Capping employees to a cheap model and letting everyone use the best model both fail. How to write an AI spend policy that lowers total cost.
Only about a quarter of enterprises can see what their AI actually costs in real time. The rest are setting spend policies with an invoice that arrives weeks later and no view of what the money bought. Under that pressure, most policies take one of two shapes, and both lose money.
The invoice falls in the first month. Then the hard tasks start taking three or four attempts. People spend more time reviewing and fixing output. Some quietly route around the policy with personal accounts. The extra attempts and the extra labor consume most of the saving, and none of it shows up next to the AI line.
Quality is high and rework is low, but the company overpays on the large share of everyday work where a cheaper model would have produced the same result. The bill grows with adoption, and the CFO eventually forces policy one.
Both policies choose a model per person or per company. The right model depends on the step. Drafting an email and reviewing a supplier contract are different jobs, and even within one task, planning and carrying out work have different needs. A single rule cannot be right for all of them.
With CompletionPrism™, the enterprise sets the policy: approved providers, quality bar, labor rates. Employees work in one place and never pick a model. Before every step, CompletionPrism™ chooses the model with the lowest cost to finish that still holds the bar, and every answer carries a receipt. Switching providers becomes a policy change, with nobody retrained.