SolarWinds’ new State of ITSM Report finds that AI is now mainstream and delivering a clear return on investment in IT service management. However, the report also shows a perception that AI has actually increased workloads in some cases. With process maturity, solid data foundations and best practice approaches, the employee AI experience is likely to improve and AI in ITSM will prove overwhelmingly beneficial.
This is according to Sean Sebring, Solutions Engineer Manager, IT Service Management at SolarWinds, who says the main theme from this year’s report is the payoff from AI. Clearly, AI is creating measurable gains across core ITSM work, he says.
The report found that every respondent reported using some form of AI, with an average of about 16 months of experience.
It also found that gains from machine learning, generative AI and agentic AI in ITSM are significant: 84% of respondents say AI has met or exceeded ROI expectations, and they report meaningful improvements across metrics such as employee productivity, incident prevention, cost per ticket, service level agreement compliance, mean time to detect and mean time to resolve. Average resolution time decreased by nearly 18% and AI saved around three hours a week on tasks such as detecting and flagging issues, responding to end-user requests, triaging and categorising tickets, creating/uploading knowledge base articles, documenting and summarising incidents and resolving and remediating. Eighty-six percent reported improved employee productivity, with 47% calling it significant.
Despite these gains, many respondents say their workload has not decreased since adopting AI, with 19% saying it stayed the same and 33% saying it increased.
Sebring says this contradiction may be due to the fact that managing, integrating and validating AI tools are relatively new functions that require more focus and thus are perceived to be harder work.
The report suggests that AI isn’t shrinking the work as much as reshaping it.
“There’s a lot that needs to be learned when it comes to the management and integration of AI. This perception that AI requires work will likely change as organisations become more aware and comfortable with AI, and more users are trained on the technology. There are also more foundational best practices being made available, such as ITIL 5, and more standard training emerging around integrating and implementing AI,” he says.
“The report findings make it very evident that AI makes organisations faster and more efficient. But the report did not deeply analyse the reasons for the perception that AI did not reduce workloads. In future, we may focus on more experience-centric metrics, looking at whether agents find their work more meaningful, or whether the quality of the service has improved,” he says. Organisations focused on activity metrics are 2.4 times more likely to report increased workloads than organisations measuring outcomes and experiences, the report found.
The report says AI maturity requires redesigning the surrounding work, including tightening data quality, clarifying ownership, consolidating tools and building internal skills, so that gains aren’t immediately eaten up by maintenance, rework and integration overheads.
Driving AI in ITSM success
Sebring says: “There are two key factors that make the difference between success and failure when implementing AI in ITSM: the data foundation and integration complexity. Organisations must have a good data foundation for AI to leverage, and they must have AI tools that seamlessly integrate with their toolsets.”
To help organisations improve their chances of success, Sebring says: “I generally recommend that organisations look at where they’re at and start where they are. They should assess their datasets, consider whether they need optimisation and address any data issues. Where this isn’t feasible, organisations could consider a scorched earth approach, in which they start fresh and give themselves a few months to start building a good data and best practice process foundation before implementing AI.”
He notes: “The guiding principle ‘start where you are’ ensures that you get systems and data ready to be leveraged by AI. Organisations should optimise and then automate – specifically in that order.”
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