Published Paper Details:

The rapid emergence of Generative Artificial Intelligence (GenAI) marks a paradigm shift in Human Resource Management (HRM), transitioning the function from traditional administrative support to a data-driven strategic partnership. This research systematically synthesizes contemporary academic literature and industry case analyses to investigate the impact of GenAI integration on recruitment efficiency and employee engagement. Findings indicate that GenAI-enabled workflows significantly optimize the talent acquisition lifecycle; documented evidence shows that automating sourcing, screening, and job description generation can reduce recruitment cycles by up to 90% while cutting operating costs by 20% to 40%. Furthermore, the study examines the technology's influence on the internal employee experience, revealing that intelligent conversational agents and adaptive learning pathways facilitate hyper-personalization, resulting in a 105.5% increase in feedback frequency and a 35.5% improvement in mental well-being scores. However, the integration of Large Language Models (LLMs) introduces critical organizational challenges, including persistent risks of algorithmic bias, the "black box" transparency-opacity paradox, and heightened data privacy concerns under frameworks such as the EU AI Act. Drawing on the Resource-Based View (RBV) and Ability-Motivation-Opportunity (AMO) theory, this paper argues that while GenAI serves as a transformative strategic enabler, its success is contingent upon robust ethical governance and a "human-in-the-loop" mandate to preserve relational integrity. The research concludes by proposing a structured implementation roadmap that balances technological efficiency with the human-centric values essential to modern workforce management.