Competing strain and resource pathways linking AI-enabled work systems to employee wellbeing.

PURPOSE: With the growing integration of Artificial Intelligence (AI) into contemporary workplaces, understanding how AI-enabled work systems influence employee wellbeing has become an important concern within HRM research, digital work literature, and future-of-work scholarship. DESIGN/METHODOLOGY/APPROACH: Drawing on the Job Demands-Resources (JD-R) model and Conservation of Resources (COR) theory, this study examines the mediating roles of technostress and psychological resilience in the relationships between AI adoption, digital work intensity, and employee wellbeing, while also testing the moderating role of perceived organisational support. Data were collected from 516 employees in the Indian IT industry and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). FINDINGS: The findings suggest that the direct effect of AI adoption on employee wellbeing remains unclear, while digital work intensity positively influences wellbeing. Both AI adoption and digital work intensity significantly affect technostress and psychological resilience, reflecting the dual nature of AI-enabled work environments. Technostress does not significantly influence employee wellbeing, whereas psychological resilience is a significant predictor and mediator. Perceived organisational support was positively associated with employee wellbeing; however, its moderating effect on the relationship between technostress and employee wellbeing was not significant. ORIGINALITY/VALUE: The study is original because it shows that AI-based work systems create both strain and resource-building processes. The results also indicate that psychological resilience has a stronger influence on employees' psychological responses than technological strains. The paper offers a fresh perspective on employees' psychological responses, digital transformation, and the impact of AI on the human factor in technology-rich work environments.