Corporate America has set itself a goal to comprehensively integrate AI across its employees’ workflows, and by some measures, their efforts have been a wild success. Around half of U.S. workers now use AI in their roles at least a few times a year, up from less than 40% a year ago, according to recent Gallup polling.

The rate of adoption has outpaced that of rival firms abroad, according to research from the Brookings Institution published in March, which found 43% of U.S. workers now use AI on the job, compared to 32% among their European counterparts. The gap holds at the firm level, too, with 7% of U.S. companies now using AI for production of goods and services compared to just just 4% of European firms.

Bosses have been under pressure to figure out how they can turn AI tools and experiments into profits. And while AI usage has in most cases yet to translate to an acceptable return on investment, the technology is starting to show up in productivity statistics—albeit modestly. The Brookings research found differences in AI use led to aggregate time savings worth 2.3% of working hours in the U.S. versus 1.4% in Europe.

It’s a potentially meaningful edge—one that companies hope will accelerate once learning curves are overcome and as the technology improves. The problem for firms now, though, is they’re making a bet that could take years to pay off, and the bills are already coming due.

U.S. companies are beginning to reckon with the reality that even if experimenting with AI seems free, it usually isn’t. Loosely monitored usage limits and a trial-and-error process of figuring out how AI can improve bottom lines has led to a series of high-profile snafus, and exposed the limitations of corporate America’s fast-tracked approach to AI adoption.

Uber reportedly spent the first four months of the year burning through its entire 2026 AI coding budget, driven largely by Claude Code usage. Andrew MacDonald, Uber’s chief operating officer, said the “link is not there yet” during a podcast interview last week, as the company has yet to apply its heavy AI usage and spending toward profitable consumer-facing products.  

Uber is far from alone in its accounting miscalculations. Corporate demand is surging for tokens—units used to determine AI usage—with Google CEO Sundar Pichai recently announcing the company is now processing 3.2 quadrillion tokens per month, a seven-fold increase from a year ago. 

Among leading tech firms where employees are more likely to use AI, and where bosses are most likely to expect it of their reports, many workers now engage in “tokenmaxxing,” a competitive process to see which employee can use the most tokens.

But with most company clients relying on pricey frontier models for their enterprise AI use, several are discussing scaling back their outlays. Meta, Microsoft, and Salesforce are just a few companies that are reportedly pushing for employees to use AI more productively, or to limit usage altogether, according to the Wall Street Journal. The nightmare scenario would likely be similar to one shared recently by Axios, citing the case of one unnamed firm that reportedly spent half a billion dollars on AI after failing to put usage caps in place for employees.

But even confronted with soaring costs, U.S. firms look unlikely to back down on their adoption plans. Adoption rates as measured by the share of U.S. companies paying for an AI subscription recently hit 50.6%, up from 46.8% at the beginning of the year, according to data collected by Ramp, which provides enterprise expense-management tools.

Early adopters in the U.S. might yet be vindicated. AI could be generating up to $250 billion in invisible economic activity that traditional indicators are not able to track, according to recent research from the Peterson Institute on International Economics. 

But those figures have yet to show up in firms’ ROI reports. An Accenture survey of U.K. firms published last month found 90% have yet to successfully integrate AI with their business and are still chasing down ways to boost revenues, with many noting productivity gains have primarily been on an individual-worker level rather than company-wide improved efficiency.

American executives might be further along on adoption than their U.K. or European counterparts. But by being ahead, U.S. firms have also set themselves the challenge of being first to figure out what, exactly, they are paying for.