SolarWinds Research Finds AI Is Delivering ITSM Gains, but Not Yet Reducing Workloads
AI is delivering measurable productivity gains in IT service management, but many IT teams are discovering that those gains come with a new layer of work and costs, according to new research from SolarWinds. The company’s 2026 State of ITSM Report, based on a global survey of more than 800 IT professionals, found that 84% of respondents say AI has met or exceeded their return-on-investment expectations. However, 52% report that their overall workload has increased since adopting AI, while just 7% say the actual cost of AI adoption matched their original expectations.
After an average of around 16 months of AI use in ITSM environments, the findings suggest that organisations are moving beyond the initial question of whether AI can deliver value and confronting a more difficult challenge: how to capture those gains without creating additional operational overhead. The research points to clear productivity improvements across everyday ITSM activities. Respondents report saving an average of 3.2 hours per week on detecting and flagging issues, 3.0 hours on end-user requests and 2.9 hours on ticket triage.
However, those time savings are being redirected towards activities associated with managing AI itself. Nearly half of respondents, 48%, say they spend time managing and maintaining AI tools and integrations, while 47% are reviewing and validating AI-generated outputs. A further 37% report spending time training and fine-tuning AI models.
The financial impact is similarly proving more complex than many organisations anticipated. Staff training, cited by 48% of respondents, is the leading unexpected AI expense, followed by data quality and cleanup at 47% and ongoing tuning and maintenance at 45%. The ongoing nature of these costs is significant. More than four in five respondents, or 83%, spend at least three hours a week keeping their AI systems running reliably.
The result is a shift rather than an outright reduction in workload. AI is taking on certain repetitive tasks, but IT teams are simultaneously developing new responsibilities around managing, validating and maintaining the technology. The research also highlights a gap between AI adoption and the maturity of how organisations are using it.
When asked where AI has had the greatest impact across the incident lifecycle, 31% of respondents pointed to identifying issues before they affect users, making it the most frequently cited area. Another 23% highlighted prioritising and routing issues.
Only 19% identified preventing issues before they occur as the area where AI has had its greatest impact. This suggests that, despite increasing AI adoption, many ITSM teams are still using the technology primarily to respond to emerging problems rather than eliminate them before they occur.
Investment patterns indicate that organisations expect this to change. Eighty-five percent of respondents say their AI in ITSM budgets have increased year over year, including 36% who report significant increases. Agentic workflows, which represent the most proactive category of AI capabilities in the survey, are also expected to see the highest investment growth.
For organisations looking to turn AI investment into sustainable value, the research points towards the importance of infrastructure, data and governance rather than simply adding more AI capabilities. SolarWinds recommends starting with high-frequency, clearly defined ITSM tasks such as ticket triage, issue detection and incident documentation, where results can be measured and feedback can be gathered quickly.
Reducing the number of disconnected AI tools and integrations is another consideration. As AI capabilities spread across different platforms, the resulting integration and maintenance requirements can create additional operational overhead. Data quality is equally important. With data quality and cleanup ranking among the leading unexpected costs, organisations need to treat their data foundation as part of the AI strategy rather than as a separate technical exercise.
Measurement is another area where organisations appear to have room for improvement. Only 21% of respondents say they measure AI using outcome or experience-based metrics. The research also finds that organisations measuring AI by activity rather than outcomes are 2.4 times more likely to report increased workloads following AI adoption.
The findings suggest that the conversation around AI in ITSM is shifting. Adoption itself is no longer the primary challenge for many organisations. Instead, IT leaders are having to determine how AI can be integrated into existing operations without creating a parallel layer of tools, processes and maintenance requirements.
People also remain central to that transition. Eighty-two percent of organisations surveyed provide formal AI training and structured change management, while 66% of respondents say their bonuses and performance reviews are tied to AI efficiency gains.
“AI adoption is no longer the hard part — the hard part is building the organisational 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.”
SolarWinds says its focus is on helping customers make that transition by providing the platform, data foundation and governance required to move from AI activity to measurable business value. The findings have particular relevance for the Middle East, where national AI strategies and enterprise investment are accelerating adoption.
According to Abdul Rehman Tariq Butt, Regional Director, Middle East, SolarWinds, the region’s pace of AI investment could make the gap between adoption and operational payoff more consequential.
“The pace and scale of AI investment in the region means the gap this report identifies, between adoption and real operational payoff, is being compressed into a much shorter window, and the resulting business impact is amplified,” he said. “The takeaway then is that AI adoption alone is never a guarantee of success. Without the same governance and data discipline the report calls out, speed just gets you to the workload problem sooner.”
For IT organisations, the message from the research is increasingly clear: deploying AI is only the beginning. The bigger challenge is building the data, governance, measurement and operational foundations that allow productivity gains to translate into sustainable improvements rather than another layer of complexity.
