This gap points to a growing paradox. As a result, AI is helping teams save time, but it is not necessarily easing their overall burden.<\/p>\n\n\n\n
On average, respondents said AI saves 3.2 hours per week on detecting and flagging issues. It also saves 3.0 hours on end-user requests and 2.9 hours on ticket triage. Despite these gains, 52% of respondents say their overall workload has increased since adopting AI.<\/p>\n\n\n\n
So, where is the time going? According to the report, those savings are being reinvested into new categories of work. These include managing and maintaining AI tools and integrations (48%), reviewing and validating AI-generated outputs (47%), and training and fine-tuning AI models (37%).<\/p>\n\n\n\n
Meanwhile, unexpected costs are compounding the issue. The top surprise expenses include staff training (48%), data quality and cleanup (47%), and ongoing tuning and maintenance (45%). Notably, these are not one-time costs. Instead, they are recurring parts of daily operations. In fact, 83% of respondents now spend three or more hours per week just keeping their AI systems running reliably.<\/p>\n\n\n\n
Furthermore, the report found that most ITSM teams are still using AI reactively rather than proactively. When asked where AI has had the greatest impact, respondents pointed to identifying issues before they affect users (31%) and prioritizing and routing issues (23%). Only 19% said AI’s greatest impact was in preventing issues before they occur.<\/p>\n\n\n\n
Even so, budget momentum suggests organizations are aware of this gap. About 85% of respondents say their AI budget for ITSM has increased year-over-year, with 36% reporting a significant increase. Agentic workflows, considered the most proactive AI capability category, show the highest expected investment growth of any area in the survey.<\/p>\n\n\n\n
To close this gap, the report recommends a more intentional approach. Specifically, it suggests starting with high-frequency, well-defined tasks such as ticket triage, issue detection, and incident documentation. It also recommends consolidating AI tools to reduce integration overhead and strengthening data quality as part of the core AI strategy, not a separate project.<\/p>\n\n\n\n
Additionally, the report emphasizes measuring outcomes rather than activity. Only 21% of respondents currently measure AI performance in outcome or experience terms. Teams that focus on activity instead of outcomes are 2.4 times more likely to report increased workloads.<\/p>\n\n\n\n
“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 at SolarWinds. He added that the goal is to help customers move from AI activity to real AI payoff.<\/p>\n\n\n\n
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<\/figure>The trend is even more pronounced in the Middle East, where national AI ambitions are accelerating adoption faster than the global average. “The pace and scale of AI investment in the region means the gap this report identifies… is being compressed into a much shorter window,” said Abdul Rehman Tariq Butt, Regional Director, Middle East, SolarWinds<\/a>. He noted that without proper governance, faster AI adoption simply leads to workload problems sooner.<\/p>\n<\/div><\/div>\n\n\n\n Overall, the findings suggest that AI adoption alone does not guarantee success. Instead, organizations need the right infrastructure, data foundation, and governance to truly benefit from it.<\/p>\n","protected":false},"excerpt":{"rendered":" AI adoption in IT service management is delivering strong returns, […]<\/p>\n","protected":false},"author":8,"featured_media":106916,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[147,9621],"tags":[10580],"contributor":[9732],"class_list":["post-106914","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-emerging-technologies","tag-ai-adoption","contributor-news-desk"],"featured_image_src":"https:\/\/techxmedia.com\/en\/wp-content\/uploads\/2026\/08\/Brad-McGinity.jpg","author_info":{"display_name":"Rabab","author_link":"https:\/\/techxmedia.com\/en\/author\/rabab\/"},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/posts\/106914","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/comments?post=106914"}],"version-history":[{"count":1,"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/posts\/106914\/revisions"}],"predecessor-version":[{"id":106918,"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/posts\/106914\/revisions\/106918"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/media\/106916"}],"wp:attachment":[{"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/media?parent=106914"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/categories?post=106914"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/tags?post=106914"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/techxmedia.com\/en\/wp-json\/wp\/v2\/contributor?post=106914"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}