The second economy

In 1850, a decade before the Civil War, the United States’ economy was small—it wasn’t much bigger than Italy’s. Forty years later, it was the largest economy in the world. What happened in-between was the railroads. They linked the east of the country to the west, and the interior to both. They gave access to the east’s industrial goods; they made possible economies of scale; they stimulated steel and manufacturing—and the economy was never the same.
Deep changes like this are not unusual. Every so often—every 60 years or so—a body of technology comes along and over several decades, quietly, almost unnoticeably, transforms the economy: it brings new social classes to the fore and creates a different world for business. Can such a transformation—deep and slow and silent—be happening today?
We could look for one in the genetic technologies, or in nanotech, but their time hasn’t fully come. But I want to argue that something deep is going on with information technology, something that goes well beyond the use of computers, social media, and commerce on the Internet. Business processes that once took place among human beings are now being executed electronically. They are taking place in an unseen domain that is strictly digital. On the surface, this shift doesn’t seem particularly consequential—it’s almost something we take for granted. But I believe it is causing a revolution no less important and dramatic than that of the railroads. It is quietly creating a second economy, a digital one.
Let me begin with two examples. Twenty years ago, if you went into an airport you would walk up to a counter and present paper tickets to a human being. That person would register you on a computer, notify the flight you’d arrived, and check your luggage in. All this was done by humans. Today, you walk into an airport and look for a machine. You put in a frequent-flier card or credit card, and it takes just three or four seconds to get back a boarding pass, receipt, and luggage tag. What interests me is what happens in those three or four seconds. The moment the card goes in, you are starting a huge conversation conducted entirely among machines. Once your name is recognized, computers are checking your flight status with the airlines, your past travel history, your name with the TSA¹ (and possibly also with the National Security Agency). They are checking your seat choice, your frequent-flier status, and your access to lounges. This unseen, underground conversation is happening among multiple servers talking to other servers, talking to satellites that are talking to computers (possibly in London, where you’re going), and checking with passport control, with foreign immigration, with ongoing connecting flights. And to make sure the aircraft’s weight distribution is fine, the machines are also starting to adjust the passenger count and seating according to whether the fuselage is loaded more heavily at the front or back.
These large and fairly complicated conversations that you’ve triggered occur entirely among things remotely talking to other things: servers, switches, routers, and other Internet and telecommunications devices, updating and shuttling information back and forth. All of this occurs in the few seconds it takes to get your boarding pass back. And even after that happens, if you could see these conversations as flashing lights, they’d still be flashing all over the country for some time, perhaps talking to the flight controllers—starting to say that the flight’s getting ready for departure and to prepare for that.
Now consider a second example, from supply chain management. Twenty years ago, if you were shipping freight through Rotterdam into the center of Europe, people with clipboards would be registering arrival, checking manifests, filling out paperwork, and telephoning forward destinations to let other people know. Now such shipments go through an RFID² portal where they are scanned, digitally captured, and automatically dispatched. The RFID portal is in conversation digitally with the originating shipper, other depots, other suppliers, and destinations along the route, all keeping track, keeping control, and reconfiguring routing if necessary to optimize things along the way. What used to be done by humans is now executed as a series of conversations among remotely located servers.
In both these examples, and all across economies in the developed world, processes in the physical economy are being entered into the digital economy, where they are “speaking to” other processes in the digital economy, in a constant conversation among multiple servers and multiple semi-intelligent nodes that are updating things, querying things, checking things off, readjusting things, and eventually connecting back with processes and humans in the physical economy.
So we can say that another economy—a second economy—of all of these digitized business processes conversing, executing, and triggering further actions is silently forming alongside the physical economy.

Aspen root systems

If I were to look for adjectives to describe this second economy, I’d say it is vast, silent, connected, unseen, and autonomous (meaning that human beings may design it but are not directly involved in running it). It is remotely executing and global, always on, and endlessly configurable. It is concurrent—a great computer expression—which means that everything happens in parallel. It is self-configuring, meaning it constantly reconfigures itself on the fly, and increasingly it is also self-organizing, self-architecting, and self-healing.
These last descriptors sound biological—and they are. In fact, I’m beginning to think of this second economy, which is under the surface of the physical economy, as a huge interconnected root system, very much like the root system for aspen trees. For every acre of aspen trees above the ground, there’s about ten miles of roots underneath, all interconnected with one another, “communicating” with each other.

The metaphor isn’t perfect: this emerging second-economy root system is more complicated than any aspen system, since it’s also making new connections and new configurations on the fly. But the aspen metaphor is useful for capturing the reality that the observable physical world of aspen trees hides an unseen underground root system just as large or even larger. How large is the unseen second economy? By a rough back-of-the-envelope calculation (see sidebar, “How fast is the second economy growing?”), in about two decades the digital economy will reach the same size as the physical economy. It’s as if there will be another American economy anchored off San Francisco (or, more in keeping with my metaphor, slipped in underneath the original economy) and growing all the while.
Now this second, digital economy isn’t producing anything tangible. It’s not making my bed in a hotel, or bringing me orange juice in the morning. But it is running an awful lot of the economy. It’s helping architects design buildings, it’s tracking sales and inventory, getting goods from here to there, executing trades and banking operations, controlling manufacturing equipment, making design calculations, billing clients, navigating aircraft, helping diagnose patients, and guiding laparoscopic surgeries. Such operations grow slowly and take time to form. In any deep transformation, industries do not so much adopt the new body of technology as encounter it, and as they do so they create new ways to profit from its possibilities.
The deep transformation I am describing is happening not just in the United States but in all advanced economies, especially in Europe and Japan. And its revolutionary scale can only be grasped if we go beyond my aspen metaphor to another analogy.

A neural system for the economy

Recall that in the digital conversations I was describing, something that occurs in the physical economy is sensed by the second economy—which then gives back an appropriate response. A truck passes its load through an RFID sensor or you check in at the airport, a lot of recomputation takes place, and appropriate physical actions are triggered.
There’s a parallel in this with how biologists think of intelligence. I’m not talking about human intelligence or anything that would qualify as conscious intelligence. Biologists tell us that an organism is intelligent if it senses something, changes its internal state, and reacts appropriately. If you put an E. coli bacterium into an uneven concentration of glucose, it does the appropriate thing by swimming toward where the glucose is more concentrated. Biologists would call this intelligent behavior. The bacterium senses something, “computes” something (although we may not know exactly how), and returns an appropriate response.
No brain need be involved. A primitive jellyfish doesn’t have a central nervous system or brain. What it has is a kind of neural layer or nerve net that lets it sense and react appropriately. I’m arguing that all these aspen roots—this vast global digital network that is sensing, “computing,” and reacting appropriately—is starting to constitute a neural layer for the economy. The second economy constitutes a neural layer for the physical economy. Just what sort of change is this qualitatively?
Think of it this way. With the coming of the Industrial Revolution—roughly from the 1760s, when Watt’s steam engine appeared, through around 1850 and beyond—the economy developed a muscular system in the form of machine power. Now it is developing a neural system. This may sound grandiose, but actually I think the metaphor is valid. Around 1990, computers started seriously to talk to each other, and all these connections started to happen. The individual machines—servers—are like neurons, and the axons and synapses are the communication pathways and linkages that enable them to be in conversation with each other and to take appropriate action.
Is this the biggest change since the Industrial Revolution? Well, without sticking my neck out too much, I believe so. In fact, I think it may well be the biggest change ever in the economy. It is a deep qualitative change that is bringing intelligent, automatic response to the economy. There’s no upper limit to this, no place where it has to end. Now, I’m not interested in science fiction, or predicting the singularity, or talking about cyborgs. None of that interests me. What I am saying is that it would be easy to underestimate the degree to which this is going to make a difference.
I think that for the rest of this century, barring wars and pestilence, a lot of the story will be the building out of this second economy, an unseen underground economy that basically is giving us intelligent reactions to what we do above the ground. For example, if I’m driving in Los Angeles in 15 years’ time, likely it’ll be a driverless car in a flow of traffic where my car’s in a conversation with the cars around it that are in conversation with general traffic and with my car. The second economy is creating for us—slowly, quietly, and steadily—a different world.

A downside

Of course, as with most changes, there is a downside. I am concerned that there is an adverse impact on jobs. Productivity increasing, say, at 2.4 percent in a given year means either that the same number of people can produce 2.4 percent more output or that we can get the same output with 2.4 percent fewer people. Both of these are happening. We are getting more output for each person in the economy, but overall output, nationally, requires fewer people to produce it. Nowadays, fewer people are required behind the desk of an airline. Much of the work is still physical—someone still has to take your luggage and put it on the belt—but much has vanished into the digital world of sensing, digital communication, and intelligent response.
Physical jobs are disappearing into the second economy, and I believe this effect is dwarfing the much more publicized effect of jobs disappearing to places like India and China.
There are parallels with what has happened before. In the early 20th century, farm jobs became mechanized and there was less need for farm labor, and some decades later manufacturing jobs became mechanized and there was less need for factory labor. Now business processes—many in the service sector—are becoming “mechanized” and fewer people are needed, and this is exerting systematic downward pressure on jobs. We don’t have paralegals in the numbers we used to. Or draftsmen, telephone operators, typists, or bookkeeping people. A lot of that work is now done digitally. We do have police and teachers and doctors; where there’s a need for human judgment and human interaction, we still have that. But the primary cause of all of the downsizing we’ve had since the mid-1990s is that a lot of human jobs are disappearing into the second economy. Not to reappear.
Seeing things this way, it’s not surprising we are still working our way out of the bad 2008–09 recession with a great deal of joblessness.
There’s a larger lesson to be drawn from this. The second economy will certainly be the engine of growth and the provider of prosperity for the rest of this century and beyond, but it may not provide jobs, so there may be prosperity without full access for many. This suggests to me that the main challenge of the economy is shifting from producing prosperity to distributing prosperity. The second economy will produce wealth no matter what we do; distributing that wealth has become the main problem. For centuries, wealth has traditionally been apportioned in the West through jobs, and jobs have always been forthcoming. When farm jobs disappeared, we still had manufacturing jobs, and when these disappeared we migrated to service jobs. With this digital transformation, this last repository of jobs is shrinking—fewer of us in the future may have white-collar business process jobs—and we face a problem.
The system will adjust of course, though I can’t yet say exactly how. Perhaps some new part of the economy will come forward and generate a whole new set of jobs. Perhaps we will have short workweeks and long vacations so there will be more jobs to go around. Perhaps we will have to subsidize job creation. Perhaps the very idea of a job and of being productive will change over the next two or three decades. The problem is by no means insoluble. The good news is that if we do solve it we may at last have the freedom to invest our energies in creative acts.

Economic possibilities for our grandchildren

In 1930, Keynes wrote a famous essay, “Economic possibilities for our grandchildren.” Reading it now, in the era of those grandchildren, I am surprised just how accurate it is. Keynes predicts that “the standard of life in progressive countries one hundred years hence will be between four and eight times as high as it is to-day.” He rightly warns of “technological unemployment,” but dares to surmise that “the economic problem [of producing enough goods] may be solved.” If we had asked him and his contemporaries how all this might come about, they might have imagined lots of factories with lots of machines, possibly even with robots, with the workers in these factories gradually being replaced by machines and by individual robots.
That is not quite how things have developed. We do have sophisticated machines, but in the place of personal automation (robots) we have a collective automation. Underneath the physical economy, with its physical people and physical tasks, lies a second economy that is automatic and neurally intelligent, with no upper limit to its buildout. The prosperity we enjoy and the difficulties with jobs would not have surprised Keynes, but the means of achieving that prosperity would have.
This second economy that is silently forming—vast, interconnected, and extraordinarily productive—is creating for us a new economic world. How we will fare in this world, how we will adapt to it, how we will profit from it and share its benefits, is very much up to us.

https://www.mckinseyquarterly.com/Strategy/Growth/The_second_economy_2853

Seven steps to better brainstorming

Companies run on good ideas. From R&D groups seeking pipelines of innovative new products to ops teams probing for time-saving process improvements to CEOs searching for that next growth opportunity—all senior managers want to generate better and more creative ideas consistently in the teams they form, participate in, and manage.
Yet all senior managers, at some point, experience the pain of pursuing new ideas by way of traditional brainstorming sessions—still the most common method of using groups to generate ideas at companies around the world. The scene is familiar: a group of people, often chosen largely for political reasons, begins by listening passively as a moderator (often an outsider who knows little about your business) urges you to “Get creative!” and “Think outside the box!” and cheerfully reminds you that “There are no bad ideas!”
The result? Some attendees remain stone-faced throughout the day, others contribute sporadically, and a few loudly dominate the session with their pet ideas. Ideas pop up randomly—some intriguing, many preposterous—but because the session has no structure, little momentum builds around any of them. At session’s end, the group trundles off with a hazy idea of what, if anything, will happen next. “Now we can get back to real work,” some whisper.
It doesn’t have to be like this. We’ve led or observed 200 projects over the past decade at more than 150 companies in industries ranging from retailing and education to banking and communications. That experience has helped us develop a practical approach that captures the energy typically wasted in a traditional brainstorming session and steers it in a more productive direction. The trick is to leverage the way people actually think and work in creative problem-solving situations.
We call our approach “brainsteering,” and while it requires more preparation than traditional brainstorming, the results are worthwhile: better ideas in business situations as diverse as inventing new products and services, attracting new customers, designing more efficient business processes, or reducing costs, among others. The next time you assign one of your people to lead an idea generation effort—or decide to lead one yourself—you can significantly improve the odds of success by following the seven steps below.

1. Know your organization’s decision-making criteria

One reason good ideas hatched in corporate brainstorming sessions often go nowhere is that they are beyond the scope of what the organization would ever be willing to consider. “Think outside the box!” is an unhelpful exhortation if external circumstances or company policies create boxes that the organization truly must live within.
Managers hoping to spark creative thinking in their teams should therefore start by understanding (and in some cases shaping) the real criteria the company will use to make decisions about the resulting ideas. Are there any absolute restrictions or limitations, for example? A bank we know wasted a full day’s worth of brainstorming because the session’s best ideas all required changing IT systems. Yet senior management—unbeknownst to the workshop planners—had recently “locked down” the IT agenda for the next 18 months.
Likewise, what constitutes an acceptable idea? At a different, smarter bank, workshop planners collaborated with senior managers on a highly specific (and therefore highly valuable) definition tailored to meet immediate needs. Good ideas would require no more than $5,000 per branch in investment and would generate incremental profits quickly. Further, while three categories of ideas—new products, new sales approaches, and pricing changes—were welcome, senior management would balk at ideas that required new regulatory approvals. The result was a far more productive session delivering exactly what the company wanted: a fistful of ideas, in all three target categories, that were practical, affordable, and profitable within one fiscal year.

2. Ask the right questions

Decades of academic research shows that traditional, loosely structured brainstorming techniques (“Go for quantity—the greater the number of ideas, the greater the likelihood of winners!”) are inferior to approaches that provide more structure.¹ The best way we’ve found to provide it is to use questions as the platform for idea generation.
In practice, this means building your workshop around a series of “right questions” that your team will explore in small groups during a series of idea generation sessions (more about these later). The trick is to identify questions with two characteristics. First, they should force your participants to take a new and unfamiliar perspective. Why? Because whenever you look for new ways to attack an old problem—whether it’s lowering your company’s operating costs or buying your spouse a birthday gift—you naturally gravitate toward thinking patterns and ideas that worked in the past. Research shows that, over time, you’ll come up with fewer good ideas, despite increased effort. Changing your participants’ perspective will shake up their thinking. (For more on how to do this, see our upcoming article “Sparking creativity in teams: An executive’s guide,” to be published in April on mckinseyquarterly.com.) The second characteristic of a right question is that it limits the conceptual space your team will explore, without being so restrictive that it forces particular answers or outcomes.
It’s easier to show such questions in practice than to describe them in theory. A consumer electronics company looking to develop new products might start with questions such as “What’s the biggest avoidable hassle our customers endure?” and “Who uses our product in ways we never expected?” By contrast, a health insurance provider looking to cut costs might ask, “What complexity do we plan for daily that, if eliminated, would change the way we operate?” and “In which areas is the efficiency of a given department ‘trapped’ by outdated restrictions placed on it by company policies?”²
In our experience, it’s best to come up with 15 to 20 such questions for a typical workshop attended by about 20 people. Choose the questions carefully, as they will form the heart of your workshop—your participants will be discussing them intensively in small subgroups during a series of sessions.

3. Choose the right people

The rule here is simple: pick people who can answer the questions you’re asking. As obvious as this sounds, it’s not what happens in many traditional brainstorming sessions, where participants are often chosen with less regard for their specific knowledge than for their prominence on the org chart.
Instead, choose participants with firsthand, “in the trenches” knowledge, as a catalog retailer client of ours did for a brainsteering workshop on improving bad-debt collections. (The company had extended credit directly to some customers). During the workshop, when participants were discussing the question “What’s changed in our operating environment since we last redesigned our processes?” a frontline collections manager remarked, “Well, death has become the new bankruptcy.”
A few people laughed knowingly, but the senior managers in the room were perplexed. On further discussion, the story became clear. In years past, some customers who fell behind on their payments would falsely claim bankruptcy when speaking with a collections rep, figuring that the company wouldn’t pursue the matter because of the legal headaches involved. More recently, a better gambit had emerged: unscrupulous borrowers instructed household members to tell the agent they had died—a tactic that halted collections efforts quickly, since reps were uncomfortable pressing the issue.
While this certainly wasn’t the largest problem the collectors faced, the line manager’s presence in the workshop had uncovered an opportunity. A different line manager in the workshop proposed what became the solution: instructing the reps to sensitively, but firmly, question the recipient of the call for more specific information if the rep suspected a ruse. Dishonest borrowers would invariably hang up if asked to identify themselves or to provide other basic information, and the collections efforts could continue.

4. Divide and conquer

To ensure fruitful discussions like the one the catalog retailer generated, don’t have your participants hold one continuous, rambling discussion among the entire group for several hours. Instead, have them conduct multiple, discrete, highly focused idea generation sessions among subgroups of three to five people—no fewer, no more. Each subgroup should focus on a single question for a full 30 minutes. Why three to five people? The social norm in groups of this size is to speak up, whereas the norm in a larger group is to stay quiet.
When you assign people to subgroups, it’s important to isolate “idea crushers” in their own subgroup. These people are otherwise suitable for the workshop but, intentionally or not, prevent others from suggesting good ideas. They come in three varieties: bosses, “big mouths,” and subject matter experts.
The boss’s presence, which often makes people hesitant to express unproven ideas, is particularly damaging if participants span multiple organizational levels. (“Speak up in front of my boss’s boss? No, thanks!”) Big mouths take up air time, intimidate the less confident, and give everyone else an excuse to be lazy. Subject matter experts can squelch new ideas because everyone defers to their presumed superior wisdom, even if they are biased or have incomplete knowledge of the issue at hand.
By quarantining the idea crushers—and violating the old brainstorming adage that a melting pot of personalities is ideal—you’ll free the other subgroups to think more creatively. Your idea crushers will still be productive; after all, they won’t stop each other from speaking up.
Finally, take the 15 to 20 questions you prepared earlier and divide them among the subgroups—about 5 questions each, since it’s unproductive and too time consuming to have all subgroups answer every question. Whenever possible, assign a specific question to the subgroup you consider best equipped to handle it.

5. On your mark, get set, go!

After your participants arrive, but before the division into subgroups, orient them so that your expectations about what they will—and won’t—accomplish are clear. Remember, your team is accustomed to traditional brainstorming, where the flow of ideas is fast, furious, and ultimately shallow.
Today, however, each subgroup will thoughtfully consider and discuss a single question for a half hour. No other idea from any source—no matter how good—should be mentioned during a subgroup’s individual session. Tell participants that if anyone thinks of a “silver bullet” solution that’s outside the scope of discussion, they should write it down and share it later.
Prepare your participants for the likelihood that when a subgroup attacks a question, it might generate only two or three worthy ideas. Knowing that probability in advance will prevent participants from becoming discouraged as they build up the creative muscles necessary to think in this new way. The going can feel slow at first, so reassure participants that by the end of the day, after all the subgroups have met several times, there will be no shortage of good ideas.
Also, whenever possible, share “signpost examples” before the start of each session—real questions previous groups used, along with success stories, to motivate participants and show them how a question-based approach can help.
One last warning: no matter how clever your participants, no matter how insightful your questions, the first five minutes of any subgroup’s brainsteering session may feel like typical brainstorming as people test their pet ideas or rattle off superficial new ones. But participants should persevere. Better thinking soon emerges as the subgroups try to improve shallow ideas while sticking to the assigned questions.

6. Wrap it up

By day’s end, a typical subgroup has produced perhaps 15 interesting ideas for further exploration. You’ve been running multiple subgroups simultaneously, so your 20-person team has collectively generated up to 60 ideas. What now?
One thing not to do is have the full group choose the best ideas from the pile, as is common in traditional brainstorming. In our experience, your attendees won’t always have an executive-level understanding of the criteria and considerations that must go into prioritizing ideas for actual investment. The experience of picking winners can also be demotivating, particularly if the real decision makers overrule the group’s favorite choices later.
Instead, have each subgroup privately narrow its own list of ideas to a top few and then share all the leading ideas with the full group to motivate and inspire participants. But the full group shouldn’t pick a winner. Rather, close the workshop on a high note that participants won’t expect if they’re veterans of traditional brainstorming: describe to them exactly what steps will be taken to choose the winning ideas and how they will learn about the final decisions.

7. Follow up quickly

Decisions and other follow-up activities should be quick and thorough. Of course, we’re not suggesting that uninformed or insufficiently researched conclusions should be reached about ideas dreamed up only hours earlier. But the odds that concrete action will result from an idea generation exercise tend to decline quickly as time passes and momentum fades.
The president, provost, and department heads of a US university, for example, announced before a brainsteering workshop that a full staff meeting would be held the morning after it to discuss the various cost-savings ideas it had generated. At the meeting, the senior leaders sorted ideas into four buckets: move immediately to implementation planning, decide today to implement at the closest appropriate time (say, the beginning of the next academic year), assign a group to research the idea further, or reject right away. This process went smoothly because the team that ran the idea generation workshop had done the work up front to understand the criteria senior leaders would use to judge its work. The university began moving ahead on more than a dozen ideas that would ultimately save millions of dollars.
To close the loop with participants, the university made sure to communicate the results of the decisions quickly to everyone involved, even when an idea was rejected. While it might seem demoralizing to share bad news with a team, we find that doing so actually has the opposite effect. Participants are often desperate for feedback and eager for indications that they have at least been heard. By respectfully explaining why certain ideas were rejected, you can help team members produce better ideas next time. In our experience, they will participate next time, often more eagerly than ever.
Traditional brainstorming is fast, furious, and ultimately shallow. By scrapping these traditional techniques for a more focused, question-based approach, senior managers can consistently coax better ideas from their teams.
McKinsey on Finance on iPad
https://www.mckinseyquarterly.com/Strategy/Strategy_in_Practice/Seven_steps_to_better_brainstorming_2767?gp=1