Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI

Authors

  • Atri Mandal IBM Research, India
  • Nikhil Malhotra IBM Global Technology Services
  • Shivali Agarwal IBM Research, India
  • Anupama Ray IBM Research, India
  • Giriprasad Sridhara IBM Research, India

DOI:

https://doi.org/10.1609/aaai.v33i01.33019381

Abstract

Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution.

The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of a classifier that is accurate is one of the main challenges, we also need to address the need of designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources.

Our system has been deployed in production of three major service providers and currently assigning over 90,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of more than 50000 man-hours of effort per annum. Till date, our deployed system has already served more than 700,000 tickets in production.

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Published

2019-07-17

How to Cite

Mandal, A., Malhotra, N., Agarwal, S., Ray, A., & Sridhara, G. (2019). Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9381-9388. https://doi.org/10.1609/aaai.v33i01.33019381

Issue

Section

IAAI Technical Track: Deployed Papers