AI in HRM: a unified model of AI capabilities and process efficiency for enhanced HRM effectiveness

Abstract The accelerating adoption of Artificial Intelligence (AI) in human resource management (HRM) raises important questions about which AI-driven interventions most effectively enhance workforce agility, decision-making, and employee outcomes. This study investigated the effects of multiple AI applications—including employee wellbeing (AIEEW), performance management (AIPM), recruitment and selection (AIRS), training and development (AITD), and strategic HR practices (AISHRP), on HR process efficiency (HRPE), decision-making agility (DMA), workplace AI integration (WAI), and enhanced HRM effectiveness (EHRE). Data was collected from 256 professionals in organizational and educational institutions and analyzed using structural equation modeling (SEM) with SmartPLS. Results revealed that eight of nine hypothesized paths were supported, with AI-enabled wellbeing exerting the strongest effect on workplace AI integration (β = 0.583, p < .001). Recruitment and performance management systems significantly enhanced HR process efficiency, while strategic HR planning and talent analytics improved decision-making agility. HR performance enhancement strongly predicted HR effectiveness (β = 0.458, p < .001), highlighting process efficiency as a key mediator of AI’s impact. Two paths were weaker: AI in training and development did not significantly affect HR process efficiency, and decision-making agility showed only marginal influence on HRM effectiveness. Theoretically, the study enriches the Resource-Based View and Dynamic Capabilities Theory by demonstrating how AI functions as both a strategic resource and a capability-building tool. Practically, findings suggest that organizations should prioritize wellbeing, recruitment, and HR performance systems when investing in AI, while ensuring that training platforms and decision-support tools are better integrated into HR processes.