Sector-specific adoption patterns of AI in Europe: a quantitative analysis using Eurostat and OECD Data

This study analyses sector-specific AI adoption across 25 European countries and 11 industries from 2020-2024 using harmonized Eurostat and OECD panel data. We develop and test the Sectoral AI Adoption Model (SAAM), proposing five drivers of adoption: technological readiness, innovation intensity, human capital, regulatory environment, and competitive pressure. Fixed-effects panel regressions show that all five factors positively and significantly predict AI adoption, with human capital and technological readiness the strongest predictors. The model explains substantial variation in sectoral adoption (adjusted R2 = 0.62). Interaction results indicate that innovation intensity strengthens the effect of human capital, suggesting complementarity between skills and innovation capacity. Robustness checks confirm results across alternative specifications. Adoption is uneven across sectors, with information and communication technologies leading and construction lagging. The study advances technology adoption research by offering a sector-level framework for AI diffusion and provides actionable guidance for managers and policymakers seeking to accelerate responsible AI integration through workforce development, infrastructure investment, and balanced regulation.