Artificial intelligence adoption and employee well-being: A job demands-resources perspective from academia and...

The rapid integration of artificial intelligence into workplaces is reshaping work design by augmenting, automating, and reconfiguring human work, with important implications for employee engagement and experience. While Job Demands-Resources (JD-R) theory provides a robust psychological framework for understanding how work characteristics influence wellbeing, insights into how AI alters demands, resources, and organisational dynamics remain fragmented across academic research and organisational practice. This scoping review synthesises academic and practitioner literature to examine how AI adoption is associated with employee engagement and wellbeing through a JD-R lens and to identify convergences and gaps between these perspectives. Guided by established scoping review approaches, academic and grey literature published since 2001 were systematically identified, screened, and synthesised. Forty-eight sources from 2019 to 2025 were included, comprising 35 academic studies and 13 practitioner publications. The literature predominantly emphasised strain-related outcomes such as stress and burnout, whereas work engagement received comparatively limited attention. Research addressing personal resources, work design factors, and proactive employee behaviours, particularly job crafting, was scarce. Integrating evidence across domains, the review conceptualises AI-enabled work as a dynamic and ambivalent work characteristic that may simultaneously generate demands and resources. The review frames job crafting as an emerging and theoretically promising mechanism through which employees may shape AI-enabled work to balance demands and resources and sustain engagement. Advancing psychologically sustainable AI-enabled work therefore requires closer and more purposeful scholar-practitioner collaboration, ensuring that theoretical insight and organisational experience jointly inform the design, implementation, and governance of AI-related work systems.