The speed of AI development is unmatched. As the technology continues to offer more advanced use cases, healthcare leaders face difficult questions: How do you know that an AI capability is ready for use? And how do you ensure it remains reliable as your organization and the broader healthcare ecosystem change?
Answering these questions requires a new level of scrutiny. Healthcare organizations already govern technology through patient safety; software development; and security, regulatory, and contractual controls. These controls remain essential, but AI can behave in ways traditional software doesn’t: the same input can produce different outputs, and performance can shift even when no code has been changed. A capability may perform differently when introduced to a new specialty, patient population, or geography. The data feeding the AI can change. Payer rules can change.
AI governance gives leaders a way to manage that variability: deciding what good performance looks like before release, setting clear thresholds for what can ship, and continuing to watch performance afterward. That’s the difference between documenting how an organization intends to use AI responsibly and building governance into how AI is developed, released, and managed.
Begin by outlining areas of impact
Governance in practice starts with deciding how you will judge an AI capability before you even begin to build. Consider evaluation across the following three areas:
- Validity: Does the feature perform its intended task accurately, reliably, and consistently?
- Bias: Does performance differ across demographic or clinical populations?
- Data quality: Is the data used to develop, validate, and monitor the feature complete, representative, and appropriate for its intended use?
How teams measure those areas depends on the capability. A predictive model’s output can be evaluated against what happened, while an AI-generated clinical summary has to be judged against a broader sample and requires an expert review against pre-defined criteria. Setting metrics and minimum thresholds before formal evaluation begins reduces the risk of choosing or changing a measure because the results look favorable.
Establish a release threshold
Once teams align on what they’re measuring, they need evidence that the capability meets the standards they set, and a clear rule for what happens with the results.
For an AI-powered documentation solution, that might mean assessing whether draft notes are accurate, complete, internally consistent, and grounded in the patient encounter, with clinician and patient-safety experts defining those standards and reviewing performance across care settings.
But measurement alone isn’t governance. The results need to inform a decision: required evaluations complete, material findings investigated, and performance requirements satisfied before final approval. That turns an evaluation metric into a release gate. The question shifts from “How did the AI perform?” to “Did it perform well enough to move forward?”
Continuing evaluating, even after deployment
Meeting a release threshold tells you how an AI capability performed at one point in time. It doesn’t tell you how it will perform indefinitely.
Post-deployment monitoring helps reveal whether a capability stays within approved performance ranges as real-world use evolves – and results must lead to action. A missed threshold should trigger a documented review. Depending on the issue, teams may increase monitoring, restrict deployment, or roll back the capability.
Human oversight is an important part of that picture, but it doesn’t lower the bar for the AI itself. Requiring a clinician to review an AI-generated output doesn’t remove the need to evaluate its quality. If clinicians routinely have to catch errors or substantially correct the output, the AI has simply shifted more work, and risk, to the people reviewing it.
This step closes the governance loop: set expectations, measure against them, use the results to make release decisions, and continue checking.
Five essential AI governance questions
Leaders don’t need every detail of a vendor’s internal governance process, but they should be able to ask how that process shapes real decisions about the AI they’re considering. The strongest vendors can respond to these questions with clear answers.
- What AI-specific criteria do you establish before developing and releasing a capability?
- How do you measure validity, bias, and data quality before broad release, and what happens if the capability falls short?
- How do you monitor performance after deployment, and what triggers a closer review?
- How do you account for changes in real-world use, such as new specialties, populations, payer rules, or workflows?
- Who is accountable for AI governance throughout the product lifecycle, and where do clinicians and other experts fit in?
What it means to be governed by design
The clearest evidence of AI governance isn’t a policy document. It’s the decisions the governance process produces: which measures are set, what evidence is required, when a capability is ready for release, and how teams respond when performance changes.
For organizations evaluating AI partners, those mechanisms show how a vendor turns its commitments around responsible AI into decisions that shape the technology clinicians and patients ultimately experience.
