Fewer than 25% of enterprises have scaled AI successfully

An article from Dive Brief Not knowing how to measure a project’s success or when to shut them down can keep companies from finding success, Gartner data found. Published Sept. 2, 2026 ciodive.com Getty Images This audio is auto-generated. Please let us know if you have feedback. Dive Brief: Despite widespread adoption efforts, fewer than one-quarter of enterprises have successfully scaled AI across multiple business units, according to Gartner data released Tuesday. The IT advisory firm surveyed more than 1,300 leaders from organizations with more than $50 million in revenue between January and April of this year. The lag in adoption isn’t slowing plans to roll out the technology, with 85% of tech leaders planning to up their AI investments next year. About 11% of the organizations that responded said they lacked visibility into what they spent on AI in 2025, according to Gartner. Low visibility into AI spending heightens the risk of missing ROI, Tina Nunno, distinguished VP and fellow at Gartner, said in the report. “Without disciplined measurement tied directly to business outcomes, organizations risk wasted resources and unmet expectations,” she said. Dive Insight: Enterprises continue to fuel AI investments despite a lack of clear ROI, but this approach can muddy an organization’s chance at finding the right use cases for it. Nearly three-quarters of senior business executives said they’ve scaled fewer than 25% of their AI pilots successfully, and two-thirds said their organization struggles to measure the ROI generated by AI to prove the positive benefits executives tout, according to an August Infosys report. Many enterprises aren’t as prepared for AI deployment — especially agentic systems — as they think. Only 1 in 5 senior managers and C-suite executives said their organization is prepared to redesign business processes to run autonomously with AI agents, an August Deloitte report found. Enterprises that constantly track the ROI of their AI initiatives, treat it as a portfolio of value and regularly assess project performance see greater returns, Gartner’s data showed. Companies that follow this framework and discontinue underperforming initiatives reported positive returns in 81% of their AI initiatives. “We want companies to get more out of AI, rather than risk spending more and either not getting appropriate return on investment, or creating more technical risk,” Nunno said in an email to CIO Dive. Tech leaders can fall prey to AI hype cycles, Nunno said. The most widely pursued AI use cases, such as cybersecurity, threat detection and IT service desk automation, don’t always deliver the highest ROI. Intelligent IT asset and cost optimization, synthetic data generation and automated code generation and refactoring were among the top three AI use cases with positive returns, according to Gartner. However, the AI use cases that will prove the most value for a company are the ones that target specific business needs and build necessary data foundations when needed, Nunno said. “CIOs should always prioritize use cases where the business case and financial logic make sense for their specific company, AI maturity and business needs,” Nunno said. “Having rigor both around the business case, and ensuring it meets your company’s strategic vision for AI will ensure CIOs choose the best use cases to maximize value.”