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This technology is now being utilized to enhance IT Service Management (ITSM) because of all the advances in Artificial Intelligence (AI). ITSM has witnessed multiple waves of new technology that promise to revolutionize the way things work. But many of them made little to no impression and are now mere fads. The most obvious question everyone is asking is "Aid in ITSM": Is AI going help make ITSM more effective and easier? This is the issue we'll examine in the two-part series "The AI Advantage In ITSM". Part one, "AI at Work" in ITSM sets the stage for our AI discussion. In the second installment, "Features and Use Cases," we'll look at specific AI-related features and apply cases that are poised to transform the ways IT service desks operate. Check over here to discover additional hints on ai service management. This is what industry experts think. Gartner's Predicts for 2018 Artificial Intelligence report[i"> states that by 2022, 40 percent of employees who interact with citizens and government workers will use an AI virtual support agent to aid in making decisions or handling their requests. Gartner says that AI capabilities will power virtual support agents to act as a resource that will allow human support agents to respond quicker and more efficiently to customer/citizen inquires or actions. AI will start having a real influence on our IT service desks as soon as it can perform actions that humans are bad at and also perform tasks that humans would rather not perform. These actions can fall into one of three categories: smart automations, strategic insights and predictive analytics. For instance, routing inbound tickets manually consumes a lot of time -- time an IT professional could be able to use for other tasks. Some help desks are automated in their ticket routing by defining rules for categorizing requests according to predefined conditions and parameters, but these rules are static, meaning they won't adapt or improve over time. Service desks may use AI technology such as Machine Learning (ML) to build a categorisation system based upon historical IT service desk information. These ML models are able to be improved as time passes by using live data. The models based on ML perform better than manual categorization as well as rule-based automations. Vendors can develop similar AI-based models to provide insights and identify anomalies in IT service desks. This typically requires a significant amount of time, effort , and skill from humans. Actual scenarios can include suggesting the best window for patches, aiding in change planning, implementation, flagging violations of an SLA and predicting IT problems. ITSM: How AI operates. AI algorithms and software are developed based on the available documented knowledge and historic data. This means that artificial intelligence is as efficient as the knowledge base and data it is based on. ITSM requires that AI-based models be created for any specific setting. This is the same as ITSM. It must have a well-documented set of solutions, workarounds, and articles as well as historic data. To train, for instance, an AI-based categorization model, we will require an archived database that includes all requests that include variables like the type of request, level, impact, urgency and site, and the entire process must be documented properly. On top of everything, AI-based models like these aren't universal, which means while a certain model might work for one particular service desk, it won't be effective for all other. Prioritization and categorizing models are trained using a particular data set. They only work for the service desk that has the data set. The models are continuously trained using live data in order to improve their effectiveness and accuracy as time passes.