Machine learning in ITSM is key to improving support productivity, alongside end user satisfaction. Having data is key to machine learning, and with most modern ITSM systems organizations will have a wealth of data that machine learning can take advantage of. Here are a few very common use cases of machine learning in ITSM:

  • Mining & Surfacing Relevant Data: Organizations have a tremendous amount of knowledge captured in formal knowledge management databases, as well as through less formal technician close notes. Machine learning can help recommend the most relevant piece of information to solving a current issue, regardless of origin.
  • Routing Incidents to Proper Support Groups and Categories: Tens of thousands of support tickets are generated monthly, and these need to be manually triaged to the proper categories and groups for support. Machine learning can improve on the manual error of mis-categorization and assignment.
  • Successfully Communicating with Customers in Support Calls: Hundreds of calls are handled daily between ITSM support staff, and customers. Machine learning could be applied to understand user sentiment and inform the staff of changes to approach.

Most enterprises today have a number of processes that can benefit from the computational superiority of machine learning to improve accuracy, reduce error, and automate decision making. This will intrinsically help apply two key competitive strategies: Cost Leadership, and Differentiation.

  • Cost Leadership – The positive impact from machine learning for cost leadership is through improved support productivity by reducing operational costs of manual, error prone tasks. In addition, newer, less experienced technicians will have be able to operate with the same abilities as more experienced technicians due to a systems ability to recommend solutions based on existing data in the system.
  • Differentiation – Machine learning provides intelligent routing and sentiment analysis in support for an improved customer experience. By having the system route issues, customers no longer need to worry about proper categorization, and this results in a friendlier user experience and a faster time to resolution. By analyzing user tone, machine learning can also empower the support personal to provide high levels of service and clear communication.

Most vendors today are offering machine learning today to help discovery insights. To help you understand what use cases you have in your organization, Pericror has developed a topic analysis tool that will help you understand what areas to focus on in your organization’s ServiceNow instance.