Execution is turning a known intent into output. Strategy is deciding what the intent should be: what’s worth doing, in what order, and why.
AI is good at the first. It’s good at translating between domains—English into code, text into images, long text into short text—and at generating output once the intent is clear. It is not good at creating meaning: deciding which items matter most, what is worth doing at all, or which route gets you from where you are to where you want to be.
Vaughn Tan calls this second kind of work meaningmaking: “any decision we make about the subjective value of a thing.” Every important judgment call in business requires it, and, according to Tan, only humans can do it (for now). It’s the business version of the idea that wisdom is knowing what’s worth wanting.
In this sense, strategy is a function, not a job title. It happens well outside the boardroom: a designer defining a brand’s visual language and an engineer choosing what a system should do are both doing strategy, because the core of their work is deciding what’s worth making rather than making it. And since strategy and execution are a spectrum, most jobs mix the two.