AI budgets are growing fast. The hard part is not tracking costs. The hard part is proving value. Without full stack attribution, teams struggle to tie AI costs to customers, features, and most importantly business outcomes.
In this lightning talk, you will learn a tool agnostic approach to cost attribution across all layers of the AI stack. With the right attribution design, cost granularity increases, and insights become more actionable as you move up the stack. The infrastructure layer provides workload level precision, such as cost per AI workload or cost per model. The model/GenAI layer adds model level attribution so spend ties cleanly to teams, products, use cases, and more. The agentic layer is the goal because it delivers the most decision ready breakdown, such as cost per agent run, cost per workflow, and cost per resolved task, grounded in the costs below it. You will see how to trace AI cost end to end from GPU and infrastructure to model calls to agent workflows to business outcomes and produce unit metrics like cost per request and cost per task, ultimately tying spend to business outcomes like cost per resolved ticket or cost per call answered so ROI can be tracked.