Closing the AI Governance Gap: A Validation Playbook

Healthcare has an AI adoption problem in reverse. The tools are everywhere; the validation is not. In a KLAS and UPMC study, ninety-three percent of health systems reported using third-party AI, but only forty-four percent had a dedicated environment to test it. That gap, in my experience, is where trust goes to die: a model that no one validated locally will eventually make a confident, wrong call on a real patient.

Closing the gap is not a policy memo. It is a function you stand up, with steps, owners, and tests that run on every change. This is the playbook I use.

The gap: everyone deploys, few validate

I start by looking honestly at the numbers, because they describe most organizations, including yours. Deployment has outrun the infrastructure to govern it, and testing before purchase is not the same as validating on your own patients.

Ninety-two percent say they test third-party tools before deployment, yet fewer than half have a dedicated place to do it. That combination usually means testing is a slide deck from the vendor, not a run on local data. The fix I push is to make validation a capability you own.

Stand up a validation function

A validation function is five repeatable steps. None of them are exotic. The discipline is doing them for every model, including the ones that arrived switched on inside a product you already bought.

The step I watch teams skip is the last one. If no one is allowed to turn a model off, you do not have governance, you have hope. Name the owner and write the kill criteria before go-live, while everyone is still calm.

Turn model cards into CI/CD test criteria

A model card is only paper until it becomes a test. The CHAI-style fields, intended use, performance, known limits, and subgroup behavior, map cleanly onto test cases, and once they are tests they run on every change instead of once at purchase. That is how I keep a model honest after the launch demo.

This is what turns governance from an annual review into continuous assurance. A prompt change, a model upgrade, or a data shift all trip the same tests, and a failure blocks the release the way a failed unit test blocks a merge.

What to monitor after go-live

Validation does not end at go-live; it changes shape. The job becomes watching for the slow failures: quiet accuracy drift, rising override rates, and inputs that no longer look like the training data. Each needs a metric, a cadence, and an owner.

I package these steps and tests as a checklist my team runs every release, the same way a pilot runs a preflight. Wire it into evaluation and guardrails and human-in-the-loop review, and the gap between deploying AI and trusting it starts to close. A downloadable validation checklist can hang off this page as a companion resource.