Today's managers and executives need to oversee humans and machines in this age of AI and RPA, but should machines be managed as humans in a way that some suggest? As artificial intelligence and robotics process automation (RPA) usage continue to expand in enterprises, managers and executives need to learn how to supervise more than just human employees. They need to manage the human-machine workforce. Some suggest that intelligent machines should be managed like people. More specifically, they suggest that, like people, virtual employees should have a job title and key performance indicators (KPIs).

Check out the "Data Science and Machine Learning" sessions at the Strata Data Conference in San Jose, March 5-8, 2018. Companies are now building platforms that facilitate experimentation and collaboration. At our upcoming Strata Data Conference in San Jose, we have many tutorials and sessions on "Data Science and Machine Learning" (including two days of sessions on enterprise applications of deep learning), and "Data Engineering & Architecture" (including sessions on streaming/real-time from several open source communities). If you want to understand how companies are using big data and machine learning to reinvigorate their businesses, there are many case studies on the schedule geared toward hands-on technologists, and sessions aimed at managers and executives. Over the past few years, companies have invested in data gathering and data management technologies, and many have began unlocking value from their vast repositories.

A gap already exists between companies' ability to collect data and managers' skills at putting it to use. Will AI increase the divide? The use of artificial intelligence in the criminal justice system offers a stark example of the contrast between knowing how to produce results and knowing how to consume them intelligently. Systems recommend bail and sentencing but offer little transparency about the basis for the recommendation, leaving the humans who digest the recommendations potentially under informed. What if we knew so little about the production processes of the food we eat?