HBR: Getting Value from Machine Learning

Makes a very obvious case.    That has existed since the beginning of computing.  Yet still a good one to repeat.  Systems must be easy enough to use.  And then actually used, to make them valuable.  Of course when you add in some level of autonomy, with a clearly measurable value, that helps.   One way to do that is to plug them into a known and measurable business process.   You can show the value of it being better, faster, or cheaper, directly.  Augmentation of people and processes is best.  Making the method automatically considered and even applied.  We did it many times.  Good example of project management below. 

Getting Value from Machine Learning Isn’t About Fancier Algorithms — It’s About Making It Easier to Use    By Ben Schreck, Max Kanter, Kalyan Veeramachaneni, Sanjeev Vohra, Rajendra Prasad  in the HBR

Machine learning can drive tangible business value for a wide range of industries — but only if it is actually put to use. Despite the many machine learning discoveries being made by academics, new research papers showing what is possible, and an increasing amount of data available, companies are struggling to deploy machine learning to solve real business problems. In short, the gap for most companies isn’t that machine learning doesn’t work, but that they struggle to actually use it.

How can companies close this execution gap? In a recent project we illustrated the principles of how to do it. We used machine learning to augment the power of seasoned professionals — in this case, project managers — by allowing them to make data-driven business decisions well in advance. And in doing so, we demonstrated that getting value from machine learning is less about cutting-edge models, and more about making deployment easier.  .... "