AI cost is often discussed too late. A prototype works, the output looks useful, and only later does the organisation ask what it will cost to run at scale.
That is the wrong order. Token usage, model calls and routing decisions should be designed into the workflow from the beginning.
Tokens are part of the operating model
Every model call consumes input and output tokens. The cost of one call may look small, but business systems rarely run once. They run repeatedly, across users, documents, customers, records and exceptions.
When the workflow grows, small inefficiencies can become meaningful. Sending unnecessary context, using a large model for a simple task or repeating calls because the process is poorly structured all add cost.
Bigger models should earn their place
Large models are powerful, but that does not mean they should be used for every step. A workflow may only need advanced reasoning at specific points.
Other stages may be better handled by rules, retrieval, deterministic code, smaller models or local models. The system should reserve expensive intelligence for the moments where it changes the quality of the outcome.
Cost control is part of trust
The aim is not simply to reduce token usage. Cheap output that is unreliable is not efficient. The better measure is value per task: did the model call improve speed, quality, accuracy, consistency or decision-making enough to justify the cost?
Designing token usage into the workflow helps AI become an operational capability rather than a novelty. It makes the system measurable, governable and easier to improve.