The Design of Business: Why Design Thinking Is the Next Competitive Advantage Read online

  As A. G. Lafley has demonstrated at Procter & Gamble, however, even organizations with a deeply ingrained bias toward analysis and mastery can develop powerful capacities for innovation. With determined leadership, they can develop the skills, structures, and processes that generate value by driving valuable insights along the knowledge funnel. Figure 1-1 shows how knowledge proceeds through the funnel.

  The first stage of the funnel is the exploration of a mystery, which takes an infinite variety of forms. A research scientist might explore the mystery of a syndrome such as autism. A hospital administrator might ask what kind of space would improve the condition of cancer patients coping with chemotherapy. Or an ambitious salesman might ask how and what Americans would like to eat on the go.

  FIGURE 1-1

  The knowledge funnel

  The next stage of the funnel is a heuristic, a rule of thumb that helps narrow the field of inquiry and work the mystery down to a manageable size. The heuristic may be a genetic anomaly, a user-centered approach to the process flow of a chemotherapy patient, or the concept of a quick-service, drive-through restaurant. It is a way of thinking about the mystery that provides a simplified understanding of it and allows those with access to the heuristic to focus their efforts.

  As an organization puts its heuristic into operation, studies it more, and thinks about it intensely, it can convert from a general rule of thumb (Americans want a quick, convenient, tasty meal) to a fixed formula (Kroc’s totally systematized McDonald’s). That formula is an algorithm, the last of three stages of the knowledge funnel. (For another perspective on the path through the knowledge funnel, see “Hunches, Heuristics, and Algorithmics: A Quick Note.”)

  Each stage of the knowledge funnel has its own unique features that are worth examining in some detail. It is said that the road to wisdom begins with ignorance, and that is where we begin.

  It Starts with a Question

  Over the course of time, phenomena enter our collective consciousness as mysteries—things in our environment that excite our curiosity but elude our understanding. The mystery of what we now know as gravity confounded our ancestors: when they looked around them, they saw that most objects—apples, famously—seemed to fall to the ground quickly; but others, such as leaves, seemed to take forever to reach the ground. And then there were birds, which didn’t seem to fall at all. In the visual arts, one of the most enduring mysteries was how to represent what we see in front of us in three dimensions on a two-dimensional surface. In both cases, people struggled for centuries to come to an understanding of the phenomena. Even the most baffling mysteries, though, eventually crumble under the force of human intelligence. With sufficient thought, a first-level understanding emerges from the question at hand. We develop heuristics—rules of thumb—that guide us toward a solution by way of organized exploration of the possibilities.

  Hunches, Heuristics, and Algorithmics:

  A Quick Note

  by Mihnea Moldoveanu

  Being precise about concepts is important, because they are the critical building blocks of any human enterprise, intellectual and otherwise. One way to analyze concepts is to describe the ways in which they are used in particular situations—that is, to highlight their “use cases.”

  The route out of a mystery begins with a hunch. Hunches are prelinguistic intuitions. You are in a dense fog high up in the Rocky Mountains. Darkness is on its way. You can see no more than five feet ahead. As you worry about your next step—and the rest of the way—your peripheral vision “sees” a slanted spruce at 11:00, and you experience a “sense” that you should turn right. If someone were to ask you at the time, “Why do you want to go right?” you could not, of course, answer the question in a way that would seem objective to that person. “Just a hunch,” you might say. “Something beyond words.” You turn and you get safely to the lodge. You never become aware of the fact that you have seen this tree before, on the way to the woods. The hunch remains a hunch. It remains beyond words but not, obviously, beyond either reason or sense.

  Heuristics are open-ended prompts to think or act in a particular way. For instance, “Look in the rearview mirror before passing,” “Go with the first instinct when trying to decide if someone is lying to you in a face-to-face interaction” (this is a heuristic that recognizes the value of a hunch), or “Buyer beware!” Heuristics offer no guarantee that using them produces a certain result. Rather, they contain the vague promise that, all things being equal, using the heuristic in the context it is meant for may, or on average, will be better for you than not using it. Heuristics are different from hunches in that they are explicit: they bring intuitions to language.

  Algorithms are certified production processes. They guarantee that, in the absence of intervention or complete anomaly, following the sequence of steps they embody will produce a particular result. For instance, an algorithm (PRIME_SEARCH) designed to figure out if a given number is a prime number—by brute force—will systematically try to divide that number by every number smaller than itself and return the answer “PRIME” if no divisor is found and “Divisor = …” if a divisor is found. Algorithms differ from heuristics in that they offer a performance guarantee that comes along with using them: you cannot use the algorithm PRIME_SEARCH on the number 209870987403987 and not get an answer, except if some catastrophe intervenes and stops you from executing the steps prescribed by the algorithm.

  Consider the falling objects. After a long period of observation and contemplation, human beings in various cultures more or less simultaneously developed the notion of a universal force that tends to pull physical objects earthward. Understanding advanced from a mystery—why do things fall to earth?—to a heuristic or a rule of thumb for explaining why things fall: a force we call gravity causes things to fall to earth.

  In art, after literally centuries of questioning and experimentation, the heuristic of perspective emerged as a solution to the mystery of three-dimensional representation. First, in about the fifth century BC, came a tool called skenographia, which historians conjecture was developed by Greek dramatists to make their sets appear to have depth. A heuristic had begun to emerge.

  Heuristics represent an incomplete yet distinctly advanced understanding of what was previously a mystery. But that understanding is unequally distributed. Some people remain stuck in the world of mystery, while others master its heuristics. The beauty of heuristics is that they guide us toward a solution by way of organized exploration of the possibilities. With a heuristic to guide his further thought and consideration, the great scientist Sir Isaac Newton derived precise rules for determining how fast an object will fall under any circumstance. Newton’s rule—that an object dropped from any height will accelerate at a constant rate of 32 feet per second squared—advanced the understanding of gravity to the third stage, the algorithm. An algorithm is an explicit, step-by-step procedure for solving a problem. Algorithms take the loose, unregimented heuristics—which take considerable thought and nuance to employ—and simplify, structuralize, and codify them to the degree that anyone with access to the algorithm can deploy it with more or less equal efficiency.

  As with gravity, the algorithm for perspective took centuries to develop. By the eleventh century AD, early physicists had arrived at the understanding that the conical shape of the eye influences how we see three-dimensional objects. A few centuries later, the Florentine painter and architect Filippo Brunelleschi studied the heuristic until he innovated a repeatable method—an algorithm—that allowed him and other artists to reliably create the illusion of three-dimensional space.

  As understanding moves from mystery to heuristic to algorithm, extraneous information is pared away; the complexities of the world are mastered through simplification. That is why my graphic model of the advance of knowledge is a funnel that tapers as knowledge moves through its stages of refinement. The gain in understanding comes from picking salient features of the environment and out of them constructing a causal explanation of the
mystery. From the inchoate phenomenon of falling objects came the concept of a universal force that pulls things earthward, which in turn was painstakingly developed, through trial and error, into a simple formula that described the unchanging properties of this once-mysterious force.

  There’s significant value to pushing knowledge to the algorithm stage. It is quite handy to have at one’s disposal a logical, arithmetic, or computational procedure that, if correctly applied, guarantees success. When Brunelleschi created the precise “vanishing point” algorithm for perspective during the first two decades of the fifteenth century, he provided a significant advantage to the Florentine artistic community until the algorithm became more widely disseminated and understood.

  The ultimate destination of algorithms as of the late twentieth century is computer code. Once knowledge has been pushed to a logical, arithmetic, or computational procedure, it can be reduced to software. Armed with the algorithm for gravity, clever engineers at Honeywell were able to create autopilot systems for giant commercial aircraft so that they could be made to fall out of the sky in a passenger-friendly fashion without human intervention. And what about Brunelleschi’s algorithm for perspective? Computers now use the three-dimensional data transferred from a camera to spit out a two-dimensional representation of it based on the formulas handed down by Brunelleschi and codified in matrix-multiplication software.

  Of course, not every mystery can become an algorithm; not all logic can be pushed through to the end of the funnel. Consider the mystery of the oldest art, music. How can certain arrangements of notes, timbres, and rhythms have such a profound effect on our emotions, and how can we harness that power to soothe or rouse our listeners? Norman Greenbaum stumbled on the answer to that mystery once and once only, coming up with the 1969 smash “Spirit in the Sky.” Wildly catchy and instantly recognizable, the song continues to spin off royalties that provide Greenbaum with a comfortable living. But the mystery of the hit song remains just that for Greenbaum. He has never produced a follow-up to the fuzzed-up hippie spirituality of “Spirit in the Sky.”

  Contrast Greenbaum’s career with that of U2, the band that developed a heuristic—a way of understanding the world and conveying that understanding through harmony, melody, and rhythm—that enables it to write songs that resonate with millions of people worldwide, not once but over and over. From the release of the earnest, anthemic album Boy in 1980 to the eclectic pleasures of Achtung Baby in 1991, U2’s mastery of heuristics produced a string of industry awards and top-forty hits. But when the band consciously stepped away from the heuristic that had served it so well—experimenting with techno, dance, and electronica on Zooropa and Pop—fans promptly voted with their feet. When, in 2000, the band reunited with producers Brian Eno and Daniel Lanois to record All That You Can’t Leave Behind, it also returned to its pre-Zooropa heuristic, leading to Bono’s famous remark at that year’s Grammy Awards: “The whole year has been quite humbling,” he said. “Going back to scratch, reapplying for the job. What job? The best-band-in-the-world job.” 2 The heuristic still worked; Rolling Stone called All That You Can’t Leave Behind U2’s third masterpiece (after The Joshua Tree and Achtung Baby). 3

  Yet even U2’s greatest albums contain some forgettable songs; its mastery of the heuristics of the pop song falls short of a surefire algorithmic formula. The occasional failures of a serial hit maker like U2 tell us something important about heuristics: they don’t guarantee success. Heuristics can do no more than increase the probability of getting to a successful outcome or at least getting there more quickly.

  Thus far at least, pop music has proven resistant to advance from heuristic to algorithm. But there have been movements in that direction: in the late 1970s, musical innovators like producer Brian Eno experimented with the sound of the human heart and determined that songs with a synthesized heartbeat as their rhythm track are instinctively enjoyed by listeners, no matter what musical setting sits atop the heartbeat. As a producer, he was able to help bands turn out hits in a variety of genres, from the jittery dance-pop of Talking Heads’ “Once in a Lifetime” to the orchestral strings of Coldplay’s “Viva la Vida” to those massively successful U2 albums. Other producers in search of a success algorithm created a succession of disposable boy bands, pop princesses, or lip-synching electro-pop acts like Milli Vanilli. And even now we have the mass populism of Simon Fuller’s American Idol, which has produced bona fide stars in Kelly Clarkson and Carrie Underwood, and a few forgettable flashes in the pan. The algorithm remains elusive. There is still nothing close to a formula for producing consistent success in the music business. Yet.

  Back to McDonald’s

  Incongruous as it might sound, the McDonald brothers and Ray Kroc followed the same path that Newton and Brunelleschi trod as they built their business from a single drive-in to a global enterprise. Their journey began with the question that so perplexed the McDonald brothers as they watched a new culture grow up around them: what and how did the mobile, leisured, mass middle class of southern California want to eat? That was their mystery.

  The brothers devised an answer by focusing on a specific facet of that emerging culture—the consumers’ desired out-of-home eating experience. The heuristic they developed—a quick-service restaurant with strictly limited menu options—emerged when they narrowed the field of possibilities to a manageable set of salient features. In doing so, the McDonald brothers discovered a way to create value from their understanding of their world.

  Kroc then picked up the baton, driving that understanding—that heuristic—all the way to an algorithm by continuing to cut away vast tracts of possibility. Hamburgers could be charbroiled or pressure-cooked. The menu could be broad or narrow. Restaurants could be smaller or larger. Ultimately, Kroc plucked one answer along innumerable dimensions to construct McDonald’s defining algorithm. Once that algorithm was in place, Kroc pushed it as far as it would go, adapting its elements to changing markets and economic conditions, but leaving its essential outlines unchanged.

  The Creation of Value in Business

  The McDonald’s story illustrates important elements of the dynamics of the march of knowledge from mystery to heuristic to algorithm: the paring away of information and the simplification of the world’s complexities. The gain in understanding comes from picking out salient features of the environment and out of them building a causal understanding of it: “I think that Californians would like a quick-service hamburger joint.” The heuristic doesn’t attempt an encyclopedic understanding of the new Californian beach culture and how the freeway system brought it into being. It focuses instead on a specific facet of that culture: the consumers’ desired out-of-home eating experience.

  To create an algorithm from that heuristic requires clear-cutting more vast tracts of possibility. Ultimately, one answer along innumerable dimensions had to be plucked to provide McDonald’s’ defining algorithm. Judgment was removed, possibilities were removed, and variety was removed.

  What is the value to a business of driving through the knowledge funnel from mystery to heuristic to algorithm? The reward is a massive gain in efficiency. By paring away possibilities from the mystery of what and how Californians want to eat to the limited menu, drive-through, quick-service burger joint, the McDonald brothers could focus on a few important things and replicate the model several times over, extending its success.

  When Kroc converted the heuristic into a precise algorithm, he was able to scale the chain to a size previously unimaginable. Restaurant site selection followed an efficient algorithm, so sites could be found and developed quickly in each desired locale. Staffing the restaurant was easy, because the procedures for hiring the unskilled labor needed were precisely laid out and the new employees could be readily taught the precise in-restaurant procedures from comprehensive manuals. Supply of food and beverage items to the new restaurant could be easily added to the precisely organized supply chain, making that supply chain even higher scale and more efficient.
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  By solving the mystery before its competitors, McDonald’s created an efficiency advantage. By honing and refining the heuristic, it extended that efficiency advantage. By converting that heuristic to algorithm, new owner Kroc drove the efficiency advantage still further ahead of its competitors, creating an enterprise worth billions of dollars—all from one new-style burger joint.

  A Fine Balance

  Matching McDonald’s accomplishment—and that of every other organization that creates value across the knowledge funnel—requires two very different activities: moving across the knowledge stages of the funnel from mystery to heuristic and heuristic to algorithm and operating within each knowledge stage of the funnel by honing and refining an existing heuristic or algorithm. We can map these two different activities onto the theories of the great management theorist James March, who posited that organizations may engage primarily in exploration, the search for new knowledge (in our terms, seeking movement across the knowledge stages), or exploitation, the maximization of payoff from existing knowledge (refinement within a knowledge stage). 4 Both activities can create enormous value, and both are critical to the success of any business organization. But they are hard to engage in simultaneously; most often, organizations choose to focus on one activity, either exploration or exploitation, to the exclusion of the other and to their own detriment.