Companies can often be too focused on a certain use case, but that isn’t always the right strategy for implementing AI. Instead, what is the right strategy is to test, test and test again. The only thing you know with AI is you don’t know but you will learn on the way.
The use case for AI can be irrelevant, but what is relevant is the success of it, and to achieve this, you need to be constantly testing and adapting. After all, with AI, experimenting and having something “done” is always better than perfect.
Testing can have another benefit as well – in ironing and rooting out bias. One famous example was when e-commerce giant Amazon decided to attempt to automate its recruiting process back in 2014. After rigorous testing of the AI project, the company realised that the new AI recruiting system was not rating candidates fairly and showed bias against women.
Amazon was forced to ditch the algorithm for recruiting purposes and go back to the drawing board. Trying and failing is a key part of business and AI is no different – that’s why testing is so important.