AI value creation meets cost accountability as FinOps evolves beyond cloud

As AI adoption accelerates, organizations are focused on balancing value creation with effective AI-driven cost management. Companies are implementing stronger governance and FinOps practices to bring greater control, accountability and visibility to this rapidly evolving landscape.

FinOps has evolved beyond its traditional focus on cloud cost management. As the scope and value of FinOps continue to expand, AI has become a major area of focus, according to Trent Allgood (pictured, right), vice president of information technology asset management and FinOps consulting at SoftwareOne Inc. and governing board member at FinOps Foundation Inc.

“When you say ‘chaos,’ that’s what we’re all trying to figure out now,” he said. “Everybody’s been thrown, ‘Here’s AI, go use it, go create value.’ How do we now manage the costs that it’s also creating?”

Allgood and Parker Nancollas (left), global FinOps practice lead at SoftwareOne Holding AG, 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 how FinOps has evolved to address AI-driven cost management and the growing importance of cross-functional collaboration to maximize business value from technology investments. (* Disclosure below.)

AI-driven cost management in focus

Organizations are approaching AI much like they did the early days of cloud adoption, recognizing that not every implementation will be perfect from the start. The core principle of FinOps is continuous improvement, with teams refining practices and adapting as technologies and business needs evolve, Nancollas noted.

“The best practices are evolving every day, and what we might say is a best practice today might not be a best practice a week from now — and that’s okay,” Nancollas said. “The point is, we do what is best today. As the industry evolves, as the best practices evolve, we will evolve as a service provider, our customers will evolve and that’s great. We’ll learn as we go.”

One of the biggest structural shifts is how quickly AI models are being retired and replaced. On-premises servers were depreciated over three years, cloud services ran for months or years — but AI models can go in and out of production in 60 days, creating a budgeting and forecasting challenge that most FinOps teams were never built to handle, according to Nancollas and Allgood.

“Models need to be looked at as a consumable portion of what we’re building that’s replaceable and will be replaced,” Nancollas said. “Part of what you need to budget for and plan for now is … research and development for those new models, updating to those new models — that has to be part of your budgeting and forecasting, not just the ongoing usage of what you’ve already implemented.”

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