Driving AI Adoption in the EU: A Quantitative Analysis of Macroeconomic Influences

This article investigates macroeconomic factors that support the adoption of Artificial Intelligence (AI) technologies by large European Union (EU) enterprises. In this analysis, panel data regression is combined with machine learning to investigate how macroeconomic variables like health spending, domestic credit, exports, gross capital formation, and inflation, along with health spending and trade openness, influence the share of enterprises that adopt at least one type of AI technology (ALOAI). The results of the estimations-based on fixed and random effects models with 151 observations-show that health spending, inflation, and trade and GDP per capita have positively significant associations with adoption, with significant negative correlations visible with and among domestic credit, exports, and gross capital formation. In adjunct to this, the regression of machine learning models (KNN, Boosting, Random Forest) is benchmarked with MSE, RMSE, MAE, MAPE, and R² measures with KNN performing perfectly on all measures, although with some concerns regarding data overfitting. Furthermore, cluster analysis (Hierarchical, Density-Based, Neighborhood-Based) identifies hidden EU country groups with comparable macroeconomic variables and comparable ALOAI. Notably, those with characteristics of high integration in international trade, access to credit, and strong GDP per capita indicate large ALOAI levels, whereas those with macroeconomic volatility and under-investment in innovation trail behind. These findings suggest that securing the adoption of AI is not merely about finance and infrastructure but also about policy alignment and institutional preparedness. This work provides evidence-driven policy advice by presenting an integrated data-driven analytical framework to comprehend and manage AI diffusion within EU industry sectors.