A true cost model doesn’t just show where money is being spent -- it reveals which work is driving impact.
June 29, 2026
As AI adoption grows, organizations have unprecedented visibility into how AI is being used. They can see token consumption, model usage, prompt volumes and adoption rates in real time. These metrics are increasingly being mistaken as indicators of success. In some cases, organizations have even introduced token leaderboards to encourage AI adoption.
However, consumption reveals little about outcomes. An engineer might consume thousands of tokens, but without attribution to a feature, fix or business objective, it's impossible to know whether that spend created value. Finance teams see costs, engineering teams see usage, but neither can easily connect the two to what was ultimately achieved.
The problem is compounded by the fact that many engineering measurement frameworks predate AI. Traditional delivery metrics remain useful, but they were never designed to capture the difference that coding assistants, autonomous agents or AI-powered workflows make. In fact, research found that 94% of engineering leaders believe the metrics that matter most are missing from their current measurement frameworks.
