On the retail side: Using computer vision to deliver alerts about shelf condition.
For farming: Using computer vision + ML to devise and track health monitoring for crops.
For manufacturing: Predictive maintenance of equipment has been a very popular area of focus.
There have been countless use cases on the finance side of things. For instance, anomaly detection techniques help with reconciling accounts and detecting fraud.
The energy industry seems to never run out of use cases for tracking commodities and/or helping predict load.
In HR, predicting turnover and education demands are some of the early use cases being approached but I expect a lot more over time.
Logistics is another area that will have a seemingly endless supply of use case. Things like loss tracking, warehouse optimization, raw material allocation and sourcing. I don't think I've ever been involved in a logistics/manufacturing project that couldn't have used some ML to add efficiency to the process.