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We have been steady in our stance that understanding the buyer for AI software, think AIaaS (intelligence as a service), is central to understanding the AI cycle, given they are the customers who will end up funding and sustaining the AI buildout. We continue to see sentiment, as well as mass confusion, around the ROI for AI as it is adopted in the enterprise. Our checks with enterprises date back to mid-2024, when enterprise software research quickly turned into AI adoption research, and most of 2024 and 2025 revealed insights around pilots being deployed, and the costs and learnings associated with those pilots. Pilots then moved to production, and most of our qualitative and quantitative work with IT decision makers has been around observations being found as AI has begun to scale in their enterprises.
The E/AI Index is our quantitative side of that work, and this is its second version. The first, published in May, surveyed 70 CIOs and CTOs and focused on budget architecture. This version surveyed 200 in September across North America and Europe. It keeps the core budget and deployment questions so we can track the move from pilot to production, and it adds new questions on verified production, full-cost ROI, token spend, and model choice. Every matched measure has moved forward since May, with AI budgets, agents executing workflows, and spending already shifted from software and services each up 7 to 9 points.
That confusion shows up most clearly in the question we hear from investors, which is whether enterprise AI pays for itself. What we found is that most enterprises do not yet have the tracking in place to answer it. Only 33 of the 200 CIOs measure return against the full cost of their AI programs, and 113 have not measured it at all. That is consistent with the top-level reports of low AI ROI, since an organization that is not tracking full cost has no return to report. When we look at the CIOs who are tracking, the story changes. Of those 33, 67% (22) are at break-even or better, and among CIOs who have scaled AI with operating controls and measure full cost, 92% (11 of 12) are
Our view, which is supported by hard data, is that the returns exist, that they are concentrated, and that they rise with production scale, the same progression from pilot to production our checks have followed since 2024. The returns show up first in software development at scale and in IT support once it spans several functions, and they are stronger where agents execute work across systems and where IT owns the AI budget.
Token spend is the other half of the debate we wanted more clarity on, and it is where we see the directional signal in the survey that connects most directly to the buildout these customers are funding. About half of CIOs are over their 2026 token plan at every stage of deployment, but the ones cutting projects because of it are mostly early. At scale, CIOs keep funding the overrun, and more of that funding is coming out of existing software budgets. That is where CIO returns start to show up in vendor revenue, and it is the part of the data we would watch most closely.
Inside the Full Report
What has changed since the first E/AI Index in May, on matched questions
The ROI evidence ladder, and how the picture changes among CIOs who track full cost
Returns by production stage, including full-cost results at scale
Which workloads pay as they scale, and returns by agent deployment
Governed production: the approval models and budget owners tied to measured value
Token spend against plan, project cuts, routing controls, and open-weight models
Where the next AI dollar comes from, the expansion gate at scale, and the realized workforce effect
Companies covered: Microsoft, GitLab, Atlassian, ServiceNow, Salesforce, Workday, Snowflake, Databricks, Amazon (AWS), Alphabet (Google Cloud), Oracle, OpenAI, Anthropic, Meta, IT services and systems integrators
10 exhibits, including a market beneficiary map, cross-tabbed by production stage, workload, governance, and token controls
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