I can at least give you some examples from my recent experience. A combination of text classification and NER can solve many business problems if the accuracy is high enough or you can mitigate errors.
For e-commerce search, a common problem is how to give relevant results for misspelled queries or unique queries. Text classification and/or NER can often identify the product type, brand, etc and hopefully give a relevant result which leads to a sale.
Another example is data entry or business analysis roles. Often they will manually extract data from sources and put it into Excel or some other BI tool. NLP ML has gotten good enough in just the last couple years that much of the data extraction can be automated, but you still need a human to verify accuracy.
For e-commerce search, a common problem is how to give relevant results for misspelled queries or unique queries. Text classification and/or NER can often identify the product type, brand, etc and hopefully give a relevant result which leads to a sale.
Another example is data entry or business analysis roles. Often they will manually extract data from sources and put it into Excel or some other BI tool. NLP ML has gotten good enough in just the last couple years that much of the data extraction can be automated, but you still need a human to verify accuracy.