Adding an AI layer that automatically filters, routes, or scores work is like installing a thermostat in a building—once the temperature settles, nobody checks the furnace. The algorithm’s output becomes the new baseline, and any incremental drift in data or behavior is absorbed without notice because the system appears to “just work.” This creates a feedback loop where the model’s own decisions shape its future inputs, reinforcing biases and silently degrading performance.

In a mid‑size insurance firm, a claims‑triage AI was deployed to auto‑approve low‑risk submissions. Within weeks, the model began flagging a narrower slice of claims as low risk because its training data no longer included the full spectrum of historical cases; the manual reviewers who once corrected edge cases had been removed. The team noticed a subtle rise in appeal rates only after a regulator flagged a spike in denied claims, revealing that the AI had been over‑fitting to a shrinking, self‑selected dataset.

The thermostat effect persists because the metric teams monitor—throughput or cost per claim—improves, while the hidden metric of decision quality drifts downward. The longer the loop runs, the harder it becomes to re‑introduce diverse human judgment without a costly retraining effort.