The new economics of enterprise AI
This article discusses the shift in enterprise AI from experimental projects to foundational business infrastructure, emphasizing the need for cost efficiency. It argues that companies should focus on the 'effective cost per useful outcome' rather than just raw GPU consumption.
Why it matters
As AI becomes a core business utility, shifting focus from experimentation to operational efficiency is critical for long-term financial sustainability.
As enterprise AI shifts from isolated experiments to becoming foundational infrastructure, companies need to focus on efficiency, not just consumption.
Imagine your company just discovered electricity. At first only a few machines run on it, and nobody worries about efficiency — consumption is low and the value is obvious. But once electricity becomes infrastructure, every process depends on it, and the question changes from "can we use it?" to "how efficiently are we using it?"
Enterprise AI is going through the same shift. What began as isolated chatbot and copilot experiments is becoming a foundational layer of the business, with models, agents, and data increasingly interconnected in production workloads. The important question stops being what a token or GPU-hour costs. It becomes how efficiently the AI environment converts paid capacity into useful business outcomes.
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