AI-Enabled Workplaces and Employee Outcomes: Examining the Mediating Role of Employee Engagement and the Moderating Role of AI Anxiety

Abstract

Organizations are adopting artificial intelligence (AI) for scheduling, decision support, customer service, analytics, and routine task automation. Whether this shift helps or harms employees depends on how workers experience it. This paper develops and proposes a moderated-mediation model in which an AI-enabled workplace influences employee outcomes (job performance and job satisfaction) through employee engagement, and in which this indirect effect depends on employees' level of AI anxiety. Drawing on the Job Demands–Resources (JD-R) model and Conservation of Resources (COR) theory, we argue that AI integration acts as a job resource when it reduces tedious work and improves information access, thereby raising engagement. However, employees who fear job loss, skill obsolescence, or loss of control experience AI as a threat, which weakens the resource-gain pathway. The paper presents the theoretical background, six hypotheses, a quantitative survey design, an analysis plan using structural equation modelling and PROCESS Model , and implications for managers. The study contributes by integrating a positive pathway (engagement) with a psychological boundary condition (AI anxiety) that is often studied separately.