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Benjamin de Moncan | April 13, 2021

Uncovering the Role of Machine Learning In ITSM​​

The term machine learning might bring to mind movies like iRobot, West World, or even The Matrix. These certainly leave an unsettling feeling, because the idea of artificial intelligence, AI, or machines learning and changing their behaviors is still relatively new. However, machine learning (ML) is nothing to fear and can have several beneficial uses and roles, including a role at the IT service desk through cloud-based IT service management software. 

Research Study: Doubling Down on Digital Transformation: Why Modern Service Management is the Answer to Creating Value Now & in the  Future

What is Machine Learning?

Machine learning is defined by Oxford Languages as:

“The use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.”

In layman’s terms, machine learning works by feeding large amounts of data into a computer/software so that it can detect patterns and learn from behaviors, effectively creating predictions based on those patterns and learned behaviors.

For a practical example, think of Google. Google collects data to learn from your behaviors and in turn, will refine your search results based on what it has learned about you. Ultimately, the goal is to show you only the most relevant results to your search by learning from human behavior and data inputs via cookies.

In ITSM, machine learning functions in a similar way. User data, incident patterns, and search habits all are continually being input and analyzed to help understand user intent, predict future issues, provide relevant search results, and even interact via intelligent automation like AI powered chatbots.

This can be taken a step further with the addition of features like Natural Language Processing (NLP) which can help predict intent based on the natural language of the user, providing a better customer experience to people of different backgrounds and regions.

In a very simple sense, the benefits of utilizing AI with machine learning may seem to lie in the improvement of the user experience. But when you look beneath the surface, there are several other benefits and uses which are outlined below. 

How Machine Learning Can Be Used in ITSM

As briefly mentioned previously, machine learning can help transform the user experience in ITSM and at the IT service desk as a whole. The following are just a few of many ways ML factors into ITSM and its role in digital transformation:

Improved Search Abilities and Results

Just like we mentioned in the earlier example of Google, a chatbot equipped with machine learning is able to improve your search results based on your past behavior and provide only relevant knowledge.

For example, imagine you are a customer working in a specific business unit who needs help from the IT service desk. ML will ­be able to understand that and provide you information specific to that unit and devices or software used in that unit, rather than providing all of the knowledge articles on a particular topic. This reduces unnecessary or unhelpful articles to search through.

This can go one step further with the use of virtual support agents and chatbot technology. With chatbots that utilize machine learning, contextualized answers are able to be provided quickly.

Improved Efficiency Handling Level-0 and Level-1 Incidents

Machine learning has the capability to predict what the users will need in self-help, effectively resolving Level-0 and Level-1 tickets and implementing the shift-left initiative. Further, machine learning can help service desk agents find information to quickly solve Level-0 tickets by providing the information faster. 

For example, if you frequently run into issues with your password, ML will detect that you typically need password resets and offer you the option to reset through self-service first. This reduces time searching and also provides a reason to use self-service portals and apps if you are hesitant or used to relying on the service desk agents themselves. 

Predictive Maintenance and Analytics

Similar to asset lifecycle automation, which we will cover shortly, machine learning can help predict and analyze when software will need to come offline for maintenance. This can prevent system downtime and give better insights into when certain tools are used the least so that you can properly plan downtime.

Further, machine learning can help analyze when tools or software is most likely to run into issues or errors, helping the IT service desk better prepare. As data is continually input, the predictions will improve over time.  

Asset Life-Cycle Management Automation

Just as machine learning can help in predictive maintenance and analytics, ML can also help predict when assets will need upgrades or replacements based on current use. This is just one aspect of what asset management solutions can do for your IT department. Other aspects of machine learning in the IT asset management automation include improved security in the asset lifecycle through predictive analytics of possible breaches, easier ordering of the assets needing to be replaced, and improved access to help articles about these assets. 

Better Planning for the Organization

Some of the points above boil down to one thing, but it is so important it bears mentioning on its own: the ability to better plan for the organization as a whole. Being able to predict and learn from trends gives insights to managers that can help them plan everything from equipment to how many people to staff at the service desk based on ticket trends. This can help you better plan for how many agents are needed, software upgrades, and more. Plus, machine learning can help streamline the budgeting process. 

Streamlined Onboarding

We have talked about the benefits of a self-service platform for onboarding time and time again. But, machine learning takes this one step further. Over time, machine learning will help you understand which information is actually useful during onboarding based on the analysis of which data is accessed more frequently and which is breezed by. Eventually, this leads to the onboarding process becoming more streamlined as you understand which information is best to remove or clarify.

Identifying Gaps in Knowledge Articles and Information

We know that a solid knowledge management strategy is all about providing the right information at the right time to the right people. But, what is knowledge management in relation to machine learning? Machine learning takes the knowledge strategy and automates it, effectively identifying gaps in articles and information to help you better refine your database and thereby enhance the user experience.

Machine learning does not only identify knowledge but can create it as well using intelligent knowledge management software. Converting documented ticket resolutions into knowledge articles, machine learning can utilize algorithms to identify the most important information to include and share.

AI and Machine Learning for the Future of ITSM

AI and machine learning are certainly futuristic topics, but they are not resigned to only live in science fiction. They represent major opportunities for the service desk and the future of ITSM. However, these are only one piece of the larger automation puzzle. Without embracing automation in other aspects in the IT service desk and the ITSM tool, you risk an incomplete picture with your efforts falling flat.

To learn how EasyVista makes intelligent automation, AI, and machine learning possible, request a demo with one of our experts today!

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Benjamin de Moncan

Benjamin de Moncan has more than 15 years of experience in the customer experience industry. Currently acting as Senior Director of Product Marketing at Easyvista, he also played a role as a senior manager at Deloitte Consulting and COO of Knowesia, which was recently acquired by EasyVista. He has spent the last several years optimizing customer care for large financial and services companies, developing expertise in building digital organizations, and developing a passion for design thinking methodology and customer journeys' optimization. Outside of work, he spends all of his time with his family, playing guitar and piano.