Artificial intelligence has rapidly become a cornerstone of modern IT service management (ITSM), promising faster issue resolution, greater automation, and improved operational efficiency. Enterprises worldwide have invested heavily in AI-powered service desks, intelligent workflow automation, and predictive analytics in pursuit of leaner, more productive IT operations.
Yet a new report from SolarWinds suggests that while AI is delivering measurable business value, it is also creating an unexpected challenge: many IT teams are working harder, not less.
The company’s 2026 State of ITSM Report, based on feedback from more than 800 IT professionals globally, reveals a striking contradiction at the heart of enterprise AI adoption. While organizations are largely satisfied with the return on their AI investments, most are struggling with increased workloads, rising maintenance demands, and unanticipated implementation costs.
The findings indicate that AI is not simply replacing work. Instead, it is fundamentally reshaping how IT departments operate.
The Promise of AI Is Being Realized
For many enterprises, AI adoption has already generated significant benefits.
According to the survey, 84% of respondents said AI has met or exceeded their return-on-investment expectations. IT teams reported meaningful productivity improvements across core service management functions, demonstrating that AI is delivering tangible operational gains.
On average, organizations reported saving:
3.2 hours per week on issue detection and monitoring
3.0 hours per week on handling end-user requests
2.9 hours per week on ticket triage and classification
These improvements translate into faster service delivery, quicker incident response, and more efficient use of IT resources.
“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.”
Brad McGinity, GM of ITSM, SolarWinds
Brad McGinity, General Manager of ITSM at SolarWinds, believes these results prove that the technology itself is not the problem.
“The teams that get this right aren’t just running a faster service desk; they’re running a fundamentally different operation,” McGinity said. “At SolarWinds, our job is to make that transition as straightforward as possible, giving customers the platform, the data foundation, and the governance they need to move from AI activity to real AI payoff.”
His comments highlight a growing industry consensus that AI’s value lies not merely in automation but in the ability to transform operational models altogether.
Why Workloads Continue to Rise
Despite delivering productivity gains, AI has failed to reduce workloads for many IT professionals.
In one of the report’s most surprising findings, 52% of respondents said their overall workload increased after introducing AI into their ITSM environments.
The reason is straightforward: AI has introduced a range of new responsibilities that did not previously exist.
Nearly half of respondents reported spending significant time:
Managing AI tools and integrations (48%)
Reviewing and validating AI-generated outputs (47%)
Training and fine-tuning AI systems (37%)
Rather than eliminating human involvement, AI often requires ongoing monitoring and governance to ensure reliable outcomes.
Organizations are discovering that AI systems must be continuously evaluated, updated, and optimized. Automated recommendations require validation. Models require training. Integrations need maintenance.
As a result, many IT departments are balancing traditional service management responsibilities alongside entirely new AI-related tasks.
The report suggests that what appears to be automation from the outside often involves a considerable amount of work behind the scenes.
“AI adoption alone is never a guarantee of success. Without the governance and data discipline needed to support it, organizations can reach the workload problem faster instead of realizing the business value they expect.”
Abdul Rehman Tariq Butt, Regional Director, Middle East, SolarWinds
The Cost Challenge Nobody Expected
Another major finding centers on the true cost of AI adoption.
Despite positive ROI perceptions, very few organizations accurately predicted the financial commitment required to support AI initiatives.
Only 7% of respondents said actual costs matched their original expectations.
The largest unexpected expenses included:
Staff training and skills development (48%)
Data cleansing and preparation (47%)
Ongoing tuning and maintenance (45%)
These costs often emerge after deployment, making them difficult to account for during initial planning stages.
The study found that more than four in five respondents spend at least three hours per week maintaining AI systems. This continual investment of time and resources highlights a crucial reality: AI is not a one-time technology implementation but an ongoing operational commitment.
For many organizations, the challenge is no longer purchasing AI tools. It is sustaining them.
AI Still Operates in Reactive Mode
Although AI has become increasingly sophisticated, the report found that most organizations continue to use it primarily for reactive functions rather than preventive operations.
When asked where AI had the greatest impact in the incident lifecycle, respondents identified:
Detecting issues before users are affected (31%)
Prioritizing and routing incidents (23%)
While these functions improve efficiency, they remain focused on responding to events that have already begun to emerge.
Only 19% of respondents said AI’s greatest contribution was preventing incidents before they occurred.
This finding points to a broader maturity gap in enterprise AI adoption.
Many organizations have successfully implemented AI tools, but far fewer have established the infrastructure, governance frameworks, and high-quality data environments necessary to unlock proactive capabilities.
The result is that AI frequently helps organizations react faster rather than preventing problems altogether.
The Importance of Governance and Data Discipline
The SolarWinds report argues that technology alone cannot deliver the full benefits of AI.
Success increasingly depends on governance, reliable data, and disciplined implementation strategies.
Abdul Rehman Tariq Butt, Regional Director for the Middle East at SolarWinds, said the challenge is particularly visible in markets experiencing rapid AI adoption.
“The pace and scale of AI investment in the region means the gap between adoption and real operational payoff is being compressed into a much shorter window, and the resulting business impact is amplified,” Butt said.
He added that organizations often assume deploying AI automatically leads to better outcomes.
“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.”
His comments underscore one of the report’s central messages: organizational readiness matters as much as technological capability.
Without quality data and strong operational controls, AI can become another source of complexity rather than a driver of efficiency.
Investment Momentum Remains Strong
Despite the challenges, organizations are not slowing their AI investments.
The study found that 85% of organizations increased their AI-related ITSM budgets year over year, while 36% reported significant increases.
Particularly noteworthy is the growing interest in agentic AI workflows, which respondents identified as the area expected to receive the strongest future investment.
Agentic AI systems are designed to work more independently, making decisions, coordinating activities across systems, and taking action without requiring extensive human intervention.
Industry leaders increasingly view these capabilities as the next evolution of IT service management, moving enterprises from automated processes toward autonomous operations.
From AI Activity to AI Outcomes
The SolarWinds findings suggest enterprises have entered a new phase of AI adoption.
The question is no longer whether AI works. The evidence shows that it does. Productivity gains are real, ROI expectations are being met, and organizations continue to invest aggressively.
The bigger challenge is ensuring that AI delivers meaningful operational outcomes rather than simply introducing new layers of complexity.
As McGinity noted, the organizations seeing the greatest success are those treating AI as a transformation initiative rather than a technology deployment.
“The hard part is building the organizational discipline to make AI actually deliver.”
For IT leaders, that may be the most important takeaway from the report. The future of IT service management will not be determined by how quickly organizations adopt AI, but by how effectively they integrate it into processes, governance frameworks, and workplace culture. Those that succeed could build more proactive, intelligent, and resilient IT operations. Those that do not may find that AI, despite all its promise, simply gives them more work to do.
