International Journal of Scientific Research and Engineering Development

This paper examines how artificial intelligence (AI) can enhance employee retention amidst significant turnover issues in modern organizations. It conducts a systematic review of 58 sources, including peer-reviewed articles and practitioner reports, predominantly from 2015 to 2026, and proposes an integrative conceptual framework. The findings indicate a discrepancy between improved predictive accuracy for voluntary turnover and inconsistent retention gains, particularly in organizations with advanced analytics capabilities. Resistance from employees often stems from concerns about fairness, transparency, and surveillance. The study introduces a dual-pathway model: the enabling pathway, where analytics lead to interventions that enhance job embeddedness and retention, and the evaluative pathway, where perceptions of justice and privacy can undermine these benefits. The paper advances nine propositions detailing these relationships and uniquely integrates four existing research areas—HR analytics, algorithmic management, organizational justice, and turnover theory—reconceptualizing retention analytics as a matter of decision quality and legitimacy rather than mere predictive accuracy. It lays out a research agenda for future empirical inquiries and offers practical recommendations for ethical AI deployment in HR decision-making.

Dr. Sameer Pawar,"AI-Driven People Analytics and Employee Retention: Assessing The Role of Data-Driven HR Decision-Making" International Journal of Scientific Research and Engineering Development, V9(1): Page(2217-2232) Jan-Feb 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.