Artificial Intelligence (AI) is rapidly altering modern organizational architecture, moving beyond automation into core functions traditionally reserved for human managerial discretion. Organizations across gig, logistics, healthcare, and white-collar sectors increasingly deploy AI and algorithmic systems for candidate recruitment, real-time employee monitoring, task allocation, workforce scheduling, performance evaluation, and predictive decision support. While these systems yield substantial improvements in operational coordination, algorithmic speed, and data transparency, they introduce profound concerns regarding worker discretion, mental fatigue, and psychological safety. This systematic review synthesizes empirical literature examining the structural relationship between algorithmic management, employee autonomy, and multidimensional well-being. Focusing on key dynamics—workplace surveillance, algorithmic transparency, perceived fairness, human oversight, work intensity, data privacy, and technostress—we analyze synthesized evidence across 172 scholarly articles. The findings reveal a fundamental organizational paradox: algorithmic management functions simultaneously as a mechanism of operational enablement and structural constraint. The review establishes that mitigating negative psychosocial risks requires meaningful human oversight, transparent decision logic, and participatory system co-design.

Shripal Malayya Durgam, Prof. Ketaki Kukde, "The Impact of AI and Algorithmic Management on Employee Autonomy and Well-being: A Systematic Review" International Journal of Scientific Research and Engineering Development, V9(5): Page(787-801) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.