The Design of Business
By: Roger Martin
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By: Roger Martin
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READ
How do you design a business, you ask?
Great question, and Roger Martin, the former Dean of the Rotman School of Business, has the answers. The most successful businesses, Martin argues, are incredibly adept at both imagining a new future and them doing what’s necessary to make it happen.
He has us rethink our definition of what a “designer” looks like, as we learn that a “design thinking” is the process of moving knowledge down a funnel - from mystery, to heuristic, to algorithm. The businesses that create the most wealth have a knack for balancing the need to work on both algorithms and new mysteries at the same time so efficiencies and new breakthroughs can happen in tandem.
One of the original and most effective algorithm creators, you’ll may or may not be surprised to learn, was McDonald’s. Ray Croc didn’t start with the McDonald’s we all know love today, he started with a mystery and drove it down into an algorithm so that a Big Mac in New York is the same as one as you’ll get in Tokyo.
You’ll learn why thinking and acting in this way is so hard - and why most people would rather just stick with what worked in the past. You’ll learn how to develop your own personal knowledge system so you can continually be working towards what Martin would call an “Opposable Mind”. You’ll learn how to take this thinking and instil it throughout whatever organisation you are working in. Most importantly, you’ll learn how to turn design thinking into a sustainable competitive advantage - and ultimately fill your bank account.
Think like a designer, and someday you might just rule the world.
There are two different kinds of companies in the world - those that make a ton of money, and those that don’t. I, for one, am interested in being a part of that former group. Rather than getting a lesson in finance or human resources, it turns out that the ticket into that club is the way you think. Let me explain.
There are a two different ways that you can think. There is analytical thinking, where we seek certainties about the world, and intuitive thinking, which is the art of knowing without reasoning.
Analytical thinking is fantastic for maintaining the status quo, which allows the organisation to build size and scale but prevents it from innovating.
Intuitive thinking is great for imagining new futures, but resists systematising what they do so cannot grow over time. Neither, as Roger Martin points out, is enough on its own. Both forms of thinking are required to create long-lasting financial success. In particular, both types of thinking are required in order to navigate the knowledge funnel.
The knowledge funnel is broken down into three separate parts.
Mystery is the first stage of the funnel, which takes on infinite forms. In this stage, you start with a question that you don’t know the answer to. For instance, McDonald’s posed the question “how and what would Americans like to eat on the go”.
A heuristic is the second stage of the funnel, which allows us to break down the mystery into a general rule of thumb.
For instance, McDonald’s went from mystery to heuristic when it posited that Americans would like a quick-service hamburger joint. Heuristics are prompts that get us to act in a certain way that guide us towards a solution by exploring possibilities.
Lastly, the knowledge is pushed down the funnel one last time when it is turned into an algorithm. For instance, when McDonald’s turned its business into a fixed formula (cook the burgers with this equipment, at this temperature, for this length of time), it became an algorithm.
Algorithms guarantee, that in the absence of an anomaly, that following a certain sequence of steps will generate a predictable result. What this essentially allows you to do is have unskilled labour producing what was previously a high-skilled job.
The benefit of pushing knowledge down the funnel to an algorithm is that it creates a significant competitive advantage over your competition who is stuck at the mystery or heuristic stage. It is a massive gain in efficiency, which leads to lower costs and higher short-term profit.
Of course, that leaves money available to invest in the next mystery to be solved. And why would you want to do that? Why not just stay in the algorithm you created forever?
Because the world you create your algorithm for will eventually change, leaving you with a solution to a problem that no longer exists. Or perhaps even worse, a competitor takes on the mystery that created your original heuristic, and creates a more powerful one. Game over, my friends.
This means you have to work on both exploration and exploitation at the same time. This is not an easy task. It requires the mind of a design thinker. Or in the words of Saul Bellow, you need to become a “first class noticer.”
Tim Brown of IDEO (the international design firm) says that design thinking is “a discipline that uses the designers sensibility and methods to match people’s needs with what is technologically feasible and what a viable business strategy can convert into customer value and market opportunity.” At the root of the designers tool kit is abductive reasoning - thinking about what might be.
Unfortunately, most of the world deems “creative types” to be flighty little creatures that believe that rules and deadlines were made for somebody else. And in some sense, they might be right. But when the good gets thrown in with the bad, we assume that anything that doesn’t focus on reliability must be bad.
Design thinkers uses abductive reasoning to find patterns in what others might still see as an amorphous whole. While looking for these patterns there are many false starts before the right inference is made. This type of exploration is expensive and risky, but the rewards are often great as well. As Martin would say, design thinking is the act of moving knowledge down the funnel, from mystery all the way to algorithm.
There is an inherent tension between reliability and validity in business. The goal of reliability is to produce consistent, predictable outcomes. The goal of validity is to produce outcomes that meet a desired objective. Although the algorithm is where you make your money in the short-term, most organisations fall into the trap of running that algorithm blindly once they find it, falling in love with it’s predictability.
It’s easy to see why: a company can’t define the resources that are necessary to solve the next mystery. It can happen in 1 month or 1 year (or longer). As Martin says, “a business that is overweighted toward reliability will erect organisational structures, processes, and norms that drive out the pursuit of valid answers to new questions.
There are three main forces at play causing organisations to seek reliability over validity: The persistence of the past: most organisations require somebody to prove that their strategy is going to produce a particular ROI. Of course, if you need to prove something, the only way you can do that is in reference to something that happened in the past, where somebody else took similar actions and produced the results you are looking to achieve. Of course, this doesn’t always work very well. Let’s illustrate with an example.
In 2007, let’s say that the the marketing team at General Motors wanted to prove that they should focus on marketing SUVs. So, they could cite scores of data from the past 10 years that included sales, margins and profit to prove the case. So, with that “proof” in hand, they get what they want. Of course, history would prove this to be a terrible decision.
Another approach would have been to request that those funds be driven into smaller, more efficient vehicles because we believed that this is where the market would head. There would be no data to back up the case. But that would have been the valid decision. Unfortunately, past experience almost always prevails over proposals that can only be proven by the passage of time.
The attempt to eliminate bias: the goal of 21st century management is to remove judgment from decisions whenever possible. Anything that can be automated, should be. This is where we get credit scoring systems, insurance pricing systems, and even marketing systems like Amazon’s product recommendations. Although these are incredibly efficient mechanisms, the attempt to eliminate all judgment from the system do not always produce the most desired outcomes, even if they are bias free.
The pressures of time: a reliable system saves a heck of a lot of time. Take the advent of automated asset allocation systems at investment advisories. The client fills out a questionnaire, the answers get fed into a system, and out pops your recommended mix of stocks and bonds. What used to take an inordinate amount of time and judgment are now accomplished by an algorithm.
Not recognising a “code”: quite often there are machines that could be doing the work of a person in your organisation. Although there are people who would rail at this idea, it’s one of the costliest mistakes that organisations can make. Martin gives the example of a retail store manager creating a weekly schedule by hand. You could also think about the work that used to be done in creating mailing lists that are now handled automatically by email marketing programs and software.
There is also a dynamic that could prevent you from getting to the algorithm at all, which is the fact that most algorithms are stuck in the heads of highly paid executives who have knowledge, turf and paycheques to defend. If they took their knowledge and turned it into the algorithm, there would be no need for their services anymore. The company could break down the information into chunks that could be executed by lower paid employees. But that’s only half the story, isn’t it?
Because the employee who can take their expensive heuristic and move it down the funnel into an algorithm is worth 1000 times more to your organisation than the employee who defends his turf and says “it can’t be done”. Are you really going to get rid of an employee who can bring that much value to your organisation?
Wouldn’t you want to get them working on taking the next heuristic and turn it into an algorithm? Yeah, I thought so. If this sounds heartless and “so 80’s”, well, that’s too bad. Because the organisations that embrace the thinking that pushes heuristics to the next stage will make more money than those that don’t. You’ll need to get over it, and so will your employees.
Sure, this all sounds like great stuff, but surely you can just send out a memo declaring that the next big initiative is going to be “design thinking” and expect a revolution to occur. Well, that’s right. Here’s how you go about getting this stuff executed across your organisation.
Set expectations and get the boss on board: if you work in a larger organisation where you are trying to get the boss on board, you’ll need to get him or her on board early. Nothing can derail an initiative like this quicker than a boss who doesn’t wholeheartedly buy-in to the idea. Design thinking is a much harder sell than Six Sigma because it’s hard to point to the ROI or some other metric you’ll achieve at the outset. Nothing but 100% support and clear expectations from the top will work. Don’t start until you have it.
Get help: as Claudia Kotchka, who got design thinking off the ground at P&G, said, “it takes at least ten, maybe fifteen years to really get mastery in design.” You’ll want to get some experienced design thinkers into the mix rather than training from the ground up. After all, who has ten to fifteen years to start creating results?
Expect some speed bumps: this stuff isn’t easy to implement, plain and simple. And because of the “test and learn” approach you’ll be taking, you can expect to run into some speed bumps along the way. Remember, this is a dramatic shift in the way you do business, so expecting this to take hold without resistance just won’t work. Don’t be afraid to use your design thinking skills to work through it.
Less talk, more action: the transformative effects of design thinking can’t be explained, they can only be experienced. So don’t bore people with reports and slide decks about how amazing this can be, get them to participate in exercises where they are in the middle of it. Demonstrate, demonstrate, demonstrate.
So there you have it. Everything you need to know about design thinking into your next competitive advantage.