AI Tokens: How to avoid AI bill shock

By Jesse Brett | 24 July 2026

The question organisations ask too late when it comes to AI

AI conversations usually start with what the technology can do. Can it summarise a meeting? Search thousands of documents? Draft responses? Automate repetitive tasks? Those questions are important because they help organisations understand the potential value of AI. But as adoption grows, another question becomes just as important:

What does it cost to run?

Unlike traditional software, AI costs are not always predictable. They are influenced by how often AI is used, how much information it processes, which models are selected and how much work happens behind the scenes.

That is why more organisations are paying attention to AI tokenomics, the discipline of understanding how AI usage translates into cost, value and business outcomes.

If AI is delivering measurable value, higher usage may be a good thing. The challenge is understanding what is driving that usage and ensuring costs remain aligned with the benefits being created.

To understand AI tokenomics, you first need to understand AI tokens

Put simply, tokens are the units AI models use to process information. Every prompt you type, every document AI reads and every response it generates is broken down into tokens.

Think of tokens as the fuel that powers AI.

A simple question might use only a small number of tokens. Asking AI to review a lengthy contract, analyse multiple documents or run an AI agent across several business systems can consume significantly more.

Most AI platforms charge based on the number of AI tokens processed, which means usage has a direct impact on cost.

This is why AI costs are often driven by consumption rather than user numbers. Two employees may have access to the same AI tool, but one could generate significantly more cost simply because of the complexity and volume of tasks being performed.

Understanding how AI token usage relates to business value is the foundation of effective AI Tokenomics.

AI agents introduce another layer of complexity

AI agents can check systems, read files, call tools, take actions and work through processes several times before producing an answer. That can be incredibly valuable, but it also means usage can increase without someone actively monitoring every step.

The aim is not to avoid AI agents, but to understand the job they are doing and the activity they generate behind the scenes.

If an agent is handling a small, controlled part of a process, the cost may be relatively easy to manage. If it is reading large volumes of information, making repeated checks or operating across multiple systems, that needs to be understood before it goes live.

Cost is a design decision, not a problem for later

This is probably the most important point in the whole discussion.

AI cost should not be treated as something you only think about when usage starts to scale. By then, many of the decisions that shape cost have already been made.

The expensive choices are often design choices:

what data the solution looks at
how often it runs
how much context it needs
which parts of the process use AI
which model is selected
where traditional automation would have done the job just as well

Choose the right model for the job

The newest or most capable model is not automatically the best choice.

For some tasks, a smaller or more focused model may be faster, cheaper and more than capable of delivering the required outcome. For others, the additional capability may be worth the investment.

The important thing is matching the model to the task rather than automatically reaching for the most powerful option available.

Good design reduces unnecessary cost

If you point AI at everything, ask it to make every decision and allow it to repeatedly process the same information, you have designed an expensive solution.

If you are more deliberate about how the solution is built, you can often achieve a better outcome for less money.

That is why cost belongs in the design conversation from the beginning rather than becoming a concern much later down the line.

The best AI solutions are not AI everywhere

A good AI solution is rarely AI from end to end. The strongest designs blend AI with automation, workflows, business rules and human review.

Not every task needs AI

If a step is simple, predictable and repeatable, automation may be the better option because it is often cheaper, faster and easier to test.

Use AI where it adds unique value

If a step requires judgement, language understanding, summarisation or interpretation, that is where AI may earn its place and deliver the greatest value.

This matters commercially, but it also matters operationally. AI introduces flexibility, although it also introduces variability. That is why it should be used where that flexibility is genuinely valuable rather than simply because the technology is available.

This is often where organisations go wrong. The technology becomes the starting point rather than the business problem.

The better starting point is the problem, the process and the data. Once those are understood properly, the right technical answer usually becomes much clearer.

Poor data does not just reduce trust. It can increase cost.

Data is not a side issue. It is one of the biggest factors in determining whether AI is useful, trusted and commercially sensible.

Messy data creates unnecessary work

If customer information, reports and documents are duplicated, outdated or stored across multiple locations, AI has to work harder to find useful information. That increases cost, slows performance and can reduce the quality of outputs.

If the wrong data is visible, or too much data is visible, the problem becomes even worse because AI can end up processing information that adds little or no value.

Rather than paying AI to help, organisations can end up paying AI to work through unnecessary noise.

Strong data foundations matter

This is why data quality, permissions and structure are so important.

AI is only as useful as the information it can safely and sensibly work with. Poor data does not just reduce confidence in the answers, it can make the entire use case more expensive and less valuable.

When organisations talk about AI readiness, the data foundation should be near the top of the list. Before building more AI on top, it is worth asking whether the data underneath is clean enough, governed enough and relevant enough to support the desired outcome.

Learn more about why data foundations matter.

Ownership and governance should be defined early

If a use case is worth building, it is worth owning properly from the very beginning.

That means deciding up front:

who is responsible for the solution
how usage will be monitored
what budgets or alerts need to be in place
how value will be measured over time

If you can’t explain the bill, you have a governance problem

The risk is not simply that AI costs money. Every business investment comes with an associated cost.

The real risk is when nobody can explain the bill, understand what is driving it or demonstrate the value being created.

Who approved it, who owns it, who monitors usage and who decides whether it is still delivering value are all questions that should have answers before the solution goes live.

If those questions are not addressed during the design stage, they usually come back later through finance, procurement or security teams, often when making changes becomes much more difficult.

Start with a clearer view of your AI readiness

AI can deliver significant business value, but successful adoption requires more than access to the right tools. It requires visibility into cost, data, governance and day-to-day usage.

Our Data & AI Readiness Assessment helps organisations understand the commercial, technical and operational considerations behind AI adoption. It provides a clearer view of your current position, highlights potential risks and identifies practical steps to help you move forward with confidence.

Whether you are exploring AI agents, automation or wider AI initiatives, understanding these foundations early can help you avoid unexpected AI costs and build a stronger platform for long-term success.

Start with a complimentary Data & AI Readiness Assessment

Jesse Brett, Principal Consultant