AI Often Doesn’t Deliver ROI for IT Departments Either

Why It Matters

The low success rate signals that many IT departments risk wasted spend and operational risk, making disciplined AI governance critical for delivering tangible business outcomes.

Key Takeaways

• Only 28% of I&O AI projects meet ROI expectations.
• 20% of AI initiatives fail outright in infrastructure ops.
• Success hinges on integration, governance, executive sponsorship.
• ITSM and cloud operations generate most AI wins.
• Centralized AI portfolio and business case essential for funding.

Pulse Analysis

The Gartner findings underscore a growing chasm between AI hype and practical returns in IT infrastructure. While last year’s MIT research warned that 95% of generative AI projects delivered no measurable financial gain, Gartner’s 28% success rate suggests modest improvement but still highlights pervasive optimism bias. Executives often launch pilots without clear metrics, assuming AI will instantly automate complex processes. This misalignment erodes confidence, stalls projects, and can jeopardize the reliability of critical systems that underpin revenue-generating applications.

Gartner identifies three pillars that separate the few successful initiatives from the majority that falter. First, embedding AI into the tools and processes teams already use accelerates adoption and surfaces quick wins. Second, top‑down sponsorship removes bureaucratic roadblocks, aligns priorities, and safeguards funding throughout the project lifecycle. Third, a disciplined, product‑style approach—complete with shared scoring models and cross‑functional governance—ensures each use case is evaluated for feasibility, risk, and expected impact. In practice, this framework has yielded the highest returns in mature domains such as IT service management and cloud operations, where 53% of reported AI victories originated.

For CIOs and CFOs, the implications are clear: AI investments must be centrally coordinated and anchored in robust business cases. Treating AI as a core strategic initiative rather than an experimental side project enables better resource allocation, risk mitigation, and measurable outcomes. As AI infrastructure spending climbs, senior finance leaders will increasingly demand transparent ROI calculations and portfolio oversight. Organizations that institutionalize AI governance, integrate solutions into daily workflows, and align initiatives with clear operational goals are poised to capture the true value of artificial intelligence in the enterprise.