After three years of demos, a pattern is stable enough to say out loud: AI creates economic value in a specific set of conditions, and destroys money everywhere else. The companies doing it well are not the ones with the boldest vision. They are the ones with the coldest arithmetic.
Three conditions, preferably all at once:
Where those three hold, the value shows up in four places: cycle time (weeks become days), coverage (you finally read all the data, not a sample), quality consistency (the 3 a.m. case handled like the 3 p.m. one), and unit costs at scale.
Artisan processes where the variance is the product. Tiny volumes where setup costs never amortize. High-stakes one-offs where the cost of a single error exceeds a decade of savings. And — the quiet one — processes whose underlying data is a mess, where AI mainly automates the chaos.
Most "AI transformation projects" should have been a subscription. The build decision deserves the same scrutiny as any capital investment — and the default answer, for most mid-sized companies, most of the time, is a good off-the-shelf tool used well. Custom AI is for the use cases your competitors can’t buy.
The practical version — where those conditions get mapped onto an actual P&L — is the AI transformation practice. How we run the machinery behind it is on the AI infrastructure page.
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