Artificial Intelligence, Machine Learning and IT Automation are emerging technologies today. As per research, organizations who started adopting these practices are growing rapidly.
Gartner report Says “AI will be a top 5 investment priority for more than 30% of CIOs”
AI Technologies will be in almost every new software product by 2020. It has the ability to learn, understand complex data and make conclusions based on analysis. AI in ITSM applies new AI models and algorithms to the established Service desk to deliver intelligent and speedy IT services. Service delivery process can become proactive and enhanced by applying Machine learning algorithms to the huge data collected by service providers.
Types of Artificial Intelligence for ITSM
There are three types of artificial intelligence emerging within the service desk environment.
Natural Language Processing
The ability of a computer program to understand human language and interact or respond.
Machine Learning
Extract data from different reports to analyze and recognize the patterns from a series of observations, and based on that make the decisions and recommendation.
Virtual Support Agents
Virtual digital assistants are chatbot programs that use virtual character acting as a service desk agent. Virtual agents are a blend of AI and Machine Learning, increasingly used in ITSM that lets users find and request services through a conversational and personalized interface. It makes it easy for employees to access IT information and services using natural language, across any channel they choose, without even leaving their current application.
Robotic Processes are Coming
According to various industry experts, RPA is going to be the next big thing. The reasoning is simple. Although AI can help you in identifying a problem, but how will you solve it? This is where RPA helps you make the most out of machine learning and lets you perform the intended action.
The use of AI in ITSM is going to be more pronounced in the near future. This is all because of its ability to improve user-experience, self-sufficiency, and the overall effectiveness of the service desk.
The technology will be able to provide a great virtual human-like experience to the end users and will make the conversations more natural, engaging, and relevant.
Use cases for Service Desk where AI in ITSM is transforming IT service delivery.
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Automatic categorization of incidents
Once the user raises a ticket to service desk through email or self-service portal in natural language, Provides Service Desk employees the ability to use Machine Learning to suggest category to address incidents
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Assignment of the requests to support Group
The ticket raised through email or on the self-service portal by end user, cognitive service desk can recognize patterns in the Subject and descriptions of incidents and service requests. Based on pattern and most popular and repetitive issues, the system can automatically classify, categorize and route to an appropriate support group.
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Learning common processes for automation
The common IT processes like employee onboarding/offboarding requires multiple activities and performed manually every day. Organizations started using machine learning models to learn how manually execute these processes so that these steps can be automated. In case of employee onboarding/offboarding, machine learning based on historical data perform certain tasks like type of employment, role and department of the employee, hardware, software, application access requirements etc.
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Generating a knowledge base and repository
Organizations started creating a centralized repository that allows developers and operations teams to keep track of changes to infrastructure as well the knowledge articles of different incidents resolutions. In future, if same incident re-occurs, AI engines can mine this knowledge base and resolve the issue quickly. Service desks are using the tools to generate knowledge repositories for service requests as well and support engineers could resolve the issue quickly based on past experiences and with the help of this knowledge base.
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Proactive problem resolution
AI and Machine learning can help to reduce issues based on predictive analysis. Based on the knowledge base and issue patterns support person can identify the solution before it gets impacted to the production environment. This will be easy for them to meet SLAs and customer expectations.
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Managing Security vulnerabilities
IT Security has to identify vulnerabilities used for application development and configurations. AI can analyze the security reports and take necessary preventive actions.
