One exception is automated language translation which is getting very good. I'm noticing that some of the articles papers I'm reading are machine translated. They appear to apply machine translation to English articles and then have some editor doing manual touch-ups which seldom is enough.
The "bad" industries such as spam and SEO can definitely benefit from ML as it exists today. There are ML algorithms (LSTM) that can generate faked web sites with images that, from Googlebot's point of view, are completely indistinguishable from real sites. Another use would be to generate realistic looking accounts in social media to steer the conversation, perhaps for political purposes. Porn obviously, could also use ML due to the huge amount of data (the porn itself and user interactions) available.
I don't think (fully) self-driving cars will exist in 2028. But who knows? Ten years is a loooong time.
This is also a problem with new datasets being generated - there is not nearly enough history available to test them or feed them to a ML system.
Furthermore, arguably, longer-term investment requires forward-looking modelling of scenarios, based on the kinds of inputs that were not seen in history. ML is not very applicable when you get big covariate shifts.
So I would say human financial analysts are not going anywhere, and any improvements would be relatively small and incremental.