The Most Popular ITSM AI Use Cases
in 2025

There's a lot of "noise" related to the adoption of artificial intelligence (AI)-enabled capabilities in IT service management (ITSM). Different ITSM tools offer a range of core and some differentiating AI use cases. However, just because an ITSM tool offers an AI-enabled capability, it doesn't mean that the capability has been adopted or is delivering additional business value.

So, what ITSM AI use cases are IT organizations employing? A recent ManageEngine survey shed some light on this. The data is included below, along with additional practical guidance on how to maximize the benefits of the AI-enabled capabilities available in your current or next
ITSM tool.

How the AI capabilities in ITSM tools are being used

Before exploring the most popular ITSM AI use cases, it's worth acknowledging the level of AI adoption in IT organizations. It's likely higher than you think.

The ManageEngine survey asked, "Have you implemented any AI features and capabilities across your ITSM practices?" A massive 82% of survey respondents stated that their organization had already implemented AI features and capabilities within their ITSM practices. In comparison, only 17% said they hadn't.

This statistic doesn't show the depth and breadth of AI use — for example, an organization might still be experimenting with the available use cases or only adopting a small fraction of what's available in their ITSM tool. This should be borne in mind.

However, the survey did reveal where AI is being used, finding that the top use cases (for the organizations using AI) were:

• Process optimization (48%)
• Risk advisory (46%)
• Knowledge discovery(42%).

These areas might come as a surprise given the ITSM market's push of conversational virtual agents for end-users and intelligent ticket handling as early ITSM AI use cases. It's also interesting to see how adoption differs by organizational size (and varies from the aggregated top three), with the respective top three use cases as follows:

• 100 - 249 employees — "Risk advisory," "Knowledge discovery," and "Conversational virtual assistant"
• 250 - 500 employees — "Risk advisory," "Process optimization," and "Intelligent categorization" (all joint top)
• More than 500 employees — "Process optimization," "Problem prediction," and "Knowledge discovery."

Hopefully, these top ITSM use cases are based on what's deemed to deliver the greatest business value relative to the ease of adoption. For example, "Conversational virtual assistant" was in joint fourth place at 39% and while promoted as an early ITSM use case, its omission from all but the 100-249-employee organizations' top three use cases is likely testament to the investment needed to get the capability consistently working at a level that meets end-user and IT staff needs and expectations.

Choosing the right ITSM AI use cases for your organization

Selecting the right ITSM AI use cases is a critical step on the path to successful AI adoption. As you might expect, there's a need to start with business value as the primary driver. While it's easy to get excited by the available technology, there's a need to focus on a real problem or opportunity.

If IT support operations are used as an example, the driver or drivers could be one or more of the following:

• Reducing ticket volumes
• Accelerating incident resolution times (and reducing downtime)
• Improving end-user satisfaction (or, ideally, improving employee and customer experiences
• Cutting operational costs
• Avoiding resource shortages.

A use-case scoring framework can be used to evaluate each use case across four dimensions:

• Business value
• Feasibility
• Risk
• Readiness

Your organization can then prioritize high-value, low-risk, high-feasibility use cases. These can often be considered "low-hanging fruit" use cases versus the high-risk early use cases that are usually harder to implement early unless your organization's AI maturity is high.

Many of the "usual suspects" in business transformation also apply. For example, ensure that you tie your AI initiatives to your ITSM and business strategies and goals. This might be improving self-service adoption or increasing first-contact resolution levels. There's also the need to get stakeholder and leadership buy-in early and to leverage other organizational change management (OCM) tools and techniques, such as:

• Keeping everyone informed of what's happening and aligned
• Equipping people with skills and confidence to work effectively with AI use cases
• Building trust and tailoring the change "perspective" to different stakeholder groups.

Use an iterative approach to AI adoption that involves using a "friendly" team or location to pilot the new capabilities/use cases, measuring the level of success via agreed-upon success metrics. Importantly, be prepared to adapt or abandon an AI initiative if the results don't meet collective expectations.

Finally, it's important to appreciate that technology implementation and early adoption aren't the finish line. Utilization habits must be reinforced, with it likely that the required new behaviors need to be embedded into workflows and performance goals.

Hopefully, this blog post has offered some helpful insights into ITSM AI use cases. If you want to download the full "The advent of AI agents in ITSM: Perception and future impact report," it's available here.