Enterprise AI is entering a new phase — one where the central question is no longer what can be built, but how to make the most of our AI investment.At VentureBeat’s latest AI Impact Tour session, Brian Gracely, director of portfolio strategy at Red Hat, described the operational reality inside large organizations: AI sprawl, rising inference costs, and limited visibility into what those investments are actually returning.It’s the “Day 2” moment — when pilots give way to production, and cost, governance, and sustainability become harder than building the system in the first place.”We’ve seen customers who say, ‘I have 50,000 licenses of Copilot. I don’t really know what people are getting out of that. But I do know that I’m paying for the most expensive computing in the world, because it’s GPUs,'” Gracely said. “‘How am I going to get that under control?'”Why enterprise AI costs are now a board-level problemFor much of the past two years, cost was not the primary concern for organizations evaluating generative AI. The experimental phase gave teams cover to spend freely, and the promise of productivity gains justified aggressive investment, but that dynamic is shifting as enterprises enter their second and third budget cycles with AI. The focus has moved from “can we build something?” to “are we getting what we paid for?”Enterprises that made large, early bets on managed AI services are conducting hard reviews of whether those investments are delivering measurable value. The issue isn’t just th …
