How Agentic AI Reshapes Work: The Roles of Cognitive Surplus, Psychological Safety, and AI-Human Task Reallocation.
This study examines how the technological characteristics of Agentic AI relate to employees' psychological states and behavioral responses in organizations. Drawing on Job Demands-Resources (JD-R) theory and Conservation of Resources (COR) theory, we conceptualize Agentic AI technological characteristics (AITC) as a higher-order construct comprising autonomous planning, multi-tool orchestration, and proactive feedback, and propose that these characteristics function as job resources associated with greater cognitive surplus and psychological safety, which in turn relate to AI-human task reallocation. We further investigate whether these relationships differ between leaders and staff. A cross-sectional, anonymous online survey was administered via an online survey platform to participants recruited through a research panel using purposive nonprobability sampling; eligibility was restricted to adults with current or prior AI-related work experience, and screened, attention-checked responses yielded a final sample of 331 employees (171 leaders, 160 staff). All constructs were measured with validated scales adapted to the Agentic AI context on 7-point Likert scales. Data were analyzed using partial least squares structural equation modeling (PLS-SEM), applying a two-stage approach for the second-order construct, bootstrapping with 5,000 resamples for direct and indirect effects, and multi-group analysis (MGA) for leader-staff comparisons. The measurement model demonstrated acceptable reliability and validity, and the structural results were consistent with the theorized resource-gain model: AITC was positively associated with cognitive surplus, psychological safety, and task reallocation, with significant mediation pathways and stronger AITC-resource associations among leaders. By focusing on psychological mechanisms rather than technical performance, this study extends emerging Agentic AI research and offers practical implications for role-differentiated AI adoption strategies.