What happens when AI stops asking for permission and starts making the buy? We dig into the turning point for retail as agentic commerce moves from recommendations to execution, and why the real challenge isn’t a shiny new model but the rules, data, and accountability that sit beneath it. As AI agents optimize for outcomes, speed, certainty, and trust, the battlefield shifts. Brand storytelling still matters, but only when it’s structured and machine-readable. Promotions still matter, but only when they’re accurate in real time. Loyalty still matters, but only when algorithms can quantify it consistently. Persuasion gives way to precision, and ambiguity becomes a liability.

We walk through the leadership choices that define this next phase: who sets spending limits, approves substitutions, and owns liability when an agent chooses perfectly on cost but poorly on margin or brand? These aren’t IT decisions; they are governance decisions. Decades of system design aimed to support human judgment. Now we must define when and how systems replace it, with clear guardrails, auditable logic, and incentives that align short-term efficiency with long-term value. If the foundations are messy, fragmented pricing, inconsistent inventory, and siloed ownership, autonomy will amplify the cracks.

Leaders preparing to win are treating APIs as core infrastructure, making product data machine-first, investing in observability to explain agent decisions, and staging tough policy conversations before autonomy scales. The pivotal question is no longer whether AI can be trusted to decide; those decisions are already happening. The question is whether they’ll happen within your systems or around them. Join us as we break down the practical steps to move from hype to readiness and build a trustworthy contract with machines. If this conversation sparks ideas, subscribe, share with a colleague, and leave a review with the first policy you’d put in place.