Despite the growing force of machine learning, businesses are struggling internally to find usable applications for the new technology.  Companies are able to figure out how models and algorithms work, but not sure how to use it yet to solve current issues they face.  Effective data-driven decision-making is the major solution machine-learning offers, and putting large sets of historic data through a machine-learning model can offer multiple benefits in cost and time savings.  

The featured HBR article goes into an example of an AI model meant to support the work of a human project manager by providing pattern recognition and insights weeks in advance of problems that a new software program will have.  Project managers have numerous projects going on at one time, all in different stages of implementation, and finding what the issue is one of these projects, down to the root cause, means delaying overall progress and added expenses.  

With a trained machine-learning model, multiple issues were identified and risks predicted about a month earlier than before and actions were taken proactively to eliminate a potential problem's effects down the road.  Having this early warning system allows project managers to have larger capacity and spend more time on critical tasks. 

As an added insight, there was no need to utilize the best data science tools out there - - a simple model that can be applied by the company experts in the field, which runs testing well, is much better than a complex or perfect ML model.