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AI-Powered Troubleshooting Co-pilots: Slash Resolution Time and Boost CSAT

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  • Mukul Garg

Abstract

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.

Suggested Citation

  • Mukul Garg, 2024. "AI-Powered Troubleshooting Co-pilots: Slash Resolution Time and Boost CSAT," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(3), pages 807-810, June.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i3:id:1544
    DOI: 10.32628/CSEIT25113365
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25113365
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