Working references from Certance Advisory: what we measure, what it costs, and what to change first. Every figure traces to a primary source.
The sourced levers that cut monthly token spend by 30–50% without cutting output quality: model routing, prompt caching, context hygiene, and moving deterministic work out of the model loop.
Adoption climbs, acceptance rates look healthy, and the board asks what the licences return. The four findings that recur in almost every team we assess, why they happen, and the seven dimensions that measure them.
New pieces go to subscribers first, together with the full AI Token Efficiency Guide.
Subscribe