CFO Optimism vs. Researcher Findings on AI’s Impact

(SeaPRwire) –   Good morning. While artificial intelligence is already boosting worker productivity, the corresponding financial benefits have not yet materialized.

A new working paper titled “Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives,” from researchers at Duke University’s Fuqua School of Business and the Federal Reserve Banks of Richmond and Atlanta, reveals a discrepancy. It finds that although chief financial officers report AI-driven productivity improvements, evidence based on actual revenue presents a more cautious picture at this stage.

The study, which surveyed nearly 750 executives, uncovers a “productivity paradox.” Firms reported average AI-related productivity gains of 1.8% for 2025. However, when researchers derived implied gains from real revenue and employment figures, the increases were significantly lower across all major industries for both 2025 and 2026.

“It’s not really hitting the top line yet in full force,” stated John Graham, a finance professor at Duke’s Fuqua School and a co-author of the study. “There is some level of delay in here for sure.”

“It is possible that CFOs are just optimistic about all the potential,” Graham noted. “By productivity, we explicitly ask output per employee.”

He primarily attributes the gap to timing. Companies that increased AI investment in late 2025 have not fully implemented new capabilities, adjusted pricing strategies, or captured revenue increases. The reported gains for 2025 align closely with the revenue-implied gains for 2026, indicating a potential one-year lag.

This pattern echoes the well-known “productivity paradox” highlighted by economist Robert Solow in 1987, who observed that computers were everywhere except in the productivity statistics for years. The paper’s authors suggest AI may be on a similar path.
 
AI’s impact varies by industry. High-skill service sectors such as finance demonstrate the most robust growth, whereas manufacturing, construction, and low-skill services trail behind, though still show positive effects. These differences stem from how AI is applied across various sectors and business models.

“For some industries, AI is going to be about replacing the call center,” Graham explained. “For another, it’s going to be about something to do with a conveyor belt in a factory. For another, it’s going to be about having fewer analysts—having the AI take the place of a financial analyst.”

Critically, these productivity improvements stem less from major capital expenditures and more from enhancements in efficiency and quality.

The central challenge for CFOs is rationalizing AI expenditures before clear returns are evident.

“ROI often depends on exactly how you calculate it—a point-in-time estimate like this year’s revenue increase divided by this year’s investment,” Graham said. “What you’d really want to do is say, the amount I’m investing today—how much will that increase value this year, next year, the year after?”

He added, “You really want to use some measure of value creation that captures several years, at least, of forward-looking improvements, rather than just a point in time.”

Graham recommends adopting a multi-year view: “If you can’t kind of show it over a three or four year horizon, then you might have to be a lot more cautious.” He suggested that a company might be following the trend without having a concrete plan for how AI will deliver tangible benefits.

“You want to look over longer than just a one-year horizon, but you have to do it with discipline, so you’re not just kind of pie in the sky hoping it gets better,” Graham concluded.

Sheryl Estrada
sheryl.estrada@.com