Empowering workforces in AI-driven environments: co-skilling, organizational support, and mitigating job insecurity

The rise of artificial intelligence (AI) has transformed workplaces, creating opportunities for innovation but also heightening job insecurity (JIN) among employees. This study examines the impact of Co-Skilling dimensions-participation, engagement, peer collaboration, and learning effectiveness (LE)-on mitigating JIN through perceived organizational support (POS), mental wellbeing (MEW), and skill confidence (SC). Using the Social Learning Theory (SLR) and the JD-R model as theoretical underpinnings, the research highlights the moderating role of employee readiness (ER) for AI in shaping these dynamics. A cross-sectional quantitative design with stratified random sampling was employed, involving 437 responses from employees across manufacturing, healthcare, technology, banking, and retail industries in China. Results demonstrate the significant mediating effects of POS, MEW, and SC, with POS emerging as a critical buffer against insecurity. However, nonsignificant findings in certain SC-related pathways and moderation effects of ER underscore the complexity of addressing JIN in technologically dynamic environments. This study contributes to theory by expanding the applications of SLR and the JD-R model and offers practical implications for organizations to design tailored Co-Skilling initiatives. Future research should explore additional contextual and psychological factors to enhance workforce adaptability in AI-integrated workplaces.