Navigating AI Transformation: How AI Anxiety and Technological Uncertainty Shape Employee Adaptability through Psychological Safety

Abstract

Artificial intelligence (AI) is reshaping work in emerging economies while creating new psychological demands for employees who must adjust to unfamiliar and rapidly changing technologies. Drawing primarily on the Job Demands-Resources (JD-R) model, with Conservation of Resources (COR) theory as supporting logic, this study examines whether AI Anxiety and Technological Uncertainty predict Employee Adaptability through Psychological Safety and whether Organizational AI Support conditions these relationships. A quantitative, cross-sectional survey of 380 employees in AI-exposed organizations in Pakistan was analyzed using Hayes' PROCESS macro (Models 4 and 7) with 5,000 bootstrap resamples. AI Anxiety and Technological Uncertainty each showed significant negative direct associations with Employee Adaptability, while Psychological Safety significantly mediated both relationships. Organizational AI Support did not show the predicted buffering pattern. The AI Anxiety × support interaction was not statistically significant at the conventional .05 level, whereas the Technological Uncertainty × support interaction was significant but negative, indicating that the adverse association with Psychological Safety became stronger rather than weaker as reported support increased. The corresponding index of moderated mediation was significant only for Technological Uncertainty and was also opposite to the hypothesized buffering direction. These findings extend emerging research on AI-related workplace stress by distinguishing direct and psychologically mediated pathways while highlighting that organizational support should not automatically be interpreted as a universal stress buffer. The results should be interpreted in light of the cross-sectional, self-report, convenience-sample design and the need for stronger confirmatory measurement validation.