Fueling innovation and mitigating burnout: the dual impact of organizational learning in AI-enabled change

Purpose This study investigates how organizational learning influences employee innovative behavior in the context of rapid artificial intelligence (AI) development and AI-enabled organizational change. It explores the dual mechanisms of knowledge acquisition and psychological strain, specifically focusing on the roles of absorptive capacity and job burnout. Design/methodology/approach Drawing on conservation of resources (COR) theory and social exchange theory (SET), this study develops and tests a dual-pathway moderated mediation model. Data were collected through a two-wave, time-lagged survey of 373 employees in Chinese enterprises experiencing AI-related workplace changes. The data were analyzed using confirmatory factor analysis, regression-based mediation and moderation analyses, and bootstrapped conditional indirect-effect analysis. Findings The results indicate that organizational learning supports employee innovation through two mediating pathways: by enhancing absorptive capacity and by reducing job burnout. Knowledge sharing and employee–AI collaboration further shape these pathways. Specifically, both contextual factors strengthen the learning-related stages of the cognitive and emotional pathways, while employee–AI collaboration also buffers the negative relationship between job burnout and employee innovative behavior in AI-enabled work settings. Originality/value These findings advance the understanding of cognitive and emotional resource mechanisms in AI-enabled workplaces. The study highlights the dual role of organizational learning in facilitating both knowledge resource gain and psychological resource protection during technological transformation and organizational change.