Learning to work with new tech

Training must change so workers can master the sophisticated skills needed.

The headlong introduction of sophisticated analytics, artificial intelligence and robotics into many aspects of work is fundamentally disrupting on-the-job training.

Critical as it is, companies tend to take on-the-job learning for granted. It’s rarely formally funded or managed, and little of the estimated $US366bn companies spent globally on formal training last year directly addressed it.

Yet decades of research show that, although employer-provided training is important, the lion’s share of the skills needed to reliably perform a specific job can be learned only by doing it. Most organisations depend heavily on on-the-job learning.

Today, on-the-job learning is under threat. The headlong introduction of sophisticated analytics, artificial intelligence and robotics into many aspects of work is fundamentally disrupting this time-honoured and effective approach.

Yet broad evidence demonstrates companies’ deployment of intelligent machines often blocks this critical learning pathway.

My colleagues and I have found it moves trainees away from learning opportunities and experts away from the action, and overloads both with pressure to master old and new methods simultaneously. How, then, will employees learn to work alongside these machines?

Indications come from observing learners engaged in norm-challenging practices that are pursued out of the limelight and tolerated for the results they produce. I call this widespread and informal process shadow learning.

Obstacles to learn

My discovery of shadow learning came from two years of watching surgeons and surgical residents at 18 top-rated teaching hospitals in the US. I studied learning and training in two settings: traditional (“open”) surgery and robotic surgery. I gathered data on the challenges robotic surgery presented to senior surgeons, residents, nurses and scrub technicians, focusing particularly on the few residents who found new, rule-breaking ways to learn.

I’ve identified four widespread obstacles to acquiring needed skills. Those obstacles drive shadow learning.

● Trainees being moved away from their “learning edge”. Training people can incur costs and decrease quality because novices move slowly and make mistakes.

As org­anisations introduce intelligent machines they often manage this by reducing trainees’ participation in the risky and complex portions of the work. Thus trainees are being kept from situations in which they struggle near the boundaries of their capabilities and recover from mistakes with limited help — a requirement for learning new skills.

● Experts being distanced from the work. Sometimes intelligent machines get between trainees and the job, and other times they’re deployed in a way that prevents experts from doing important hands-on work. In robotic surgery, surgeons don’t see the patient’s body or the robot for most of the procedure, so they can’t directly assess and manage critical parts of it.

● Learners are expected to master both old and new methods. Robotic surgery comprises radically new techniques and technologies for accomplishing the same ends that traditional surgery seeks to achieve. Promising greater precision, it was simply added to the curriculum and residents were expected to learn robotic as well as open approaches.

But the curriculum didn’t include enough time to learn both thoroughly, which led to a worst-case outcome: residents mastered neither. I call this problem methodological overload.

● Standard learning methods are presumed to be effective. The pressure to rely on approved learning methods is so strong that deviation is rare: surgical-training research, standard routines, policy and senior surgeons all continue to emphasise traditional approaches to learning, even though the method clearly needs updating for robotic surgery.

Shadow learning responses

Faced with such barriers, shadow learners are bending or breaking the rules out of a view to get the instruction and experience they need. We shouldn’t be surprised. Expertise — perhaps the ultimate occupational goal — is no exception. Given the barriers I’ve described, we should expect people to find deviant ways to learn key skills. The following are the shadow learning practices that I and others have observed:

● Seeking struggle. Recall that robotic surgical trainees often have little time on task. Shadow learners get around this by looking for opportunities to operate near the edge of their capability and with limited supervision. They know they must fight to learn and that many attending physicians are unlikely to let them. The subset of residents I studied who did become experts found ways to get the time on the robots they needed.

● Tapping frontline know-how. As discussed, robotic surgeons are isolated from the patient and so lack a holistic sense of the work, making it harder for residents to gain the skills they need. To understand the bigger picture, residents sometimes turn to scrub techs, who see the procedure in its totality. The best scrubs have paid careful attention during thousands of procedures. When residents shift from the console to the bedside, therefore, some bypass the attending and go straight to these “superscrubs” with technical questions.

● Redesigning roles. The new work methods we create to deploy intelligent machines are driving a variety of shadow learning tactics that restructure work or alter how performance is measured and rewarded. People tacitly recognise and develop new roles that are better aligned with the work — whether or not the organisation formally does so.

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

(c) 2019 Harvard Business School Publishing Corp.