The activation playbook was written for knowledge workers with a laptop and a broadband connection. It does not translate cleanly to a warehouse operative with a handheld scanner, a field technician with a ruggedized tablet, or a store manager checking in for thirty seconds between shifts. Getting adoption in operational, industrial, and frontline B2B contexts requires a fundamentally different approach to onboarding, interface design, and what you even count as successful activation. This panel brings together operators who have built for those environments. Panelists will share the specific adaptations they made to their activation model for non-desk users: how they defined the first-value moment in a workflow context, how they designed onboarding for intermittent and interrupted use, how they measured adoption in environments where traditional analytics instrumentation does not work cleanly, and what the support model needs to look like when your user cannot easily stop and read a help article. Expect a practically grounded conversation with examples from manufacturing, logistics, retail, and field services.

Product managers are starting to hand real work to AI agents. They draft requirements, run market analysis, build prototypes, and synthesize customer feedback. The demos look effortless. The production reality is harder, and most teams learn that the hard way. This keynote is a practical guide to rebuilding PM workflows around agents that actually hold up. I will walk through which parts of the product job are ready to delegate today, which still need human judgment, and how to tell the difference. We will cover the best practices that separate a useful agent from a confidently wrong one. That includes scoping the task tightly, defining a quality bar before you build, keeping a human in the loop for consequential steps, and measuring whether the agent is saving time or just moving the work. You will leave with a clear framework for putting agents into your own workflow, a checklist of what to measure, and the common failure modes to avoid. The goal is simple. Do more of the work that needs a product manager, and less of the work that never did.

Piloting an AI feature is easy. Getting it past security review, a risk-averse legal team, and a compliance function that has never seen a model card before is where most enterprise AI rollouts stall. This panel brings together product leaders who have moved AI capabilities from demo to default inside large, regulated organizations, and asks what actually changed procurement's mind. Expect a candid look at the objections that keep coming up (data residency, model transparency, "who is accountable when it's wrong"), and the specific tactics, documentation, and internal allies that got AI features signed off rather than shelved. Attendee takeaways: - The real objections: What compliance, legal, and security teams are actually pushing back on when an AI feature reaches procurement, and why "it works in the demo" isn't enough. - Building the paper trail: What documentation, audit evidence, and model transparency buyers now expect before they'll sign off. - Internal champions: How to identify and equip the person inside the buyer's org who will fight for your feature when you're not in the room.