Algorithms Won’t Replace Managers, But Will Change Everything About What They Do

by Walter Frick  |   12:00 PM December 20, 2013

Workers more and more will come to be classified into two categories. The key questions will be: Are you good at working with intelligent machines or not? Are your skills a complement to the skills of the computer, or is the computer doing better without you? … If you and your skills are a complement to the computer, your wage and labor market prospects are likely to be cheery. If your skills do not complement the computer, you may want to address that mismatch. Ever more people are starting to fall on one side of the divide or the other. That’s why average is over.

But what about management? I interviewed Cowen last month about his vision of the future, and where he sees managers fitting into it. An edited version of our conversation follows.

WF: What do you see as the main career lessons of the book?

TC: One thing the book suggests is that only being technically skilled may not be that useful, because those jobs can be outsourced or even turned over to smart machines. But people who can bridge that gap between technical skills and knowing some sector in a way that’s more creative or more intuitive, that’s where the large payoffs will come.

A classic example is Mark Zuckerberg with Facebook. Obviously a great programmer, but had he just gone out to be paid as a programmer he wouldn’t be that well off. He was a psychology major — he understood how to appeal to users, to get them to come back to the site. So he had that integrative knowledge.

For people who are not technically skilled, marketing, persuasion, cooperation, management, and setting expectations are all things that computers are very far from being good at. It comes down to just communicating with other human beings.

WF: Where does management fit into this?

TC: In any company, you need someone to manage the others, and management is a very hard skill. Relative returns to managers have been rising steadily; good managers are hard to find. And, again, computers are not close to being able to do that. So I think the age of the marketer, the age of the manager are actually our immediate future.

WF: I was talking recently with Andrew McAfee of MIT, whom you reference the book, and he mentioned the way software might even replace some pieces of management, though not managers themselves. Do you see algorithms and software encroaching majorly into that area?

TC: If it’s just measuring how hard people work, how long they’re at their desks, how good a job they do, how good a doctor or a salesman is adjusting for quality of customer, there’s a huge role there for software. But to actually replace managers, for the most part I don’t see that. At least not for the time horizon I’m writing about, 10 to 20 years out.

WF: One thing that McAfee talks about is the idea that people want to get their information from a human; they can be very distrustful of a computer just spitting out a recommendation.

Do you see that being true in a management context as well? That part of my job, if I’m running a company or division, is being able to understand machine intelligence and deliver it personally?

TC: That’s right. You will translate what the machine says and try to motivate people to do it. Professors and teachers will be more like coaches or tutors, rather than carriers of information. They’ll steer you to the program, tell you which classes to take, and be a kind of role model to get you excited about doing the work.

It’s a very important skill, and hard to learn. But I think you’ll see this kind of pattern again and again.

WF: How else do you see management changing?

TC: Management — for all the change we like to talk about — has actually been pretty static for a while. But smart machines and smart software are going to change management drastically, and in general we’re not ready for this. We will need truly new managerial thinking, not just new in the cliched sense of repackaging with new rhetoric and new categories.

WF: I wanted to ask you a little more about the machine-human teams. Freestyle chess — where human and computer teams play together, and outperform either on their own – is the example throughout the book.

What skills does the freestyle chess master have that the grandmaster doesn’t?

TC: The program, of course, does most of the calculations. The one skill the human needs when playing freestyle is how to ask the program good questions. Knowing what questions to ask is how you beat a solo program playing against you, and you don’t even have to be very good at chess. You need to understand chess at some core level, and you need to understand what different programs can and cannot understand.

It’s a kind of meta rationality. Knowing not to overrule the programs very much, but also knowing they’re not perfect, and knowing when to probe. And I think that, in management, those will be the important human skills.

WF: How do you determine where the line is between when the employee is able to add value above and beyond what computer intelligence is giving them, and when the software itself can replace them?

For example, I’m imagining some analytics software. Someone is very skilled at using it, they’re drawing some conclusions, they’re presenting them to people. But the next version of software may have built-in the ability to make those inferences. What skills keep that human from being replaced?

TC: People who can judge that there’s more to the matter than the software can grab; people who can judge the fact that there’s a need for a different kind of software for the problem; people who know when to leave the software alone and get out of its way.

Those are difficult to acquire and often quite intangible skills, but I think they’re increasingly valuable. You can think of other professional areas, like law or medicine, where you let the software do a lot of the work but you can’t uncritically defer to it. Software is bad at common sense in a lot of ways and it misses a lot of context. It’s people who can provide context.

WF: You have an interesting section in the book with respect to specialization in science. Do you see the path towards greater specialization as the path to career security? Or is there still a role for generalists?

TC: There’s a role for both, but you need to ask whether these terms lose some of their meaning. If someone says to you “I specialize in being a generalist,” it’s not actually a crazy claim. Most people cannot be a generalist, and you have to work really hard at a bunch of particular things to be good at it. You’re specializing in doing that. These are people who integrate and understand the contributions of others — that’s a lot like managing. So what you call generalists — I would not oppose them to specialists — there’s a big and growing role for them.

WF: You can’t go a day without seeing a story about who should learn to code or not learn to code. The same thing with respect to statistics. Given the way you see the labor market breaking out, are there specific things you advise people go out and learn?

TC: Statistics will be an increasingly big area. And even knowing a little can have a pretty high return. Coding’s tricky. If you can learn it, great. But if you can’t do it right, you really shouldn’t bother. There’s no half way.

But if you’re a manager or you work in health care, you might not ever be doing statistics, but if you can grasp some basic stuff that you can teach yourself, there’s a very high return. And it’s really quite feasible, unlike coding where it’s a major undertaking. If you’re a doctor trying to figure out which parts of the hospital are bringing in the money and someone hands you statistics — if you’re helpless, that’s really bad.

WF: One area that strikes me as one of the more difficult for machine intelligence is strategy. Where to position your business in a marketplace seems like on the far end of what machines can tell us.

TC: Yeah, that’s all humans, though you might consult machines for background information. But in no sense are machines close to being able to do that. That’s a very long way away.

All of our sectors are all on different paths, and the differential timing actually will be useful because we won’t have to figure it out all at once. We’ll get lessons from different areas sequentially and adjust. Humans will switch into the sectors they’re still good at in a rolling way. And that will make this socially more stable and better for most people.

If you woke up one morning and the machines were better than you at everything, that would be pretty disconcerting. That’s the science fiction scenario but it’s not that realistic.

Should Leaders Focus on Results, or on People?

by Matthew Lieberman  |   8:00 AM December 27, 2013

A lot of ink has been spilled on people’s opinions of what makes for a great leader. As a scientist, I like to turn to the data.  In 2009, James Zenger published a fascinating survey of 60,000 employees to identify how different characteristics of a leader combine to affect employee perceptions of whether the boss is a “great” leader or not. Two of the characteristics that Zenger examined wereresults focus and social skills. Results focus combines strong analytical skills with an intense motivation to move forward and solve problems.  But if a leader was seen as being very strong on results focus, the chance of that leader being seen as a great leader was only 14%. Social skills combine attributes like communication and empathy. If a leader was strong on social skills, he or she was seen as a great leader even less of the time — a paltry 12%.

However, for leaders who were strong in both results focus and in social skills, the likelihood of being seen as a great leader skyrocketed to 72%.

Social skills are a great multiplier.  A leader with strong social skills can leverage the analytical abilities of team members far more efficiently. Having the social intelligence to predict how team members will work together will promote better pairings.  Often what initially appear to be task-related difficulties turn out to be interpersonal problems in disguise.  One employee may feel devalued by another or think that she is doing all the work while her partner loafs – leading both partners putting in less effort to solve otherwise solvable problems. Socially skilled leaders are better at diagnosing and treating these common workplace dilemmas.

So how many leaders are rated high on both results focus and social skills?  If this pairing produces especially effective leaders, companies should have figured this out and promoted people to leadership positions accordingly, right?  Not hardly.  David Rock, director of the Neuroleadership Institute, and Management Research Group recently conducted a survey to find out the answer.  They asked thousands of employees to rate their bosses on goal focus (similar to results focus) and social skills to examine how often a leader scored high on both.  The results are astonishing.  Less than 1% of leaders were rated high on both goal focus and social skills.

Why would this be?  As I describe in my book, Social: Why our brains are wired to connect, our brains have made it difficult to be both socially and analytically focused at the same time.  Even though thinking social and analytically don’t feel radically different, evolution built our brain with different networks for handling these two ways of thinking.  In the frontal lobe, regions on the outer surface, closer to the skull, are responsible for analytical thinking and are highly related to IQ.  In contrast, regions in the middle of the brain, where the two hemispheres touch, support social thinking.  These regions allow us to piece together a person’s thoughts, feelings, and goals based on what we see from their actions, words, and context.

Here’s the really surprising thing about the brain. These two networks function like a neural seesaw. In countless neuroimaging studies, the more one of these networks got more active, the more the other one got quieter.  Although there are some exceptions, in general, engaging in one of the kinds of thinking makes it harder to engage in the other kind.  Its safe to say that in business, analytical thinking has historically been the coin of the realm — making it harder to recognize the social issues that significantly affect productivity and profits.  Moreover, employees are much more likely to be promoted to leadership positions because of their technical prowess.  We are thus promoting people who may lack the social skills to make the most of their teams and not giving them the training they need to thrive once promoted.

How can we do better?  For one, we should give greater weight to social skills in the hiring and promotion process.  Second, we need to create a culture that rewards using both sides of the neural seesaw.  We may not be able to easily use them in tandem, but knowing that there is another angle to problem solving and productivity will create better balance in our leaders.

Finally, it may be possible to train our social thinking so that it becomes stronger over time. Social psychologists are just at the beginning stages of examining whether this kind of training will bear fruit.  One exciting prospect, one that would make the training fun, is the recent finding that reading fiction seems to temporarily strengthen these mental muscles.  Wouldn’t that be great — if readingCatcher in the Rye or the latest Grisham novel were the key to larger profits?