DIGITAL MATURITY OF EUROPEAN UNION ENTERPRISES IN THE CONTEXT OF AI IMPLEMENTATION – AN EMPIRICAL APPROACH USING CLUSTER ANALYSIS

Purpose: The aim of this study was to analyze the extent of AI technology implementation in the economic activities of EU countries and to classify them based on their level of digital advancement. Need for the study: AI plays a central role in the digital transformation of enterprises and the emergence of intelligent management practices. Despite its growing importance, significant disparities remain in AI implementation across the EU, warranting a structured, data-driven assessment of digital advancement levels. Methodology: The study employs cluster analysis techniques – specifically the k-means algorithm and Kohonen self-organizing maps (SOM). The empirical data were sourced from Eurostat (2023) and cover 27 EU member states across ten quantitative indicators, including AI adoption, ICT human resources, GDP per capita, cloud infrastructure, and advanced data analytics. Findings: Three distinct country clusters were identified: high, medium, and low levels of AI adoption. Countries such as Finland, Denmark, and Belgium were classified as highly advanced, while Poland, Bulgaria, and Romania fell into the least advanced group. The results reflect substantial differences in digital infrastructure and economic development levels among EU countries. Practical Implications: The classification can inform the development of targeted digital and innovation policies. Policymakers may use the findings to address regional gaps, allocate resources more effectively, and promote responsible AI deployment within the EU.