Clinical AI Governance
At month five post-go-live, the performance data is mixed, the physician steward has been logging issues for three months, and the sunk cost framing has made its way into every leadership meeting. The organizations that exit bad AI tool relationships cleanly are the ones that wrote the exit criteria before this moment arrived.
At some point in the Q2 leadership meeting, someone says it:
"We've invested eighteen months in this. If we exit now, we lose all of that."
That sentence is not a financial analysis. It is the sunk cost trap, and it is the most reliable signal that the exit decision is being shaped by the vendor instead of by your governance structure.
Eighteen months of investment is not recoverable. It was spent whether you continue or exit. The actual decision is whether you commit months six through thirty-six to a tool that is not performing. The sunk cost framing collapses that distinction, and most governance conversations do not re-frame fast enough.
None of these are automatic deal-breakers in isolation. Together, they mean your governance documentation is doing less work than the vendor's contract language.
New features replace the original commitment. The metrics that mattered in your evaluation get quietly retired. Every performance question gets answered with a future capability. The goalpost moved after the tool went live, and nobody has formally acknowledged that it moved.
Getting your clinical data out requires the vendor's active cooperation. The export path is manual, delayed, or requires a support ticket. This is not a technical constraint: it is a leverage structure, and vendors build it intentionally for exactly this moment.
No one at the vendor holds the institutional context of your implementation. Every escalation restarts from baseline. The relationship that closed the deal does not exist anymore, and the new team's incentive is retention, not resolution.
The SLA covers uptime. It does not cover clinical accuracy, adoption rates, or outcomes. There is nothing in the contract that defines failure in terms your organization actually cares about. Without it, the exit conversation has no forcing function and no timeline.
The pilot showed one set of results. The current deployment is measured against a different framework. Nobody negotiated that shift: it happened incrementally. The original baseline is not in any document anyone can find, which means the vendor's roadmap has replaced your evaluation criteria as the de facto standard.
The health systems that exit bad AI tool relationships cleanly are not the ones that made smarter evaluation decisions at the start. They are the ones that defined failure criteria before the tool went live.
Pre-implementation exit criteria are not complicated. They look like this:
Those three sentences do not require a special governance structure. They require someone to write them before the sunk cost calculus is available.
The organizations that skip this step are the ones holding the exit conversation at month eighteen while a vendor sells them on one more quarter of data.
In most contexts, a sunk cost conversation is about financial resources. In clinical AI, it is also about clinical risk.
Every day a clinical AI tool that is not performing as designed stays live in your environment, you are accumulating risk: not necessarily immediate liability, but a slow drift away from the evidence standard you held the tool to during evaluation. The tool is shaping clinical decisions. The gap between its current performance and its pilot benchmark is not visible in the weekly dashboard. It shows up in outcomes data six to nine months later.
Exit criteria that include clinical performance thresholds, not just financial ones, close that gap before it costs you something that cannot be recovered with a vendor roadmap conversation.
In the implementation kick-off meeting. Not at the first governance review. Not when the data starts showing problems.
Before the tool goes live, when the vendor is cooperative, the clinical team is engaged, and no one has a personal investment in proving the deployment succeeded. That is when the exit criteria cost nothing to be specific about.
The answer to "Who owns the exit decision?" belongs in the same document. In most health systems, exiting a clinical AI tool requires consensus from the CMO, clinical informatics, the vendor relationship owner, and often the CFO. Without a pre-negotiated decision structure, that consensus takes months. The vendor stays in the gap while you build it.
Define the decision authority before launch. Write the transition protocol at the same time. An exit that requires a two-month operational crisis to execute is one the sunk cost argument will win.
If your organization implemented a clinical AI tool in Q1, you are now at month four or five: the point in the deployment cycle where the early adoption energy has worn off and the performance picture is starting to clarify. This is when the sunk cost framing first becomes available to everyone in the room.
Two or more signals from the list above means the exit criteria conversation has a deadline. The longer the governance gap stays open, the more organizational capital the vendor's framing absorbs.
The advisory work in this zone: portfolio audit against current performance benchmarks, exit criteria documentation, vendor conversation scripts, and the executive briefing that makes the case at the board level.
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