In 2017, IDEO acquired longtime partner Datascope and integrated the company’s data scientists and engineers. Datascope team members aren’t just passionate data scientists; they’re human-centered designers who happen to work with data, as illustrated by successful collaborations like this one:

If Procter and Gamble (P&G) were a city, it would be the size of Boulder, Colorado. The company employs more than 100,000 people in 70 countries. Its research and development team is made up of more than 20,000 specialists working across locations, languages, and areas of expertise, from cleaning chemicals to paper products to over-the-counter medications.

P&G approached Datascope to help the company connect these researchers to one another and the rest of the organization. In working with P&G leaders, Datascope found that the company needed a better way to access and leverage employee expertise. Managers routinely got requests from researchers looking to work with others in their subject areas, but didn’t know how to identify the right partners or set them up for fruitful collaboration.

Datascope gathered internal data, including unstructured research data on the scientists’ interests and who they partner with most often. The team used that information to develop two new systems that bridge the gap for P&G researchers and managers. The first is an internal search engine that lets managers quickly find and connect the right experts across P&G offices worldwide. The second is a data visualization tool that highlights subject areas ripe for more collaboration among experts and research teams, who may already be asking similar questions. This intelligent tool can surface relevant, timely information managers need to connect their teams.

P&G already had a practice of collecting information for product research, but it wasn’t until Datascope came on the scene that the company took advantage of large, multi-layered data sets and made them more intelligent, so leaders could expand P&G’s expertise through new global partnerships.

In 2017, IDEO acquired longtime partner Datascope and integrated the company’s data scientists and engineers. Datascope team members aren’t just passionate data scientists; they’re human-centered designers who happen to work with data, as illustrated by successful collaborations like this one:

The second a smartphone debuts, buyers, bloggers, and journalists begin writing comments and reviews—and users start troubleshooting. Motorola Mobility wanted to tap into this river of data, but didn’t have the right tools to collect and respond to it.

Motorola’s design, engineering, and service teams hoped to identify product problems early, address them quickly through customer support, and ultimately fix them in future releases.

So Datascope built a custom engine that aggregates and analyzes feedback from hundreds of millions of phrases and internet references, drawn from consumer sites, user forums, news coverage, social media feeds, and product reviews. The tool parses key, product-specific information and routes it to the right Motorola teams: hardware, software, connectivity, and interface design, to name a few.

The analytics and visualization engine not only studies product performance in real time, but it also uses algorithms to gauge customers’ emotional reactions: Do they like the phone or hate it? Do they have complaints? Are they recommending it to friends?

Datascope’s tool translates all this data into simplified to-dos that are sent to the correct service and repair staff before user issues can mar a product launch or even the company’s reputation. And as engineers discover more about how a phone performs in the real world, they learn how to avoid making similar mistakes in the future.

The tool proved valuable as soon as it launched in 2012: When Motorola released the RAZR M smartphone, customers had a hard time figuring out how to use wifi and voice features at the same time and lodged complaints online. The engine recognized the pattern of complaints and alerted Motorola's customer support team, which was able to post responses with clear instructions in every relevant forum, turning a potential customer satisfaction problem into a positive interaction.

Motorola continued to use the engine to address issues in later releases of the RAZR M. The advance warning made engineers’ lives easier, while better handsets and software made for a more seamless customer experience. Improvements were also evident in the RAZR M’s well-reviewed next generation, the RAZR MAXX.

Based on the tool’s success, Motorola used it to improve the company’s supply chain and product development process. And beginning in 2014, with the Moto 360 smartwatch, Datascope’s analytics engine has been used to gauge performance and public sentiment not just for new smartphones, but for Motorola Mobility’s entire line of products.

For most parents, ensuring their child’s proper nourishment is a top priority. But that’s not always easy, especially for parents on the go. Plum Organics, an organic baby food company, set out to solve that problem by providing families with pureed fruits and veggies in pouches, making it easy for them to feed their babies. Plum approached IDEO for help designing a non-pouch package that could showcase the vibrancy of the ingredients within.

By conducting research and in-person interviews with families, Plum set out to ensure that the new product was both desirable and functional for parents and their babies. By joining families in their homes, IDEO was able to gather essential insights that helped inform prototypes and the final design, like the fact that nothing can replace the feeling of making an intimate connection with a child through spoon feeding; that baby food is often wasted if a parent can’t remember how long a product has been in the fridge; that families struggle with how to spoon-feed their children on the go without making a mess, and that both parents and retailers were concerned that baby food jars don’t stack securely on shelves.

With those insights in mind, the team designed a transparent bowl that allows consumers to see the natural color of Plum’s vibrant, organic food. A resealable lid makes it easy for parents to store food between feedings, and the container includes a feature that allows them to track the day that they opened the pack to ensure that they don’t end up throwing away perfectly good food. Lastly, the new package is easily and safely stackable and includes a divot in the lid for parents to rest a spoon on, making for a much cleaner and more sanitary feeding experience.

Baby Bowls for Plum are now available at retailers nationwide and are a natural evolution for the brand, helping Plum continue to provide high-quality, organic baby food for consumers and their children, even if they’re eating on the go.

Accessing our genetic code is as easy as spitting in a tube, but how can we benefit from all that DNA data? IDEO surveyed more than 1,000 Americans about the gene sequencing services they want. "Give me things I can act on,” one respondent said. “Give me advice, action plans, apps, and tools. Information is just air until I can put it to use." As it turns out, tools that can translate our genetic information into better life choices—to improve our health, chart our family history, or even find an effective exercise routine—are hard to come by.

Inspired to organize and apply the wealth of genetic data in all of us, Helix envisioned the first online platform for products that offer insights based on a person’s unique DNA sequence. The startup’s founders approached IDEO to help them get to launch by identifying their future market segments and designing their brand and product offering.

Helix sequences all 22,000 of your genes from one saliva sample—sent in a mail-in kit—and hosts a marketplace of products developed by trusted brands and innovative developers that explore your health, ancestry, entertainment, family, fitness, and nutrition. Similar to the Apple app store, Helix lets you pay individually for the products you want and is a platform for developers to launch new offerings over time. After you've provided a DNA sample once, every product in the Helix store is made available to you on-demand. So, as new products are launched and new discoveries are made in the field of genomics, the data from your original sample is all you’ll need to keep exploring your personal genetic insights.

IDEO engaged a range of users, including early adopters of at-home genetic tests and “quantified self” enthusiasts, and surveyed potential Helix customers across the country. That qualitative and quantitative research validated Helix’s “app store” business model and provided key insights into the products people were most interested in.

Through the partnership, Helix uncovered what users need most from a genomics company:

• to prioritize the accuracy and privacy of DNA test results
• to encourage a sense of discovery and exploration of our genes
• to celebrate each individual’s unique qualities

A team of ethnographers, data scientists, and designers at IDEO helped direct the startup’s brand approach (including its name, Helix) and crafted a blueprint for how the company connects with customers and presents its product. The Helix store launched in July 2017.

Our DNA used to be a mystery—something we guessed at by observing our parents. Now, easy access to our genetic code—and the tools to truly understand it—arrives on our doorstep.

In one sentence, who are you?

I am an illustrator who enjoys bringing any amount of joy to people through my creations.

What do you do? How did you learn how to do it?

I create different drawings using pencil and ink. The illustrations range from patterns to caricatures, unique creatures, and other one-off ideas. I grew up sketching and doodling and it has continued. When I moved to LA, I was suddenly surrounded by an amazing cadre of artists and creators who constantly inspire me, as well as push me to keep improving myself. That is how I learned my craft and continue to learn, because I believe you never truly finish learning.

What are your 3 favorite possessions?

Miniature Back to the Future 2 hover board; Magnum P.I. Tiki Mug; a drawing of my fiancé and me, which she drew. Not in that order.

What does a typical work session look like?

I have a running list of ideas and sketches that I pull from if I don't have an art show with a specific topic that I am working towards. Usually, I'll do some Google searches on the topic at hand and pull a bunch of reference photos for perusal during the pencil drawing stage. I sketch out the drawing inside a pre-drawn area, such as 4x6 or 5x7. Art that is easy to frame is always a goal of mine, because I love framed art and it drives me mad when I see things tacked onto a wall or cork board. After finalizing the pencil drawing, I'll usually step away for a moment or two to play a video game or watch a show. I like to do something that differentiates the time between stages. Once I come back to the drawing, I pull out all the different micron pens that I think I will use for the piece. I check the nibs, and then I begin inking. I put on dramas in the background to listen to, or things I've seen a ridiculous number of times. West Wing and Parks and Recreation are two such shows.

I don't like to start inking unless I am going to finish a drawing that night. Sometimes, a piece is too large, and I have to break it up over two nights. If that happens, I make sure the pens I've used on the previous night are away from all my other pens. I do this to achieve a consistency within the lines. Once finished, I scan the piece, put it within a predetermined frame, and pack it up for a craft fair or deliver it if it's for an art show. Rinse and repeat as necessary.

How do you get over creative block?

I used to stop drawing and just stew for a bit until it finally passed, which could take a bit of time. As I've gotten older, and ever so slightly more confident in my abilities, I push through it. I just keep drawing. Yes, sometimes the drawings are awful, but as Lauren (my future wife) always says, "sometimes you just have to pass a rat."

How do you see the world in 2078?

A lazy utopia, filled with autonomous everything. Humanity will have moved past Earth and outward into the stars while robots cater to our every whim and we become fat and happy. I could be confusing my vision of the world in 2078 with the timeline in Wall-E—who doesn't yearn for the saccharine dreams of Pixar in 2017. It is tough to image the future right now because at this moment it feels as though we are hanging by a loose wire, and any minute now someone with a spray tan is going to cut the wire and tell us all how huge his ratings are, as we all fall to our collective doom. I hope we make it to 2078, if only so that I can enjoy my hover chair and 128-ounce Slurpee.

What’s number one on your bucket list?

I should say something epic like bungee jumping off of Thomas Jefferson's nose on Mount Rushmore, but honestly, I'd like to have my own artist's studio. Some place I could retreat to and tune out the world and draw silly pictures. That to me is something I want to do before I fade out of this existence.

Who are you creative crushin’ on lately?

I've had a one-sided love affair with Dustin Harbin for a long time. I own two of his original pieces and his diary comics book. The man can use an ink nib like Aaron Sorkin can turn a phrase.

I’ve been creative crushing on Austin Salmi since I saw his doodles, patterns, and adorable characters on Instagram almost two years ago. Austin’s work, learned through practice and osmosis in his job surrounded by artists, has a giggly character about it that fits his intention to hide the ugliness of the world for a moment, and make lovely order out of chaos. I got to learn a bit more about him and his process in this installment of Creative Crushes.

We had just begun work on a project exploring how we might help a medical-liability insurance company increase patient safety by learning from past incidents. As we talked, our conversation shifted away from excitedly imagining ideas for data-driven tools toward ways our work might potentially do harm.

What if our designs ended up raising premiums for doctors and, in turn, healthcare costs for patients? What if they kicked doctors off their insurance? What if doctors stopped reporting adverse events to keep their premiums from rising? There was real potential that our work could result in reduced patient safety or increased cost of care. We could inadvertently build a tool that could be used against the very people we were trying to help.

Today, data systems and algorithms can be deployed at unprecedented scale and speed—and unintended consequences will affect people with that same scale and speed. How can we always make sure we’re putting people first when designing large scale systems? Especially when those systems will change over time and evolve without direct human supervision?

The seed planted during that bar conversation has grown into a set of principles, activities, and now a set of cards that our teams—both data scientists and designers across every other discipline—use to ensure we’re intentionally designing intelligent systems in service of people.

We’re far from the first people to ponder this. We’ve been inspired by organizations like AI Now and Data + Society, books like Weapons of Math Destruction and Technically Wrong, academic communities like FATML and CXI. In particular, we’ve been eagerly following O’Reilly’s series on data ethics (and encourage you to read their free ebook, Ethics and Data Science).

To develop our own set of guiding principles, we started with people. We talked to folks all across the globe: We interviewed IDEO teams about where they found challenges. We spoke to our clients about where they saw intelligent systems go awry. We spoke to the public about where smart designs seemed to cross lines. We observed and read about AI systems that had gone off the rails and worked to understand how this might have been avoided. We learned a lot.

We came up with an original design, iterated on them, and landed on a set of four design principles and ten activities that can help guide an ethically responsible, culturally considerate, and humanistic approach to designing with data. These activities are meant to provoke thought; they’re a vehicle for introducing new ideas and stimulating conversations around ethics throughout the design process.

To start, here are our principles:

1. Data is not truth

Data is human-driven. Humans create, generate, collect, capture, and extend data. The results are often incomplete, and the process of analyzing them can be messy. Data can be biased through what is included or excluded, how it is interpreted, and how it is presented. Unpacking the human influence on data is essential to understanding how it can best serve our needs.

2. Don’t presume the desirability of AI

Just because AI can do something doesn’t mean that it should. When AI is incorporated into a design, designers should continually pay attention to whether people’s needs are changing, or an AI’s behavior is changing.

3. Respect privacy and the collective good

While there are policies and laws that shape the governance, collection, and use of data, we must hold ourselves to a higher standard than “will we get sued?” Consider design, governance of data use for new purposes, and communication of how people’s data will be used.

4. Unintended consequences of AI are opportunities for design

Just as with any design endeavor, we know that we’re not going to get it right the first time. Use unanticipated consequences and new unknowns as starting points for iteration.

To get started with the activities, you can download the cards here. We hope that these activities provoke dialogue and provide concrete tools to help our community ethically design intelligent systems.

Thank you to the larger team who helped make these cards come to life—Ben Healy, Jane Fulton Suri, Jess Freaner, Mike Stringer, Justin Massa, Connie Oh, and KP Gupta, who designed them.

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A design researcher, a business designer, and a data scientist were sitting at a bar. It sounds like the setup of a joke, but the conversation was actually quite serious.

As a user experience and interaction designer with a hybrid art and tech background, I’ve long been a fan of the concept of using art and design as a Trojan horse to make tricky concepts accessible. Through intrigue, curiosity, and play, these experiences can bypass our biases and open the door to understanding, without requiring deep comprehension. If you’ve ever visited a children’s science museum and watched kids excitedly engaging with subjects that would bore them to tears in book form, then you’ve seen the power of experience and play at work.

Meet ERNEST

That’s why my colleagues and I created ERNEST, an installation that gives the viewer the job of training a rudimentary artificial intelligence. Using facial and emotional recognition tech, ERNEST shows the user imagery and tries to see how happy it makes them. Our goal for this project was to engage users in a game-like experience to help them understand how machine learning models are trained, and subtly hint at the areas where bias could creep in.

Fellow IDEOer Matt Visco and I wanted to make sure ERNEST didn’t look like a modern computer—we designed it to look vintage. With its old hardware and nostalgic graphics, ERNEST doesn’t feel smart on the surface. Its look is meant to evoke a time when computers carried the promise of a utopian future, prior to mass connectivity and the ubiquitous collection of personal data.

The full installation uses five monitors, two computers, and three Raspberry Pis, which power several video loops that reveal the training data behind ERNEST, and give passersby a sense of the experience even if they don’t engage directly with it.

How it Works

ERNEST uses computer vision to perceive the user ambiently through facial recognition, allowing it to detect when a user is looking at it and how far away they are. When a user gets close enough, ERNEST comes alive and asks the user to help it learn. It then shows the viewer images intended to make the user happy, and gauges their emotional reaction based on their facial expression using a pre-trained model. The facial tracking and emotional data is displayed to the viewer in real time, encouraging users to make faces at ERNEST. Viewers can then validate the accuracy of the model to help ERNEST know if it was correct or not.

(Sound complicated? The video below might make it clearer.)

We installed ERNEST in IDEO San Francisco’s lobby for a couple of months, and also brought it to the San Francisco Art Institute Fort Mason Gallery for the CODAME Intersections exhibition in November 2018.

The Results

Many participants loved to challenge ERNEST, often emoting in varying ways through numerous loops of the game. Once, a group of participants spent nearly 20 minutes playing with ERNEST, takings turns to compare how it perceived each person.

When viewers interact and play with ERNEST, we’re helping them see that machines can have almost human-like perception, reacting differently to different faces, and sometimes being uncannily accurate...or completely wrong. The wrong guesses were in many cases more interesting, and were in fact a very intentional part of the design. We wanted users to see that artificial intelligence is sometimes not as intelligent as we might think.

ERNEST is meant to be a game, but what happens when real-world AI makes the wrong guesses? Or what if those wrong guesses are uneven, indicating an unintentional bias, such as working well for some faces but not others?

We wanted people to realize that designing systems that can ambiently perceive humans without direct input is rife with ethical concerns. And already, systems like these are being deployed at scale. The Guardian predicts emotionally-aware tech is already a $20B industry. Even something seemingly benign like ERNEST causes us to wonder—if an AI is trying to make the viewer happy, who determines what “happy” is? What if bad actors train Ernest to think that disturbing content is “happy?” What if the technology is repurposed to provoke anxiety or anger? What happens if we deploy technology like ERNEST widely?

Of course, ERNEST only scratches the surface of these big ethical questions, but we hope that it helps give people insight into how artificial intelligence thinks, and the ways its thinking can be flawed. The more that we as a society can start to understand these systems, the better we can judge their quality, and work to design them to be in line with our shared human values.

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Artificial intelligence and machine learning are among the hottest topics of the tech world, but some of us at the frontier forget that many people feel intimidated—or even afraid of—these technologies. The pop-culture trope of machine intelligence gone rogue has been a part of our cultural psyche for a good 150 years. Think 2001: A Space Odyssey’s HAL 9000, The Terminator’s Skynet, and The Matrix’s... Matrix. Combine that fear with arcane tech-speak like “recurrent neural network” and “generative adversarial network,” and in addition to feeling emotionally intimidating, AI and ML can also feel incomprehensible.

Feedback is a tricky thing. There’s always going to be differing opinions of right and wrong, and even if it’s explained to you in a calm, clear voice, it can sting like nothing else. But getting feedback on your work is the only way you'll grow. It forces you to rationalize your thinking and creative decision-making, and teaches you to swallow your pride.

I learned the hard way: A few years ago, I designed my first typeface called Emotiv. It was a concept for a messaging app that would help people express their emotions through language (instead of emojis).

Eager to get my work out there, I decided to send a sample to a very well-respected typographer whose work I greatly admired. Given this individual’s reputation (and my lack of experience) I was reluctant to share at first, but with encouragement from friends, I sent it along with an explanation of how it came to be, not really expecting a response.

A few days passed, and much to my surprise, an incredibly lengthy reply fell into my inbox and hit me like a ton of bricks. It was the toughest feedback I’d ever received. Alignment issues, inconsistencies, readability problems, the email went on and on. It felt like my career was ending before it even had a chance to begin. We designers consider our creations extensions of ourselves—meaning that we take the pain of feedback personally.

While I can't offer up permanent thick skin, I have come up with 4 mindset shifts that will help you get—and give—negative feedback in a constructive way:

1. Introduce constraints

The least helpful feedback goes something like, “Hmmm, I’m not sure I like this.” We all have personal tastes, but there is no build-off point from that—no way to develop and improve. Instead, ask the person giving you feedback to be constructive, give their rationale, and ask as many questions as they’d like, rather than give you a gut reaction.

2. Tag team

Rather than putting yourself on the receiving end, get involved in the process. Imagine that the work being critiqued isn’t yours and offer your own thoughts. That will help you stay focused on the work itself. Plus, by making the feedback process more of a collaborative effort, you can understand the work from new angles to make it better together.

3. Be Spock

Do your best to take your emotions out of the equation. Instead of hanging on every negative word, look for unbiased points that will help you better define and execute the work. Even if the person giving you feedback is using words like “I like...”, or “I don’t like…”, those words aren’t aimed at you—they’re aimed at uncovering the fundamental thinking or execution behind the work.

4. Don’t ask for the answer

When the feedback process unearths a problem with your work, it’s tempting to simply ask for a clear solution to the problem. But that won’t push you to go further and explore other possibilities. Solving the problem yourself gives you another opportunity to learn and grow your skills.

Looking back, I now see now that the typographer who crushed me was actually providing a great service. (Honestly, you could tell someone they are terrible at something in considerably fewer words.)

I’d made the same mistake my fellow design student had made in that first class—I reacted as if it was a personal attack on me as a designer. But that feedback prodded me to become much more critical about my own work, studying details that others might not notice, with an eye toward readability and alignment.

Getting negative feedback can hurt. You pour your time and effort into creating something, and with the swiftest of comments, it gets cut down. But receiving it not only makes your work better, it makes you better... especially at giving feedback.

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Nothing prepares you for the reality of receiving negative feedback. In one of my first design classes, I watched in horror as a fellow student had his work ripped in two for the first time in front of all of us. Shortly afterwards, he quit the course.

I help organizations cut through complexity by shaping how they show up, through brand strategy, identity, and storytelling.

Working at the intersection of brand and business, I bring an editorial eye and a bias toward making—turning ideas into tangible experiences that people can understand, trust, and believe in.

My work spans government, healthcare, financial services, media, and consumer goods, from launching Gen Z-focused ventures to building innovation labs and reimagining legacy brands. I relish moments of ambiguity and enjoy translating across teams, perspectives, and priorities to move ideas forward.

I started my career in editorial and digital design, shaping cross-platform experiences and identity systems in publishing and agency environments. That foundation still shapes how I work today: I’m detail-oriented, collaborative, and overreliant on the Oxford comma. I hold a BFA in Visual Communication from the School of the Art Institute of Chicago.

For 25 years, I've asked the same question: Who does this design exclude—and what are we going to do about it?

My work spans human-centered strategy, inclusive design, and qualitative research, with clients ranging from Microsoft, Google, and Verizon to the National Science Foundation, and San Francisco Unified School District.

Before IDEO, I taught elementary school in East Palo Alto and spent a decade doing design work with social service organizations—where I learned firsthand what it costs people when systems are built without them in mind.

I am currently writing User Error, a nonfiction book about digital access and the design decisions behind it. I live in Oakland, California.

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My expertise spans brand, communication, and product design across tech, education, the arts, and social impact.

I believe great work demands both high-level vision and obsessive attention to detail, and only happens through collaboration.

I bring an inclusive, hands-on approach and a deep understanding of business, which enables me to consistently deliver excellence while always asking why.

Before joining IDEO, I worked at the brand firms Pentagram and VSA Partners. I began my career as a designer, leading teams at the School of the Art Institute of Chicago and the Milwaukee Art Museum. I hold an MFA in graphic design from Maryland Institute College of Art and a BFA in communication design from the Milwaukee Institute of Art & Design, and have taught at some of the best design schools in the US.

I am responsible for IDEO’s long-term success in China and working with clients to use design as a tool to enable growth. Specifically, I have helped Chinese companies design holistic brand solutions through the development of their products, communication, services, and innovation teams, and I have helped multinationals expand their presence and influence in China.

I advise global leaders on developing China-led innovation and capabilities.

In over 15 years in IDEO Shanghai, I have worked on projects that use design to elevate the quality of the experience of healthcare products and services, streamline processes that increase productivity, create spaces and programs that promote and enable inclusive communities, and build next generation mobility solutions that are planet-positive.

Before joining IDEO, I worked at Philips Electronics and the Electrolux Group in Italy, the Netherlands, and Singapore on a number of breakthrough commercial products. I am a member of the Young President Organization in Shanghai.

Most AI products today are built for work—a space with clear problems and established systems. One founder noticed a gap: personal life is messier, harder to systematize, and mostly left behind by the AI boom. So she built Cue, a personal AI that lives inside iMessage and group chats, to go where the other tools haven't.

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In this episode, Becca Carroll, IDEO's Chief Strategy Officer, talks with Sida Li, co-founder and CEO of Shared Context Lab, about staying anchored to a human need while the technology around her keeps changing shape, why she treats her business model with the same rigor she'd bring to a product, and what it feels like to build a company at this particular, disorienting moment in AI.

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The conversation also gets into how Sida makes design decisions: the language Cue uses to describe itself, the tradeoffs behind building inside iMessage instead of a new app, and a real story about a business idea that didn’t pan out.

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This is the second in a two-part series profiling founders from IDEO's Startups-in-Residence program. The first conversation is with Johannes Seemann, founder of Sooner, on designing GenAI for the emotional side of money.

Most personal financial tools are built to run the numbers and optimize towards a budget. While that works for some, most people experience money as a lived relationship that does not neatly fit into a spreadsheet. Johannes Seemann and Becca Carroll discuss why money is emotional before it is mathematical, and what a human-centered approach to building a generative AI financial product looks like in practice.

Mina Seetharaman talks with Scott Shigeoka, author of Seek and Head of Curiosity Cultivation at the Eames Institute, about what distinguishes genuinely curious leadership from performative curiosity, how power dynamics shape curiosity, and why practicing curiosity can restore energy rather than drain it.

Mina Seetharaman talks with David Epstein, author of Inside the Box and Range, about why total freedom often produces average work, how leaders can design useful limits on purpose, and what organizations such as General Magic and Pixar reveal about the relationship between boundaries and creativity.