'AI', 'machine learning', and 'big data' are relevant today as buzzwords; having succumbed to misuse and overuse by the media in the past decade. One of the worst offenders in recent times seems to be the financial media, which seems to rarely draw distinctions between the core ideas behind these terms.
In contrast, there are a lot of examples out there on of how to do it right. I liked this Bloomberg piece from last October, which looks at the opportunity for disruption in finance. One aspect of that article which struck me as especially well thought out was their recognition that the impact of such technology would relate in some degree to the asset class and type of institution (buy-side/sell-side). To illustrate my point, I'll leave you with the following paragraph , which summarizes this very neatly:
Sell side credit markets: Natural-language processing, data collection and machine learning are being applied to automate subjective human decisions.
Sell side foreign exchange: Big data and machine learning are being used to anticipate variations in client demand and the resulting price swings.
Sell side commodities: Trader and salesperson conversations are being catalogued to create profiles of clients.
Sell side equities: Artificial intelligence is being applied to order execution.
Buy side equities: Predictive analytics is being applied to time stock purchases and assess risk based on market liquidity.
Buy side credit: Computer programs are being trained to scan and understand bond covenants, legal documents and court rulings.
Buy side macroeconomics: Natural-language processing is being used to analyze central bank commentary for clues on monetary policy. Other software is analyzing data such as oil-tanker shipments and satellite images (e.g., Chinese industrial sites, Walmart parking lots and more) to spot trends in the economy.