AI in IT service management (ITSM) departments may be delivering a reasonable return on investment, but it’s not yet making life any easier for IT professionals.
A new survey by SolarWinds found that 84% of respondents believe AI has met or exceeded ROI expectations, and many have unlocked meaningful time savings across core tasks.
Yet despite these apparent benefits, more than half (52%) revealed their overall workload has increased since adopting the technology. Notably, just 7% said the cost of AI adoption has matched what they planned for.
Even after an average of about 16 months using AI in ITSM environments, most teams are still managing AI’s overhead rather than realizing its full potential.
Real gains, but new workloads
SolarWinds said the survey highlights the productivity benefits of using the technology in ITSM tasks.
Respondents reported that AI is saving them an average of 3.2 hours per week detecting and flagging issues, 3.0 hours on end-user requests, and 2.9 hours on ticket triage.
Most of the time saved is being swallowed up by new work, however. Nearly half (48%) said they now spend time managing and maintaining AI tools and integrations.
A similar number (47%) spend more time reviewing and validating AI-generated outputs, while 37% end up training and fine-tuning AI models.
As for unexpected costs, 48% cited staff training, 47% data quality and clean-up, and 45% tuning and maintenance – all of which are ongoing, rather than one-time costs.
More than four-in-five respondents (83%) said they now spend three or more hours per week just keeping their AI systems running reliably.
ITSM teams still stuck in reactive mode
Most ITSM teams seem to be taking a reactive rather than proactive approach. According to SolarWinds.
When asked where AI has had the greatest impact across the incident lifecycle, three-in-ten cited identifying issues before they impact users and 23% pointed to prioritizing and routing issues.
Only 19% cited preventing issues before they occur as the area of greatest impact.
The answer, according to SolarWinds, is to concentrate efforts where they’re most likely to pay off – high-frequency, well-defined tasks where gains are measurable and feedback loops are tight, such as ticket triage, issue detection, and incident.
Teams should consolidate AI closer to existing service workflows rather than spreading it across disconnected tools and strengthen the data foundation.
Notably, data quality is the top reason AI fails to deliver expected value, and treating it as part of the AI strategy rather than a separate clean-up project directly determines output quality.
“We’re at an inflection point in IT service management. AI adoption is no longer the hard part — the hard part is building the organizational discipline to make AI actually deliver,” said Brad McGinity, GM of ITSM, SolarWinds.
“The teams that get this right aren’t just running a faster service desk; they’re running a fundamentally different operation.”
Lingering ROI woes
ROI with AI has become a common recurring talking point for IT leaders over the last three years, with the financial benefits still up for debate at some enterprises.
Recent IDC research found one-in-five firms admitting they’re investing aggressively in AI with little evaluation of the likely ROI.
However, ROI isn’t necessarily all about the money. According to recent research from KPMG, other key metrics include the performance and quality of work and the speed and accuracy of decision making.
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