Nobody knows where their AI budget is going

The early stages of AI adoption focused primarily on getting organizations to adopt AI-enabled solutions such as copilots, agents, assistants and AI-powered workflows. The priority was demonstrating that AI had the potential to deliver value, not scrutinizing every dollar spent along the way.

At the time, this made sense. There were relatively few people using AI, budgets were often funded through innovation initiatives and the cost of experimentation was low compared to the potential upside.

Organizations treated AI spending as a learning experience, so finance teams had little reason to scrutinize every model call or workflow because the priority was learning what AI could do, not optimizing what it cost.

When AI moves from pilot to production

After AI goes from pilot programs to production environments, the financial implications of using AI become very different. Every time a user enters a prompt, calls a model, retries an action, takes an action with an AI agent or executes an AI workflow, the total amount of money spent on AI goes up.

The amount at stake is rising quickly. Gartner expects worldwide spending on AI to reach $2.59 trillion in 2026, an increase of 47 percent from 2025. As companies put AI into more products and daily tasks, even small inefficiencies will repeat across millions of requests and add up to substantial costs.

The movement toward autonomous AI agents will only accelerate this trend. An agent may take much longer to perform its task than a single chatbot interaction. It may also use several different models, interact with other systems and continue to operate autonomously until it completes its task. Since organizations are likely to deploy many agents, AI costs will increase due to two factors: more people using AI and AI performing more work.

These concerns are already affecting which projects survive. Gartner predicts that more than 40 percent of projects involving AI agents will be canceled by the end of 2027 because of rising costs, unclear value or weak controls. A company may approve an agent because it works during a pilot, then reconsider it once thousands of people begin using it and every task triggers several paid requests.

A small number of experimental interactions will become thousands or millions taking place throughout an organization each day. Organizations that were previously asking how to expand their use of AI will now be asking: Do the benefits derived from each AI initiative outweigh the long-term operational costs associated with it?

This change in perspective is a good thing. Organizations are starting to look at AI as an operational expense instead of a shiny new toy. They are now evaluating AI initiatives based upon whether the initiative’s benefits exceed its long-term operational costs, rather than evaluating them based upon enthusiasm for the new technology.

Organizations can see the total AI bill but not where it comes from

The biggest issue for organizations is understanding where those costs originate.

Model providers typically send invoices detailing total usage for a given period. Companies know how much they spent on AI, but they often cannot explain which workflows generated those costs, whether that spending created meaningful business value, or which teams should ultimately own it.

Research from the FinOps Foundation shows that practitioners rank controlling the cost and use of tokens in software delivered as a service as their top concern related to AI. The reasons include bills that reveal little about what caused the expense and systems that provide no built-in way to trace costs back to the people or work responsible for them. A total on an invoice cannot tell a company whether one useful product feature caused the expense or whether an agent repeatedly called a model without improving the result.

That lack of visibility represents a significant blind spot. Imagine receiving your monthly cloud infrastructure bill without knowing which applications consumed your compute capacity, or receiving your monthly utility bill without knowing which buildings consumed your electricity. Most organizations would never tolerate that level of uncertainty elsewhere in their technology stack, yet many are currently managing AI spending in precisely this way.

Without attribution, organizations cannot determine which AI systems are delivering measurable business value, which workflows are inefficient or where unnecessary costs are accumulating.

Optimizing AI spending requires visibility into workflows

Much of today’s discussion about optimizing AI spending revolves around selecting a lower-priced model or negotiating better pricing with model providers.

Those discussions are valid because pricing is one of the few variables organizations can easily measure. Lower model prices alone will not solve the problem. As AI becomes embedded in more workflows and autonomous agents perform more work, organizations often consume far more tokens than they save through lower pricing. The greater opportunity to reduce AI costs often lies in workflow design.

OpenAI provides a clear example. Developers often send the same instructions or previous conversation back to a model each time they make a request. OpenAI introduced Prompt Caching in 2024 so developers could reuse material the model had recently processed and receive a 50 percent discount on those input tokens. The company later increased the discount to 75 percent for repeated material sent to its GPT-4.1 models. The savings come from changing how an application sends information to the model, which means a company can lower its bill without choosing a less capable model or negotiating a new contract.

Optimizing AI spending therefore requires organizations to develop visibility into how work flows through their AI systems. Organizations need to understand how agents interact with one another, how workflows execute, where redundant processing occurs and which steps provide the greatest value relative to their cost.

They also need to know when a system retrieves information it never uses, repeats a failed request, sends the same material several times or calls an expensive model for work that a cheaper one can complete. Each decision may add only a fraction of a cent to one task, but the same mistake repeated across millions of tasks can erase the financial benefit the system was supposed to produce.

Once organizations gain this level of understanding, they can optimize intelligently instead of simply selecting the least expensive model.

AI spending will drive organizations toward greater selectivity

Over the last several years, the AI industry has focused on determining whether AI belongs everywhere. The next stage will focus on identifying where AI creates the greatest value.

Some workflows will produce enough business value to justify substantial AI investment, while others simply will not. Long-term success will depend on understanding where AI creates meaningful value, where the costs outweigh the benefits and which AI systems actually justify their ongoing expense.

Companies should begin by recording which team, product, customer and task caused each paid request. They should compare that expense with the result the system produced, set limits that stop agents from retrying work indefinitely, and alert the people responsible when the cost of a task rises unexpectedly. Engineers can then inspect the costly work, remove repeated steps, reduce the amount of information sent with each request or choose a less expensive model when the quality remains acceptable.

A monthly invoice arrives too late and says too little. Companies need to trace spending while the work takes place, assign responsibility for it and decide whether the result earned its cost. Those practices will help leaders determine where AI deserves more investment, where the system needs repair and where it should be turned off.