""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.""
""The 20 percent 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.""
""Among I&O leaders who faced setbacks, 38 percent said persistent skill gaps continue to hamper AI success. The same proportion cited poor data quality or limited data availability as a direct cause of AI project failure.""
""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 priorities.""
A Gartner survey reveals that only 28% of AI use cases in IT infrastructure yield a return on investment, with one in five projects failing outright. Many AI initiatives falter due to unrealistic expectations regarding automation and cost reduction. Skill gaps and poor data quality are significant barriers to success. Successful applications of AI are more common in mature technologies like GenAI for IT service management. Funding challenges persist, necessitating greater involvement from CEOs and CFOs in AI infrastructure spending decisions.
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