On Thursday 28th October we are carrying out essential upgrades. The website may be unavailable for a period of time starting from 6am BST. We apologise for any inconvenience this may cause you.

Machine learning is the key to AI adoption in ITSM

Android figure extending arms and hands over lit up computer surface wtih technical diagrams

With so much hype today about artificial intelligence, IT service providers need to weed out the facts from the fiction to develop as successful strategy. The most promising area of study and research in artificial intelligence for ITSM is in machine learning or teaching computer systems to think and learn like humans.

Machine Learning – How Does it Work?

Machine learning focuses on feeding computer systems large quantities of data and information to help the computers to learn, act and think as humans do autonomously. A common example of machine learning is how web browsers learn how to improve results by tracking how we interact with search results. Behind the scenes, the search algorithm is attempting to understand if results are successful.

Machine learning is an integral part of developing IT services and ITSM tools. However, to be successful in using machine learning requires data – lots and lots of data. Traditional applications use knowledge gained about a process or business requirements to produce a specific, predictable outcome. Machine learning is where data about IT services and processes is used to learn from the collected data without pre-programming the outcome.

Machine learning also utilizes advanced statistical techniques and computing systems to improve ITSM tasks over time. With machine learning, the application can write additional programs to interpret input and predict outcomes. Advancements in machine learning are at the root of the current explosion of artificial intelligence.

Machine Learning and ITSM

From an ITSM perspective, machine language is building the ability of IT service and service management tools to learn from collected data. From a value stream perspective, the organization does not just define activities, workflows, controls, and procedures needed to achieve business objectives – the organization can also learn from those activities, workflows, controls, and procedures to create new ones or improving existing outcomes without or with limited human intervention.

An example of a common use of machine learning in ITSM is the use of Chatbots or virtual assistants. While initially the goal is to program a bot with known questions and answers, there is also a significant focus on learning over time to respond to questions that have not been programmed previously based upon an attempt to understand the question’s intent.

ITSM and Automation

In ITSM, a vast amount of the work that is done to deliver and support services is repetative and is a likely candidate for automation. Teaching ITSM tools to look for patterns and learn from past events, incidents, problems, known errors – could hold potential for significant advancement in the coming years. With such a rapid pace of change in the field of artificial intelligence, the future is not the same long distant dream of the past but instead a reality that is just around the corner.

If you are an IT manager, AI and the emerging field of machine learning should be a consideration both in your ITSM strategy but also a consideration when hiring employees. The more we learn and attempt to use these advanced statistical techniques the more likely we are to make advancements in ITSM strategies.
If you want to read more about AI, machine learning and its relationship to newly released ITIL4, check out the AXELOS White Paper, ITIL 4 and Artificial Intelligence.

Current rating: 0 (0 ratings)


There are no comments posted.
You must log in to post a comment. Log in
ITIL 4 and artificial intelligence White Paper Industry
In this white paper, the advancements of artificial intelligence are explored, along with its current adoption in ITSM and its potential for the future. Read