This article is part of VentureBeat’s special issue, “The Real Cost of AI: Performance, Efficiency and ROI at Scale.” Read more from this special issue.
Recent surveys and VentureBeat’s conversations with CFOs suggest the honeymoon phase of AI is rapidly drawing to a close. While 2024 was dominated by pilot programs and proof-of-concept demonstrations, in mid-2025, the pressure for measurable results is intensifying, even as CFO interest in AI remains high.
According to a KPMG survey of 300 U.S. financial executives, investor pressure to demonstrate ROI on generative AI investments has increased significantly. For 90% of organizations, investor pressure is considered “important or very important” for demonstrating ROI in Q1 2025, a sharp increase from 68% in Q4 2024. This indicates a strong and intensifying demand for measurable returns.
Meanwhile, according to a Bain Capital Ventures survey of 50 CFOs, 79% plan to increase their AI budgets this year, with 94% believing gen AI can strongly benefit at least one finance activity. This reveals a telling pattern in how CFOs are currently measuring AI value. Those who have adopted gen AI tools report seeing initial returns primarily through efficiency gains.
“We created a custom workflow that automates vendor identification to quickly prepare journal entries,” said Andrea Ellis, CFO of Fanatics Betting and Gaming. “This process used to take 20 hours during month-end close, and now, it takes us just 2 hours each month.”
Jason Whiting, CFO of Mercury Financial, echoed this efficiency focus: “Across the board, [the biggest benefit] has been the ability to increase speed of analysis. Gen AI hasn’t replaced anything, but it has made our existing processes and people better.”
But CFOs are now looking beyond simple time savings toward more strategic applications.
The Bain data shows CFOs are most excited about applying AI to “long-standing pain points that prior generations of technology have been unable to solve.” Cosmin Pitigoi, CFO of Flywire, explained: “Forecasting trends based on large data sets has been around for a long time, but the issue has always been the model’s ability to explain the assumptions behind the forecast. AI can help not just with forecasting, but also with explaining what assumptions have changed over time.”
These recent surveys suggest that CFOs are becoming the primary gatekeepers for AI investment; however, they’re still developing the financial frameworks necessary to evaluate these investments properly. Those who develop robust evaluation methodologies first will likely gain significant competitive advantages. Those who don’t may find their AI enthusiasm outpacing their ability to measure and manage the returns.
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