Enterprise AI is transitioning from isolated experiments where consumption and value were straightforward, to becoming foundational infrastructure that affects nearly every process. This shift changes the central question companies must ask themselves: moving from whether they can use AI to how efficiently they are deploying it.
The analogy to electricity adoption illustrates the pattern. Early electricity adoption was simple—machines ran, value was clear, and nobody measured consumption closely. But once electricity became essential to operations, efficiency became critical because the cost of waste grew proportionally with dependence.
Enterprise leaders building or investing in AI businesses need to understand this transition is already underway. The priorities that made sense for pilots—speed to value, proof of concept—no longer work when AI becomes embedded in core workflows. Optimization, cost control and measurable ROI are now central concerns.
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