Artificial intelligence, labor markets, and institutional adjustment: A systematic synthesis

Abstract

This article examines how artificial intelligence (AI) reshapes labor markets through a systematic synthesis of theoretical, empirical, and policy-oriented research. We adopt a structural perspective in which employment outcomes arise from task recomposition, organizational redesign, demand dynamics, and institutional mediation rather than from direct job substitution. Methodologically, we rely on a structured qualitative review of influential contributions across economics, management, and governance. The analysis shows that AI alters job content, coordination, and firm boundaries before its effects appear in aggregate employment. Findings point to heterogeneous outcomes across sectors and skill groups: high-skill workers more often benefit from complementarities and task expansion, whereas routine-intensive roles face stronger adjustment pressures shaped by firm strategy, market structure, and labor relations. We further find that governance conditions matter directly, as documentation standards, risk-management frameworks, and accountability mechanisms influence whether productivity gains support inclusive adjustment or reinforce asymmetries. We conclude that the labor-market effects of AI depend less on technical capability alone than on organizational and institutional conditions. For firms and policymakers, the practical implication is straightforward: AI adoption requires governance capacity and worker adjustment strategies if productivity gains are to translate into broad-based labor-market benefits.