ARTIFICIAL INTELLIGENCE AS A DRIVER OF CHANGE IN REGIONAL LABOR PRODUCTIVITY: ASSESSING DIFFERENTIATED EFFECTS FOR THE RUSSIAN ECONOMY

Abstract

Artificial intelligence (AI) technologies are regarded in public policy as one of the main sources of labor productivity growth, yet their diffusion across Russia is highly uneven, and the question of which regions will reap the main benefits has not received a convincing answer. The aim of the article is to assess the differentiated effects of AI adoption on labor productivity in Russian regions, taking into account differences in innovation potential, industrial structure and firm size. The methodology combines a typology of regions by their potential to create and adopt AI, an analysis of sectoral data on technology penetration, estimates of job exposure to automation, and a comparison with empirical findings on regional productivity dynamics. An index of the relative intensity of AI expenditure is proposed; calculations show that average expenditure per AI-using organization in Moscow is roughly 28–29 times higher than in the rest of the country, while the gap in AI adoption between large and small organizations reaches 3.6 times. Five groups of regions with different mechanisms of AI influence on productivity are identified, ranging from technology generation in the capital to selective automation in regions with minimal potential, where relative lagging is likely. It is argued that in the short run AI tends to widen rather than narrow interregional gaps, and that its positive effect on productivity materializes only where skilled personnel and complementary investment are available. The practical significance lies in proposals for differentiating support measures for AI adoption across types of regions.