it service management

Planning Machine learning for your ITSM operation?

November 30, 2018

Implementation of machine learning in help-desks can be trained to auto-approve service requests based on the employee's role other parameters.

Ex: when a ticket is created, the help-desk will be able to automatically approve the request and initiate a workflow without waiting for the manager's approval.

1. Level one incidents will be resolved without technicians

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

2. SLA performance prediction

Using correlation and regression models applied to the assets with which services are delivered, it is possible to build predictive models that indicate the likelihood that an organization will be able to achieve its service-level agreement (SLA) and performance indicators. With these insights, organizations are able to adjust focus in time so that they can allocate resources more efficiently to optimize SLA performance

3. Intelligence will impact asset life cycle management

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 like 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.

4. Problems will be anticipated and prevented

With machine learning, help desks will be able to analyze incident patterns and anticipate problems. In addition, trained help-desks could automatically trigger notifications or create problem tickets for anticipated issues so 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.


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