It’s interesting how Andrew Ng explained in a simple manner on what machine learning can do and cannot do in his “AI for Everyone” course that I’m currently taking. I think it helps me at least to think of the projects that can be tested for machine learning.
Rather than listing down all the problems we have (in an organization), think of the activities/tasks that do not require you to think by more than 1 second to do/decide. This is called ‘simple concept’ tasks which do not need a lot of mental thought especially the ones that you are currently doing manually. This can be replaced by computers – machine learning can help cut down your manual work. Provided, you have enough data to supply – in terms of volume, richness and completeness (i.e. there’s input and output).
Try it and start cracking your head to list down all the relevant tasks.
By doing so, we can start doing pilot projects and assess whether it’s feasible to continue in a bigger scale. The idea is to execute multiple projects in a year. According to Andrew, implementing 1 AI project in 1 year is extremely long. We need to do more than that to speed up our learning process.