With all the advances in the area of Artificial Intelligence (AI) and its wide-ranging application across a variety of disciplines, this new technology is making its way to IT Service Management (ITSM). Many waves of technological innovation have had an impact on ITSM and are promising to revolutionize the way things work. A lot of these innovations had minimal or no impact, and were soon forgotten.
The obvious question that everyone is asking is "Aid in ITSM": Is AI going to assist in making ITSM more effective and less complicated? This is the issue we'll discuss in this two-part seriesentitled "The AI Advantage in ITSM." In part one, "AI at Work in ITSM," we'll set the stage to our AI discussion. In the second part, "Features and Use Cases," we'll look at specific AI-based features and use case scenarios poised to change the ways IT service desks operate. Browse around this website to find out an article source about machine learning help desk.
These are the opinions of industry experts. consider to be the case. Gartner's Predicts 2018 Artificial Intelligence report[i] states that by 2022, 40% of citizen-facing employees and government workers will use an AI virtual support agent to help in making decisions or processing their requests. Gartner says that AI capabilities will empower 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 actions.
AI will start having an impact on our IT service desks once it can perform actions that humans are bad at and perform actions humans would prefer not to perform. The actions that AI performs fall into three categories: strategic insights and smart automations. They are also known as predictive analytics.
For example, the process of routing incoming tickets manually takes up a lot of time, time an IT technician could be able to use for other tasks. Some help desks employ rules that automatize ticket routing. These rules categorize requests based on predetermined conditions and parameters. However, these rules are not permanent, which means they won't change or develop in time.
Service desks can use AI technologies like Machine Learning (ML) to build a categorisation system based upon previous IT service desk data. Best of all, the ML models will become more precise over time, when they take live data into consideration. These models based on ML are more effective than manual categorization or automated systems based on rules.
Vendors are able to create AI-based models that can produce insights and detect anomalies within IT service desks. This is a huge advancement over what would take for humans to perform. Real-life scenarios include suggesting the best window for patch updates, helping in change planning and implementation, flagging breaches of an SLA and predicting IT issues.
How AI is used how AI works in ITSM. AI algorithms and software are based on the historical data of the past as well as well-documented information. AI is only as good as the data and knowledge it was able to build on. Similarly, in ITSM for instance, in order to build an AI-based model that is suitable for any specific situation it is necessary to have an appropriately documented set of resolutions, workarounds, knowledge articles, and properly maintained historical data. To train an AI-based categorization/prioritization model for instance it is necessary to have a historical database that includes the types of requests, their levels, impacts, urgency, site and other parameters. This data must be recorded.
Apart from that AI-based models like they don't function everywhere. Categorization and prioritization models are trained on the specific data set and are only used by the service desk from which that data set is pulled. The models are continuously trained using live data in order to improve their effectiveness and accuracy as time passes.
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