Watch Your AI Spending
To curtail costs, a growing number of firms are imposing limits on how much their employees can effectively use. Could this backfire?
You have $29 to spend on AI this month. Use it wisely.
Wary of escalating costs around their AI investments, an increasing number of organizations are imposing limits on AI use by a big swath of their employees. In some cases, firms are lowering the amount of money an employee can spend on AI each month to less than what they likely spend each month on morning coffee. It’s a move that experts say could make sense in the short term but could cause multiple headaches down the road. “If I can’t innovate with it, or it runs the risk stopping midwork, then I won’t rely on AI,” says Bryan Ackermann, Korn Ferry’s head of AI strategy and transformation.
In some cases organizations, hitting the new limit triggers a conversation; managers (or the team tasked with implementing AI within the organization) ask employees to justify why they must exceed the limit. In others, it’s a hard cap, and an employee cut off from AI use.
Experts say it’s not surprising that more companies have become more cost conscious around their AI spending. US firms are forecast to spend $280 billion on AI this year alone, more than 50% more than what they spent in 2025 (these numbers don’t count the spending done by the AI model-makers themselves). Each time an AI is asked a question, develops a financial model, or anything else, it spends “tokens.” This spring, many AI creators moved away from a flat-fee pricing model to charging for each token. Pricing for some software subscriptions jumped between 20-40% instantly. Other plans, which once allowed for unlimited AI use, now have hard limits, forcing firms to buy more tokens, akin to having to buy more minutes for a cellphone plan.
Over the summer, many organizations placed caps on how muchtheir technology employees were using AI, since software developers were theones who were racking up the biggest bills. But the latest round of limits cap theuse of employees outside the IT department. Some organizations have found thatemployees are using pricey AI tools to perform low-value tasks, such asconverting PDFs into slide presentations. In those so-called “token chewing”cases, the employee might save time and effort, but the end product doesn’thelp clients and costs the organization financially.
Ackermann says the limits and subsequent exception conversations aren’t practical for any large organization. In a 20,000-personorganization, for instance, if 2% of employees had to have conversations about their AI usage that would entail 200 additional meetings each month. “It’s unsustainable,” Ackermann says. The system also puts the burden on employees to keep track of their usage, and many don’t have the tools or knowledge to do so.
The new limits also could curtail people from experimenting with AI. Training, which in many cases had been geared towards letting employees develop their own ways of effectively using AI, now could focused solely on making sure employees know how to use AI on processes that have already been shown to increase productivity. “You’ve had three years (of AI of throwing things at the wall and see what sticks,” says Janet Mertens, director of research for AMS, a division of Korn Ferry.
Ackermann, who speaks with the AI model makers regularly, says they know that pricing is an issue. The onus is on the developers to make AI with affordable and dependable pricing for its corporate customers. “Otherwise, it will only be used as a toy,” he says.
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