A Study on Human-AI Interaction and Strategic HRM Practices: Challenges and Potential

Abstract

The rapid integration of artificial intelligence (AI) in organizational practices has transformed human resource management (HRM), demanding a nuanced understanding of AI–human interaction within strategic and operational HR functions. This study investigates the determinants of AI adoption in HRM, focusing on both organizational and HR role-related factors. A structured survey-based research design was employed, targeting senior HR professionals—including HR Heads, Managers, and Chief Human Resource Officers (CHROs)—drawn from a professional HR database. After rigorous data screening, valid responses were retained for analysis. The study examined major AI adoption determinants, including Behavioural Intention, Top Management Support, Performance Expectancy, and Competitive Pressure. Additionally, it considered critical HRM role dimensions derived from Ulrich’s model, such as Administrative Expert, Employee Champion, Strategic Partner, and Change Agent. Measurement items were refined from established constructs and assessed using a five-point Likert scale. Descriptive statistics revealed that respondents exhibited positive attitudes toward AI adoption, reflecting expectations of efficiency gains and improved decision-making. Validity and reliability analysis confirmed the robustness of the measurement instrument. Hypothesis testing using structural equation modeling demonstrated that all proposed hypotheses were supported, highlighting the significant influence of both organizational factors and HRM role responsibilities on AI adoption. The findings suggest that alignment of strategic objectives, managerial support, and change facilitation synergistically enhances AI–human collaboration. This study provides empirical evidence for practitioners and policymakers to design effective AI-driven HR strategies that optimize talent management, operational efficiency, and organizational competitiveness.