REGIONAL DIMENSION OF ECONOMIC DEVELOPMENT IN THE CONTEXT OF ARTIFICIAL INTELLIGENCE IMPLEMENTATION IN PUBLIC ADMINISTRATION

Vollständiger Abstract

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The rapid advancement of artificial intelligence technologies and their growing application across various domains of public life are driving fundamental changes in approaches to public administration. Despite the increasing scholarly attention to AI integration in government, the regional dimension of economic development in the context of AI implementation remains insufficiently studied. The purpose of this article is to identify regional features of artificial intelligence implementation in public administration systems and to assess the relationship between the level of AI readiness and the level of economic development across world regions.The study draws on data from the Government AI Readiness Index. The methodological framework includes comparative analysis, statistical analysis, grouping method, and correlation-regression analysis applied across nine world regions: North America, Western Europe, Eastern Europe, East Asia, South and Central Asia, Middle East and North Africa, Latin America and the Caribbean, Pacific, and Sub-Saharan Africa.The results indicate that AI implementation in public administration has resulted in differences in economic development across regions. North America demonstrated the highest level of readiness with an integral score of 81.51 points, followed by Western Europe (64.42 points) and Eastern Europe (54.31 points), all exceeding the global average of 48.17 points. Regions including Sub-Saharan Africa, the Pacific, and Latin America and the Caribbean recorded the lowest scores, reflecting limited digital infrastructure, constrained financial resources, and weaker institutional capacity. The regression analysis showed that over 90% of the variation in AI readiness across regions could be explained by GDP per capita.The findings confirm a strong positive relationship between economic development and government AI readiness, while also indicating that institutional, regulatory, and political factors independently influence outcomes. The results provide a basis for developing differentiated national strategies for AI integration in public administration that account for regional specificities and development levels.