AI adoption in businesses is still low, but as it scales, so does token consumption. Think of it like a gas meter that starts spinning the moment your team fires up these tools.

Sitting across from people rolling out AI lately, the concern of mounting token costs with little visible ROI keeps coming up. Every prompt, every response, every automated workflow burns tokens. So companies are starting to ask: does everyone need access to the most powerful models? Should we really be burning frontier compute just to fix the grammar in an email?

Not every task needs the latest shiny model. Sorting email, summarizing a doc, answering routine questions, you simply don’t need Claude Opus 4.6 or ChatGPT 5.4 for that.

One solution is to use tools like Ollama that let you run open-weight models locally for everyday tasks at near zero cost, saving your frontier budget for work that actually needs it.

Tiered AI, matching the model to the task or job role, might be the most underrated cost strategy in the room right now. It’s what allows businesses to expand AI access across the org instead of cutting usage just to save at the pump.