The New Economics of Enterprise AI: From “Can It Be Used?” to “How to Achieve Efficient Output”
The article uses the widespread adoption of electricity as an analogy, noting that enterprise AI is transitioning from a phase of isolated experiments to one of infrastructure‑level deployment. The central question has shifted from “Can we deploy AI?” to “How can we deliver measurable business outcomes at the lowest effective cost?” It introduces “the effective cost per useful output” as a key metric and analyzes the economic differences among three AI operating models—building an in‑house stack, leveraging on‑demand cloud services, and integrating a private AI platform—while emphasizing the decisive impact of operational practices such as utilization rates, model selection, and cache management on ROI.