Learning to Work with Intelligent Machines

Companies are deploying AI and robotics in ways that disrupt traditional training techniques such as mentoring and on-the-job learning. We need to understand how to combine the old with the new.

by

Matt Beane

by

Matt Beane

From the Magazine (September–October 2019) · Long read

John W. Tomac

Summary.

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.

Idea in Brief

The Problem

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).

The Outcome

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.

The Solution

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.

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.

A version of this article appeared in the September–October 2019 issue of Harvard Business Review.

MB

Matt Beane is an assistant professor of technology management at the University of California, Santa Barbara, and a research affiliate with MIT’s Initiative on the Digital Economy.

Learning to Work with Intelligent Machines

Idea in Brief

The Problem

The Outcome

The Solution

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