Vol. 7, Issue 2, Part B (2025)

This study examines the dual impact of artificial intelligence (AI) adoption on operational efficiency and socio-economic dynamics across industries. Through a synthesis of global case studies and academic research, the paper highlights AI's transformative potential in sectors such as healthcare, manufacturing, logistics, and retail, where it drives cost reduction, productivity gains, and innovation. Key findings include AI's role in predictive maintenance (reducing downtime by 45%), demand forecasting (cutting overstock costs by 25%), and precision diagnostics (lowering misdiagnoses by 30%).
However, the study also identifies significant ethical and operational challenges, such as algorithmic bias (e.g., hiring tools favoring elite university graduates), privacy violations (e.g., non-consensual employee monitoring), and workforce displacement. Mitigation strategies, including bias-detection tools, federated learning, and hybrid human-AI collaboration models, are explored to balance efficiency with ethical accountability.
The paper underscores the need for robust policy frameworks (e.g., EU's Digital Services Act, California's AI Accountability Act) and workforce reskilling initiatives to address disparities and ensure sustainable AI integration. By merging insights from computer science, economics, and policy design, this research provides actionable recommendations for stakeholders to harness AI's benefits while mitigating its risks, ultimately advocating for a future where AI augments human potential equitably.