Regional Specificities of AI Adoption

The relevance of the study is due to the implementation of the national project "Data Economy and Digital Transformation of the State," which has identified a pronounced spatial unevenness in the regions of the Russian Federation, requiring the systematization of regional models for the implementation of AI (artificial intelligence) and the identification of factors that determine the success of digital transformation in territories. The purpose of the study is to identify significant factors of interregional asymmetry in the implementation of regional policy in the field of AI, to determine the types of regional models of digital transformation of regions, and to develop recommendations for improving the effectiveness of a differentiated approach at the level of the constituent entities of the Russian Federation. To achieve this goal, the authors analyzed the regional conditions for AI implementation using the example of five territories. The scientific and practical significance of the work lies in the development of a typology of five regional AI models and in substantiating the direct correlation between the level of infrastructure development and the digital maturity of the region. Key findings of the study : it has been established that the most successful model is a systemic one, combining a high level of institutional maturity, large scale implementation of AI projects, and long-term planning of human resources potential; it has been demonstrated that “blind” replication of successful practices in regions with a different economic structure and budgetary capacity is ineffective; the key problem areas of the regional AI implementation policy have been identified: digital inequality, a shortage of interdisciplinary personnel, barriers to scaling up scientific developments, and infrastructure constraints; the need to move from unified approaches to a differentiated regional policy has been substantiated, taking into account the level of digital maturity, budgetary capacity, and infrastructure development of each constituent entity. Conclusions of the study : a transition from unified approaches to a targeted, differentiated AI implementation policy is required, taking into account the level of various development metrics of the territories. It is necessary to strengthen individual support for depressed regions through the development of human resources potential and the institutional environment. The success of digital transformation is determined not by the volume of funding, but by the region’s ability to integrate effective management mechanisms, which requires a revision of the budgetary approach to resource allocation and methodological support for the territories.