I’ve been seeing more teams talk about “adding AI” as a default next step, even when the underlying problem isn’t fully clear.

In practice, the failures I’ve seen rarely come from bad models or tools. They come from skipping basic questions: – What decision or task is being improved? – Are rules still working at their current scale? – Is there real usage, or just expectations?

I put together a simple breakdown of how AI projects tend to succeed when they do, and where teams usually go wrong early on.

Curious how others here decide when AI is worth the added complexity and when it’s better to wait.