recent

To help improve the accuracy of generative AI, add speed bumps

4 capabilities of a real-time business

Slack CEO: How to roll out artificial intelligence internally

Credit: gorodenkoff / iStock

Ideas Made to Matter

Future of Work

How can we preserve human ability in the age of machines?

By

From ancient Greece to modern warehouses and operating rooms, skill-building has relied on a relationship between experts and novices: Novices need to learn, and experts often need help.

Intelligent technologies threaten this vital skill-building bond between experts and novices, according to Matt Beane, SM ’14, PhD ’17, an assistant professor at the University of California, Santa Barbara and a digital fellow at the MIT Initiative on the Digital Economy. Beane’s research focuses on how humans work with machines, including field research on robotic surgery and robotic telepresence in health care and knowledge work.

In his new book, “The Skill Code: How to Save Human Ability in an Age of Intelligent Machines,” Beane looks at how technologies such as robots and AI can interfere with old ways of learning, with potentially devastating consequence — and how people can build skills today, including making intelligent technologies part of the solution.

The following excerpt has been lightly edited for style and length.

+++

Ernie trudges down an abandoned, sandy road in the midday sun, with a 109-degree breeze at his back. It’s just another day at the office: He’s off to defuse a bomb.

He’s wearing a 75-pound protective suit and shrapnel-proof helmet, both made of thick layers of Kevlar and plastic, all coated with fire-retardant materials. His hands, however, are exposed to the breeze, fingers flicking through rehearsed disarmament techniques as he makes his way toward his target. As he gets close to his target — in this case, a tattered Pokémon backpack near a schoolyard — his vision sharpens up and he scans the area for disturbances or wires. Step by step, he works his way closer to the device. He reports via his headset in spare, dry language that he’s about to move in. By the book, step by step, he works his way closer to this device. He takes a knee, a deep breath, and reaches for his tools: It’s go time.

A person in business attire holding a maestro baton orchestrating data imagery in the background

Leading the AI-Driven Organization

In person at MIT Sloan

Before robots entered the picture, the task looked a lot like I’ve just described here: You dealt with an improvised explosive device by walking up to it and going through a set of procedures. Where’s the novice in this picture? A hundred meters away in a bombproof truck. Hard for them to learn from Ernie while he’s working: By protocol, he’s not all that chatty, especially because they don’t know how long they have. Even if he could talk more, there’s only so much information he could convey about what he was seeing, his assessment and decision-making, and his technique. And they have only one shot at the problem. Of course, after-action reviews help a bit, but hands-on training was a serious bottleneck. Ernie remembered the old pain of being a novice all too well — it felt like breathing through a straw.

To be a specialist in explosive ordnance disposal takes more than a year of intensive training and is one of the most dangerous and prestigious positions in the military. It also happens to be one of the only jobs that I have come across where the use of robots didn’t block skill development but enhanced it.

In 2001, the PackBot arrived for military duty. This 40-pound, tanklike, one-armed robot entered military service in 2002 and opened up new possibilities for explosive ordnance disposal almost immediately. After an initial period of experimentation and design changes, a new work pattern emerged that took advantage of the technology’s capabilities.

Now Ernie sits side by side with his trainee, Deshaun, in the same bombproof truck, and Ernie guides him through the procedure of approaching the bomb. Deshaun is the one in physical control, not Ernie, using a controller that looks remarkably similar to Deshaun’s PlayStation back home. That frees Ernie up to focus on situational awareness and tactics to prompt Deshaun with questions to develop the same skill. They still have to work briskly, but their side-by-side interactions have a lot more give-and-take where there was virtually none before. The introduction of the PackBot made it a lot easier for Deshaun to learn from Ernie on the job.

Right now, the positive outcome with bomb disposal is the exception to the rule. The way we’re handling most intelligent technologies blocks healthy challenge, complexity, and connection instead of enhancing them. On average, we’ve started a war between technological productivity and human skill, and skill is losing. Going back to good old-fashioned apprenticeships isn’t often a good option in our increasingly hybrid, digital, and fragmented workplaces. But if we continue to undermine healthy challenge, complexity, and connection and sever the traditional expert-novice bond with no new one in place, we’re going to find ourselves in a world of trouble.

That’s why it’s important to rework the skill code. First, we need to learn which sequences and approaches to challenge, complexity, and connection are working, and how they’re different from the status quo. We also need to be putting these skill-related innovations into practice — changing techniques, technologies, work systems, and even organizations. If we do this, we can do far more to preserve its vital role as the world changes. With a reworked skill code in hand, we can put it straight to work, no matter what technology you’re dealing with. We don’t have to give up new technologies or productivity gains to preserve healthy challenge, complexity, and connection, and we shouldn’t: The path to a healthy future requires them all. In fact — and this is key — there are probably many cases where insisting on both productivity and skill will get us better outcomes in both categories than we would otherwise.

The PackBot itself is not the point. We can’t solve our skill problems by building robots just like the PackBot for each industry. The robot design was only part of what made the novice-expert relationship work so well. The controller could just as easily have ended up in Ernie’s hands, with Deshaun looking on, trying to work his way into the action — saving the day, perhaps, but killing skill development in the process. What matters most — but is too often missing when we adopt gee-whiz tech — is how that tool will be used and whether that will enhance skills for a novice. Some of that is shaped by design, but a lot of it is down to implementation. In fact, there might well be times where the senior explosive ordnance disposal tech does take control too frequently. But overall, someone, somewhere, decided that the trainee would take primary physical control. We need to begin our journey to save our skill right at that moment, because that’s where the power is. Right here, right now, we can take control of the way we’re designing and using technology so we get results and improve healthy challenge, complexity, and connection at the same time.

Excerpted from “The Skill Code: How to Save Human Ability in an Age of Intelligence Machines” by Matt Beane, published by Harper Business. © 2024 by Matt Beane. 

For more info Sara Brown Senior News Editor and Writer