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.

As soon as the rain stopped, we got on our bikes to survey the damage. We saw the flowers on the horizon: droopy and sad-looking. Then, suddenly, they started illuminating, one after the other—opening up, blooming. To this day, we still don’t know how they came to life. But the sight was exactly what we had envisioned. Better even.

That was 2014: the official start of FoldHaus Art Collective. Since then, our art installations have become bigger and more audacious. Shrumen Lumen, five massive, origami mushrooms, followed in 2016 (they’re now on exhibit at the Smithsonian American Art Museum), and we most recently built a five-story tall, geodesic sphere covered with 42 origami shells called RadiaLumia.

Each project takes months to design, prototype, and build. Which means I often get asked: Why do you spend your nights and weekends doing this when you have a fulfilling day job as a creative leader?

For me, it means that I get to work with a bunch of other creative types who pitch in to build something beautiful that’s bigger than what any one of us could do on our own. I get to work with my hands in a way that’s rare in my day job, and every project stretches my own sense of what’s possible.

And because FoldHaus projects are always a group effort, we need to broaden the question: Why do any of us—between five and 30 volunteers contribute to our projects on any given weekend—give up sleeping in to solder? Or skip the beach to code LED patterns? As one of the leaders of the group, I have learned a few things about what it takes to build a strong community and take a bold vision from a Post-it to the Playa.

Here are the 5 principles that have helped this team pull off increasingly ambitious projects:

1. Set an audacious—and contagious—vision

Drawing in a team requires not only that you love your idea, but that others do, too. After all, if you’re asking them to volunteer their time, they’ve got to be pulled in week after week, when they could easily be at the taco truck.

Blumen Lumen was a hit because everybody loves flowers. With the Shrumen Lumen, it took some convincing—many worried about associations with 'shrooms at Burning Man. This year, I made sure to test out the RadiaLumia concept with a few key members of the team, so I knew I had broad support. The buy-in worked, and has yielded our biggest team of volunteers by far.

The bottom line: Pressure-test your idea early—before you get too attached to it—so others will want to join up and help you realize the vision.

2. Stand on your own shoulders

Leading people to a grand vision is impressive only if you can actually pull it off. Ladder up from where you (and your team) actually are to where you want to go. And, acknowledge that it may not all happen at once.

Even before our Blumen Lumen, we built three shade structures for Burning Man. One was a disaster, but we learned from it. Now, we ensure that we are leveraging all we mastered the previous year to create the next installation. People are drawn to us because of our bold vision, but they stay with us because they know we have what it takes to make it happen.

The bottom line: Learn from previous outings that you can get over the mountain, so you can ask people to stretch and meet a new challenge.

Folding the origami pieces looks easier than it is.

3. Make it accessible

Part of the fun of leading a creative team is building camaraderie and community. But that can be constrained if a project requires too much specialized knowledge. Ensuring that part of the project is achievable by those without technical or unique skills is key to drawing a crowd when you need it.

On all of our projects, a big part of the effort is folding the plastic origami pieces. That requires pure manual labor, and anyone can be trained to do it, so we hosted all-hands-on-deck build parties every weekend leading up to Burning Man.

The bottom line: If you want to draw a crowd, create easy ways for people to participate without specialized expertise.

4. Leave room for individual passions

When people are motivated to do something, get out of their way and let them go for it. Given the freedom, teammates will create something together that’s more amazing than what a single creative leader could pull off. Set the vision, then let people self-select into groups to get it done.

With RadiaLumia, no one person knows everything that’s going on at every given moment, which is kind of scary but also amazing, because it’s coming together despite—or because—of that!

When we shared the vision at the kickoff meeting in February, there were a bunch of people who’d only just met. Within minutes, they were already troubleshooting light placement. By the end of the afternoon, they’d demonstrated a cool light show—on day one! We now have several self-directed teams that are loosely connected, but working independently.

The bottom line: If you create a compelling enough vision and rough guardrails, you’ll create the conditions for people to self-organize in a way that will surprise you.

5. Embrace ambiguity

The beauty of large-scale creative endeavors is in all of the unknowns. Try to remain flexible about who realizes the vision (and how).

Each year there are moments of extreme uncertainty. Weeks before installing the Shrumen Lumen at Burning Man, the origami kept getting stuck when we tried to pull the stems over the steel substructure that would anchor them to the ground. This was completely unexpected and took two weekends to debug. (Spoiler alert: We fixed it, but not without panic!). Troubleshooting is part of the process.

The bottom line: Expect the unexpected, and look for the beauty and lessons in it rather than the difficulty.

One of the most rewarding parts of FoldHaus is that while I may set the vision, once we pull off a communal project, the whole team feels immense ownership and pride. Being able to share both the process and the result is what makes the late nights and desert rainstorms worth it.

See RadiaLumia come to life at Burning Man 2018.

Also: We’d love to hear about your ambitious team efforts, at Burning Man or elsewhere, using the hashtag #MakerArt.

Special thanks to Amy Bonsall for her massive help crafting this story.

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We’d been working 16-hour shifts to install our art piece, a series of 10 larger-than-life, undulating, mechanical origami flowers. It was our first big build at Burning Man, and it took way longer than we expected to set up. After days, the flowers still weren’t working. Then came a torrential rainstorm.

Direct feedback that teaches rather than chides requires trust, and trust takes time.

Unlike humans, AI craves to know when it’s wrong. The entire premise behind machine learning is that it learns from failure or success. For example, we may feel that weather algorithms always get it wrong, but they can only improve by comparing their forecast to what actually happened. It’s therefore our job to tell the system when it’s wrong and acknowledge when it gets it right—but that requires a level of trust.

We can often tell if our friends or partners are unhappy with our actions, even if they don’t directly tell us. We can read their facial expressions, subtle changes in their tone of voice, and body language. Machines, however, don’t have that kind of intuition. Until we’re all walking around with some kind of lightweight EEG machine strapped to our head, machines will need to find proxies for how to sense human emotions, desires, and preferences. The obvious way to do this is to just to ask us, but that is manufacturing a transactional relationship, not a trusting one.

All I’m asking AI is for a little respect

A good example of an AI asking for trust rather than earning it can be found in the personal styling services that are popping up. They are doing some amazing things in integrating data science into every component of their business to drive growth. But my experience put me in the awkward position of feeling like I needed to explain myself and share sensitive personal details with someone I’d just met.

Signing up for the subscription service begins with a long and detailed survey about body type and style preferences. I spent a good 30 minutes thoughtfully answering myriad questions to create my style profile, and yet I returned everything that they sent me in the first box. None of it jived with my personal style, and only one thing fit.

I had put the time into creating a relationship with the AI—training it with my intimate information, such as how my body was shaped—and that process had given me a false sense of mutual understanding. If it had just taken a look at my Instagram profile and sent me a box, I would have given it permission to get it all wrong. But its surprisingly clunky and unnatural way of “sensing” my preferences for clothes set an unrealistically high expectation of what was to follow. Questions about slightly nuanced styles of plaid let me to believe that it could understand I like simple patterns—and then it sent me a shirt better fit for my grandfather.

By asking me to train a system before we had established a relationship of trust, the AI had breached it before it had even formed. When I’m in a trusting relationship, I can forgive someone for an error. But this felt different—like I’d been duped into a false sense of intimacy. I’m going to try one more box, but I have a strong hunch I’ll be cancelling my subscription soon.

AI is a relationship

While rare, there are a few AI with whom I’m in a trusting relationship of mutual respect. I relish my moments of training them because they respect my time and provide me with value as I train them—even when the AI is mostly wrong. I’ve come to truly value those moments when they tell me I’m wrong, because they do so in a way that deeply respects my position as a human and their position as a machine.

The format of the playlist is likewise brilliant: I don’t need to rate the songs but simply listen to them or skip them after I’ve heard a bit, and songs that I love get added to my own playlists or saved to my phone. Over time, it learns my preferences and can dish me up even better tunes. There’s no artificiality to training Spotify, and each and every time I train it, there’s value back to me as the user. Most importantly, Spotify has earned my trust through helping me discover a slew of new artists and, in doing so, earned its right to be wrong. (I don’t really love St. Vincent, but I understand why Spotify thinks I would.)

But we’re not the only one doing the teaching. When it comes to “training” humans, Waze and Google Maps are setting the standard. Their features are clearly based on a deep understanding of the common human emotional conditions that we endure during traffic: stress, anxiety, impatience, and a deep temptation to bail on our algorithmically recommended route in favor of our own “shortcuts” (which are almost never shorter).

Maybe the most brilliant bit of human-centered design inside of these navigation apps is how they highlight alternative routes and how much longer they’d take. Instead of having to blindly trust the route the AI has chosen, it considers its own possible fallibility and assures us by showing us the other routes we might be considering. Thinking of bypassing the highway? That’ll take another four minutes. Want to cut through that residential area? Here’s some construction of which you might not be aware.

In this way, Waze and Google Maps serve as a sort of angel on our shoulder, counseling us away from our inner traffic-hating demons. They’re not “AI-splaining” or chiding us for considering alternate routes, but rather giving us the necessary information to make the right decision. They’re rooted in a deep respect of human agency.

At IDEO, we prefer to think of AI as Augmented Intelligence rather than Artificial Intelligence. Taking a human-centered approach to building relationships between AI and humans compels us to meet humans on their terms, building relationships of trust and respect, and always remembering that intelligent systems must exist in service of humanity, not the other way around.

Beautiful, human-centered, human-AI relationships are about understanding human beings and our wonderful, weird intricacies and inconsistencies. They’re about designing the experience of AI around humanity, rather than the other way around. AI can only function well if it learns from its mistakes, and that means establishing trusting relationships with humans so they’ll feel comfortable saying when it’s wrong.

This article was originally published in Quartz Ideas.

Illustrations by Cassandra Fountaine.

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It’s awkward to correct a stranger when they’re wrong. How did you feel when Miguel from IT, whom you’ve only met once, told you that “learnings” wasn’t a real word? Or when Robin lectured you about the correct pronunciation of “macaron” at the office holiday party?

But, for the most part, that relationship is passive. And that begs the question, what if we could teach our machines more actively? Could we school them on very specific things that are important to us? That thought became the perfect setup to build a little experiment involving selfies.

A self-portrait or a selfie is actually more than just one picture. It’s one picture out of endless tries which all look pretty much the same ... except that they don’t. At least not to the person taking the self-portrait. That person’s individual aesthetic, packed with subtle and subjective nuances, must be captured at exactly the right moment. Adding to the challenge, it's hard to explain to someone else the way you squint your eyes when you nail your smile or how you purse your lips to look roguish.

Enter Brainchild: a machine learning–based camera that can be taught individual preferences and make them accessible as a function. You can train the camera to understand how you like to see yourself without going through 100 iterations. And most importantly, it allows you to share your sensibility with somebody else.

Brainchild is just a prototype for now, but I'm hoping others will be inspired to build on the idea.

Beautiful, baby, beautiful

Machines are a billion times faster in quantitative tasks than we are. For a long time, the problem was one of not understanding quality. But that has changed with the improvement of so-called deep learning techniques—a subset of AI or machine learning.

By teaching Brainchild how you would like to be portrayed, it can assess the quality of what it sees in real time, and give you haptic feedback when it perceives you looking your best, so you can simply take that one picture.

It also allows others taking a snapshot of you to see you as if through your own eyes. Taking portraits is a very intimate art. Rather than taking the human out of the loop, Brainchild augments this human-to-human interaction by making it more collaborative.

A feel-good feedback loop

The intervals between interacting with machine-learning products and experiencing their learnings are often very long. There are many technical challenges, but more accessibility and immediate feedback would help us teach more actively and develop a better intuition for them. In return, machines could learn better and become more personal.

Brainchild is designed to provide immediate feedback. You teach it how you like to be portrayed, instead of letting it guess, and every picture you take based on its haptic recommendation serves as feedback on how well it learned and how well you taught it. (Plus, it's private. Brainchild works offline and stores all of your information locally, so there's no danger of it being shared.)

Smile!

How does the Brainchild prototype work? There are countless technical parameters we could talk about, but in the most simple terms, it makes use of a so-called transfer learning process based on a fine-tuned convolutional neural network—a technique frequently used in computer vision.

Brainchild needs to be taught five fundamentals:

1. What a human face looks like
2. How to learn about new faces
3. How to isolate the face in a picture
4. The way the portrait subject looks normally
5. The way the portrait subject looks when they deem themselves looking their best

The first two points require huge amounts of coding, data, number crunching, and time. That’s what consumed most of my time (next to working on the wrong file for a day!).

The third point addresses the fact that when you take a portrait there is always something behind you. To focus only on you without distraction, Brainchild extrudes your face from the background. That is something it learns before you use it.

Then you become the teacher. In order to learn how you look your best, it needs to know how you look normally. So, you feed it a few examples of both, either by taking new snapshots, or by using pictures stored on your phone.

To help Brainchild differentiate between the two picture sets you simply twist the front plate, switching between two learning modes. The serious smiley means every picture you take will teach Brainchild how you look “normally”. Switching to the happy smiley means every picture you take will teach it what it looks like when you look your best. This is how you get to know each other.

As soon as the camera spots you in a way that matches what you taught it about looking good, it will gently vibrate and light up the camera’s trigger button. The vibration and brightness increase the closer your match with your own ideal.

Brainchild doesn’t snap the photo for you, it just signals you at the optimal moment to do so. You can also hand the camera to someone else to capture you in the way you like to be captured. And the more you teach it, the more accurate it becomes.

It might seem mundane to teach a camera how to take a better selfie, but the technology has already inspired one of our clients to rethink parts of a high precision medical device that could dramatically reduce treatment costs for patients, and another software engineer to work on a playful version of FaceID.

I myself am now working on a body pose add-on for fashion photography, and am curious to see all of the other ways we might actively teach our machines and help them augment us in more personal ways.

Instagram: @brain_children
Concept and Technology: Jochen Maria Weber
Visual Design: Tiffany Yuan
Industrial Design: Leo Marzolf
Special thanks: Tobias Toft

We teach our machines every day. Voice assistants learn how to talk to us based on what we say to them. Navigation apps guide us based on the routes we take. Without millions of teachers like you, our machines would be much dumber.

What do you do when a panel is truly the star of the show? Can you still design meaningful participation from your guests beyond the Q&A?

This was our challenge when IDEO CEO Tim Brown, pioneering social entrepreneur Jacqueline Novogratz (Founder of Acumen), and visionary CEO Carlos Rodriguez-Pastor (CEO of Intercorp and Founder of Innova Schools) sat down for a conversation about design and bravery in our New York office. We knew these three would inspire guests with heroic stories of using design to create change at scale and challenging orthodoxies. But could we keep the crowd inspired after the conversation ended?

Our best bet, we decided, was to design a series of activities that would send guests on a heroic journey of their own—from hearing the call to adventure to facing fears to celebrating triumph over adversity. The result? A happy swirl of exploration and discovery, with guests staying late to gather around the hands-on exhibits like family in the kitchen at the holidays.

Here are seven ways to leverage a panel discussion and design an event they’ll never forget.

1. A moment to unwind

Create a ritual to help guests wash away the baggage of the day and be present for all that follows. We invited guests to partake in a sonic shower, an immersion in music that stretches the ears and awakens the senses.

Try this: An aromatherapy bar for deep breaths of invigorating or relaxing scents.

2. Active imagining

Even the most thought-provoking panels amount to a type of passive contemplation for listeners. Follow a panel with a prompt to actively imagine. We asked guests to hear the call of service, by imagining they were a person they serve and writing in that person’s voice in response to questions like, “What would it take for you to feel confident about the future?”

Try this: A finish-the-doodle mural wall or a listening booth with a guided meditation.

3. Space to be vulnerable

Offer your guests the surprising jolt of inspiration that comes from being vulnerable and naming your fears. We designed an anonymous confessional, tricked out with blacklight and digital wizardry, where guests wrote their fears on a private screen, then watched their words magically appear on a monitor in type for party guests to see.

Try this: Pair strangers up using matching name tags and asking them to find each other and swap secrets.

We designed an anonymous confessional to help guests embrace vulnerability.

4. Fast-tracked inspiration

Inspiration can also be fast and light. We sent guests on a tour of themed rooms devoted to New York’s legendary cultural movements—Punk, BeBop, Hip Hop, the Beats, and the Harlem Renaissance. Each room was chock-a-block with images, sound, and strips of paper bearing anthemic words from the movement. It was a blast of culture felt through the senses.

Try this: Give guests a deck of cards created from your favorite inspirational Instagram memes.

Guests were invited to tour the most iconic cultural movements in New York history.

5. Skin in the game

Putting one’s body on the line—through movement, physical sensation, or embodied action—is the ultimate form of engagement. For our biggest test of bravery, we dared guests to stick their arm through a hole in a black screen to blindly receive a semi-permanent Jagua-ink tattoo from an artist on the other side. No requests or restrictions permitted.

Try this: Ask guests to enter a space through one of two thresholds marked by declarations of identity, like “I’m an artist” or “I’m an activist.”

The ultimate trust exercise—blindly allowing a stranger to draw on your skin with semi-permanent ink.

6. Making

Who doesn’t like to roll up their sleeves and craft something fun? Making gives your guests an outlet for any new, creative ideas your event has inspired. We handed guests shortbread cookies, pipette bags full of icing, and examples of exquisite cookie decoration to emulate.

Try this: A collage-making table with old magazines, construction paper, scissors, and glue sticks.

7. Silly celebration

Even the most buttoned-up among us appreciate the chance to let their hair down. We gave permission to get silly with an IDEO designer’s GIF Dance Party, accompanied by another designing spinning live music. Guests grooved in front of a camera for a few seconds, then watched their avatar populate a digital dance floor on monitors throughout the space.

Try this: A box of masks and capes and the promise of a free drink for anyone who orders as a superhero.

We asked guests to get jiggy with it, and they obliged.

To summarize: Get creative. Get physical. Get weird. Your guests will thank you for it.

Got an idea for an out-of-the-box event activity? Share it with us on Twitter using the hashtag #DisruptThePanel.

Ever attend a conference? Then it won’t surprise you to know that every event planner out there wants to disrupt the panel conversation. (We’ve been experimenting with the format for years and sometimes a panel is still the best choice.)

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.