People.ai x Red Hat Break the Illusion: AI Didn’t Miss, Your Inputs Did (March 19)
Revenue teams are running harder than ever, but the signal they depend on keeps slipping through their hands. Dashboards light up, pipelines swell, activity logs stack to the ceiling, and still the forecast walks into the room with a limp. Not because people are missing effort, but because the system underneath that effort cannot hold a clean line of truth. Data arrives late, fractured, dressed like certainty but missing the one thing operators actually need, context. So leaders toggle, sellers guess, and the machine that promised clarity starts sounding like a group chat with no owner.
That is the tension sitting underneath People.ai’s upcoming session, “Stop Toggling, Start Selling: How AI Actually Works for Revenue Teams,” landing March 19, 2026 in a fully virtual room built for operators who are done pretending the tooling is the strategy. The premise is blunt and earned. Most AI fails because it feeds on incomplete data. Not bad models. Not bad intentions. Incomplete inputs. And when the inputs are broken, the outputs just get louder, not smarter.
The room pulls in the people who carry the number when the story stops working. CROs, heads of Revenue Operations, leaders responsible for sales tools and acceleration. The ones who have to defend the forecast in real time, not in theory. At the center is Richard Harris, a name that tends to show up when sales needs to get honest with itself. Richard Harris is not there to decorate the idea of AI. Richard Harris is there to interrogate it, to walk through what actually has to be true for AI to help a revenue team instead of shadowboxing with one.
Alongside him, Jamie Craig Gainey brings the enterprise weight of Red Hat into the frame, not as a logo but as a live system. Jamie Craig Gainey is hosting and unpacking how Red Hat, in partnership with People.ai, is building what she calls a seamless, agentic experience across a global field sales organization. Not a pilot. Not a slide. A working model across thousands of sellers where AI is expected to act, not just observe.
People.ai sits underneath it all with a point of view that cuts through the noise. If the data is incomplete, the AI is compromised. If the AI is compromised, the decisions are theater. So the work is not adding more tools. It is making the data whole enough that the answers can carry weight. What starts to emerge is less about dashboards and more about trust, less about activity capture and more about revenue answers that hold up when the room gets quiet and someone asks, why this deal, why now.
There is a shift happening here that does not announce itself loudly. It shows up in how leaders start measuring truth inside their systems, how sellers spend less time narrating work and more time moving it, how AI stops being a mirror and starts becoming an operator. The teams that lock this in will not look faster at first glance. They will look certain. And in this market, certainty is the edge nobody can fake for long.