Model Drift
The degradation of an AI model's performance over time as the real-world data it encounters diverges from the data it was trained on. Requires continuous monitoring and retraining to maintain quality.
Model drift occurs when the statistical properties of incoming data change compared to training data, causing AI performance to degrade. In customer service, this might happen as product offerings evolve, new terminology emerges, customer demographics shift, or business policies change. Without intervention, a model that initially performed excellently can gradually become less accurate and relevant.
Managing model drift requires continuous monitoring of AI performance metrics like accuracy, resolution rate, and customer satisfaction. When degradation is detected, organizations must refresh training data, retrain models with recent examples, or update AI knowledge bases to reflect current reality. Sophisticated AI platforms automate drift detection and retraining cycles, ensuring sustained high performance without manual intervention.
The challenge of model drift is particularly acute for AI deployed in dynamic environments. Customer service is inherently evolving—new products launch, policies update, seasonal patterns shift, and customer expectations change. Leading AI platforms address drift through continuous learning systems that adapt in real-time, active learning mechanisms that flag uncertain scenarios for expert review, and feedback loops that capture outcomes to improve future performance.