How Artificial Intelligence Is Reshaping Labor Productivity: A Narrative Review of Theoretical Perspectives and Emerging Empirical Evidence

Artificial Intelligence (AI) has emerged as a transformative general-purpose technology with far-reaching implications for labor markets and productivity dynamics worldwide. This narrative review synthesizes theoretical perspectives and emerging empirical evidence covering literature published between 2010 and 2025 to examine how AI influences labor productivity through pathways of automation and augmentation. Drawing on interdisciplinary insights from economics, management, and labor studies, the review finds substantial firm-level productivity gains associated with AI adoption, driven by enhanced task efficiency, decision support, and workflow optimization. However, macro-level productivity effects remain uneven due to slow diffusion, disparities in digital readiness, and the need for complementary organizational and human-capital investments. The analysis highlights that AI’s productivity impact is strongly mediated by worker skills, data infrastructure, and strategic implementation within firms, while also generating important distributional consequences such as widening productivity gaps between early and late adopters, skill polarization, and regional divergence. This review contributes to business and economic communication by bridging economic theory and managerial practice, offering an integrated framework that clarifies how AI-driven productivity mechanisms are translated into organizational strategy and policy discourse. By consolidating fragmented evidence across disciplines, the study provides managers, economists, and policymakers with a coherent understanding of when and how AI can enhance labor productivity. The review concludes that realizing AI’s productivity potential requires sustained investment in reskilling, digital transformation, ethical governance, and supportive public policy.