AI Projects in Infrastructure & Operations Stall Ahead of Meaningful ROI Returns: Gartner

Q&A with Melanie Freeze, Director of Research, Gartner —

Only 28% of AI use cases in infrastructure and operations (I&O) fully succeed and meet ROI expectations, while 20% fail outright, according to a Gartner, Inc. survey of 782 I&O leaders in November and December 2025.

To ensure success, I&O leaders should connect AI to the business during execution, not just planning. The survey found that among 77% of I&O leaders who deliver at least one successful AI use case, success is attributed primarily to integrating AI into existing workflows and systems and securing full support from business executives.

Ahead of the Gartner IT Infrastructure, Operations & Cloud Conference in Sydney, we spoke with Melanie Freeze, Director Research at Gartner, about the key success factors for AI use cases in I&O and how leaders can prioritize and fund them.

Q: Which types of AI in I&O fail, and why?

A: The 20% failure rate is largely driven by AI initiatives that are either overly ambitious or poorly scoped. AI that doesn’t fit into the organization’s operations simply can’t deliver ROI

For the 57% of I&O leaders who reported 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.

I&O leaders most frequently observe AI failures in auto-remediation, self-healing infrastructure, and agent-led management of workflows within and between systems. Failure most commonly occurs when I&O leaders expect these solutions to outperform what AI tools can reliably deliver for complex and unpredictable IT operations.

Factors such as skills gaps and lack of data readiness also play a role in AI project failures. Thirty-eight percent of I&O leaders who faced setbacks said persistent skill gaps continue to hamper AI success. Additionally, 38% of I&O leaders said poor data quality or limited data availability was a direct cause of AI project failure.

Q: What separates I&O leaders who achieve AI ROI from those who struggle?

A: The Gartner survey showed that ROI from AI is not driven by the sophistication of the model, but by how well the technology is integrated, governed, and aligned with real operational needs.

Gartner identified three success factors:

• Successful I&O leaders don’t run AI as side projects. For 33% of I&O leaders with AI success, they embed it 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.
• A strong correlation between success and leadership buy‑in. Twenty-six percent of successful I&O leaders reported full executive support and 25% could count on cross-functional collaboration to successfully implement their AI use case. This matters because executive backing helps remove roadblocks, align priorities, and ensure the investment stays funded and focused.
• High‑performing I&O leaders start with realistic business cases and upfront preparation. Today, most AI success in I&O comes from GenAI applied to IT service management (ITSM) and cloud operations, where markets are mature and have proven business value. In fact, 53% of I&O leaders reported their AI wins occur in ITSM. Whether these 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.

High‑performing I&O leaders start with realistic AI business cases and upfront preparation.
Melanie Freeze, Gartner

Q: With AI infrastructure expected to represent 54% of global IT spending in 2026, how should I&O leaders prioritize and determine funding for AI use cases?

A: To start, I&O leaders should ensure every AI use case is linked to a business goal. They should begin 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, 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.

Once priorities are clear, I&O leaders can determine which use cases deserve funding and at what level. 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.

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 adoption, not just prioritization, determine AI’s real ROI.

Gartner clients can learn more in “Infographic: Where AI is Delivering Value Now for Infrastructure and IT Operations” and “AI Portfolio: How to Vet, Prioritize and Fund AI Use Cases”.

Learn how to accelerate digital transformation by integrating AI, optimizing cloud costs and building a resilient infrastructure in the complimentary Gartner roadmap Boost Your I&O Leadership: Proven Tactics to Align CIO Priorities.

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Source: Gartner