Stanford University


Learning to Work with Intelligent Machines

Harvard Business Review
September-October 2019

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Although companies spend billions on formal training for employees, most of the skills needed to perform a specific job can be learned only by doing it. This on-the-job learning (OJL) has long depended on mentorship, with experts coaching apprentices. But today OJL is under threat from the headlong introduction of sophisticated analytics, AI, and robotics into many aspects of work. These technologies are moving trainees away from learning opportunities and experts away from the action. The author describes the deviant, rule-breaking workarounds shadow learning that surgeons in training, police officers, M&A analysts, and others are figuring out on their own to overcome these obstacles and suggests how companies can benefit from studying them.

Stanford University