The most interesting use of AI in product right now is not in the product itself, but in how product leaders make decisions. Teams are using AI to synthesise customer research at a scale that was previously impossible, to model the second-order effects of roadmap decisions, to identify patterns in usage data that no analyst would have found, and to stress-test strategic assumptions before they get built into a plan. This panel brings together leaders who have gone furthest down this path and are willing to share what they have actually learned. Panelists will discuss the specific ways they have integrated AI into their strategy and decision-making process, the places where it has made a genuine difference, and the places where they tried it and found it wanting. They will also tackle the harder question of what it does to a product organisation when the tools for analysis and synthesis become dramatically more powerful, what changes about the PM role, what changes about team structure, and what human judgement remains irreplaceable.

AI can summarize research, generate roadmap updates, analyze feedback, and support prioritization, but it cannot fix weak product signals. If customer insights are fragmented, priorities are unclear, outcomes are poorly defined, dependencies are hidden, or decision rights are fuzzy, AI may simply create faster, more polished noise. The result is not better product leadership. It is automation applied to confusion. In this session, Mark Moskovitz explores how product leaders and product operations teams can strengthen the signals AI will consume and amplify. Using the Sorcerer’s Apprentice as a memorable metaphor, the talk shows why organizations need clearer inputs, stronger decision systems, and better learning loops before scaling AI into product planning, reporting, research synthesis, and executive decision support. Attendees will leave with the AI Signal Readiness Diagnostic, a practical framework for assessing whether their product organization is ready to benefit from AI.