The systems behind enterprise AI adoption success - IBM

Everyone's Buying GPUs. Almost Nobody's Ready to Feed Them. The enterprise AI conversation has a blind spot the size of a data center. Every budget meeting I've sat in over the past 18 months has the same shape: GPU allocation gets 70% of the discussion time, model selection gets 20%, and the data infrastructure that actually feeds those models gets whatever's left over. Usually about ten minutes and a vague reference to "we'll figure out storage later." This is why most enterprise AI deployments stall after the proof of concept. The Bottleneck Nobody Budgets For Here's what happens in practice. A team spins up a promising AI workload — retrieval-augmented generation, a fine-tuning pipeline, an inference service. It works great on a curated dataset in a dev environment. Then they try to run it against production data at scale and everything falls apart. Not because the model is wrong, but because the storage layer can't deliver data fast enough, the pipeline can't unify sources across