Retail learned a hard lesson over the past decade. The companies that won were not always the ones with the most data or the flashiest algorithms. They were the ones who could trust their own numbers, explain how a recommendation was made, and stand behind it when a regulator or a customer asked.
Healthcare is now living through the same reckoning, and the parallels are worth a retail leader’s attention.
The setup is familiar
In retail, the temptation was always to optimize for the short-term number: push the sale, chase the conversion, let the model run. The brands that did this without guardrails ended up with messy data, decisions no one could explain, and trust problems that cost more to fix than they ever saved.
In healthcare, the equivalent number is the risk score. Private insurers covering older Americans get paid more for sicker patients, so there has always been pressure to find and report every possible condition. For years, the tools built for this job were optimized to do one thing: add more.
The reckoning arrives
In 2026, federal auditors reviewed a set of these insurers and found that 80 to 91 percent of the diagnoses they sampled were not fully supported by the patient record. A federal advisory panel told Congress the broader pattern adds up to roughly 22 billion dollars in excess payments a year. One major insurer paid 117.7 million dollars to settle claims tied to a system that only ever added conditions and never removed the ones that did not hold up.
Any retail executive who lived through an audit of their own data practices will recognize the shape of this. A tool optimized for one direction, with no check on quality, eventually meets someone asking to see the receipts.
The shift in what buyers want
The interesting part is how fast the buying criteria changed. Healthcare organizations used to evaluate risk adjustment software on a single question: how much additional revenue will this find? Now the first questions are different. Can it show why it suggested a diagnosis? Can it remove a code that is no longer valid, not just add one? Will the evidence trail survive an audit?
This is the same maturity curve retail walked. The market stopped rewarding tools that simply maximized a number and started rewarding tools that could be trusted, explained, and defended. Governance moved from a nice-to-have to the first item on the checklist.
Why explainability becomes the product
The healthcare tools gaining ground now are built on what is called Neuro-Symbolic AI, an approach that pairs pattern recognition with explicit rules. In plain terms, it does not just guess. It links every suggestion to a specific piece of evidence and shows the logic, so a human can check it and an auditor can follow it.
Retail leaders already know why this matters. Once a model touches money and trust, opaque automation becomes a liability, not an asset. The winning systems are the ones where a person stays in the loop and every decision has a paper trail.
The lesson, stated plainly
Every data-heavy industry eventually reaches the same fork. One path optimizes a number until someone forces a reckoning. The other builds for accuracy and transparency from the start, and treats the audit not as a threat but as a test it is ready to pass.
Retail has mostly chosen the second path, the hard way. Healthcare is choosing it now, under real regulatory pressure. For any leader watching from another sector, the takeaway is the same: the tools worth buying are the ones that can show their work.
