SolarWinds 2026 State of ITSM Report: From AI Potential to Real Payoff

The 2026 SolarWinds State of ITSM Report, released today, shows that AI helps IT teams do more work. But there is a catch. Instead of making the job easier, AI is changing the kind of work IT professionals do.

The report surveyed over 800 IT workers. It found that 84% of them see the value in AI. However, 52% of teams say their workload has increased. Only 7% say the costs matched what they planned.

This creates a split: teams have high productivity, but they also have a full plate. Sean Sebring, Global Manager, Solution Engineering – ITSM at SolarWinds, explained why this happens.

“We shouldn’t expect the workload to decrease; it should shift,” Sebring told ITOps Times. “We don’t want to be replaced, we don’t want to become obsolete. Instead, our workload will just change.”

Moving from Doing to Checking

AI saves time on tasks like finding issues and handling user requests. But that time does not just disappear. Instead, teams spend those hours managing the AI tools themselves. About half of the teams surveyed say they now spend time reviewing what the AI produces and keeping the systems running.

Sebring noted that this is part of using AI. “If you take any AI training today, it is all about ‘human in the loop,’” he said. “You need a person there managing these things, overseeing them, and validating things. It is signing off: yes or no, we can move forward with this.”

He warns against treating “human in the loop” as a passive term. It is not just about being told when things happen. It is active management.

Preparing for AI

Many teams rush to use AI because they fear falling behind others. But in their hurry, they often ignore the basics. The report says that data quality is the biggest barrier.

Sebring says IT leaders should follow the “optimize and automate” rule. This means fixing processes before adding AI.

“If you’re automating garbage, you’re going to get garbage really quickly,” Sebring explained. “When we’re talking about data and AI, if we have good processes, they are reliable, and we want to automate them—absolutely, let’s automate it. But let’s start where we are and see what we have. If what we have is a poor process, and we go to leverage AI and automate it, then it’s only going to produce poor results more quickly.”

For companies with systems that are too broken to fix, Sebring suggested a “scorched earth” approach. This means building a fresh foundation designed specifically for AI, rather than trying to fix old, messy systems.

“I think it should be a consideration,” Sebring says. “Let’s start fresh in the sense that let’s entertain that the environment I’m introducing AI in was built for AI, not something from a legacy system where we didn’t have the proper considerations in place.”

Focusing on Results

Teams that measure AI success by how much they do often report higher workloads. Teams that measure success by the outcomes report fewer issues.

Sebring said that IT teams need to revisit how they track progress. Instead of just counting tickets, they should look at user experience and the value of the work.

“If I said that my workload hasn’t changed, but my efficiency has increased, let’s unpack that,” he said. “Is the workload more meaningful work? Are we able to focus more on experience?”

By focusing on real outcomes, IT teams can stop fighting an endless queue of tickets and start using AI to improve the service they provide. As the report concluded, teams that get this right are not just faster; they are running a different kind of operation.