AI-driven digital transformation and ICT integration: Implications for uneven economic development pathways in Europe

Research background: European economies are increasingly differentiated not only by traditional macroeconomic indicators but also by their position in digital trade, artificial intelligence (AI) ecosystems, and high-technology value chains. While prior research has examined innovation, ICT integration, and cluster dynamics separately, there remains a need for an integrated empirical framework that explains how AI-related human capital, digital trade intensity, and innovation capacity jointly shape structural development gaps across Europe. Purpose of the article: This paper examines how high-technology factors, innovation capacity, information and communication technologies (ICT) trade integration, and artificial intelligence-driven digital transformation impact economic development disparities across European countries and shape distinct development pathways. Methods: A panel dataset of 16 European countries for the years 2019–2022 is analyzed using a three-stage framework. First, indicators from three domains (economic performance, innovation and technological capacity, and AI and digital transformation) are min-max normalised. Second, k-means clustering is applied to group countries, with the optimal number of clusters selected using the Silhouette coefficient. Third, XGBoost is used to identify the variables that most strongly differentiate between clusters, and multinomial logistic regression is employed to interpret the direction and magnitude of the key determinants of cluster membership. Findings & value added: The analysis identifies three distinct development clusters in Europe: advanced AI-intensive economies, ICT trade-driven digitalisers with weaker AI talent bases, and structurally lagging countries with limited digital integration. AI-related human capital, especially AI labour migration and talent concentration, together with ICT trade intensity, emerge as the strongest empirical differentiators of development pathways. Methodologically, the study contributes an integrated framework combining clustering, explainable machine learning, and econometric modelling that can be replicated in other regional contexts. Practically, the results highlight that long-term competitiveness increasingly depends on AI talent ecosystems and digital trade specialization rather than on traditional macroeconomic growth factors alone.