoming the mechanism that translates macro recovery signals into quicker, lower‑cost decisions across the real estate lifecycle - think predictive rent forecasting and smarter asset operations, not just neat demos.

"The speed of artificial intelligence development is staggering. However, in a rapidly changing environment, there are times when it's important to stop for a moment and reflect on where we are, what is happening around us, and to identify our own strengths and areas where we have the opportunity to succeed and make an impact. The Finnish AI Landscape Report has been conducted precisely for this need." - Timo Sorsa

Finland's national AI strategy marries ambition with practical plumbing: since the 2017 Finland's Age of Artificial Intelligence roadmap the government has pushed open‑data policies, large‑scale funding (for example the AI Business Programme was allocated EUR 100 million over four years and the Finnish Centre for Artificial Intelligence received EUR 8.3 million in flagship funding for 2019–2022), and tools to move experiments from lab to market - think Business Finland and VTT support, an AI maturity tool, innovation vouchers and testbeds - so companies can pilot real systems rather than just demos; the updated Finland AI Strategy Report - EU AI Watch overview documents these programmes and the strong emphasis on skills, lifelong learning and ethics, while the government's Finland Artificial Intelligence 4.0 programme - Ministry of Economic Affairs and Employment focuses that effort on digitalisation for SMEs and the “twin transition” (green + digital) through cross‑sector cooperation and targeted measures.

That mix - funding, open data, regulatory sandboxes, national research infrastructure and ethical oversight - creates a predictable national context where Finnish firms can experiment with AI in production settings, attract talent via Talent Boost, and scale responsibly; the memorable detail: a national strategy that explicitly funds ecosystems and testbeds means pilots can tap public resources instead of relying only on risky internal bets.

“the AuroraAI program is being developed via concrete life-events.”

Finland's AI case is pragmatic: a compact, highly educated ecosystem and strong public plumbing make it easy to move from experiment to impact, with Business Finland and VTT helping translate lab science into deployed services and a lively startup scene anchored in Helsinki's campuses (think MARIA01) that gives the market real momentum; the joint “State of AI in Finland 2025” report maps this vibrant mix of startups, research and industry and flags where bold investment can scale wins (State of AI in Finland 2025 report - Business Finland).

Strengths include predictable national programmes, open‑data efforts and named funds and testbeds from the national strategy described by the EU's AI Watch (Finland AI Strategy report - EU AI Watch), while practical challenges remain: a sizeable reskilling need (about one million people), evolving regulation around automated decisions and procurement, and the classic scale gap between promising pilots and commercial rollouts - all reasons networks like AI Finland network - aifinland.fi exist to connect talent, industry and public actors.

Picture this: a Finnish pilot turns into a live building‑operations tool because a local testbed, public grants and a startup on MARIA01 all lined up - that orchestration is Finland's distinctive advantage, and the thing to prioritise when assessing fit for real‑estate AI projects.

"The speed of artificial intelligence development is staggering. However, in a rapidly changing environment, there are times when it's important to stop for a moment and reflect on where we are, what is happening around us, and to identify our own strengths and areas where we have the opportunity to succeed and make an impact. The Finnish AI Landscape Report has been conducted precisely for this need." - Timo Sorsa

The 2025 industry outlook for AI in Finland is one of rapid rollout tempered by sharp governance focus: corporate uptake is already mainstream in financial services - nearly 90% of firms either use AI or plan to within two years - driving practical wins in automation, fraud detection and customer triage even as organisations wrestle with fairness and data‑quality rules (see the Fondia/FIN‑FSA survey summary Fondia/FIN‑FSA survey on AI adoption in Finland's finance sector); at the same time generative models and agentic AIs are spreading fast (global studies show big jumps in GenAI use), pushing demand for compute, new CapEx and energy planning that will shape where Finnish data centres and real‑estate owners invest next (AI adoption trends and generative AI growth in 2025).

Expect a bifurcated market: larger, well‑resourced players scale integrated AI stacks with strong risk teams, while SMEs and property managers use off‑the‑shelf tools or partner with startups to add value - think faster deal screening and predictive analytics for cash flows and energy retrofits (see practical prompts for property modelling at practical property modelling prompts for real estate investment analysis and predictive analytics).

The “so what?” is simple: Finland's predictable regulation, public testbeds and growing budget lines mean 2025 is the year pilots either prove commercial or fall back to governance lessons - so plan for measured scaling, data audits and clear KPIs now.

“2025 will mark a significant milestone in AI agent adoption across industries such as finance, supply chain, sales, services, marketing, and tax… OpenAI's ‘Operator' framework and Amazon's Bedrock Agents framework will enable companies to incorporate AI agents into their enterprise.” - Igor Epshteyn

Across Finland the story is practical: AI is being stitched into every stage of the real‑estate lifecycle from smarter site selection to post‑occupancy operations.

At the front end, location‑intelligence and site‑selection platforms bring together GIS, foot‑traffic and demographic layers so teams can

predict performance, not just monitor it,

helping avoid costly mis‑buys and find whitespace opportunities faster (CARTO site selection and location intelligence tools).

In planning and design, Finnish consultancies show how simulation and machine learning speed decisions - Ramboll's Brutus transport model and related ML tools let planners test regional mobility and development scenarios, while Granlund's AI Energy Mapping turns machine‑readable energy certificates and internal cost/CO2 databases into concrete retrofit proposals and scalable professional services, moving from idea to working tool in months rather than years (see the Ramboll/Granlund examples and service notes for real projects in Finland).

Generative models can accelerate design iterations and automate report writing, and on the construction and asset‑management side AI shortens schedules, predicts maintenance needs and optimises HVAC and lifecycle costs.

For data‑centre or heavy‑infrastructure plays, AI layers in power, connectivity and flood‑risk data to pinpoint viable sites quickly. The practical takeaway: in Finland AI isn't a gadget - it's a workflow multiplier that can evaluate whole portfolios at once, surface clear renovation actions and turn pilots into repeatable services that cut time and cost while preserving expert judgment (Ramboll and Granlund AI-driven planning and energy mapping case studies).

Finland's pragmatic AI toolbox for real estate mixes tried‑and‑tested SaaS with sensor fleets and specialist analytics: cloud property platforms that include utility‑consumption analytics and tenant workflows (see Saru Tech Finland property management with utility-consumption analytics), portfolio and KPI dashboards that visualise assets on interactive maps and pull market feeds (Exquance portfolio and market-data tools for Finland), lightweight capture and vision tools that “create a floor plan in 5 minutes with your phone” for fast asset onboarding (CubiCasa), and a growing IoT stack for energy, occupancy and predictive maintenance - TEKTELIC LoRaWAN gateways and smart room sensors built to scale for smart buildings.

Add niche analytics vendors like Skenario Labs and video‑analytics integrators to spot safety or tenant‑experience wins, and use flexible APIs (Assetti and Exquance both emphasise integrations) to stitch these components into one workflow; the memorable payoff is this: a single property visit can feed automated floor plans, sensor baselines and an initial DCF‑ready file, turning days of admin into next‑week investment decisions.

For Finnish teams, the practical aim is predictable integrations, clear KPIs and reusable data loops rather than one‑off demos.

“The KONA Micro Gateway and Smart Room Sensors were chosen by Soobr because of their “Always On” connection, extended battery life, and outstanding indoor RF performance.”

For Finland's real‑estate teams the legal checklist is straightforward but non‑negotiable: comply with the EU AI Act while keeping GDPR front and centre, build documented risk management and human‑oversight into every AI use case, and plan to use national testbeds and the upcoming regulatory sandbox to de‑risk pilots - the EU overview notes each Member State must set up at least one national AI regulatory sandbox by 2 August 2026 and sandboxes can be used to demonstrate compliance and even allow limited reuse of personal data for testing under safeguards (EU AI regulatory sandbox approaches - Member State overview).

Key obligations include conformity assessments and detailed technical documentation for high‑risk systems, post‑market monitoring, transparency rules for generative AI and user notification, and alignment of DPIAs with the AI Act's Fundamental Rights Impact Assessment expectations (see the summary of how the AI Act supplements GDPR) (IAPP analysis: Top operational impacts of the EU AI Act and GDPR alignment); remember that non‑compliance carries real penalties (up to €35M or ~7% of turnover) so governance, clear vendor contracts and a budget for compliance are practical necessities (PwC guide: EU AI Act compliance and transformation).

Think of the sandbox as a supervised test lab that can shield a pilot from administrative fines if it follows regulator guidance while still requiring careful liability and data‑handling controls - start with a data‑flow inventory, classify risk tiers, plan DPIAs/FRIAs, and map interfaces for human oversight so pilots can graduate to production without regulatory surprises.

Mitigating AI risk in Finnish real‑estate projects means turning governance from an afterthought into a procurement requirement: start by mapping use‑case risk tiers and DPIAs, bake human oversight and audit trails into contracts, and require model “nutrition‑labels” and bias reports with every purchase order so asset managers can scan safety at a glance; Finland's national strategy and testbed-friendly policy environment make it practical to pair these controls with sandboxed pilots (Finland national AI strategy report (EU AI Watch)).

Operationally, use explainability, bias assessment and model‑card standards to simplify audits and vendor management - tools that vendors like SAS AI governance and model interpretability solutions package as model‑interpretability, fairness checks and automated compliance trails - so monitoring is continuous, not episodic.

Finally, adopt a lifecycle framework for ownership, legal checks and incident response (the Databricks AI Governance Framework is a practical, pillar‑based blueprint) to align people, processes and infrastructure and to make procurement a lever for safe scaling rather than a source of hidden liability (Databricks AI Governance Framework (blog post)).

The memorable rule of thumb: require a readable model card before the first live decision - if it can't be summarized on a page, don't buy it yet.

"The AIGA programme is a unique opportunity and a first-class partner network to study and develop governance models for AI as well as the services and the business ecosystem emerging around responsible AI."

Getting started in Finland means choosing low‑risk, high‑impact pilots, and predictive maintenance is the perfect first move: local lists of specialists and strong R&D partners make it easy to stand up a measurable experiment quickly (see the Top Predictive Maintenance Companies in Finland for vendor options and profiles) - pick a small fleet or a handful of building systems, deploy sensors and edge/cloud pipelines, then run a 3–6 month trial that tracks clear KPIs like percent downtime reduction, OEE, mean time between failures (MTBF), share of planned vs emergency repairs, tenant satisfaction and energy savings (HLB reports energy and cost savings can be material).

Design the pilot so alerts map directly to work orders and procurement (the Deloitte playbook shows how sensor data plus ML models can cut downtime - one example delivered a 30% fall in downtime and ~€250K in annual savings), and partner with Finnish research or integrators (VTT's smart predictive maintenance services are a practical option) to accelerate model development and ensure production‑grade monitoring.

Protect the pilot with a data inventory and DPIA, require vendor model cards and explainability, and treat “time to measurable intervention” as the success metric: if a sensor‑to‑action loop doesn't save a repair or schedule a pre‑emptive visit within weeks, iterate fast.

The memorable benchmark: a single well‑tuned sensor and model can move a repair from emergency overnight call‑out to a planned job next week - and that shift pays for the pilot.

Finland's real‑estate sector is poised to convert promise into practice: solid public plumbing (LUMI, data‑centre investment and national testbeds), active industry hubs (see KIRAHub's urban and built‑environment network) and a mapped AI ecosystem mean pilots can scale fast if paired with clear KPIs and governance; expect more repeatable wins in predictive maintenance, energy retrofits and portfolio screening rather than one‑off demos, and remember the practical payoff - one well‑tuned sensor and model can shift a repair from an emergency overnight call‑out to a planned job next week.

For teams choosing where to invest effort, the Business Finland “Finnish AI Landscape 2025” frames the opportunity and highlights where startups, research labs and investors are aligning, while PropTech clusters inside KIRAHub are the natural places to find partners and testers.

Prepare for measured scaling: embed DPIAs, human oversight and sandboxed tests early, train operational teams in prompt design and tool use, and consider upskilling via Nucamp's AI Essentials for Work bootcamp to make sure product owners and asset managers can translate model outputs into trusted decisions and measurable savings.

"The speed of artificial intelligence development is staggering. However, in a rapidly changing environment, there are times when it's important to stop for a moment and reflect on where we are, what is happening around us, and to identify our own strengths and areas where we have the opportunity to succeed and make an impact. The Finnish AI Landscape Report has been conducted precisely for this need." - Timo Sorsa