The bad news was that all this expertise was scattered in different business units, each with its own strategic objectives, its own profit and loss incentives, and its own culture. These divisions operated independently. The corporate operating philosophy was one of extreme decentralization. Business unit heads were like CEOs of their own independent companies. Whatever interactions the divisions had in the past were done at arm’s length (e.g., the custody division provided custodial services to the money management business unit on the exact same terms as all its other customers). Some of this was done for good legal and regulatory reasons, but some of it was a choice of business philosophy. While all the groups worked in reasonably close proximity to one another (all within the same major metropolitan city), they had never had reasons to interact before. And they had no incentives to do so. Bonuses were based strictly on how your business performed.
Even more challenging were the deep cultural divides between the groups. The investment banking unit and the money management unit were classic Wall Street cultures, dominated by MBAs and PhDs from top programs whose compensation depended heavily on performance-based bonuses. In both groups, speed was everything. They had to be able to pounce on market trends and move quickly in and out of positions. The custody group was composed of IT systems specialists and accountants. Pay scales differed between the two groups by an order of magnitude. For the custody group, reliability was everything. Systems had to be secure and 100 percent reliable. In describing the differences among the various groups, one senior executive told me, “The systems people think in terms of years and big projects. The money management folks are obsessed with quarters because that’s how they are measured. And the traders—their attention spans can be measured in seconds.” The custody people viewed the traders and money managers as arrogant; the trader and money management people viewed their counterparts in custody as dull. The traders and money management units made the bulk of the company’s profit; custody generated most of the revenue.
It is not surprising that, for several years, talk about creating new ancillary services remained just that—talk. There were no mechanisms to mesh the capabilities and expertise of these different parts of the company. The strong decentralization philosophy inhibited cross-divisional collaboration. A new group was created to coordinate cross-divisional innovation efforts, but this group lacked a budget and any direct authority over innovation projects (those all remained in the divisions). Its impact was predictably limited. The different sets of skills and know-how needed to execute its strategy remained trapped in their own silos.
The company had a great strategy for exploiting information. It was in many ways decades ahead of its time. It had done a great job searching for and seeing an incredible opportunity that was well within its reach. But it lacked the capability for synthesis. It could not bring the pieces together.
Over the course of my career, I came to realize that what I observed at this financial services company was not unique. It is an inherent feature of many multidivisional corporations. Each division is focused on its own business and its own innovation agenda. I’ve seen it in other financial services companies, in health-care companies whose own pharmaceutical and diagnostic divisions struggled to collaborate on the potential of genomics to transform both businesses, in medical device companies whose different market-focused business units could not collaborate on developing common technology platforms, in hospitals whose attempts to create novel services were crushed by their inability to overcome walls between different medical and surgical departments. I’ve seen it stifle innovation in food and beverage companies as different divisions battled over “ownership” of the program and in the inability of hardware and software groups within the same company to synchronize their efforts. And, coming closer to home, frankly, I see it all the time in universities.
The consequences of failures to integrate existing knowledge and capabilities from within a corporation can be severe. An ominous example is Sony. For a couple of decades, Sony was a star of the consumer electronics industry. It launched a string of hit products like the Walkman and was a pioneer in digital photography. Academics fawned over its design prowess in case studies and articles in the Harvard Business Review. Yet, as we all know today, Apple clobbered Sony in portable electronic devices (a segment invented by Sony) with the iPod and then the iPhone. Walter Isaacson’s biography of Steve Jobs has a terrific chapter on the development of the iPod that provided a vivid picture of how Sony missed the opportunity despite having had all the pieces in place. It already had a portable music player; it had expertise in critical component technologies like disk drives, displays, and batteries. Sony had its own music division with a strong ensemble of recording artists. But each of these was housed in a different division, each with its own profit and loss accountability and each with its own strategic interests. Sony had no capacity for bringing these pieces together. The head of a music company that had tried to work with Sony commented to Isaacson, “How Sony missed this is completely mind-boggling to me.… Steve [Jobs] would fire people if the divisions didn’t work together, but Sony’s divisions were at war with one another.”5
Building a Capability for Synthesis
There is a magical quality to creative synthesis. Ideas of distant ancestry find their way to a common place and then give birth to something completely new and compelling. But there is nothing magical about this process. An organization’s capacity for synthesis stems from the choices made by managers about how the organization works. We discussed examples above of how organizations are often designed to stifle synthesis. They create boundaries that block the flow of ideas across divisions or functions. They balkanize knowledge. Those examples help us understand what not to do, but how might we design organizations that are actually good at connecting ideas across markets, technical domains, and functions? Like any organizational capability, synthesis involves choices about people, processes, and structures.
People: Find, Develop, and Retain the Synthesizers
Earlier I made the point that being a business polymath is not possible for most of us. We tend to focus our attention on our well-ploughed fields of expertise. This specialization is, in general, helpful to both our own careers and to our organization. Having access to a broad portfolio of experts from different disciplines or backgrounds is probably one of the best ways to ensure your organization gets exposed to a rich diversity of innovation ideas. But, ultimately, capitalizing on this diversity—finding the novel combinations of seemingly disparate ideas that form the basis of transformative innovations—requires individuals who can span boundaries, who have the capacity to sort and filter ideas from different fields and see how they connect. These are people good at what I call “intellectual arbitrage.” They leverage ideas across fields. They can see how a theory in physics might apply to biology, how the configuration of our brain’s neurons might provide an apt analogy for computer architectures, how a business model designed for the consumer products industry might work for Internet-based advertising, or how a product concept for subcompact cars might revolutionize light jets. Posed simply, for an organization to be good at synthesis, it needs good synthesizers.
Some of the greatest inventors, scientists, and entrepreneurs of all time were spectacular synthesizers. Leonardo da Vinci, Isaac Newton, Thomas Edison, Nikola Tesla, Guglielmo Marconi, Albert Einstein, Henry Ford, James Watson and Francis Crick, Bell Labs’ John Bardeen, and Steve Jobs were all incredible at seeing connections across fields. These are, of course, extreme examples of individuals with extraordinary intellects. The good news is that you do not have to be or have access to the likes of these geniuses to be a successful innovator. They are out there among us in “mere mortal” form, and innovating companies find them and cultivate them. In the drug industry, scientists who are particularly good at discovering drugs are called “drug hunters.” Successful drug hunters are examples of good synthesizers. They integrate insights from chemistry, biology, physiolog
y, and clinical practice to “see” molecular structures that might treat a particular disease. Drug hunters not only need to understand quite a bit about these different disciplines, but they need to be able to connect insights across them. In the entertainment industry, the animation studio Pixar fuses the highly technical world of computer graphics with the highly creative world of motion picture animation because it has people throughout its organizations—software engineers, technical artists, editors, or directors—who themselves bridge those worlds. Pixar has software engineers with interests (and even training) in art, and it has artists who understand technical aspects of computer graphics. The company’s cofounder and CEO, Ed Catmull, is perhaps the epitome of this fusion. A PhD in computer science, Catmull had dreamed of becoming a film animator since childhood.
Paolo Fazioli, the founder of Fazioli Pianoforte S.p.A, an Italian company making ultra-high-end pianos played by some of the world’s leading concert pianists, is an example of a person who spawned innovation by bridging different worlds. After graduating from the University of Rome with a degree in mechanical engineering, Fazioli, a gifted pianist from an early age, went on to study both piano and music composition. He dreamed of becoming a concert pianist. He earned a degree in piano (from the Conservatorio Gioachino Rossini) and a master’s degree in music composition (Academy of St. Cecilia).6 Despite his considerable musical talents, Paolo realized that a career as a professional concert pianist was beyond his reach. At that point, he joined the family furniture business, which specialized in office furniture made from exotic woods. Within a few years, though, Paolo left the company to start his own piano company with the mission to engineer and produce the best-sounding pianos in the world. This mission required Fazioli to integrate three different streams of know-how. The first was music and the piano in particular. How should a piano feel when it was played? How should it sound? He started by studying contemporary grand pianos to understand how their structure and design influenced their performance. The second was engineering. A piano is a highly complex piece of mechanical and acoustical equipment, consisting of thousands of parts. The design of each part, the mechanical connections between them, and choices of materials shape the performance and sound of the piano. Fazioli conducted extensive experiments on different types of wood to better understand their acoustical properties (Fazioli was fortunate that his first piano factory, located in Sacile, Italy, is only about a hundred miles from the forests of Fiemme Valley, where Antonio Stradivari sourced wood for his violins). Fazioli was helped by the fact that his brother was an expert in wood technology. The final piece of the puzzle was manufacturing. The best-designed piano will be ruined by a poorly designed manufacturing process. Choices about myriad processes (such as joining techniques, wood forming processes, and finishing) impact a piano’s performance. Here, Fazioli was able to draw on his own and his family’s expertise in precision-crafted wood furniture (Fazioli’s first piano factory was actually located inside the family’s furniture factory). Today, Paolo Fazioli plays every piano before it leaves the company’s factory.
How can you identify, attract, and cultivate synthesizers? Identifying them is actually not that hard. There is usually something revealing in their educational background, career path, or even personal interests that has caused them to cross fields. Maybe they are like Catmull, who studied and became proficient in one field and yet had dreams of another. Or maybe they are like Fazioli, who studied music and engineering (or other pairs of seemingly distant fields). Or maybe they are like the Israeli scientist I recently interviewed who was trained in physics and computer science but became interested in how principles from these fields could be used to study the evolution of bacteria. He would now describe himself as a “systems biologist” (a description well supported by his publication record) even though he has no formal training in biology. Career-path movements and personal history can also hint at people’s potential to be a synthesizer. Have they always worked in the same industry or in the same function for most of their career? That provides great depth but probably doesn’t help them understand insights from other industries or functions, let alone leverage those creatively.
One problem that many organizations have in recruiting potential synthesizers is that they tend to undervalue people from “nonconforming” backgrounds. The “standards” established by many human resource departments are exactly that—standards. They create uniformity. They filter out the people whose backgrounds just don’t quite fit the mold. And, in so doing, they screen out your future potential synthesizers.
Career paths within organizations can also be used to cultivate synthesizers. Whenever I am working in an organization doing research or consulting, I always ask the most senior people I meet about their previous positions inside the company: “How did you get to this position?” I am less interested in how they “climbed the ladder” than in how they moved across it. I am always surprised by how many companies’ career ladders are etched within functional or divisional silos. Sales and marketing people enter and rise through the commercial ranks. R&D people stay in R&D—and often within their own technical or scientific disciplines. Movements away from your specialty are viewed as signs of failure (i.e., you weren’t good enough to “cut it” in, say, R&D so you got “repurposed” to operations or marketing). From an innovation point of view, this model of human resource management is a disaster. It completely destroys your ability to promote and develop synthesizers. In fact, if the most capable people stay within their silos, it means that the only people moving across boundaries are your weakest players. And this means that the people potentially most important for your innovation efforts are not your strongest.
There is nothing easy about synthesis or being a synthesizer. Synthesizers have to be incredibly capable people. They need intellectual firepower across multiple domains and a habit of mind that can entertain complexity, contradictions, and ambiguity. They need the capacity to both learn from and communicate to specialists from diverse fields. This type of work requires people with the right backgrounds and temperament. It also requires recognition. The synthesizers in your organization should be among the most-coveted (and best-rewarded) positions.
Processes: Design for Exploration and Experimentation
The act of combining diverse streams of knowledge and experience is inherently unpredictable. And the more diverse the contributing streams get, the more unpredictable the outcome of the process becomes. Consider a cooking analogy. Let’s say you have a lot of experience preparing Italian cuisine. You not only have a long repertoire of recipes, but you also have a good sense of which ingredients pair well with others (fresh egg pasta with butter and cream-based sauces; dry pasta with olive oil–based sauces, etc.). This familiarity helps you improvise and come up with new dishes that dazzle your friends. In fact, you are planning to dazzle a group tonight, and so you head to your favorite Italian food specialty market to see what looks good. To your horror, you discover the store is closed in celebration of the feast of Saint Anthony, and it is now too late to find another Italian market. Next door, though, is an Indian food market. In a fit of culinary desperation, you decide to prepare an Indian meal despite your utter lack of familiarity with this cuisine and the fact that you lack an Indian cookbook. You realize you will have to innovate, and since you have so far read up through Chapter 5 of this book, you know a good strategy is to search broadly in the market for a variety of ingredients and spices. When you get home, you will have just enough time to try a few experiments with different combinations of ingredients to see which ones come out best. As you can well imagine, this is a risky strategy. Unlike when you prepare Italian food, you have no prior experience about what combinations of ingredients yield sumptuous sauces and which produce acrid ones. You will have to learn through trial and error, and you do not even know where to start. The large list of ingredients you brought home provides a lot of potential combinations to explore. This is, of course, both the good news and bad news. Th
e good: some random combinations of these ingredients may produce extraordinary results. The bad news: you have a lot of possible combinations to test, and many are likely to turn out horribly! To use the terminology, you are facing a high-variance outcome. You might win big, but the odds are stacked against you.
Lee Fleming’s research (mentioned earlier) suggests that a similar dynamic is at work when it comes to combining different “ingredients” (component technologies, etc.) for innovation. He found that the blockbuster innovations tend to result from exploring more novel combinations of component technologies that are also highly interdependent (that is, each one influences the performance of the other). But this approach is also the riskiest in that it usually results in failure. If you get the combination “just right,” you may just have the big winner, but getting it just right is very difficult if the ingredients are unfamiliar to you.
Because the right combination of “ingredients”—whether they are component technologies, capabilities, engineering methods and principles, or business concepts—cannot be predicted in advance, creative synthesis demands a fluid innovation process rather than the more structured processes that have become popular over the past decade. Let’s contrast these two approaches. Structured approaches like phase-gate models (and the waterfall variant used in software engineering) typically prescribe a detailed sequence of steps and activities for every innovation project. These are generally grouped into well-defined “phases” (e.g., concept development, detailed engineering) delineated by criteria for a project to move from one phase to the next. These structured models draw their inspiration from manufacturing processes. Indeed, they aim to make innovation as predictable as manufacturing.
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