Artificial Intelligence Adoption, Labor Market Inequality, and Firm Transformation: A Systematic Narrative Review
DOI:
https://doi.org/10.54097/zca8f782Keywords:
Artificial Intelligence, Generative AI, Labor Market, Inequality, Firm Innovation, ESG, Green Innovation, Task Model, Global BusinessAbstract
This article provides a systematic narrative review of recent literature on artificial intelligence adoption, labor market inequality, and firm transformation. Drawing on theoretical models, firm-level empirical research, quasi-experimental policy studies, and international organization reports, the paper examines how artificial intelligence changes economic activity through three connected mechanisms: task reallocation, productivity complementarity, and organizational capability formation. The review argues that the economic consequences of artificial intelligence cannot be understood through a simple substitution-versus-complementarity distinction. Instead, artificial intelligence reshapes the boundary between labor and capital, changes the relative value of different tasks, and creates uneven firm-level capacities to convert digital tools into innovation, sustainability performance, and competitive advantage. The literature suggests that aggregate productivity effects may be meaningful but more modest than optimistic public forecasts imply; wage inequality effects are ambiguous and depend on task exposure, adoption intensity, worker skill, and institutional context; and firm-level benefits are concentrated among organizations with complementary assets, data capability, and absorptive capacity. The article contributes by integrating macroeconomic, labor-market, and corporate-sustainability perspectives into a unified review framework. It concludes by identifying research gaps concerning developing economies, small and medium-sized enterprises, cross-border value chains, long-term employment adjustment, and the governance of AI-enabled inequality.
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