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Two teams say they are building an AI product. One has a roadmap that makes sense. The other spends six months confused. The difference is a mental model they did not know to install.
Every AI product pays a hidden tax that no traditional product spec accounts for. The tax is called non-determinism, and it rewrites your definition of done, your testing story, and your roadmap.
Every AI project eventually hits the same question. Train our own model, pay a vendor, or wrap an API. Here is the decision matrix that cuts it down from two weeks of meetings to one afternoon.
Scope against what the model will be able to do in six months, not what it did yesterday. Here is the framework for betting on capability trends without wagering on vapour.
Most 'AI features' do not earn their keep because they were bolted onto problems that were not AI-shaped to begin with. Here is how to find the problems where AI actually wins, and how to reject the ones where it does not.
Your magic AI demo works on Tuesday and a month later you cannot ship it. The gap between a demo that wows and a product that survives GA is specific, measurable, and where almost every AI project loses time.
