Artificial Intelligence and Job Design: Insights from Job Demands–Resources Theory

Artificial intelligence (AI) is transforming work by reshaping how jobs are designed, experienced, and performed. This article reviews emerging evidence on the implications of AI adoption for job design from the perspective of Job Demands–Resources theory. We develop a conceptual framework that explains how AI adoption may alter the balance between job demands and job resources, and thereby influence both motivational and health-impairment processes. We further propose AI readiness resources (AI-R) – including personal resources, such as AI self-efficacy, and job resources, such as colleague support for AI – as important conditions that help explain when AI adoption is more likely to strengthen rather than undermine employee well-being and performance. Building on this framework, we formulate three guiding principles for future research. First, scholars should distinguish the level of AI adoption, namely top-down versus bottom-up adoption, from the type of AI use, namely automation versus augmentation, because these dimensions may have different job design implications. Second, researchers should differentiate AI-R resources at the personal and job levels, as they point to different mechanisms and intervention opportunities. Third, research should approach AI adoption as a dynamic process in which employee well-being, performance, and AI readiness may influence one another over time. The article concludes with an agenda for future research and practical directions for designing AI-enhanced jobs that are meaningful, motivating, and sustainable.