[proverbialbunny]

ML is typically used to find correlations in data. If something happens over and over again, there is a high chance it will happen again. Having such an algorithm that has identified this correlation allows it to identify when it will happen again. This allows for what is called predictive analytics.

This can be as simple as identifying when a customer will end their service with a business, as there might be a pattern before previous customers have left, predicting when new customers are going to leave, and giving them a coupon or similar right before they would otherwise leave. This problem is called customer churn.

It can be as complex as identifying when hardware will fail ahead of time, or even bio-ware. For example, I did a project that predicted when people were falling into depression before they could tell they were with a high accuracy rate. I also predicted other future medical issues ahead of time, like the probability an elderly person is going to fall over within the next handful of days.

On the business side there are a lot of use cases for ML, but it falls more into analytics than engineering, as it's about predictive insight.