CIOs are starting to use different AI and machine learning capabilities to improve IT service management processes....
Use cases of AI in ITSM typically combine natural language processing with AI-infused mining of ITSM data. Natural language processing (NLP) is being used to automate user requests for IT services, while applying AI and machine learning to ITSM data is giving IT practitioners a richer understanding of their infrastructure and processes.
"AI technology is new and still very much on the bleeding edge when it comes to its application within the ITSM space," said Milind Wagle, CIO at Equinix Inc. "Within the last one to two years, there has been a dramatic increase in start-up companies that are trying to do just that -- many of which are still very early in their maturity state."
Natural language processing -- the underlying technology in virtual agents -- is often configured through integrations with chat services like Slack that are already used by employees for communicating with each other.
Using AI in ITSM processes, in principle, makes a lot of sense due to the volume of data ITSM systems generate. The systems collect lots of data about what's being requested, along with information about when, by whom and why. This data also provides a glimpse into the IT assets and processes that are in place and can help identify who owns them, how they are used and whether they are still relevant.
"Data is the fuel that AI needs to deliver relevant and valuable insights," said John Peluso, CTO of AvePoint's Public Sector. Quick wins from applying AI in ITSM come from insights that can drive priority, predictive action and critical alerts.
Ready to dig a little deeper? Here are 10 ways in which machine learning and AI in ITSM is changing how IT services are delivered.
1. Automatic categorization of incidents using chatbots
Chatbots integrated into ITSM infrastructure can be used to categorize the underlying problem in employee requests. For example, Genpact recently went live with the BMC Chatbot integrated with BMC Helix SaaS suite, which automated connectivity with ITSM infrastructure across cloud and on-premises infrastructure. The chat interface simplified the ability to prioritize and automate level one and two service requests for 50,000 Genpact users.
2. Intelligent assignment for incoming requests
Service desk teams have different skill sets, and some are better at resolving different types of IT requests than others. Using AI in ITSM can automatically triage tickets to the correct support groups without having to have humans up front reading the content in the ticket to make a decision, said Equinix's Wagle.
3. Automatically fulfill basic requests through task automation
NLP can help chat agents handle categories of requests and incidents. These agents can help answer common questions using historical ticket data and an ITSM knowledge base.
But this application of AI in ITSM requires creating a repository of proper documentation of past request history and relevant knowledge articles, said Kumaravel Ramakrishnan, product manager for ManageEngine, an IT operations and service management provider under the Zoho Corp. umbrella.
4. Generating a solutions repository
Enterprises are starting to embed IT operations provisioning tools directly into the chat services used by developers. These tools help generate a unified repository that allow developers and operations teams to keep track of changes to infrastructure and how different types of incidents were successfully resolved. Later, when similar problems emerge, AI engines can mine this repository to help operations teams resolve the problem more quickly.
IT service desks are adopting the same types of tools to generate knowledge repositories for broader sets of IT service requests as well. "Good IT professionals with experience could tell you that most of the issues they solve today are based on past experiences with solving issues," said Oded Moshe, vice president of products at SysAid Technologies Ltd., an IT help desk provider. AI can also help mine IT service request data outside of the chat channel into repositories that can be used to solve current issues. This augmented repository speeds up the process for solving issues.
5. Event resolution guidance
A good ITSM repository can also guide issue resolution through the ITSM tool. AI-generated advice could be as simple as suggesting a related incident, solutions article or configuration item, reducing the time required to think about how to locate an item and then search for it.
Guided use of the ITSM tool is even better, said Matt Cox, senior manager of solutions consulting at Samanage Ltd., an IT service desk provider. In this case, using AI in ITSM creates best practices and automatically points IT service desk practitioners toward that behavior, rather than forcing them to identify the best use of an ITSM tool. One example is to create an algorithm to identify related incidents and their trends for problem management and suggest that agents open a problem record.
6. Learning process flows optimized with machine learning
Many IT requests, like employee onboarding, require human agents to perform a complex set of steps to fulfill the requests. Enterprises are now using machine learning models to watch how humans execute these processes so that future requests can be more automated. In the case of employee onboarding, machine learning models learn from a historical database of requests that cover a range of actions taken based on type of employment, role and department of the new employee. The trained models then assign new requests to the right technicians. By recognizing patterns in the employee onboarding request database, machine learning-based models can also suggest what hardware or software an employee needs right when the onboarding request is created, said ManageEngine's Ramakrishnan.
7. Proactive problem resolution improved by big data analytics
Advances in big data and analytics are improving the predictive and correlative capabilities for ITSM. Based on analysis of the repository and user behavior patterns, AI and machine learning tools can help reduce the number of IT issues users experience, or forecast and fulfill users' requests before they even know they have a problem.
"Issues ranging from IT outages to individual user hardware malfunctions can be predicted, and solutions automatically applied or at least suggested with an increasingly higher rate of success as the system learns from past experiences," said Ambarish Kayastha vice president of product management for ITSM and automation at CA Technologies.
AI enables better, faster, proactive and automated problem resolution of issues introduced through changes in the environment, changes in end-user behavior or changes within your apps and services, said Andreas Grabner, DevOps activist at Dynatrace. For example, Citrix is using ITSM integrations across Dynatrace, ServiceNow and AWS. When the Dynatrace engine predicts a problem, it can do root cause analysis before customers are impacted.
8. Anomaly detection by flagging unusual repeat incidents
The signs of some IT incidents may not be apparent through traditional ITSM monitoring tools. AI machine learning models are being trained to detect anomalous behavior that may occur across multiple IT systems. These models can help alert IT staff to a problem before an incident has occurred.
9. Using predictive analytics to flag requests that could violate SLAs
IT service requests can result in spinning up software and hardware configurations that reduce applications performance or, worse, break entirely. Predictive analytics are being used to mine performance data within and even across enterprises to identify potential problems. This insight can provide guidance to users or the IT service desk on alternative approaches for fulfilling a request that meets service-level agreements.
10. Identifying security vulnerabilities
Security researchers are constantly identifying vulnerabilities with commonly used IT infrastructure applications and configurations. This can include libraries for developing applications as well as infrastructure. AI tools can interpret new reports and prioritize issues for security teams to address before a recently discovered vulnerability can be used by attackers.