The first time I encountered the potential application of machine learning was a project where I was helping a company protect itself from competitors infringing on their trademark. One key component of trademark infringement is whether there is the likelihood of confusion by a consumer. High-profile examples include Apple suing Samsung for copying their minimalist smartphone designs, or Tiffany & Co. suing Costco for selling Tiffany-inspired jewelry.
The rise of social networks means consumers are generating tremendous amount of content about brands. My project involved writing a prototype software program to analyze online user comments that mentioned two competing brands, then determine if the user was identifying the product with the wrong brand. An example could be someone sharing a photo of a Captain Morgan rum bottle and calling it by their competitor's name, Admiral Nelson.* The goal was to run a daily scan and gather examples as evidence of brand confusion over time. The field of analyzing human text using computers is a form of machine learning called Natural Language Processing (NLP).
In a world where physical goods are easily copied, the only competitive advantage left is your reputation.