Most "AI integration" content falls into one of two camps: breathless hype about transforming your business overnight, o...

Most "AI integration" content falls into one of two camps: breathless hype about transforming your business overnight, or dense academic papers about model architecture. Neither helps you decide whether the customer support ticket triage your team keeps complaining about is actually a good candidate for machine learning, or whether you'd be better off writing forty lines of regex.This is a framework for making that call, and for integrating ML into a system that already works without breaking it. Start With the Problem, Not the ModelThe fastest way to waste three months is to pick a model first and go looking for a use case to justify it. Work backwards instead. A good ML integration candidate usually has all of these properties:It's repetitive. The task happens often enough that automating it saves real time, not just once a quarter.It's data-rich. You have historical examples of the task being done, ideally with an outcome you can label as "good" or "bad" in hindsight.It tolerates im...