What Machine Learning Actually Means for a Small Business
Separating the analytical techniques that create leverage from the AI hype that only creates invoices. When prediction matters, when explanation matters more, and when to skip both.
Machine learning is useful in exactly one situation: you have to make the same narrow prediction many times, and being slightly more accurate has real financial value. Which customers are about to churn. Which invoices will pay late. Which inventory items will stock out next month.
If the decision happens once a quarter in a room full of people, prediction is the wrong tool. Those decisions need explanation, and explanation comes from econometrics: a model that tells you the estimated effect of a lever you control, along with how confident you should be in it.
There is a third case that gets neglected: neither. Plenty of high-value questions are answered by careful counting. Cohort retention tables, contribution margin by segment, and cost per outcome have moved more budgets than any model I have built.
The practical test before spending on anything sophisticated: name the decision, name the action that would change, and name the dollar value of being right more often. If any of the three is blank, the model is a purchase, not an investment.
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