Good thoughts, though not quite enough cautions on the implementation. It contains risk as well as payout. Also very much like the immediate inclusion of business subject matter experts. To best measure potential value and understand risk. Also make clear the need for the right data. This approach is very much the same in any kind of analytical method.
How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist By Kathryn Hume in HBR
Artificial intelligence is no longer just a niche subfield of computer science. Tech giants have been using AI for years: Machine learning algorithms power Amazon product recommendations, Google Maps, and the content that Facebook, Instagram, and Twitter display in social media feeds. But William Gibson’s adage applies well to AI adoption: The future is already here, it’s just not evenly distributed.
The average company faces many challenges in getting started with machine learning, including a shortage of data scientists. But just as important is a shortage of executives and nontechnical employees able to spot AI opportunities. And spotting those opportunities doesn’t require a PhD in statistics or even the ability to write code. (It will, spoiler alert, require a brief trip back to high school algebra.)
Having an intuition for how machine learning algorithms work – even in the most general sense – is becoming an important business skill. Machine learning scientists can’t work in a vacuum; business stakeholders should help them identify problems worth solving and allocate subject matter experts to distill their knowledge into labels for data sets, provide feedback on output, and set the objectives for algorithmic success .... "
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