Artificial intelligence has already arrived in European companies. It is part of everyday work for many teams, is being tested in projects and is on the agendas of executive boards and management teams. The decisive question is therefore no longer whether companies use AI, but what for. Here, the Conclusion Tech Reality Check 2026 shows a clear pattern: most companies use AI primarily to make existing processes faster and cheaper. A fundamental redesign of working methods and business models remains the exception.
The study is a survey of digital practice in Europe. It does not ask which tools companies own. Instead, it asks what they have under control, where they reach their limits, and which risks and opportunities they see.
Key results first: 74 percent of the surveyed companies use AI primarily to increase efficiency, not to work differently or to transform processes.
This result is not a fringe phenomenon. It holds across all countries and regardless of how digitally mature an organisation is. That is what makes it relevant: AI initially seems to work as a tool for optimisation, no matter how far a company has progressed digitally.
The Results at a Glance
Efficiency Takes Centre Stage
The benefits companies actually see from AI confirm this picture:
51 percent cite lower costs and higher efficiency.
53 percent report faster or better decision-making.
49 percent see better support for employees and relief of resources.
45 percent see more room for innovation.
43 percent see improved services for customers or citizens.
6 percent have seen no noticeable effects so far.
The order is revealing. Costs, efficiency and decision quality lead clearly. Innovation and customer benefit, precisely the areas in which AI could create new business models, follow well behind. This does not mean that efficiency gains are worthless. They are real and measurable. But it also shows that the biggest lever is not where many companies would expect it to be. Anyone who treats AI purely as a cost and efficiency topic leaves untapped the part of its potential that enables growth and differentiation.
Leadership Determines the Change
The question of who actually steers technological change is particularly revealing. 59 percent of the surveyed leaders actively steer it. 34 percent set the direction and promote adoption, and a further 25 percent explicitly take responsibility and visibly stand behind the transformation.
On the other side, 19 percent wait until changes become unavoidable. Another 17 percent support teams but do not take on a steering role themselves.
For companies, this means that whether AI takes hold in the organisation is less a question of budget than of mandate. If nobody clearly bears responsibility, AI remains stuck in pilot projects and isolated initiatives instead of changing how work gets done.
Skills and Experimentation Remain Limited
New technologies only work when people understand and can try them out. Here the study shows a clear gap. Only 36 percent of companies have a coherent, company-wide learning programme for digital skills. This means a structured programme, not a collection of individual training sessions.
On experimentation, the picture is similar:
29 percent of companies actively encourage their teams to explore new technologies.
The large majority of 64 percent tolerate experimentation but do not actively promote it.
6 percent even advise against using AI tools.
This middle ground is risky. It signals openness to the outside world without providing the resources, time and security that experiments require. If you let teams try things out but create no spaces for learning, you usually get few results that can be turned into new ways of working.
Country Comparison: Different Patterns
The study compares four European markets. The differences are instructive because they show that added value can come about in different ways:
Germany: 40 percent of leaders actively set the direction, and a further 31 percent visibly take responsibility. That is a strong leadership profile. At the same time, Germany lags clearly behind the other countries in launching new products or services, at 13 percent. Leadership alone therefore does not guarantee innovation. The possibilities of AI must be translated into concrete offerings.
Netherlands: Only 11% of leaders actively and deliberately drive technological change—the lowest figure among the countries surveyed. However, 33% of the executives surveyed passively monitor current developments and look for opportunities to optimize their services.
Spain: 36 percent of leaders actively set the direction. At 55 percent, Spain leads in launching new products or services. In addition, 31 percent of Spanish organisations actively encourage experimentation with AI, the highest share in the comparison.
Portugal: The value of AI use is particularly clear here: 59 percent of respondents cite lower costs and higher efficiency as a result. 56 percent see an improvement in the services they offer to their target groups.
From this comparison, a dangerous fallacy may seem plausible at first glance: practical AI use succeeds in the first steps even without a mature digital foundation, so entering AI could be faster than building a perfect data foundation. The early successes in efficiency and costs are real. But they say little about whether the foundation will carry the next stage.
That is why a second look is worthwhile. A strong foundation becomes decisive at the latest when the next step arrives: the transition from individual AI tools to agentic processes, in which AI systems carry out tasks independently and intervene in existing workflows. Such processes are only as reliable as the data, interfaces and responsibilities they rest on. If these are missing today, the results of tomorrow will suffer considerably. Anyone who invests now in data quality, clear responsibilities and a robust system architecture creates the conditions for initial successes to turn into scalable transformation. It is precisely at this transition that the Agentic Experience Foundation (AXF) comes in.
Why Is AI Transformation Still Out of Reach?
The study cites several reasons, and most of them lie in management rather than in technology. The three most important points:
Optimisation instead of redesign: As long as AI is used as a tool for optimisation, its transformative potential remains limited. Anyone who only speeds up processes without questioning them often preserves the existing structures.
Unclear responsibility: The lever lies in adapting processes and decisions, not in the technology. As long as it remains unclear who decides on change, it also remains unclear which changes will be initiated.
Missing skills: Technological change is shaped by human behaviour. Clear leadership and targeted skills development are crucial for new ways of working to emerge.
What Companies Can Do Now
Five fields of action can be derived from the findings:
Explicitly account for technical debt: Those who include these costs in investment decisions create transparency about short- and long-term consequences. In concrete terms, this can mean deliberately assessing the quality of the underlying systems and data for AI initiatives, rather than assuming it.
Understand AI as organisational change: What matters is adapting processes, roles and decisions, not just deploying tools. An AI project that merely speeds up the existing workflow is only a first step. The second step asks whether the workflow itself still fits.
Manage data quality strategically: Clear agreements, common standards and clearly defined responsibilities are the prerequisites here. This applies especially when AI applications are meant to access data from several teams or organisations.
Build leadership and skills: Leaders should visibly take responsibility for the change. Learning programmes should be anchored in a structured way rather than organised as one-off measures.
Turn awareness into concrete action: Anyone who knows the risks must define how to deal with dependencies and outages. Awareness alone is not enough. What matters is that insights turn into binding rules and clear responsibilities.
Practical check for your company
Do you know what share of your AI projects you deliberately aim at efficiency, and what share at new ways of working?
Is there a clearly named person who is responsible for technological change?
Do your investment decisions take technical debt into account?
Do you actively encourage experimentation, or merely tolerate it?
If your leadership team can answer these questions, you have a sound basis for the next steps. If not, that is exactly where to start.
Conclusion
AI already improves costs, processes and decisions in many companies. The greater benefit, namely new ways of working, new offerings and innovation, usually remains out of reach. The cause lies less in the technology than in leadership, governance and skills. Anyone who regards AI only as a tool for efficiency uses only part of its potential. Anyone who sees it as an occasion for organisational change opens up new possibilities, but must also clearly regulate responsibilities and enable experimentation.
Dive Deeper into the Results: The full Tech Reality Check 2026 contains the results for all four countries as well as analyses of costs, digital autonomy and data ecosystems. Anyone who wants to go deeper into the findings will find the detailed evaluations for each country there.
Want to tackle the move from individual AI tools to agentic processes in a concrete way?
The Agentic Experience Foundation (AXF) helps you structure AI initiatives from the idea through to implementation. Check now where your company stands and plan the next step with a clear roadmap.
