A model feedback loop is an operational cycle where an AI model’s outputs (such as intent, sentiment, compliance flags, or next-best-action suggestions) are compared against real call outcomes and human-labeled reviews, then used to retrain or recalibrate the model. It includes capturing errors, adding corrected labels, and updating the model so future predictions better match how the contact center actually works.
It matters because call drivers, policies, scripts, and customer behavior change, and models drift if they are not regularly corrected. A working feedback loop reduces recurring misclassifications, improves the reliability of alerts and dashboards, and helps leaders trust that coaching, QA sampling, and operational decisions are based on current, accurate signals.
It also creates accountability: teams can track which error types are increasing, how quickly they are fixed, and whether model changes improve downstream metrics like handle time, transfers, repeat contacts, and compliance exceptions.
