EU AI adoption by country
AI adoption varies hugely across the EU. In 2025, the highest shares of enterprises using AI were in Denmark (42.0%), Finland (37.8%) and Sweden (35.0%), while the lowest were in Romania (5.2%), Poland (8.4%) and Bulgaria (8.5%) — an eight-fold gap between the leading and trailing member states.
Source: Eurostat — 20% of EU enterprises use AI technologies (dataset isoc_eb_ai) (2025)
What it means
An eight-fold gap separates Denmark from Romania, with Nordic economies far ahead of southern and eastern members. For an operator the signal is that the competitive baseline for AI use depends heavily on location: in a Nordic market AI is fast becoming standard, while in lower-adoption countries an early mover can still build a meaningful lead over local peers.
Context
Eurostat publishes country-level AI-adoption shares from its annual ICT-usage survey of enterprises with 10 or more employees. These figures are for the 2025 reference year, published December 2025. Denmark also recorded the largest year-on-year rise (+14.5 percentage points). Cross-country comparisons are harmonised, though structural differences — industry mix, firm size distribution and digital infrastructure — explain much of the spread.
How to interpret this data
About the source: This data comes from Eurostat. Public datasets like this are the foundation of fact-based decision-making in industry. When you see these numbers cited in vendor proposals or consultant reports, remember: the raw data is freely available, and the value is in how you interpret it for your specific plant and situation.
Where this matters: Generative AI in manufacturing are built on insights like the data shown here. Rather than treat data in isolation, read the deeper guides to see how these trends translate into actionable levers for your plant.
Sector relevance: This dataset is especially relevant to Chemicals, Food Processing. These sectors face the trends and challenges you see in this chart daily — energy cost pressure, the push for decarbonization, adoption of AI and predictive maintenance. Use this data to benchmark your plant against the industry average and identify where you lag or lead.
How to use this data: Take the headline number but look deeper at the chart. Is it growing or shrinking? Which segments or regions drive the trend? Does your plant's data align, or are you an outlier? Outliers are often where the best opportunities hide — either an efficiency gap you can exploit, or a leading practice you can copy.
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Related topics
Generative AI in Manufacturing: Practical Examples · Generative AI · Machine Learning (Industrial)
Relevant to: Chemicals · Food Processing · Brewing & Beverage