5 ITSM automation use cases with Machine Learning (ML)

Auto approvals

With the implementation of machine learning, help desks can be trained to auto-approve service requests based on the employee’s role, department, work site and other parameters. For example, when a designer requests additional design tools or software, the help desk will be able to automatically approve the request and initiate a workflow without waiting for the manager’s approval. The help desk also can be trained to automatically check the workstation assigned to that designer for minimum system requirements to install the requested tools or software and create a request to upgrade the system, if necessary — by itself.

Resolving incidents

End users will be able to search for solutions and resolve incidents without the involvement of any technicians. Through machine learning, help desks can be trained to scan incoming tickets and provide end users with solutions automatically, based on the system’s previous experience. Google Assistant-style chat boxes will also help end users resolve incidents or get information without even logging a ticket into the help desk.

Help desks also could learn from past experience and data to route tickets or tasks to the appropriate technicians or support groups, thereby automating the ticket assignment process without having to create any rules or workflows. Machine learning would help reduce resolution times and improve the efficiency of the help desk team.

“With the implementation of machine learning, help desks can be trained to auto-approve service requests”

Problem anticipation

With machine learning, help desks will be able to analyse incident patterns and anticipate problems. In addition, trained help desks could automatically trigger notifications or create problem tickets for anticipated issues so that the help-desk technicians can investigate at the earliest. Say the performance of an application server starts deteriorating, help desks would be able to anticipate any application failures from the past performance data of that particular server, warn end users who might be affected, create a problem ticket and associate any relevant incident tickets with the problem ticket.

Change management

Change implementations are always associated with a certain level of risk. Without a proper plan and workflow in place, change implementations can be costly. Help desks can learn from previous change implementation data and experience to help create highly dynamic workflows. For example, with the implementation of machine learning, help-desk systems might recognise potential signs of change implementation failure and prompt administrators to stop the implementation and execute the backup plan even before the failure occurs. Change management modules guided by machine learning will also be able to make recommendations during the planning phase based on previous experiences.

Asset lifecycle

A sizeable number of incidents occur due to old IT assets whose performance has degraded. Machine learning can help automatically identify which assets might repetitively break down based on factors such as their performance levels and incidents associated with them. Once those assets are detected, the help desk can use machine learning to send notifications to technicians and facilitate ordering replacements. The simplest case could be the help desk automatically creating requests for printer toner replacements after a specific number of pages have been printed. ITSM is full of opportunities for machine learning. The scenarios above are some of the simplest use cases showing how machine learning can make life easier for both the help-desk team and end users. Though these might not be readily available as out-of-the-box solutions, they are not too far away into the future.How to view the contract owner details

This article was originally published in Intelligent tech channels