Artificial intelligence usage at work: understanding dual self-regulation mechanisms and goal orientation in career growth

Purpose – Grounded in the Self-Regulation Theory, this study examines the mechanisms and boundary conditions through which artificial intelligence (AI) usage shapes employee career growth. Design/methodology/approach – Based on a three-wave survey of 495 AI-active employees across industries, this study examines the impact of AI usage on career development. We test thriving at work and identity threat as dual mediators, and further explore how learning goal orientation (LGO) and performance goal orientation (PGO) moderate these pathways using the PROCESS. Findings – AI usage positively contributes to career growth by boosting thriving at work and inducing identity threat, which drives increased employee effort. LGO significantly moderates the relationship between thriving at work and career growth. Conversely, PGO positively moderates the relationship between identity threat and career growth but negatively moderates the relationship between thriving at work and career growth. Originality/value – Demonstrating how AI usage concurrently fosters thriving at work and elicits identity threat, the study offers a nuanced understanding of career growth. The proposed dual-pathway framework deepens theoretical discourse and provides practical insights for organizations aiming to optimize AI implementation and support employee career advancement in the intelligent era.