Why AI Adoption in Finland Isn't Becoming Business Value
Let’s state the fact. Finland is using AI. It just isn’t profiting from it yet. Across the Nordic region, 79% of organisations report efficiency gains from AI — but only 18% report revenue growth. That gap is not a technology problem. It is an organisational one.
Text by Dr Umair Ali Khan & Martti Asikainen, 28.5.2026 | Photo by ADobe Stock Photos
Seventy-nine per cent of Nordic organisations report that AI has improved their efficiency. Eighteen per cent report revenue growth. Those two figures, sitting side by side in Deloitte’s 2026 Nordic survey, represent the central puzzle of AI adoption right now: organisations are getting better at using the tools, but it is not showing up where it counts (Deloitte 2026).
The picture looks similar at the individual level: Statistics Finland’s February 2026 survey found that 80% of Finnish workers using AI report their work speeds up — yet the average time saved is 5.5 hours per month. Real gains, stubbornly small ones (Kangassalo 2026).
This is not a rounding error or a measurement lag. In our consultancy work with Finnish companies through FAIR, we see the same pattern consistently: tools deployed, processes streamlined, time saved — and gains that stay stubbornly local to the team that produced them. They do not propagate. They do not compound.
Recent ILO analysis describes this as the aggregation paradox of AI — measurable task-level productivity improvements that fail to translate into organisational-level gains (Chan & Shedania 2026). This also aligns with what Brynjolfsson, Rock and Syverson (2021) describe as the modern productivity paradox: gains remain delayed until complementary organisational changes and workflow redesign are implemented. When leadership looks at the numbers and asks the obvious question, the obvious answer is rarely comfortable.
The Gap Nobody Planned For
Finnish companies are adopting AI faster than they are building the structures needed to turn AI use into business results. Efficiency gains are real. They are also local. A team saves time. A routine gets automated. Documentation improves. But without deliberate effort, these gains do not accumulate — they dissolve at the boundary of the team that made them.
One structural explanation stands out. Deloitte’s same report finds that only one in five Nordic organisations has appointed someone accountable for AI value realisation (Deloitte 2026). No ownership means no measurement. No measurement means no scaling. It also means that when a pilot works, the lesson stays inside the pilot rather than propagating across the business.
Organisations that do translate AI gains into business value tend to share a small number of structural features. They have appointed someone whose job it is to track whether AI initiatives are delivering measurable results — not adoption metrics, but business outcomes. They invest in moving successful pilots across team boundaries rather than treating each deployment as a self-contained project.
And they also treat knowledge transfer as a deliberate management task rather than something that happens organically. None of this is technically complex. All of it requires organisational intent that most companies have not yet developed. The tools are not the constraint. The system around the tools is.
The Fragmentation Problem
AI use in Finland is growing, but much of it is fragmented, individualised, and experimental. Companies buy tools, launch pilots, and automate isolated tasks. What they rarely do is redesign the wider processes, roles, and business models through which value is actually created and captured. The result is a collection of local wins that do not add up to a strategic advantage.
FAIR’s analysis of Finnish companies tells a related story. The share of Finnish companies intending to hire an AI specialist has fallen from 60% to 33% in five years, even as AI becomes more embedded in daily operations (Khan 2025a). This is not indifference. It reflects a genuine shift in how companies think about AI skills — from a specialist hire to a general capability. The risk is that neither ends up being properly resourced.
The barriers slowing this down are not primarily technical. FAIR’s 2025 survey identifies skills and knowledge gaps, data security and regulatory uncertainty, lack of time, and unclear return expectations as the main obstacles (Khan, Kauttonen & Kudryavtsev 2025b).
Companies are not simply deciding which AI tools to deploy. They are also trying to understand what data they can legally use, what regulations apply, how risks should be managed, and whether the investment will produce sufficient return. That is a substantial burden to carry alongside a day job.
The same barriers also surface in a different context. According to FAIR’s 2026 survey of 200 Finnish business leaders, conducted by Taloustutkimus Oy, the most cited obstacles were a skills and knowledge gap (33%) and data security and regulatory compliance challenges (28%) (Asikainen 2026; FAIR 2026).
Organisational Level Skill Gap
FAIR’s survey identifies the main barriers to broader AI adoption as skills and knowledge gaps at 33%, data security at 29%, regulation and compliance at 28%, lack of time and resources at 26%, and cost and uncertain return expectations at 24% (Asikainen 2026; FAIR 2026). The report states that these challenges are not merely technical but also linked to organizational readiness, leadership, and working practices.
This trend remains consistent with our earlier findings (Khan 2025a; Khan et al. 2025b), in which the skill gap and organizational readiness appear as the main challenges. Companies are not only asking which AI model they should use. They are asking how to use AI safely, protect data, comply with regulations, find time and resources, and justify investment.
FAIR’s survey is also clear that AI regulation is not a future issue. The report states that the EU AI Act entered into force on 1 August 2024; its first obligations, including prohibitions on certain AI practices and AI literacy requirements, became applicable on 2 February 2025; and the remaining obligations take full effect on 2 August 2026.
At the same time, regulatory awareness remains limited. FAIR’s key findings indicate that the GDPR is the best-known framework, but familiarity with other AI-related regulations remains limited. Only 25% of companies are moderately or well acquainted with the EU AI Act. This is not simply a knowledge gap. When deploying AI costs almost nothing, the incentive to examine whether its use is compliant is correspondingly low — which is precisely when compliance failures accumulate quietly (Asikainen & Masala 2026).
This creates a practical challenge for Finnish companies. AI adoption is increasing, but many companies still need clearer knowledge of what responsible and compliant AI use requires. Deloitte’s Nordic report shows the same broader pattern. It states that 84% of Nordic organisations cite data privacy and security as a key concern, while 67% are prioritising investments in security and compliance (Deloitte 2026).
The R&D Blind Spot
Here is where the picture gets more uncomfortable. Finland has genuine research depth in AI. Universities publish, labs innovate, and the country consistently ranks well in applied AI capacity. Yet the connection between that capacity and the companies that could use it remains weak.
Even among Finnish companies running advanced AI projects, only one in five involves direct R&D collaboration with a university or research institute (Demos Helsinki 2026). For a country that positions research as a competitive asset, that is a striking underutilisation. Companies engaged in research collaboration are more likely to productise AI solutions than those working alone — which means the gap is costing something real.
The standard response to this finding is to call for more partnerships. That is true, but it misses the more difficult point: research collaboration requires internal capability to absorb and act on what comes out of it. Companies without dedicated AI leadership, without redesigned processes, and without a clear theory of where AI creates value for them are poorly placed to benefit from external expertise, however good it is.
Not a Technology Problem
The gap between AI adoption and business impact in Finland is not mainly about access to technology. Finnish companies have the tools, and an increasing number are using them. The gap is strategic and organisational — and in that respect, it is harder to close than a technology deficit.
Closing it requires treating AI as a strategic function rather than an operational add-on. Deloitte’s 2026 Nordic survey makes the scale of the gap concrete: 5% of Nordic organisations plan to increase AI investment this year, while strategic preparedness has fallen from 61% to 43% and talent preparedness from 33% to 14%. Money is accelerating. Readiness is declining.
In practice, that means several things at once: appointing someone accountable for AI value — not adoption, value; investing in people alongside platforms; redesigning processes where AI is deployed rather than layering it on top of workflows built for a different era; and taking compliance readiness seriously before it becomes an emergency.
The EU AI Act is not a future consideration — it applies now, and it applies to any organisation whose AI use touches EU markets or citizens (EU 2024/1689). When AI costs almost nothing to deploy, the incentive to examine whether its use is compliant is correspondingly low — which is precisely when compliance failures accumulate quietly (Asikainen & Masala 2026).
The next time someone in your organisation reports that an AI tool is saving the team time, the right question is not whether that is welcome news. It is: what would need to change for that to show up in the numbers? If no one has a convincing answer, you have identified the problem — and it has nothing to do with the tools.
Authors
Dr Umair Ali Khan
Senior Researcher
Finnish AI Region
umairali.Khan@haaga-helia.fi
Martti Asikainen
Communications Lead
Finnish AI Region
martti.asikainen@haaga-helia.fi
References
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Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–372.
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