The conversation about AI in enterprise product teams has moved on from whether to use it to how to integrate it without creating new dependencies, new risks, and new forms of technical debt. This panel brings together product leaders who have gone past the experimentation phase and are now running AI as a core part of how their teams operate. Panelists will cover where AI has genuinely changed how they work, where it has underdelivered, and the governance and process questions that most teams are still figuring out in real time. Expect honest disagreement about what good AI integration actually looks like inside a complex, regulated, or legacy-constrained enterprise environment. Attendee takeaways - Where AI is creating real leverage in enterprise product workflows versus where the hype has outrun the reality - The governance and process guardrails that make AI integration safe in regulated or risk-sensitive environments - How to evaluate AI tooling for enterprise product teams when every vendor is making the same claims - What changes about team structure and skill requirements when AI becomes a core part of how the product org operates

The most durable AI value is often invisible: embedded in operational workflows, running behind ERP systems, quietly removing manual work from processes unchanged for fifteen years. This session is about that harder, less glamorous category of AI work. The speaker covers the data quality problems to solve first, the integration constraints that shape what is possible, and how to make the ROI case to a finance team skeptical of AI promises. Attendee takeaways - The data quality baseline required before AI can add reliable value in a legacy operational context - How to scope AI features in back-office workflows so they reduce friction without creating new failure modes - How to build the ROI case for back-office AI with a finance team that has heard too many AI promises