What it means
AI exception handling is the process for detecting, routing, reviewing, and resolving cases where an AI-enabled HR workflow cannot safely produce or apply an output. Exceptions can include low-confidence summaries, missing data, conflicting source records, policy conflicts, unusual candidate or employee records, restricted permissions, integration failures, or reviewer disagreement.
Why buyers should care
AI tools are most useful when they handle routine work and clearly identify the cases that need human attention. Without exception handling, a system may hide uncertainty, create incomplete records, send unsuitable communications, or move sensitive workflows forward without review. Buyers should expect the product to make uncertainty visible rather than treating every AI output as equally reliable.
Evaluation checks
Review how exceptions are detected, labeled, assigned, escalated, and closed. Check whether users can override, reject, annotate, or reopen an exception without losing audit history. Buyers should also ask how exceptions affect downstream systems such as ATS, HRIS, ticketing, analytics, or employee communication tools, and whether exception trends are reported to administrators.
This glossary entry is buyer-oriented guidance, not legal, compliance, or financial advice.