IT service management (ITSM) and machine learning

 IT Service Management (ITSM) is a form of strategy and operation for executing, delivering, and organizing IT services for end-users that meet the stated needs of the end-users and the stated goals of the business. Technology has enhanced the way companies operate in all industries around the world. At the same time, traditional IT service management (ITSM) solutions have failed to maintain customer satisfaction levels and meet the growing expectations of consumers in the fast-paced digital world.

Machine learning

Machine learning is an artificial intelligence application that enables systems to learn and improve from experience without explicitly programming spontaneously. Machine learning (ML) focuses on developing computer software that can access data and use it to learn for themselves. Machine learning has already begun to make a difference in our daily lives more than anyone could have imagined. Say, for example, a pair trains their sprinkler system to turn on automatically when cats are prevented from entering their lawn. In simple words, Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to be more exact in predicting results without explicitly programming them.


The service desk management is about creating the “one-go-to” place for all the IT related needs, helps and issues. The desk is responsible for managing incidents or service disruptions, fulfill any IT related requests, and changes. The service desk scope of work is generally enormous and wide-ranging hence it needs to be managed effectively and efficiently. There are some benefits of ITSM.

1. ITSM makes it easy for teams to provide quick, proactive, shock-free responses to unforeseen events, new opportunities, and competitive threats.

2. By authorize enhanced method concerts, better accessibility, and fewer service barriers, ITSM helps clients work harder and do more business.

3. By systematically accelerating incident resolution, minimizing incidents and problems, and preventing or resolving issues automatically, ITSM helps businesses achieve greater productivity at a lower cost than IT infrastructure.

4. By incorporating observance into IT service aim, delivery, and management, ITSM can get better compliance and trim down risk.

5. ITSM helps the institute set and convene a sensible outlook for the service, leading to superior clearness and enhanced customer contentment.

Benefits of machine learning

There are some benefits of machine learning.

Automation for everything

One of the most potent benefits of machine learning is its ability to automate various decision-making tasks. It gives developers more time to use their time for more productive use. For example, we see some expected benefits in our daily lives: social media emotion analysis and chatbots. A chatbot responds immediately as first-level customer support when a negative tweet is related to a company’s product or service. Machine learning is shifting the world with its computerization for approximately everything we can think of.

Recommending the Right Product

Product recommendation is an essential aspect of any sales and marketing strategy, including up-selling and cross-selling. ML models will analyze a customer’s purchase history, and based on that; they will identify products from your product inventory that the customer is interested in. This process is known as non-supervised learning, which is a particular type of ML algorithm. Such a model would enable businesses to make better product recommendations for their customers, thus encouraging product purchases. Therefore, supervised learning helps to create a high product-based recommendation system.

 Application of Machine Learning in ITSM

We need to understand that in this ever changing world of technology, the traditional IT service management (ITSM) solutions and practices have become inefficient and does not work effectively to keep the customer satisfaction levels at the highest hence its obvious that the organizations are moving towards ML application to enhance their business scalability and improve business operations. Machine Learning algorithms have enabled the ITSM pratice to improve its speed and quality of service while keeping the cost low. Still there is lost of scope for the usage. In this short article, I will cover top 7 use cases which can elevate the service level of service desk.

 a) Predictive analytics – The first application of ML in any field that comes up for discussion is predictive analytics. What kind of predictive analytics can be make in ITSM? Here we can predict the number and nature of incidents, problems and issues. Even the risks associated with proposed changes, measure and predicting the future levels of customer satisfaction across different types of service desk offerings can be other areas of implementing predictive analytics.

 b) Demand planning – This use case is an extension of predictive analytics itself where we use machine learning algorithms to predict the future demand for both IT services and IT support capabilities which can help the management in the budgeting decisions as well as manage the entire process effectively with minimum cost. The result from demand planning can also help us to gauge the required levels of variables like cost, pricing, benefit, capacity, stock, etc.

 c) Predictive maintenance – We can treat this as extension of demand planning on engineering side than operations, which is the theme in demand planning. Machine learning algorithms enables to selectively apply maintenance to the IT infrastructure and critical business services to prevent disruptions in services or failures. The various processes that are being designed around this is mostly about the ability to track important parameters in real time and with the help of algorithms working on these real time data and give us the trigger to take prevention actions.

 d) Improved search capabilities – This might sound a trivial capability but its one of the important features as we are talking beyond the traditional search options and results. We are talking about intelligent search capabilities, which can predict the search criteria and provide the data related to relevant search keywords. This would give us number of relevant search result with a high degree of accuracy to use the content for our work.

 e) Providing recommendations – Everyone is probably aware of the product recommendations algorithm usage by Amazon to predict what we might want to buy. Similarly, Netflix and Spotify can give us best entertainment content. Extending the similar logic, the product recommendations in ITSM field can provide self-help in recommending knowledge base or solutions for service desk agents, or for end users. Thus, this can help us in speeding up processes to deliver resolutions or services more quickly and accurately which can not only help to improve customer satisfaction but also improve employee efficiency.

 f) Identifying and filling knowledge gaps – Machine learning algorithms capabilities are generally linked to identifying  and distribution of knowledge from the data but we can also use it to create the knowledge. Some of the top applications under this use case, we can think of are identification of knowledge-article gaps based on the analysis of aggregated incident ticket data. While converting a  resolution note of documented ticket into knowledge, algorithms can be used to identify the most pertinent and valuable information from which to create a new knowledge article.

 g) Intelligent autoresponders – Probably we might be trying to stretch the capabilities little too far in this case but its possible to achieve what we are trying. Depending upon the issue type and the nature of the problem, we could use technology to work on a ticket, understand the problem, find the solution, apply it and close the ticket without any human intervention with highest accuracy level. It’s a high-value use case scenario hence even we can use technology to close 1-2% of the issues created, it can give us a great benefit in terms of time, cost and convenience.

First published on BlogPost