Dr. Friedman is a physician turned product manager with 20+ years building enterprise software and leading digital transformation. He writes about the intersection of technology, human behavior, and healthcare, where solutions directly impact lives.
Your AI worked in the lab. Your users bypassed it in the real world. The gap isn’t the model, it’s the environment. Without the right incentives, workflows, and accountability, even the best-designed AI gets reduced to output generation. The system around it determines everything.
The physician was the gatekeeper. The hospital was the hub. AI changed both. Now Amazon, Google, and OpenAI are racing to own what comes next. The patient relationship is the prize, and the bidding has started.
Patients aren't waiting for the healthcare system to catch up. They have wearables, direct-access labs, referral-free MRIs, and AI interpreting all of it. The parallel system is already running."
She arrives with a plan her AI already helped her build. The physician now has two choices: become a trusted continuum who adds what AI cannot, or become a friction point blocking a plan she already made. Only one of those sustains the relationship.
Enterprise AI has a structural catch-22: context lives where you cannot run agents, and compute lives where context does not exist. Move the data and you lose the meaning. That gap is why most deployments produce outputs that are technically impressive and operationally thin.
1 in 3 Americans uses AI for health advice. The heaviest users are uninsured, low-income adults who cannot access a doctor. The AI they are relying on was built on data from patients who look nothing like them. That is not an equity talking point. It is a product failure.
A developer, an accountant, a graphic designer, a film director, a composer, and a product manager all use the exact same interface to communicate with AI: a text box. That has never been true of mature technology. It will not stay true for this one.
Agentic AI is not a smarter chatbot. It’s a system that can hold the full clinical picture across fragmented data, coordinate actions, and escalate when needed. But its value depends on data, interfaces, and accountability designed right from the start.
New peer-reviewed research named 4 critical challenges blocking healthcare AI deployment. The research got the problems right. But three of those four share one root cause nobody is building toward. One doesn't. Here's the response from the real world.
AI doesn't hallucinate randomly. It hallucinates in patterns. A new paper from MIT, Harvard, and Google maps five of them, each with a distinct failure mode and a distinct way of passing review. The taxonomy was built for medicine. The failure modes are universal.
OpenAI had one of the best models in the world. Walmart still fired it. Not because the model was weak. Because it had no idea what it was working with.
The people most affected by your AI product are almost never in the room when you build it. They don't appear in your analytics. They weren't in the meeting when the roadmap was approved. They will never give you feedback. You owe them something anyway