The Convergence of ITSM and Machine Learning in the Coming Decade


Corporations rely on the organizational service desk to solve problems such as technical incidents, service disruptions, management inquiries, and other IT-related requests. Depending upon the size of the company, the scale of the incident, and the volume of tickets, operating a service desk can quickly become a monumental task. Efficient service desk solutions are essential to keeping IT operations afloat and avoiding the risk of functional breakdown.

In today’s digital landscape, previous IT service management (ITSM) solutions may not be enough to match the demands of senior officers or to meet rising consumer expectations. Moreover, the ever-increasing proliferation of data and rapidly advancing technology means that both employees and customers have grown accustomed to fast, accurate, and next-generation ticket resolution.

When IT professionals attempt to resolve a flood of service tickets with manual overtime, this burden can cause incorrect decisions or human errors that eventually hamper business growth. Fortunately, new tools like automated processing, artificial intelligence (AI), and machine learning can help mitigate these problems. Machine learning is no longer a catchphrase of the future; experts project that it will be one of the most prominent features of service desks in the coming decade. The following applications provide insights on how implementing machine learning into the service-desk workflow can help businesses attain their objectives and goals.

Greater Efficiency Through Automated Solutions

Machine learning uses past patterns, algorithms, and statistical models to perform tasks without needing explicit instructions. As a subdivision of artificial intelligence, machine learning can use past inferences and data to present viable solutions. According to the ITSM blog by SysAid, advanced machine learning streamlines many technologies to provide end-users with the most relevant answers from all existing information. In addition to making service tasks more efficient, machine learning allows end-users to self-resolve certain tickets without needing further troubleshooting or technical support.

Another benefit of automated solutions is that machine learning can curate and dispense information that reduces the need for logging a support ticket in the first place. For example, many service desks enhanced with machine-learning technology include settings for assistant chats that resolve basic to moderate issues. Users can “ping” or “contact” the service desk with a question, and the machine-learning assistant can quickly scan knowledge-base articles to find a fast and accurate solution. If the result does not answer the customer’s question or if the problem is more advanced, the machine-learning tool can forward the request to the IT professionals as the service desk. This automated process empowers the customer with self-help resolutions and expedites ticketing workflow for IT employees.

Improvement of Overall Asset Management

Most businesses depend on technology assets for short-term and long-term operations. But whether software or hardware, technology assets run the risk of performance degradation or depreciation if a company does not deploy patches efficiently or prepare for emerging technologies. Furthermore, companies who have not ensured successful help-desk integration or scheduled asset monitoring can forfeit the benefits of software and hardware assets altogether.

Machine learning uses statistical data and past reports to improve asset management and optimize the benefits of the latest software and hardware. For example, if the algorithm associates a specific technology with a high number of incidents, the system can deploy the appropriate patch or recommend replacing that technology completely. Moreover, if the statistical model identifies a sector’s significant drop in performance, machine learning can help locate the origin so that your company corrects it and prevents future losses.

Better Problem-Solving and Prevention Capabilities

Due to its ability to provide intuitive reports on curated data, machine learning is a beneficial tool for preventing incidents and solving problems. Service desks updated with the latest machine learning can create automatic notifications for a range of common issues so that IT professionals can investigate and resolve errors immediately.

For example, by taking a holistic approach to historical data like frequency of incidents and rate of incident escalation, machine learning can create a predictive statistical model to prevent further occurrences. Similarly, machine learning can use a department’s cumulative past performance data to identify why an asset is under-performing and deploy the right patches as necessary.

Fewer Risks Associated with Transitions and Transformations

As technology changes and industries require next-level solutions, IT departments must remain prepared for the latest software upgrades and transformations. When companies adopt new technology, there is always a natural risk associated with the learning curving during such transitions. Without the plan of action or protocol, IT departments may either fail to adopt the technology correctly or fail to execute performance effectively.

Machine learning mitigates this risk by preparing the system against an advanced list of common errors and malfunctions (based on previous installations as well as the latest reports from the original developer). The best machine learning also features change-management modules and templates to manage best-case and worst-case scenarios. With a tested plan in place, a business can make a smooth transition for either the entire organization or specific departments in need of an upgrade.

The Bottom Line

Machine learning is no longer merely an exciting prospect or futuristic buzzword. Instead, there is now a distinctive convergence between IT service desk management and the latest machine-learning technology. Machine learning can improve a company’s ability to resolve level one (L-1) incidents, improve prediction and problem-solving capabilities, and supercharge asset and change management. As the field of machine learning expands, current IT professionals can expect to work with this technology to improve their existing skill sets.




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