The AI adoption spending spree is over. Time to focus on value.

IT leaders and CFOs are starting to push back on unrestrained AI spending within their organizations, with many enterprises now looking for ways to get better value out of their automation tools, observers say.

In recent months, several companies have blown through AI token budgets while encouraging employees to experiment with the technology. Several companies have deployed AI token usage leaderboards to advance adoption, but the practice has led to employee tokenmaxxing, with one Disney employee interacting with the Claude AI 460,000 times in a nine-day period.

Over at Uber, employees burned through the company’s entire 2026 AI budget in four months, prompting executives to cap AI coding assistant use.

At the same time, Anthropic and other major AI vendors are rolling out new metered pricing models.

Now, to counter runaway token use, some companies have begun to focus on “valuemaxxing,” by combining visibility, governance, and financial accountability to create better returns on their AI spending.

Value as a new metric

The focus at many enterprises in past years has been using AI to increase productivity, but value is increasingly becoming an important metric, says Becky Trevino, chief product officer at FinOps vendor Flexera.

“Before, the value was, ‘Let’s just deploy it to as many people as possible,’” she says. “It’s now quickly becoming, ‘Let’s be smart about this because we have a set amount of tokens.’”

AI budget surprises have become a serious talking point among customers of the technology, she adds.

“The metric for success is not if I have 99% of my organization using Microsoft Copilot,” Trevino says. “It’s now, show me the money. Show me what’s changing in your organization.”

FinOps companies such as Flexera aren’t the only organizations pushing AI customers to focus on value. In early June, the Linux Foundation announced the new Tokenomics Foundation to establish open industry standards, benchmarks, and best practices for the economics of AI infrastructure. 

The bill comes due

A reckoning about AI value and cost is overdue, some experts say. IT leaders who aren’t worried about the price of AI services are underestimating the coming problems, says Cliff Jurkiewicz, vice president of global strategy at HR-focused AI vendor Phenom

“AI is underpriced right now, and that’s masking the real cost,” he adds. “Behind every prompt is expensive infrastructure — data centers and energy — and providers aren’t absorbing that forever.”

There seems to be a casual approach to AI token use at many organizations, he says, with the current cheap access phase encouraging overconsumption.

“There’s very little discipline around what actually drives business value versus what’s just convenience,” Jurkiewicz says. “Every interaction has a cost, input and output, and these systems tend to overdeliver, which means you’re paying for more than you need. Multiply that across an entire workforce, and costs scale fast.”

As Anthropic and OpenAI both appear to be headed toward IPOs, pricing models will change, he predicts. Smart AI users will respond by limiting use, he adds.

“As pricing shifts to metered models, companies are going to get a wake-up call,” Jurkiewicz says. “The winners will be the ones that put guardrails in place early by prioritizing high-impact use cases and treating AI like a finite, strategic resource, not an unlimited utility.”

IT leaders need to recognize that costs can vary widely between AI models, adds Andy Sen, CTO of IT services marketplace AppDirect. Many companies are allowing employees to use models without considering the cost, he says.

“A lot of companies are turning on AI features everywhere, riding the productivity wave until they are blindsided by a huge bill,” he adds.

Encourage savings

Sen encourages IT leaders to research the costs of individual models and to encourage employees to use the most economic choice available. IT leaders should consider turning off premium features for small tasks, such as writing emails.

“The key is understanding the difference in cost between some of these models, because there are some that can be one hundred times more expensive than others,” he says. “Instead of getting an answer in fifteen seconds, you’re getting it in five seconds, and for most work, that’s not really worth it.”

CIOs can also help employees use AI more economically by pointing out the workers who are using it well, Sen says.

“If you want to see more people leveraging AI across the board, point to the people who are already using it successfully,” he says. “Role models can be a great way to show people how they can be more effective at their job and encourage AI use.”

Flexera’s Trevino recommends that organizations consider FinOps services and prioritize which departments have more access to AI tools.

“If you have a limited amount of budget and you have 20 departments, which of those departments matter the most?” she says. “Allocate most of your AI budget there. If you know three of those 20 are the ones that are you need to drive growth or competitiveness for your business, then you should really ensure that those three business units have the access they need.”