AI factories may be a watershed moment for industry, but the means must line up with the ends: outcomes that CIOs can measure. The contemporary AI moment speaks to the early days of cloud, when CIOs weren’t buying a service so much as adopting a way of operating, according to Sid Nag, president and chief research officer of Tekonyx. AI factory must prioritize scalable intelligence to deliver real, tangible results. To address the question of value, enterprises need orchestration engines, domain-specific models and the ability to fine-tune, train and run inference on their own data, according to Nag. Just as important, they need governance, predictability and repeatability so that AI can move from one-off experiments to reliable business systems. In other words, the AI factory matters not as an engineering marvel, but as a model that enables a scalable intelligences which CIOs can use to drive measurable outcomes, he explains. “To me, that’s a successful outcome of AI,” Nag said. “If that’s the way the world can think about it and all the vendors can respond to that, I think that’s a winning ticket.”