The ROI challenge: Measuring success at scale
With the foundation in place, the critical next step is demonstrating concrete business value. Yet despite aggressive AI deployment across organizations, measuring and achieving ROI remains a significant hurdle. The preponderance of 2026 State of the CIO respondents are going hard at AI deployment, yet they are less evolved when it comes to establishing formal processes for approving and measuring the value of AI projects. Slightly more than half of respondents (53%) have established some type of official approval process, but 28% are still hammering out details with plans to activate something within the next 12 months.
47% of IT leaders say they have formal metrics to measure AI project value.
Respondents are also lagging on defined success metrics or KPIs, yet most are committed to making this a priority in 2026. Less than half (47%) of IT leaders say their organizations have formal metrics for measuring AI project value with another 34% planning to implement such benchmarks within the year. Those that are measuring AI success called out operational efficiency and process improvement as the top metrics, cited by 40% of respondents. Employee productivity (34%) and cost reduction (30%) also ranked high as important ROI targets. AI's impact on revenue or growth is less important as of now, cited by only 27% of respondents.
With most organizations knee deep in efforts to formalize AI processes and create new organizational structures, ROI remains somewhat elusive. Less than a fifth of this year's IT leader respondents (19%) say AI initiatives have met or exceeded ROI goals. The reality lies somewhere in the middle—40% confirm some projects have hit the mark while 18% say fewer than a third of AI use cases are meeting defined metrics.
IT leaders acknowledge the importance of addressing the ROI shortcomings. Nearly a third (32%) of IT leaders confirmed inconsistent and incomplete ROI metrics are a critical barrier to AI success. Additionally, respondents called out lack of clarity on corporate AI strategy (31%) and a shortage of in-house expertise (40%) as hurdles to meeting ROI goals. This ROI gap reflects a hard truth: translating AI investments from promising pilots into measurable business value requires more than enthusiasm and governance frameworks. It demands rigorous discipline around measurement, realistic operational planning, and a clear-eyed assessment of what success actually looks like in each organization.
