# Improving IT Support by Enhancing Incident Management Process with Multi-modal Analysis

## TL;DR

This paper proposes a multi-modal analysis approach to improve incident management in IT support, leveraging both text and image data to enhance ticket routing and resolution efficiency.

## Contribution

It introduces a novel multi-modal analysis system for IT incident tickets, integrating image and text data to improve automation in incident resolution processes.

## Key findings

- Significant improvement in ticket routing accuracy.
- Enhanced incident resolution speed.
- Effective use of multi-modal data in real-world tickets.

## Abstract

IT support services industry is going through a major transformation with AI becoming commonplace. There has been a lot of effort in the direction of automation at every human touchpoint in the IT support processes. Incident management is one such process which has been a beacon process for AI based automation. The vision is to automate the process from the time an incident/ticket arrives till it is resolved and closed. While text is the primary mode of communicating the incidents, there has been a growing trend of using alternate modalities like image to communicate the problem. A large fraction of IT support tickets today contain attached image data in the form of screenshots, log messages, invoices and so on. These attachments help in better explanation of the problem which aids in faster resolution. Anybody who aspires to provide AI based IT support, it is essential to build systems which can handle multi-modal content. In this paper we present how incident management in IT support domain can be made much more effective using multi-modal analysis. The information extracted from different modalities are correlated to enrich the information in the ticket and used for better ticket routing and resolution. We evaluate our system using about 25000 real tickets containing attachments from selected problem areas. Our results demonstrate significant improvements in both routing and resolution with the use of multi-modal ticket analysis compared to only text based analysis.