AI Model Drift Monitoring detects when production models generate less accurate predictions due to shifts in input data distributions, changes in underlying relationships, or evolving target populations. Organizations implement automated monitoring of prediction accuracy, data distribution statistics, and model outputs against validation datasets. Detection triggers model retraining, recalibration, or decommissioning decisions. A credit card fraud detection model experienced performance drift when pandemic-driven shopping pattern changes caused legitimate transactions to appear fraudulent; monitoring systems detected rising false positive rates, prompting emergency retraining with recent transaction data to restore accuracy.