“Only Humans Need Apply”: A Stimulating Look Into the Future of Work

Over the course of 2016, one of the most interesting debates I have been having with World 50 members and outside experts has been about the speed and impact of automation on our workplace in the coming years. While some executives see automation as a normal evolution of technology, others think that what is happening today is a different and that we must begin to plan for a new world in which traditional human workplace roles are no longer a given. A recent book, Only Humans Need Apply (by Thomas Davenport of Babson College and Julia Kirby of Harvard University Press), examines this issue with solid arguments and clear (if perhaps too optimistic) thinking.

The authors start out by making the case for why pretty much all of us should be worried about machines replacing us in the workplace. In fact, they give a long list of criteria that define which roles will go first:

  1. Machines have already taken over a part of your job (e.g., radiologists)
  2. Your job requires little physical effort or manipulation of objects  (e.g., most office workers)
  3. You don’t have to transmit complex information  (e.g., junior analysts or lawyers)
  4. Most, if not all, of your work is straightforward content analysis with little personal interpretation  (e.g., financial analysts)
  5. Your job is based on answering data-dependent questions  (e.g., junior consultants)
  6. It’s based on quantitative analysis  (e.g., engineers)
  7. Your job involves tasks that can be performed virtually  (e.g., teachers)
  8. Consistency is important on your output  (e.g., analysis, engineers, etc.)
  9. On of your tasks is to create data-based narratives  (e.g., financial analysts, consultants)
  10. There are well-defined rules for doing what you do  (e.g., medical technicians)

Of course, the more characteristics your job shares with the list above, the authors note, the more likely it is that a machine will replace you in the (perhaps near) future. Though the material is mostly familair to anyone who follows this topic, the authors do a good job of making the case that no one — from doctors to engineers to teachers — are immune from this evolution. Anyone skeptical of this prediction should read the interview in Sloan Management Review with Bernd Schmitt (Robert D. Calkins Professor of International Business at Columbia Business School). He thinks the today’s image of what workplace robots are will soon be outdated:

Human-machine interaction may be quite different from how it is today, with humans in full control. As creative AI systems become more active, robots will make suggestions on how to do work, where to look for answers, how to make decisions, and how to organize and to lead.

Moreover, because the physical form of robots will change and become less mechanical, humans may feel more comfortable around them — although, admittedly, these robots may give some people the creeps. Robots will appear more and more in humanoid forms, and they will display emotions in their faces and move their bodies increasingly naturally.

These robots will have capabilities as idea generators for new product development, as consultants and counsellors, and they may take over many HR roles. They will engage with others in office conversations and office chat. In other words, they may become full-fledged and fully integrated employees and be part of a company’s culture. Human office workers will likely end up working next to robot workers.

Even a job as vague as “management consultant” is also under the microscope argues a recent HBR piece (by two BCG consultants), who outline the framework for what they call an integrated strategy machine:

 An integrated strategy machine is the collection of resources, both technological and human, that act in concert to develop and execute business strategies. It comprises a range of conceptual and analytical operations, including problem definition, signal processing, pattern recognition, abstraction and conceptualization, analysis, and prediction. One of its critical functions is reframing, which is repeatedly redefining the problem to enable deeper insights. Within this machine, people and technology must each play their particular roles in an integrated fashion.

Digesting all of these predictions might make reading the first part of Only Humans Need Apply a grim task for anyone who thinks she is safe from machines or that there are special human traits that will protect most “knowledge workers” from automation’s job-destroying march. However, after presenting the bad news for humans, the book gets more interesting because it lays out five strategies that humans can employ to survive or even excel in this new world:  Stepping Up, Stepping Aside, Stepping In, Stepping Narrowly, and Stepping Forward.

  1. Stepping Up: “Moving up above automated systems to develop more big-picture insights and decisions that are too unstructured and sweeping for computers or robots to be able to make.” Example: someone who designs the automation machines in the first place.
  2. Stepping Aside: “Moving to a type of non-decision oriented work that computers aren’t good at, such as selling, motivating people, or describing in straightforward terms the decisions that computers have made.” Example: someone who counsels patients after receiving a bad diagnosis from a robot doctor.
  3. Stepping In: “Engaging with the computer system’s automated decisions to understand, monitor, and improve them.” Example: someone who choses which automated trading strategy to execute based on the day’s political events.
  4. Steeping Narrowly: “Finding a speciality area within your profession that is so narrow that no one is attempting to automate it — and it might never be economical to do so.” Example: someone who can underwrite insurance in highly specialized risk classes, such as movie production or emerging technologies.
  5. Stepping Forward: “Developing new systems and technologies that support intelligent decisions and actions in a particular domain.” Example: someone who changes business processes based on automation and analytics recommendations.

All of these strategies are examples of what the authors call augmentation, and this idea — that the role of most human work in the future will be to enhance a machine-dominated work world — takes up the remainder of the book. Indeed, in their closing chapters, Davenport and Kirby present some general recommendations for policy makers and business leaders to embrace this new world and to help prepare the workers of tomorrow for augmentation careers:

For enterprises, an augmentation agenda is the way to ensure the ongoing innovation and flexibility required to survive in a fast-paced, competitive economy. Only with deep human skills, well leveraged by powerful machine analysis, can they continue to offer solutions that resonate with their all-too-human customers. When employers invest in augmentation, they create settings in which knowledge workers are empowered to do more, not asked to do less — and where as a result more value accrues to them as well as to the business’s customers and owners.

Turning next to people, the authors note that:

For individuals, augmentation represents the antidote to automation and the removal of a threat to their ability to have positive impact in the world. It is the invitation to take five kinds of steps, at the least, that they might not have recognized as options before. It’s the invitation to either add value to what machines do, or have the machines add value to their work.

At the end of the day then, Only Humans Need Apply, argues that in the future survival will depend on either making the machines better or filling in gaps where they won’t or can’t work. For them, this is a fine outcome, and they are not the only authors who look into the future and see a better working world. Harvard’s Martin Feldstein, for example, recently wrote on Project Syndicate that he, for one, is not worried about “disruptive technology”:

Why am I so optimistic? Simply put: history. Rapid technical change is not something new. We have experienced technological change that substitutes machines and computers for individual workers for many years. And yet, despite the ups and downs of the business cycle, the US economy continues to return to full employment.

This has been most dramatic in manufacturing. Robots and automated machines have replaced production workers in manufacturing for many years, driving employment in the sector from 13 million in 1950 to only nine million now, even as the real value of manufacturing output rose by 75%. And those who are no longer employed in manufacturing have found jobs elsewhere in the economy.

Computers have also replaced workers in a wide range of service industries. We no longer see many elevator operators. Switchboard operators are gone. Most of us get our boarding passes at airports from automated check-in machines. Law firms and accounting firms use computers to do what professional employees used to do.

And yet the US unemployment rate is now just 4.9%, even lower than its average in recent decades.

Even putting aside the fact that the 4.9% unemployment rate he cites does not reflect all the people in the U.S. who have given up on finding a job or have gone on disability to survive, I think most of us would agree that people who lost manufacturing jobs have not all “found jobs elsewhere in the economy.” As Simon Kuper wryly noted in the FT recently:

When your industry goes, you lose both your income and your identity. Woody Allen has a nice comedy sketch about his father being made “technologically unemployed” — “They fired him. They replaced him with a tiny gadget … that does everything my father does, only it does it much better. The depressing thing is, my mother ran out and bought one.”

That’s a funny quote, but lost earning power is not funny at all. As we all know, for most white collar workers lost factory jobs are one thing but lost lawyer and doctor jobs are quite another (though engineers have been living with the effects of outsourcing and automation for decades now). The wage drop for someone in manufacturing moving to a low-level service job may be -20 to 30%, but think about what that gap would be for a doctor or management consultant?

It’s interesting to me that several reviewers have called Only Humans Need Apply optimistic in outlook, since I am sure a lot of people would find a future as a “machine augmenter” not such a cheery prospect. That said, the authors are right in that everyone — from CEO to painter — must come to terms with the new generation of smart machines just around the corner. I have been lucky enough to meet a few machine learning scientists, ethicists and entrepreneurs this year, and the picture this book paints is pretty much in line with everything I heard privately. In the end, we will all have to make peace (or perhaps war) with the world around the corner. As the book’s title hints, “only humans need apply” will be said about fewer and fewer jobs. Finding our place in this emerging world is something we all can, and should, start thinking about today.

Carlos Alvarenga

Founder and CEO at KatalystNet and Adjunct Professor in the Logistics, Business and Public Policy Department at the University of Maryland’s Robert E. Smith School of Business.

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