This study examines the impact of AI-driven Human Resource Management (HRM) practices in remote and hybrid work environments, with a focus on both organizational and employee outcomes. Using a qualitative research design based on secondary data analysis of 35 peer-reviewed articles, the study is grounded in key theoretical frameworks including the Technology Acceptance Model (TAM), Ability-Motivation-Opportunity (AMO) model, and Resource-Based View (RBV). The findings highlight that AI-driven HR practices improve productivity, organizational efficiency, and decision-making while enhancing employee engagement and flexibility. However, challenges such as data privacy concerns, algorithmic bias, and employee trust issues remain significant. This research contributes to the existing literature by integrating fragmented studies on AI in HRM and hybrid work models, particularly in the Indian context, and provides practical and policy-level implications for organizations adopting AI-enabled HR systems.
Subha Laxmi Mandal (Mon,) studied this question.