OpenAI introduces new AI ROI framework as enterprise cost concerns mount

OpenAI pitches new AI ROI metrics

Why it matters: The framework is OpenAI's response to a growing enterprise AI cost reckoning, as companies increasingly route work to cheaper models and demand clearer returns on their investments. Zoom in: Friar is proposing a new enterprise AI metric: "useful intelligence per dollar." Instead of measuring AI spend by expenses or cost per token, she argues businesses should ask four questions: * Is AI completing work that matters? Think customer issues resolved, code shipped or contracts reviewed, not just lots of usage. * What does each successful task cost? Measure the full cost of a task, yes including AI usage and retries but also any cost of human review, rather than token prices alone. * How often does it get the work right? The fewer corrections or escalations to humans, the greater the potential return. * Does each AI dollar produce more value as usage grows? Companies should track whether AI is completing more high-quality work over time without exponential cost growth. Between the lines: OpenAI is trying to shift the conversation from the price of AI to the value it creates. * If companies judge AI by the amount of useful work completed rather than benchmark scores or token costs, frontier AI companies can make the case that more capable -- and often more expensive -- models ultimately deliver better economics. Zoom out: CFOs are widely flying blind when it comes to their AI costs. * One executive oversaw a half a billion dollar accidental Claude bill over the course of one month, as Axios reported. * OpenAI CEO Sam Altman said costs are the second biggest challenge customers talk to him about, behind AI deployment within their organizations. Reality check: OpenAI has an incentive to steer customers toward measuring outcomes instead of sticker prices, since its models are among the most expensive. * OpenAI argues its most capable models will prove cheapest given the efficiencies associated with using top-tier intelligence. * "The basic economic question facing CFOs and other business leaders is whether the value of the work AI completes grows faster than the cost of producing it," Friar writes. * She argues businesses shouldn't just flock to the cheapest AI, but they should buy the AI tools that produce the most value, delivering on both costs and performance. Yes, but: Many enterprises have already reached this conclusion, but they're responding differently than OpenAI might like. * Rather than defaulting to the most capable frontier model, executives tell Axios they're increasingly routing everyday tasks to the cheapest model that can do the job, using only frontier AI models for the most intensive tasks. * Others are experimenting with open-weight models or AI routers that automatically choose the best balance of cost and performance for each task. The bottom line: As businesses worry about AI spend, OpenAI hopes to change how budgets for this technology are calculated.

OpenAI's CFO: 4 questions that reveal if your AI spend is paying off | Fortune

Is your AI paying off? Today, OpenAI CFO Sarah Friar published the scorecard she uses for telling whether you are actually getting economic value from AI spend. For years, software success was measured through adoption -- seats, active users, and renewals, Friar notes. She argues that AI is different: it must be measured by the work it actually accomplishes. "The basic economic question facing CFOs and other business leaders is whether the value of the work AI completes grows faster than the cost of producing it," Friar wrote in a blog post. Answering that question, she says, requires going deeper than simple metrics like cost per token. She argues that the metric that matters for AI is what she calls "useful intelligence per dollar." It has four elements: Is AI completing work that matters? What does each successful task cost? Can people depend on the result? And does each dollar produce more value as usage grows? In practice, that means leaders should track the volume of AI-completed work that meets a defined quality bar, add up the full cost of completing that work, and then divide by the number of successful tasks to get a cost per successful task. From there, the test is whether people can reliably depend on the output and whether, over time, high-quality completed work grows faster than total cost while quality holds or improves. If it does, each AI dollar is producing more value -- and compute sits at the center of that equation, Friar explains. "Our job is to make that equation better with every generation: more capable models, faster and more dependable results, and lower costs for the work customers need done," she writes. For OpenAI, a hyperscaler, compute is not just a technology expense -- it is a strategic asset. As a private company, it does not publish formal capex guidance, but the Stargate initiative announced in January 2025 outlined a plan to invest up to $500 billion over roughly four years to build large-scale AI infrastructure in the U.S. -- with the initial phase targeting about $100 billion and the broader buildout accelerating toward a 10-gigawatt capacity goal in the U.S. by 2029. Just over a year later, it has already surpassed that milestone, as demand for AI continues to accelerate. According to reports, OpenAI's IPO could come as soon as this summer or as late as 2027. The company is already valued at $852 billion and approaching the $1 trillion range. While finance chiefs have long led capital allocation and investor communication, they are increasingly expected to help determine strategy, including where the company places its biggest long-term bets, like AI spend, alongside the CEO. McKinsey recently held its 24th annual Global CFO Forum, an exclusive gathering that brought together about 100 finance chiefs from over 30 countries, representing some of the world's largest organizations. Andy West, a senior partner at McKinsey and global co-leader of the firm's Strategy and Corporate Finance practice, told Fortune he conducted an informal poll, asking CFOs whether the strategy function now reports to them. About two-thirds raised their hands. Five years ago, it would have been less than a third, he said. "We've been talking about AI at this conference for a couple of years now," West said. Last year, finance leaders were still experimenting with AI. This year, the conversation shifted decisively toward enterprise-wide transformation, he said.