Agentic AI is remaking jobs, not just automating tasks: across firms it consolidates, segments, elevates, displaces and creates roles, changing hierarchies and the skills employers seek. Policymakers and organisations must shift from task-level responses to role-focused strategies for workforce planning and governance.

Main Finding

Agentic AI shifts the unit of analysis from discrete tasks to whole roles: rather than simply substituting individual tasks, agentic systems reconfigure job boundaries and workflows across industries. The study identifies five dominant patterns of role transformation—consolidation, segmentation, elevation, displacement, and creation—and shows that these changes alter organizational hierarchies, coordination mechanisms, and skill requirements, with broad consequences for labor demand, wage structure, and policy design.

Key Points

• Role-focused lens: Agentic AI enables multi-step, goal-directed automation, so impacts are better understood at the role level (bundles of tasks, authority, coordination) rather than by isolated tasks.
• Five transformation patterns:
  • Consolidation: Several complementary roles or functions are merged into a single, agent-plus-human role (e.g., AI handling workflow orchestration formerly split across teams).
  • Segmentation: Existing roles are split into more specialized sub-roles, separating routine agent-handled parts from human-only judgment tasks.
  • Elevation: AI automates lower-level work, raising the remaining human tasks toward higher-value judgment, relationship, or oversight functions.
  • Displacement: Entire roles become obsolete when agents can autonomously perform core responsibilities end-to-end.
  • Creation: New roles emerge around AI design, monitoring, orchestration, and human–agent teaming (e.g., “agent supervisors,” safety engineers).
• Organizational effects: Changes in reporting lines, centralization vs. decentralization of control, and shifts in coordination costs and bottlenecks.
• Skill dynamics: Demand shifts toward skills in supervision, systems thinking, AI governance, and complex judgment, while routine operational skills may decline in value.
• Strategic heterogeneity: Industries and firms differ in which pattern dominates depending on task structure, regulatory constraints, and organizational incentives.

Data & Methods

• Mixed-methods design combining:
  • Cross-industry case studies to identify recurring transformation patterns and contextual drivers across sectors.
  • Role-content analysis that maps roles as bundles of tasks, decision authority, coordination links, and required skills—contrasting pre- and post-agent deployment role definitions.
  • Qualitative interviews and workshops with organizational leaders, managers, and practitioners to capture adoption rationales, governance practices, and experiential insights on workforce impacts.
• Analytical approach emphasized pattern identification and conceptual framing rather than precise causal estimation; used qualitative triangulation across industries to increase generalizability.
• Note: The summary reflects the study’s conceptual and empirical synthesis; specific sample sizes and statistical estimates are not reported here.

Implications for AI Economics

• Reframe labor-impact models: Standard task-level automation models understate systemic effects. Economic models should incorporate role-level bundles, coordination costs, and endogenous role redesign.
• Labor demand and wage effects:
  • Potential increase in wage premiums for supervisory, orchestration, and governance skills.
  • Polarization risks where elevated roles capture value but displaced workers face downward pressure unless retrained.
• Measurement and statistics: Labor market measurement should move beyond task proxies to track role composition, cross-role consolidation, and new occupations tied to agentic systems.
• Policy and workforce strategy:
  • Active labor-market policies (reskilling, credentialing for supervisory/governance roles) are critical to smooth transitions.
  • Support for job redesign and internal mobility (incentives for firms to reallocate displaced workers into elevated or newly created roles).
  • Regulatory frameworks for agentic systems (safety, accountability, certification) to shape how roles can be delegated to agents.
• Organizational design: Firms should anticipate changes to coordination and hierarchy, invest in human–agent teaming practices, and design clear oversight roles to manage risk and maintain value capture.
• Research directions: Empirical quantification of role-level impacts, dynamic models of role evolution, and evaluation of policy interventions to manage transitions in an agentic-economy.