AI is accelerating how quickly teams can generate ideas, but without structure, it also accelerates risk. Too often, organizations continue to invest time, budget, and effort based on assumptions that have never been tested.
This 30-minute session introduces a practical approach for using AI to identify assumptions, prioritize what must be true, and generate evidence before committing further resources. It outlines a simple structure for improving decision quality and shows how AI can support faster, more disciplined experimentation — without replacing clear thinking.
Attendees will leave with a clear, actionable method for moving from uncertain ideas to evidence-backed decisions: Commit, Correct, or Cut.