The Intersection of ITSM and Machine Learning

Increased Efficiency in Handling Level-1 Incidents

Based on previous experiences, machine learning can scan inbound tickets and offer end-users automated solutions. This helps end-users to self-resolve issues with no involvement of technicians. Machine learning also enables the development of Google Assistant-like chat boxes to resolve simple problems, allowing end-users to gain information and answers without the need to log service desk tickets. 

For instance, if a printer is malfunctioning, a user just needs to 'ping' the service desk, and through machine learning, can initiate a service request instantly, after checking issues like if the printer requires a cartridge replacement. The help desk can also send relevant Knowledge Base articles automatically for additional self-help, thereby automating the entire process and expediting resolution times.

Effective Management of Asset Life-Cycle

Companies that rely on technology assets (who doesn't?), often face the challenge of performance degradation if their IT assets are incapable of handling new applications and emerging technologies. Even though companies spend a great deal of money on software and hardware, IT assets are still not optimized, mainly because of little or no transparency in IT asset management solutions.

Using machine learning, businesses can track and manage the performance of IT assets efficiently, through insights on incidents and performance levels over time. If many incidents associated with a technology asset, enter the system, or if there is a consistent performance drop, machine learning can identify it and help address future and related incidents.  

Dynamic Problem Prediction and Proactive Prevention

Machine learning allows support desks to analyze a range of incident models to predict problems. Additionally, service desks can create problem tickets or trigger notifications for expected issues automatically, enabling technicians to look at the issue as quickly as possible.

For example, if a server is underperforming, service desks, with the help of machine learning, can anticipate the failure in advance based on previous performance data and create a ticket connected to similar incident tickets that occurred in the past.  

Taking into account several key factors, such as frequency of issues and rate of changes, machine learning can help to create predictive models for the service desk, which enable early prediction of service degradation that will cause additional incidents to occur. This enables service desk teams to determine what they will need to ensure optimum service levels are met.   

Eager to begin your career in the Machine learning industry? Test your knowledge with thess Machine Learning Multiple Choice Questions.

Reduced Transformation Risks

There is always risk involved when technology changes are implemented. If there is no precise plan in place, transformations can not only be expensive, but they can also be ineffective, in terms of performance delivery. Machine learning enables service desks to learn from past implementation data, which helps create dynamic workflows.

Service desk systems, making use of machine learning, can discover signs of implementation malfunctions and prompt IT, administrators, to mediate before a fault occurs. Besides recognizing failures in advance, change implementation modules, driven by machine learning, can also provide key inputs at the time of planning, based on prior experiences.  

Machine learning, over the last decade, is at the core of many new technologies, from IT service management to self-driving cars, speech recognition, improved web search, and much more. As data continues to proliferate, more applications of machine learning rapidly are emerging, heightening the demand for professionals with the right skill sets.

Today, machine learning skills are a must-have for aspiring data scientists, and a great way to gain these skills is through a reputed e-learning platform. Simplilearn's AI and ML Course, IIT Kanpur Machine Learning Course and the Artificial Intelligence Master's Program, for instance, provides comprehensive training for professionals that want to up their game in a career in AI and Machine Learning. Further, you can become certified the latest version of the world's most popular ITSM framework through Simplilearn's ITIL 4 Foundation Certification Training course. ITIL 4 has significant improvements from earlier versions, in particular, support for AI and Machine Learning technologies.