Cost optimization FinOps AI

The AI Effect: When Technology Cost Became Unpredictable

Ingrid Roodenburg
Ingrid Roodenburg

FinOps in 2026: From Cost Control to Technology Value (Part 2 of 5 ) 

FinOps has officially redefined its mission from managing cloud value to managing the value of technology as a whole. AI is the single biggest reason why. It's introducing cost drivers that don't fit existing financial models. So, we can no longer treat cloud as a separate bucket. It now has to cover SaaS, licensing, and hybrid infrastructure together, all in one connected view.

In Part 1, we saw that FinOps has officially redefined its mission from managing cloud value to managing the value of technology as a whole. AI is the single biggest reason why. In just two years, the number of FinOps teams managing AI costs has surged from 31% to 98%, introducing cost drivers (tokens, model usage, GPU consumption) that don't fit existing financial models. AI often gets cheaper per unit while total spend keeps rising, because usage grows faster than the price drops. As a result, FinOps can no longer treat cloud as a separate bucket. It now has to cover SaaS, licensing, and hybrid infrastructure together, all in one connected view.

AI is the biggest driver of change in FinOps

Artificial Intelligence has been the single biggest driver behind the rapid evolution of FinOps. Consider the pace of change: in just two years, the number of FinOps teams managing AI costs has surged from 31% to 98% (Source: FinOps Foundation).

This sudden rise has exposed a clear gap between how organizations used to manage costs and what is needed today. Traditional cloud spending, while complex, was still relatively predictable. Costs typically scaled with these familiar drivers:

  • Storage
  • Compute power
  • Network usage

AI changes that completely. It introduces new and unfamiliar cost drivers that don't fit neatly into existing financial models, such as:

  • Tokens
  • Model usage
  • GPU consumption

AI spend keeps rising even though it gets cheaper

This is the trend many organizations are now experiencing firsthand: on the surface, AI is becoming cheaper, the cost per unit has dropped significantly. But in reality, overall spending is going up.

Why? Because usage is increasing at a much faster rate than the cost per unit is falling:

  • As organizations experiment, scale, and embed AI into more processes, consumption grows rapidly.
  • Costs grow right alongside that consumption.

And in the end, what looks like a cost reduction on paper, often turns into higher total spend in practice.

This mirrors what we see more broadly in the market: companies that had finally started to get cloud spending under control are now facing rising costs again, largely driven by generative AI. The result is a new kind of financial challenge. AI spending is:

  • Higher
  • Less predictable
  • Harder to track
  • More difficult to link directly to business outcomes

Many organizations are investing heavily, but still struggle to answer a simple question: is this money delivering value?

How does FinOps fit in this new environment?

In this environment, FinOps becomes essential. Its role is no longer just to reduce costs, but to bring structure and clarity to increasingly complex spending. It helps organizations to create visibility, set guardrails and ensure accountability for how AI is used.

Most importantly, it shifts the conversation. Instead of asking, "How much did we spend?", organizations are starting to ask a far more meaningful question: "What value did we create for every euro we spent?"

As AI adds more complexity, the scope of FinOps is expanding alongside it. Today, most FinOps teams are no longer focused on cloud alone. They are also dealing with:

  • SaaS applications
  • Software licenses
  • Hybrid infrastructure

This reflects how technology is used within organizations. Cloud is no longer a separate category, it has become just one piece of a much larger, interconnected technology landscape.

What happens when organizations manage these cost areas separately?

Costs are spread across multiple platforms, tools, and environments, all of which contribute to total IT spend. Because of this, managing each area separately no longer works. Organizations that treat cloud, SaaS, licensing, and AI as isolated cost buckets often run into the same problems:

  • Fragmented reports
  • Inconsistent numbers
  • Slow, unclear decision-making

It becomes difficult to answer even basic questions about where money is going and what it is delivering. And this is where FinOps plays a crucial role. It brings everything together into one clear view:

  • Connecting cost to ownership
  • Connecting ownership to usage
  • Connecting usage to business outcomes

This helps organizations understand not just what they are spending, but why, and whether it makes sense. In that way, FinOps is becoming the layer that connects technology spending to real business value, across the entire organization.

What's next in this series?

A wider scope - cloud, SaaS, AI, and licensing all managed together - doesn't just change what FinOps covers. It's also changing where FinOps sits in the organization and how teams work day to day. In Part 3, we'll look at how FinOps is moving upstream into strategic decision-making, and shifting from a reactive, after-the-fact function to a proactive one.