Share of EU enterprises using AI
In 2025, 20.0% of EU enterprises with 10 or more employees used artificial-intelligence technologies to run their business — up 6.5 percentage points in a single year from 13.5% in 2024, and more than double the 8.1% recorded in 2023. Adoption among European firms is rising sharply.
Source: Eurostat — 20% of EU enterprises use AI technologies (dataset isoc_eb_ai) (2025)
What it means
The share of European firms using AI more than doubled in just two years, from 8.1% in 2023 to 20.0% in 2025. For an operator the practical signal is that AI is moving from a minority experiment to a mainstream business tool across the EU — the question is shifting from whether peers are adopting it to how quickly.
Context
Eurostat measures AI use each year through its survey on ICT usage and e-commerce in enterprises, covering firms with 10 or more employees. The 2025 figure of 20.0% was published in December 2025 and reflects the 2025 reference year; earlier points cover 2021, 2023 and 2024. Definitions of 'using AI technologies' are harmonised across the survey, so the year-on-year jump reflects genuine diffusion rather than a change in method.
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, AI agents for industrial maintenance 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 · AI Agents for Industrial Maintenance: What They Are and Where They Help · Generative AI · Large Language Model (LLM) · Machine Learning (Industrial)
Relevant to: Chemicals · Food Processing · Pharmaceuticals