How Machine Learning is Benefiting ITSM
Machine learning is improving the quality and pace of IT services. Be it the help desk or the end-users, the fusion of ITSM and machine learning, and their automated solutions are benefiting both.
FREMONT, CA: Today, as we see the organizations branch-out, the load on their service desks also witness voluminous service requests. This is the primary reason why organizations must lay importance on the efficient IT service management system (ITSM). Several technologies have fused with ITSM to render better services, but the ever-growing customer expectations resist the smooth flow of services. Thus to give away satisfactory solutions, the IT environment has reset its features to incorporate Machine Learning (ML) that can tailor the requirements of the IT professionals to meet customer expectations and trigger business growth.
Below listed are four applications of ML that can enhance service desk operations.
Efficiency in dealing with level-1 incidents
ML technologies shrink the workload of the technicians in resolving the service requests. Based on prior experiences, the software can issue tickets and offer some preliminary solutions to the users. At times when some device stops functioning, the user can place a service request through the ML software. Accordingly, ML can provide some 1st level checks and also provide some guidance to fix the minor issues. Therefore, technicians do not need to rush for trivial issues.
The IT assets occupy the prime position within an organization. With the implementation of new technologies, these IT assets often deteriorate in their performances, impeding the normalcy of the business operations. In such scenarios, to proliferate the business cycle and functions, ML algorithms aid companies now to track the performances of these assets, receive valuable insights that can help to evaluate the incidents, and manage the issues better in the future.
Forecasting and preventing issues
With ML, support desks can address several issues and analyze them much efficiently. For instance, when ITSM is automated, service desks can examine the underperformance of a system and the frequency of the issues occurring, build predictive models, and forecast any future performance issues. Hence it becomes easy for the service desk team to take action promptly and prevent serious system damages.
Lower transformation risks
Each tech-transformation entails huge monetary and functionality risks. The ML algorithms train the system to acquire information from the previous implementation experience and establish a stable workflow.
As data hikes in volume, this hour needs sophisticated Machine learning applications to contest the challenges of customer expectations and also reduce the chances of manual error in their processes. From enhanced voice assistance tools to self-driven cars, ML, therefore, is the key need of the hour in managing assets.
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