the most expensive ai decision your company made this year was never approved
someone grabbed an api key, shipped something that worked, and three months later finance gets hit with a $40k invoice nobody signed off on. now multiply that across every team running their own experiment.
the finops foundation changed their mission this year, they went from "advancing people who manage the value of cloud" to "advancing people who manage the value of technology." j.r. storment calls it "technology value management." the stats back up why: 98% of organizations track ai costs now, compared to 31% two years ago. 90% monitor saas spend versus 65% last year. missions change when bills arrive.
everyone watches gpu costs. but the real money drain? rate limits that were never configured. prompt caching that's disabled. usage logs no one checks. teams using flagship models when gpt-5.4 nano costs 12x less per token. most ship on the expensive model because it's the default. ask them what their last 10,000 api calls cost and they can't tell you.
every cloud audit plays out the same way: nobody owns the ai budget line. engineering blames finance. finance blames engineering. product blames infrastructure.
where does your ai spend actually live? and who's responsible when it spirals?