From Tasks to Roles: How Agentic AI Reconfigures Occupational Structures Across Industries

Agentic artificial intelligence (AI) represents a significant departure from traditional task-level automation by enabling autonomous, goal-directed systems capable of performing multi-step workflows. This research investigates how agentic AI reconfigures occupational structures across industries, shifting the analysis from discrete task substitution to the transformation of entire roles. Using a mixed-methods approach that integrates cross-industry case studies, role-content analysis, and qualitative insights from organizational leaders, the study identifies five dominant patterns of role transformation: consolidation, segmentation, elevation, displacement, and creation. Findings reveal that agentic AI not only reshapes job boundaries but also alters organizational hierarchies, workflow coordination, and skill requirements. The study offers a conceptual framework for understanding the transition from task-based to role-based redesign and provides strategic recommendations for policymakers, organizations, and workforce planners navigating the emerging agentic-economy landscape.