Artificial Intelligence Adoption in European Enterprises: Empirical Evidence from Eurostat Data
The AI-ImpactSK project — Empirical Examination of Artificial Intelligence Adoption and its Consequential Impact on Enterprise Performance, Market Dynamics, and Policy Formulation in the Slovak Economy — aims to comprehensively examine the impact of artificial intelligence adoption across key sectors of the Slovak economy, analysing how AI influences business performance, market dynamics, and innovation diffusion. Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia (project No. 09I05-03-V02-00003/2025/VA), the project combines large-scale market surveys, qualitative interviews with AI-adopting firms, comparative international analysis, diffusion forecasting, corporate performance modelling, and targeted knowledge transfer to produce both academic knowledge and actionable policy recommendations. This proceedings volume presents a set of empirical studies that form the analytical foundation for the project’s subsequent primary research phases.
Artificial intelligence has ceased to be a distant technological prospect and has become a tangible determinant of competitiveness, productivity, and innovation capacity across the European economy. The pace at which enterprises adopt AI technologies, the barriers they encounter, and the functional domains in which they deploy these tools are questions of immediate relevance to researchers, policymakers, and business leaders alike. Yet despite growing policy attention — most notably the European Union’s Digital Decade targets — systematic empirical evidence on enterprise-level AI adoption across EU member states has remained fragmented and methodologically inconsistent.
This proceedings volume addresses that gap. The sixteen studies assembled here draw on harmonized Eurostat data from the Community Survey on ICT Usage and Employment in Enterprises to provide a comprehensive, multi-dimensional portrait of AI adoption in European enterprises. Each article examines a distinct analytical angle — from longitudinal adoption trends and firm-size differentials, through cross-country benchmarking and digital intensity associations, to the adoption of specific AI application domains such as machine learning, generative AI, business process automation, and AI for R&D and innovation. The collection also investigates the structural barriers to adoption, including cost and knowledge constraints, ethical and legal concerns, and the prevalence of AI bias mitigation practices.
A distinctive feature of this volume is its consistent methodological approach. All articles employ descriptive statistical analysis complemented by appropriate inferential tests — including OLS regression, Mann-Kendall trend tests, Spearman rank correlations, chi-square tests of independence, and coefficient-of-variation analyses — applied to officially collected, publicly available Eurostat microdata. This methodological coherence ensures that findings are directly comparable across articles and that the volume as a whole constitutes a unified analytical resource rather than a loosely connected anthology.
The studies presented here form the empirical foundation upon which subsequent phases of the AI-ImpactSK project will build. The project’s broader agenda encompasses primary market surveys, qualitative interviews with AI-adopting firms, policy formulation, comparative international analysis, diffusion forecasting, and corporate performance modelling. By establishing a rigorous evidence base from secondary data, this volume ensures that the project’s later primary research is informed by a thorough understanding of the existing landscape of enterprise AI adoption in Europe.
We trust that this collection will serve not only as a reference for the academic community but also as a practical resource for policymakers designing AI adoption support mechanisms and for enterprise leaders navigating their own digital transformation journeys. The empirical patterns documented in these pages — the persistent size-class gaps, the cross-country heterogeneity, the uneven diffusion of ethical AI practices — represent both challenges to be addressed and opportunities to be seized as Europe pursues its ambition of becoming a global leader in trustworthy and competitive artificial intelligence.
