The field of machine learning is quite a hot topic. We know that this type of artificial intelligence (AI) provides computers with the ability to learn without being explicitly programmed. For people like me who need it in simpler terms, machine learning deals with systems that can learn from past data and experience to improve performance of a particular task.
Machine learning already is touching our daily lives, both personally and professionally, more than anyone could’ve imagined. For example, a home’s lawn sprinkler system can be trained to tell the difference between squirrels and cats, and to turn on when cats walk on the grass in order to shoo them away. Processing online search requests, filtering spam automatically out of our email inboxes, as well as understanding and replying to our speech commands on smartphones are all machine learning tasks being done on a daily basis.
Sooner or later, machine learning will also be applied to ITSM to change the way help desks work. The benefits might include predicting issues and problems proactively, improving search capabilities and knowledge management, and classifying and routing issues with greater ease. To be more specific, you can expect the following scenarios in the near future:
Service Requests Will Have Auto-Approvals and Custom Workflows
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. Furthermore, the help desk 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 — all by itself.
Helpdesk systems will also be able to learn from past onboarding experiences and make suggestions such as the type of software and hardware the user needs, the access permissions they need based on their role or department, and a printer configuration setup. These are all options for improving the speed of service delivered to end users.
Level 1 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.
For example, a user would just have to ping the help desk that “the printer is not working,” and the help desk would be able to check the printer’s print threshold level, and create a request for a toner replacement, if needed; the system would also be able to immediately and automatically send any relevant knowledge base articles that might help the end user check network connectivity issues or reset the printer configurations in their machines.
Help desks could also 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.
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 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.
Highly Dynamic Change Workflows Will Be Created
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 recognize potential signs of change implementation failure and prompt administrators to stop the implementation and execute the back-out 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.
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. 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, keep an eye out as they are not too far away into the future.
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Staff Writer
