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Dive Brief:
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OpenAI is urging companies to rethink how they measure returns on artificial intelligence investments, arguing that traditional software metrics fail to capture the business value the technology can create.
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In a Friday blog post, OpenAI CFO Sarah Friar said organizations should use metrics such as the number of tasks completed successfully with AI, the total cost of completing those tasks and the cost per successful task over time.
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“For years, software was measured through adoption: seats, active users, renewals,” Friar said in a separate post on LinkedIn. “AI is different — it needs to be measured by work accomplished.”
Dive Insight:
The push comes as companies face mounting pressure to show that AI investments are delivering measurable business value.
Worldwide spending on AI is forecast to total $2.59 trillion in 2026, a 47% increase year-over-year, according to Gartner.
In a study released by PwC in January, only 12% of CEOs said AI had delivered both cost and revenue benefits. Overall, 33% of respondents reported gains in either cost or revenue, while 56% said they had so far seen no significant financial benefit.
The issue has come into sharper focus amid growing scrutiny of AI token consumption, or the amount of AI processing companies pay for when using large language models.
Palantir CEO Alex Karp recently argued that many business leaders are growing increasingly frustrated with the economics of LLMs, questioning whether rising token costs are translating into ROI.
“The enterprises are just tired of it,” Karp said in a CNBC interview.
Karp made the comments while promoting Palantir’s own technology, acknowledging that his company has a commercial interest in the debate.
In her blog post, Friar introduced the concept of measuring AI investments based on “useful intelligence per dollar.”
Rather than measuring AI success by the number of licenses purchased or employees using a tool, she said companies should focus on specific workflows and outcomes — such as resolving customer issues, completing software changes, reviewing contracts or preparing forecasts.
The cost of AI, Friar said, should include not only model and computing expenses but also factors such as employee time, human review and rework required to achieve a usable result.
“Tokens create value when they transform into work people can use,” Friar wrote. “As models become more capable, they can take on longer and more complex tasks: maintaining context, reasoning through multiple steps, working across tools, and adapting as they go.”
