AI Quality Assurance Scoring of Support Conversations

Traditional contact-center QA often reviews a sample of calls or chats. AI-based quality assurance can transcribe or inspect interactions, apply a rubric, and flag conversations for human review. Some vendors describe automated coverage of every interaction; that is a product capability, not proof that every score is accurate or that every organization should use it for employment decisions. A model can consistently score the wrong thing at scale. The scorecard matters as much as the model. Criteria should be observable and tied to service goals: Did the agent verify identity using the approved process? Was the answer consistent with current policy? Did the agent explain the next step? Vague criteria such as “sounded positive” invite subjective judgments and may penalize different communication styles, accents, disability-related speech patterns, or emotionally difficult calls. Teams should document what counts as evidence and when a criterion is not applicable. Calibration means reviewers score the same conversations and compare interpretations. Zendesk’s QA documentation describes calibration as a way to align reviewers and make feedback more consistent. A practical program can compare human ratings with automated scores, review false positives and false negatives, and update examples when policy changes. This does not make the scorecard inherently fair; it makes disagreement visible. Use automated scoring first to find themes, not to impose discipline without context. Keep recordings and transcripts access-controlled, set retention periods, disclose monitoring as required, and let agents see the evidence and challenge an inaccurate score. Check results across languages, channels, issue difficulty and relevant employee groups. If a low score clusters around one policy or tool failure, repair the workflow. The goal is better service and useful coaching, with a person accountable for consequential decisions.