innovation in problem solving

Author Talks: Why problem solving is the key to innovation

In this edition of Author Talks , McKinsey Global Publishing’s Raju Narisetti chats with Dr. Sheena Iyengar, S.T. Lee Professor of Business at Columbia Business School, about her new book, Think Bigger: How to Innovate (Columbia Business School Publishing, April 2023). Iyengar shares insight into her research on problem solving and explains how adaption and critical decision making affect innovation. An edited version of the conversation follows.

What problem are you trying to solve with this book?

Think Bigger: How to Innovate is a book that walks you step by step through how you can create a solution to any problem you’re trying to solve.

It seems like everything that there is to know about innovation  or that we could know about innovation has already been done. Yet if you read all those books about innovation carefully, essentially all of them are based on knowledge that’s old.

Our current practices in the business world—and even, I might add, in academic settings—have not updated their approaches to teaching people how to be creative, how to ideate in line with those recent advances. We still tell people, effectively, to either mind wander—sort of daydream—or to brainstorm. Brainstorming was invented in 1930, although we have updated it and made it better than it was in 1938. Given our current knowledge about the way the mind works, we can do better than waiting for a mind wander to have a result or a so-called aha moment or a flash of insight to happen. We can do better than brainstorm.

Essentially, the problem that the Think Bigger: How to Innovate methodology solves for is the question, “What do you need to do to have an idea?” It’s not about waiting for an aha moment. It’s not about going out and brainstorming. It’s what you can actually do, step by step, to get an idea.

There is also a personal reason behind the book for you.

There is a very personal, emotional reason. I was born with a rare eye disease: retinitis pigmentosa. I have a rare form of it; I went blind as a very young person. One of the things that happens when you’re growing up disabled is that you’re forever told about all the choices you can’t have. At the same time, you have this message that you’re always given, particularly in American culture, that you can grow up and do and be whatever it is you want to do and be, as long as you put your heart and mind to it.

If you think about it, both those messages are essentially extreme and incorrect. It’s not the case that I can’t do anything. It’s not the case that I can do anything. The questions are, how do I figure out what choices I have? What choices can I create?

That was a lifelong struggle for me. It was something that I very much started to try and tackle growing up and then as an undergrad. And certainly my dissertation and much of my research up until Think Bigger: How to Innovate had to do with choice and how I, as a disabled person, have choices.

What I also began to realize, though, is that my way of creating choices, either when there are no known choices out there for me or when people just don’t realize what choices might be available to me, could be a method that was based on science. What I realized is that this isn’t just me who has this struggle of, “How do you create meaningful choices?”

When I look around me, every single person is wondering, “What dreams are possible for me? Which of those dreams can I turn into reality, and what’s that process, step by step?” Rather than waiting for chance encounters or waiting for an aha moment to hit you, maybe I can actually make it more systematic for you, so you have a how-to toolkit.

As step one, why is it important to choose the right problem?

Let’s take the invention of ice cream. Who made ice cream this globally accessible thing? She was a woman by the name of Nancy Johnson  who, in the 1800s, was the wife of a chemist. She was a 50-year-old woman who was a missionary. Well one of the things that happened back then is that, yes, you had ice cream, but it was very, very expensive. George Washington paid close to $200 for a little thing of ice cream when he was president.

In the early 1800s, Nancy Johnson asks the question, “How do you make ice cream accessible?” Now back then, they would take a bowl, and they would fill it with ice. Then they would take a smaller bowl, fill it with cream, and then stir, stir, stir, stir, stir, stir. It would form lots of lumps, and it would get harder and harder to stir as it’s thickening. It was backbreaking labor.

The first question was, “How do you keep it cold as you’re stirring it?” Because it would often melt as they would be stirring it. “How do you make it easier to make so it’s not backbreaking labor, and how do you keep it from forming lumps?”

What does Nancy Johnson do? She takes a water pail, which had been around already for 400 years. But the pail was much bigger than the bowl. She then fills that with ice, and then inside it, she puts the cream into something made of pewter. She asked herself, “How do you keep it cold? Well when men go to the tavern, what do they drink their beer in that keeps it cold? Pewter.”

She puts the cream in the pewter container, then said, “How do I make the labor less backbreaking?” Well let’s use a hand crank,” which was used for grinding up spices and coffee. “Let’s attach to that hand crank spatulas.” But the spatulas would have holes in them so that as you’re stirring, the liquid could go through, which would make it a lot easier to stir. She learned about the role of spatulas with holes in them from runaway slaves who were often coming from sugar plantations where they had to mix hot, sugary liquids to make molasses. And to prevent it from forming crystals, they would have these holes in the spatula.

Essentially, you create a culmination of water pail, plus the pewter bowl, plus the hand grinder, plus the spatula with the holes in it. You’ve now created what was dubbed as a disruptive technology back in 1843.

What are the steps here? You define the problem, which is step one. You break it down into its most important subparts: How do I keep it cold? How do I make it less cumbersome to make? How do I reduce the formation of lumps? For each subpart of your problem, you search far and wide so you can go beyond your industry. You go beyond your main domain  of inquiry. You ask yourself how other industries solve for this subproblem—for example, with pewter, the hand grinder, the spatula with the holes in it. You now combine those pieces together in a unique way. And voilà, you have an innovation that not only solved the problem but now becomes scalable .

That is essentially the Think Bigger: How to Innovate method; that is essentially what I teach people how to do. In step one, you start by defining the problem . Most of the time, it’s actually not as self-evident as, “How do I make ice cream accessible?” I would suspect that even Nancy Johnson took a while before she understood how to define that problem. As Einstein was reported to have said, “If I had an hour to save the planet, I would spend the first 55 minutes thinking about the problem and the last five minutes thinking about the solution.”

Step two is to break the identified problem into subcomponents?

Once you have your problem statement, which we always phrase as a question—"What’s the problem I’m trying to solve?”—in order that you can have an open mind, you then break it down. You break it down into its most important pieces.

Every problem has a bazillion things that have to be solved. You’re never going to solve everything. I call it “the 80 percent rule.” You break it down into the highest-priority parts. If I were to solve for these three to five different subparts, then I’ll solve for about 80 percent of my problem.

Let’s take a sport that’s very near and dear to our hearts: basketball. The guy who invented basketball was James Naismith, who, in 1891, was this gym teacher in Massachusetts. He was asked to come up with a sport that young people could play in the winter. In spring and summer, when the weather was nice, they could play football, they could play rugby, they could play lacrosse, and they could play soccer. But how do you keep them occupied and burn off their energy in the winter in Massachusetts, when there was a lot of snow?

What were the things he had to solve for? Well he had to make sure the sport was playable indoors. He had to make sure that whatever sport they played wouldn’t be so rough—you couldn’t have them falling on the ground; it was going to be a rough floor, and that could hurt somebody. It had to feel challenging. It had to be fast, competitive, and burn off some energy.

He’s looking around at soccer, football, et cetera, and says, “What if we take a ball, like from soccer? Think about a ball, what can I do with it indoors? Well passing it sounds like a good idea. But obviously we don’t want to push. That could lead to injury. But I don’t want to have them throw the ball to a line; that seems awfully easy in an indoor space. A net seems a little too complicated. What do I do? Well how about this sport that nobody ever really knows about? It’s called ‘duck on the rock.’ A little ‘duck’ sits on a rock, and you throw things at it to get the duck to fall off. What if we did something like that indoors?” He took a peach basket, and he made a hole in it. “What if we throw the soccer ball into that?”

The reason that James Naismith was able to create basketball was that he understood what subparts of his problem he needed to solve for. That’s what enabled him to create the game.

Step three is asking what the problem will solve for?

You’ve got your problem, and you’ve broken it down. Now most people tend to start generating solutions. That’s certainly a very natural temptation to have. I always say, at that point, create a “sparking lot.” These are just sparks. Whatever solution you’re going to come up with right now is partial.

It’s important at this stage, when you know a bit about the problem, to step back and ask yourself, if you were to find the ideal solution, how should it feel? How are you going to know what solution is better for you versus worse for you? By really knowing how you want that solution to feel.

You know, we think feelings are bad things and shouldn’t be a part of any creative or decision-making process. That’s incorrect. Feelings are the only things that can truly guide you in determining what your selection criteria is. You still have to be systematic about it. You can’t be random about when you use your feelings. That’s why we do it right now in step three.

How do you want your final solution to feel? You have to uncover your wants. You know there’s the famous story about Bill Gates. It is about when he first created his basic software; he attached his software to a desktop computer called Altair. People really weren’t interested in the computer, but they were very interested in his software. He kept finding people who were pirating his software. It made him mad, and he would write these nasty letters to all these computer hobbyists saying, “You guys are just pirates.”

He was pretty angry about it as long as he thought his fate and his desires were attached to Altair. Then one day, when he was walking around a conference floor with computer hobbyists, he discovered they were all using his software. They were exchanging it on all kinds of machines. And then a light bulb went off in his head. “Wait, what is it that I really want? Is it that I want Altair to succeed? Or is it that I want people to start using this software?” After he had that insight, he essentially terminated his contract and went on to take his software to IBM and other companies. The world has never been the same.

Step four is to search both in and out of the box?

Once you get to step four, you’re now ready to start the ideation process, the solution-generation process. Step four I call “search in and out of the box.” The reason that I call it that is, so often, we tell people to do out-of-the-box thinking. Then we stick them in the room and tell them to brainstorm.

Well, brainstorming is a great way to share the knowledge  that’s in the room. But it’s not out-of-the-box thinking. Out-of-the-box thinking requires you to search far and wide for how different industries and different people at different points in time have solved for analogous problems.

Out-of-the-box thinking requires you to search far and wide for how different industries and different people at different points in time have solved for analogous problems.

You collect those tactics or strategies. Let’s take the case of Henry Ford and the invention of the Ford car. Henry Ford did not invent the car. Henry Ford did not invent the assembly line. Henry Ford did not invent any of the elements that went on to create the Model T. He searched far and wide and found the pieces he needed to put together.

Back then, a car cost $2,000, which was unaffordable. So Ford thought, “How do I reduce the cost of labor? How do I reduce the amount of time it takes to make a car? How do I reduce the cost of materials?” Very simple, subparts to the problem.

“How do I reduce the cost of labor? Well by creating specialization.” The assembly line was actually already being used by Oldsmobile. Now you have a system where one person knows about putting together the engine, another person knows about putting together the frame, and so forth. Each person has to learn only their particular thing, which makes them faster and faster at doing that thing.

“How do I reduce the amount of time that it takes to make a car?” At that time, it took 12.5 hours to build a car. When one of Ford’s engineers was visiting the slaughterhouses of Chicago, they observed something very interesting. In the early 1900s, when they would take an animal apart to pack it and send it on a train to various locations, they would use something called “the moving disassembly line.” Ford already had an assembly line for the car. What happens if you add the moving business to this? It reduces the amount of time it takes to build a car from 12.5 hours to about two hours. That’s huge!

“Now how do I reduce the cost of materials?” Back then, you could have your car any color you wanted. But Ford was famous for saying you could have your car in any color you wanted, as long as it was black. That’s because there was this new paint that had come on the market called japanning. It looked like a black lacquer—very much like Japanese art— and would dry in less than 24 hours. The average paint color back then would take about seven to 14 days to dry. Once you put together japanning with a moving assembly line, not only do you reduce the amount of time it takes to build a car, but you also can bring down the price. They brought down the price of that car to around $250. It was tremendous.

Notice what’s happening here. It’s not like Ford’s trying to become an interdisciplinary businessperson or scientist. No, he’s just learning from different industries and importing into his own world tactics that worked in other industries. He’s importing them into his world and adopting and editing them for use for his problem. And that’s the core to thinking bigger, whether it’s creating a business, whether it’s being a revolutionary scientist, whether it’s being a revolutionary leader .

What does step five, choice mapping, do?

In Think Bigger: How to Innovate , the alternative to brainstorming that I present is choice mapping. The way to think about choice mapping is just that it is a more efficient and deliberative approach to getting that flash of insight. Rather than waiting around for that flash of insight to happen, perhaps randomly, I am essentially telling you what you can do for your cognitive functioning to have that flash of insight. I’m very structured about it and very deliberative about teaching people how to do it.

The alternative to brainstorming that I present is choice mapping. The way to think about choice mapping is just that it is a more efficient and deliberative approach to getting that flash of insight.

Let me give you an example of how choice mapping works. I’m going to use one of our great heroes. Up until now, we’ve mainly talked about products. But the Think Bigger: How to Innovate method is not just to use for products, big and small. It also explains the ways ideas are formed. That’s true of any idea, including such big ideas as democracy, for example.

Let’s consider Mahatma Gandhi. He was not just an amazing person who did an amazing thing in his lifetime, but he essentially created a technique that we use even now for how to voice discontent when you don’t have power. Now when we analyze somebody like Gandhi, we try to analyze what his childhood was like. We try to analyze what his character was. What was the complexity of his character? Who were the people he knew? What were the ideas that influenced him? All of that is a really interesting part of his narrative. But those are not the elements that made his idea.

Stepping away from his story—the story itself is of course amazing, and everybody should learn it—I want to just focus in on the pieces he brought together to create his idea. He is trying to solve the problem of getting a large group of people who are very different from one another—different in caste, different in religion, different in language—in a bazillion different ways. It’s a very diverse population, the Indian colony. Now he wants to help them get freedom. How do they create a form of rebellion that has some likelihood of success, given that they’re fighting against a mighty power?

First, is there any method that anybody’s ever used to go against powerful entities and win? Turns out he has an example from the Brits themselves: the women’s suffragette movement. Hunger strikes. In fact, Gandhi notes in various writings how the Indians should take a page from the women’s suffragette movement. Then he was influenced by the work of Tolstoy and the communal farms that he created in Russia. In fact, if you look at the original farms that Gandhi created in South Africa, they have many of the same elements that Tolstoy created.

Now Gandhi has the problem of how to bring a bunch of people who are very different from one another to all agree with each other and have a kind of common cause. That’s where, drawing from Tolstoy, he creates the ashram.

Third, how to get people who are naturally suspicious of foreign ideas to adopt the principle of nonviolence and more of a community feeling with one another? That’s where he brings in very traditional garb and very traditional language from Hinduism.

Bring these elements together, and you have Gandhi’s idea of nonviolent civil disobedience. I would say the best demonstration of how he put all these pieces together was the Salt March.

Step six, the final one, you call ‘the third eye.’

The third eye is asking the question, “Do you see what I see?” Imagine at the end of step five, you’ve generated a whole bunch of ideas. Choice mapping can generate a lot more solutions than any other method. They’ll be unique solutions to the problem that you’ve set forth. Now that you’ve picked a solution, and you like it, it’s up in your head. Now the question is, how do I figure it out if it’s worth taking to the next level?

We have a method for that. That method is not to go out and ask people, “Do you like it?” We don’t even know if they know what we’re talking about. What is this thing we want them to either like or dislike? The third eye is learning what others see or hear or experience or imagine as we present to them our idea.

This is not prototyping; this is before that. One of my favorite examples of someone who very effectively used the third eye, without calling it that, was Paul McCartney. I had the honor and privilege of being able to interview him when I was working on my book. We talked about the process he used when creating the song “Yesterday.” The apocryphal story that we often hear is just that he woke up one morning and the tune was in his head; that was it. That’s certainly a true part of the story, but it’s not the whole story.

He woke up one morning with this tune in his head, and he didn’t want to forget the tune. So he put some nonsensical words to the tune, and then he began to hum the tune to people. He wouldn’t ask them, “Hey do you like it?” He would ask them, “Have you ever heard this tune before?” He would hum this tune to lots of people, his fellow band members, other professional musicians, strangers, and friends. Again and again, what he heard was, “Well no. It sounds familiar, but I’ve never heard this tune.” Little by little, as he’s doing this, and he’s watching their reactions, he’s realizing there is some magic to this tune.

This inquiry is not just about discovering yes or no. He’s continuing to iterate, building out his tune. As he’s building it out, he finally gets to the point where he is sitting in a car in Portugal, and he starts coming up with lyrics.

Visit Author Talks to see the full series.

Sheena Iyengar is the S. T. Lee Professor of Business at Columbia Business School. Raju Narisetti is the leader of McKinsey Global Publishing and is based in McKinsey’s New York office.

Comments and opinions expressed by interviewees are their own and do not represent or reflect the opinions, policies, or positions of McKinsey & Company or have its endorsement.

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• Understand cognitive preferences (yours and others’) to adapt the creative thinking process and drive your team’s success
• Develop techniques that promote effective brainstorming and enable you to reframe problems in a way that inspires innovative solutions

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• The cognitive preferences that drive how we approach problems, and how to leverage those cognitive preferences for individual and team success
• How to develop—and implement— a methodology that overcomes barriers to innovative thinking and fosters the generation of new ideas, strategies, and techniques
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How to solve problems using the design thinking process

The design thinking process is a problem-solving design methodology that helps you develop solutions in a human-focused way. Initially designed at Stanford’s d.school, the five stage design thinking method can help solve ambiguous questions, or more open-ended problems. Learn how these five steps can help your team create innovative solutions to complex problems.

As humans, we’re approached with problems every single day. But how often do we come up with solutions to everyday problems that put the needs of individual humans first?

This is how the design thinking process started.

What is the design thinking process?

The design thinking process is a problem-solving design methodology that helps you tackle complex problems by framing the issue in a human-centric way. The design thinking process works especially well for problems that are not clearly defined or have a more ambiguous goal.

One of the first individuals to write about design thinking was John E. Arnold, a mechanical engineering professor at Stanford. Arnold wrote about four major areas of design thinking in his book, “Creative Engineering” in 1959. His work was later taught at Stanford’s Hasso-Plattner Institute of Design (also known as d.school), a design institute that pioneered the design thinking process. 

This eventually led Nobel Prize laureate Herbert Simon to outline one of the first iterations of the design thinking process in his 1969 book, “The Sciences of the Artificial.” While there are many different variations of design thinking, “The Sciences of the Artificial” is often credited as the basis. 

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A non-linear design thinking approach

Design thinking is not a linear process. It’s important to understand that each stage of the process can (and should) inform the other steps. For example, when you’re going through user testing, you may learn about a new problem that didn’t come up during any of the previous stages. You may learn more about your target personas during the final testing phase, or discover that your initial problem statement can actually help solve even more problems, so you need to redefine the statement to include those as well. 

Why use the design thinking process

The design thinking process is not the most intuitive way to solve a problem, but the results that come from it are worth the effort. Here are a few other reasons why implementing the design thinking process for your team is worth it.

Focus on problem solving

As human beings, we often don’t go out of our way to find problems. Since there’s always an abundance of problems to solve, we’re used to solving problems as they occur. The design thinking process forces you to look at problems from many different points of view. 

The design thinking process requires focusing on human needs and behaviors, and how to create a solution to match those needs. This focus on problem solving can help your design team come up with creative solutions for complex problems. 

Encourages collaboration and teamwork

The design thinking process cannot happen in a silo. It requires many different viewpoints from designers, future customers, and other stakeholders . Brainstorming sessions and collaboration are the backbone of the design thinking process.

Foster innovation

The design thinking process focuses on finding creative solutions that cater to human needs. This means your team is looking to find creative solutions for hyper specific and complex problems. If they’re solving unique problems, then the solutions they’re creating must be equally unique.

The iterative process of the design thinking process means that the innovation doesn’t have to end—your team can continue to update the usability of your product to ensure that your target audience’s problems are effectively solved. 

The 5 stages of design thinking

Currently, one of the more popular models of design thinking is the model proposed by the Hasso-Plattner Institute of Design (or d.school) at Stanford. The main reason for its popularity is because of the success this process had in successful companies like Google, Apple, Toyota, and Nike. Here are the five steps designated by the d.school model that have helped many companies succeed.

1. Empathize stage

The first stage of the design thinking process is to look at the problem you’re trying to solve in an empathetic manner. To get an accurate representation of how the problem affects people, actively look for people who encountered this problem previously. Asking them how they would have liked to have the issue resolved is a good place to start, especially because of the human-centric nature of the design thinking process. 

Empathy is an incredibly important aspect of the design thinking process.  The design thinking process requires the designers to put aside any assumptions and unconscious biases they may have about the situation and put themselves in someone else’s shoes. 

For example, if your team is looking to fix the employee onboarding process at your company, you may interview recent new hires to see how their onboarding experience went. Another option is to have a more tenured team member go through the onboarding process so they can experience exactly what a new hire experiences.

2. Define stage

Sometimes a designer will encounter a situation when there’s a general issue, but not a specific problem that needs to be solved. One way to help designers clearly define and outline a problem is to create human-centric problem statements. 

A problem statement helps frame a problem in a way that provides relevant context in an easy to comprehend way. The main goal of a problem statement is to guide designers working on possible solutions for this problem. A problem statement frames the problem in a way that easily highlights the gap between the current state of things and the end goal. 

Tip: Problem statements are best framed as a need for a specific individual. The more specific you are with your problem statement, the better designers can create a human-centric solution to the problem. 

Examples of good problem statements:

We need to decrease the number of clicks a potential customer takes to go through the sign-up process.

We need to decrease the new subscriber unsubscribe rate by 10%. 

We need to increase the Android app adoption rate by 20%.

3. Ideate stage

This is the stage where designers create potential solutions to solve the problem outlined in the problem statement. Use brainstorming techniques with your team to identify the human-centric solution to the problem defined in step two. 

Here are a few brainstorming strategies you can use with your team to come up with a solution:

Standard brainstorm session: Your team gathers together and verbally discusses different ideas out loud.

Brainwrite: Everyone writes their ideas down on a piece of paper or a sticky note and each team member puts their ideas up on the whiteboard. 

Worst possible idea: The inverse of your end goal. Your team produces the most goofy idea so nobody will look silly. This takes out the rigidity of other brainstorming techniques. This technique also helps you identify areas that you can improve upon in your actual solution by looking at the worst parts of an absurd solution. 

It’s important that you don’t discount any ideas during the ideation phase of brainstorming. You want to have as many potential solutions as possible, as new ideas can help trigger even better ideas. Sometimes the most creative solution to a problem is the combination of many different ideas put together.

4. Prototype stage

During the prototype phase, you and your team design a few different variations of inexpensive or scaled down versions of the potential solution to the problem. Having different versions of the prototype gives your team opportunities to test out the solution and make any refinements. 

Prototypes are often tested by other designers, team members outside of the initial design department, and trusted customers or members of the target audience. Having multiple versions of the product gives your team the opportunity to tweak and refine the design before testing with real users. During this process, it’s important to document the testers using the end product. This will give you valuable information as to what parts of the solution are good, and which require more changes.

After testing different prototypes out with teasers, your team should have different solutions for how your product can be improved. The testing and prototyping phase is an iterative process—so much so that it’s possible that some design projects never end.

After designers take the time to test, reiterate, and redesign new products, they may find new problems, different solutions, and gain an overall better understanding of the end-user. The design thinking framework is flexible and non-linear, so it’s totally normal for the process itself to influence the end design. 

Tips for incorporating the design thinking process into your team

If you want your team to start using the design thinking process, but you’re unsure of how to start, here are a few tips to help you out. 

Start small: Similar to how you would test a prototype on a small group of people, you want to test out the design thinking process with a smaller team to see how your team functions. Give this test team some small projects to work on so you can see how this team reacts. If it works out, you can slowly start rolling this process out to other teams.

Incorporate cross-functional team members : The design thinking process works best when your team members collaborate and brainstorm together. Identify who your designer’s key stakeholders are and ensure they’re included in the small test team. 

Organize work in a collaborative project management software : Keep important design project documents such as user research, wireframes, and brainstorms in a collaborative tool like Asana . This way, team members will have one central source of truth for anything relating to the project they’re working on.

Foster collaborative design thinking with Asana

The design thinking process works best when your team works collaboratively. You don’t want something as simple as miscommunication to hinder your projects. Instead, compile all of the information your team needs about a design project in one place with Asana. 

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Design Thinking: New Innovative Thinking for New Problems

Einstein was certainly right — we can’t solve problems by using the same kind of thinking we used when we created them. In addition, with the rapid changes in society, the methods we have previously used to solve many of the problems we face are no longer effective. We need to develop new ways of thinking in order to design better solutions, services and experiences that solve our current problems. Design Thinking steps in with a bold newly systematised and non-linear human-centred approach. This will help us radically change how we go about exploring problems and creating solutions to those problems.

The problems designers, business owners, and engineers face today are in a whole new level of scale compared to the challenges we’ve faced in the past few decades. In a largely globalised world, where the changes in economic and natural resources can be felt halfway around the globe, our challenges are becoming more intertwined with the systems that connect us all. To solve the new wave of problems we face today and in the future, we need a new kind of thinking, a new approach towards innovation . Design Thinking is a large part of that new approach towards innovation, as it allows people, teams, and organisations to have a human-centred perspective, and yet a scientific approach, towards solving a problem. Tim Brown, CEO of the international design consultancy firm IDEO, makes this point in the introduction of his book, Change by Design :

“A purely technocentric view of innovation is less sustainable now than ever, and a management philosophy based only on selecting from existing strategies is likely to be overwhelmed by new developments at home or abroad. What we need are new choices—new products that balance the needs of individuals and of society as a whole; new ideas that tackle the global challenges of health, poverty, and education; new strategies that result in differences that matter and a sense of purpose that engages everyone affected by them. It is hard to imagine a time when the challenges we faced so vastly exceeded the creative resources we have brought to bear on them.” – Tim Brown

Which Problems Can Design Thinking Help us Solve?

One of the first questions people ask when hearing about Design Thinking is, "What is Design Thinking best used for?" Design Thinking is suited to addressing a wide range of challenges and is best used for bringing about innovation within the following contexts.

Redefining value

Human-centred innovation

Quality of life

Problems affecting diverse groups of people

Involves multiple systems

Shifting markets and behaviours

Coping with rapid social or market changes

Issues relating to corporate culture

Issues relating to new technology

Re-inventing business models

Addressing rapid changes in society

Complex unsolved societal challenges

Scenarios involving multidisciplinary teams

Entrepreneurial initiatives

Educational advances

Medical breakthroughs

Inspiration is needed

Problems that data can't solve

A Holistic approach to Challenges

Design Thinking is best suited to addressing problems where multiple spheres collide, at the intersection of business and society, logic and emotion , rational and creative, human needs and economic demands and between systems and individuals. We would most likely not require Design Thinking to tackle tame problems — that is, problems that are simple and that have fixed and known solutions — unless we were seeking a novel or innovative means to solving the problem with a different desired goal than the typical available solutions.

It's NOT Just a Process or Set of Steps

However, Design thinking is not necessarily only to be understood as a process or method for solving a set-in-stone collection of problems. It is also a mindset that can be applied in almost any scenario where innovation or thinking differently is required. It can also be combined with other methodologies, business strategies, social innovation models, and management practices. It's something that changes depending on its context and can use tools and techniques from other disciplines.

It's About Human-Centred Innovation

Design Thinking works best where we need to make human sense of things, approaching challenges in ways that best suit human needs regardless of the scale or authority of the challenge. A conformist, controlled, technical or linear approach is no longer able to grapple with the newly complex and sensitive needs of modern society.

It starts with an intention, a desire, a need or yearning towards a better situation or state. We have no way of knowing whether this is a mere dream or a practical and viable path to take. Design Thinking gives us the tools to explore What Could Be .

As Bruce Mau, founder of the Massive Change Network, put it:

"It's not about the world of design, but the design of the world". – Bruce Mau

Cope with Disruptions in Society

Since the disruptions in human development caused by the Industrial Revolution, analysts have been strategizing ways of streamlining just about every business, production and economic process imaginable with the aim of extracting the maximum benefit from the least amount of time and resources. While this may have had some degree of success on the level of productivity and efficiency, the recipe to that much-needed innovation within all sectors has been somewhat of a conundrum. This is where Design Thinking steps in with a bold new human-centred approach at radically changing how we go about exploring problems and finding solutions to those problems, helping us break out of the old moulds we've become stuck in, so as to take a fresh look at the world around us.

Besides the ongoing struggles between the analytical and creative worlds, other factors have dramatically disrupted the way we see, understand, experience, and interpret the world around us. Technology is developing at such a rapid pace that job descriptions can barely keep up, let alone entire industries. Consumers demand much more now that they are constantly switched on, always informed, and obsessively sharing everything with their networks.

Focus on Humans, Not Users

In order to remain relevant, companies and organisations are also fighting a battle for attention on an unprecedented level. Besides the constant scrutiny and accountability, information overload is also reaching its peak. People are increasingly seeking out those products, services, and organisations that they personally connect with on a meaningful level. Many people are selecting the few options that speak directly to their human needs and experiences . This has driven Human-Centred Design and Design Thinking approaches of all types to mushroom in the last few years. Approaches to business and social innovation are increasingly looking for alternatives to the old models of adding value, by focusing on human needs and experience as primary motivating factors.

Innovative solutions need to be found that can keep up with massive disruptions affecting Human Resources, Energy, Sustainability, Education, Economic Constraints, Political Instability—these large, systemic and complex problems with capital letters—and a whole plethora of other challenges which existing strategic and management practices and processes are unable to pick apart.

Innovate or be Swept Away with the Tide

Idris Mootee, CEO of Idea Couture and a leading expert on applied Design Thinking in large-scale strategy innovation, wrote his book Design Thinking for Strategic Innovation about the implementation of Design Thinking methodology within business. The book outlines a number of disruptions in the business environment , including new consumer behaviour and expectations, forcing companies to rethink their every move.

“This disruption has not been so kind to businesses operating by the rules of the old model. We don't have to watch their ads anymore. We don't believe their marketing hype anymore. We don't want to eat their junk ingredients anymore. We don't have to buy from their stores anymore. And we don't want the best of them to just be profit machines anymore. We want more, when we want it, how we want it, and at the price we want it.” – Idris Mootee

Idris Mootee uses the analogy of the study of weather systems, where it was determined that even the slightest changes in atmospheric conditions may have dramatically varying results in the way weather patterns developed. The current climate of rapid change and upheaval is even more difficult to forecast for the future. We are unable to see what lies around the next corner, let alone months or years down the line. This means we need a completely new and dynamic approach to innovation and strategic planning: something less rigid that can quickly and easily adapt to the varying conditions we find ourselves in and those dramatic changes which lie around the next corner.

The abilities to understand and act on changes rapidly in our environments and changes in human behaviour are becoming crucial skills we are still developing and refining. Design Thinking offers a means for grappling with all this change in a more human-centric manner. In order to embrace Design Thinking and innovation, we need to ensure that we have the right mindsets, collaborative teams, and conducive environments.

Form the Right Mindsets, Teams, and Environments for Innovation

Creating the right mindsets , selecting the appropriate team, and setting up environments which encourage innovation to take place are three of the essential aspects of fostering successful innovation within companies, organisations, and society at large.

1. Form The Right Mindsets for Innovation

One of the amazing things about Albert Einstein was the connection between his creative and analytical thinking . He was an extremely creative individual, deeply reflective of the human condition, weaknesses and failings while at the same time years ahead of most in terms of his analytical thinking capacity. His ability to join and synthesise worlds of influence, merging creative thinking with intense analytical abilities brought about the breakthroughs he achieved as a thinker and a scientist. Like Design Thinking, Albert Einstein relied on and celebrated both logic and imagination.

“Logic will take you from A to B. Imagination will take you everywhere.” – Albert Einstein

The notion that creativity or "artistic" talent is only the domain of those gifted with these abilities is one of the most inhibiting factors in our lives today. However, it is becoming a more widely held belief that creativity and lateral thinking can be learnt, and with the implementation of the appropriate steps, process and mindset, can be unleashed to solve some of the "wickedest" problems (i.e., most complex and tricky problems) we find ourselves faced with. The challenge is that most modern corporations, organisations and institutional settings tend to kill creativity with an overly conformist notion of things.

The struggle between creative and logical thinking is an old one, which is yet to be understood fully, even with scientific breakthroughs in neuro- and cognitive science . It has been a common belief that those who tend to be more analytical, logical and rational in nature have always relied more heavily on the left side of their brains, while those who are more creative, expressive and emotional have relied more on the right side. This myth seems to have recently been busted, with studies indicating both sides of the brain are involved in both creative and logical processes of all kinds and work.

We need to develop more open, collaborative, and explorative cultures and mindsets, which combine both logic and imagination, in order to create new innovative solutions. And Design Thinking will help us do just that.

2. Create Cross-disciplinary and Innovative Teams

It is the norm in many organisations to encourage the development of skills and abilities relevant to a specific role. For instance, creativity is encouraged in graphic designers, while analytical skills are encouraged for marketing, business, and operations-related jobs. However, such a “boxed” organisation of talent, where different skills are developed and used in silos throughout different departments, will not be able to produce much of the innovation we need for the new wave of wicked problems .

We now know that a healthy collaboration between the creative and logical ways of thinking is crucial in creating the kind of holistic thinking that is required to understand and solve new kinds of multi-dimensional problems. This is also true for people working in multidisciplinary teams, where teams possessing a range of thinking styles, expertise, and experiences come together to develop solutions more effectively than narrow-focused, specialist individuals are able to working alone. In Design Thinking, cross-disciplinary collaboration plays an important role — it is when designers, ethnographers, business analysts, and marketers work together that we create truly revolutionary ideas. To facilitate Design Thinking and innovation, thus, organisations need to start thinking about truly cross-departmental, cross-disciplinary collaboration, and abandon the silo model of skills.

3. Create Environments Conducive to Innovation

The environments we inhabit and the activities we most engage in influence our thinking patterns, our understanding of things, and our ability (or lack thereof) to innovate.

This is why innovative companies like Google spend money to create workspaces that are filled with toys and unconventional equipment, and areas for creative thinking throughout their offices. It’s also the reason that many companies clear space in their busy annual schedules to send their entire staff on team-building getaways where they build rafts together, jump around in circles and, in the best way possible, behave like kids. However, it’s not only to make the company a fun and interesting place to work. It's about allowing for and tapping into the type of thinking which results in breakthrough innovation as opposed to churning out more of the same cookie-cutter patches to problems. Playing is risky business. You put yourself out there. Likewise, it takes courage to question status quo and come up with innovative solutions.

That’s why we need to create dynamic spaces, both physically and metaphorically, where people are able to embrace change, explore the unknown, experiment with radically new ways of thinking, and work together collaboratively.

Author/Copyright holder: Mathew Ingram. Copyright terms and licence: CC BY 2.0

Google is one of the major companies which prioritise to spend time and money creating playful workspaces filled with toys and unconventional equipment. The goal is to help employees feel safe and that it’s okay to come up with new and unconventional solutions in a playful manner.

IDEO Formed the Right Mindsets, Teams, and Environments for Innovation

How can you start forming the right mindsets, set up cross-disciplinary teams, and create playful environments to foster innovation? Let’s take an example. After the 2000 dot-com bubble burst, IDEO CEO Tim Brown decided that it was time to do a redesign of the organisation. In the redesign, IDEO transformed the way collaboration within the organisation, as well as with external partners, worked fundamentally. IDEO created the concept of “One IDEO”, which underscores the need to act not as independent design studios, but rather a single interconnected network of talents. The company also changed the way it organised its offices by abandoning the classic design studio model. Instead, they started adopting a “global practices” model, which helped teams organise according to global systems in areas like “Health Practice” and “Zero20” (which focuses on the needs of children up to the age of 20).

New organisational structures like that in IDEO — which are themselves subject to change as and when needed to better serve the needs of clients and the world — are needed to spur innovative collaboration between teams and create impactful solutions that make the world a better place. However, the changes do not have to be large-scale. While it’s nice to have adult-sized playgrounds like those in Google and Facebook campuses, it is more than enough to ensure that the organisational layout and philosophy is one that encourages and prioritises collaboration and innovation.

The Take Away

The challenges organisations and countries face today are much more complex and tricky than the ones we faced a few decades ago. Part of the reason is globalisation, which brought together different agents across the globe into an interconnected web of systems that affect one another. To solve these new, complex problems, Design Thinking steps in with a bold and newly systematised, non-linear human-centred approach. Design Thinking allows us to adopt a human-centred perspective in creating innovative solutions while also integrating logic and research. In order to embrace Design Thinking and innovation, we need to ensure that we have the right mindsets, collaborative teams, and conducive environments. When we align our mindsets, skills and environments, we are able to create innovations that allow us to survive the disruptions we might face in the near future. Keep in mind a deep desire to create a better situation for the world around us, and start creating a better world for yourself and the world.

References & Where to Learn More

Tim Brown, Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation , 2009

Idris Mootee, Design Thinking for Strategic Innovation , 2013

See Bruce Mau here .

Don Norman . “Rethinking Design Thinking” , 2013.

Bill Moggridge, “Design Thinking: Dear Don” , 2010.

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No one goes through life without experiencing an issue or problem they have to solve. As such, everyone needs to develop critical thinking and problem-solving skills to be successful. This statement is particularly true in the workplace, where people face significant and minor problems on a daily basis. When it comes to problem-solving, Americans rank among the worst in the developed world. We have previously discussed some decision making and problem solving examples and team activities for the workplace. However, this article will cover some of the ways that people approach problem solving in the workplace and their personal lives, as well as some problem solving steps you can implement today.

Different Approaches of Creative Problem Solving

There are many different approaches that people can take to develop and grow their problem solving skills. Big Think expert, theoretical physicist, and author Geoffrey West says that one method of problem solving is a systems thinking approach. This holistic, integrative, and systemic approach supplements the reductionistic approach to problem solving by focusing on the way different parts of an adaptive system interrelate and how systems evolve over time. In his Big Think course “Systems Thinking 101: A New Approach to Problem-Solving,” West says:

“In terms of my own career, I made a shift from thinking in this very traditional reductionistic way, which I love and admire and respect and still do, to some extent, to thinking in terms of complex adaptive systems. And I think systemic thinking requires this extraordinary integration of the big picture, the holistic picture, the systemic picture, with this reductionistic picture. And it’s not one or the other, and that’s something I’d like to stress: I think we need both.”

According to another Big Think expert and author Tim Ferriss, some of the best and most creative decisions are discovered from what he refers to as “empty space.” According to Ferriss in a Big Think interview :

“Three to five hour uninterrupted blocks of time are extremely critical if you want to connect the dots, if you want to have the space to allow yourself to have original ideas or at least original combinations of ideas you really need to block out that time and protect it at least once a week… there are many people who do this, Remet Set, for instance, who has a very, very successful multi, multimillion-dollar business that he built out of a blog he started long ago in college, which was very, very niche in its focus, he blocks out I believe it’s every Wednesday for three to five hours of time he’ll block it out for learning. Noah Kagan, another entrepreneur, does the same thing… Because as soon as you go into bullet dodging — or, like Wonder Woman, bullet blocking — mode with everyone else’s agenda for your time, which is very often the inbox or text messages, you’re DOA; you’re done.”

Problem Solving Steps You Can Implement in the Workplace

1. define the problem.

While you may have a general idea of the issue you wish to solve, it’s vital that you specifically define the issue and write it down. Read it over to ensure that you have properly defined the issue and know that it’s the exact problem to try solving.

2. Analyze the Issue and List Pertinent Factors That You Must Consider

This is where you can ask yourself some of the necessary creative problem solving questions: Who is affected by the issue? How will they be affected by any changes? Will this address all of or just some of the issue at hand?

3. Generate Potential Solutions

Do not just come up with one idea and run with it; take the time to come up with several viable alternatives to help further develop your problem solving skills. According to the American Society for Quality (ASQ), it’s essential to create a standard with which you can compare the intended results of the various proposed solutions. Don’t use these standards to judge which solution is best; just use it to come up with potential solution ideas at this stage.

4. Analyze the List of Solutions to Determine the Most Viable Option(s)

This is where you can also ask yourself some creative problem solving questions to determine the positive and negative aspects of each proposed solution. Will each solution resolve the issue without creating new problems? Does the solution fit within the confines of your organization’s culture, processes, etc.? And, is it scalable (or does it need to be)?

5. Select the Best Solution for the Issue

Evaluate each solution as a whole to determine whether it is the right fit for your employees, operations, and organization as a whole. This decision can be based on a set list of factors or even your “gut feeling” based on your years of professional experience. Together, these factors can help you narrow down the list.

6. Plan Your Next Course of Action and Implementing the Solution

This is where you should write down what you will do to solve this issue and chart out how you want to make it happen. This may involve time from you and others who will be included in the process. It also should entail planning follow-ups in the future to ensure that the work has been implemented.

7. Implement the Solution to the Issue

Now, all that is left is to put your plan into action and showcase the results of your problem solving skills. Follow the plan that you and your team have set out and be sure to follow-up to ensure the work is complete.

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Innovation in Business: What It Is & Why It’s Important

• 08 Mar 2022

Today’s competitive landscape heavily relies on innovation. Business leaders must constantly look for new ways to innovate because you can't solve many problems with old solutions.

Innovation is critical across all industries; however, it's important to avoid using it as a buzzword and instead take time to thoroughly understand the innovation process.

Here's an overview of innovation in business, why it's important, and how you can encourage it in the workplace.

What Is Innovation?

Innovation and creativity are often used synonymously. While similar, they're not the same. Using creativity in business is important because it fosters unique ideas . This novelty is a key component of innovation.

For an idea to be innovative, it must also be useful. Creative ideas don't always lead to innovations because they don't necessarily produce viable solutions to problems.

Simply put: Innovation is a product, service, business model, or strategy that's both novel and useful. Innovations don't have to be major breakthroughs in technology or new business models; they can be as simple as upgrades to a company's customer service or features added to an existing product.

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Types of Innovation

Innovation in business can be grouped into two categories : sustaining and disruptive.

• Sustaining innovation: Sustaining innovation enhances an organization's processes and technologies to improve its product line for an existing customer base. It's typically pursued by incumbent businesses that want to stay atop their market.
• Disruptive innovation: Disruptive innovation occurs when smaller companies challenge larger businesses. It can be classified into groups depending on the markets those businesses compete in. Low-end disruption refers to companies entering and claiming a segment at the bottom of an existing market, while new-market disruption denotes companies creating an additional market segment to serve a customer base the existing market doesn't reach.

The most successful companies incorporate both types of innovation into their business strategies. While maintaining an existing position in the market is important, pursuing growth is essential to being competitive. It also helps protect a business against other companies affecting its standing.

Learn about the differences between sustaining and disruptive innovation in the video below, and subscribe to our YouTube channel for more explainer content!

The Importance of Innovation

Unforeseen challenges are inevitable in business. Innovation can help you stay ahead of the curve and grow your company in the process. Here are three reasons innovation is crucial for your business:

• It allows adaptability: The recent COVID-19 pandemic disrupted business on a monumental scale. Routine operations were rendered obsolete over the course of a few months. Many businesses still sustain negative results from this world shift because they’ve stuck to the status quo. Innovation is often necessary for companies to adapt and overcome the challenges of change.
• It fosters growth: Stagnation can be extremely detrimental to your business. Achieving organizational and economic growth through innovation is key to staying afloat in today’s highly competitive world.
• It separates businesses from their competition: Most industries are populated with multiple competitors offering similar products or services. Innovation can distinguish your business from others.

Innovation & Design Thinking

Several tools encourage innovation in the workplace. For example, when a problem’s cause is difficult to pinpoint, you can turn to approaches like creative problem-solving . One of the best approaches to innovation is adopting a design thinking mentality.

Design thinking is a solutions-based, human-centric mindset. It's a practical way to strategize and design using insights from observations and research.

Four Phases of Innovation

Innovation's requirements for novelty and usefulness call for navigating between concrete and abstract thinking. Introducing structure to innovation can guide this process.

In the online course Design Thinking and Innovation , Harvard Business School Dean Srikant Datar teaches design thinking principles using a four-phase innovation framework : clarify, ideate, develop, and implement.

• Clarify: The first stage of the process is clarifying a problem. This involves conducting research to empathize with your target audience. The goal is to identify their key pain points and frame the problem in a way that allows you to solve it.
• Ideate: The ideation stage involves generating ideas to solve the problem identified during research. Ideation challenges assumptions and overcomes biases to produce innovative ideas.
• Develop: The development stage involves exploring solutions generated during ideation. It emphasizes rapid prototyping to answer questions about a solution's practicality and effectiveness.
• Implement: The final stage of the process is implementation. This stage involves communicating your developed idea to stakeholders to encourage its adoption.

Human-Centered Design

Innovation requires considering user needs. Design thinking promotes empathy by fostering human-centered design , which addresses explicit pain points and latent needs identified during innovation’s clarification stage.

There are three characteristics of human-centered design:

• Desirability: For a product or service to succeed, people must want it. Prosperous innovations are attractive to consumers and meet their needs.
• Feasibility: Innovative ideas won't go anywhere unless you have the resources to pursue them. You must consider whether ideas are possible given technological, economic, or regulatory barriers.
• Viability: Even if a design is desirable and feasible, it also needs to be sustainable. You must consistently produce or deliver designs over extended periods for them to be viable.

Consider these characteristics when problem-solving, as each is necessary for successful innovation.

The Operational and Innovative Worlds

Creativity and idea generation are vital to innovation, but you may encounter situations in which pursuing an idea isn't feasible. Such scenarios represent a conflict between the innovative and operational worlds.

The Operational World

The operational world reflects an organization's routine processes and procedures. Metrics and results are prioritized, and creativity isn't encouraged to the extent required for innovation. Endeavors that disrupt routine—such as risk-taking—are typically discouraged.

The Innovative World

The innovative world encourages creativity and experimentation. This side of business allows for open-endedly exploring ideas but tends to neglect the functional side.

Both worlds are necessary for innovation, as creativity must be grounded in reality. You should strive to balance them to produce human-centered solutions. Design thinking strikes this balance by guiding you between the concrete and abstract.

Learning the Ropes of Innovation

Innovation is easier said than done. It often requires you to collaborate with others, overcome resistance from stakeholders, and invest valuable time and resources into generating solutions. It can also be highly discouraging because many ideas generated during ideation may not go anywhere. But the end result can make the difference between your organization's success or failure.

The good news is that innovation can be learned. If you're interested in more effectively innovating, consider taking an online innovation course. Receiving practical guidance can increase your skills and teach you how to approach problem-solving with a human-centered mentality.

Eager to learn more about innovation? Explore Design Thinking and Innovation ,one of our online entrepreneurship and innovation courses. If you're not sure which course is the right fit, download our free course flowchart to determine which best aligns with your goals.

About the Author

NEW BOOK: Pivotal: Creating Stability in an Uncertain World 

Innovation Insights by Stephen Shapiro

Innovation vs Problem-Solving

Language is important.

Although I’ve spent 25 years focused on innovation, not everyone likes that word.

In this 1:56 video, I share why I often use the term problem-solving instead – and how that perspective can accelerate change in any organization.

Learn more about my latest innovation book at  www.InvisibleSolutionsBook.com .

How We Created a 20,000-Person Culture of Innovation at Accenture… And You Can Do It, Too

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Rebels and the Innovation of Healthcare

Creative problem-solving in the healthcare sector..

Posted September 2, 2024 | Reviewed by Lybi Ma

• A recent book examines healthcare innovators that are outside of the medical field.
• The four roles of innovators parallel the creative problem-solving process.
• Collaboration can be a common theme in innovation in the field.

Many people have experienced situations when the medical system didn’t live up to expectations. Various complaints would likely be heard: the wait time for an appointment, expensive medication , and frustration with insurance not covering a procedure.

And that is not to disparage anyone working in or connected to the medical field. No doubt they are difficult jobs working in a complex system.

Yet some people—patients, that is—strive for healthcare changes.

Susannah Fox knows this. Her book, Rebel Health , celebrates innovators. For over 20 years, she has talked to people moving to improve and advance healthcare.

It is a story of people like Dana Lewis, diagnosed with Type 1 diabetes, who feared dying in her sleep because the alarm monitoring her blood sugar wasn’t loud enough to awaken her if she experienced nocturnal hypoglycemia. Despite being told by the manufacturer that the volume was sufficient, Lewis powered forward to create her own device, which was subsequently shared online.

Improvements come from problem-solvers in different arenas, often outside of the healthcare profession. In Fox’s words, “This revolution is the start of something big—for them and all of us” (p. 2).

Fox’s paradigm of innovators offers four roles: Seeker, networker, solver, and champion. A seeker takes action after realizing that something is not quite right. A networker finds community to learn more about a condition or diagnosis. The solver sees the challenge and starts to prototype solutions. Finally, the champion pivots a new idea to innovation by sharing resources with others.

Her notion of rebels in solving healthcare mirrors the creative problem-solving process, especially the framework found in the Creative Education Foundation (2016). In this creative problem-solving (CPS) model, the first step, clarify , narrows down the challenge at hand. The second part, ideate , involves divergent thinking in producing potential ideas for the challenge. The next segment, develop , takes a promising idea and tests it in various ways before the last step, implement finalizes a plan for moving forward.

The healthcare innovators taken from Fox have parallels to the CPS model.

Take the solver in healthcare, the person who resembles the develop notion in CPS. Just as the person(s) in the develop phase analyzes and improves identified solutions, the solver gets access to needed information, “bending and sometimes breaking rules in pursuit of a goal” (Fox, 2024, p. 11). The develop component can include questions surrounding the positive points of the challenge and the beneficial outcomes of the solution. Data to the solver is crucial and challenging, especially when it is needed, for instance, to build a new device to improve patients’ health.

Meanwhile, the champion, that person in healthcare innovation who takes rough ideas to scale, involves an implementation mindset in the CPS model. In Fox’s words, they “fast-track innovations” (p. 11). From the CPS perspective, they work to identify “assisters” and “resisters” and create action statements to bring a solution to fruition.

“Whatever health challenge people are facing, they are not alone,” she told me via email. “There are people who would love to help them if only they knew how to be found. Seekers stand ready to go on the hunt for information. Networkers will provide peer support and advice. Solvers may have invented the device or a needed solution, and champions can boost a team toward mainstream recognition.”

Fox maintained that seekers, networkers, solvers, and champions are dedicated to healthcare transformation throughout the world. Perhaps their efforts, which can run consistent with the CPS design, may reduce the list of healthcare complaints in the years ahead.

Creative Education Foundation (CEF). (2016). Educating for creativity: Level 1 resource guide . Author.

Fox, S. (2024). Rebel health: A field guide to the patient-led revolution in medical care . The MIT Press.

John McCarthy, Ph.D., is an educator, author, and international speaker specializing in creative thinking/innovation, strengths-based approaches, and wellness.

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TechBullion

Quantum computing and ai: the future of problem solving in business.

The world of business is constantly evolving, and at the forefront of this evolution are two groundbreaking technologies: quantum computing and artificial intelligence (AI). Separately, these technologies are reshaping industries, but together, they represent the future of problem-solving for businesses. As companies look for ways to gain competitive advantages, streamline operations, and unlock new value, the fusion of quantum computing and AI promises to bring an unprecedented wave of innovation.

Understanding Quantum Computing

Quantum computing isn’t just an incremental advancement in technology; it’s a leap forward that promises to fundamentally change the way we process information. Unlike classical computers that operate using bits in binary states (0s and 1s), quantum computers use qubits , which can exist in multiple states at once thanks to the principles of quantum mechanics. This superposition enables quantum computers to process vast amounts of data and perform complex calculations at speeds unimaginable with today’s classical systems.

For businesses, this means that problems previously considered unsolvable due to their complexity can now be tackled head-on. From optimising supply chains to modelling financial markets, quantum computing holds the potential to handle data and calculations at a scale that would otherwise be impossible.

The Role of AI in Today’s Business Landscape

AI has already demonstrated its value across a wide range of industries. Machine learning algorithms are analysing massive datasets, making predictive decisions, and automating processes that once required human intervention. AI has transformed customer service, enhanced marketing strategies, optimised logistics, and revolutionised healthcare, to name just a few examples.

However, as powerful as AI is, it has its limitations. The sheer amount of data required to train advanced machine learning models, as well as the computational power needed to perform certain tasks, can be bottlenecks for even the most advanced businesses. This is where quantum computing comes into play.

The Synergy Between Quantum Computing and AI

The real magic happens when quantum computing and AI come together. Quantum computing offers the computational power to train AI models faster, optimise them for better performance, and process larger datasets more efficiently. Here are a few ways this synergy could transform industries:

• Optimisation at Scale : In industries like logistics and transportation, AI is already being used to optimise routes, reduce fuel consumption, and streamline operations. But with quantum computing, AI can handle exponentially more variables, leading to more precise and efficient solutions. Imagine supply chains that adapt in real-time, factoring in weather patterns, traffic conditions, and geopolitical events to deliver the best possible outcomes.
• Drug Discovery and Healthcare Innovation : The healthcare industry is notoriously slow when it comes to developing new treatments and drugs. AI is already helping researchers identify potential candidates for drug therapies by analysing huge datasets of molecular structures. Quantum computing could take this a step further by simulating complex molecular interactions with far greater accuracy, significantly speeding up drug discovery processes.
• Financial Risk Modelling : AI has become an essential tool in financial services for managing risk, detecting fraud, and automating trading. Quantum computing will enhance these AI models by processing multiple complex variables simultaneously. In risk modelling, for instance, quantum-enhanced AI could better predict market fluctuations, helping businesses mitigate risks and optimise investments in ways that were previously impossible.
• Accelerated AI Training : One of the biggest challenges businesses face in deploying AI at scale is the time and computational power required to train models. Quantum computing can reduce this bottleneck by processing training data faster and enabling more complex models to be built and tested in less time.

Preparing for a Quantum-AI Future

While quantum computing is still in its early stages, businesses that start preparing now will be better positioned to reap the benefits as the technology matures. Early adopters can already begin experimenting with quantum algorithms through cloud-based quantum computing platforms offered by companies like IBM, Google, and Microsoft. At the same time, businesses should continue to invest in AI talent and infrastructure, ensuring they are ready to integrate quantum computing capabilities as they become more accessible.

Quantum computing and AI together have the potential to solve problems that are simply too large or complex for classical computing methods. As more businesses explore this convergence, they will unlock new opportunities to improve efficiency, reduce costs, and innovate in ways that give them a significant edge in the market.

Real-World Applications

Some forward-thinking companies are already exploring the intersection of quantum computing and AI. For example, pharmaceutical companies are testing quantum algorithms to model drug interactions more quickly and accurately. Financial institutions are using quantum computing to enhance AI-powered trading systems, creating more robust risk management models. Manufacturers are deploying quantum-enhanced AI to optimise factory processes, reducing waste and improving output.

These are just a few examples of how businesses can leverage quantum-AI integration to transform their operations. Over the next decade, as quantum computing becomes more viable, we can expect to see this synergy revolutionise industries at a pace not seen since the advent of the internet.

Quantum computing and AI are no longer just futuristic concepts. They are rapidly evolving technologies that are reshaping how businesses approach problem-solving. The ability to process vast amounts of data, run complex simulations, and optimise operations with unprecedented accuracy will give businesses a powerful toolset to tackle the most pressing challenges of the 21st century.

As we move into this new era, businesses that invest in understanding and adopting these technologies will not only survive but thrive. The future of problem-solving is here, and it’s being driven by the incredible potential of quantum computing and AI. The question for businesses is no longer whether to embrace these technologies, but how quickly they can harness their transformative power.

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Jason Miller helps influential brands and celebrities create generational wealth with their businesses | CEO, Strategic Advisor Board .

Traditionally, a CEO has been able to use intuition to foresee market trends and identify opportunities to lead the company to success. Based on years of experience in the industry, they would make strategic decisions.

Today, a CEO still needs to have a vision and be able to lead the company to growth by taking new, bold directions. But as AI is enhanced and becomes even more powerful, it can provide insights into customer decision making through historical data, market trends and research and development (R&D).

To be the best CEO you can be today, you can use AI to your advantage to help blend intuition with data and shift to faster problem-solving and innovation. With AI, I've found employees can also have more autonomy and become empowered, and you can stay on top of regulations and your brand perception as seen from your customers’ eyes.

Shifting Away From Top-Down Leadership

Many organizations operate with a top-down approach to decision making. This means the CEO is the top decision maker, and commands trickle down to the various teams of management for execution. This style is very centralized, and a small group of executives are making decisions for the entire organization. This can lead to a lack of autonomy from employees and stifle any potential collaboration between departments to make more informed decisions.

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I've found that shifting away from this approach can allow the innovation process to speed up. Since ideas can come from anywhere in the organization, innovation is decentralized. In order to use AI to the company’s advantage, the CEO has to be open to collaborating with others from the organization.

One of the biggest shifts is often from rigid and static leadership styles to adaptive and agile leadership approaches. With this style, decision making can become faster and more dynamic. The CEO can use AI to analyze real time and make adjustments as needed to stay ahead of problems with contingency plans.

AI can allow an organization to be flexible and adapt faster to market or customer changes. Employees at all levels of the organization will be able to help the company make better choices. This leads to flattened hierarchies as the need for so many layers of management decreases. This allows employees to have access to leadership and be more involved in decision making.

The Use Of Regulatory And Compliance Data

CEOs can use AI to regularly monitor any changes that may take place within compliance data, which is used to make sure companies follow laws and industry standards. CEOs can use AI data to avoid penalties. AI also tracks environmental, social and governance metrics , which can help companies stay in line with social and sustainability strategies.

This not only works with local or national laws, but also globally, so you can be prepared in any place you operate in or sell to. In turn, your company could become a leader in your industry and set the gold standard for others to follow. I've noticed this type of compliance is important for attracting investors and maintaining your company’s reputation for high standards.

Brand Perception

CEOs can use AI to analyze everything from customer feedback to social media interactions and reviews in order to gauge what the public thinks of the brand. The brand’s reputation can be handled in real time if there’s an issue that needs fixing. Specifically, AI can track how the company is viewed in different markets and within different target audiences.

Using natural language processing (NLP) algorithms, CEOs can use AI to identify emotions and opinions customers have about the brand or products/services. A CEO can know in real time if a product launch isn’t going well and if there needs to be immediate action to address an issue publicly. All of this information can help the CEO form better marketing strategies and communication to keep the brand favorable in the customers’ eyes.

Empathetic Leadership

I’ve talked about how CEOs can leverage AI data to provide customer insights, but let’s talk more about how it can help employee well-being, engagement and performance within your organization.

Using NLP algorithms, AI can detect sentiment and collect data from employee feedback and other communication channels to see if there is a pattern of burnout or dissatisfaction. The CEO can then take targeted actions like offering wellness programs, providing more mental health days or adjusting workloads. Employees may feel more valued and appreciated, which can lead to better retention and the cultivation of an empathetic corporate culture.

Using AI in the role of CEO leadership is uncharted territory, and there may be ethical dilemmas that come up. It’s important to use your intuition, your feelings and real-life experience along with AI data to make the best decisions for your employees, customers and company.

As leaders, we must proceed with caution in the use of AI. Just like there are many strengths to integrating AI into your company's operations, it can also have negative effects on your staff. One of the least-thought-of drawbacks of AI is considering the health and welfare of your employees. Employee stress over the potential drawbacks of AI is a real problem we are facing today.

This being said, as a leader, ensure that you have an open dialog with your employees to ensure they feel safe and protected in their jobs despite the integration of AI. If we work hand in hand with AI, I think we can achieve far greater results for our companies and workforces.

Integrating AI within the CEO’s traditional role and tasks can allow companies to be more efficient and have both an intuition-based style and a data-driven style of decision making. This can lead to adaptability and innovation on a faster level. CEOs will be able to create a collaborative and agile work environment where decisions get made faster, and employees on all levels of the organization participate in problem-solving and decision making tasks.

CEOs will need to make ethical decisions and override AI when appropriate by using their skills, experience and insights. This way, they can help companies succeed by using empathy and integrity.

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The Arizona State University Artificial Intelligence Cloud Innovation Center (AI CIC), powered by Amazon Web Services (AWS), collaborated with the International Swaps and Derivatives Association (ISDA) to address the challenge of automating the extraction and classification of clauses from the ISDA Credit Support Annex (CSA), a key document used in the derivatives market to manage credit risk between parties involved in derivative transactions. This collaboration aimed to streamline the complex process of identifying and categorizing clause variants, ultimately presenting the information in a JSON format that aligns with the Common Domain Model (CDM), a framework developed to standardize and streamline the representation of financial transactions and their associated data.

CSAs are a key component in the derivatives market, essential for managing counterparty credit risk, while the CDM is crucial for standardizing and improving the efficiency of financial transactions. However, the process of extracting and classifying clauses from legacy CSAs is time-consuming and prone to errors. The challenge was to develop a Proof of Concept (POC) that could automatically extract clauses, identify variants, and present this information in a structured JSON format.

The AI CIC team employed a multi-phase approach to develop a scalable solution using AWS cloud services. The project involved creating an agent architecture that leveraged prompt engineering techniques to accurately extract and classify clauses from the ISDA Master Agreement. The solution utilized Amazon Bedrock, AWS Lambda, and AWS S3 to manage document uploads, trigger clause extraction processes, and store the resulting CDM JSON structures. Key milestones in the project included the development of specialized prompts for each clause, testing various large language models (LLMs) to determine the best fit, and integrating a bedrock agent that could handle the extraction and classification tasks. The Claude 3 OPUS LLM was selected for its superior performance in reasoning, classification, and handling complex prompt instructions.  

Industry Impact and Problem Solving

The successful implementation of this project demonstrates a significant advancement in automating legal document information extraction. By leveraging AI and cloud technologies, the project provides a scalable and efficient solution for the derivatives industry, reducing manual labor and minimizing the risk of errors. This automation not only enhances the accuracy of clause extraction but also enables faster decision-making processes, ultimately contributing to more robust and stable financial markets.

"I was introduced to the Generative AI work at the Cloud Innovation Center by our AWS contact and had the privilege of collaborating with them to build a proof of concept (PoC) for an AI process that converts legacy legal documents into our common domain model format. I want to extend my gratitude to Arun, Sai, and the entire team for their excellent work in developing the PoC. This achievement allows us to further develop and promote the Common Domain Model and facilitates the integration of legacy documents using the CDM. The team's work was fast and professional, making the experience outstanding. I highly recommend this program to other organizations in the public sector."

David Lee, Senior Director, Digital Transformation,International Swaps and Derivatives Association, Inc. (ISDA)

Potential for wider application.

The techniques and architecture developed in this project have the potential to be applied across various industries where legal document processing is a critical task. The solution can be adapted to other complex agreements, such as contracts in finance, real estate, and corporate law, providing a powerful tool for organizations to automate the extraction and analysis of critical information.

Supporting Artifacts

Building on the success of this POC, the next steps include fine-tuning the solution by curating additional example statements for each clause variant and further optimizing the prompt engineering process. Additionally, the project will explore extending the agent’s capabilities to handle a broader range of clauses and variants, increasing the system’s versatility and application across different legal documents.

About the ASU CIC

The ASU Smart Cities Cloud Innovation Center (CIC) is a strategic relationship with Amazon Web Services (AWS) and is supported by AWS on ASU’s Innovation campus - SkySong. The mission of the CIC is to drive Innovation Challenges that materially benefit the greater Phoenix metro area and beyond. The CIC will do this by solving pressing community and regional challenges, using shareable and repeatable technology solutions from ideation through prototype, as a service for the greater human good.

The CIC also provides real-world problem-solving experiences to students by immersing them in the application of proven innovation methods in combination with the latest technologies to solve important challenges in the public sector. 

The challenges being addressed cover a wide variety of topics including homelessness, water conservation, vandalism, pedestrian safety, digital service delivery and many others. The CIC leverages the deep subject matter expertise of government, education and non-profit organizations to clearly understand the customers affected by public sector challenges and develops solutions that meet the customer needs.

For more information on the ASU CIC, to read about projects or to submit a challenge, please visit  https://smartchallenges.asu.edu .

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How to Manage Breakthrough Innovation

A conversation with Alphabet’s Astro Teller on taking big bets on new ideas and tolerating the fear of failure.

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How do you guide a team working on innovative projects—when there is no existing playbook?

Astro Teller says he uses a vetted approach to decision-making for the innovative projects that he and his teams undertake at X, Alphabet’s R&D engine.

Teller is the Captain of Moonshots at X, which he helped launch at Google in 2010. His mission there is to invent and launch new technologies that address serious problems in the world. But those technologies must also create the foundations for substantial new businesses for Google’s parent company, Alphabet. They’ve worked on a pill that detects cancer, cars that drive themselves, and mega-kites that work as turbines to collect wind energy, to name just a few examples.

In this episode, Teller offers key lessons for managing the process that delivers breakthrough innovations. You’ll learn how he decides to keep investing in a project, and how he knows when it’s time to pull the plug. You’ll also learn how he assembles teams and what qualities he looks for in potential new hires.

Key episode topics include: leadership, innovation, technology and analytics, leadership and managing people, experimentation, creativity, breakthrough, Alphabet, Google.

HBR On Leadership curates the best case studies and conversations with the world’s top business and management experts, to help you unlock the best in those around you. New episodes every week.

• Listen to the original HBR IdeaCast episode: X’s Astro Teller on Managing Moonshot Innovation
• Find more episodes of HBR IdeaCast .
• Discover 100 years of Harvard Business Review articles, case studies, podcasts, and more at HBR.org .

HANNAH BATES: Welcome to HBR on Leadership , case studies and conversations with the world’s top business and management experts, hand-selected to help you unlock the best in those around you.

Astro Teller says there IS a vetted approach to creativity and decision-making for innovative projects that he and his teams use at X, Alphabet’s R&D engine.

Teller is the captain of moonshots at X, which he helped launch at Google in 2010. His mission there is to invent and launch new technologies that address serious problems in the world and create the foundations for large, new businesses for Google’s parent company, Alphabet. They’ve worked on a pill that detects cancer, cars that drive themselves, and mega-kites that collect wind energy.

In this episode, Teller shares key lessons for managing the process of developing breakthrough innovations.

You’ll learn how he decides to keep investing in a project, and how he knows when it’s time to pull the plug. You’ll also learn what qualities he looks for when he hires for innovation projects and assembles teams, and why humility is so essential.

  This episode originally aired on HBR IdeaCast in March 2023. Here it is.

ALISON BEARD: Taking big swings isn’t always easy in business, especially when you don’t know what will pay off – or how long it will take. Today’s guest has a high tolerance for that kind of uncertainty – exploring big problems, experimenting with solutions, failing, trying again, sometimes succeeding, sometimes not.

Astro Teller is Captain of Moonshots at X, Alphabet’s dedicated innovation factory. He helped launch it after cofounding a number of other companies, teaching at Stanford University, and studying computer science there and at Carnegie Mellon. His teams at X work on everything from getting remote populations online, to monitoring ocean health, and to using machine learning to improve supply chains.

I spoke with Teller during a live virtual conference – HBR at 100: Future of Business – where audience members were also able to submit questions. Here’s our conversation.

ASTRO TELLER: Thanks for having me.

ALISON BEARD: First I have to ask about your name. It seems like kismet that someone called Astro would become the head of a moonshot factory. So how did that happen?

ASTRO TELLER: I agree, it seems like fate, but it was a typo at Stanford. I didn’t want to leave any blanks on my application to Stanford. I don’t have a middle name, so I felt like an idiot that I wrote my last name, comma, first name. I had to leave a blank because I don’t have a middle name. Almost nobody called me Astro in high school. It was a not friendly nickname from the soccer team. Basically they thought my flat top looked like a patch of AstroTurf, so I wrote in Astro. And then I think there was a type-in error because I didn’t have a middle name, and so someone typed in Astro as my legal name, and just stuck.

ALISON BEARD: And the rest is history. So, you co-founded X within Google, and the mission is to solve real problems and have a real impact, not create gadgets or technology for its own sake. So, how do you identify the problems that you want to work on?

ASTRO TELLER: So, as you were just saying, X’s mission is to invent and launch breakthrough technologies that can help tackle a huge problem with the world and create the foundations for large sustainable businesses for Alphabet. As a result, our remit is very wide. It needs to be something that can really help the world, something that can be good for Alphabet, but there’s no specific industry that it’s in. We’ve sort of caught different waves over time. The wave that I would say X is in right now, we’re largely focused on sustainability, but it used to be robotics more, mobility.

And in any particular area, really we say, what is an idea that sounds like science fiction but would be really important if it turned out to be true? And how cheaply can we ask the question: is this just a bad idea or is it once in a generation opportunity?

And we look at thousands of these things every decade. That’s our job is to sort of have a very wide funnel and then to filter very aggressively. So, at the wide part of the funnel, we’ll look at almost anything as long as it has those basic characteristics: that it could be a breakthrough technology, it could help the world tackle a really serious problem, and build a foundation for a large sustainable business for Alphabet.

ALISON BEARD: And so how does that filtering work? How do you narrow down with so many good ideas?

ASTRO TELLER: At the beginning, we’ll try anything that has those characteristics. And on day one, we don’t need anything except that it has those characteristics. It’s a hypothesis to test. But afterwards, what we’re saying is for every dollar that we put into this machine, we don’t care if the answer is yes or no. The answer is no for almost everything that we look at. The question is how cheaply, how wisely can we get to the answer? Is this a great idea or one of the bad ones? And again, most of them are bad ones.

And so, we’re always looking for evidence. How can de-risk this? How can we learn, turn uncertainty into risk? Because we don’t even mind risk. But what we’re buying down, especially in the first couple of years of this thing is, what is this really? What does it want to be when it grows up? What are the hard parts actually about this thing? And that how can we get it into the world very early on so that we start to learn even faster about the ways in which it might not work, so that we can kill it and get onto the next idea?

ALISON BEARD: Related to that, I’ve read about a project management concept that you call monkeys and pillars to help you make those decisions. So, tell this audience about it.

ASTRO TELLER: So, I was on a conversation very much like this one. It was about seven years ago, and up until then I had said we have to work on the hardest parts of the problem first. And that made sense to me, but for whatever reason, it hadn’t really taken at X. And so, the interviewer, like you are now, asked, “What do you mean by that?”

And so, I gave this hyperbolic statement, which was, let’s say that you’re trying to train a monkey to stand on the top of a 10-foot pedestal and recite Shakespeare. Which should you do first, train the monkey or build the pedestal? And so, this has become a joke inside X, but because it’s easy for people to remember.

In that extreme case, it should be obvious to all of us that if you build the pedestal, you could be like, “Hey, look boss, I built a pedestal.” And the boss would be like, “Hey, good job, Astro.” But you haven’t actually made any progress. There was no chance that you couldn’t make the pedestal. All of the risk was on training the monkey. So clearly what we should do first is try to train the monkey because if we can’t, the pedestal is a total waste of time, and if we can, we can always build the pedestal afterwards. So, something about that hyperbolic statement has become a meme inside X, and people actually put little icons of monkeys next to the parts of their effort, which they believe are the really critical parts to push on to understand whether or not this could actually be a really once in a generation opportunity.

ALISON BEARD: Okay. So, let’s assume that people are doing a good job of beginning to train the monkey. You have some positive results, you have some negative results. How are you deciding when a project needs further investment, you’re green lighting it to keep going, or that something should be killed?

ASTRO TELLER: It depends, I have to be honest with you, there isn’t a single answer, but let me give you some of the kinds of things that we look for. If a team says, “We know what the right thing to do is. Just leave us alone so we can build it.” And that’s so the wrong answer when it comes to moonshots, that we might stop it just because they said that, and we will definitely stop it as soon as they turn out to be wrong, which they inevitably will. A team that shows up saying, “We’re going to audaciously try thing after thing,” but say from the first moment, “We’re probably wrong,” they have a better chance of turning the loop faster, so we’re going to bet on them longer.

Sometimes projects are inherently slow in their learning loops because of what they’re trying to do. And when that happens, all things being equal, it seems like less of a good bet than a project that has somehow figured out how to get into the real world and learn something every single week. Our experience is the faster you’re learning, the more likely you are to be successful, kind of independent of how things are going this month, even this quarter. So, it’s really a measure of learning per dollar that we’re getting. The ones where the learning per dollar is high, we tend to keep betting on, and the ones where the learning is low, even if the progress looks good, we tend to slow down or just stop.

ALISON BEARD: I like that, learning per dollar, it’s a new metric. So, we’re going to dig into some of the specific projects that you’re working on in a little bit, but more generally, what sort of time horizons are you looking at when you are thinking about a successful spinoff or that a project has been completed in X’s term, and is ready to move on to the next thing?

ASTRO TELLER: Ten years is sort of what we say here at X, and that allows for us to play the long game and there’s a lot of incremental value that can be produced, and a lot of incremental goodness for the world that you can go after when you play the long game. 10 years though, I mean, I would say on average things that ultimately graduate from X to become other bets, our most recent other bet for example was minerals in the computational agriculture space. It’s our moonshot for agriculture. It was here for about six years before it graduated. So, by saying 10 years, we don’t necessarily mean that it will be at X for 10 years. We mean that certainly within 10 years, the thing that we are starting from a cold start should be pretty interesting and important within 10 years.

ALISON BEARD: So, I’d love to just better understand your place within Google and Alphabet because I think other large corporations – or even smaller ones – can learn from it. How do you have an incubation factory within your own company? So how does your funding work? How much interaction do you have with the rest of the company, and then how do you do that spinoff process?

ASTRO TELLER: We’re not the only source of innovation at Alphabet of course, but we are an innovation engine for Alphabet. So, our job is to help Alphabet, Google’s parent, have new problems and hopefully find new solutions to those problems. Over time, some of the ones that we created earlier on like Google Brain, Verily, the life science business for Alphabet, Waymo, the self-driving cars, Wing, the drones for package delivery. More recently, Intrinsic, an attempt on our part to democratize how the manufacturing process works and the automation of robotics in manufacturing.

As I was mentioning, we’ve just recently spun out this new effort in and made it a company in Alphabet, Other Bet as we call it, in the agriculture space. In each of these cases, these are still nascent businesses, and what we would hope over time is that at least some of these become large, important, good for the world and valuable to Alphabet. So we care very much what’s happening at Google, but we’re like a little sister to Google on the side, trying to make things that will ultimately be important to Alphabet and help Alphabet to continue to grow and do good things for the world.

ALISON BEARD: So, let’s talk about talent. What kind of people are you looking for to help you with these moonshots, and is there enough of it around?

ASTRO TELLER: I think that there’s an incredible amount of it latent inside people, but finding people who have unleashed themselves is pretty hard. We think about this a lot, and we actually spend a lot of time and energy, even once we’ve hired people, helping them to unleash themselves. So really a lot of our interviewing process is about trying to decide if people are ready to unleash themselves rather than that they’re sort of done or perfect in any way.

The top four things that we look for: fearlessness, which tends to map to audacity and creativity and things like that. Humility, because audacity, fearlessness is critical so that you will try really out there things, but then you need humility to be able to say right after you start trying it, “You know what? This probably isn’t going to work. Let’s use evidence, verify that it isn’t one of the really great ones so we can throw it away and get onto the next one.” Teamwork, because innovation is fundamentally a team sport. And then a growth mindset. If we’re trying to build learning machines inside of a moonshot factory, if each of the teams is supposed to be a learning machine, then we need each of the humans here at X to be a learning machine.

ALISON BEARD: You have what I imagine are very brilliant, creative, probably a little quirky people. How do you decide who will work well together in those teams that you’re talking about, and do they require a different style of management?

ASTRO TELLER: The style of management is somewhat different. I would say that the difference probably is even bigger at the sort of X level. We don’t have org charts the way you would think of normal businesses working. There’s a lot of fluidity inside of X. Because if you were to come to X and start, I don’t know, flying car company or whatever it is you were trying to start here at X, you’re going to turn out to be wrong. Almost everybody is almost all the time. So then your thing stops, and then you’re going to find a new thing to be a part of. And so that fluidity kind of ruins the sort of hierarchy and politics that often goes on inside of groups.

So, I think of myself as a culture engineer, and a lot of the way that X is wired is if you ask people to do a bunch of really basic things like play the long game, show up with a lot of audacity, but also a lot of humility. If you’re asking them to practice running these experiments and then being intellectually honest about the experiments after they’ve run them, this is all really simple stuff. It’s easy to say. And just like a diet, actually practicing the diet is super hard. Everything I’ve described, even at X, ferociously hard to do.

And so, everything at X is wired around trying to make you not feel stupid about actually showing up humble and open-minded, with a growth mindset. Why are you going to kill your project if you think that your bonus or your ability to get promoted or the next thing that you’re going to get to do is going to be harmed by that intellectual honesty? Which is why there isn’t a lot of intellectual honesty floating around. And so we are like back at basics all the time, saying what do we need to do to send the hundreds and hundreds of signals necessary so that everyone at X naturally does the things we’re actually asking them to do, and that they tend not to do at most other businesses?

ALISON BEARD: Yeah, so I mean, if you’re struggling with it, you can only imagine what it feels like at more traditional organizations that want to be more X-like. So, what advice do you give leaders of other organizations, particularly outside the tech sector, about how to develop the kind of culture you’re talking about?

ASTRO TELLER: I think it really comes down to, A, how serious are you about the thing that you want? And then, if you’re really serious about it, then you have to commit to the practice of actually making people feel good about doing it. So, here’s my one-hour innovation lecture in 60 seconds. Choice A, choice B. Choice A, you can give a million dollars of value to your business this year guaranteed, or choice B, you can give a billion dollars of value to your business this year, but it’s not guaranteed. It’s one chance in 100. A, million guaranteed. B, billion, one chance in 100.

I’ve done this all over the world and I say, “Who’s choosing choice A?” Nobody raises their hand. “Who’s choosing choice B?” Everybody, big smile on their face, raises their hand. And I say, “Okay, now leave your hand up if in your wildest dreams, on their best days, your manager, your CEO, your board of directors supports you choosing choice B, even kind of a little bit.” And every hand in the room goes down and then I say, “You don’t need a lecture on innovation. You need a new manager.” This is the problem, is everyone asks for innovation, but they’re not actually willing to support the innovation because innovation is mostly about making mess. And you can try to do it efficiently, that’s what we try to do, but you can’t make the mess go away and almost nobody is actually tolerant of the mess.

ALISON BEARD: So how do you get leaders, managers to be more tolerant of the mess?

ASTRO TELLER: How badly do you want a factor 10 increase in value? The reason everyone raised their hand for choice B is because it has 10 times the expected utility of choice A, and that’s what innovation is. It is literally worth that much more. So I guess you have to decide whether you know want your 10% improvements or your 10X improvements. If you want the 10X improvements, you have to take a really long time horizon.

You have to have a portfolio because you will only get the payoff, the expected utility payoff over long periods of time, over a wide range of things. And then you have to be able to help everyone there be in it to sort of do the card counting. We’re not going to be gamblers of innovation, we’re actually going to be card counters of innovation, following a process and trusting that that process over very long periods of time will get us that 10X that we’re looking for.

ALISON BEARD: Before we get to audience questions, I want to ask you rapid fire about some specific projects that you’re excited about, because everyone wants to know what the next X moonshot is. So, Chorus.

ASTRO TELLER: People have been trying for 30 years to track all the physical things in the world so that we can improve the logistics supply chains, and it looks like we might have a way to do that that is much less hardware intensive, and that would be transformative for the world of logistics and supply chains.

ALISON BEARD: Yeah, particularly coming out of the COVID-19 crisis. So, Taara…

ASTRO TELLER Yeah. We have a way of shooting a laser up to 20 kilometers. It’s eye safe, so you could just go up like this and it still wouldn’t hurt you, and it moves information at 20 gigabits per second. So, you have to have line of sight between these two things, but you can just strap them to two poles as long as they can see each other. If a bird flies in between, then you lose one 1000th of a second of data, and it’s less than 1% of the cost of trenching fiber. We’ve been rolling them out for the last two years in Africa and India. We’re really excited about that one.

ALISON BEARD: Very cool. Okay: Tidal.

ASTRO TELLER: Ocean health. Fundamentally, humanity gets several trillion dollars a year of value from the oceans, and we’re killing the oceans faster than we’re killing our land or our air. We have to stop. And because humanity needs the oceans and derives so much value there, we have to somehow get more value from the oceans while regenerating the oceans. And that’s not going to happen unless we take automation to the ocean so that we can understand it and so that the value that we’re producing in and with the oceans is healthy for the oceans. Now, we’re starting in aquaculture, but we have a lot of other ideas about the maritime industry, about blue carbon, et cetera.

ALISON BEARD: Okay, last one before we go to audience questions. Tapestry.

ASTRO TELLER: That’s X’s moonshot for the electric grid. If you want to be able to plan, build and operate a clean, resilient electric grid, you have to start by understanding your grid. And the grid worldwide is the most complex machine that humanity has ever made. It is literally the case that it is so complex that there isn’t currently a digital map of where every wire is and where every transformer is, even for the people who are running the grid. So, when someone asks, “Hey, can I put this new solar field onto the grid?” The reason that they’re waiting in a five to 10 year line waiting to be added to the grid is because the grid operators, who are responsible for keeping the grid safe, don’t know what will happen if they plug that solar field onto the grid. So, we are trying to make the digital tools, the virtualization of the grid that will allow grid operators around the world to actually understand their system, play what if games, and ultimately operate their grid much faster in a sort of 21st century way.

ALISON BEARD: We do have a lot of questions from the audience. I’m going to try to get to as many as we can. Lizette from Cape Town is asking whether the monkey and pedestal approach can apply to other less ambitious projects as well. Should you always start with the most difficult part first?

ASTRO TELLER: Only if money is precious to you. I don’t know what to say. Look, if you know you can succeed, if you’re making a 10% improvement on something that already exists, then everything’s the pedestal. There isn’t a monkey. So maybe the order doesn’t matter very much and do whatever will get you the bonus first. I don’t know. But if what you’re doing has a lot of risk in it, if it’s a moonshot, if it’s a 10X opportunity, not a 10% opportunity, you’re probably going to be wrong and you’re going to have to stop entirely or pivot dramatically.

The faster you find out that you’re on the wrong track, the thing is, learning is not driven by success. You learn nothing when you succeed, except maybe to do that again. You learn exclusively when you fail. You have a model of the world and you find out you were wrong. And so failure is learning. They’re identical. So you should chase that if you want to go fast.

ALISON BEARD: Okay, so more talent questions. Lots of people are wondering how to really unleash talent in the way that you do at X. Gabby from New York City says, how do you help your employees do that? Juan asks, how can managers encourage people and teams to unleash themselves in more traditional organizations?

ASTRO TELLER: I want to be fair. Unleashing yourself in a traditional organization is hard if the organization, in being traditional, doesn’t totally want you to be unleashed. I wear rollerblades all day every day at the office. They’re on my feet right now. And I do that, it’s fun. But I also do that to remind people I don’t take myself seriously. I don’t take anyone else here seriously. We’re having fun together because fun and humor are the wellspring from which creativity comes.

If you can’t embrace silliness, if you can’t acknowledge that we’re all a work in progress and that most of why we waste time at work is fear, and we can’t get past fear until we can understand why we’re afraid and get really vulnerable with each other. It’s all just going to be like suit and ties and wasting time. I don’t know what to say. So I guess if you are a manager and you really want people to be unleashed, you need to first put down all your armor, take off all your masks, and then you need to start rewarding people when they do it.

ALISON BEARD:  Okay, so this is related again, if people aren’t necessarily unleashing themselves yet. Sandeep from Cincinnati, Ohio is asking how do you train them? He says, I’m curious to know what training looks like at X.

ASTRO TELLER: There’s a lot of different parts of that. There isn’t a single answer, but for example, we have a program here called Thrive where we take people who we think are ready through a nine, 10 month process. It costs us a lot. We can’t do it for everybody, but we do it for a non-trivial fraction of the people here. And it is about helping them to understand better what’s holding them back, their limiting beliefs, the ways in which their fear shows up in controlling them and stifling their creativity, their audacity, their humility, sometimes. A hundred percent of us have things to get over, and it does take support for people to learn that. But I think really, I mean, there’s a lot of training. I can give you other examples, but if you want it and you don’t actually do it yourself and reward it, I don’t think you’re going to get it no matter what training you give to people.

ALISON BEARD: Terrific. So, Yusuf from Pittsburgh, Pennsylvania is trying to get at this idea of tackling these incredibly challenging problems that others have failed at, that you might fail at as you’re experimenting. So, what is your advice to companies when they’re in a situation where they need to make a decision on an idea to invest or not, knowing that people have failed before and that failure is quite a possibility?

ASTRO TELLER: Well, I’m going to answer the question under the assumption that this is a company that’s placing lots of bet, and this isn’t a small startup that where that’s their only bet. But assuming that this is a more sizable company, it’s placing lots of bets. And so it’s trying to decide, should I place a bet here? What I would say is, number one, good chance you’re wrong. That does not mean don’t do it, but just start with that in mind. Then number two, go learn why those other ones didn’t, and make sure that you at least make a new interesting mistake when you fail, instead of making the same mistake somebody else already made. What a waste that would be. I also think there’s a lot of benefit to failing more than once in the same area. When I look at the things that we ultimately make successful here at X, there’s so much moonshot compost that goes into them. Because when a project ends, the people stay, the code stays, the patents stay, the learning stays.

So, in ocean health, in agriculture, that’s not the first time we tried. That was like the 10th time we tried. And so, we actually have these sort of reverse org charts of this moonshot compost and all of these different ideas and people and how they ultimately culminated in something which looks like a really good idea and in a super exciting business. But that’s because we kept failing and keeping all of those learnings. So, if you’re only willing to fail once and learn once, and then you’re just going to run off to another space, I think it’s harder.

ALISON BEARD: So, I have to end by asking about generative AI. Your first novel, which you wrote when you were in your twenties, was about Edgar who is sentient AI. He basically becomes HAL and starts talking back to his creator. So, what do you think of the recent developments? How close are we to that Edgar sentient AI character?

ASTRO TELLER: Let me answer like this. I think that computers have been levers for our minds for a long time, and robots increasingly are becoming levers for our bodies, doing physical work for us in lots of different situations. And I see a lot of ongoing opportunity just here at X to use artificial intelligence and machine learning as raw materials that go into trying to make the world better. And in the same way that if we were 100 ago, and electricity was relatively new on the scene, everyone would be excited about electricity. And rightly so, but electricity isn’t the end of the story. Electricity is the beginning of the story. If we can now put more intelligence into things that we’re making, great, then we can find better and better ways to solve huge problems in the world. That’s how it feels at X.

HANNAH BATES: That was Astro Teller, Captain of Moonshots at X, in conversation with Alison Beard on HBR IdeaCast .

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This episode was produced by Mary Dooe, Anne Saini, and me, Hannah Bates. Ian Fox is our editor. Music by Coma Media. Special thanks to Rob Eckhardt, Maureen Hoch, Erica Truxler, Ramsey Khabbaz, Nicole Smith, Anne Bartholomew, and you – our listener. See you next week.

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