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With all the advancements in the area of Artificial Intelligence (AI) and its broad application across different disciplines, this new technology is making its way to IT Service Management (ITSM). Multiple waves of new technology have impacted ITSM and are promising to revolutionize the way things are done. Many of these technologies made little or no impact and were soon forgotten.
Now, the obvious question on everyone's mind is: Can AI actually make ITSM simpler and more efficient? This is the question we'll address in the two-part series "The AI Advantage In ITSM". Part one, "AI at Work" in ITSM will set the scene for our AI discussion. In the second installment, "Features and Use Cases," we'll look at specific AI-based features as well as use case scenarios poised to change the manner in which IT service desks function. Navigate to this website to get additional resources about next-gen itsm.
These are the opinions of industry experts. consider to be the case. Gartner states, in Predicts 2018, Gartner's Artificial Intelligence report[i], that by 2022, 40 percent of employees who interact with customers and government employees will be able to consult an AI virtual support agent to provide decision or process support. Gartner states that AI capabilities will empower virtual support agents as a source of support that will allow human support agents to respond faster and more effectively to citizen or customer inquiries or requests.
AI will have a significant impact on IT service desks when it's able to perform tasks that humans can't do, and perform actions that humans would not be reluctant to perform. These actions fall under one of three categories: strategic insight intelligent automations, smart insights, or predictive analytics.
For example, routing incoming tickets manually takes up a lot of time, time an IT technician could use for more important tasks. Some help desks have automated ticket routing by creating rules that categorize requests based on preset parameters and conditions, however these rules are static, which means they will not change or evolve with time.
With the aid of AI technology, such as Machine Learning (ML), service desks are able to create categorizing models that is based on historical IT service desk data. These ML models are able to be improved as time passes by using live data. These models based on ML are more effective than manual categorization and rule-based automations.
Vendors can create similar AI-based models to generate insights and identify anomalies in IT service desks. This otherwise takes a lot of effort, time and ability from humans. In real-world scenarios, this could include providing the best time to patches, assisting in changing planning and implementation, flagging requests that could violate an SLA, and predicting IT problems.
ITSM What is ITSM? AI works. AI algorithms and programs are based on historical data and documented knowledge. AI is only as good as the knowledge and data it is able to build on. ITSM requires that an AI-based model must be designed for a specific situation. This is the same as ITSM. It must be well documented with solutions, workarounds, and knowledge articles as well as historic data. To train an AI-based categorization/prioritization model for instance, we need a historical database that contains the types of requests, their levels and impacts, as well as urgency website and other parameters. This data must be documented.
On top of everything AI-based models, like they aren't universal. This means that while a particular model may work on one service desk, it likely won't perform for all of them. Models for categorizing and prioritizing are trained using a particular data set. They are only available to the desk with the same data set. To improve their effectiveness and accuracy the models are continually taught using live data.
Now, the obvious question on everyone's mind is: Can AI actually make ITSM simpler and more efficient? This is the question we'll address in the two-part series "The AI Advantage In ITSM". Part one, "AI at Work" in ITSM will set the scene for our AI discussion. In the second installment, "Features and Use Cases," we'll look at specific AI-based features as well as use case scenarios poised to change the manner in which IT service desks function. Navigate to this website to get additional resources about next-gen itsm.
These are the opinions of industry experts. consider to be the case. Gartner states, in Predicts 2018, Gartner's Artificial Intelligence report[i], that by 2022, 40 percent of employees who interact with customers and government employees will be able to consult an AI virtual support agent to provide decision or process support. Gartner states that AI capabilities will empower virtual support agents as a source of support that will allow human support agents to respond faster and more effectively to citizen or customer inquiries or requests.
AI will have a significant impact on IT service desks when it's able to perform tasks that humans can't do, and perform actions that humans would not be reluctant to perform. These actions fall under one of three categories: strategic insight intelligent automations, smart insights, or predictive analytics.
For example, routing incoming tickets manually takes up a lot of time, time an IT technician could use for more important tasks. Some help desks have automated ticket routing by creating rules that categorize requests based on preset parameters and conditions, however these rules are static, which means they will not change or evolve with time.
With the aid of AI technology, such as Machine Learning (ML), service desks are able to create categorizing models that is based on historical IT service desk data. These ML models are able to be improved as time passes by using live data. These models based on ML are more effective than manual categorization and rule-based automations.
Vendors can create similar AI-based models to generate insights and identify anomalies in IT service desks. This otherwise takes a lot of effort, time and ability from humans. In real-world scenarios, this could include providing the best time to patches, assisting in changing planning and implementation, flagging requests that could violate an SLA, and predicting IT problems.
ITSM What is ITSM? AI works. AI algorithms and programs are based on historical data and documented knowledge. AI is only as good as the knowledge and data it is able to build on. ITSM requires that an AI-based model must be designed for a specific situation. This is the same as ITSM. It must be well documented with solutions, workarounds, and knowledge articles as well as historic data. To train an AI-based categorization/prioritization model for instance, we need a historical database that contains the types of requests, their levels and impacts, as well as urgency website and other parameters. This data must be documented.
On top of everything AI-based models, like they aren't universal. This means that while a particular model may work on one service desk, it likely won't perform for all of them. Models for categorizing and prioritizing are trained using a particular data set. They are only available to the desk with the same data set. To improve their effectiveness and accuracy the models are continually taught using live data.