AI ROI in IT: Gartner Finds Only 28% of Infrastructure and Operations Use Cases Fully Succeed
For business leaders weighing another round of AI investment, a new study from Gartner offers a sobering reality check. Released on 7 April 2026 and reported by Computerworld, the research found that only 28% of AI use cases in infrastructure and operations (I&O) fully succeed and meet ROI expectations. A full 20% fail outright.
The findings come from a survey of 783 I&O leaders conducted in late 2025. While the 28% success rate is an improvement over the 95% of generative AI projects that produced no measurable financial return in an MIT study released last year, it still means that nearly three in four AI initiatives in IT infrastructure and operations are not delivering as hoped.
Refer & earn: 30% of a client’s first month, then 10% of every month after that, for up to 24 months per client. See the partner programme →
Melanie Freeze, a director of research at Gartner, told CIO.com that failure most commonly occurs for reasons including unrealistic expectations of what AI tools can do and skills gaps during the actual pilot. In a Gartner Q&A advisory about the survey, she noted that of the 57% of I&O leaders reporting at least one failure, many said their AI initiatives failed because they expected too much, too fast. They assumed AI would immediately automate complex tasks, cut costs, or fix long-standing operational issues. When expectations are not realistically set and the results don’t appear quickly, confidence drops and projects stall.
For any business leader who has been told that AI will transform IT operations overnight, this is a crucial corrective. The technology is not magic. It requires deliberate integration, realistic goal-setting, and sustained executive support.
Why AI Projects Fail: Unrealistic Expectations and Skills Gaps
The Gartner study identifies two primary drivers of failure. The first is unrealistic expectations. Many I&O leaders assumed AI would immediately automate complex tasks, cut costs, or fix long-standing operational issues. When those results didn’t materialize quickly, confidence dropped and projects stalled. This pattern is familiar to anyone who has watched a promising pilot lose momentum after the first quarter.
The second driver is skills gaps during the pilot. AI tools require not just technical know-how but also the ability to integrate them into existing workflows and to interpret their outputs correctly. Without those skills, even well-funded projects can flounder.
Freeze also pointed to a cultural tendency among IT departments to treat AI as an experiment rather than a strategic initiative. She described a team of I&O professionals who will just go out and try something. That kind of ad-hoc experimentation, while valuable for learning, rarely produces measurable ROI. The reality, Freeze said, is that in order to achieve an anticipated ROI, IT departments must not opt to run AI projects as side projects.
What Separates Success from Failure: Three Factors
Gartner identified three success factors that distinguish AI wins from losses. First, successful organizations embed AI into the systems and processes people already use. As AI becomes part of day-to-day operations, it boosts adoption and creates visible impact within the organization, the company noted. This is a critical insight: AI that lives in a separate tool or dashboard is easy to ignore. AI that is woven into existing workflows becomes part of how work gets done.
Second, successful I&O executives receive full support from top executives. That support helps remove roadblocks, align priorities, and ensure the investment stays funded and focused. Without executive backing, AI initiatives are vulnerable to budget cuts and shifting priorities.
Third, successful leaders create realistic business cases. This means grounding AI projects in specific operational needs rather than chasing technology for its own sake. Freeze told CIO.com that starting without a plan is never a good idea. She said, It’s always a bad situation for any technology to say, ‘we built it. It’s going to succeed.’ It needs to be grounded in the business case. What does your business need? What are their ambitions? What are the problems within your function that your current tool set is not able to solve? Within that upfront strategic framework, then success follows.
Where AI Wins Are Happening: ITSM and Cloud Operations
The Gartner study found that the bulk of AI success comes from generative AI applied to specific areas: IT service management (ITSM) and cloud operations. Freeze noted that 53% of I&O leaders reported their AI wins occur in ITSM. These are areas where markets are mature and have proven business value.
For business leaders, this suggests that AI investment should be concentrated where there is a clear, measurable use case and a track record of success. Trying to apply AI to every operational problem at once is a recipe for the kind of failure that 20% of I&O leaders experienced.
Freeze also emphasized that whether wins occur in the cloud or in ITSM, I&O leaders must ensure they are shared broadly within the organization, and the AI strategy remains cohesive and centrally led. This means avoiding the trap of isolated pilots that never scale.
Managing AI Use Cases as Products
To improve ROI, Freeze recommends that I&O leaders prioritize and determine funding for AI use cases by managing AI use cases as a product to avoid duplication, drive synergies, and track their collective impact on I&O and business outcomes. From there, she said, I&O leaders can work alongside their CIOs, data and analytics, security, legal, and finance stakeholders to assess each use case for feasibility, risk, cost, and expected business impact. A shared scoring model makes it easy to compare and rank all use cases and guide investment decisions.
This product-management approach is a departure from the common practice of funding AI initiatives through individual business units. Freeze observed that today, many AI initiatives are still funded by individual business units. However, as AI infrastructure spending continues to rise, CEOs and CFOs need to play a more active role in setting funding criteria and approving major investments.
For executives, this means moving AI decision-making out of the IT department alone and into the C-suite. It also means creating a centralized portfolio view of AI investments, rather than letting each unit pursue its own experiments.
The High Stakes of Failure
Freeze also warned that a failed AI project can affect an entire organization. Not being able to provide secure, reliable, available infrastructure can have major implications for business outcomes. In other words, AI failures in I&O are not just wasted budget; they can disrupt core business operations.
She noted that the drivers of failure are slightly different from the drivers of success. I&O leaders must remember that a clearly defined, centrally endorsed AI portfolio helps their organization focus resources where they matter most. Above all, strong execution and business adoptions, not just prioritization, determine AI’s real ROI.
What Business Leaders Should Do Now
Based on the Gartner findings, business leaders should take several practical steps. First, set realistic expectations. AI will not fix long-standing operational issues overnight. Second, invest in skills. Ensure that teams have the training to integrate AI into existing workflows. Third, embed AI into day-to-day systems rather than running it as a side project. Fourth, secure executive sponsorship to keep funding and focus aligned. Fifth, concentrate on proven areas like ITSM and cloud operations. Sixth, manage AI use cases as a portfolio, with a shared scoring model and centralized governance.
Finally, involve the CFO and CEO in funding decisions. As AI infrastructure spending rises, the financial stakes grow. A disciplined, business-case-driven approach is more likely to deliver returns than scattered experimentation.
The Gartner study makes one thing clear: AI ROI is not about the sophistication of the model. It is about how well the technology is integrated, governed, and aligned with real operational needs. For business leaders, that is both a warning and a roadmap.
Disclosure: Faida Workforce provides consulting services related to AI adoption and workforce transformation. This article is for informational purposes and does not constitute a sales pitch. Our editorial team maintains independence in reporting.
Byline: Faida Workforce Editorial. AI tools assist in production; a human editor is accountable for all content.
Sources
- AI often doesn’t deliver ROI for IT departments either — Computerworld, published 7 April 2026.
30% of their first month, then 10% every month after that
Refer a business to Faida Workforce and you earn 30% of the client’s first paid month, then 10% of every month for up to 24 months per client — rising to 15% from the accrual after you pass 10 active clients. Paid by PayPal, batched on the 1st.
10 Pro clients = $2,598.70 over 24 months — $3,748.13 at the 15% tier.
Figures are potential earnings worked from the published rates, assuming every referred client pays for the whole cap — not a promise, not an average. The $1 trial is never commissionable.
