An Approach for Machine-First Incident Management Shailesh K.S. 1 , G.Sharada 2 and P.Venkata Suresh 3 1 Independent Researcher, Bengaluru, India Email: shailesh.shivakumar@gmail.com 2 Department of Information Technology, Malla Reddy College of Engineering & Technology, Hyderabad, India Email: gsharada8@gmail.com 3 School of Computer and Information Sciences, Indira Gandhi National Open University, New Delhi, India Email: pvsuresh@ignou.ac.in Abstract—The objective of the paper is to propose a Machine Learning based model for incident management. Incident management is a key activity during the operations phase. It’s essential to address the incidents in an optimal time so that there is no disruption to the operations. As the number of incidents increase, it’s essential to have algorithms which allocate the incident to the right bot or concerned department through which the incident gets resolved or closed. The paper takes machine-first, smart, automation focused operations approach for incident management. The machine first approach proposes various tools, methods and processes that use machine learning to optimize the overall ticket management process. The Machine First Incident Management (MFIM) approach improves the productivity as well as quality. We elaborate the detailed method for smart ticket management, pro-active failure prediction, smart problem management, proactive maintenance and smart shift left in this paper. Index Terms— Incident management, Ticket management, Machine-first operations, Automated operations, Operations management. I. INTRODUCTION Incident management system (also known as ticket management system) is a labour intensive activity. Given below are the key problem areas in the ticket management system:  Effort intensive: A significant volume of tickets are repetitive, but the ticket handling team repeats the solution process for each of the tickets leading to high manual effort.  Resolution time: Since majority of the ticket management process is manual, the resolution time is high.  Resolution quality: Manual resolution of tickets lead to quality issues  Resource availability: For supporting tickets in multiple geographies and multiple time zones we need to staff the resources across all the geographies.  Impact on mission critical setup: Mission critical applications need strict Service Level Agreements (SLA) that are difficult to manage. II. LITERATURE REVIEW The global smart ticketing market size was valued at USD 11.3 billion in 2018 and is expected to register a CAGR of 14.9% from 2019 to 2026 [1]. [2] demonstrates defining requirements for an incident management Grenze ID: 01.GIJET.9.1.556 © Grenze Scientific Society, 2023 Grenze International Journal of Engineering and Technology, Jan Issue