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.

At IDEO, we like to breathe new life into the mundane with design. In an effort to shape a new paradigm of meeting-dom, here are some of our best resources on making your next conference-room gathering stand out—in a good way.

1. When you want to kick things off with a bang

Stretch! Sketch! Dance! Build in a few minutes for a new opening exercise and you’ll have a team that’s fully present and ready to get down to business.

READ: 6 Ways to Kick Off Creative Meetings

2. When you have a mixed bag of introverts and extroverts

An IDEOer once called the San Francisco space an “extroverted office full of introverts.” If you have a team of folks all across that spectrum, these exercises will strike a balance between team-together-time and solitude.

READ: 5 Brainstorming Exercises for Introverts

3. When you want people to really get to know each other

Whether bringing together a brand new team or sitting down with old colleagues, a jam-packed room can make for an overwhelming and impersonal experience. Ditch the canned icebreakers and try one of these instead—any of these exercises will help reveal your group’s commonalities.

READ: 5 Exercises for Breaking Down Barriers

4. When morale needs a boost

Do any of your meetings have a jingle? Didn’t think so. Reset your whole meeting philosophy with some out-of-the-box tips.

READ: How to Design a Meeting That Doesn’t Suck

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Meetings. Maybe you’re in one right now. (Are you paying attention?) Maybe you have one in ten minutes. (Are you prepared?) We know them well—and we know they’re not usually the highlight of your day. In fact, they can be quite the opposite.

A GAN (Generative Adversarial Network) is a type of AI that pits two neural networks against each other, each one training the other to improve its ability. Unlike other types of AI, GANs don’t just recognize objects—they can create them. GANs borrow a feature from biology and pit two competing neural networks against each other. One network, the “generator,” produces synthetic images, while another network, the “discriminator,” tries to guess whether the images are synthetic or real. The discriminator learns from both synthetic and real images, getting better over time. As it improves, it challenges the generator to produce ever more realistic synthetic images.

During a bite-sized sabbatical from my day job at IDEO CoLab, I dove head first into building and exploring the visual potential of GANs. I learned a lot about the differences between how humans and computers see the world, and how an AI’s output is shaped by the data we provide it. As designers, it’s important for us to understand the limitations of emerging technologies likes GANs—or else we risk creating products that harm, rather than help, humans. Here’s what I created, and what I learned. (Want to learn how to set up your own GAN? Read more here.)

1. Choosing a dataset

GANs can generate convincing fake pictures of almost anything—faces, cats, and even anime characters. How do they do it? It all depends on the dataset. GANs need to be fed a large set of images (think tens of thousands or more) so they can learn patterns. For example, GANs that produce faces learn that they tend to be roughly round, have two eyes, one nose, one mouth, and possibly hair on top. They learn pixel-level patterns, too—the textures that make up skin, eyes, and hair.

The trick with GANs is finding sets of images large enough and diverse enough for a network to pick out patterns. There are plenty out there—like CelebA (hundreds of thousands of celebrity faces) or LSUN (images of scenes like rooms and buildings)—but these are all meant for this kind of research, and training with them tends to illustrate how successful GANs can be.I wanted to do the opposite. What if we try to stretch GANs to their limit, so that we can get a better understanding of how they work? In this case, we’re using images from The Simpsons.

2. Training the GAN

Once I had my dataset of images from the show, it was time to train the GAN. For those familiar with machine learning, read my step-by-step process here. For everyone else, here’s the gist of it: I installed an open-sourced machine learning software called TensorFlow on my computer, which I souped up with a hefty graphics card originally bought for virtual reality and gaming. I then used a series of tools to reformat and resize images from my Simpsons dataset.

I trained the network overnight (sometimes over multiple days) and saved examples of images created by the generator network. The video below documents the learning process of the generator. With time, the images began look more and more like the actual Simpsons.

Scrub through the video to see the network’s progress. At 1:10 (about 13 hours of training), the first outlines of Simpsons eyes appear. At 2:00 (about 23 hours), the color scheme becomes noticeably brighter and figures start to emerge. At 3:00 (about 34 hours), there is less variation as shapes start to solidify and the networks reach some stabilization on what is classified as “real.” At 5:00 (about 57 hours) and beyond, the shapes start to vaguely resemble Simpsons characters, though the result is still too psychedelic and abstract for us to truly classify the images as real Simpsons frames.

3. Making sense of the output

The GAN manages to generate very Simpsons-esque images, but they’re definitely not going to fool the human eye. What’s going on? GANs (and any neural network or machine learning method, for that matter) only learn patterns that are in the training data. As humans, we have a deep and complex understanding of what people and objects should look like, including Simpsons characters and scenes in the show. The GAN, however, doesn’t bring any preexisting knowledge. It has to learn everything from the raw pixel values of the images it sees, and more complex patterns take more examples to learn.

In this case, the GAN easily learns the color palette of The Simpsons and its simple, flat illustration style, and starts to learn the features of each character. With enough training data, it may learn more complex patterns, like complete images of characters, or which characters show up in which settings (like the Simpson family at home, or Mr. Burns at the power plant).

What’s it all mean?

The varied and strange outputs of these networks are a succinct example of both the promise and risks of building products, services, or systems with machine learning inside. They’re seemingly magical, learning from patterns in data without being told what to look for, but they’re also limited by that data. Imagine you came into this world yesterday and had to make sense of it all just from still frames of The Simpsons—how would that inform your thinking?

Biases in training data, intended or not, will be reflected in the output of the network, which, if embedded into a product or service, can codify the bias at an unprecedented scale. As designers, we are uniquely positioned to help with this problem. As Kate Crawford, cofounder of the AI Now Institute has said, the problem is neither a specifically social nor technical problem: It’s a socio-technical problem. Designers have a responsibility to understand the unique role data plays in machine learning, so that we can create networks that equitably serve human needs.

Learn more about training GANs on IDEO Labs. Special thanks to CoLab’s Adam Lukasik for his help wrangling GANs for this post.

Pop quiz: What the heck is a GAN? You might not have heard the term, but you’ve likely seen one in action. From generating images of fake celebrities, to creating training data for self-driving cars, and even recreating suggestive (and weird) nude portraits, GANs are everywhere these days. But what exactly is this bleeding-edge technology, and what can we do with it?

In one sentence, who are you?

I am Vince Kadlubek, CEO and cofounder of Meow Wolf in Santa Fe, New Mexico.

What do you do? How did you learn it?

I am a momentum artist. I have dedicated my life to the study and sculpting of momentum, which primarily entails finding authentic, collaborative understandings and influences with the brilliant people around me.

What does a typical work session look like?

I'm usually dealing with these questions: What is the brightest vision? How do we articulate that vision? What steps are necessary for the most effective articulations? Work sessions are meetings or checking email. Those are the two kinds of work sessions I have these days. Sometimes I get to have creative work sessions, which are really just big meetings with beer and Indian food.

When it came to building something like the House of Eternal Return, we imagined, then found scrap armature materials, started producing structure and skin, utilized a bunch of air drying sculpting medium, and started turning large amounts of the exhibit into plaster forms. Lots of stuff was sculpted around the armature produced by the architects and general contractors, using more standard finish practices like painting.

3 favorite possessions?

Oh gosh. Together, “favorite” and “possession” are kinda an oxymoron for me. All I can think about are non-physical things like my memory, my spirit, and my compassion.

How do you get over creative block?

Collaborate with someone who is not stuck in a creative block.

What was the last item on your to-do list?

The last item on my to-do list was to donate money to Beto O'Rourke. But as for work, my last to-do item was to send our SVP of art direction a copy of a PowerPoint that she can use in an upcoming presentation.

How do you see the world in 2078?

Um... well, warm. And I also see a massive network of interconnected spatial computing, photo-realistic artificial intelligences, the liveable internet, monumental creator tools that result in instant manifestation—essentially, we will have transitioned into the 5th dimension.

What’s your number one bucket list item?

Chilling poolside in the Oia Village in Santorini, Greece with a bottle of Krug Clos d’Ambonnay 1998 and three of my closest friends.

Who are you creative crushin’ on lately?

Young Thug, constantly. It is actually a problem. I have a hard time getting excited for any other musician. And I have recently fallen for food and cocktails, so Grant Achatz from Alinea in Chicago is on my mind a lot. LeBron James is creating a reality that is mind-blowing and way beyond basketball; the world is wrapped in his storytelling. And finally, I am a huge coaster guy and what RMC is doing in the coaster world is unbelievable—Steel Vengeance at Cedar Point has completely flipped the script.

Vince Kadlubek is a visionary. He leans into tension, ruffles feathers, and has spent more than a decade constructing new worlds with the art collective Meow Wolf. In 2016, they transformed an abandoned 20,000-square-foot bowling alley into the House of Eternal Return—an immersive experience full of creativity and curiosities—drawing more than a million people to Santa Fe, New Mexico for a dose of childlike wonder. Now their sights are set on two new activations in Las Vegas and Denver, as they aim to create the modern-day Disneyland. As an immersive experience designer, I’ve been personally inspired by Meow Wolf and the big-thinking and resourcefulness of Vince, the collective's CEO. I was stoked to chat with him about his inspiration and aspirations for the future.

The answer is yes, of course. And when companies don’t fix themselves, we feel it in our products and services. The company has designed something—often a digital thing—that fails to give us what we need, and we walk away cursing the air, wondering if the leaders of the company have ever used the service themselves.

But making things that work for humans isn’t easy. We change every five minutes. How can organizations be confident that what works today will work tomorrow? It’s a lot easier to just slap on a Band-Aid and hope it won’t rip off with the first sudden move.

There are vastly different perspectives out there about how to make change happen and stick, and we’re curious about them all, so this fall, we invited five friends who’ve led major transformation projects at their companies to IDEO’s London studio and for an open conversation.

Here are the top 5 takeaways from our Make Change event:

1. Be a curious leader

“The problem I see in a lot of organizations is they have a lot of great tools but the top leaders don’t use them. If you’re not prepared to do what you’re asking everyone else to do, then why should you expect them to? If you want to understand what’s broken in your company, try entering your own expenses.”

—Mike Bracken (Public Digital), the guy who led digital transformation for the UK government (GDS), a model that’s been replicated in Canada, Argentina, Peru, Australia and the U.S. (and he has a letter from Obama to prove it).

If Mike had one piece of advice for making change happen, it would be this: “Go and sit next to a user when they’re using your service or product. I make sure to do six hours of that every month.”

2. Question accepted behaviors

“If you’re in a self-contained moment as an organization, I’d highly recommend that you build an innovation team that sits on the side and role models something really weird and strange, questioning the accepted behaviors. Having a constant dialogue and exchange with other perspectives is what makes companies competitive. But not a team that sits in the ivory tower. Our innovation team has reinvented itself three times in three years according to what the business needs."

—Anne Pascual, VP of Design at Zalando, Europe’s leading online fashion platform, which delivers to 24 million customers and grew its revenue to around €4.5 billion last year.

Anne's advice? “Think big. We tend to be obsessed with the details, but there’s much bigger opportunities out there.”

3. Don't build an innovation lab

“Innovation is far too important to be outsourced. I’m not a believer in gluing an innovation lab onto the side of your organization. If your culture is not innovative, that’s not going to sustain. You’ve got to fix your culture first if you want to be in this for the long term. It’s got to be everyone’s job when they wake up in the morning. And you then have to create the air cover as a leader for that to happen, which is all about creating a no-blame culture. You can’t innovate as fast as we do without sometimes screwing up. We do it regularly, and when we do, we try to learn from it, but we know it just goes with the territory of being a disruptor.”

—Paul Clarke, Chief Technology Officer at Ocado. After a deal with Kruger in May, Ocado’s market cap blew past Marks & Spencer's—a sign of the tectonic shift happening in retail.

Paul's advice would be this: “Tell the truth about what's broken.”

4. Be a DJ

“A good leader is like a DJ—they play the music that makes people move, but they should accept that people will move in different ways and what’s important is that you move to the same rhythm and move forward.”

—Beatriz Lara Bartolomé, former Chief Innovation Officer of BBVA, and EMEA Advisor Director of Globant, a digitally-native technology services company.

To make change happen, Beatriz would advise you to “ask your teams what they think should be improved.”

5. Launch beacon projects

“Launching a product to market in a short period of time is the best organizational change program—you’re going to learn the most about how the company behaves. We call these projects that have a change agenda 'beacons.' Select them for their ability to help you build a new muscle. If you’re not building a new muscle with every project, you’re wasting an opportunity to learn.”

—Luis Cilimingras, IDEO partner and managing director of our London studio. He leads digital transformation and change projects, including starting an innovation studio called La Victoria Lab that creates new products, services, and experiences for Peru’s emerging middle class.

Luis's best advice: “Keep seeking out inspiration. It’s your job to be inspired.”

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Take a behind-the-scenes look at the event.

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People are always looking for ways to improve themselves. But what about companies? Companies are just collections of people, so it makes sense that they’d build up all kinds of bad habits over time. Shouldn’t our organizations be on a self-improvement regimen, continuously trying to fix what’s broken?

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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.