For today's CFOs, as AI technology becomes more deeply integrated into business operations, accurately understanding its cost-effectiveness is becoming increasingly important.

CFOs are not only focused on the return on investment of technology and which AI use cases are most effective, but are also thinking: "Just because we can use AI for this, should we?" said Gordon Pothier, CFO of Board.

"I think we first need to truly understand and master the cost of this technology," Pothier told CFO Dive in an interview focused on how finance executives are addressing AI ROI. "How do we plan around it?"

Healthy skepticism

Since OpenAI launched ChatGPT in 2022, generative AI has quickly become a standard tool for many professionals and business processes.

For example, Board's engineering team was an early adopter, "and I think if we tried to take these tools away now, there would be an outright revolt," Pothier said.

According to a company press release, Pothier became the finance chief of the Boston, Massachusetts-basedcorporate planning platformin November 2025. Before joining Board, he spent three years at code security provider Sonar, including two years as CFO. His previous CFO experience also includes serving as finance executive at Onapsis Inc. and Carbon Black.

However, as more employees rely on AI, CFOs are paying closer attention to its price tag—and the potential risks of over-reliance, both in terms of cost and effectiveness.

For example, last month, Uber executivesrevealed that the ride-hailing companyhad exhausted its 2026 AI budget in just four months. This news has fueled growing concerns among tech and business executives, with many needing tobalance the rising costs of AI tokens(data processing units purchased when companies use models) against their yet-to-be-realized ROI.

When it comes to current AI projects and spending, "there is more 'healthy skepticism' among CFOs and other key company leaders," Pothier said. He added that the momentum for adopting the technology remains—Gartner projectsAI spending will exceed $2.5 trillion by the end of this year(according to a May report)—and that generative AI still has valuable use cases.

But, "I do think skepticism is starting to emerge in a healthy way, like, 'Okay, are these cost savings really scalable, or are they just short-term?'" Pothier said. "What is the ROI? Now we're starting to really talk more about what the costs will look like over the next few years?"

Filtering the noise

Regarding AI over-reliance, Pothier said the danger for companies can arise when they "move too quickly for some reason, without considering all aspects of the problem."

For example, with token usage, companies may be calculating the cost of current tokens, but "how do I do scenario planning for what the next three to five years look like?" he said.

For CFOs, this means stricter scrutiny both in internal AI experiments and in choosing truly sustainable third-party vendors, Pothier said.

For example, as many companies begin leveraging AI to improve marketing efforts, not doing so can seem like falling behind—but CFOs must "really question" what the offered AI tools are actually doing and how they operate, he said.

"Is one chatbot really doing more than another chatbot?" he said. "What about token usage? Is it processing... using tokens in a way that the cost would be really, really high?"

This is not to say CFOs should avoid integrating AI altogether—if there is a solution that addresses a specific problem, "you absolutely should consider it," Pothier said. "But I think you have to try to filter out some of the noise and prioritize, and that's not always easy."