Machine learning use cases are easy to list and hard to launch. This guide organizes ML by the six business outcomes it reliably delivers, grounds each in a real industry example, and explains why data and integration, not the model, decide whether a project ever reaches production.

Most enterprise AI initiatives stall not because the models are weak, but because the data underneath them is. Before you scale compute or add use cases, your pipelines, governance, and lineage need to be production-ready. Here is how to build data foundations that let AI scale without scaling your errors.