Good morning. AI is everywhere in your board materials right now—but how much of that spend is actually earning its keep on the P&L?
OpenAI CFO Sarah Friar believes that, when it comes to AI, there’s a better way to measure value than focusing on model specifications, vendor promises, or cost per token. She suggests that CFOs and FP&A leaders instead track something far more fundamental: how much “useful intelligence per dollar” they’re getting from every AI deployment. In a blog post, she outlines four questions she uses as a scorecard for AI investments—a concise checklist that any finance team can adopt and apply to its own pilots and programs.
“The question I hear from CFOs everywhere is simple: how do we get more value from our AI spend?” she writes. Her questions aim to push leaders to define which work actually matters in their business, quantify the fully loaded cost when AI does that work, and test whether employees can reliably build on those outputs instead of redoing them. The goal: prove that the value of AI-completed work is compounding faster than the cost to produce it—or reallocate capital before hype turns into drag.
Finance leaders plan to devote even more money to AI. A recent Bain & Company survey found that 56% of senior finance executives are increasing enterprise-wide AI investment by more than 15% this year. Over the next two years, 83% of CFOs surveyed plan AI budget increases above 15%, with 42% expecting increases above 30%.
Yet results from early AI investments remain mixed. Just 31% of CFOs rated AI outcomes in finance as strongly positive. Finance chiefs are doubling down not because early returns have been spectacular, but because the gap between those who have scaled AI and those who haven’t is becoming too large to ignore, according to Bain. CFOs are increasingly being tapped to steer AI value creation—many are still searching for a viable scorecard for what’s working.
Sheryl Estrada [email protected]
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