Not every problem needs AI; start with whether complexity demands it.
We created an AI fit analysis with portfolio companies. It starts with questions around precision and output quality, focusing on data quality. Then ask: Do you benefit from modeling complex decisions versus just a few? If it’s a linear problem, a rule engine works. But if you have a complex decision space or nonlinear problems like search engines or recommendation engines, then it’s time to deploy AI to find tiny clues. This doesn’t always mean large language models. Sometimes simple regression analysis is good enough and more precise.
— Building AI products: 5 lessons from our founders’ workshop · The Edge