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

AI is turning Finland's real estate sector from a cost centre into a smart efficiency engine: pilots like the Saving Energy with Data project (TTL research) are building nationwide, user-friendly tools so property managers can turn fragmented consumption and maintenance data into everyday savings, while market leaders prove it works in practice - Kojamo reports AI heating optimisation has cut heat consumption in Lumo homes by about 7% and powers new tenant-facing services like an AI Apartment Agent that speeds leasing and improves service (Kojamo AI heating optimisation news release).

Finnish startups and pilots show AI can save double‑digit energy and cost percentages without sacrificing comfort, making emissions targets and lower operating budgets achievable for building owners, managers and tenants across Finland.

“Artificial intelligence is one of the most powerful tools for aligning economic value with climate goals.” - Ahmet Köse, R8 Technologies

Smart energy management in Finland is moving from rule‑of‑thumb schedules to minute‑precise, data‑driven control: AI platforms can nudge air‑handling units on or off by a few minutes based on weather, tariffs and real‑time indoor data, trimming energy use without upsetting comfort.

Homegrown and international tools are already proving the point - R8tech's AI Digital Operator, now part of Proptech Finland, reports up to 20% energy savings in commercial sites and even uncovered 300 technical faults in a hotel where engineers had found only 30 (R8tech AI Digital Operator energy management case study), while Danfoss' Leanheat Building shows up to 20% lower energy bills and up to 30% CO₂ reductions in district‑heated buildings and has supported Finnish partners like Vantaa Energy, Avain and the City of Espoo in real deployments (Danfoss Leanheat Building energy savings and CO₂ reductions).

At scale, unified platforms such as the one described by C3 AI combine ML and optimization to cut total energy costs by double digits and predict equipment failures before they become costly - a practical route for Finnish landlords and district heating operators to meet climate targets and shrink operating budgets at the same time (C3 AI HVAC optimization platform for energy cost reduction).

Underused office space is a hidden drain on Finnish balance sheets and emissions targets, and accurate people‑flow data is the key to fixing it: Supersight's privacy‑preserving computer‑vision model now "reports utilisation rates" with over 99% accuracy, turning everyday Android phones into cost‑effective, top‑down counting sensors that reveal who uses which desks and when (Supersight achieves over 99% accuracy in utilisation reporting - press release).

Developed with compute power from Europe's LUMI supercomputer and supported by Business Finland, this level of precision gives Finnish landlords and facility managers actionable occupancy maps to right‑size portfolios, cut heating, lighting and cleaning costs, and reduce emissions tied to empty square metres without creating GDPR risks.

Combined with research partnerships and larger pilots, such utilisation intelligence makes it practical to convert wasted space into revenue or simpler, greener operations - an Android phone in a neat case can now point the way to millions saved and tonnes of CO₂ avoided when scaled across a portfolio (CSC article on Supersight and LUMI supercomputer reducing emissions, €6M privacy-protected data collection research project with Supersight).

“Companies are wasting hundreds of billions on empty office space. If the use of office buildings could be better optimised, numerous client companies could save millions and reduce energy related emissions by eliminating unnecessary space,” - Kimmo Pentikäinen, Supersight CEO

AI is fast becoming the investor's sidekick in Finland: predictive models can forecast rental growth, pull comparables and standardize datasets so analysts spend hours on insight instead of weeks on paperwork (see UBS's overview of AI in real‑estate investing for concrete examples UBS AI in real‑estate investing overview); Finnish practitioners are already putting that into practice - Vuono Group's AzureAI proof‑of‑concept shows how crawling tens of thousands of ads and combining public rent data, renovation histories and cash‑flow models can surface the tiny fraction of deals with positive short‑term cash flow and compute NPV and margin‑of‑safety at scale, a process the team says can be thousands to ten thousand times cheaper than manual searching (Vuono Group AzureAI real estate investment opportunities).

That speed matters in a market that's stabilising slowly - national price forecasts point to modest recovery in 2025 and strong city‑level variation - because AI helps investors spot neighbourhoods and asset types poised to outperform (for example, Investropa's 2025 Finland outlook highlights urban momentum in Espoo and Oulu Investropa Finland 2025 real‑estate forecasts), and it can also read leases, estimate renovation debt and flag distressed or off‑market opportunities so capital is deployed with better timing and clearer downside protection.

Predictive maintenance is already a practical cost‑saver across Finnish buildings because connected gear and cloud AI let landlords and service teams fix problems before tenants notice: Helsinki‑based KONE uses IoT sensors that ingest some 3,000 events per second to detect anomalies in century‑old elevators, cut customer‑reported issues and entrapments by around 40%, and push near‑real‑time diagnostics and software updates from the cloud (KONE AWS IoT case study); technicians in Hyvinkää report that remote checks and AI helpers speed troubleshooting and keep people flowing, while parts‑and‑supply systems such as Servigistics have trimmed inventory by double digits so spare parts are where they're needed without excess cost.

Secure, carrier‑grade connectivity and managed SIMs are making national rollouts feasible, so landlords gain transparency, fewer emergency callouts, and smoother tenant communication about service windows - imagine elevators that phone home and schedule their own preventative visit before a tenant ever presses the alarm.

For Finnish property teams, the payoff is lower operating budgets, higher uptime and clearer sustainability gains as fewer emergency repairs and truck rolls mean less waste and lower emissions.

“This has significantly changed the way we do elevator maintenance. We can do software updates, checks and troubleshooting remotely over the cloud. Being able to continuously monitor elevator conditions enables new maintenance capabilities that make our elevators more reliable and safer than ever before.”

Finland's construction sector is turning AI and BIM into a practical toolkit for planning, procurement and the full building lifecycle: student startup Complink is already using AI+BIM to match ventilation ducts and other components to market products - slicing procurement time from months to minutes while optimising cost, CO₂ and on‑site logistics (Aalto University: Complink uses AI and BIM to streamline construction procurement in Finland); larger projects pair takt scheduling with digital twins and reality capture to boost delivery certainty - Laakso Hospital's workflows showed how process discipline plus tech can yield measured daily gains (up to 250 m² completed floor area per day in takt trials) and tighter just‑in‑time logistics (BuildNews: NFB delegation examines AI and takt-based construction methods in Finland).

On a practical level, 360° photos, automated clash detection and continuous BIM-fed updates mean designers get near‑real‑time site context and teams can sequence deliveries, reduce rework and lower lifecycle costs - provided companies prioritise quality data, standards and staff training (RT.fi: AI across the construction lifecycle in Finland), turning smarter planning into tangible schedule and cost savings.

“Collecting data is the number one priority,” - Eija Lantta

AI-driven workloads are reshaping a very local corner of Finland's real estate market: data centres that serve high‑performance inference and training need new cooling and power strategies, and that's exactly where Finnish buildings can win or lose.

CBRE's overview shows the sector moving from air to liquid cooling - direct‑to‑chip and immersion systems - to handle denser racks and cut facility power, with studies suggesting up to an ~18% reduction in facility power and meaningful total‑power savings (CBRE report on liquid cooling energy savings for AI data centers).

At the same time, CBRE Investment Management flags growing power needs, the push for renewables and local permitting as real constraints to new AI capacity (CBRE Investment Management report on AI data center power needs and infrastructure).

Finland's natural edge is its climate: operators here enjoy over 8,000 hours of free cooling annually, a tangible sustainability and cost advantage when paired with modern cooling tech (Brightlio data center statistics on free cooling hours in Finland).

For landlords and planners, that combination - cold climate, smarter cooling and careful power planning - turns AI demand from a headache into a marketable, lower‑carbon offering for data‑intensive tenants and sectors that depend on reliable, low‑latency compute.

Wiring AI into Finnish real estate isn't just about algorithms - it runs on public infrastructure, targeted grants and national competence hubs that make experimentation affordable and responsible: the LUMI supercomputer in Kajaani (a 380‑petaflop system with performance equivalent to about 1.5 million laptops) offers a serious runway for training models and large simulations and even reserves roughly 20% of Finland's quota for companies so landlords and proptech teams can prototype at scale (LUMI supercomputer overview and specifications); Business Finland and EuroCC Finland pair funding and expert support to lower the cost and risk of using that compute (SME/start‑up subsidies and RDI channels accelerate access to LUMI resources) so pilots move to production faster (Business Finland funding and partnership routes for companies, EuroCC Finland HPC competence centre and services).

The practical payoff is clear: Try&Buy projects, training courses and joint calls mean a small property manager can run energy, occupancy or predictive‑maintenance models with expert help - turning pilot learnings into lower bills, fewer truck rolls and measurable emissions gains without a Silicon Valley sized budget.

A practical Finnish roadmap starts with low‑friction pilots that prove value: deploy privacy‑first occupancy sensors like Haltian's plug‑and‑play system to collect real‑time and historical usage data without cameras or complex wiring, then feed that data to analytics and energy controls so heating, lighting and cleaning follow actual demand (see Haltian Occupancy Analytics (GDPR-friendly occupancy sensors)).

Parallel pilots can combine inexpensive, privacy‑preserving sensing (Supersight even uses an Android phone “in a neat case” as a computing sensor) with cloud or HPC model training to reach high‑accuracy utilisation models and realistic emissions reductions - leveraging shared compute and funding where available accelerates that step (Supersight and LUMI case study - harnessing AI to reduce real-estate emissions).

Once occupancy and equipment telemetry are feeding a single platform, move to integrated use cases recommended by industry guides - automated HVAC tuning, predictive maintenance work‑orders, smarter cleaning schedules and tenant‑facing services - to capture the operational wins JPMorgan highlights for proptech adoption and tenant experience improvements (JPMorgan proptech adoption report - tenant experience and operational wins).

Keep pilots short, measure energy/uptime/portfolio‑utilisation KPIs, and scale where net operating cost and carbon savings are clear; the payoff in Finland is practical and rapid when privacy, interoperability and targeted funding are prioritised.

“Companies are wasting hundreds of billions on empty office space. If the use of office buildings could be better optimised, numerous client companies could save millions and reduce energy related emissions by eliminating unnecessary space,” - Kimmo Pentikäinen, Supersight CEO

Conclusion and next steps for Finnish real estate beginners: start small, measure everything, and learn fast - run a short pilot that proves value (think privacy‑first occupancy sensing, a predictive‑maintenance feed, or even an immersive 3D tour) and treat it like a product: define KPIs (energy saved, utilisation uplift, lease velocity), iterate, then scale.

Cheap, privacy‑aware sensors - even an Android phone

in a neat case

used as a top‑down sensor - can expose wasted space and unlock savings, while immersive virtual walkthroughs have been shown to lift digital sales (Tech Mahindra's metaverse case increased digital sales by ~20% for a Finnish firm).

Pair pilots with skills training so teams can own outcomes: Nucamp's AI Essentials for Work (15 weeks) teaches prompt engineering and practical AI tools that make pilots reproducible without a huge data‑science team.

With the global AI‑in‑real‑estate market expanding quickly (2025 estimate ~$301.6B), early, measurable wins buy lower operating costs and a competitive edge; link pilots to clear funding or HPC support and prioritise privacy and interoperability from day one - that combination turns experiments into routine savings and better tenant experiences.