Suppose a retailer wants to use AI to understand its customers better. The first question is not which algorithm to use. It is whether the retailer already knows what it is looking for.
If the goal is to predict which customers will cancel their subscription, and the retailer has historical data showing who actually cancelled, that is a supervised learning problem. The system trains on labeled examples, meaning each data point comes with a correct answer attached. The model learns the relationship between customer behaviour and the outcome.
But what if the retailer has no predefined outcome in mind and just wants to discover natural groupings among customers? That is an unsupervised learning problem. The system receives no labels. It finds structure in the data on its own, grouping customers by similarity without being told what similarity means in advance.
The distinction matters practically because it determines what data you need. Supervised learning requires labeling, which is expensive and time-consuming. A dataset of 80,000 customer records needs a human to mark each one as churned or retained before training can begin. Unsupervised learning skips that step but gives you results that are harder to evaluate, since there is no ground truth to compare against.
Neither approach is better. The right choice depends entirely on whether your question has a known answer you are trying to predict, or an unknown structure you are trying to discover.