Leading Teams in the AI Era

Every leader walks into the AI era carrying a task portfolio that was built for a world where humans held the monopoly on judgment, analysis, and decision authority. This session opens the Sprint by asking participants to place their own role inside the Narrow Corridor framework and the Stacey Matrix, so that they can see with clarity which of their current tasks AI now substitutes, which ones AI enhances, and which new leadership responsibilities are emerging. The session is deliberately personal before it becomes organizational, because leaders who cannot name how their own desk is changing are poorly positioned to lead others through the same transformation.

Learning objective: Produce a personal task map covering what to stop, what to start, and what to adapt, along with the constraints the leader must manage as AI takes on more of the work around them.

The most overlooked reality of AI adoption is that teammates are already using AI at very different speeds, with very different tools, and with very different levels of disclosure, which silently reshapes trust, credit, accountability, and influence inside the team. This session gives participants a framework for reading team dynamics under conditions of uneven and often invisible AI use, and then puts them through a group task that forces them to observe those dynamics in real time. The debrief is where the work really happens, because participants share how they actually used AI during the exercise, which makes the invisible visible.

Learning objective: Diagnose how team dynamics shift when AI use varies across members, and role model the disclosure practices that make AI use visible, shared, and productive within the team.

This session shifts the role of AI from individual productivity tool to a shared team resource capable of observing, diagnosing, and improving team dynamics in real time. Participants engage in a group decision exercise, after which the transcript is handed to the AI so that the team can interrogate what actually happened in the room, including the patterns of participation, the quality of disagreement, and the moments where dynamics helped or hindered the decision. The session teaches leaders how to deploy AI as an observer with intent, how to read its feedback critically, and how to use its diagnosis to intervene skilfully on the dynamics that matter most.

Learning objective: Deploy AI as an observer of team dynamics and translate its feedback into concrete leadership interventions that improve how the team works together.

The most unsettling moment in the AI era arrives when the AI in the room holds more relevant knowledge than the leader, more than any team member, and at times more than the entire team put together, yet a high stakes decision still needs to be made and owned. This session is the simulation. Participants interact with AI as a genuine team member during a consequential decision, and the faculty then surfaces what actually happened in the group, which is always revealing. The core question is who was really in command, under which conditions leadership shifted toward the AI, under which conditions it shifted back, and what signals told participants when to trust the AI and when to go against it.

Learning objective: Make a high-stakes decision with AI acting as a full team member, and identify the specific conditions under which the leader must trust the AI’s recommendation and the conditions under which the leader must override it.

The sprint closes by placing participants in front of a question that is easy to avoid and impossible to outsource, which is what kind of future, among the many that are now technologically possible, they actually want to lead their organizations, their teams, and their societies into. The premise is that the AI future is a possibility space shaped by the choices leaders make, and human agency is the mechanism through which one future, among many, actually gets built. Using a Future Back exercise anchored in 2036, participants work backward to the leadership decisions, trade offs, and skills that make their chosen future reachable. The session also offers a dedicated space for genuine debate and open Q&A on the questions participants are still carrying.

Learning objective: Build a personal roadmap that commits the leader to an agentic path into the future they have chosen, including the skills to build, cultivate, and retire, within the possible set of futures that AI now makes available.