Multimatics Insight

Machine Learning Optimization to Reinforce IT Service Management Performance

Machine Learning Optimization to Reinforce IT Service Management Performance

As technology and invention vastly improved, IT Service Management (ITSM) leaders are moving toward effective resource allocation and aligning strategic goals. With the increasing volume of data flooding the databases, ITSM leaders need to build effective solutions for higher effectiveness in how the IT team supports its users.

According to data from InformationWeek's 2022 State of ITSM and ESM survey, most organizations are putting more effort and strategies into dealing with ITSM, however, this leads to few results in procedural and repetitive tasks like user management and data updating. The condition leaves them to manually handle the issues and remain a lot of room for improvements. In addition, the survey shows that only 8% of organizations operate with a very high level of maturity where their ITSM technology is fully optimized. More shockingly, 40% of organizations reported that the last time they refreshed their ITSM technology was around six years ago or longer.

With such ecosystems and challenges, ITSM leaders need to utilize new technologies and inventions, such as machine learning and AI to accelerate their ITSM processes and develop successful strategies. Machine learning, or teaching computer systems to think and learn like people, is the most promising field of study and research in machine learning for ITSM.

ITSM and Machine Learning: the key concept

In ITSM, machine learning improves the processes by analyzing user data, incident patterns, and search habits, understanding user intent, predicting future issues, providing relevant search results, and utilizing intelligent automation like AI for customer experience. Additionally, machine learning can also be utilized in day-to-day uses such as processing online search requests, filtering spam automatically from email, and understanding and replying to speech commands. Machine learning focuses on feeding computer systems with large quantities of data and information to help the computers to learn, act and think as humans do autonomously.

Below are the key benefits of machine learning implementation for ITSM:

1. Increased efficiency in handling level-1 incidents

Machine learning can scan incoming tickets and provide end users with automated solutions based on prior experiences. In doing so, end users can now address problems with or without the assistance of specialists. A recent development on chat boxes like Google Assistant is also made possible by machine learning, enabling end users to get information and replies without submitting service desk tickets.

2. Effective management of asset life-cycle

When a company heavily relies on its technological assets only, there is a chance of performance deterioration if the IT infrastructure is unable to handle new applications and developing technologies. IT assets are still not optimized even though businesses spend a lot of money on software and hardware, mostly because IT asset management solutions offer little to no insight. Through insights into problems and performance levels over time, machine learning enables businesses to efficiently analyze and manage the performance of IT assets. Machine learning can detect a technology asset and assist in addressing related and ensuing incidents if several events connected to it enter the system or if performance consistently drops.

3. Reduce transformation risks

Implementing technological advancements is not without risks. Transformations may not only be costly but also unsuccessful in terms of delivering performance if a detailed plan is not in place. Service desks can learn from data from previous implementations thanks to machine learning, which helps develop dynamic workflows. Machine learning-based service desk solutions can identify early warning indications of implementation issues and alert IT managers to resolve them before a problem arises. In addition to anticipating errors, machine learning-driven change implementation modules can offer crucial planning inputs based on prior experiences.

4. Dynamic Problem Prediction and Proactive Prevention

Support desks can examine a variety of event models using machine learning to foresee issues. In order to help technicians address problems as quickly as possible, service desks can also automatically create problem tickets or send out notifications for impending problems. For instance, if a server is performing poorly, service desks can use machine learning to predict the failure in advance using performance data from the past and produce a ticket linked to earlier tickets for incidents with a similar nature. Machine learning may assist in the development of predictive models for the service desk that allows for the early detection of service deterioration that will result in future incidents by taking into consideration a number of important parameters, such as the frequency of difficulties and the pace of change.

Without adapting machine learning and automation in their ITSM companies are at risk of failing flat and being unable to compete in the global market. The implementation of machine learning in ITSM holds potential for significant advancement in the coming years. Machine learning represents major opportunities for the service desk and the future of ITSM.

With such high demand for CSDS professionals, many firms are required to employ consultants to help the different businesses that require cybersecurity data science with these necessary demands in order to meet them. Consultants bring a lot of information and expertise to the table. In applying CSDS, many practitioners find it challenging to optimize available tools achieve specific goals. For example, white hat tools (i.e Pentest) often quickly end up being repurposed for black hat purposes. There is also found adversarial machine learning practice, which is a reverse engineering and confusing/tricking machine learning models that intend to seeding system with false data. To start a CSDS profession, the framing and hosting of targeted training and credential programs is suggested for the professionalization of the CSDS domain. In this sense, there would be a lot of benefit from the research, instruction, and curriculum creation on this subject.

The Future Path of CSDS

From the organization perspectives, several obstacles that may be encounter when applying CSDS are confusion, regulation uncertainty, marketing hype, and few resources of CSDS practitioners or professionals. Uncertain regulation between organizations and related stakeholders will complicate data analytics process and decrease security system and data integration. From the process perspective, several obstacles in applying CSDS are inherent costs, false alerts volume, decision uncertainty, and scientific process. From the technology perspectives, the obstacles are include data preparation, normal vs anomalous condition, whether the organization has own their infrastructure or shadow IT, and lack of labeled incidents. These obstacles in the organizations can be resolved by establishing management-driven change and conducting training & program governance.

IT Service Desks and Machine Learning: the core concept

Most of the large IT service companies have dedicated service desk systems in order to manage the problems faced by the customers, employees, or any other end users. The service desks will collect tickets from customers and assign tickets to designated categories to resolve.In this process, machine learning plays important role in smoothening the process by providing accurate analysis of ticket submissions and making them available to be resolved in real-time.

Incorrect routing of tickets results in the reassignment of tickets, unnecessary resource utilization, user satisfaction deterioration, and adverse financial implications for both customers and service providers (Paramesh, S. P.; Shreedhara. K. S. 2019).

Then, how machine learning can enhance IT Service Desks performance?

1. Enhanced model prediction

Machine learning utilizes training data and the ITIL incident management process to an automated solution to the ticket classification techniques, allowing the IT Service Desk to improve its model prediction accuracy.

2. Effective human resource

A machine-learning-based IT service desk eases the process of categorization of ticket id, owner, contact source, and severity leading to effective management of skillsets and competence owned by the human resources.

3. Elevate user satisfaction

A machine-learning-based IT problem resolution becomes more accurate and IT staff becomes more responsive, presenting a capability in managing ticket resolution time. This leads to improved user satisfaction.

4. Assess the performance of IT staff and processestrong

Machine learning helps to define business processes by measuring KPIs to assess the performance of IT staff and processes. In addition, machine learning also generates management reports and conducts continual customization to meet the ongoing requirements needed by the IT staff.

Nevertheless, a machine-learning-based IT service desk enables model prediction accuracy, reduces the ticket resolution time, and ensures customer satisfaction including the ticket comments, as well as measuring IT staff performance.


Machine language is improving the capacity of IT service and service management solutions to learn from gathered data from an ITSM perspective. From a value stream perspective, the organization not only defines the activities, workflows, controls, and procedures necessary to achieve business objectives, but can also learn from those activities, workflows, controls, and procedures in order to develop new ones or enhance current outcomes with limited to zero human intervention.

Al-hawari, F. (2019). A Machine Learning Based Help Desk System for IT Service Management. Journal of King Saud University. Computer and Information Sciences. German Jordania University.
InformationWeek (2022). 2022 State of ITSM and ESM Survey Report. Team Dynamix

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