Unsupervised Learning - Using the Outcome of Learning
Unsupervised Learning is a collection of tools and you can use those tools for many purposes.
Sometime, it's just for data visualisation / data exploration (see UMAP for example), to better understand the problem and get the idea how to tackle it. Sometimes it will lead to business decision like building processes for a specific cluster or building submodels for specific clusters.
Sometime it helps deal with sub-problems of data-science, like outliers identification (see Hdbscan or isolation forests for example). What to do with those outliers depends on your business case.
Sometimes it helps define intermediate 'targets' to perform semi-supervised or supervised learning, like using some self-organisaed map, identify a cluster and use that cluster as a target. It help when you are not sure you have identified all your positive targets or when you don't wnat to label everything.