Instructions
One of the most common ways AI products fail after a promising start is premature expansion. The product works for one use case, users are happy, and the temptation is to immediately add more — more features, more audiences, more AI capabilities. But each expansion adds complexity, increases quality requirements, and divides the team's attention. Expansion before the foundation is solid is how you end up with a product that does many things poorly instead of one thing well.
Defining expansion criteria is about having an honest, pre-committed answer to the question: what would need to be true before we expand? The discipline of answering this before you're in the moment — when enthusiasm for new ideas is high and the problems with the current product are temporarily invisible —...
