Now, I think that design thinking is one of the most important “dangerous ideas” of the 21st century. Its impact on product design, on how organizations go about solving problems, on how we live our everyday lives has been profound. But it’s been 15 years—a generation—since David Kelley had his epiphany to stop calling IDEO’s approach “design,” and start branding it as “design thinking.” And a lot’s changed.
Each day now we generate 2.5 quintillion bytes of data—from internet posts, mobile phone activity, IoT sensors, purchase transactions, and more. So much data that over 90% of it in existence was created in just the last two years. Two years of Twitter tweets produce more words than are contained in all the books ever printed, combined. This year, 85% of the 1.2 trillion photos taken will be captured on smartphones. The first camera phone was manufactured in 2000. At about the same time that David’s “design thinking” lightbulb was going off, in 2002 a full human genome sequence cost $100 million. These days it can be done for $1,000. And by 2020 it’ll cost less than a movie ticket.
In the intervening period, design thinking has enjoyed endless press coverage. Universities, businesses, non-profits, and science labs run design sprints based on its principles. The concept is even taught at some elementary schools. It’s perhaps inevitable that when an idea gets this popular, it becomes a victim of its own success. And I think, to some degree, this has happened with design thinking. People who are barely trained in the process become so-called design-thinking instructors. Practitioners struggle to define the term clearly. And, worst of all, some of the core tenets of design thinking have, in my observations, been watered down or misapplied.
What’s more, as the world has grown more complex, I believe that the version of design thinking that we’ve been working with for the past generation needs to evolve, in order to account for these dramatic changes. I think, in other words, that design thinking needs tuning up and updating (Design Thinking 2.0, anyone?). Therefore, in the spirit of suggestions for further thought/study/debate, let me offer two directions for a critical refresh.
I’ve had my fill of empathy. Or to be more specific, all the talk of empathy in recent years. Don’t get me wrong. I’m all for a human-centric approach to design, one that puts the user first and attempts to understand how the world looks to them, as I’ve argued throughout this book. But in design circles and many other fields, empathy has become little more than a buzzword, which, at its most vacuous, seems to mean nothing more than a soft bleating sound made when a small animal is in pain. At its most cynical, it’s a Silicon Valley euphemism for market research.
As one colleague pungently put it, “Empathy is a rathole.” I’m not sure I would go quite so far, but for the sake of semantic integrity, alone, I think that we as a community of design thinkers should self-impose an 18-month moratorium on using the word.
There are other reasons to be cautious of being overly led by empathy. For one, empathy as an emotion has its limits. As the recent presidential election underscored, there’s only so far the average tech worker in Silicon Valley can go in understanding the thinking of Trump voters in the Rust Belt and South, and vice versa. When we’re talking about building things to be used by hundreds of millions of people, there’s no way a highly paid 20-something white male designer at Uber or Instagram or Google can reasonably hope to empathize with end-users in parts of the country or world with which he’s had no meaningful contact. To truly understand this audience, he would have to go live among them: interview them; gather intel on their behaviors, lifestyle, and concerns; probe how they make use of the products he makes.
“Empathy” was David Kelley’s shorthand for this type of ethnographic research. And, to be fair, that’s what some design thinkers still have in mind. But over time, through overuse, when most designers talk about empathy, they don’t seem to me to be referring to fact-gathering at all, but something more like feeling-broadcasting. Empathy in design has gone from an outward-facing action to an inward-turned affect. I think it might be too late to protect the design-thinking denotation of the word from the layman’s definition. Regardless, I would urge us as a discipline to practice rigorous evidence-based compassion, rather than trying to feel people’s pain.
In my own experience working with designers, it’s struck me that decisions were often made at the end of sentences that began with phrases like “I believe” or “I feel.” But today, we don’t have to rely solely on gut emotions like empathy, and we can go even further than ethnography. We can let the data tell us what will work and what won’t. We can use tools like Optimizely to test multiple designs in real-time; to compare alternative concepts in minutes and hours rather than weeks or months; to let data weave its way into the design process. At Airbnb, for example, they’re using structured data to help ensure that the quality of the homes is improving, to create a better experience.
Most designers and many engineers have heard of the concept of “T-shaped” people—individuals with depth in a given domain complemented by a familiarity and, at a minimum, a healthy respect for the adjacent disciplines required to build and launch a successful product. But if you want to build enduring companies and really earn your seat at the table, I think you need to be π-shaped. That is, you need to have depth in both the creative and the analytical. You need to be left- and right-brained. Empathetic and data driven.
Build something, put it out in the world, collect data, collect feedback, make adjustments.
This isn’t to say you should always defer to the data. Algorithms can’t fully account for the human element. Joe Gebbia said that if he had listened to the analytics in 2008, when Airbnb had zero growth, no investors, and a lot of credit card debt, he would’ve shut the service down and cut his losses. For months, the data were telling him this idea was never going to take off, and he should go work on something else. But he refused to listen to the data. In a way, he was refusing to listen to the users, as well, because they were telling him that they weren’t very interested in what he was currently offering. Instead, he soldiered on and did still more things that couldn’t be defended by the numbers—like fly to New York to try to plumb the causes underlying their lack of growth, in order to save the company. Because the data can tell you what’s happening, but they can’t tell you why it’s happening—especially when it comes to radical new ideas. And, most importantly, Joe didn’t give up because he had a vision. In the final analysis, no amount of empathy is a substitute for having a vision.
In fact, too much empathy can kill your company. If you think design is going out, ex ante, asking users what they want and then trying to give it to them, you will fail. As Jobs said, “It’s really hard to design products by focus groups. A lot of times, people don’t know what they want until you show it to them.” Or, as I sometimes like to put it, invention is the mother of necessity. Build something, put it out in the world, collect data, collect feedback, make adjustments. Build; don’t ask. Listen to your users in real-time, but don’t be a slave to narrow consumer cravings.
Now, not every idea hits its target, and of course, there are plenty of products that don’t deserve to exist. But I can tell you for sure that the most successful startups are those that created the markets that they ultimately owned. And at one time in their life, many—if not most—onlookers thought it was a crazy or stupid idea. These founders—like Jobs, like Joe—navigated their way through the fog by figuring out when to listen to the market and when to listen to their inner compass.
So, have a vision of the future that you want to bring people into the light of. Then provide the one thing that we as designers are best capable of providing: creative leadership.
The world is now a profoundly interconnected place. Here are some additional factoids to illustrate how we touch, and are inextricably in touch with, each other at every moment. We are halfway to connecting everyone on the planet, with 3.7 billion internet users worldwide. In the U.S., 99% of 18- to 29-year-olds use the internet. Smartphones have become ubiquitous: roughly half the world’s adult population owns one and it’s projected that by 2020 the figure will climb to 80%. WhatsApp was founded less than a decade ago, but now traffics in 10 billion more messages a day than the SMS global text-messaging system. And never mind six degrees of separation—just off the top of my head, I’m one degree removed from both Barack Obama and a Bhutanese Sherpa.
We live in a massively, intricately interconnected global system. Your startup will be enmeshed in this system. And it’s increasingly impossible to be designers (or human beings) without taking into account how we affect and are, in turn, affected by all the moving pieces of this organic machine. “The more complex an organism is,” says artist and teacher Adam Wolpert, “the more capable it becomes. And the more capable it is, the more it can address challenges and seize opportunities. The downside of that is, the more complex it becomes, the more vulnerable it becomes.” The challenge for designers, increasingly, is learning how to balance the production of ever more complex capability against the threat of a resultant breakdown. That’s why I think design thinking, which emphasizes solving problems holistically, needs to look at a bigger whole by incorporating another body of thought: systems thinking.
Systems thinking isn’t new—though most designers I’ve spoken with are unfamiliar with it. It’s a mode of analysis that’s been around for decades. But I think it has newfound relevance for today’s everything-is-networked, Big Data world. Systems thinking is a mindset—a way of seeing and talking about reality that recognizes the interrelatedness of things. System thinking sees collections of interdependent components as a set of relationships and consequences that are at least as important as the individual components themselves. It emphasizes the emergent properties of the whole that neither arise directly, nor are predictable, from the properties of the parts.
Systems thinking can be used to explain and understand everything from inventory changes in a supply chain, to populations of bacteria and their hosts, to the instability in Syria, to the seemingly irrational behavior of certain elected officials. The vocabulary of formal systems thinking is one of causal loops, unintended consequences, emergence, and system dynamics. And practicing systems theorists employ tools such as systemigrams, archetypes, stock and flow diagrams, interpretive structural modeling, and systemic root cause analysis—all of which is waaay beyond the scope of this book.
For the purposes of this treatment, I’ll simply introduce the Iceberg Model and briefly discuss two key concepts in systems thinking, emergence and leverage points.
The Iceberg Model is a helpful way to explain the concerns that drive systems thinking. Events are at the top of the iceberg. They’re incidents that we encounter from day to day—the hurly-burly of life. Patterns are the accumulated habits or behavioral “memories” that result from repeated, unconsidered reaction to events. Systemic structures are how the components of the system are organized. These structures generate the patterns and events that confront us. Mental models are the assumptions we have about how the world works; they give birth to systemic structures. Values are the vision we have for our future—what we aspire to. They’re the basis for our mental models.
Systems-savvy designers will know the real answer is to unearth what patterns or assumptions are generating those suboptimal behaviors. Not just what happened and when, but how and why these things happened.
Mostly we live at the level of events, because it’s easier to notice events than it is to discern hidden patterns and systemic structures. Even though it’s underlying systems that are actually driving the events we’re captive to. It’s there, at the tip of the iceberg, that we expend most of our energies and attention, and like the Titanic, it’s there that we run aground because we don’t see the truth of the problem—the variables and influences lying below the surface. We take actions without understanding the impact of those actions on the system, making the situation worse.
As an apocryphal illustration, let’s say, there’s a cup of coffee made at Philz that isn’t perfect (WUT!). That would be an event. A pattern would be noticing that there’s a higher frequency of imperfect coffees that are produced during shift changes from the morning to afternoon to evening barista staff. Perhaps the systemic structure generating this pattern of defective coffees is that the shift changes are scheduled so as there’s no overlap between the incoming and outgoing teams of baristas.
The mental model that the baristas hold leads them to believe that they’re only responsible for the Canopies of Heaven and Philharmonics that they make, not the team after them. And say, the value that drives that belief is one of competition—of wanting to make better cups of Philz than the other shifts, and therefore not being concerned about the pour-over apparatus being properly cleaned, or the beans correctly ground and apportioned, at the end of a shift.
End nightmare scenario. Back to your regularly scheduled Mint Mojitos.
It’s usually the case that moral character or human error are blamed for what are really system failures. The people who made the mistakes—the “bad apples”—need to be reprimanded, retrained, or fired. But systems thinkers understand that these are symptoms and not causes. Systems-savvy designers will know the real answer is to unearth what patterns or assumptions are generating those suboptimal behaviors—the bad containers, as Stanford psychologist Philip Zimbardo puts it, rather than bad apples. Not just what happened and when, but how and why these things happened.
Adam Wolpert, my systems-thinking Obi-Wan, shared a real-life example with me of putting this mindset into practice. He was asked to help ameliorate fraught conditions at a cohousing development in Sebastopol. The development, which was made up of 35 people living in 22 units, was riven with conflict and coming apart. When Adam arrived on the scene, he conducted a systems mapping exercise to determine what the boundaries and priorities of this landscape looked like. What soon became apparent to him was that…
This thing is not a thing. It’s actually a couple of things. There’s one boundaried system of people who want to live in a community, and be really connected and engaged … and really make a family. And then there’s another group of people who want to live in the neighborhood, and they want to be good neighbors and live in a cool place … but they’re not interested in being an intentional community….
The people who … just wanted to live in the neighborhood, they were being vilified by the intentional-community people….They were being thought of as slackers, of not showing up … But if you really looked at it from their point of view, you saw this whole other framework. Which was really what the whole needed to come into a healthy balance and move forward.
Two key concepts to understanding systems thinking. The first is emergence. What makes a system a system rather than just a collection of parts is that the components are interconnected and interdependent. Their interconnectedness creates feedback loops, which change the behavior of the system—in fact, they define the behavior of the system. Emergent properties arise that exist only in the system as a totality, and not in its disparate components, making it impossible to understand the system without looking at the whole.
You can’t understand how we get to an anthill by looking at a single antenna or thorax. A Tesla driving down 280 is an emergent property of the innumerable parts that go into making the car—as well as the national grid of recharging stations that had to be built and the web of regulatory oversight that needed to be navigated. In the inextricably connected world we now live in, it’s no longer possible or wise to solve for the part without due consideration of the sum of the parts.
It was an awareness of this reality that led Alta Motors, an electric motorcycle startup, to delay going to market. “We took a systems-design approach,” said CTO Derek Dorresteyn, “We optimized all of these things to work in concert together to get to the goal, which was the user experience. If we went to market too early, we would get locked into certain technological approaches….So we could only make a change in the future by changing three things at once instead of just one.”
Rather than attempt to design a wholly new, perfect solution, oftentimes it’s better to find areas where an incremental change will lead to significant renovation in the system. The smallest nudge for the biggest effect.
So how are designers supposed to address this onslaught of socioeconomic, techno-political complexity? I think the trick is to analyze systems with an eye towards finding leverage points—the second key concept in systems thinking. Rather than attempt to design a wholly new, perfect solution, oftentimes it’s better to find areas where an incremental change will lead to significant renovation in the system. The smallest nudge for the biggest effect.
The challenge is to rise above the distraction of the details and widen your field of vision. Try to see the whole world at once and make sense of it. It’s a heady challenge, yes. But you either design the system or you get designed by the system.
“Everything is networked now,” in the world according to Evan Sharp. “All of culture, all of communications, it all is going through networks.” Therefore, at the scale of seven billion people, “any small little improvement you make has massive aggregate value.” This will cut against the grain of most designers’ instincts, because the end-result will likely be far from an ideal proposed design, but designing for the real world means dealing with the practical constraints of that reality and trying to make refinements in the face of compromise.
Now, I don’t want to oversell systems thinking. It’s not always possible in real-world cases to reasonably model very complex systems in ways that lead to good design strategies and outcomes. Systems thinking will also be novel and perhaps somewhat jarring to many designers, because as designers we’re usually laser-focused on a single, discrete design problem. But when appropriate, applying a systems mindset to design thinking will give designer founders a powerful tool for circumnavigating the problems of the age. Focus on relationships over parts; recognize that systems exhibit self-organization and emergent behaviors; analyze the dynamic nature of systems in order to understand and influence the complex social, technological, and economic ecosystem in which your startup exists.
Some designer founders, like Moxxly’s Gabrielle Guthrie, understand this even without training in systems thinking:
The outcome could be a physical product, a system, or culture….To be a designer founder, you have to care about the broader situation. It’s a Russian doll, or a “Powers of Ten” thing. You’re working on a particular thing, and you think about how it fits into the mindset of the larger team, how it works with your users, or for the company. You have to be looking at many different angles and be very agile.
The challenge is to rise above the distraction of the details and widen your field of vision. Try to see the whole world at once and make sense of it. It’s a heady challenge, yes. But you either design the system or you get designed by the system. Moreover, while this nonlinear way of thinking might seem alien at first, rest assured that it won’t be long before it feels like second nature—because it is. No one put this more beautifully than the late sustainability pioneer and systems scholar Donella Meadows:
Only a part of us, a part that has emerged recently, designs buildings as boxes with uncompromising straight lines and flat surfaces. Another part of us recognizes instinctively that nature designs in fractals, with intriguing detail on every scale from the microscopic to the macroscopic. That part of us makes Gothic cathedrals and Persian carpets, symphonies and novels, Mardi Gras costumes and artificial intelligence programs, all with embellishments almost as complex as the ones we find in the world around us.