Managing Machine Learning: How to Bridge the Man/Machine Divide

• Begin with small steps to build confidence. Identify a tangible, specific business problem that responds to a data-driven strategy. Continually refine the algorithms through trial and error. You'll find that as the successes grow, so will comfort with the technology.
• Set manual checkpoints. Allow employees to both interact with the data and confirm its conclusions. Let the confidence levels in the algorithms guide the direction of your check-ins — e.g., a 95 percent confidence level likely doesn't need human intervention whereas an algorithm with a lower level of confidence may benefit from more frequent checkpoints.
• Analyze the data; trust your instincts. Machine learning provides unexpected insights, but it can be difficult to accept seemingly counterintuitive findings. For example, if your inventory management system automatically recommends ordering hundreds of units of a seasonal product that you expect to drop off in demand, listen to your intuition. Even if the machine may recommend certain actions, a human expert can overrule action. However, be sure to go back and check to see if the demand was there. If the machine was right, you'll have more faith in future recommendations.
• Define key performance indicators. Consistently track how successfully your machine learning algorithms are performing. Tracking performance will not only quantify your successes, but also increase confidence and provide the data necessary to continually improve.

As a machine learns project by project, so will your organization. As trust in the technology grows, machine learning will uncover ways that your company can succeed today and into the future!