How to measure what your AI work really costs before changing anything, what a shadow baseline reports, and how to use it to build a business case.
Every AI cost initiative eventually faces the same question from finance: compared with what? Savings claims without a baseline are guesses. The most credible way to start is to measure current spending and current rework on real traffic, for long enough to be representative, before rerouting a single request.
In a shadow baseline, requests go exactly where they go today, on the models people already use. Alongside each request, the cost to finish is measured and the alternatives are priced, but nothing is changed. Two weeks is usually enough to capture a normal mix of work across a team.
Start with one developer team and its approved AI traffic. Developers generate high volumes of AI requests across a wide range of difficulty, from simple edits to complex design decisions, so the baseline shows where model choice matters. The results translate easily to other teams.
A good baseline separates what was observed from what was estimated. Token spend is observed from the provider. Rework is estimated. The projected saving depends on both. A CFO who can see which numbers are which can decide how much of the saving to believe, and the business case survives scrutiny.
Every CompletionPrism™ engagement begins with a two-week shadow baseline on one team's traffic. Nothing is rerouted during the baseline. At the end, you have a measured cost to finish your work and a projected saving you can defend. For software vendors, the Embedded License starts the same way, on fourteen days of the product's own traffic.