The AI Cost Nobody Approved

The financial approval was built on implementation cost and an initial usage projection. On paper, it makes sense. The problem lies in what was left out of the spreadsheet.

Inference scales with real adoption. The more the system is used, the more API calls, the more tokens processed, the more automations running in parallel. Variable OPEX grows right alongside the project's success.

The business case was built for the launch, not for success. And that distinction comes at a steep price.

The company discovers AI's real cost at the renewal meeting, not at the moment of approval. Someone pulls up the statement, looks at the usage growth, and asks: was this in the plan?

What's happening there isn't waste. It's the system working exactly as it should. The problem is that the ROI wasn't structured for that scale.

Building the business case with real volume scenarios from the start completely changes that conversation: cost per transaction at scale, projection of growing adoption, a return model that tracks usage, not just the launch.

Those who ignore this arrive at renewal with a critical system, a cost nobody approved, and an argument to cut precisely what is working.

How is your company's AI cost model holding up? Was it built for the pilot or for operation at scale? Let me know in the comments.