AI economics reshape FinOps as enterprises seek greater visibility and control

As AI spending accelerates across the enterprise, organizations are grappling with a new generation of cost and optimization challenges while seeking greater AI spend visibility. The next phase of FinOps is increasingly focused on improving visibility and embedding financial accountability into everyday technology decisions.

FinOps has evolved well beyond cloud cost management, with organizations now expanding their focus to a broader range of technology spending. As AI adoption accelerates, enterprises are facing new challenges, according to Rajeev Laungani (pictured, left), head of product at Virtasant LLC.

“Every day there’s a new model. Every day there’s a new service,” Laungani said. “What is the right model? Are we using it efficiently? And that’s the first problem to solve in the world of AI.”

Laungani and Colby Rozell (right), technical product manager of information technology optimization at Chevron Corp., spoke with theCUBE’s John Furrier and Paul Nashawaty at the FinOps X 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed AI spend visibility and the growing role of FinOps in software development. (* Disclosure below.)

AI spend visibility challenges

Organizations are increasingly looking to consolidate their FinOps toolsets, using AI to automate repetitive tasks while reserving human judgment for higher-stakes decisions. While AI can automate many repetitive FinOps tasks, human judgment remains essential because organizations still rely on multiple tools and have yet to find a single platform that can address every need, according to Rozell.

“It’s really this combination of multiple tools bringing those insights together where we can really leverage the insight that we’re gaining there,” he said. “In addition to that, we actually have opportunities where we need to remove the friction.”

Many cost-saving recommendations deliver relatively small individual savings, but implementing them often creates enough engineering effort and friction to delay action. When aggregated, however, those recommendations can add up to millions of dollars in potential savings, according to Rozell.

“That’s really where I see AI for FinOps removing that friction, helping us get to value faster in those decisions,” Rozell said. “It’s not just making the decision, still having the human in the loop, but it’s all the way through to the execution and modifying their code for them.”

Optimization at the code level is where the real granularity lies, with every architectural decision carrying a cost impact that compounds over years, Laungani noted.

“With every code-level change, what is the cost impact of that?,” Laungani said. “With every development or architectural consideration, how is that going to impact the environment five years down the road?”

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

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