Agentic AI in organizations: reimagining learning, autonomy, and human oversight

Purpose This viewpoint examines organizational implications of agentic AI systems in planning, selecting tools, executing multistep actions, and evaluating their own outputs with limited human intervention. Design/methodology/approach This viewpoint synthesizes academic and practitioner insights to identify key organizational challenges associated with agentic AI drawing on organizational learning theory and recent research on human–AI collaboration, and propose practical directions for HR and learning and development. Findings As agentic AI becomes more autonomous, it is reshaping organizational learning, human oversight, and accountability. Organizations will need to redesign learning practices and governance mechanisms to ensure that people and AI work together effectively. Research limitations/implications This conceptual viewpoint does not include empirical validation. Future research should examine the proposed ideas across different organizational settings. Practical implications Organizations should assess workforce readiness, redesign learning around human–AI collaboration, and establish feedback mechanisms that strengthen learning, oversight, and accountability. Originality/value This viewpoint shifts the discussion from technological capabilities of agentic AI to its organizational implications, offering practical guidance for organizations adapting to increasingly autonomous forms of work.