Why AI Projects in I&O Stall Ahead of Meaningful ROI Returns?

Why It Matters

The findings expose a critical gap between AI hype and tangible value in infrastructure, urging leaders to realign investments toward integrated, executive‑backed initiatives that deliver measurable ROI.

Key Takeaways

• Only 28% of AI use cases in I&O meet ROI expectations.
• 38% cite skill gaps and data quality as failure drivers.
• Embedding AI in daily workflows boosts adoption and ROI.
• Executive sponsorship correlates with successful AI implementations.
• ITSM and cloud ops deliver 53% of AI wins.

Pulse Analysis

The promise of artificial intelligence in infrastructure and operations (I&O) has outpaced its delivery, with Gartner’s 2025 survey revealing that just 28 % of AI initiatives achieve their projected return on investment. A notable 20 % of projects fail outright, often because expectations are set too high or the use case does not align with the complexities of modern IT environments. Common failure points include over‑ambitious auto‑remediation and self‑healing solutions that exceed current tool capabilities, as well as persistent skill gaps and poor data quality that undermine model performance.

Success stories share three common threads. First, organizations that embed AI directly into existing workflows see faster adoption and measurable impact, turning the technology into a routine productivity aid rather than a siloed experiment. Second, strong executive sponsorship removes bureaucratic roadblocks, aligns cross‑functional priorities, and secures the budget needed for iterative refinement. Third, realistic business cases grounded in clean, readily available data set achievable targets; in practice, most wins now arise from generative AI applied to IT service management and cloud operations, where the value proposition is already proven.

To translate these insights into sustainable ROI, I&O leaders should treat each AI use case as a product, establishing a centralized portfolio, scoring model, and clear business‑outcome metrics. This approach prevents duplication, enables cross‑team synergies, and gives CEOs and CFOs a transparent view for funding decisions. As AI infrastructure spend climbs, executive oversight will shift from isolated business‑unit budgets to enterprise‑wide governance, ensuring that only initiatives with validated use cases, data readiness, and executive backing receive capital. The result is a tighter alignment between technology investment and measurable business performance.