  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.
