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Ashish

Garg

Principal Product Manager

Walmart

Ashish Garg is a product and AI leader with extensive experience building enterprise-scale planning, decision intelligence, and GenAI-enabled product operations across Walmart, Sam’s Club, eBay, and Gap Inc. Currently a Principal Product Manager at Walmart, he leads intelligent enterprise planning initiatives focused on AI-assisted product delivery, portfolio governance, and operational scalability. Known for building the “operating systems” behind high-performing product organizations, Ashish specializes in continuous planning, roadmap governance, AI-enabled workflows, and decision intelligence platforms that help large enterprises improve execution, prioritization, and cross-functional alignment. His work spans forecasting, supply chain optimization, conversational automation, and GenAI adoption frameworks including RAG architectures, prompt standards, and enterprise AI governance. A frequent keynote speaker and thought leader on enterprise AI, product operations, and AI trust, Ashish brings a practitioner’s perspective on moving organizations from AI experimentation to measurable operational impact. His recent talks have focused on intelligent product development lifecycles, enterprise AI governance, and scaling GenAI responsibly inside large organizations.

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25 February 2027 15:00 - 15:45

Panel | The AI rodeo: Who's holding the reins?

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.