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Use Cases of AI & ML Impact on ITSM
Machine learning provides insights that help firms prioritize ITSM issues, take proactive action, reduce resolution time, and increase staff productivity.
Fremont, CA: IT firms are using artificial intelligence and machine learning approaches to enhance and optimize IT service management procedures. Because ITSM systems create a large amount of data, adding machine learning to ITSM processes makes a lot of sense because it can give IT workers a better knowledge of their architecture and procedures.
Machine learning provides insights to help firms prioritize ITSM issues, take proactive action, reduce resolution time, and increase staff productivity.
Let's see some of the top AI and machine learning use cases in ITSM that are changing how IT services are delivered.
• Virtual Agents
Using "virtual agents" to give consumers quick access to self-service abilities or a suitable IT assignment group that can solve their problems as promptly as feasible is one of the most prevalent and rapidly developing uses of AI in ITSM.
• Proactive Problem Resolution
Advances in big data and analytics are expanding ITSM's predictive and correlative capabilities. Machine learning and artificial intelligence solutions based on repository analysis and users' online patterns can assist end-users in reducing the amount of IT issues they face and even forecast and fulfilling user requests before they even know there is a problem.
• Service Desk Automation
70-80 percent of resources get consumed by the service desk and operational duties such as implementing service requests, resolving incident tickets, and delivering updates. Organizations may employ AI to intelligently automate such processes, allowing technicians to spend more time inventing and aiding the organization in meeting its objectives.
• AI-powered Knowledge Management
Knowledge management can utilize deep learning technology to propose solutions from the repository or browse the cloud to aid users in resolving IT issues. As a result, companies may spend less time administering the knowledge base and more time sharing expertise with technicians and end-users.
• Anomaly Detection
Some IT incidents may be undetectable with standard ITSM techniques. AI/ML models may detect abnormalities and report recurrent incidences across many IT systems. They can also help notify IT staff of an IT problem, before an event happens.