Most businesses know they should be "doing something with AI." Few know where to start. After building ML systems for clients across healthcare, finance, and education, here is what we have learned about turning machine learning from a buzzword into a working business tool. Start With the Problem, Not the Technology The biggest mistake we see is companies starting with "we want to use AI" instead of "we have a problem that costs us X per month." Machine learning is a tool. Like any tool, it is only useful when applied to the right problem. Good ML problems share three characteristics: You have data. Not "we could collect data someday." You have it now, in a structured or semi-structured format, with enough volume to train a model. The task is repeatable and pattern-based. Classifying images, predicting churn, detecting anomalies, extracting information from documents. If a human does it by recognizing patterns, ML can probably do it faster. The cost of being wrong is manageable. ML models make mistakes.…