{"id":24962,"date":"2026-04-22T13:41:18","date_gmt":"2026-04-22T13:41:18","guid":{"rendered":"https:\/\/insurance-canada.ca\/?p=85476"},"modified":"2026-04-22T13:41:18","modified_gmt":"2026-04-22T13:41:18","slug":"ai-projects-in-infrastructure-operations-stall-ahead-of-meaningful-roi-returns-gartner","status":"publish","type":"post","link":"https:\/\/blog.lifeinsurance-orleans.ca\/index.php\/2026\/04\/22\/ai-projects-in-infrastructure-operations-stall-ahead-of-meaningful-roi-returns-gartner\/","title":{"rendered":"AI Projects in Infrastructure & Operations Stall Ahead of Meaningful ROI Returns: Gartner"},"content":{"rendered":"

Q&A with Melanie Freeze, Director of Research, Gartner<\/strong> \u2014<\/span><\/em><\/h4>\n

Only 28% of AI<\/a> use cases in infrastructure<\/a> 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.<\/p>\n

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.<\/p>\n

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

Q: Which types of AI in I&O fail, and why?<\/h3>\n

A:<\/strong> The 20% failure rate is largely driven by AI initiatives that are either overly ambitious or poorly scoped. AI that doesn\u2019t fit into the organization\u2019s operations simply can\u2019t deliver ROI<\/p>\n

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\u2011standing operational issues. When expectations are not realistically set and the results don\u2019t appear quickly, confidence drops and projects stall.<\/p>\n

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.<\/p>\n

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.<\/p>\n

Q: What separates I&O leaders who achieve AI ROI from those who struggle?<\/h3>\n

A:<\/strong> 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.<\/p>\n

Gartner identified three success factors:<\/p>\n