@inproceedings{laskar-etal-2023-ai,
    title = "{AI} Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching",
    author = "Laskar, Md Tahmid Rahman  and
      Chen, Cheng  and
      Fu, Xue-yong  and
      Azizi, Mahsa  and
      Bhushan, Shashi  and
      Corston-oliver, Simon",
    editor = "Sitaram, Sunayana  and
      Beigman Klebanov, Beata  and
      Williams, Jason D",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-industry.57",
    doi = "10.18653/v1/2023.acl-industry.57",
    pages = "599--607",
    abstract = "In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts, AI can quickly determine which calls are most relevant for coaching purposes, and provide relevant feedback and insights to the contact center manager or supervisor. In this paper, we present {``}AI Coach Assis{''}, which leverages the pre-trained transformer-based language models to determine whether a given call is coachable or not based on the quality assurance (QA) queries/questions asked by the contact center managers or supervisors. The system was trained and evaluated on a large dataset collected from real-world contact centers and provides an efficient and effective way to determine which calls are most relevant for coaching purposes. Extensive experimental evaluation demonstrates the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents.",
}

In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts, AI can quickly determine which calls are most relevant for coaching purposes, and provide relevant feedback and insights to the contact center manager or supervisor. In this paper, we present {``}AI Coach Assis{''}, which leverages the pre-trained transformer-based language models to determine whether a given call is coachable or not based on the quality assurance (QA) queries/questions asked by the contact center managers or supervisors. The system was trained and evaluated on a large dataset collected from real-world contact centers and provides an efficient and effective way to determine which calls are most relevant for coaching purposes. Extensive experimental evaluation demonstrates the potential of AI Coach Assist to improve the coaching process, resulting in enhancing the performance of contact center agents.
Laskar, Md Tahmid Rahman Chen, Cheng Fu, Xue-yong Azizi, Mahsa Bhushan, Shashi Corston-oliver, Simon
AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching