Call centers handle thousands of calls daily, making effective call quality monitoring essential. This study explores the potential of large language models (LLM) to improve these processes by supporting the human evaluator. We investigate how LLM can help identify human errors in quality monitoring forms, focusing on their effectiveness in flagging potential mistakes. Our research employs a three-step approach: 1) comparing the model’s assessments with primary human evaluator decisions, 2) validating discrepancies through blind secondary human reviews, and 3) analyzing patterns in cases where LLM flag potential human errors. This methodology aims to uncover both the advantages and limitations of integrating LLM into quality monitoring processes. Specifically, we examine when the model identifies human oversight and when it underperforms. Our findings provide insights into the advantages and disadvantages of LLM as supportive tools in call quality monitoring. They highlight the potential of human-AI collaboration in quality monitoring to create a more consistent and reliable evaluation process.