Every pricing model, seat or meter, rests on one number: what the work actually costs. Finding that number is a data job. Someone has to track the cost of every step, every retry and every human review, then tie it to a finished claim. That work falls to the data team, which makes pricing a CDO’s problem as much as procurement’s.

But first, agents need data they can read. “Most enterprises are nowhere near ready,” Bustamante wrote. He describes claim data trapped in a 47-page document with inconsistent formatting, and loan applications with missing fields and illegible handwriting. “You can’t orchestrate agents on top of chaos.”

The research points the same way. IDC, in research with Lenovo, found that for every 33 AI proofs of concept a company launched, only four reached production. A proof of concept is a small trial built to test an idea. Bustamante blames the gap on two things. The first is data: Trials run on clean data and simple workflows but real operations are messy.

The second is liability. In a trial, a wrong answer is a lesson. In production, “if the AI approves a fraudulent claim or denies a legitimate one, someone is liable,” Bustamante wrote.

In his view, that someone is the buyer. “Responsibility sits with the enterprise because the agent is a tool,” he wrote. It is a convenient position for a vendor, but the practical point stands. If a regulator asks why a system denied a claim three years ago, the company needs an audit trail: the data the system saw, the logic it followed and the reason for its decision. “The seat model doesn’t give you that,” Bustamante wrote.

Neither does a meter. A bill counts work. but doesn’t explain a decision.

Europe has set a deadline for that test. Under the EU AI Act, AI systems used to judge creditworthiness or price insurance face high-risk rules from Dec. 2, 2027. Those rules include keeping system logs.

So what should a buyer ask before signing?