Often, employees look forward to an organization's service desk to solve problems, such as reporting service disruptions and incidents, requesting changes, and other IT-related requests. Depending on the organization's size, the nature and scale of the problem, and the volume of tickets that a service desk receives, it can be overwhelming to manage. Thus, the efficient management of a service desk is crucial, considering the wide range of critical functions it is responsible for.
In today's fast-paced, ever-changing digital world, legacy ITSM (IT service management) solutions are failing to keep up with rising customer expectations, and they are ineffective in offering high-level experiences that next-generation customers seek.
As stated in several studies, ITSM professionals aren't able to focus on tasks that add value to the business because of interruptions caused by everyday IT support and service-related issues. When flooded with support tickets, service desks are succumbing to human or manual errors, which lead to incorrect decision-making that impedes business growth.
With IT ecosystems, these days, transforming at an accelerated rate, adoption of emerging technologies is becoming increasingly vital for service and support desks. Exponential technological growth and data proliferation are intensifying the work pressure of IT managers, but machine learning and automated processes can mitigate the load significantly. Machine learning and artificial intelligence are not buzzwords anymore, and businesses across the globe are incorporating such technologies to improve and heighten IT service management efficiencies.
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Be it for performance management of systems, applications, and networks, business intelligence, or predictive analytics, machine learning, and artificial intelligence are disrupting the IT landscape in a major way. IT service management teams, as a fundamental driver of business operations and growth, can use machine learning to reduce time-intensive, manual tasks, and to streamline processes. Here are some key ITSM-related machine learning applications that will liberate IT professionals to help them impact the business's bottom line.
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.
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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 Machine Learning Certification 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.