AI is now drafting specs, writing code, and shipping features faster than most governance models were built to handle. When something goes wrong, the question is no longer just what broke, but who answers for it: the product manager who approved it, the engineer who reviewed it, the leader who set the pace, or the model itself. Too often the answer stays unclear until the moment it matters, and that gap puts customers, revenue, and trust at risk. This panel moves beyond abstract talk of responsible AI to get specific about how senior product leaders are assigning ownership, building oversight into fast moving workflows, and deciding where a human has to stay in the loop without slowing the team down. Expect real examples, honest disagreement, and no tidy consensus. Attendees will leave with practical ways to define accountability before an AI shipped feature goes wrong, and a clearer sense of where to trust the system and where to check its work. It is AI harmony over AI hype, with someone clearly holding the reins.

Getting an AI agent into production is the part most teams plan for. What decides whether it lasts is everything settled before development starts. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls. In this keynote, Shalini Dinesh shares a playbook for deciding what to build before development begins. It starts with intent mapping: how to identify and prioritize the customer intents worth investing in, and why not every intent needs an agent. She then walks through a framework for planning fallback mechanisms and the guardrails and stakeholders that need to stay in the loop from discovery to launch. Attendees walk out with the confidence to lead agent initiatives from day one, knowing what to plan and which questions to settle before a single line of development begins.