The new economics of enterprise AI
TL;DR
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?
Nauti's Take
Small teams should measure three real workflows for two weeks: token usage, processing time, and error costs. That baseline reveals which AI applications hold up over time and where cheaper models, caching, or tighter approvals will outperform adding more automation.
Summary
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?
”…
Sources