Managing Grassroots and Boardrooms: Change Strategies for Navigating Agentic AI in Organizations

Agentic artificial intelligence (AI) systems are entering the workplace at an unprecedented pace, making the associated organizational change a unique challenge for senior leaders. Traditional change models assume controllability and sequential implementation. By contrast, agentic AI operates autonomously, and adoption often emerges through employee exploration, rendering established blueprints a limited guide. Conceptualizing top-down steering and bottom-up enactment as independent dimensions rather than opposing choices, we outline four prototypical approaches to agentic AI change: (1) light-touch drift, (2) grassroots experimentation, (3) command-and-scale, and (4) integrative change . Rather than pinpointing a superior strategy, we argue that senior leaders must continuously diagnose and rebalance their approach to fit their context. We call for treating agentic AI change not as a one-time implementation challenge but as the ongoing orchestration of an evolving portfolio of top-down and bottom-up processes. We close with directions for future research on the enactment, temporal patterns, and governance of agentic AI change.