An explainability threshold is a set minimum standard for how clearly an AI output (such as a compliance risk flag, QA score, or coaching recommendation) must be justified before it is acted on in a contact center. It specifies what supporting evidence needs to be available to a supervisor, QA analyst, or auditorfor example, the exact call moments, transcript excerpts, and the rule or policy the model is applying.
Operationally, it matters because AI outputs often drive high-impact actions like coaching, score adjustments, escalations, or disciplinary steps. A defined threshold reduces disputes and rework by ensuring teams can quickly verify why something was flagged and whether it is correct.
It also supports compliance and audit readiness by making decisions traceable and consistent across agents and teams. When the threshold is not met, the output can be treated as informational only, routed for human review, or excluded from formal scoring.
