AI-Powered Troubleshooting Co-pilots: Slash Resolution Time and Boost CSAT
DOI:
https://doi.org/10.32628/CSEIT25113365Keywords:
AI Troubleshooting, Support Co-pilot, LLM, NLP, Resolution Time, CSAT, Data PipelinesAbstract
AI co-pilots utilizing Large Language Models (LLMs) and Natural Language Processing (NLP) significantly enhance technical support by analyzing structured (tickets, CSAT) and unstructured (logs, chats) data in real-time to suggest solutions. This article details the data engineering pipelines and AI model integration required, demonstrating through e-commerce and finance case studies reductions in Mean Time to Resolution (MTTR) (35-60%), increases in First Contact Resolution (FCR) (15-25%), and corresponding Customer Satisfaction (CSAT) improvements (10-20 points). The direct link between accelerated, accurate resolutions and higher CSAT is established.
Downloads
References
Amazon AWS. (2023). Implementing AI-powered support agents with Amazon Connect and Amazon Lex. AWS Whitepaper. Retrieved from https://docs.aws.amazon.com/whitepapers/latest/ai-powered-support-agents/welcome.html
Brown, A., & Davis, K. (2024). The human-AI partnership in technical support: Augmenting expertise, not replacing it. Journal of Service Management, 35(1), 112-130. https://doi.org/10.1108/JSM-05-2023-0187
Chen, L., Wang, Y., & Liu, Q. (2024). Large Language Models for Real-Time Technical Support: Architecture, Evaluation, and Challenges. Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS).
Forrester. (2024). The Total Economic Impact™ Of AI-Powered Customer Service Solutions. Forrester Consulting Study commissioned by [Vendor Name Redacted].
Garcia, M. (2023). Data Engineering for AI: Building Reliable Data Pipelines for Machine Learning. O'Reilly Media.
Gartner. (2023). Market Guide for AI in Customer Service. Gartner Report ID G00775823.
IBM. (2023). IBM watsonx Assistant: Product Documentation - Troubleshooting Co-pilot Features. Retrieved from https://www.ibm.com/docs/en/watsonx-assistant
Lee, S., & Patel, R. (2024). Quantifying the Impact of AI Assistants on Contact Center Performance and Customer Satisfaction. International Journal of Research in Marketing, 41(2), 345-362. https://doi.org/10.1016/j.ijresmar.2023.11.005
Miller, J. (2024). Unstructured Data: The Fuel for Next-Gen AI Support. Communications of the ACM, 67(3), 54-61. https://doi.org/10.1145/3631310
Smith, T., & Johnson, P. (2023). The rising complexity of technical support: Challenges and opportunities in the digital age. Journal of Technical Support Management, 18(4), 221-238.
Wang, H., & Brown, E. (2024). Explainable AI (XAI) for Troubleshooting Co-pilots: Building Trust in Real-Time Support. IEEE Transactions on Human-Machine Systems, 54(1), 78-89. https://doi.org/10.1109/THMS.2023.3325678
Zhang, Y., Gupta, A., & Kim, S. (2023). Leveraging Log Data and Language Models for Automated Incident Resolution. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23), 3214–3224. https://doi.org/10.1145/3580305.3599901
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.