Artificial Intelligence and Labor Market Adjustment in Türkiye : Exposure and Complementarity

Overview & Synopsis

Artificial intelligence (AI) has attracted growing attention as a potential source of productivity growth and structural transformation in emerging economies. Yet the labor market implications of AI depend not only on technological capabilities but also on how those capabilities interact with firm behavior, workers’ skills, and institutional conditions. This paper provides a systematic diagnostic of how AI-related technological potential is distributed across Türkiye’s labor market and examines the conditions under which that potential may translate into observable labor market outcomes. The analysis applies occupation-level measures of AI exposure and task complementarity to nationally representative labor force survey data and employer-employee administrative records. It documents how AI-related technological potential is distributed across workers, jobs, firms, sectors, and regions. The results show that approximately 41 percent of employment in Türkiye is in occupations with relatively high exposure to AI, although the scope for productivity-enhancing complementarity varies substantially across occupations and worker groups. Given the early stage of AI diffusion and the rapid evolution of AI capabilities, these estimates should be interpreted as a descriptive mapping of technological exposure and potential complementarity rather than as forecasts of realized employment, wage, or productivity effects. Despite this widespread exposure, employment and labor demand patterns across exposure categories have remained largely stable in recent years, suggesting that the translation of technological potential into observable labor market effects depends critically on adoption dynamics and complementary adjustments within firms and labor markets. Drawing on additional evidence on firm adoption, hiring patterns, and labor market structure, the paper argues that the economic consequences of AI are likely to unfold gradually through changes in task organization, skill demand, and production processes rather than through abrupt economywide disruption. By distinguishing technological exposure from complementarity and adoption capacity, the paper provides an analytical framework for interpreting emerging evidence on AI and work. It also highlights the policy margins most relevant to shaping labor market adjustment in middle-income economies.