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With the rapid advancements in the field of Artificial Intelligence (AI) and its broad application across different disciplines, this new technology is making its way to IT Service Management (ITSM). ITSM has seen multiple technological advancements that promise to revolutionize the way things operate. However, many of them have made little to no impression and are now simple fashions. The question on everyone's mind is: Will AI actually make ITSM simpler and more efficient? This is the issue we'll discuss in our two-part series "The AI Advantage In ITSM". Part one, "AI at Work" in ITSM will set the scene for our AI discussion. Part two "Features and Utilization Cases," will focus on specific AI-based features as well in use cases that can alter the way IT service desks operate. [url=https://aisera.com/ai-service-desk-solutions]Check out this site[/url] to find out special info about IT service desk. Industry experts make some solid forecasts about this. Gartner declares in Predicts 2018: Artificial Intelligence report[i], that in 2022, 40% of employees who interact with customers and government employees will use an AI virtual support agent for decision or process support. Gartner states that AI capabilities will power virtual support agents as a source of support that will allow human support agents to respond faster and more efficiently to inquiries from customers or citizens or requests. AI will start having an impact on our IT service desks when it is able to perform tasks which humans do not excel at and do things that humans would rather not do. These actions can be classified into three categories: intelligent automation, strategic insights and predictive analytics. Routing incoming tickets manually takes a lot of time that an IT technician might be able to use to perform other duties. Help desks may use rules that automatize ticket routing. These rules categorize requests based on predetermined conditions and parameters. However the rules aren't dynamic, so they don't alter or evolve over time. Service desks can use AI technology such as Machine Learning (ML) to create a categorisation model that is based on historical IT service desk data. Most importantly, these ML models will become more accurate over time when they take live data into consideration. These ML-based models perform better than manual categorization or automated based on rules. Vendors can create similar AI-based models that can generate insight and spot anomalies within IT service desks. This typically requires a significant amount of time, effort , and ability from humans. Actual scenarios can include suggesting the optimal time to apply patch updates, helping with change management and implementation, identifying violations of an SLA and predicting IT problems. ITSM: How AI operates. AI algorithms and applications are based on data from the past and well-documented information. AI is only as good as the information and data it could build on. ITSM requires that an AI-based model must be created for any specific setting. Similar to ITSM. It must have a well-documented set of solutions, workarounds, and knowledge articles as well as historical data. For example, to train an AI-based categorization model, we need an archived database that includes all requests, with parameters such as request type degree, level, impact urgency, and location, and it all needs to be properly documented. Additionally AI-based models such as they aren't universal. This means that while a particular model might be useful for one service desk, it won't perform for all of them. Categorization and prioritization models are trained on a specific data set and only work for the desk that that data set is pulled. To increase their efficiency and accuracy the models are continually taught using live data.