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

Artificial Intelligence (AI) has emerged as a transformative force in enhancing operational efficiency across industries. This study examines AI's dual role in driving productivity gains while introducing complex ethical and socioeconomic challenges. Through sector-specific case studies in healthcare, manufacturing, and logistics, we demonstrate AI's capacity to reduce costs (e.g., 45% decrease in equipment downtime through predictive maintenance), improve accuracy (e.g., 99.5% defect detection in manufacturing), and optimize resources (e.g., 40% shorter ER wait times via diagnostic AI). However, the research also highlights significant concerns including algorithmic bias in hiring systems, privacy violations in employee monitoring, and workforce displacement effects.
The paper analyzes mitigation strategies such as bias-detection tools, federated learning for data privacy, and hybrid human-AI collaboration models. We evaluate emerging policy frameworks like the EU Digital Services Act and corporate transparency initiatives. Findings suggest that while AI delivers measurable efficiency improvements, its sustainable adoption requires: (1) robust ethical safeguards, (2) workforce reskilling programs, and (3) international governance standards. The study concludes with recommendations for balancing technological innovation with equitable outcomes, proposing a "human-in-the-loop" paradigm as essential for responsible AI integration.