A model that performed well at launch doesn’t stay that way automatically. Customer language shifts, product lines change, new edge cases appear, and an underlying API-based model can be updated by its provider without notice. Any of these can quietly widen the gap between what the model was tuned to handle and what it’s now actually seeing, and accuracy degrades without any code changing.
The danger is that drift is usually invisible until someone notices a pattern of bad outputs, by which point it’s often been happening for weeks. Unlike a system outage, there’s no alert that fires; the system keeps running and keeps producing answers, just gradually worse ones.
Catching it requires deliberate monitoring: tracking output quality against a real baseline over time, not just confirming the system is technically online. This is one of the concrete reasons “we deployed it” and “we’re governing it” are different states, and why ongoing measurement, not a one-time launch review, is the actual safeguard.