A running joke amongst a few friends is: AI companies spend 20% of their time building a model, and 80% of their time explaining why their model does not work.
The challenge is, the more you build, the more complex the system becomes, the more likely it is not going to work.
The value of AI is to automate things, and NOT to explain things. Often, AI will derive an action of which nobody knows the rationale, however the action reaches its objective.
This is the total opposite of business analysis, which seeks to understand and explain.
The machines will eventually understand business as well as or even better than we humans do. However, they derive their understanding not by reading BI reports.
From the example of AlphaGo, we already know that machines approach problems much differently compared to humans.
That’s why we mentioned that Ai companies need 80% of their time to explain the models. Because, the customers of AI companies are humans, and they are not able to immediately accept the behaviour of the machines, instead they seek to understand why.
I have been doing data for years, and I know that human analysts often just try to fit their analysis into a story.
The machine, however, offers an interesting alternative.
