Data products were built on a quiet assumption: the designer could anticipate the questions. Agents break that assumption without breaking the discipline. What survives is not the shape we prepared but the honesty of the contract underneath it. Five questions for every data product owner.
LLMs are getting faster, cheaper, and better every week. The comparison charts prove it. They also prove nothing about who wins. I've watched six technology wars end. The best product lost every time. The winner owned the right layer of the stack.
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.
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.
Everyone talks about governance. Nobody agrees on what it means. Data governance, AI governance, master data governance: they're not separate programs. They're one spectrum. And most enterprises already have 70% of what they need. They just can't see how the pieces connect.
Data products are the missing layer in healthcare AI. There is no harmonization at scale without them. Instead of fragmented, untrusted datasets, data products create governed, reusable, semantically clear assets with defined ownership and value. That’s how AI moves from pilots to production.
The healthcare data integration problem is 20 years old and not going away. So why are we still building AI that assumes clean data? A case for designing AI that works in the real world, not the one we keep promising to build.
Healthcare AI fails not from lack of data, but from fragmentation. Each system sees a piece of data, no one sees the patient. Physicians reason in connected patterns, "Knowledge Graphs" formalize that reasoning, creating a unified, governed layer that lets AI see meaning, not noise.
Healthcare is rapidly adopting Customer Data Platforms to unify patient data and personalize engagement. Used as infrastructure, CDPs improve identity resolution, consent management, and outreach. But without clear governance, they risk optimizing for engagement over clinical need.
The narrative says AI pilots fail in healthcare because the industry is slow and resistant. It is backwards. No industry changes faster than healthcare when the change actually works. The problem is not resistance. It is that most AI pilots have not earned the right to change it.
We can’t talk seriously about AI in healthcare without understanding the data infrastructure behind it. From HL7 v2 to FHIR, SNOMED, and MCP, I break down how clinical data actually moves and why every healthcare leader needs to grasp this before approving the next AI pilot.