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Strategy advice has taken a rather negative tone of late. Consultants and scholars alike seem obsessed with eradicating bias and error in human judgment and decision-making. A virtual cottage industry has emerged to offer advice how to do that, often pushing managers to replace flawed human judgment with big data analytics and various computational tools. Given this abysmal view of human judgment, it’s no wonder that some authors have suggested that algorithms and artificial intelligence (AI) should play a greater role in strategic decisions.
No doubt bias and error are important concerns in strategic decision-making. Yet it seems quite a stretch to suggest that the original strategies developed by people like Apple’s Steve Jobs, Starbucks’ Howard Schultz, or even Walmart’s Sam Walton had much to do with error-free calculations based on big data. Their strategies, like most breakthrough strategies, emerged in settings with remarkably little data to process and little basis for calculation — situations in which the paths to value creation were highly uncertain and evidence was sparse. We are highly skeptical that debiasing decision-making, eradicating errors, or ceding strategy to AI will improve strategizing, let alone lead to breakthrough strategies.
What Do You See?
Composing valuable strategies requires seeing the world in new and unique ways. It requires asking novel questions that prompt fresh insight. Even the most sophisticated, deep-learning-enhanced computers or algorithms simply cannot generate such an outlook.
But where does the uniqueness and novelty so essential to innovative strategic thinking come from? It comes from contrarian, perhaps even “distorted,” perceptions and beliefs about reality and the “facts” that surround us. We think that venture capitalist and PayPal cofounder Peter Thiel gets it roughly right when he asks prospective entrepreneurs to tell him something they believe is true that nobody agrees with them about. If everyone believes the same thing — or if everyone uses the same variables, information, and computational tools — the logical result is computational consistency, shared conclusions, and me-too strategies. Thus, while renowned behavioral economist Daniel Kahneman and his coauthors Andrew M. Rosenfield, Linnea Gandhi, and Tom Blaser argued in a 2016 Harvard Business Review article that it is problematic that professionals “often make decisions that deviate significantly from those of their peers,” it is this seeming pathology that provides the underlying raw material — the essential ingredient — for valuable strategies. In setting strategy, deviation in judgment is a feature, not a bug.
Examples abound. In the mid-1970s, computers were used for large-scale industrial and office applications. A mass-market personal computer was a reality few envisioned to be feasible, and any number of facts, surveys, expert opinions, and data could corroborate that conclusion. Yet despite the evidence and widespread agreement, Steve Jobs, cofounder of Apple, somehow believed otherwise. Similar narratives could be told about Herb Kelleher of Southwest Airlines or Jeff Bezos of Amazon.com. All three entrepreneurs ignored current evidence to pursue a future reality that only they and perhaps a handful of others envisioned.
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It is tempting to believe that the right evidence and the right analysis will yield the right strategy. But just as customer surveys seldom lead to breakthrough products that capture the imagination of customers and markets, substantive strategy-making requires that we see well beyond the available data. As Polaroid Corp. founder Edwin H. Land once noted, “every significant invention … must be startling, unexpected, and must come into a world that is not prepared for it.” The story is no different for managers seeking to advance valuable new functional strategies — supply chain solutions, product development ideas, or marketing strategies. Paths to substantive value creation emerge from those capable of envisioning a reality that others simply can’t imagine.
We view the strategist’s task as akin to an inkblot test, where participants are presented with highly ambiguous evidence and signals that afford many possible realities, but offer no single correct answer. With such tests, the very same evidence — an ambiguous picture or set of marks — can be interpreted correctly in many different ways. Indeed, Jobs and the rest of the nascent computer industry all had the same data. But in the words of an old Apple slogan, Jobs did indeed see and “think different.” Valuable strategizing demands this novel perception — an ability to see in ambiguous cues and data what others can’t see. Strategic thinking is fueled by the novelty of our observation, not its consistency. The object of strategic thinking is not to ensure that we all observe the same information and derive the same conclusion. It is precisely the opposite: If your desire is to be a value creator, you must aspire to see what others cannot.
Strategy as Theory
This is not to say that we believe strategic thinking sits outside the realm of logic, science, and experimentation. Quite the contrary: We argue that strategic thinkers engage in an exercise that parallels that of scientists. Like scientists, they start with a significant problem to solve, and then use this problem as a prompt to compose a theory — in this case a theory of value creation. This theory then becomes their unique perspective and point of view about the opportunity they see.
One role of a theory is to shape sight and perception, to enable seeing — often from simple observations — what was previously unnoticed. As Albert Einstein observed, “whether you can observe a thing or not depends on the theory which you use.” Through a novel business theory, you see value in choices, in combinations, and in purchases that others cannot. And most importantly, like theories in science, your theory of value should lead to hypotheses and experiments that help realize opportunities unseen by others.
Of course, whether you are an entrepreneur, a corporate strategist, or a mid-level manager, generating the value you envision typically demands convincing others of the merits of your theory. Convincing others to believe in your envisioned reality over theirs is no small task. In 2009, the founders of Airbnb Inc. pitched their now-famous idea to the venture capitalist Fred Wilson and his firm Union Square Ventures, known for its prescient investments in entrepreneurial growth companies such as Twitter, Tumblr, and Kickstarter. Airbnb needed an infusion of cash, but Wilson and his partners were tremendously skeptical — and with good reason. After all, why would anyone want to stay with strangers while traveling? Why would individuals agree to rent their homes to complete strangers? And how on earth would a small startup — without any experience in the industry — take on large established players and brands in the sophisticated hotel market? Given these concerns, Wilson’s company passed on the investment opportunity. The rest, of course, is history. In 2017, Airbnb claimed more than three million listings in 65,000 cities in 191 countries.
Only with hindsight is it easy to see the value in Airbnb. After all, Wilson’s company’s decision was entirely consistent with the facts at the time. But selling a theory like Airbnb’s takes more than selling facts. It is instead about selling assumptions and logic — convincing those whose resources you need that your assumptions and logic are reasonable and compelling. It is about selling a series of if-then statements. For the Airbnb founders, those entailed convincing investors that if they could solve a number of key problems — including secure payment, efficient matching of those seeking accommodations with those renting homes, and development of a mechanism to generate reputation and trust between the two parties — then the business would thrive. Of course, Airbnb could point to eBay Inc. and Amazon as examples of partial solutions to the trust problem. But fundamentally, the path to gaining others’ support and resources depended on selling their theory through a compelling logical narrative.
Keep in mind that the most valuable theories often face the greatest resistance. In both the world of science and the world of entrepreneurship, stories abound of persistent scientists or entrepreneurs facing consistent rejection — until one day they don’t. Novel theories are consistently resisted. And you too will likely face similar resistance in selling your novel theories. But clarity of assumptions, persuasive logic, and persistence are key to breaking through this resistance.
Of course, the ultimate test of any theory of value rests on whether the strategic experiments you undertake generate the value anticipated. Fortunately, successful theories do tend to share some common features.
First, valuable theories are novel. As discussed above, they are built around novel beliefs and often try to solve previously unrecognized problems. Think of Uber, Apple, Airbnb, eBay, Amazon, or Walmart. At their origin is some form of contrarian or divergent thinking.
Second, valuable theories are simple and clear. They indicate clearly what problems to solve and experiments to run. They also make it easier to spot solutions others have overlooked. Consider the famous 1979 visit of Steve Jobs to the Xerox PARC research center, where he observed many of the central technologies that today shape personal computers: the graphical user interface, bitmapped graphics, and networking technology. Jobs’ theory of value in personal computers focused on generating seamless and intuitive interactions between a user and a computer. Thus, when he walked into Xerox PARC and found technologies that were languishing there, he instantly recognized that they could solve problems framed by his theory. He later recalled one of the technologies he saw that day as the “best thing I’d ever seen in my life.”
Third, particularly valuable theories have broad and general application. They solve not one but a host of problems, and then continue to identify problems to solve. This happened with Apple. Jobs’ theory of seamless interaction between a user and a device has continued to direct Apple’s value-creating efforts, leading to a remarkable succession of devices that have included computers, music players, phones, tablets, and watches. Something similar also happened with The Walt Disney Co. In the 1920s and 1930s, Walt Disney began creating fantasy worlds and fantasy characters through animated film; then, once opportunities for licensing those characters started to emerge, Disney developed a broader theory of value, recognizing that these characters could be replicated and resold in other entertainment businesses, including books, music, character licensing, and later theme parks, hotels, and television. This theory has continued to fuel Disney’s strategic experiments for decades, prompting moves into retail stores, cruise ships, and Broadway shows. More recently, it prompted Disney to purchase Marvel Entertainment LLC and Lucasfilm Ltd. LLC and expand its content into superheroes and science fiction characters.
Getting Strategy Right
The human capacity for calculation is admittedly flawed and error-prone. Strategic decision-makers should do their best to avoid succumbing to any number of biases, including overconfidence, confirmation, and anchoring biases. But the cumulative negative effects of these biases pale in comparison to the capacity for enhanced strategic decision-making that can be provided by a well-crafted theory. Humans in general are endowed with a remarkable capacity to compose theories that facilitate novel perception, experimentation, and value creation. We believe strategic leaders should focus their efforts on positing theories, testing their underlying logic and assumptions, and crafting strategic actions and experiments. It is those activities — rather than computation or the avoidance of biases and errors — that lead to true breakthroughs.