For the past two years, the story around enterprise AI adoption has mostly been about getting people to use it. Over the past few months, that story has rapidly been changing in many organizations. AI observability that once measured success by token volume is starting to measure success by return on investment, and the enterprises that figure out cost management first are the ones that will be able to scale AI without their spend outrunning their revenue.

At its core, AI cost management is the practice of measuring, attributing, controlling and optimizing AI spend across models, agents, applications and teams. The goal isn't simply to use fewer tokens, but to maximize the business value generated for every dollar spent. As AI agents take on more autonomous work, that discipline increasingly extends to managing the cost of agent reasoning, tool calls, and the models powering them.

We sat down with Kevin Stumpf, a Senior Staff Engineer at Databricks and Technical Lead for Unity Gateway, to talk through how teams should actually put that discipline into practice: not treating AI cost as a line item to minimize, but as a system to manage.