Nearly 4 in 10 CFOs at large U.S. companies now expect generative AI to deliver very positive returns within one to two years, a sharp reversal from five months earlier when none held that view. The shift suggests finance leaders are finding concrete value in targeted AI projects even as they push full integration further out.
The findings come from a PYMNTS Intelligence report surveying 60 CFOs at companies with at least $1 billion in annual revenue. In December, 39.1% expected very positive returns within one to two years, while 26.1% said the payoff would take six years or longer. That split reflects a more practical approach to the technology than the earlier, all-or-nothing outlook.
In July 2025, no surveyed CFO expected strong returns within two years. Five months later, the share expecting that level of return in three to five years fell to 34.8% from 65.9%. The movement suggests targeted projects are creating a clearer path to value.
Measuring returns differently
CFOs are broadening how they measure AI success. Customer experience is the most commonly cited metric, at 78.3%, followed by improved margins at 75%. Sixty percent tracked lower operating costs, and 56.7% measured increased revenue per customer, up from 18.3% in July.
Only 25% used headcount reduction as a measure, down from 36.7%. The emphasis has shifted toward productivity, growth, and service quality rather than cost cutting through layoffs.
Full integration remains a long-term project. CFOs estimated that embedding gen AI across the organization would take an average of 6.28 years, nearly double the 3.19 years forecast in July. Finance leaders appear to treat the process like renovating a building one floor at a time: individual operations improve quickly, while the whole structure takes much longer.
That distinction helps explain why near-term confidence and longer implementation schedules can rise together.
Implementation lessons so far
Early use cases are spreading. The report found gen AI's role expanded sharply in financial reporting, capital management, and working capital optimization. Meanwhile, the average number of drawbacks cited by CFOs fell to 4.23 from 6.92, with errors, and implementation delays all declining.
Remaining challenges are more focused. Skills shortages were cited by 78.3% of CFOs, followed by data security and privacy at 70%, and vendor dependence at 46.7%. Those concerns point to the next phase: building talent, and oversight around programs that are already producing results. For finance leaders planning their own adoption, a structured approach to applying these tools can shorten the learning curve - an AI for CFOs Learning Path covers forecasting, automation, and financial strategy. Executives weighing adoption timelines and business impact can also find relevant material in the AI for Executives & Strategy collection.
Why this matters for CFOs and executives
The data changes the conversation around AI investment. CFOs can justify pilot projects with a 24-month payoff horizon, citing this report. The same data also supports budgeting for a company checklist of roughly six years, with skills and data security as the constraints to plan around.
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