How AI Could Keep Young Workers From Getting the Skills They Need

Whenever people talk about the dangers AI holds for the workforce, they usually have one thing in mind: technology stealing jobs. But artificial intelligence poses a much more subtle threat than that—one that will have consequences for business unless we address it.

Simply put, the way we’re handling AI is keeping young workers from learning skills.

For more than 12 years, I have been studying how work changes as a result of intelligent technologies like robots and AI. Across a number of industries, I’ve seen the same thing over and over: This new, sophisticated technology makes it easier for experts to do their jobs. Seasoned surgeons can operate more quickly and efficiently, for instance, when they use robots in the operating room.

But the efficiency comes at a cost. The technology allows experts to do more, independently, so they don’t need younger, less-experienced workers to help them out anymore—so those novices are left without mentors to teach them the skills they need to do their job. Looking at operating rooms again, it takes two people to perform most complex procedures with traditional tools. The senior surgeon generally provides “exposure” by retracting tissue while the resident does what most of us think of as surgery—incisions, suturing and so on. Residents are on task the entire time. Focused. Learning.

Now the residents mostly sit around during operations and watch veteran surgeons get the job done thanks to help from a robot. Limited work. Limited learning.

As learning opportunities like these are lost throughout more industries, the results could be profound for both individual workers and the economy. We are sacrificing skill building and human bonds of mentoring on the altar of productivity. No matter our role, tenure, occupation or industry, if we can’t collaborate with someone who knows more, we’re not going to learn effectively, and we won’t be able to keep up. And our organizations will struggle where they might otherwise race ahead—because workers won’t have the deep knowledge they need to innovate and step into senior roles.

Turning history on its head

We have decades of research showing that this situation is the opposite of what we want. We build skill by collaborating across the expert/novice divide, so novices get to see the work, help out at the edges and earn the privilege of doing more next time.

Now that mechanism is being lost. My observations, combined with primary data from other field researchers, show a destructive dynamic at work, across a range of industries. In industrial-process engineering, I have seen experts use software to do modeling on their own, instead of involving a junior engineer. In warehousing, I’ve watched area managers rely on dashboard analytics to understand staffing and process flows, instead of uncovering those things collaboratively with less-experienced line leads and workers.

My collaborator Callen Anthony at New York University found that junior investment-banking analysts were being separated from senior partners as those partners started to use algorithms to help create company valuations for mergers and acquisitions. Junior analysts—instead of collaborating with the senior partners as they had before—essentially just pulled data for the algorithms to use in their valuations.

The rationale for this arrangement was twofold: reduce errors by junior people in sophisticated work and maximise senior partners’ efficiency. Explaining the work to junior staffers pulled partners away from higher-level analysis.

This setup produced short-run productivity improvement, but it moved junior analysts away from challenging, complex work, making it harder for them to learn the entire valuation process and diminishing the firm’s future capability. Junior bankers become senior bankers, after all.

Less time at the table

One of the most striking examples of the widening skill gap is surgery. I observed hundreds of procedures at some of the top teaching hospitals in the country, where robots deeply reshaped how work was done. Surgery, as I said, used to take four hands; minimally invasive surgical robots can supply three, all controllable from a single console. They make things so much easier for surgeons that the million-dollar tools have become the de facto standard for many complex procedures.

Most important, robots make it possible for surgeons to perform operations solo, no residents needed. And, since residents are slower and make more mistakes than an experienced surgeon would, those surgeons are opting to cut residents out of the action. Before, residents might operate for four hours during a 4½-hour procedure. In my nationwide data, their robotic average time hovered in the 10- to 15-minute range. And residents got less operating time in 88% to 92% of cases.

In this situation, we end up with much-less-capable surgeons. My data shows that many newly minted surgeons struggle mightily when they get their first jobs—not just because they don’t have robotic skill, but because their failed quest to learn robotics took so much effort they lost key learning opportunities in other procedures and practice areas, from ureteroscopy to kidney stones to vasectomies, that they would be expected to handle in most new surgical jobs.

The long-term loss

The consequences of poor training go beyond day-to-day competence. Consider what happens to the culture of a hospital when it loses healthy expert/novice collaborations. Less teaching and learning, to be sure, but also more-limited career advancement as experts advocate less for trainees. What about hospitals’ ability to innovate in surgical practices? Limits there, too, as discoveries made by colleagues get tamped down by increasingly focused, efficient, expert-driven surgical performance. The ability to service skyrocketing surgical demand? In the short run, you serve more patients, but in the medium term you scramble to keep up as the pool of new talent dwindles.

Of course, different organisations, industries and professions in different places will feel the pinch on different time scales. They will also compensate in different ways. But in general, organisations will not sense the problem directly: Instead, they will incrementally accumulate a larger cost base—in areas such as (re)training and reduced billable or applied time—and build a bureaucracy to manage this skills gap. At law firms, new attorneys might take longer to ramp up to normal caseloads, while senior attorneys would have to spend more non billable time to handhold them.

Now imagine the consequences of similar skills gap across all types of companies, throughout the economy. Without a firm, immediate correction, this is what we can expect. This is our trillion-dollar skills problem.

A way forward?

Solving the problem is vital, but how should we do it? My collaborator and I found evidence of one approach that can work.

Remember, the problem right now is that senior workers are learning new technologies, such as robotic surgery, that make junior workers unnecessary. In our research, though, we found cases where junior and senior workers teamed up to learn about new technologies together .

By working closely with seniors in this way, the juniors didn’t just learn about the new technologies, they ended up collaborating with seniors on other aspects of the job. Since the older and younger workers were figuring out how the tech worked, they also needed to figure out how to integrate it into vital day-to-day tasks. So, the novices got to see firsthand how those jobs were done while performing actual work.

For instance, in my research, I saw some residents and senior urologists team up to learn robotic techniques in live surgical procedures. In those cases, the residents got much more actual hands-on operating time than residents who mostly just watched robotic procedures—10 times more. And the quality of that time was far better: Expert and novice were jointly figuring out how to use the tech, just as they had a patient on the table.

Granted, this process isn’t easy. In our research, we found that these collaborations often failed. But when they did work, they were powerfully effective. We need more companies to take the chance and implement this strategy, to figure out how to make it most effective and serve as examples.

It will not only help close the skills gap, it will give old and new workers a new sense of purpose on the job—through strengthened relationships. Research shows very clearly that we get motivation for our work when it builds trust and respect with those who share our values. Progressing to more competence therefore involves questions of the heart, like, “Have I earned this expert’s trust and respect?” or “Does this novice look up to me?”

We often treat these issues as unconnected with hard-nosed skill and results, when they are a core part of why we try at all in the first place. They are the animating force for the journey.

Matthew Beane is an assistant professor at the University of California, Santa Barbara, and author of  “ The Skill Code: How to Save Human Ability in an Age of Intelligent Machines.”