Abstract
Call monitoring at contact centers is still very inefficient and subjective. Call resolution involves post-hoc analysis leading to latency and delay in the identification of urgent issues. We propose a comprehensive novel generative AI (Gen-AI) system, integrated with our custom-built sentiment Analysis and transformer-based topic modeling components, that automates call summarization and topic labeling using a large language model (LLM) and other transformer-based model architecture. The system enhances operations by converting call audio to text transcript, identifying call dispositions, generating structured summaries in a SOAP (Subjective, Objective, Assessment, Plan) format, using in-house custom LLM to classify calls as satisfied, neutral, or dissatisfied, and assigning thematic labels using the integrated transformer-based models for detailed categorization. In addition to the conceptual building blocks of the proposed system, we also built and described a functional software prototype that demonstrates these capabilities through a user-friendly UI that allows for the searching and filtering of calls based on various criteria. This Gen-AI approach can potentially reduce the administrative workload for agents, improve documentation accuracy, and provide actionable insights for leadership, thereby enhancing auditing processes and member satisfaction. Our proposed next-gen Gen-AI-based system has the potential to transform contact centers by improving operational efficiency and service quality, with further validation planned to assess its effectiveness compared to current practices.
Recommended Citation
Patel, Rushabh; Gupta, Ashish; Qin, Xiao; and Brickner, Carlin, "A Generative AI and NLP-Based Research Prototype for Call Resolutions and Automation: The Case of a Healthcare Contact Center" (2024). Proceedings of the 2024 Pre-ICIS SIGDSA Symposium. 16.
https://aisel.aisnet.org/sigdsa2024/16