Most teams don’t wait for perfect ground truth. They rely on early signals like data drift, input distribution changes, and proxy business metrics to flag risk. When ground truth arrives late, it’s used for periodic back-testing, recalibration, and retraining rather than real-time correction.

In practice, teams combine delayed labels with monitoring, human review for edge cases, and clear retraining triggers. The goal is not to eliminate drift entirely, but to detect it early and control its impact until reliable feedback becomes available.