Healthcare AI job postings keep asking for unicorns: 14 years of experience in a field that is three years old, deep AI expertise, deep clinical expertise, deep product expertise. That person does not exist. And even if they did, they would not fix the real problem.
In healthcare, AI can already predict. The hard problem is reasoning, and reasoning depends entirely on context. The physician who takes a complete history, examines carefully, and aggregates everything from the EHR to the nursing home fax is not being thorough. They are building the product.
People don't buy a drill. They buy a hole. AI is the drill. Every major technology wave produces the same confusion. SQL became infrastructure. Java became infrastructure. Cloud became a checkbox. AI is doing the same thing. We are in the expensive middle of that arc right now.
Twenty years of customer interviews, workshops, and journey maps. Then agentic AI arrived, and every framework I trusted turned out to share one assumption I had stopped noticing: that the human is always smarter than the tool. Here's what breaks when that stops being true.
For 20 years, I translated between customers and engineers. Now I architect decision systems with AI. But here's the catch: you're designing for two customers: humans and their AI agents. Design for one, forget the other, and it fails.
MVP was meant to test ideas, not become architecture. Now, we’ve started stacking prototypes into production systems nobody designed as systems. When one layer fails, the whole structure drifts silently. In medicine, that’s not technical debt. It’s patient risk.
My first AI-assisted article was polished, accurate, and completely unrecognizable as mine. The problem wasn’t the model. It was that I never taught it how I think. If you want AI to sound like you, you have to externalize your reasoning first.
In healthcare AI, policy shifts now appear first in journals like JAMA and NEJM, then quietly become grant conditions and RFP requirements. Many tech teams miss this signal. The winners will be those who translate medical literature into architecture before it becomes mandatory.
I’m seeing a flood of self-proclaimed “AI experts” with little real depth. After decades building enterprise systems, I still hesitate to claim the title. In healthcare AI, experience matters more than hype. Projects fail from old debts and governance gaps, not lack of buzzwords.
The bottleneck in tech is no longer writing code, it’s deciding what to build. As AI collapses development time, value shifts upstream to product judgment. In healthcare especially, models work, but projects fail without real product thinking. The new question isn’t can we build it, but should we.
At 2am, every product manager runs the same simulation, an imaginary room of stakeholders. The problem? You built the room. AI can become the same echo chamber. There are five levels of working with AI, and only the higher ones truly challenge your thinking.
I ran a full design thinking workshop on a Sunday afternoon, no flights, no sticky notes, just nine AI agents configured to disagree. For under $10, they reframed the problem entirely.