Worked at a large bank and saw /worked on a few interesting projects. Obviously a lot of work on trading. One group of market makers had an RL agent built that

Worked at a large bank and saw /worked on a few interesting projects. Obviously a lot of work on trading. One group of market makers had an RL agent built that would trade small sizes. The value here wasn't in profitability, it was that it freed up the traders to serve larger tickets while being catering to a broader swathe of the market. Another group dealt with underwriting commercial loans. In that process, borrowers submit hundreds of documents that need to be classified (This is an architects license, this is a pest inspection report ... ). The data was too varied for simple heuristics, but fairly straightforward NLP eliminated a good chunk of work. If you extrapolate, a lot of the problems I've seen "the average" (not Google) company solve with AI is optimizing an internal process, as opposed to making a new product offering. So "huge success" these were not, but they were successfull. I think that for the average company, expecting "AI" to make a "huge success" in terms of business impact is often a sign of weak product thinking.