Hospital teams are getting better at launching AI pilots, but many programs fail between demo and deployment. The root cause is usually not model quality. It is hand-off failure: unclear model ownership, weak monitoring, and no escalation path when performance drifts.
A practical governance baseline includes model cards for every live model, predefined rollback criteria, and operational dashboards that combine latency, error rates, and clinical override events. Without this, clinical trust erodes quickly.
Hospitals that treat MLOps governance as a patient-safety control, not just engineering hygiene, are the ones scaling AI beyond pilots.
