The Gap Between AI Potential and Payoff in ITSM

Survey of 800+ IT pros finds AI is meeting expectations on paper, but only 7% say the true cost matched what they planned for
Learn more about SolarWinds

AI is broadly meeting ROI expectations in IT Service Management (ITSM), but for most teams, it is not yet easing the burden on the people doing the work, according to the 2026 State of ITSM Report from SolarWinds, a study of how IT teams are using AI in their ITSM workflows. 

84% of respondents say AI has met or exceeded ROI expectations, and they report meaningful time savings across core ITSM tasks. Yet 52% say their overall workload has increased since adopting AI, and only 7% say the cost of AI adoption matched what they planned for. After an average of about 16 months using AI in their ITSM environment, most teams are still managing AI’s overhead rather than realizing its full potential.

The AI Productivity and Cost Enigma

The data makes clear that AI creates real productivity gains. Respondents report AI saves 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. But those savings are largely being reinvested into a new category of work that did not exist at the same scale before AI:

  • Managing and maintaining AI tools and integrations: 48%
  • Reviewing and validating AI-generated outputs: 47%
  • Training and fine-tuning AI models: 37%

The costs that catch organizations off-guard compound this further. The top surprise expenses — staff training (48%), data quality and cleanup (47%), and ongoing tuning and maintenance (45%) — are not one-time setup costs. They are recurring parts of the operating model, and more than four in five respondents (83%) now spend three or more hours per week just keeping their AI systems running reliably.

Despite clear productivity gains, IT teams are discovering that AI isn’t reducing their workload; it's reshaping it.

Reactive Instead of Proactive

Despite significant maturity in AI adoption, most ITSM teams are still using AI to respond to problems rather than prevent them. When asked where AI has had the greatest impact across the incident lifecycle, respondents cited identifying issues before they impact users (31%) and prioritizing and routing issues (23%) as the top two areas, which are both fundamentally reactive responses to problems that have already emerged. Only 19% cited preventing issues before they occur as the area of greatest impact.

This reactive posture points to a gap between AI adoption and AI maturity. The tools are in place. The missing piece is the infrastructure, data foundation, and organizational discipline to move AI upstream into prevention. Budget momentum suggests organizations know this: 85% say their AI in ITSM budget has increased year-over-year, with 36% saying it has increased significantly, and agentic workflows, the most proactive AI capability category, show the highest expected investment growth of any area in the survey.

An Intentional Approach is Best

Organizations can avoid inflating AI workloads and unexpected costs by taking a more intentional approach to AI implementation. IT leaders should build the right infrastructure and governance to enable proactive AI adoption, including:

  • Start where the path to value is clear. Focus AI on high-frequency, well-defined tasks first — ticket triage, issue detection, incident documentation — where gains are measurable and feedback loops are tight.
  • Reduce friction around the work. Consolidate AI closer to existing service workflows rather than spreading it across disconnected tools. Every integration point is another source of maintenance overhead.
  • Strengthen the data foundation. Data quality is the top reason AI fails to deliver expected value — treating it as part of the AI strategy, not a separate cleanup project, directly determines output quality.
  • Measure outcomes, not just activity. Only 21% of respondents measure AI in outcome or experience terms. Teams that measure AI by activity, rather than outcomes, are 2.4 times more likely to say their workload has increased since adopting AI.
  • Bring people along intentionally. Eighty-two percent of organizations offer formal AI training and structured change management, with 66% of respondents seeing their bonuses and performance reviews tied to AI efficiency gains.

"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."

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The Gap Between AI Potential and Payoff in ITSM

Survey of 800+ IT pros finds AI is meeting expectations on paper, but only 7% say the true cost matched what they planned for
Learn more about SolarWinds

AI is broadly meeting ROI expectations in IT Service Management (ITSM), but for most teams, it is not yet easing the burden on the people doing the work, according to the 2026 State of ITSM Report from SolarWinds, a study of how IT teams are using AI in their ITSM workflows. 

84% of respondents say AI has met or exceeded ROI expectations, and they report meaningful time savings across core ITSM tasks. Yet 52% say their overall workload has increased since adopting AI, and only 7% say the cost of AI adoption matched what they planned for. After an average of about 16 months using AI in their ITSM environment, most teams are still managing AI’s overhead rather than realizing its full potential.

The AI Productivity and Cost Enigma

The data makes clear that AI creates real productivity gains. Respondents report AI saves 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. But those savings are largely being reinvested into a new category of work that did not exist at the same scale before AI:

  • Managing and maintaining AI tools and integrations: 48%
  • Reviewing and validating AI-generated outputs: 47%
  • Training and fine-tuning AI models: 37%

The costs that catch organizations off-guard compound this further. The top surprise expenses — staff training (48%), data quality and cleanup (47%), and ongoing tuning and maintenance (45%) — are not one-time setup costs. They are recurring parts of the operating model, and more than four in five respondents (83%) now spend three or more hours per week just keeping their AI systems running reliably.

Despite clear productivity gains, IT teams are discovering that AI isn’t reducing their workload; it's reshaping it.

Reactive Instead of Proactive

Despite significant maturity in AI adoption, most ITSM teams are still using AI to respond to problems rather than prevent them. When asked where AI has had the greatest impact across the incident lifecycle, respondents cited identifying issues before they impact users (31%) and prioritizing and routing issues (23%) as the top two areas, which are both fundamentally reactive responses to problems that have already emerged. Only 19% cited preventing issues before they occur as the area of greatest impact.

This reactive posture points to a gap between AI adoption and AI maturity. The tools are in place. The missing piece is the infrastructure, data foundation, and organizational discipline to move AI upstream into prevention. Budget momentum suggests organizations know this: 85% say their AI in ITSM budget has increased year-over-year, with 36% saying it has increased significantly, and agentic workflows, the most proactive AI capability category, show the highest expected investment growth of any area in the survey.

An Intentional Approach is Best

Organizations can avoid inflating AI workloads and unexpected costs by taking a more intentional approach to AI implementation. IT leaders should build the right infrastructure and governance to enable proactive AI adoption, including:

  • Start where the path to value is clear. Focus AI on high-frequency, well-defined tasks first — ticket triage, issue detection, incident documentation — where gains are measurable and feedback loops are tight.
  • Reduce friction around the work. Consolidate AI closer to existing service workflows rather than spreading it across disconnected tools. Every integration point is another source of maintenance overhead.
  • Strengthen the data foundation. Data quality is the top reason AI fails to deliver expected value — treating it as part of the AI strategy, not a separate cleanup project, directly determines output quality.
  • Measure outcomes, not just activity. Only 21% of respondents measure AI in outcome or experience terms. Teams that measure AI by activity, rather than outcomes, are 2.4 times more likely to say their workload has increased since adopting AI.
  • Bring people along intentionally. Eighty-two percent of organizations offer formal AI training and structured change management, with 66% of respondents seeing their bonuses and performance reviews tied to AI efficiency gains.

"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."

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Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

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