Discussion about machine learning has become a hot topic these days. It is a known fact that it is a type of AI or Artificial Intelligence that offers an ability to learn through computers without any explicit programming. To put it in simpler language, machine learning is related to systems, which can take lessons from past and historical experience and data to enhance the performance of a specific task.

Today, machine learning has succeeded to touch people's lives professionally, as well as, personally.  No one could have thought of its disruptive features even a few years ago. For instance a lawn sprinkler solution can be given the training to make the distinction between cats and squirrels. Machine learning can perform a wide variety of tasks ranging from online processing of search requests to automatic filtering of spam for email inboxes, understanding as well as responding to human speech commands given on smartphones.

It is only a matter of some years within which this technology is also expected to disrupt the ITSM or IT service management to alter the way a helpdesk operates. The merits of ITSM software suite and ITSM tools could also include proactive prediction of problems and issues, enhancing search capabilities, and improving knowledge management. Other benefits could be routing problems with more ease. Here are some of the most common scenarios where you would find the application of machine learning quite soon:

Service requests could have custom workflow and auto-approvals

If machine learning is deployed, a helpdesk can be given the training to make sure that the service requests are auto-approved on the basis of criteria like department, worksite, role and other parameters of the employees. For instance, if a designer requests for extra software or design tools, the helpdesk would be able to approve the service request automatically. It will also start a workflow instead of waiting for the approval of a manager. The helpdesk could also be trained to check the workstation, which has been assigned to the designer automatically all by itself for minimum system requirements for installing the requested software or tools.  

 An IT helpdesk software can also learn from its past experiences and also recommendations like the kind of hardware and software needed by the user, the configuration setup of a printer, and the access permissions required by them based on their departments or roles. These are different alternatives to improve the service speed delivered to the end users.

Technician help will not be required to resolve Level1 incidents

 It will be possible for the end users to search for their solutions and subsequently resolve such incidents without the intervention of any agent or technician. Machine language can be used to train the helpdesks to scan all incoming tickets so that end users can get automatic solutions on the basis of the previous experience of the system.

Such a system would also be able to automatically and immediately send any appropriate knowledge base articles, which might come handy for the end users to check connectivity issues in a network or even change the configuration of their printer in their computers.  

Data and past experiences can also teach a helpdesk to route tasks or tickets to the relevant agents/support groups or technicians, thus automating the process of ticket assignment without creating any kind of workflows or rules. Machine learning is also expected to help in lowering the time for problem resolution and enhance the efficiency of the teams working at the helpdesk.

Issues can be anticipated beforehand and prevented

Machine learning will also help a helpdesk to anticipate problems and analyze incident patterns. Plus, a trained helpdesk would automatically create problem tickets or trigger notifications for the anticipated problems to ensure that the technicians at the helpdesk can conduct investigations as soon as possible.

Creation of highly dynamic workflows will be possible

There is always a risk element associated with implementing changes. If there is no proper workflow or a plan in place, implementing changes can be quite expensive. A helpdesk can always take lessons from its past change implementation experience and data so that highly dynamic workflows can be created.

For instance, when machine language is implemented, a helpdesk system could recognize the potential symptoms of failure while implementing changes. It can then ask its administrators to immediately halt the change implementation while executing a backout plan to prevent the occurrence of a failure that was anticipated. Machine learning can guide change management modules to make suggestions during the planning stage based on earlier experiences.

Asset Life Cycle Management can be impacted by intelligence

Many incidents occur because of the degrading performance of the old IT assets. Machine learning can be useful for automatic identification of those assets that could break down repetitively based on criteria like their associated incidents and performance level. Once such assets are identified, the helpdesk may utilize machine learning for sending notifications to support staff/technicians, as well as, trigger ordering replacements. A simple such case could be when a helpdesk creates automatic requests for replacing the printer toners once the device has printed a set number of sheets.

It can be seen from the above discussion that ITSM offers plenty of opportunities pertaining to machine learning.