As AI spending surges, organizations are expanding their focus from AI cost optimization to value measurement. The trend is driving demand for deeper visibility into how AI investments impact productivity and business performance.

One of the biggest changes AI introduced was unpredictability, because usage and costs can scale far beyond the patterns typically seen in traditional cloud environments. That volatility has forced organizations to rethink how they track and manage AI spending as adoption grows, according to Hunter Harris (pictured), director of cloud FinOps at Pegasystems Inc.

“AI can more or less scale infinitely until the [cloud service provider] runs out of capacity. We haven’t had any runaway like that, but the only thing that’s predictable is that it has been very unpredictable in many ways,” he said. “With us, a lot of it was like, ‘How do we classify our AI spend? Where is it going? Is it for internal productivity? Is it for our customers and products that we’re building? Is it experimentation?’ We had to put it through that lens.”

Harris spoke with theCUBE’s John Furrier and Paul Nashawaty at FinOps X 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the growing challenge of AI cost optimization and the importance of connecting technology spending to measurable business value. (* Disclosure below.)

AI cost optimization decisions

As AI adoption grows, FinOps conversations are increasingly shifting toward complex decisions around agents, model routing, token economics and more, reflecting a broader convergence of deep technical systems challenges and business strategy. FinOps teams must act as a “Rosetta Stone” for the business, according to Harris.

“You have to be able to translate everyone’s language. You have to be able to talk to an engineer or a support engineer or a salesperson or a product engineer or an ops person and get everyone on the same page,” he said. “That’s why I think we’ve been successful.”

Going beyond cost visibility, connecting AI investment to revenue at the contract level is what allowed Pegasystems to demonstrate real margin impact — mapping cloud and AI costs back to individual customers to show overall contribution margin, Harris noted.

“You have to connect all of this into a data model,” he said. “It’s not just cloud cost data — it’s your operational data, your support data, your product data, your bug data, your revenue. If you can actually have all of that come together, you can tell real stories about how the business is performing and make much more accurate forecast and budget decisions.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the FinOps X 2026:

(* Disclosure: TheCUBE is a paid media partner for the FinOps X event. Neither the FinOps Foundation, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE