AI is drafting specs, writing code and shipping features faster than traditional governance can keep up. But when something goes wrong, who takes responsibility: the PM, the engineer, the product leader or the AI itself? This panel moves beyond abstract discussions of responsible AI to explore how product leaders are defining accountability, building oversight into fast-moving workflows and deciding where human judgment needs to stay in the loop. Expect real examples, honest debate and practical advice on balancing AI autonomy with customer trust and business risk. Key takeaways: - Understand how to assign clear ownership and accountability for AI-driven product decisions. - Explore how to build meaningful human oversight into fast-moving AI workflows without slowing teams down. - Learn where to trust AI outputs, where human judgment is essential and how to manage the risks when things go wrong.

Getting an AI agent into production is one thing; making it successful is another. Gartner predicts over 40% of agentic AI projects will be cancelled by the end of 2027, often due to unclear business value, rising costs or inadequate risk controls. In this keynote, Shalini Dinesh shares a practical playbook for planning agent initiatives before development begins from identifying valuable customer intents to designing fallback mechanisms, guardrails and stakeholder oversight. Key takeaways: - Learn how to identify and prioritise customer intents worth automating and when an agent isn't the answer. - Understand how to plan fallback mechanisms and guardrails before development starts. - Discover how to involve the right stakeholders from discovery through to launch.