Many business leaders make the mistake of believing that AI and machine learning are like regular IT, but this could not be further from the truth. In large part, this is because, unlike simple software solutions for discreet business problems, it can be very difficult to measure the ROI of machine learning.
This is because the costs associated with adopting AI are often amorphous. They are never as simple as the sticker price of a vendor solution. For example:
How much does it cost to have a VP of wealth management collaborate with a team of data scientists for three months? Six months? Eighteen months?
How much does it cost to rearrange a bank's technology infrastructure and reinvent the way it collects and stores data on its wealth management clients?
How much does it cost to go back to the drawing board if an AI software that suggests investment decisions actually fails to invest as optimally as actual wealth managers?
As a result, many companies will struggle to g...
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