Planning for the Future of Work

Digital technologies are poised to disrupt how work is done. Consider the popular example of the impending arrival of autonomous vehicles. When self-driving vehicles are mainstream — within the next decade or two (or less) — the impact on work in the United States alone will be massive.

According to the United States Bureau of Labor Statistics, 1.5 million people in the U.S. are commercial truck drivers, 800,000 work as delivery drivers, and another 1 million people make a living as other types of transportation professionals — including bus drivers, taxi drivers, and Uber drivers. The secondary effects of self-driving cars and trucks are also significant; they would significantly reduce accidents, thereby also affecting auto-body shop workers, insurers, hospital emergency room workers, and a number of others.

Autonomous vehicles are only one maturing digital technology that will disrupt work. Add artificial intelligence, blockchain, additive manufacturing, and virtual and augmented reality to the disruptive mix, and the impact these technologies will have on work will be staggering. Many companies and executives are not planning for this future, and while some employees and leaders are considering how these technologies will affect their careers or their organizations, they may be doing it wrong.

The common approach, which focuses on identifying types of work that only humans can do, is an unproductive way to plan for the future of work. If one primarily fits human work into the gaps left by what computers cannot do, people will increasingly be squeezed out as technology becomes more advanced. As a general rule, computers have become capable of most things that we once thought outside the realm of computer expertise, such as facial recognition and language translation. This logically begs the question: How are people truly better than computers?

The Rise of Emotional Robots

We don’t know exactly how people will adapt or what the majority of jobs will look like in the future, but several pundits have attempted to identify the areas in which humans are superior to computers. Columnist and author Tom Friedman suggests that caring is a trait distinguishing people from machines, noting:

“We used to work with our hands for many centuries; then we worked with our heads, and now we’re going to have to work with our hearts, because there’s one thing machines cannot, do not, and never will have, and that’s a heart. I think we’re going from hands to heads to hearts.”

Anthony Goldbloom, the founder and CEO of Kaggle Inc., suggests that making decisions from incomplete data is another. This insight is reminiscent of what Pablo Picasso once said of computers: “But they are useless. They can only give you answers.”

But what happens when we create caring robots? Research has shown that people are more likely to open up to robots than humans, because the fear of judgment is significantly diminished. To that end, Cynthia Breazeal of the MIT Media Lab is designing so-called “sociable robots” that can approximate empathetic connections.

Simulations can also allow AI to make novel insights from past data that humans cannot. For example, when the AI system AlphaGo competed solely against itself to learn the game Go, instead of using data from human players, it was able to create insights and strategies that people working at the game had not developed over the centuries of playing it.

Seeking the Right New Opportunities

If, as Picasso implied, people are good at asking questions, what questions should we be asking? In the near term, one certainly might be, “What are the new opportunities that arise as technology takes over certain aspects of work?” MIT economist David Autor notes that there are actually more bank tellers in the U.S. today than there were before the advent of the ATM; they are just doing different work today than they did before.

At first, autonomous vehicles will certainly give rise to different types of work. Doctors, nurses, lawyers, and other professionals may be more apt to conduct house calls, as they’ll be able to use the travel time productively. People may be able to use their kitchens to start restaurants that rely on self-driving vans for food delivery. Certainly, still other new jobs are possible. Autor reminds us that just because we cannot envision them now, doesn’t mean they won’t happen. The farmer disrupted by changes in agricultural technologies in the 1900s probably did not envision the future job of the data analyst predicting yield.

We must not be ignorant to the fact that technology is likely to evolve to take over those new roles eventually as well — the sympathetic robot may one day replace the traveling human doctor. But we expect these changes to take place over time. Marco Iansiti and Karim Lakhani argue that it will likely be 20 years or more, for example, before blockchain becomes mainstream. Even if technologies evolve more quickly, societies and institutions often change more slowly.

Ironically, asking questions about new opportunities for work in the light of technological disruption may be the one task for which humans are inherently superior than computers. In many ways, the ability to ask these questions combines the earlier examples of tasks in which computers are inherently superior to humans. It is part empathy, since it involves identifying unmet human needs and desires in this new environment. It is part decision-making based on incomplete data, since it means identifying needs in a new environment created by technological evolution.

Implications for Work

This perspective suggests that work will still exist in a digital future, but it will be different — and shifts will be unpredictable. This demands that people be prepared to be lifelong learners. Successful employees will pivot to new careers as their skillsets become undervalued in one job or sector, requiring them to repurpose them in new roles or industries.

Companies should seek to support this need for lifetime learning in order to retain employees and guide their development. As we learned from our 2017 research on digital maturity, a few organizations employ this practice today. Some companies allow their employees to spend a certain portion of their work week contributing to open-source software communities. Insurer Cigna Corp. conducted a strategic analysis of their future talent needs, and now reimburses employees at a higher rate if they pursue degrees in those strategic areas. Employees value their organizations’ investment in their future; we saw that talent is up to 15 times more likely to stay with a current employer if that company provides them opportunities to continue developing skills.

Organizations that want to stay ahead of the talent curve will help their employees develop new skills appropriate for a digital business environment. We have argued that this may mean that organizations manage talent differently, building a workforce of long-term employees that assemble teams of on-demand workers, allowing the organization to nimbly adapt to changes as well. Although these types of employees will be managed differently, both should be developed and evaluated in a way that maximizes the opportunity to adapt their skillsets to changes in the technology landscape. This is the future of work.