SolarWinds’ 2026 State of ITSM Report finds that artificial intelligence is delivering measurable productivity gains for IT teams, but those gains are being offset by new demands around managing, validating, and maintaining AI systems.
AI ROI rises even as ITSM workloads increase
Based on a survey of more than 800 IT professionals, the report found that 84% of respondents said AI has met or exceeded their return-on-investment expectations. At the same time, 52% said their overall workload has increased since adopting the technology.
The findings suggest that AI is not necessarily reducing the amount of work IT teams perform, but instead changing where that work is concentrated. Respondents reported saving an average of 3.2 hours per week on detecting and flagging issues, 3 hours on handling end-user requests, and 2.9 hours on ticket triage.
Those savings are being partially redirected toward a new set of responsibilities. Nearly half of respondents said they spend time managing and maintaining AI tools and integrations, while 47% cited reviewing and validating AI-generated outputs and 37% pointed to training and fine-tuning AI models.
Speaking with Channel Insider, SolarWinds Global Manager of Solution Engineering Sean Sebring said the findings raise a broader question about how IT organizations are using the time AI gives them back.
“If your workload increased, are you doing more meaningful work?” Sebring said.
Time savings on ticketing coupled with new AI management tasks
The report found AI is already producing measurable time savings across several common IT service management (ITSM) tasks. Respondents reported saving an average of 3.2 hours per week on detecting and flagging issues, 3 hours on handling end-user requests, and 2.9 hours on ticket triage.
At the same time, respondents reported spending time on a new set of AI management tasks, including:
- Managing and maintaining AI tools and integrations: 48%
- Reviewing and validating AI-generated outputs: 47%
- Training and fine-tuning AI models: 37%
Sebring said that when he added the time savings reported across the survey and compared them with the hours respondents reported spending on AI maintenance, teams could still net roughly eight hours per week.
“We’re talking about increased workload. Well, we still, on average, saved eight hours, if you add up how much was saved versus how much is spent taking care of it,” Sebring said.
Data quality and integration limit proactive AI use
Despite the productivity gains, SolarWinds’ research suggests that ITSM teams have yet to fully shift AI from reactive workflows to proactive operations.
When respondents were asked where AI has had the greatest impact across the incident lifecycle, 31% cited identifying issues before they affect users, while 23% cited prioritizing and routing issues.
Only 19% said that preventing issues before they occur has been AI’s greatest area of impact.
Sebring pointed to two foundational challenges standing in the way of that transition: data quality and integration complexity.
“Stronger, more trusted data, and then a simpler, more streamlined integration for your service management and your IT operations,” Sebring said.
He explained that service management is often reactive, responding to requests, complaints, or incidents after they arise. IT operations, meanwhile, provide visibility into the health of infrastructure and systems.
Bringing those two sides together can give organizations the information needed to identify potential problems earlier and make more proactive use of AI.
Why teams should fix processes before trying to automate them
However, Sebring cautioned that organizations need to address weaknesses in their existing environments before attempting to automate them.
“If we’re trying to automate, meaning we’re implementing AI against poor data, poor practices, poor processes, then we’re speeding up poor processes, poor practices,” Sebring said. “We’re going to speed up poor results.”
For Sebring, these foundational issues make AI readiness an important consideration for organizations looking to expand adoption.
Rather than implementing AI simply to keep up with the broader market, he said organizations should first evaluate whether their data, processes, and existing technology stacks are prepared for it.
AI costs expose budgeting and readiness gaps
The AI readiness question also extends to how organizations budget for AI.
According to the report, only 7% of respondents said the actual cost of AI adoption matched their plans.
The top surprise expenses listed were:
- Staff training at 48%
- Data quality and cleanup at 47%
- Ongoing tuning and maintenance at 45%
More than four in five respondents (83%) also said they spend at least three hours each week keeping their AI systems running reliably.
Sebring said part of that budgeting gap may come from the unusually high expectations organizations have placed on AI compared with previous emerging technologies.
“Our standard and expectation around AI is already higher than most other new technologies that came out,” Sebring said.
That can include expectations around how quickly organizations should realize value from AI, as well as differing interpretations among business leaders about what the technology can realistically accomplish.
“Well, it’s AI. It should solve all of these problems for me,” Sebring said, describing the type of expectation organizations can bring into AI deployments.
MSPs can guide AI deployment through tech stack expertise
When asked about the role MSPs and other channel partners can play in addressing AI adoption and maturity gaps, Sebring emphasized the importance of understanding customers’ technology stacks.
“The technology stack is what’s important for partners, distributors, resellers, right, the channel, to make sure that they understand,” Sebring said.
Sebring said understanding a customer’s existing technology environment will be especially important as partners help organizations determine where and how AI should be deployed.
SolarWinds, for its part, is positioning its portfolio around closer integration between IT operations and service management. Sebring said a more consolidated technology stack can help customers reduce integration complexity and avoid relying on multiple vendors to address different parts of their environment.
However, he acknowledged that consolidating around a single vendor will not necessarily be the right answer for every organization.
“It’s not to say that multiple vendors isn’t an answer at times,” Sebring said. “It’s just knowing what makes the most sense in the technology stack.”
AI ROI could shift toward employee and customer experience
Looking ahead, Sebring said one question SolarWinds hopes to explore in future studies is how IT teams are actually using the time they save through AI.
“When we look at things, and this report is very evident, we’re still kind of just thinking in the same box of if I was a service desk manager, my time doing these menial tasks got faster. My workload feels more,” Sebring said.
“So let’s unpack why. Are you doing more meaningful work? Has it yielded in other parts of the business, increasing that user or consumer experience because of that?”
In the same vein, Sebring expects the way organizations define AI ROI to evolve beyond simply measuring how much faster IT teams can complete existing tasks.
He pointed to employee and customer experience as areas that could heavily influence how organizations determine whether their AI investments are producing meaningful results.
“That’s where I think that the next phase of kind of interpreting ROI from AI is, is on the experience side,” Sebring said. “And that’s both consumer and employee or service provider.”
Channel Insider also recently spoke with Hatz AI CEO Jimmy Hatzell about the mounting pressure on MSPs to formalize their AI strategies, particularly as SMB adoption accelerates. Read more about why deployment, cost, and governance are becoming key considerations for providers.