Auditability (AI systems) is the capability to reliably reconstruct how an AI-assisted decision or output was generated, including what data was used, which model and configuration were active, and what steps or rules influenced the result. In a contact center, this typically means keeping time-stamped records that link a call, transcript, prompts, model version, and any downstream actions or recommendations.
Operationally, auditability matters because supervisors and compliance teams need to verify why an agent was guided a certain way, why a call was flagged, or why a customer outcome changed. Without audit trails, it is hard to investigate disputes, correct errors, demonstrate adherence to policies, or prove that changes in performance are tied to specific model updates or process changes.
Good auditability supports consistent QA, faster root-cause analysis, and safer change management by making AI behavior observable over time. It also helps meet regulatory and internal requirements for recordkeeping, explainability, and accountability when AI influences customer interactions.
