Predicting and Preventing Turnover in Industry 4.0: Understanding the Impact of Artificial Intelligence Adoption on Employee Turnover

Abstract

With the increasing adoption of Artificial Intelligence (AI) in the workplace, employees' career paths have become more diverse and less predictable in the era of Industry 4.0. As technological transformations accelerate, employee turnover patterns are also changing, as reflected in the growing prevalence of occupational transitions and large‐scale layoffs. These shifts highlight the need for more developmental approaches to understanding and managing retention. To address this issue, the present paper examines how AI implementation influences employee turnover by focusing on both the motives and psychological states underlying the withdrawal process. Drawing on proximal withdrawal states theory, the paper provides a deeper understanding of the dynamic nature of employee withdrawal patterns. This perspective elucidates the withdrawal process shaped by AI adoption, emphasizing that employees' responses to technological disruption are diverse in both psychological and behavioral terms rather than uniform. Furthermore, the paper proposes Human Resource Development (HRD)‐based interventions aimed at preventing undesirable turnover through employee education and career development initiatives. By integrating these elements, we introduce an HRD‐focused framework for addressing turnover issues associated with AI adoption in a more developmental and sustainable manner. Finally, the paper outlines future research directions for empirically testing the proposed theoretical mechanisms and intervention strategies within the rapidly evolving technological landscape of work.