Enterprise IT infrastructure isn't built for AI inference
A TechRadar Pro perspective piece argues that enterprise IT architecture still centers on supporting model training, while inference is becoming the dominant workload. It cites IDC saying global AI investment surpassed $300 billion in 2025, Deloitte saying inference already accounts for roughly two-thirds of compute, and McKinsey projecting inference will represent 30% to 40% of total data center demand by 2030. The article says training relies on centralized GPU clusters and high-speed interconnects, whereas inference needs geographically distributed compute, commodity ethernet and low-latency connections — nearly opposite requirements — and that autonomous agents' bursty, multi-step tool calls multiply network traffic further. It also cites Gartner's prediction that 40% of enterprise applications will integrate task-specific agents by the end of 2026, IDC's projection of a tenfold surge in agent usage by 2027, and a Gartner survey finding only 28% of AI use cases fully meet ROI projections while 20% fail outright.
It warns enterprises to rebuild compute, networking and storage around inference and agent workloads before scaling deployments, or risk spending that never converts into returns.