"Pilots lie." ​Chris Talbot isn't knocking proof-of-value projects. He still believes in them. But he's blunt about why they create a false sense of readiness. Pilot units represent the hospital at its best: engaged staff, freshly configured devices, vendor support, and extra IT attention. Strip that support away, put a float nurse on the floor at 3 a.m. with half the devices dead off the charging rack, and that's the real test. As Chris puts it, "What breaks first is almost never the software. It's that consistency underneath." ​When Gregg Malkary brought Chris Talbot (SMG3 Partners), Ron Remy (Mobile Heartbeat), and Angus Douglas (GE HealthCare) together to discuss what happens after AI identifies a problem in the hospital, the conversation quickly moved beyond the technology itself and into something harder: who owns the next step, how work gets coordinated across the enterprise, and what happens when no one acts in time. ​Ron Remy points out that even an urgent clinical message is still "one of a hundred signals that the recipient receives."Without context or prioritization, a stalled discharge competes for attention alongside every other notification arriving in the same inbox. More alerts and more data don't automatically produce better decisions. Sometimes they simply create more noise. ​Angus Douglas frames the stakes clearly. Sending a bad alert can be just as damaging as missing a good one, "because once you do that and staff stop trusting something, you really lose that adoption momentum." AI is already helping health systems stratify emergency department admissions and identify discharge opportunities, but, as Angus notes, "I don't think they're flipping a switch at all yet." Clinical judgment still matters, and trust remains difficult to earn back once it's lost. ​The conversation also explores the execution-layer realities that AI can't solve on its own: device fleets where 15 to 20% are dead, missing, or misconfigured on any given day; the difficulty of getting data out of enterprise clinical systems and into the workflows where action happens; and why, as Ron argues, no deployment succeeds without sustained investment in change management. ​This episode asks a practical question that extends well beyond AI: what does it take to move from a successful pilot to a capability an entire health system can depend on? Communication, workflow ownership, clinician trust, and organizational discipline all prove to be just as important as the intelligence of the technology itself.