Human-in-the-loop review is a process where supervisors, QA analysts, or compliance staff review AI-generated items from customer calls—such as transcripts, summaries, sentiment, or detected compliance events—and confirm, edit, or reject them before they drive actions or become part of the official record.
Operationally, it matters because automated analysis can miss context, mishear audio, or misclassify regulated statements. Adding a required human checkpoint for high-risk cases helps prevent incorrect coaching, improper customer follow-up, and incomplete or inaccurate compliance documentation.
Teams typically apply it through rules that route certain calls or flags for review (for example, low-confidence transcription, mentions of payment details, or potential disclosures). The reviewed outcomes can also be used to refine guidelines and improve future model performance and QA consistency.
