Gartner surveyed 782 infrastructure and operations leaders. Only 28% of their AI use cases fully succeeded and met ROI expectations. One in five failed outright. 57% of the leaders had seen at least one failure.
That's most organisations running AI programs.
The failures follow a pattern
Melanie Freeze, Gartner's Director of Research, found a consistent thread: teams expect AI to fix deep operational problems fast. When results don't come quickly, confidence collapses and projects stall.
“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 organisation's operations simply can't deliver ROI.”
The areas where AI struggles most — auto-remediation, self-healing infrastructure, agent-led workflow automation — are exactly the environments where edge cases are frequent and reliability is non-negotiable. Current tools can't consistently perform there. The ambition was ahead of the tool.
Two more failure drivers, cited by 38% of respondents each: skill gaps, and poor data quality or limited data access. Both sit in the foundations, underneath any choice of model or vendor.
The gap between ambition and execution is a delivery problem. Most conversations about AI treat it as a technology problem, which is why the same failures keep recurring.
What the 28% do differently
The organisations that succeed run AI differently.
Gartner's data shows three patterns.
- They put AI inside the systems people already use. That cuts adoption friction and makes results visible faster.
- They get executive alignment before they start. Projects with leadership backing survive the rough patches. Without it, the moment results are slow or the business case gets fuzzy, the project gets cut.
- They start narrow. IT service management and cloud operations came up most often as starting points: well-defined environments with structured processes and measurable ROI.
The board pressure problem
A lot of AI initiatives are still funded by individual business units, with no CFO or CEO oversight. As AI infrastructure spend rises — Gartner expects it to make up 54% of global IT spending this year — that's changing. Boards are asking harder questions, and leaders running projects on “trust us, the value is coming” are increasingly exposed.
A Harris Poll of 600 CIOs, run for Dataiku, found 98% said board pressure to demonstrate measurable AI ROI was increasing. 71% said their AI budget would likely be cut or frozen if targets weren't met by the end of the first half of 2026.
That's an uncomfortable spot for leaders who skipped the delivery discipline at the start.
What the pattern means for delivery
AI fails for operational reasons: expectations that weren't grounded, data that wasn't ready, people who weren't brought along, scope too ambitious to produce early wins.
Every one of these failure modes is old. ERP rollouts, CRM implementations, and digital transformation programs have followed the same pattern for decades. The hype cycle just makes it harder to see coming.
The teams getting ROI treat AI as a delivery program: scoped business cases, realistic timelines, executive accountability, and a concrete plan for how people will actually use it.
