Abstract: As artificial intelligence adoption accelerates across sectors, organizations face mounting pressure to demonstrate immediate returns on AI investments, often through workforce reductions that outpace actual automation capabilities. This pattern reflects longstanding corporate short-termism rather than genuine technological displacement, yet it foreshadows deeper structural challenges as AI systems mature. Drawing on labor economics, organizational behavior, and technology adoption research, this article examines how managerial incentives drive premature workforce contraction, the macroeconomic risks of AI-led unemployment, and evidence-based policy responses. The analysis argues that gradual, policy-led work-time reduction represents not merely a quality-of-life enhancement but essential economic stabilization infrastructure. Through examination of historical work-time transitions, contemporary pilot programs, and cross-sector implementation strategies, the article demonstrates how coordinated reduction in standard working hours can preserve employment, maintain aggregate demand, and distribute productivity gains equitably. Organizations and policymakers that treat work-time policy as foundational economic planning will better position their economies to harness AI's benefits while mitigating systemic instability.