Using behavioral science to improve the customer experience 10-16

By guiding the design of customer interactions, the principles of behavioral science offer a simple, low-cost route to improved customer satisfaction.

          

Service operations seem a natural setting for the ideas of behavioral science. Every year, companies have thousands, even millions, of interactions with human beings—also known as customers. Their perceptions of an interaction, behavioral scientists tell us, are influenced powerfully by considerations such as its sequence of painful and pleasurable experiences. Companies care deeply about the quality of those interactions and invest heavily in effective Web sites and in responsive, simplified call centers.

Yet the application of behavioral science to service operations seems spotty at best. Its principles have been implemented by relatively few companies, such as the telecommunications business, which found that giving customers some control over their service interactions by allowing them to schedule field service visits at specific times could make them more satisfied, even when they had to wait a week or longer. Many more companies ignore what makes people tick. Banks, for example, often disturb the customer experience by altering the menus on ATMs or the interactive-voice-response (IVR) systems in call centers. They fail to recognize the psychological discomfort customers experience when faced with unexpected changes.

Likewise, for every restaurant that surrounds a bill’s arrival with a succession of complementary desserts—thereby capitalizing on the customer’s preference for service encounters that end positively—there are a lot of call centers that ignore the importance of a strong finish. Indeed, many companies actively work against one by placing so much emphasis on average handling times that they inadvertently encourage agents to end a call once its main business is complete, leaving customers with memories of brusque treatment.

It doesn’t have to be this way. Academics such as Professor Richard Chase at the University of Southern California’s Marshall School of Business have used research on how people form opinions about their experiences to design actual services. In a 2001 Harvard Business Review article,¹ 1. For more, see Richard B. Chase and Sriram Dasu, “Want to perfect your company’s service? Use behavioral science,” Harvard Business Review, June 2001, Volume 79, Number 6, pp. 78–84. Chase and his team even laid out principles for managers to consider when designing any customer interaction. Get bad experiences over early, so that customers focus on the more positive subsequent elements of the interaction. Break up pleasure but combine pain for your customers, so that the pleasant parts of the interaction form a stronger part of their recollections. Finish strong, as the final elements of the interaction will stick in the customers’ memory. Give them choice, so they feel more in control of the interaction. And let them stick to their habits rather than force them to endure the discomfort and disorientation of unexpected change.

Here we review the experience of an insurance company that used those principles to improve its customers’ satisfaction significantly, with no incremental costs or fundamental changes in people or infrastructure. A systematic approach like this one is needed to counteract the natural tendency of service operations to focus on the needs of IT systems and work flows, not to mention the preferences of employees, managers, and service providers, largely ignoring the way customers perceive their service interactions. If companies in a broad range of service industries—including banking, telecommunications, and retailing—applied a rigorous approach, they would reap significant economic benefits, ranging from reduced churn to greater cross-selling to additional customer referrals.

Setting the stage

Executives at a leading North American health insurer sought to help patients manage their treatment programs for serious long-term illnesses, such as diabetes or congestive heart failure. Conditions like these are difficult to manage because treatment is often protracted and outcomes can depend on the patients’ willingness to make significant lifestyle changes.

Patients participating in an experimental health-management program received regular, scheduled calls from a team of nurses over a period of several months. The calls aimed to deliver additional support to patients undergoing long-term treatment, by helping them understand the available options and stick to their treatment regimes, as well as reinforcing lifestyle changes recommended by their doctors. Improved compliance helps insurers too, as better outcomes reduce the overall cost of treatment.

In the past, the clinical-treatment program for each patient had determined the content of such calls, and the company used what it considered to be a tried-and-true method for managing them. Team members had received guidelines on the objectives of the calls and used a checklist to sequence discussions with customers.

Behavioral science in action

To see if this approach could be improved, the company divided the nurses into two groups—approximately 20 in a pilot group and another 20 in a control one—and began applying a behavioral-science lens to the interactions of the former to test different versions of the call structure. Postcall surveys measured the customers’ satisfaction with each call and with the company. Key customer and operational metrics (including sign-up rates) helped estimate the financial impact. The pilot team used behavioral-science principles throughout the interactions.

1. Get bad experiences over with early

The team identified difficult issues—for example, the forthcoming lapse of certain insurance benefits or the need to transfer from one facility to another—and moved them to the start of the call. It also set up a later phase built around constructive coaching from the nurses on how to deal with the issues raised earlier. In addition, general questions that were likely to make patients uncomfortable (about current pain levels, smoking habits, eating patterns, and alcohol consumption, for instance) were moved from the end of the call to the beginning.

2. Break up pleasure and combine pain

By combining the most challenging elements of a call in its first phase, the health-management team could focus on positive aspects during the rest of it. The team found that patients responded very positively to coaching by nurses, so there was an effort to ensure that coaching on multiple topics was an explicit part of every phase of the call. A nurse might, for example, discuss the next treatment steps, how the patient could take advantage of all covered benefits, and ways of minimizing out-of-pocket expenses. There was also an effort to resolve all possible issues within a call and to transfer it to other groups only as a last resort.

3. Finish strongly

The conclusion of the health-management calls was scripted to finish on a positive note by emphasizing the tangible insurance benefits available to patients and, where medically appropriate, the likelihood of a successful outcome to the agreed-upon action plan. At the end of a program lasting several months, with calls taking place every month or so, patients received a final call from their health-management nurse. This call ended by celebrating their progress, reviewing the goals they had met, and summarizing the positive steps they had taken to achieve those goals.

4. Give customers choice

The company made an effort to give customers explicit choice on three critical elements: the type of treatment plan, which facilities to visit and which doctors to see, and the timing of future calls. In each area, the nurse was guided to tell the customer, “You have a choice; let me give you some options.” Customers explicitly had the right to make the ultimate decision, though the outcome may have been limited or strongly suggested—for example, “Hospital A is closest to your home, but B is only 15 minutes further away, and it has a specialist unit with a great track record at treating your condition.”

5. Let customers stick to their habits

In many situations, it was important for patients to change their lifestyles—say, by eating different foods, consuming less alcohol, or exercising. To encourage patients to make these changes while minimizing the discomfort they generated, nurses introduced them gradually over a series of calls. Dietary changes might be discussed initially, for instance, followed by encouragement to begin exercise. The nurses also tried to reframe the patients’ perceptions of the severity of the changes by comparing them with more unfavorable alternatives: for example, “instead of eliminating your favorite foods altogether, why not just try picking low-fat varieties next time you are in the store.”

The team also worked to ensure that the calls themselves became a positive habit for the patients. This approach gave them the option of having the same nurse on follow-up and promoted a consistent approach for every call, so that they became used to the interactions.

Results

The effect of the changes was significant. Patients in the test group reported an average satisfaction level seven percentage points higher than that of patients in the control group—for calls with the same basic content. These patients’ satisfaction levels with the company was on average eight percentage points higher than that of the control group. More important, patients in the test group were on average five percentage points more likely to say that the calls had motivated them to make positive changes in their behavior.

Notably, the program didn’t significantly affect the company’s costs or change key operational metrics, such as the length of a call or the number of calls a day. Moreover, test group nurses reported an average level of job satisfaction higher than that of the control group nurses. Finally, the impact was rapid. Most of the increase in the satisfaction levels of the test group patients happened within two weeks.

---

Many other service industries could benefit from a similar approach. By breaking down frontline transactions and rebuilding them with behavioral and experiential principles, companies could systematically achieve rapid, measurable improvements in customer satisfaction.

About the author(s)

John DeVine is a principal in McKinsey’s Miami office, and Keith Gilson is a consultant in the Toronto office.

View at the original source

The manager and the moron 10-04

The manager and the moron

The computer is a moron. And the stupider the tool, the brighter the master must be, says Peter Drucker. In this Quarterly archive article, he explains how “the dumbest tool we have ever had” will compel managers to think through their actions.

As all of us know, during the last 20 years the free world has had the greatest, most sustained economic advance in history. Most of us believe that this has been a time not merely of forward movement, but of vast economic change.

The facts and figures, however, do not support this impression. They show, instead, that our era has actually been a time of unprecedented non-change. It has been largely a period of linear forward movement along old trend lines, of adding new stories to an old building according to the old architectural design.

Imagine an economist in 1913, just before World War I, taking the economic trend lines of what were then already the advanced countries, and projecting each of them ahead to 1966. He would have hit it on the nose for Japan, Western Europe, and the United States, in fact for every one of the developed nations with one important exception—the Soviet Union, which is significantly below where it would have come out on our economist’s projection. 

The reason for this, as all of us know, was that the Russians imposed a political straitjacket on agriculture and froze farm technology just at the worst possible moment, when the technological revolution in farming was getting under way. Worse, they froze the agricultural population. By making it possible for anybody who stayed on the farm to be fed, no matter how poorly, they removed the economic pressure that elsewhere in the world has pushed the farmer off the farm, brought about fantastic productivity jumps in agriculture, and provided labor for the expansion of industry.

Suppose, again, that our economist, having made his projections in 1913, fell into a 50 years’ sleep. When he woke up, he would have found the industrial geography of the world virtually unchanged. Every country that is today an industrially advanced nation was well past the takeoff point in 1913. Not a single new one has joined the club, unless you count satellite economies like Canada, Australia, South Africa, and Mexico. Brazil, which has a long and distinguished history as a country of the future, may join the club tomorrow, but it isn’t quite there yet.

Compared to this linear movement, the 50 years before 1913 present the greatest imaginable contrast. During those five decades the industrial map of the world had been changing as rapidly as the physical map of the world changed in the fifteenth and the early sixteenth centuries—the Age of Discovery. Right after the Civil War, the United States and Germany emerged as economically advanced countries and rapidly overtook the old champion, Great Britain. A quarter of a century later Russia and Japan emerged, along with the western part of Austria-Hungary—the present Czechoslovakia and Austria, with Northern Italy. In short, the 50 years before 1913 were a period of very rapid shifts in economic power relationships.

Since World War I, however, such changes have been absent. This explains why economists of today are so concerned with economic development. Before 1913, it was taken for granted, but since then we’ve apparently gone sterile. And we don’t know how to start it up.

Perhaps the greatest shock to our Rip Van Winkle economist, however, would be the fact that, with the exception of the plastics industry, the main engines of growth in the past 50 years were already mature or rapidly maturing industries, based on well-known technologies, back in 1913.

The dynamos of growth

Our most rapidly advancing industry in the last 20 years of expansion has been agriculture. The productivity of farming has been increasing twice as fast as the productivity of manufacturing in all the developed countries except Russia. Yet the average farmer of today in the United States is not farming in a much more advanced way than the top farmer of 1913. Hybrid seed is about the only new development of any consequence.

And the next dynamo has been the steel industry. World steel capacity has expanded fivefold since 1913. Yet 99 percent of all steel capacity in existence today is built on a technology that was considered antiquated—and Lord knowswas antiquated—in 1913.

Our third engine of growth has been the automotive industry. Yet in 1913 Henry Ford was already producing and selling 183,000 Model T’s, and a year later the figure had climbed to 261,000—more cars than the Soviet Union has ever produced in a single year. Even the Ford Motor Company of 1913 would be a major producer in today’s free-world automotive industry.

Much the same is true of the electrical apparatus industry. Neither Westinghouse, nor GE, nor Siemens was exactly unknown in 1913. They were blue chips. And this is also true of the organic chemical industry.

Plastics is the only industry based on new technology that is economically important today in terms of contribution to gross national product, employment, and so on. As far as the economic statistician is concerned, other industries hardly exist as yet. The airplane began to have an economic impact when the jets came. But the real impact will come with the big freight jets, which will make every airstrip in the world a deep-water port. In a few years, they may make the ocean-going freighter, man’s oldest efficient transportation, look roughly the way the railroads began to look around 1950. This will be one of the greatest changes in transportation we’ve ever had. But it is still ahead of us.

Much ado, little impact

The computers, despite all the excitement they have been generating, are not yet economically important. It’s only now that IBM is shipping them out at a rate of a thousand a month that they’re even beginning to have an impact. But we haven’t begun to use the potential of the computer. So far we are using it only for clerical chores, which are unimportant by definition. To be sure, the computer has created something that had never existed in the history the world—namely, paying jobs for mathematicians. But that is hardly a major economic contribution, no matter what the graduate dean thinks.

So the economic impact of the new technologies is still in the future. If we subtracted every single one of them from the civilian economy, we would hardly notice it in the figures—perhaps a percentage point or two.

But this situation of linear movement is rapidly changing in every respect. And the greatest change is one that our Rip Van Winkle economist, looking only at the figures, wouldn’t even notice: In the past 20 years we have created a brand-new form of capital, a brand-new resource, namely knowledge.

Up until 1900, any society in the world would have done just as well as it did without men of knowledge. We may have needed lawyers to defend criminals and doctors to write death certificates, but the criminals would have done almost as well without the lawyers, and the patients without the doctors. We needed teachers to teach other ornaments of society, but this too was largely decoration. The world prided itself on men of knowledge, but it didn’t need them to keep the society running.

As late as the mid-forties, General Motors carefully concealed the fact that one of its three top men, Albert Bradley, had a PhD. It was even concealed that he had gone to college, because, quite obviously, a respectable man went to work as a water boy at age 14. A PhD was an embarrassing thing to have around.

Nowadays, companies boast about the PhDs on their payrolls. Knowledge has become our capital resource, a terribly expensive one. A man who graduates from a good business school represents some $100,000 of social investment, not counting what his parents spent on him, and not counting the opportunity costs. His grandparents and great-grandparents had to go to work at the age of 12 or 13 with the hoe in the potato patch so that he could forgo those ten years of contribution to society. And that’s a tremendous capital investment.

Besides spending all that money, we are also doing something very revolutionary. We are applying knowledge to work. Seven-odd thousand years ago, the first great human revolution took place when our ancestors first applied skill to work. They did not use skill to substitute for brawn. The most skilled work very often requires the greatest physical strength; no ditchdigger works harder than the surgeon performing a major operation. Rather, our ancestors put skills on top of physical labor. And now—a second revolution—we’ve put knowledge on top of both. Not as a substitute for skill, but as a whole new dimension. Skill alone won’t do it anymore.

Now, this has two or three important implications for management.

First, we must learn to make knowledge productive. As yet we don’t really know how. The payroll cost of knowledge workers already amounts to more than half the labor costs of practically all business I know. That represents a tremendous capital investment in human beings. But so far neither productivity trends nor profit margins show much sign of responding to it. Pretty clearly, although business is paying for knowledge workers, it isn’t getting much back. And if you look at the way we manage knowledge workers, the reason is obvious: we don’t know how.

One of the few things we do know is that for any knowledge worker, even for the file clerk, there are two laws. The first one is that knowledge evaporates unless it’s used and augmented. Skill goes to sleep, it becomes rusty, but it can be restored and refurbished very quickly. That’s not true of knowledge. If knowledge isn’t challenged to grow, it disappears fast. It’s infinitely more perishable than any other resource we have ever had. The second law is that the only motivation for knowledge is achievement. Anybody who has ever had a great success is motivated from then on. It’s a taste one never loses. So we do know a little about how to make knowledge productive.

The obsolescence of experience

Another implication flows from the creation of this new knowledge resource. The new generation of managers, those now aged 35 or under, is the first generation that thinks in terms of putting knowledge to work before one has accumulated a decade or two of experience. Mine was the last generation of managers who measured their value entirely by experience. All of us, of necessity, managed by experience—not a good process, because experience cannot be tested or be taught. Experience must be experienced; except by a very great artist, it cannot be conveyed.

This means that the new generation and my generation are going to be horribly frustrated working together. They rightly expect us, their elders and betters, to practice some of the things that we preach. We don’t dream of it. We preach knowledge and system and order, since we never had them. But we go by experience, the one thing we do have. We feel frustrated and lost because, after devoting half our lifetimes to acquiring experience, we still don’t really understand what we’re trying to do. The young are always in the right, because time is on their side. And that means we have to change.

This brings us to the third implication, a very important one. Any business that wants to stay ahead will have to put very young people into very big jobs—and fast. Older men cannot do these jobs—not because they lack the necessary intelligence, but because they have the wrong conditioned reflexes. The young ones stay in school so long they don’t have time to acquire the experience we used to consider indispensable in big jobs. And the age structure of our population is such that in the next 20 years, like it or not, we are going to have to promote people we wouldn’t have thought old enough, a few years ago, to find their way to the water cooler. Companies must learn to stop replacing the 65-year-old man with the 59-year-old. They must seek out their good 35-year-olds.

For all its importance, however, the appearance of knowledge as a new capital resource is not the most vivid change in our environment, if only because it does not yet have a visible impact on the world’s economic figures. Probably the most vivid change is in technology.

Many of the old technologies, of course, still have a lot of life in them. I think it’s quite clear that the automobile, for instance, has yet to experience its greatest growth period. In the developed countries, however, it’s in a defensive position. I don’t think we need a great deal of imagination to foresee the day when the private car will be banned in the midtown areas or the day when the internal combustion engine will be limited to over-the-road use.

Or consider steel. I think one can quite easily foretell technological changes that will cut the cost of steel by about 40 percent. But whether that’s enough to re-create momentum for the steel industry is debatable. I think that steel would probably need a greater cost advantage to make it again the universal material it used to be. Since steel, like all multipurpose materials, isn’t ideal for any one use, it has to compete on price. And, as you know, the steel industry has lost 20 percent of the markets it had before World War II. 

It’s concrete here, plastic there, and so on. Whether steel will lose the automotive-body business to one of the new composition materials in the next ten years is a moot question. Only a fool would bet on it at this point, but by the same token only a fool would bet against it. If it does happen, it’s very doubtful whether even a 40 percent reduction in cost might be enough to keep steel from joining the long parade of yesterday’s engines of economic growth.

In agriculture, the great need is for an advance in productivity—but again, not in the developed countries. By now, the agricultural population in the developed countries has shrunk to such a small percentage of the total that even tripling its productivity would make little difference in the overall economic picture.

And so on. I’m not saying that the industries based on old technologies can’t advance, but I am saying they’re unlikely to provide the impetus we need for continuing expansion. From now on, I think, the expansion will have to be powered by new industries based on new technologies, something we have not seen to any extent since before World War I.

Enter the knowledge utility

One of the most potentially earthshaking forces in our economy is the technology of information. I don’t mean simply the computer. The computer is to information what the electric power station is to electricity. The power station makes many other things possible, but it’s not where the money is. The money is in the gimmicks and gizmos, the appliances, the motors and facilities made possible and necessary by electricity, that didn’t exist before.

Information, like electricity, is energy. Just as electrical energy is energy for mechanical tasks, information is energy for mental tasks. The computer is the central power station, but there are also the electronic transmission facilities—the satellites and related devices. We have devices to translate the energy, to convert the information. We have the display capacity of the television tube, the capability to translate arithmetic into geometry, to convert from binary numbers into curves. We can go from computer core to memory display, and from either one into hard copy. 

All the pieces of the information system are here. Technically there is no reason why Sears, Roebuck could not offer tomorrow, for the price of a television set, a plug-in appliance that would put us in direct contact with all the information needed for schoolwork from kindergarten through college.

Already the time-sharing principle has begun to take hold. I don’t think it takes too much imagination to see that a typical large company is about as likely to have its own computer 20 years hence as it is to have its own steam-generating plant today. It is reasonably predictable that computers will become a common carrier, a public utility, and that only organizations with quite extraordinary needs will have their own. 

Steel mills today have their own generators because they need such an enormous amount of power. Twenty years hence, an institution that’s the equivalent of a steel mill in terms of mental work—MIT, for example—might well have its own computer. But I think most other universities, for most purposes, will simply plug into time-sharing systems.

It would be silly to try to predict in detail the effects of any development as big as this. All one can foresee for certain is a great change in the situation. One cannot predict what it will lead to, and where and when and how. A change as tremendous as this doesn’t just satisfy existing wants, or replace things we are now doing. It creates new wants and makes new things possible.

A new age of information

The impact of information, however, should be greater than that of electricity, for a very simple reason. Before electricity, we had power; we had energy. It was very expensive and rather scarce, but we had it. Before now, however, we have not had information. Information has been unbelievably expensive, almost totally unreliable, and always so late that it was of little, if any, value. Most of us who had to work with information in the past, therefore, knew we had to invent our own. One developed, if one had any sense, a reasonably good instinct for what invention was plausible and likely to fly, and what wasn’t. But real information just wasn’t to be had. Now, for the first time, it’s beginning to be available—and the overall impact on society is bound to be very great.

Without attempting to predict the precise nature and timing of this impact, I think we can safely make a few assumptions.

Assumption No. 1: Within the next ten years, information will become very much cheaper. An hour of computer time today costs several hundred dollars at a minimum; I have seen figures that put the cost at about a dollar an hour in 1973 or so. Maybe it won’t come down that steeply, but come down it will.

Assumption No. 2: The present imbalance between the capacity to compute and store information and the capacity to use it will be remedied. We will spend more and more money on producing the things that make a computer usable—the software, the programs, the terminals, and so on. The customers aren’t going to be content just to have the computer sitting there.

Assumption No. 3: The kindergarten stage is over. We’re past the time when everybody was terribly impressed by the computer’s ability to do two plus two in fractions of a nanosecond. We’re also past the stage of trying to find work for the computer by putting all the unimportant things on it—using it as a very expensive clerk. Actually, nobody has yet saved a penny that way, as far as I can tell. Clerical work—unless it’s a tremendous job, such as addressing 7 million copies of Lifemagazine every week—is not really done very cheaply on the computer. But then, kindergartens are never cheap.

Now we can begin to use the computer for the things it should be used for—information, control of manufacturing processes, control of inventory, shipments, and deliveries. I’m not saying we shouldn’t be using the computer for payrolls, but that’s beside the point. If payrolls were all it could do, we wouldn’t be interested in it.

Managing the moron

We are beginning to realize that the computer makes no decisions; it only carries out orders. It’s a total moron, and therein lies its strength. It forces us to think, to set the criteria. The stupider the tool, the brighter the master has to be—and this is the dumbest tool we have ever had. All it can do is say either zero or one, but it can do that awfully fast. It doesn’t get tired and it doesn’t charge overtime. It extends our capacity more than any tool we have had for a long time, because of all the really unskilled jobs it can do. By taking over these jobs, it allows us—in fact, it compels us—to think through what we are doing.

But though it can’t make decisions, the computer will—if we use it intelligently—increase the availability of information. And that will radically change the organization structure of business—of all institutions, in fact. Up to now we have been organizing, not according to the logic of the work to be done, but according to the absence of information. Whole organization levels have existed simply to provide standby transmission facilities for the breakdowns in information flow that one could always take for granted. Now these redundancies are no longer needed. We mustn’t allow organizational structure to be made more complicated by the computer. If the computer doesn’t enable us to simplify our organizations, it’s being abused.

Along with vastly increasing the availability of information, the computer will reduce the sheer volume of data that managers have had to cope with. At present the computer is the greatest possible obstacle to management information, because everybody has been using it to produce tons of paper. Now, psychology tells us that the one sure way to shut off all perception is to flood the senses with stimuli. That’s why the manager with reams of computer output on his desk is hopelessly uninformed. That’s why it’s so important to exploit the computer’s ability to give us only the information we want—nothing else. The question we must ask is not, “How many figures can I get?” but “What figures do I need? In what form? When and how?” We must refuse to look at anything else. We no longer have to take figures that mean nothing to us and read them the way a gypsy reads tea leaves.

Instead, we must decide on our information needs and how the computer can fill those needs. To do that, we must understand our operating processes, and the principles behind the processes. We must apply knowledge and analysis to them, and convert them to a clerk’s routine. Even a work of genius, thought through and systematized, becomes a routine. Once it has been created, a shipping clerk can do it—or a computer can do it. So, once we have achieved real understanding of what we are doing, we can define our needs and program the computer to fill them.

Beyond the numbers barrier

We must realize, however, that we cannot put on the computer what we cannot quantify. And we cannot quantify what we cannot define. Many of the important things, the subjective things, are in this category. To know something, to really understand something important, one must look at it from 16 different angles. People are perceptually slow, and there is no shortcut to understanding; it takes a great deal of time. Managers today cannot take the time to understand, because they don’t have it. They are too busy working on things they can quantify—things they could put on a computer.

This is why the manager should use the computer to control the routines of business, so that he himself can spend ten minutes a day controlling instead of five hours. Then he can use the rest of his time to think about the important things he cannot really know—people and environment. These are things he cannot define; he has to take the time to go and look. The failure to go out and look is what accounts for most of our managerial mistakes today.

Our greatest managerial failure rate comes in the step from middle to top management. Most middle managers are doing essentially the same things they did on their entrance jobs: controlling operations and fighting fires. In contrast, the top manager’s primary function is to think. The criteria for success at the top level bear little resemblance to the criteria for promotion from middle management.

The new top manager, typically, has been promoted on the basis of his ability to adapt successfully. But suddenly he’s so far away from the firing line that he doesn’t know what to adapt to—so he fails. He may be an able man, but nothing in his work experience has prepared him to think. He hasn't the foggiest notion how one goes about making entrepreneurial or policy decisions. That’s why the failure rate at the senior-management level is so high. In my experience, two out of three men promoted to top management don’t make it; they stay middle management. They aren’t necessarily fired. Instead, they get put on the Executive Committee with a bigger office, a bigger title, a bigger salary—and a higher nuisance value because they have had no exposure to thinking. This is a situation we are going to eliminate.

On the other hand, we are going to open up a new problem of development at the middle-management level. It isn’t difficult for us to get people into middle management today. But it is going to be, because we shall need thinking people in the middle, not just at the top. The point at which we teach people to think will have to be moved further and further down the line. We can already see this problem in the big commercial banks.

We will have to manage knowledge correctly in order to preserve it. And this gets us into myriad questions of teaching and learning, of developing knowledge and techniques of thinking—not only in the developed nations, but in countries that are yet unaware of the distinction between management-by-experience and management-by-thinking, countries that are unaware of management itself. But that is another subject.

View at the original source

Artificial intelligence meets the C-suite 10-02

Artificial   intelligence   meets   the   C-suite

Technology is getting smarter, faster. Are you? Experts including the authors of The Second Machine Age, Erik Brynjolfsson and Andrew McAfee, examine the impact that “thinking” machines may have on top-management roles.

The exact moment when computers got better than people at human tasks arrived in 2011, according to data scientist Jeremy Howard, at an otherwise inconsequential machine-learning competition in Germany. Contest participants were asked to design an algorithm that could recognize street signs, many of which were a bit blurry or dark. Humans correctly identified them 98.5 percent of the time. At 99.4 percent, the winning algorithm did even better.

Or maybe the moment came earlier that year, when IBM’s Watson computer defeated the two leading human Jeopardy! players on the planet. Whenever or wherever it was, it’s increasingly clear that the comparative advantage of humans over software has been steadily eroding. Machines and their learning-based algorithms have leapt forward in pattern-matching ability and in the nuances of interpreting and communicating complex information. The long-standing debate about computers as complements or substitutes for human labor has been renewed.

The matter is more than academic. Many of the jobs that had once seemed the sole province of humans—including those of pathologists, petroleum geologists, and law clerks—are now being performed by computers.

And so it must be asked: can software substitute for the responsibilities of senior managers in their roles at the top of today’s biggest corporations? In some activities, particularly when it comes to finding answers to problems, software already surpasses even the best managers. Knowing whether to assert your own expertise or to step out of the way is fast becoming a critical executive skill.

In this interview with McKinsey’s Rik Kirkland, Erik Brynjolfsson and Andrew McAfee explain the organizational challenge posed by the Second Machine Age.

Yet senior managers are far from obsolete. As machine learning progresses at a rapid pace, top executives will be called on to create the innovative new organizational forms needed to crowdsource the far-flung human talent that’s coming online around the globe. Those executives will have to emphasize their creative abilities, their leadership skills, and their strategic thinking.

To sort out the exponential advance of deep-learning algorithms and what it means for managerial science, McKinsey’s Rik Kirkland conducted a series of interviews in January at the World Economic Forum’s annual meeting in Davos. Among those interviewed were two leading business academics—Erik Brynjolfsson and Andrew McAfee, coauthors ofThe Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (W. W. Norton, January 2014)—and two leading entrepreneurs: Anthony Goldbloom, the founder and CEO of Kaggle (the San Francisco start-up that’s crowdsourcing predictive-analysis contests to help companies and researchers gain insights from big data); and data scientist Jeremy Howard. This edited transcript captures and combines highlights from those conversations.

The Second Machine Age.

What is it and why does it matter?

Andrew McAfee: The Industrial Revolution was when humans overcame the limitations of our muscle power. We’re now in the early stages of doing the same thing to our mental capacity—infinitely multiplying it by virtue of digital technologies. There are two discontinuous changes that will stick in historians’ minds. The first is the development of artificial intelligence, and the kinds of things we’ve seen so far are the warm-up act for what’s to come. The second big deal is the global interconnection of the world’s population, billions of people who are not only becoming consumers but also joining the global pool of innovative talent.

Erik Brynjolfsson: The First Machine Age was about power systems and the ability to move large amounts of mass. The Second Machine Age is much more about automating and augmenting mental power and cognitive work. Humans were largely complements for the machines of the First Machine Age. In the Second Machine Age, it’s not so clear whether humans will be complements or machines will largely substitute for humans; we see examples of both. That potentially has some very different effects on employment, on incomes, on wages, and on the types of companies that are going to be successful.

Machine-learning experts Anthony Goldbloom and Jeremy Howard tell McKinsey’s Rik Kirkland how smart machines will impact employment.

Jeremy Howard: Today, machine-learning algorithms are actually as good as or better than humans at many things that we think of as being uniquely human capabilities. People whose job is to take boxes of legal documents and figure out which ones are discoverable— that job is rapidly disappearing because computers are much faster and better than people at it.

In 2012, a team of four expert pathologists looked through thousands of breast-cancer screening images, and identified the areas of what’s called mitosis, the areas which were the most active parts of a tumor. It takes four pathologists to do that because any two only agree with each other 50 percent of the time. It’s that hard to look at these images; there’s so much complexity. So they then took this kind of consensus of experts and fed those breast-cancer images with those tags to a machine-learning algorithm. The algorithm came back with something that agreed with the pathologists 60 percent of the time, so it is more accurate at identifying the very thing that these pathologists were trained for years to do. And this machine-learning algorithm was built by people with no background in life sciences at all. These are total domain newbies.

Andrew McAfee: We thought we knew, after a few decades of experience with computers and information technology, the comparative advantages of human and digital labor. But just in the past few years, we have seen astonishing progress. A digital brain can now drive a car down a street and not hit anything or hurt anyone—that’s a high-stakes exercise in pattern matching involving lots of different kinds of data and a constantly changing environment.

Why now?

Computers have been around for more than 50 years. Why is machine learning suddenly so important?

Erik Brynjolfsson: It’s been said that the greatest failing of the human mind is the inability to understand the exponential function. Daniela Rus—the chair of the Computer Science and Artificial Intelligence Lab at MIT—thinks that, if anything, our projections about how rapidly machine learning will become mainstream are too pessimistic. It’ll happen even faster. And that’s the way it works with exponential trends: they’re slower than we expect, then they catch us off guard and soar ahead.

Andrew McAfee: There’s a passage from a Hemingway novel about a man going broke in two ways: “gradually and then suddenly.” And that characterizes the progress of digital technologies. It was really slow and gradual and then, boom—suddenly, it’s right now.

Jeremy Howard: The difference here is each thing builds on each other thing. The data and the computational capability are increasing exponentially, and the more data you give these deep-learning networks and the more computational capability you give them, the better the result becomes because the results of previous machine-learning exercises can be fed back into the algorithms. That means each layer becomes a foundation for the next layer of machine learning, and the whole thing scales in a multiplicative way every year. There’s no reason to believe that has a limit.

Erik Brynjolfsson: With the foundational layers we now have in place, you can take a prior innovation and augment it to create something new. This is very different from the common idea that innovations get used up like low-hanging fruit. Now each innovation actually adds to our stock of building blocks and allows us to do new things.

One of my students, for example, built an app on Facebook. It took him about three weeks to build, and within a few months the app had reached 1.3 million users. He was able to do that with no particularly special skills and no company infrastructure, because he was building it on top of an existing platform, Facebook, which of course is built on the web, which is built on the Internet. Each of the prior innovations provided building blocks for new innovations. I think it’s no accident that so many of today’s innovators are younger than innovators were a generation ago; it’s so much easier to build on things that are preexisting.

Jeremy Howard: I think people are massively underestimating the impact, on both their organizations and on society, of the combination of data plus modern analytical techniques. The reason for that is very clear: these techniques are growing exponentially in capability, and the human brain just can’t conceive of that.

There is no organization that shouldn’t be thinking about leveraging these approaches, because either you do—in which case you’ll probably surpass the competition—or somebody else will. And by the time the competition has learned to leverage data really effectively, it’s probably going to be too late for you to try to catch up. Your competitors will be on the exponential path, and you’ll still be on that linear path.

Let me give you an example. Google announced last month that it had just completed mapping the exact location of every business, every household, and every street number in the entirety of France. You’d think it would have needed to send a team of 100 people out to each suburb and district to go around with a GPS and that the whole thing would take maybe a year, right? In fact, it took Google one hour.

Now, how did the company do that? Rather than programming a computer yourself to do something, with machine learning you give it some examples and it kind of figures out the rest. So Google took its street-view database—hundreds of millions of images—and had somebody manually go through a few hundred and circle the street numbers in them. Then Google fed that to a machine-learning algorithm and said, “You figure out what’s unique about those circled things, find them in the other 100 million images, and then read the numbers that you find.” That’s what took one hour. So when you switch from a traditional to a machine-learning way of doing things, you increase productivity and scalability by so many orders of magnitude that the nature of the challenges your organization faces totally changes.

The senior-executive role.

How will top managers go about their day-to-day jobs?

Andrew McAfee: The First Machine Age really led to the art and science and practice of management—to management as a discipline. As we expanded these big organizations, factories, and railways, we had to create organizations to oversee that very complicated infrastructure. We had to invent what management was.

In the Second Machine Age, there are going to be equally big changes to the art of running an organization.

I can’t think of a corner of the business world (or a discipline within it) that is immune to the astonishing technological progress we’re seeing. That clearly includes being at the top of a large global enterprise.

I don’t think this means that everything those leaders do right now becomes irrelevant. I’ve still never seen a piece of technology that could negotiate effectively. Or motivate and lead a team. Or figure out what’s going on in a rich social situation or what motivates people and how you get them to move in the direction you want.

These are human abilities. They’re going to stick around. But if the people currently running large enterprises think there’s nothing about the technology revolution that’s going to affect them, I think they would be naïve.

So the role of a senior manager in a deeply data-driven world is going to shift. I think the job is going to be to figure out, “Where do I actually add value and where should I get out of the way and go where the data take me?” That’s going to mean a very deep rethinking of the idea of the managerial “gut,” or intuition.

It’s striking how little data you need before you would want to switch over and start being data driven instead of intuition driven. Right now, there are a lot of leaders of organizations who say, “Of course I’m data driven. I take the data and I use that as an input to my final decision-making process.” But there’s a lot of research showing that, in general, this leads to a worse outcome than if you rely purely on the data. Now, there are a ton of wrinkles here. But on average, if you second-guess what the data tell you, you tend to have worse results. And it’s very painful—especially for experienced, successful people—to walk away quickly from the idea that there’s something inherently magical or unsurpassable about our particular intuition.

Jeremy Howard: Top executives get where they are because they are really, really good at what they do. And these executives trust the people around them because they are also good at what they do and because of their domain expertise. Unfortunately, this now saddles executives with a real difficulty, which is how to become data driven when your entire culture is built, by definition, on domain expertise. Everybody who is a domain expert, everybody who is running an organization or serves on a senior-executive team, really believes in their capability and for good reason—it got them there. But in a sense, you are suffering from survivor bias, right?

You got there because you’re successful, and you’re successful because you got there. You are going to underestimate, fundamentally, the importance of data. The only way to understand data is to look at these data-driven companies like Facebook and Netflix and Amazon and Google and say, “OK, you know, I can see that’s a different way of running an organization.” It is certainly not the case that domain expertise is suddenly redundant. But data expertise is at least as important and will become exponentially more important. So this is the trick. Data will tell you what’s really going on, whereas domain expertise will always bias you toward the status quo, and that makes it very hard to keep up with these disruptions.

Erik Brynjolfsson: Pablo Picasso once made a great observation. He said, “Computers are useless. They can only give you answers.” I think he was half right. It’s true they give you answers—but that’s not useless; that has some value. What he was stressing was the importance of being able to ask the right questions, and that skill is going to be very important going forward and will require not just technical skills but also some domain knowledge of what your customers are demanding, even if they don’t know it. This combination of technical skills and domain knowledge is the sweet spot going forward.

Anthony Goldbloom: Two pieces are required to be able to do a really good job in solving a machine-learning problem. The first is somebody who knows what problem to solve and can identify the data sets that might be useful in solving it. Once you get to that point, the best thing you can possibly do is to get rid of the domain expert who comes with preconceptions about what are the interesting correlations or relationships in the data and to bring in somebody who’s really good at drawing signals out of data.

The oil-and-gas industry, for instance, has incredibly rich data sources. As they’re drilling, a lot of their drill bits have sensors that follow the drill bit. And somewhere between every 2 and 15 inches, they’re collecting data on the rock that the drill bit is passing through. They also have seismic data, where they shoot sound waves down into the rock and, based on the time it takes for those sound waves to be captured by a recorder, they can get a sense for what’s under the earth. Now these are incredibly rich and complex data sets and, at the moment, they’ve been mostly manually interpreted. And when you manually interpret what comes off a sensor on a drill bit or a seismic survey, you miss a lot of the richness that a machine-learning algorithm can pick up.

Andrew McAfee: The better you get at doing lots of iterations and lots of experimentation—each perhaps pretty small, each perhaps pretty low-risk and incremental—the more it all adds up over time. But the pilot programs in big enterprises seem to be very precisely engineered never to fail—and to demonstrate the brilliance of the person who had the idea in the first place.

That makes for very shaky edifices, even though they’re designed to not fall apart. By contrast, when you look at what truly innovative companies are doing, they’re asking, “How do I falsify my hypothesis? How do I bang on this idea really hard and actually see if it’s any good?” When you look at a lot of the brilliant web companies, they do hundreds or thousands of experiments a day. It’s easy because they’ve got this test platform called the website. And they can do subtle changes and watch them add up over time.

So one of the implications of the manifested brilliance of the crowd applies to that ancient head-scratcher in economics: what the boundary of the firm should be. What should I be doing myself versus what should I be outsourcing? And, now, what should I be crowdsourcing?

Implications for talent and hiring

It’s important to make sure that the organization has the right skills.

Jeremy Howard: Here’s how Google does HR. It has a unit called the human performance analytics group, which takes data about the performance of all of its employees and what interview questions were they asked, where was their office, how was that part of the organization’s structure, and so forth. Then it runs data analytics to figure out what interview methods work best and what career paths are the most successful.

Anthony Goldbloom: One huge limitation that we see with traditional Fortune 500 companies—and maybe this seems like a facile example, but I think it’s more profound than it seems at first glance—is that they have very rigid pay scales.

And they’re competing with Google, which is willing to pay $5 million a year to somebody who’s really great at building algorithms. The more rigid pay scales at traditional companies don’t allow them to do that, and that’s irrational because the return on investment on a $5 million, incredibly capable data scientist is huge. The traditional Fortune 500 companies are always saying they can’t hire anyone. Well, one reason is they’re not willing to pay what a great data scientist can be paid elsewhere. Not that it’s just about money; the best data scientists are also motivated by interesting problems and, probably most important, by the idea of working with other brilliant people.

Machine learning and computers aren’t terribly good at creative thinking, so the idea that the rewards of most jobs and people will be based on their ability to think creatively is probably right.

View at the original source

View 

Management intuition for the next 50 years 09-27

Management   intuition   for   the   next   50   years

The collision of technological disruption, rapid emerging-markets growth, and widespread aging is upending long-held assumptions that underpin strategy setting, decision making, and management.

Intuition forms over time. When McKinsey began publishing the Quarterly, in 1964, a new management environment was just beginning to take shape. On April 7 of that year, IBM announced the System/360 mainframe, a product with breakthrough flexibility and capability. Then on October 10, the opening ceremonies of the Tokyo Olympic Games, the first in history to be telecast via satellite around the planet, underscored Japan’s growing economic strength. Finally, on December 31, the last new member of the baby-boom generation was born.

Fifty years later, the forces symbolized by these three disconnected events are almost unrecognizable. Technology and connectivity have disrupted industries and transformed the lives of billions. The world’s economic center of gravity has continued shifting from West to East, with China taking center stage as a growth story. The baby boomers have begun retiring, and we now talk of a demographic drag, not a dividend, in much of the developed world and China.

We stand today on the precipice of much bigger shifts in each of these areas, with extraordinary implications for global leaders. In the years ahead, acceleration in the scope, scale, and economic impact of technology will usher in a new age of artificial intelligence, consumer gadgetry, instant communication, and boundless information while shaking up business in unimaginable ways. At the same time, the shifting locus of economic activity and dynamism, to emerging markets and to cities within those markets, will give rise to a new class of global competitors. Growth in emerging markets will occur in tandem with the rapid aging of the world’s population—first in the West and later in the emerging markets themselves—that in turn will create a massive set of economic strains.

Any one of these shifts, on its own, would be among the largest economic forces the global economy has ever seen. As they collide, they will produce change so significant that much of the management intuition that has served us in the past will become irrelevant. The formative experiences for many of today’s senior executives came as these forces were starting to gain steam. The world ahead will be less benign, with more discontinuity and volatility and with long-term charts no longer looking like smooth upward curves, long-held assumptions giving way, and seemingly powerful business models becoming upended. In this article, which brings together years of research by the McKinsey Global Institute (MGI) and McKinsey’s Strategy Practice,¹ we strive to paint a picture of the road ahead, how it differs from the one we’ve been on, and what those differences mean for senior executives as they chart a path for the years to come.

Forces at work

In an article of this length, we can only scratch the surface of the massive forces at work.² Nonetheless, even a brief look at three of the most important factors—emerging-markets growth, disruptive technology, and aging populations—is a useful reminder of the magnitude of change under way.

Dynamism in emerging markets

Emerging markets are going through the simultaneous industrial and urban revolutions that began in the 18th century in England and in the 19th century in the rest of today’s developed world. In 2009, for the first time in more than 200 years, emerging markets contributed more to global economic growth than developed ones did. By 2025, emerging markets will have been the world’s prime growth engine for more than 15 years, China will be home to more large companies than either the United States or Europe, and more than 45 percent of the companies on Fortune’s Global 500 list of major international players will hail from emerging markets—versus just 5 percent in the year 2000.

The new wave of emerging-market companies now sweeping across the world economy is not the first. In the 1970s and 1980s, many US and European incumbents were caught unaware by the swift rise of Japanese companies that set a high bar for productivity and innovation. More recently, South Korean companies such as Hyundai and Samsung have shaken up the leading ranks of high-value-added industries from automobiles to personal electronics. The difference today is that new competitors are coming from many countries across the world and in numbers that far outpace those of past decades. This new wave will be far tougher on some established multinationals. The shift in the weight of the global economy toward emerging markets, and the emergence of nearly two billion consumers who for the first time will have incomes sufficient to support significant discretionary spending, should create a new breed of powerful companies whose global expansion will take place on the back of strong positions in their home markets.

Within those markets, the locus of economic activity is also shifting, particularly in China (Exhibit 1). The global urban population is growing by 65 million a year, and nearly half of global GDP growth between 2010 and 2025 will come from 440 cities in emerging markets. Ninety-five percent of them are small and medium-sized cities that many executives haven’t heard of and couldn’t point to on a map: not Mumbai, Dubai, or Shanghai, of course, but Tianjin (China) and Porto Alegre (Brazil) and Kumasi (Ghana), among many others. Hsinchu, in northern Taiwan, is already the fourth-largest advanced-electronics and high-tech hub in the China region. In Brazil, the state of Santa Catarina, halfway between São Paulo and the Uruguayan border, has become a regional hub for electronics and vehicle manufacturing, hosting billion-dollar companies such as WEG Indústrias.

Exhibit 1

Previously unknown cities are becoming significant economic players in many emerging markets, particularly China.

• Enlarge

Technology and connectivity

From the mechanization of the Industrial Revolution to the computer-driven revolution that we are living through now, technological innovation has always underpinned economic change and disrupted the way we do things. But today is different—because we are in the “second half of the chessboard.” The phrase comes from the story told by Ray Kurzweil, futurist and director of engineering at Google, about the inventor of chess and the Chinese emperor. The inventor asked to be paid in rice: a single grain on the first square, two on the second square, four on the third, and so on. For the first half of the chessboard, the inventor was given spoons of rice, then bowls, and then barrels. The situation changed dramatically from there. According to one version of the story, the cost of the second half of the chessboard bankrupted the emperor as the continued doublings ultimately required 18 million-trillion grains of rice, enough to cover twice the surface area of the Earth. Similarly, the continuation of Moore’s law means that the next 18 months or so will bring a doubling of all the advances in computational power and speed we’ve experienced from the birth of the transistor until today. And then it will happen again. We’re accustomed to seeing Moore’s law plotted on a logarithmic scale, which makes all this doubling look smooth. But we don’t buy computers logarithmically. As power increases, prices decrease, devices proliferate, and IT penetration deepens, aggregate computing capacity surges at an eye-popping rate: we estimate the world added roughly 5 exaflops of computing capacity in 2008 (at a cost of about $800 billion), more than 20 in 2012 (to the tune of just under $1 trillion), and is headed for roughly 40 this year (Exhibit 2).

Exhibit 2

Businesses and consumers will add roughly 40 exaflops of computing capacity in 2014, up from 5 in 2008 and less than 1 in 2005.

• Enlarge

These extraordinary advances in capacity, power, and speed are fueling the rise of artificial intelligence, reshaping global manufacturing, ³ and turbocharging advances in connectivity. Global flows of data, finance, talent, and trade are poised to triple in the decade ahead, from levels that already represent a massive leap forward.⁴ For example, less than 3 percent of the world’s population had a mobile phone and less than 1 percent was on the Internet 20 years ago. Today, more than two-thirds of the world’s population has access to a mobile phone, and one-third of it can communicate on the Internet. As information flows continue to grow, and new waves of disruptive technology emerge, the old mind-set that technology is primarily a tool for cutting costs and boosting productivity will be replaced. Our new intuition must recognize that businesses can start and gain scale with stunning speed while using little capital, that value is shifting between sectors, that entrepreneurs and start-ups often have new advantages over large established businesses, that the life cycle of companies is shortening, and that decision making has never had to be so rapid fire.⁵

Aging populations

Simultaneously, fertility is falling and the world’s population is graying dramatically (Exhibit 3). Aging has been evident in developed economies for some time, with Japan and Russia seeing their populations decline. But the demographic deficit is now spreading to China and will then sweep across Latin America. For the first time in human history, the planet’s population could plateau in most of the world and shrink in countries such as South Korea, Italy, and Germany.

Exhibit 3

Aging populations in much of the developed world and China will create long-term growth headwinds.

• Enlarge

Thirty years ago, only a few countries had fertility rates considerably below those needed to replace each generation (approximately 2.1 children per woman), comprising only a small share of the global population. But by 2013, about 60 percent of all people lived in such countries.⁶ This is a sea change. Germany’s Federal Statistical Office expects that by 2060 the country’s population will shrink by up to one-fifth and that the number of people of working age will fall to 36 million (from roughly 50 million in 2009). Thanks to rigorous enforcement of the one-child policy, the size of China’s core, working-age population probably peaked in 2012. In Thailand, the fertility rate has fallen from 6.1 in 1960 to 1.4 in 2012. These trends have profound consequences. Without a boost in productivity, a smaller workforce will mean lower consumption and constrain the rate of economic growth. (For more on these dynamics, see “A productivity perspective on the future of growth.”)

The great collision

Declaring an inflection point, particularly when the underlying forces at work have been operating for some time, is a major claim. What justifies it, we believe, isn’t just the growing pace and scale of these forces, but the ways in which they are coming together to change the dynamics we are accustomed to experiencing on both the demand and the supply side of the global economy.

On the demand side, since the 1990s we’ve been enjoying a virtuous cycle of export-led emerging-market growth that created jobs, raised incomes, and generated enormous opportunities in those markets, while also reducing prices for goods in developed ones and enabling faster consumption growth in the West. For example, in the United States, real prices for nonpetroleum imports fell more than 30 percent between the early 1990s and today. As emerging markets get richer, it will be harder for them to play the low-cost-labor arbitrage game, making it critical for local consumers to emerge as growth drivers in place of ever-rising exports to developed markets. It will also be harder for Western consumers to continue enjoying de facto gains in living standards resulting from ever-falling import prices. As all this happens, trade between emerging markets, already on the rise, should continue growing in importance.

On the supply side, we’ve been operating for many years on a two-track productivity model, with developed markets continually pushing forward and emerging markets playing catch-up. Emerging markets are still less productive than developed ones, and those with capital-intensive catch-up models will find them difficult to maintain as their economies become more consumer and service oriented. As anyone who has seen row after row of empty brand-new high-rise apartments in overbuilt Chinese fringe cities can attest, the transition from investment-led growth is unlikely to be smooth, even for countries like China with explicit policies aimed at shifting to more consumer- and service-oriented economies. On the other hand, digitization and mobile technologies should provide a platform for product and service innovation, as we are already seeing in Africa, where 15 percent of transactions are carried out via mobile banking (versus 5 percent in developed markets), and in China, where Alibaba has proved that consumer online markets can take on unprecedented scope and scale.

How these interdependencies in supply and demand will play out is far from clear. We’ve modeled optimistic and pessimistic global GDP scenarios for a decade from now. They diverge by more than $17 trillion,⁷ a spread approaching the size of current US GDP. Variables at play include the pace and extent of the shift to emerging-market consumers as the critical global growth engine, the adjustment of developed markets to a world where they can no longer draft off the combined benefits of low-cost imports and low-cost capital enabled by emerging markets, and the emergence of new productivity solutions as developed and emerging markets alike try to advance the frontier in response to their demographic and other growth challenges.

It’s likely that different regions, countries, and individuals will have different fates, depending on the strength and flexibility of their institutions and policies. Indeed, we’re already seeing this in portions of Southern and Eastern Europe that remain mired in recession and debt and in the United States, where some local governments are on the verge of failure as their economic bases can’t keep up with the needs of their aging populations. Similarly, as aging boosts the importance of productivity-led growth in many emerging markets, progress will be uneven because many known productivity solutions depend on effective regulatory regimes and market mechanisms that are far from standard in emerging markets.

Given the multiple stresses that are occurring at once in the global economy, we should not expect uniform success—but neither should we become too pessimistic. The massive pressures created by the dynamism of emerging markets, technological change, and rapid aging will help stimulate the next era of innovation and growth in a variety of areas. They will include the more productive natural-resource use that will be necessary to support the world’s growing global consuming class, the more efficient use of capital, and the more creative management of talent.

Management implications

Emerging on the winning side in this increasingly volatile world will depend on how fully leaders recognize the magnitude—and the permanence—of the coming changes and how quickly they alter long-established intuitions.

Setting strategic direction

McKinsey research suggests that about two-thirds of a company’s growth is determined by the momentum—the underlying growth, inflation, income, and spending power—of the markets where it competes. Harnessing market momentum in the years ahead will require covering more geographies, more industries, and more types of competitors, prospective partners, and value-chain participants—as well as more governmental and nongovernmental stakeholders. Rather than thinking of a primary national market broken into three to five value segments, tomorrow’s strategist must comprehend a world where offerings may vary by city within a country, as well as by distribution channel and demographic segment, with aging and income inequality necessitating increasingly diverse approaches. All this will place a premium on agility: both to “zoom out” in the development of a coherent global approach and to “zoom in” on extremely granular product or market segments.

The importance of anticipating and reacting aggressively to discontinuities also is rising dramatically in our increasingly volatile world. That means monitoring trends, engaging in regular scenario-planning exercises, war-gaming the effects of potential disruptions—and responding rapidly when competitive conditions shift. For example, few of the traditional mobile-phone manufacturers protected themselves against Apple’s disruption via the iPhone. Samsung, however, managed to turn that revolution into an opportunity to rise dramatically in the mobile-phone league tables.

Finally, the strategist increasingly needs to think in multiple time frames. These include a company’s immediate tactics and ongoing improvements to counteract new competitive threats, market selection and emphasis given current capabilities and competitive positions, investments to enhance capabilities within the current strategic construct and to enable entry into adjacent markets, and, for the longest term, the selection and pursuit of new, long-lived capabilities. The latter point is worthy of emphasis—advances in technology and the interconnectedness of geographic and product markets make the half-life of “normal” competitive advantages very short indeed. This puts a premium on the selection and development of difficult-to-replicate capabilities. (For more, see Dan Simpson’s essay in “Synthesis, capabilities, and overlooked insights: Next frontiers for strategists.”)

Building new management muscle

It will be increasingly difficult for senior leaders to establish or implement effective strategies unless they remake themselves in the image of the technologically advanced, demographically complex, geographically diverse world in which we will all be operating.

Everyone a technologist. Technology is no longer simply a budget line or operational issue—it is an enabler of virtually every strategy. Executives need to think about how specific technologies are likely to affect every part of the business and be completely fluent about how to use data and technology. There is a strong argument for having a chief digital officer who oversees technology as a strategic issue, as well as a chief information officer, who has tended to be in charge of the nuts and bolts of the technology the company uses. Technological opportunities abound, but so do threats, including cybersecurity risks, which will become the concern of a broader group of executives as digitization touches every aspect of corporate life.

Managing the new workforce. Technology is increasingly supplanting workers, and the pace of IT innovation is transforming what constitutes work as well as how, when, and where we work. MGI research suggests that as many as 140 million full-time knowledge workers could be displaced globally by smart machines—at the same time aging workforces are becoming commonplace and labor shortages are emerging for pockets of technical expertise. New priorities in this environment include ensuring that companies are using machine intelligence in innovative ways to change and reinvent work, building the next-generation skills they need to drive the future’s tech-led business models, and upskilling and retraining workers whose day-to-day activities are amenable to automation but whose institutional knowledge is valuable. (For more on artificial intelligence, see “Manager and machine: The new leadership equation.”)

For workers with more replicable skills, there’s a danger of doing less well than their parents—which will create social stresses and challenge managers trying to energize the entire workforce, including employees dissatisfied about falling behind. Developed and emerging markets will experience different flavors of these issues, making the people side of the equation particularly challenging for geographically dispersed organizations. (For more, see “The past and future of global organizations.”)

Rethinking resources. The convergence of IT and materials science is spawning a surge in innovation that will dramatically change when, where, and how we use natural resources. In their new book Resource Revolution, our colleague Matt Rogers and his coauthor, McKinsey alumnus Stefan Heck, argue that combining information technology, nanoscale materials science, and biology with industrial technology will yield substantial resource-productivity increases. Taken together, those improvements represent an extraordinary wealth-creation opportunity and will be the key to achieving high-productivity economic growth in the developing world to support billions of new members of the global middle class. Capturing these resource-technology opportunities will require new management approaches, such as substitution (replacing costly, clunky, or scarce materials with less scarce, cheaper, and higher-performing ones), optimization (embedding software in resource-intensive industries to improve, dramatically, how companies produce and use scarce resources), and virtualization (moving processes out of the physical world).⁸

Breaking inertia

Change is hard. Social scientists and behavioral economists find that we human beings are biased toward the status quo and resist changing our assumptions and approaches even in the face of the evidence. In 1988, William Samuelson and Richard Zeckhauser, economists at Boston University and Harvard, respectively, highlighted a case in which the West German government needed to relocate a small town to mine the lignite that lay beneath. The authorities suggested many options for planning the new town, but its citizens chose a plan that looked “extraordinarily like the serpentine layout of the old town—a layout that had evolved over centuries without (conscious) rhyme or reason.”⁹

Businesses suffer from a surprising degree of inertia in their decisions about how to back up strategies with hard cash to make them come to fruition. Research by our colleagues showed that between 1990 and 2010, US companies almost always allocated resources on the basis of past, rather than future, opportunities. Even during the global recession of 2009, this passive behavior persisted. Yet the most active companies in resource allocation achieved an average of 30 percent higher total returns to shareholders annually compared with the least active.¹⁰ The period ahead should raise the rewards for moving with agility and speed as digitization blurs boundaries between industries and competition in emerging markets heats up.

It would be easy, though, for organizations and leaders to become frozen by the magnitude of the changes under way or to tackle them on the basis of outdated intuition. Taking the long view may help. In 1930, the great British economist John Maynard Keynes boldly predicted that 100 years on, the standard of living in progressive countries would be four to eight times higher. As it turned out, the upper end of his optimistic expectation turned out to be closer to the truth. Those who understand the depth, breadth, and radical nature of the change and opportunity that’s on the way will be best able to reset their intuitions accordingly, shape this new world, and thrive.

View at the original source