After AI Go-Live, Who Owns Monitoring? Often No One

Health systems have become disciplined about vetting AI tools before deployment. What happens after go-live has proven far harder to standardize, according to Becker's Hospital Review.

The core problem is ownership. Once an AI model is live, responsibility for monitoring its performance often shifts without a clear designated holder. Models can drift as patient populations, workflows, and data change, but many contracts do not spell out who watches for that degradation, how often, or who acts when something breaks. The result is that the first sign of trouble frequently arrives as a frontline clinician complaint rather than a monitoring alert. As one framing puts it, AI is a living organism, not a static product that performs the same way on day 500 as on day one.

In practice, health systems should negotiate post-deployment terms up front: defined monitoring metrics, alert thresholds, retraining commitments, and clear accountability for both vendor and buyer. Treating AI oversight as ongoing operational work, not a one-time procurement checkbox, is the emerging expectation.