Proceedings of the 70th International Academic Conference, Madrid

CHANGING PATTERNS OF AI ADOPTION IN EUROPE: A CLUSTER-BASED COMPARISON OF COUNTRY POSITIONS

VLADIMÍR HOJDIK, DIANA PALLÉROVÁ

Abstract:

This article examines changing patterns of AI adoption in Europe through a cluster-based comparison of country positions. The analysis is based on two comparative data environments: the 2020 benchmark dataset derived from the European enterprise survey on the use of AI technologies and the current Eurostat dataset based on enterprise-level AI indicators for 2024. To ensure comparability, the clustering procedure uses four harmonised variables: overall AI adoption, AI adoption among small enterprises, AI adoption among medium-sized enterprises and AI adoption among large enterprises. The common analytical sample consists of 26 European countries, with Slovakia interpreted as part of the wider European AI adoption landscape rather than as an isolated national case. The article applies hierarchical cluster analysis, supported by descriptive statistics, cluster centroids, scree plots, PCA-based visualisations and comparison of country membership across cluster solutions. The results show that the 2020 benchmark dataset produced a three-cluster structure consisting of lower-intensity adopters, high-intensity AI leaders and upper-mid adopters. In contrast, the current Eurostat dataset indicates a more polarised two-cluster structure, distinguishing between AI frontrunners and lower-adoption followers. This suggests a reconfiguration of relative country positions, with some countries consolidating or improving their position, while others remain in the follower space or are reclassified into less advanced clusters. Slovakia remains positioned within the lower-adoption follower group across both data environments. Its position indicates partial but insufficiently broad AI diffusion rather than convergence toward the European AI frontier. The findings highlight firm-size diffusion as a key structural dimension of AI adoption and show that broad-based AI transformation requires more than adoption by large enterprises. The article contributes to the literature by demonstrating the value of cluster-based comparison for understanding AI adoption patterns, country reconfiguration and the relative position of small open economies within the European digital transformation landscape.

Keywords: artificial intelligence adoption; cluster analysis; European countries; firm-size diffusion

DOI: 10.20472/IAC.2026.070.006

PDF: Download