Recent research reveals that deploying Large Language Models (LLMs) for automated IT ticket routing without careful consideration of costs can significantly degrade operational efficiency. The study found that LLMs often exhibit overconfidence and poor calibration, leading to misrouting that incurs substantial expenses due to increased handling time and errors. To mitigate this, a "selective abstention router" architecture is recommended, using calibrated classifiers to defer tickets to human triage when the LLM's confidence is too low.
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