Matt Beane

<article-ideainbrief class="article-ideainbrief">
 <h4 class="ideainbrief-title">Idea in Brief</h4>
 <div class="ideainbrief-contents">
  <h5>The Problem</h5>
  <p>The rush of intelligent machines and sophisticated analytics into many aspects of work means that trainees are losing opportunities to acquire skills through on-the-job learning (OJL).</p>
  <h5>The Outcome</h5>
  <p>In medicine, policing, and other fields, people are finding rule-breaking ways to acquire needed expertise out of the limelight. This “shadow learning” is tolerated for the results it produces, but it can exact a personal and an organizational toll.</p>
  <h5>The Solution</h5>
  <p>In response, organizations should carefully uncover and study shadow learning; adapt practices that develop organizational, technological, and work designs that enhance OJL; and make intelligent machines part of the solution.</p>
 </div>
</article-ideainbrief><p>It’s 6:30 in the morning, and Kristen is wheeling her prostate patient into the OR. She’s a senior resident, a surgeon in training. Today she’s hoping to do some of the procedure’s delicate, nerve-sparing dissection herself. The attending physician is by her side, and their four hands are mostly in the patient, with Kristen leading the way under his watchful guidance. The work goes smoothly, the attending backs away, and Kristen closes the patient by 8:15, with a junior resident looking over her shoulder. She lets him do the final line of sutures. She feels great: The patient’s going to be fine, and she’s a better surgeon than she was at 6:30.</p>

fBcFmo3RaHgyhO7tKuM+ZoXN3W2o29z+YwsZCkFe1io5J41c/4Lxm5+CbNj47j4+LJY2j/Pp3m27brom6iTHmQ3pSThHX98dwJRMw6pHZNcz0vP3Y+ZGIHpHZPtABQvDPfL6NSw3wf/JkbY1cWPedg==

<p>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 &#8220;deviant,&#8221; rule-breaking work-arounds&#8212;&#8220;shadow learning&#8221;&#8212;that surgeons in training, police officers, M&#38;A analysts, and others are figuring out on their own to overcome these obstacles and suggests how companies can benefit from studying them.</p>

Learning to Work with Intelligent Machines

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 work-arounds—“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.

Learning to Work with Intelligent Machines

Summary.

Partner Center