in short:
AI cost management is becoming one of the next major challenges in financial operations. Token-based pricing, model routing, caching, workload placement, and vendor consumption models are creating new complexities for enterprise technology leaders. Standards are still emerging, but the spend is already real, which means organizations need to start building visibility, governance and business discipline before today’s experiments become tomorrow’s uncontrolled run rates.
Coming out of FinOps X, one thing is clear: the most honest conversations in enterprise technology right now are about things no one knows yet.
The issue of AI cost is real and accelerating, and the frameworks for managing it are still being invented. Generative and agentive AI are moving from pilot projects to products, workflows, and business processes. Each prompt, retrieval step, model call, generated output, evaluation, and agent loop incurs a cost. On a small scale, these costs may seem harmless. At an enterprise scale, they become commercial, operational and governance issues.
According to Gartner® First Take 2026: Tokenomics Foundation Marks a Turning Point in Controlling Runaway Artificial Intelligence Costs,”Leading organizations are increasingly taking a more thoughtful, post-hype approach, which requires economic AI sustainability. ”*
The challenge is that no one really knows how to solve this problem yet. This is not a criticism. That’s the point. Organizations that are now prone to uncertainty will be in a better position when standards, benchmarks and buying patterns begin to stabilize.
My main lessons learned from FinOps X 2026:
1. The indicator does not exist yet
Every organization using AI at scale is measuring something. Almost none of them can truly confidently measure the right things. The industry has yet to determine what token consumption, AI value or efficiency will be across different models, vendors, workloads and use cases.
The danger is familiar: Once an indicator becomes widely adopted, it can be exploited. Then it no longer reflects anything real. Cost per token can be useful, but it doesn’t tell the whole story. For some use cases, cost per query may be better. The cost per successful outcome may be reduced again. The correct answer depends on what the AI system actually wants to do.
This is no reason to stop measuring. This is a reason to be skeptical of your own dashboards and invest in a better framework before the wrong standards solidify. When shared definitions do emerge, organizations doing this work today will be in the lead.
2. Model routing is more dangerous than it looks
Routing workloads to cheaper models sounds like a simple cost optimization. This is not the case. Route to the wrong model and you risk corrupting cache, increasing latency, reducing quality, or triggering a rebuild that costs more than the original saved model selection.
This is where AI cost management differs from traditional cloud cost optimization. You cannot optimize the cost of tokens at one tier and assume the system improves. Model routing, hint architecture, caching, abstraction, workload placement, quality thresholds, and business outcomes all interact. If you only optimize unit price, the money you save may backfire.
3. Opaque supplier pricing is a business model risk
Software vendors are moving from familiar seat-based models to consumption, credit and usage-based pricing. In theory, this should bring cost into line with value. In practice, many models make true costs more difficult to understand.
Points run out quickly. Mathematics is difficult to understand. Unit definitions vary. The downstream effects are real. If your vendor changes its token pricing, usage limits, or consumption rules, it could force you to rethink the economics of your products, services, internal workflows, or AI-enabled customer experiences.
Before these models become the default and leverage disappears, now is the window to drive contract visibility. Businesses should demand clearer terms of consumption, better reporting, more transparency into unit economics, and the ability to attribute the use of AI to team, product, and business outcomes.
4. The industry is organizing around the issue
The launch of the Tokenomics Foundation is the clearest signal yet that AI cost management is being taken seriously at the institutional level. As token-based AI becomes a new form of variable technology spending, our goal is to work closely with the FinOps Foundation to create open standards, benchmarks, and best practices for the economics of AI infrastructure.
This problem exists on both sides of the AI economy. Buyers demand transparent, vendor-neutral standards for AI consumption. Vendors need clearer ways to define, price, benchmark and explain the economics of the infrastructure they sell. Neither side benefits from a market that gets bills before people understand the model.
5. Open-ended questions are work
The most important thing about where this space stands is the list of questions that have yet to be answered:
- How do we standardize metrics for vastly different token pricing models?
- How do we price uncertainty, retries, and failures in AI experiments?
- Who gets the token budget and how do you decide?
- Should humans be doing some of this work at all?
- When should a workload use cutting-edge models, smaller models, cached responses, rules-based workflows, or no AI at all?
- How do we connect AI consumption to business value, rather than just usage?
These are not hypothetical questions. They are practical decisions that organizations are making right now, without a mature framework to guide them.
What should business leaders do next?
The worst move is to wait for the market to mature before taking action. AI spending is already growing faster than the operating models surrounding it. Leaders don’t need perfect standards to start building better discipline.
- Start with visibility. Understand where AI is used, which models are called, who owns the workload, what business outcomes the use supports, and how costs are allocated. Then bring FinOps, procurement, engineering, finance, legal, risk and business stakeholders into the same conversation. This cannot be shouldered by one team.
- From there, push for transparency. Ask how the vendor measures usage, how pricing changes, what reporting is available, and whether consumption can be mapped to internal teams, products, or cost centers.
- Build internal guardrails before AI adoption becomes too fragmented to govern. And don’t separate cost from quality, performance, safety or value. In the field of artificial intelligence, the cheapest option can quickly become the most expensive option.
The FinOps community is at the same level as 2012 cloud costs. The payout is real, the pain is real, and the discipline is being built in real time. Practitioners investing in this now will define what the situation will look like five years from now.
Next step: Talk to an SHI expert and let’s work together to solve this emerging challenge.
Want to learn more about this topic? Read our blog on AI FinOps – How to stop chasing tokens and start measuring results
*Gartner, First Take: Tokenomics Foundation Signals a Turning Point in Taming Runaway AI Costs, June 5, 2026 GARTNER is a trademark of Gartner, Inc. and/or its affiliates.
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