Obesity exists at the complex intersection of a clinical condition and a cultural force. Clinically, it comes with a diagnosis and treatment plan. Culturally, it is influenced by beliefs, identity, and self-worth, making it deeply personal and often taboo. Families, healthcare providers, and experts tend to avoid the fraught “o-word,” opting for euphemisms instead. For families on Medicaid, these challenges are even more pronounced. Award-winning health-tech startup Clarity Pediatrics, together with designers from IDEO, IDEO.org, and with support from Rise Together Ventures, sought to uncover the obstacles to healthier lifestyles and create a free report for US pediatricians who want to bend the curve on the childhood obesity epidemic.

Research with kids and parents on Medicaid in Dallas, Texas, and California’s Bay Area, along with interviews with healthcare providers and experts across the country, uncovered stories of stigma and frustration. Parents spoke of the daily challenges of balancing multiple jobs, sourcing affordable healthy food, and finding time to make necessary lifestyle changes. They described rushed pediatric visits that left them with unclear next steps and feelings of blame. Provider conversations echoed these struggles, noting time constraints and limited follow-up options.  

Ultimately, food had become a daily torment for these families. Eating was unavoidable and also one of the few affordable indulgences low-income parents were able to provide. Once kids left their own homes, they were surrounded by unhealthy temptations, making it feel impossible for parents to maintain control. The popularity of costly new weight-loss medications and surgical procedures added another layer of complexity for families who had been underserved by—and therefore distrustful of—America’s healthcare system. With so much of the responsibility placed on parents to self-direct care, inequities deepened, adding to the burden of families already at a breaking point.

To design a path forward, IDEO and IDEO.org mapped a family’s needs before, during, and after pediatric visits. This work revealed critical parts of the journey where families needed more support: recognizing early signs of obesity, navigating provider conversations, and sustaining healthier habits at home.

These insights informed a theory of change framework that provides actionable guidance for stakeholders involved in obesity care. Grounded in three opportunity areas along the patient journey—building awareness, confidence, and capacity—the framework highlights how trust, dignity, and systemic support can lead to lasting outcomes.

IDEO and IDEO.org also developed 10 user-centered concepts spanning digital, physical, and service offerings to reduce stigma and empower families to sustain healthier lives. These ideas address dignified diagnoses and structural inequities, among other pressing issues. Complementing these concepts, IDEO created a short documentary of a mother discussing her family’s experience navigating childhood obesity to help pediatric providers—many of whom have had very different lived experiences from the families and patients they serve—better understand the realities of the daily struggles of many families on Medicaid.

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Designed for the public good, the interactive website and free downloadable report, Reimagining Childhood Obesity, contain research findings, design recommendations, and a theory of change framework. The goal: provide practical, actionable guidelines for pediatricians, educators, healthcare organizations, and innovators to meet families and kids where they are and support them as they move toward a healthier future.

21% of US children are obese. 40% of Latino youth are either overweight or obese.

26% of kids on Medicaid experience childhood obesity, compared with 11% on private insurance.

525,600 minutes: Total time per year parents spend caring for their children. 17 minutes: Average time a pediatrician spends with a child during their annual exam, a small window to address a complex chronic condition such as obesity.

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user-centered ideas to help pediatricians reduce the stigma of childhood obesity in an accessible report

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opportunity areas for building trust with young patients and providing family support

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With its reach, Acer saw an opportunity to power positive choices in the billions. ‍

But new products and services won’t succeed if consumers don’t want them, no matter how sustainable they are. A few years go, the Harvard Business Review’s report, "The Elusive Green Consumer," found that about two-thirds of those surveyed said they wanted to buy brands that advocated sustainability. But only a quarter actually did.

Meanwhile, the market for climate era products is only growing: In 2022, Forbes found that nearly 90 percent of the Gen X consumers they surveyed said they’d be willing to spend more on sustainable products, compared to just over a third two years before. The desire was only higher among Millennials and Gen Z; they just didn’t like what was on offer.

To drive sales and growth, and meet its sustainability commitments, Acer needed to capitalize on desirability, deepening the connection between Acer’s sustainable options and consumer needs, lowering the barrier to purchase. To that end, Acer and IDEO created the Acer Climate Lab, a cross-organizational team and initiative, to launch the company on a transformative journey to deepen the connection between Acer's conscious technology and the realities of people's lives and needs across four strategic areas.

Together, the combined team conducted extensive research to better understand consumer attitudes and behaviors toward sustainable products, and identified everyday areas where Acer’s conscious technology might be able to have the most significant impact: living, working, moving, and learning.

• Living: Envisions homes as hubs of energy efficiency and climate resilience, leveraging technology to optimize energy use, enhance air quality, and enable proactive climate management. These concepts include technologies like smart energy systems, adaptive air solutions, and integrated home management platforms explore how technology can support sustainable daily living.
  
  
• Working: Reimagines the workplace with flexible, sustainable technology solutions that prioritize resource optimization and circular practices. Concepts such as Acer Loop could provide a subscription-based model that ensures access to the latest technology, supports remote work, and reduces electronic waste through effective recycling and rehoming practices.
  
  
• Moving: Focuses on the transformation of urban mobility by integrating technology into systems that promote sustainable transportation. Concepts could include e-mobility hubs that highlight how urban spaces could offer seamless access to shared, low-emission travel options, reducing reliance on traditional carbon-heavy methods.
  
  
• Learning: Explores pathways to sustainable education by imagining environments where technology supports circularity and accessibility. This includes ideas like refurbished devices for students, repair and reuse programs, and educational spaces designed with climate-positive principles, nurturing eco-conscious habits in future generations.

Together with Red Peak and Sid Lee, the team launched the culmination of the work at COP28, the United Nations Climate Change Conference in Dubai, positioning Acer as a pioneer in climate positive technology. The exhibit included immersive experiences and interactive displays demonstrating how Acer's sustainable solutions seamlessly integrate into everyday life. Visitors could engage with scenarios depicting sustainable living, working, moving, and learning, emphasizing the practical benefits of Acer’s innovations.

Acer's journey through the Climate Lab project underscores a commitment to sustainability that goes beyond mere compliance. With this new cross-business team tackling the real-world barriers faced by consumers, Acer is paving the way for a more inclusive, sustainable future. This initiative not only aligns with global climate goals, but also sets a precedent for the tech industry, demonstrating that with the right approach, sustainability and innovation can go hand in hand.

In a survey, 65% of respondents said they want to buy brands that advocate sustainability, yet only about 26% actually do. 

Nearly one in two consumers say they either don’t know what information to trust, or that nothing can influence how much they trust a business’s commitment to sustainability.

1 cohesive strategy

for climate era products across 6 different fields

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With the introduction of generative AI, Ethiqly and IDEO saw a unique opportunity to help teachers, by cutting down the time it takes to provide each student with feedback on their writing assignments. But to get there, they had to learn from the experts themselves. To kick off the project, the team headed into the classroom to co-design with students and teachers.

One of the first problems they discovered? Traditional methods of grading essays are laborious and inefficient for high school teachers, who are already notoriously busy and overworked. What teachers really wanted was to spend time giving each student the personalized feedback that helps them grow. Meanwhile, students really struggled to get started on their writing assignments, overwhelmed by looking at a blank page.

Together, the team started to think about how it could harness AI to make personalized instruction more accessible for all students, helping teachers provide better feedback at scale, and giving students the support they need to not only get started on their writing assignments, but improve their work. 

With insights directly from students and teachers, the IDEO team built working prototypes, allowing them to beta test the product in schools, and demonstrate value to investors. They also developed a brand identity and style guide for launch, which came to life in a pitch deck, landing page, and other brand expressions.

The resulting product is a sophisticated blend of AI technology and user-friendly interfaces that empower teachers and inspire students. The goal isn’t just to get to a finished essay, but to assist students in developing critical thinking skills. Ethiqly's tools help students organize their thoughts and get writing by providing contextual prompts and suggestions—without doing the work for them. And the AI assistant supports teachers by suggesting comments based on evaluation criteria they have set, and teachers choose which feedback is relevant for their individual students. Students never see it without teacher approval.

As one teacher told the team, “With leveraging AI more in education, there’s this fear that it’s deprofessionalizing the field or it’s a direct replacement of a teacher in a classroom. I think it gives us meaningful data, so that we can actually teach the way that we want to in order to support our students.” Now, Ethiqly is in use in classrooms in 25 countries, positively impacting students across the globe.

Teachers know 1:1 interaction is key to student success, but an EdWeek survey found only 46% of teachers’ time is spent teaching, while 25 hours/week goes to other tasks like grading and making lesson plans.

25 countries…and growing

Ethiqly’s reach in classrooms around the world

Designing a piece of a city, like a park, doesn’t start with a blank slate. There are always traces of the past and the present, and the lived history of its residents.

To create a full picture of that story, the IDEO team built workshops for the community centered around questions about the unique experiences of living in Frisco. They invited them to reflect on the communities they grew up in, the city of today, and the public spaces that have played formative roles for them. 

From their answers, a common value emerged: heritage. Frisco is a rapidly growing city, with residents whose families have lived there for centuries living side-by-side with more recent neighbors from diverse cultures and ethnic groups. Participants shared a vision of Grand Park as a unifying, shared space, a backdrop where many families can form foundational memories in Frisco.

Next, IDEO brought in multisensory design prompts to help participants further conceptualize the park. How did they want it to feel, sound, and smell? In the built-up, suburban city of Frisco, residents imagined a space that was less planned, enabling guided wandering and exploration. They also hoped that the park would inspire loved ones and descendants to feel a sense of curiosity and wonder.

At the end of workshops and sessions with community groups, the IDEO team took Polaroids of participants, asking, “What’s one word you want to describe Grand Park in the future?” Those words informed the collective vision that IDEO delivered to the city of Frisco, which included spatial concepts, user journeys, and branding for the park, all designed to showcase its unique landscapes. In January 2024, Frisco City Council approved the vision statement, and officials expect to break ground in the second half of 2025.

The population of Frisco is growing rapidly; its population is up 516% since the year 2000.

At more than 1,000 acres, Grand Park in Frisco is even larger than Central Park in New York City.

Less than 1% of Blackland Prairie soil remains in the region, and some of it is in Grand Park.

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Frisco residents were engaged in the process, from City Council members to high school students

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respondents shared anonymous feedback on what they imagined the park should be

What will it mean when an algorithm can handle 90 percent of your job? What will working with dozens of agents be like?  When anyone can build enterprise tools—instantaneously? 

That future has already arrived at the major AI labs—and faster than almost everyone expected, even Anthropic. Founded in 2021 by a group of researchers who set out to build AI that is safe and useful, it’s now used by millions and was valued at $965 billion in its most recent funding round.

The company operates on the assumption that AI capabilities will keep improving quickly—and that its own internal ways of working have to keep pace. That means fomenting and accepting exponential change. Speaking to Joel Lewenstein, Anthropic’s refreshingly honest head of product design, provides a visceral sense of what the future we’re all heading toward feels like—both exhilarating and, occasionally, overwhelming. 

I spoke with Lewenstein about why protecting our thinking matters more than ever, why his team members don’t feel AI is atrophying their craft skills, and why enterprise collaboration is the next frontier for research and product.

Ed White (EW): Do you remember a moment when you first realized AI’s true potential?

Joel Lewenstein (JL):  My wife is an appellate lawyer. It was around 2023, and she was trying to explain this extremely dense legal case to me. After three tries, I still didn’t understand it. So one night, while she was asleep, I pulled out my phone and asked a gen AI to explain the case. It got two-thirds right, but then it started hallucinating, providing incorrect but insistently confident information. I remember having this conspiracy theorist-like out-of-body experience. I felt so alive because I’m learning this deep thing, and I felt so scared and confused because I’m being lied to, but it was insistent. I remember thinking that there’s so much good here, along with confusion and complexity that would be interesting to work on. 

The second moment was hearing Dario [Amodei, CEO of Anthropic] on the Dwarkesh Podcast. This was also in 2023. Dwarkesh asked him: “If we reach AGI [artificial general intelligence] and it cures cancer, should it be governed by a company, the US government, or the international government?” I thought it was a crazy premise. But Dario launched into a deep reflection on governance, ethics, and international order. I realized these people fully believed in the goodness of AI. That made me very excited.  

EW: What’s your role at Anthropic?

JL: I lead the product design, user research, and content design teams. There are about 30 folks, and we basically put a product designer, a user researcher, and a content designer on our platform API business (Claude Code, Cowork), our consumer apps, and our enterprise and growth areas. Our designers are deeply embedded and trying to figure out what the hell this role is in 2026. 

EW: Having come from more classic product design roles at organizations like Airtable and Quora, how is this experience different?

JL: The most interesting dimension here is speed. You hear this constantly, right? But the weird part is the time compression. There’s a measure-twice-cut-once quality to the work. You do a lot more preparation, thinking, and consideration before you build because the time to build is so short. The question is: How do we keep up in an environment where everyone’s shipping all the time?  

We also build a ton of internal tools. Our content design team created a GitHub agent that monitors strings sent to production. It checks for adherence to our content guidelines and opens PRs [pull requests] for any that don’t comply. That’s crazy because this used to be 50 percent of the job. Now it’s just 5 percent, allowing us to focus on other things. All of our designers are writing code.  

EW: Where does the design or craft bit fit in, then?

JL: With design, there are three steps to the process. Step one: determining what we should build, why we’re building it, and what problem we’re solving. Step two: creating user flows and ensuring the basic components are in place. Step three: crafting the last mile of delight, those little details that many people may not notice, but subtly make a difference. Step two is now gone. We just prompt Claude, and it just does it. Step one—deciding whether we should build something, why we should do it, and where it leads—is still a very human and messy process. As for the last step of getting all of those little interactions and visual details right, our designers are still better than the models. Maybe that won’t be the case forever, but for now, it is.  

EW: Tim Brown, IDEO’s former CEO, co-authored an article about this recently titled “The AI dividend.” Now that AI has reduced a task that used to take 50 percent of your time to 5 percent, what do you feel it’s allowing you to do?  

JL: I would love to tell you that all the boring tasks have been automated, allowing the designers to sit at whiteboards thinking bigger, more ambitious thoughts. But our Slack and GitHub are flooded with PRs from dozens of engineers. We’re juggling 17 different Claude Code instances, and each one is pinging, “I’m done! Can you review this?” It’s a cacophony of tiny projects. It’s similar to the attention-economy notification overload we experienced in our personal and social media lives. Now, it feels the same way in our work life. We’re fighting harder than ever for half a day or a day to just think, dream, and explore.  

EW: Do you think the pendulum will eventually swing back? 

JL: It has to because the human condition will require it. I think two things will happen. First, we haven’t invented an interface for managing hundreds or thousands of Claude instances.  There’s still an unsolved UX problem. The best analogy is the American presidency. There are hundreds, thousands, or even millions of staffers running around making big and small decisions, producing piles of status updates. A human chief of staff manages all that. Eventually, we’ll need a digital equivalent who will serve up a concise summary from a few of your SVPs [senior vice presidents] on Monday morning—agents who have dealt with 10,000 individual contributors—so that you don’t have to think about each task.

EW: How else are you seeing designers’ roles change?  

JL: Being close to the code and adopting a code-first, prototype-first mindset is really central. We’re not too precious about our work. If something has a decent UX, we put it out there in a beta and collect feedback. 

As someone who has always worked in the digital space, I think about how creative professionals in the past, like musicians, operated: once an album was finished and the record was pressed, that was it. I could never have worked that way creatively. Back when I started, in the Ruby on Rails era, we could simply deploy again next week and fix any issues. But for AI native folks, if something goes wrong on a Tuesday night, they just submit a PR on Wednesday morning to fix it. Everything is malleable and adaptable all the time.

This lack of preciousness, the idea that “done and shipped” is better than “perfect,” is crucial. Of course, there are challenges. Sometimes the coded output doesn’t look great, but we accept we’re going to learn from this vibe-coded thing and get on with it. We refer to this approach as “intentional craft,” meaning that if we need to make this outstanding, we still have the chops to do it, but we also need the discipline and the judgment to recognize when it’s appropriate to let a rough version go out.

User expectations are changing fast. During the heart of the mobile app era, people had incredibly high bars for their experience. We’re no longer in that era. When we put out these vibe-coded, early experimental things, we get feedback like, “Guys, this broke three times, and this button is called three different things.” But they also say, “I love it. Please make it better, and I'll keep using it.” We’re in a part of the S-curve where users have a lot of tolerance for jank. Eventually, this field will mature.  

EW: How do you maintain your craft in a world where models can perform many design tasks?

JL: Collaboration with these models is not a replacement story. You get better as a writer, editor, and thinker with a creative partner who pushes you, asks questions, and explores alternative theories. There’s a nice sparring partner quality to collaborating with AI that I appreciate.  

The models aren’t doing anything better than a thoughtful designer can with enough time. In a head-to-head competition between Claude and an outstanding designer, the designer still wins most of the time. I don’t hear our designers being concerned about skill atrophy. They’re worried about not having the time and mental space to practice the skills they already have and care about.   

EW: That’s very heartening to hear, to be honest. With so many options of what to build and many more people who can do many more things, how do you find a sense of direction? Do you and Mike [Krieger, co-lead of Anthropic Labs] share an ethos on product development?

JL: We’re letting the model lead the way. For example, in the pre-AI era, a product like Claude Design would have emerged through a TAM [total addressable market] analysis: evaluating our position as market leaders in coding, identifying adjacent spaces, interviewing stakeholders, spinning up a team, and committing to a 6- to 18-month timeline. 

In reality, the complete opposite unfolded. People using Claude Code were organically making UIs [user interfaces]. One of our designers noticed that these URIs [uniform resource identifiers] were pretty good and wrote a skill to coax the models into producing even better front-end designs, specifically using our design system. He realized it was useful and spoke with our researchers, who confirmed it wasn’t a fluke. He then refined the skill into a standalone app. That’s the genesis of Claude Design. We felt comfortable launching it at a research preview because we believe the models will continue to advance. 

EW: That’s interesting because organizations don’t generally use what I’m going to call “academic research” as a way of thinking about how products come about. 

JL: It’s crazy because at any other place that I worked, nothing changes on a product unless you assign an engineering team to change it. If your product engineers all went on vacation, no features would launch. But at Anthropic, if our product team went on vacation, the models would just keep improving.

EW: What’s it like working alongside agents daily, and how does it impact the organization?

JL: Everyone at Anthropic has dozens of Claude Code agents running in the background. We recently worked on a navigation IA [information architecture] refresh. Anyone who’s done a navigation redesign knows there are a million questions in the kickoff meeting: Are people really using this tab? Are the users of this tab and this tab the same, etc. In my old-school, pre-AI brain, I’m like, “We’ll need a week to explore, a follow-up meeting, and then we’ll come back with all these questions.” But in the meeting, the designers are just tap, tap, tapping on their Claude Code agents, asking, “Will you look at our internal documents, Slack, and our data to figure out how many people click on this tab?” And 45 seconds later, someone chimes in, “Hey, it turns out that tab is still used a lot.” It’s like everybody has a team of research assistants behind them, finding answers for them in real time. 

EW: What’s inspiring you right now? 

JL: I’m curious what the next three to five years hold for the unit of computing. We learned how to use websites, then apps, and now chatbots. What will be the next unit, and will it be vibe-coded or developed by individuals, corporations, or models that generate it in real time?

EW: Looking ahead, what’s most exciting to you and your team?

JL: We’re thinking a lot about teams and team productivity. Claude is amazing in single-player mode. But while we’ve become more productive individually, the collaborative aspect of work hasn’t changed dramatically. Right now, we have a World Cup collection of individual superstars producing amazing things, but their collective chemistry remains unmediated by AI. But then again, you and I are speaking right now completely unmediated by AI, and that’s also beautiful.  

EW: What advice would you give designers? What should they be doing and thinking about?  

JL: When users have a problem, our instinct as designers is to create a new feature—design the rectangles and the flows—and let AI build it. We’ve started to invert that approach. If users have a problem, we ask, “Can Claude solve it on its own with prompting?” Then, if it can’t or needs additional structure, we add a feature or product architecture. 

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“Design in the Age of AI” is a series of conversations with designers and makers from across industries and disciplines, building the future with AI today.

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“Being close to the code and adopting a code-first, prototype-first mindset is really central.”

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Travel is fundamentally a human experience, and personalization has always been the ideal: the concierge who remembers your name, the host who anticipates your needs, the perfect room tailored to your preferences. 

But true personalization has traditionally been a luxury, reserved for those with wealth and elite status. Even as technology has enabled mass travel and big data and CRM systems have laid the foundation for personalization, brands have struggled to scale hyperpersonalization to everyone. Loyalty programs have become the industry’s best attempt at bridging this gap: Points and tiers quantify who deserves what, with status serving as a proxy for real relationships and customer understanding. 

Until now. With the introduction of AI agents, hyperpersonalization can scale from the segment level to the individual traveler. Done right, it has the potential to democratize what was once a luxury good: the experience of being recognized. 

Of course, serendipity and delight remain a huge part of the travel experience: unsolicited dinner recommendations from local shopkeepers, wrong turns that lead to hidden gems, and conversations with strangers that become core memories. Our research shows that, especially for Gen Z customers, agency and spontaneity play a big part in shaping their tastes and identities. The opportunity is for AI to know travelers well enough to make their experience feel personal—but also to know when to get out of their way.

Capturing this opportunity means redesigning loyalty so that rewards are legible to agents, personalization scales to the individual, and the system delivers an ecosystem of value matched to what each traveler actually cares about. In doing so, brands can finally deliver the kind of personalization that has always been the industry’s ideal.

The dual audiences challenge

Every travel brand is now designing for two distinct audiences. The first is the person who is comfortably waking up in their hotel bed, enjoying the aroma of morning coffee from the lobby brunch, or grabbing a beer with their new Scottish best friend by Fenway Park. The other is an AI agent that’s handling the searching, comparing, and booking tasks on that person’s behalf. 

These two audiences have different priorities, attention spans, and methods for evaluating information. Hyper-optimize a webpage for one audience, and you may inadvertently block traffic coming from the other.

It’s tempting to treat these dual audiences as a marketing problem—a matter of making pages readable to agents without alienating people. And that is part of it. Stripe has already built a wallet for agents, and AI coworkers are beginning to handle multi-step bookings. But the consumer trust gap is wider than the technology gap. The travel industry has not gathered enough data to determine whether travelers will allow an agent to optimize their preferences about brand, travel occasion, or loyalty points.

As brands strive to cater to both audiences, agents in the funnel are revealing three outdated assumptions baked into the way most travel and hospitality loyalty programs operate. First: transactions are the best measure of loyalty. Second: personalization doesn’t scale. Third: points are the primary reward. Once these assumptions are challenged, a different kind of reward emerges: the reward of travel itself. In the age of AI, loyalty programs should be designed with enough emotional intelligence to preserve the romance of travel while harnessing the superpowers of machine intelligence. 

False assumption 1: Transactions are the best measure of loyalty

When American Airlines launched its AAdvantage program in 1981, it introduced the first large-scale frequent-flyer program by mining its reservation system for recurring phone numbers. This marked the first time the airline could identify returning customers and begin building direct relationships with them rather than with their travel agents. The airline built the program on the data it had at the time: transactions. Forty-five years later, most loyalty programs still follow a similar structure—offering points for spending, tiers for hotel nights, and miles for distance traveled. 

This transactional dynamic is increasingly ineffective. The average American belongs to 17.4 loyalty programs, but actively engages in fewer than half. Many brands claim loyalty membership sizes that rival the entire adult population of the United States. These transactions may look like loyalty, but are they really?

The next generation of travelers will have an agent doing the work that only the most dedicated points gurus do today: discerning what each traveler finds valuable and surfacing the options that match. Imagine a Gold-tier member with a brutal travel schedule who is looking for a hotel close to tourist attractions. In a regular search, they filter by price, reviews, and location. An agent can do that, plus spot the perks that actually matter at six in the morning after a red-eye: early check-in and a quiet room away from the elevators. 

Now, the measure of loyalty broadens beyond the transactional into the experiential and emotional utility of being seen and cared for. Agents simultaneously reveal which programs offer real value and match them to what each traveler values for each and every trip. 

False assumption 2: Personalization doesn't scale

Top loyalty programs like Amazon Prime offer around 30 distinct benefits. They range from free next-day shipping to cash-back options and streaming media subscriptions. Brands surface long lists of these perks because they can’t possibly know which one will land with a given member. The list is often a coping mechanism for the brand’s inability to fully personalize and the consumer’s inability to remember everything the brand offers.

Marketers often use robust quantitative surveys, such as conjoint analyses, to gain insight into consumer preferences. Run enough permutations with enough travelers, and you can statistically estimate which combination of benefits they value most. Marketing teams still debate granular tactics, like the order of perks to list in an email. But those debates are entirely unnecessary when agents can just choose based on the traveler’s preferences.

Consider the scale this can reach. Expedia Group alone encompasses more than 3.5 million lodging and vacation rental properties. One can imagine that in the near future, by analyzing a traveler’s bookings and reviews across this vast selection, an agent can ensure that a guest’s preferences are prioritized for every booking, regardless of who operates the property.

Personalization scales when agents do the matching. The new design challenge is enabling agents to interpret hundreds of personal preference variations and reward ecosystems, making offerings sufficiently readable for an agent to match and communicate well on the consumer’s behalf.

False assumption 3: The points are the reward

Ask anyone who has been a road warrior what real loyalty feels like, and the answer is rarely the points. It’s often Shirley at the front desk, who remembers you like a higher floor, a firm pillow, and a bigger room rather than a view. The recognition itself was the reward, a relationship rather than a transaction. But for too long, it was too expensive to offer to anyone beyond elite travelers. 

Now, the behaviors that used to earn that recognition are fading just as technology is emerging to recognize customers without them. Today’s consumers are less loyal and less inclined to align themselves with a specific loyalty program or brand because they value choice, flexibility, simplicity, and convenience. The number of people who used to go on mileage status runs in December just to hit status is decreasing. Instead, they’re seeking something more, and brands are increasingly able to provide it, thanks to advancements in AI.

Expedia’s recent partnership with CLEAR offers a glimpse into the future of travel loyalty: It is evolving from individual brand perks to an integrated ecosystem of complementary benefits that enhance each step of the journey (think: Apple ecosystem logic, but applied to travel). For example, CLEAR offers Expedia members discounted memberships to CLEAR+ lanes and access to its Concierge services, providing VIP treatment as they head to their next destination. What stitches the ecosystem together is the underlying agent layer. It reads each traveler’s  preferences across the partner brands and surfaces the right benefit at the right moment, without the traveler having to coordinate it themselves. No longer just a perk for road warriors, recognition becomes an experience any traveler can enjoy at every stage of their journey.

The opportunity to make travel more human

People often talk about compound interest when it comes to money. You invest a small amount, it grows over time—that’s the magic of compound interest. But we believe the memories, stories, and friendships formed through travel compound even harder. Years later, a distinctive smell, a friend, a menu item—whatever—can magically transport you back to that travel moment. 

Loyalty has never really been about earning points. It’s about the memories of being cared for while away from home, discovering something new and unexpected, and feeling seen and recognized. It’s about the desire to return because those memories capture the joy of travel.

Nights, miles, and dollars are the receipts for a trip. They are not what the trip leaves behind, which is more sensorial and more personal than anything a tier structure has ever captured. The next era of travel loyalty will be about making travel more human, powered by the combined strengths of machine intelligence and emotional intelligence.

“In the age of AI, loyalty programs should be designed with enough emotional intelligence to preserve the romance of travel while harnessing the superpowers of machine intelligence."

I’ve never been a big fan of medical dramas, but I couldn’t stop watching The Pitt, the HBO Max show about a fictional Pittsburgh ER at a public teaching hospital. The high-stakes medical cases, relentless pace, and scrappy, sleep-deprived team you can’t help but root for had millions of viewers counting down to Thursday nights earlier this year. But for me, it was about watching the work itself. 

Apparently, I’m not alone. The Washington Post called the Emmy-winning show “perhaps the purest example of ‘competency porn’ on TV”—a term that surged into the cultural vocabulary this year to name the satisfaction of watching people who are really, really good at their jobs. 

But what they’re really good at goes beyond the practice of medicine. They excel at teaching and learning on the job. They think out loud, trust learners with calls that matter, and dissect what just happened before moving on to what’s next. I find it inspiring to watch.

Here are three lessons on teaching and learning in the workplace that we can take from The Pitt, and ways to activate them in your own work. 

Make mastery observable

Every scene on The Pitt has two things going on: someone doing the work, and someone learning to do the work. A recent nursing school grad shadows as the charge nurse guides a survivor through a rape kit exam—every choice a lesson in how to move at the patient’s pace. A resident observes as the attending decides out loud whether a procedure is worth trying if it might cost their patient her sight. A specialist performs an emergency procedure the attending has never seen before, teaching the room as she does it. Watching people at the top of their game work out a problem from beginning to end—including the messy middle, not just the outcome—is a sight to behold.

Most of us don’t work in a teaching hospital, but “see one, do one, teach one”—the century-old learning model attributed to Johns Hopkins co-founder and surgeon William Stewart Halsted—is how most of us pick up a craft, medical or otherwise. But that kind of learning is harder to come by these days. For years, most knowledge work has been evolving to a less readily observable state. Increased remote work makes it challenging to know what other people are working on, receive mentorship, get feedback from peers, or signal when you need help. The explosion of single-player AI tools has increased output, but made the thinking that informed it harder to see and, some research suggests, to trust. 

When my San Francisco colleague Thomas Overthun worked at Philips early in his career, he would pass by hundreds of drawing boards on his way out of the studio, displaying an amazing range of work in progress. Our workplaces look very different now, but it’s entirely possible to design tools, rituals, and ways of working that can help us see and get inspired by one another’s in-process work.

Here are a few practices that are helping us tilt our work toward each other:

• Use video shares: Kaii Tu in our Shanghai studio records short Loom videos to walk his team through the design choices he made—and why. The format forces him to make his reasoning legible, and makes it possible to share across time zones.
• Host “Screen Share Fridays”: On Fridays at our Cambridge studio, the studio-wide Slack channel explodes with screenshots of work in progress, sketches, project Post-it Notes, photography from the field, and presentation decks. Questions naturally follow, which then leads to dialogue about the work.
• Think in public: My Chicago colleague Leah Marcus builds incredible FigJam boards at the start of each project with relevant research, inspiration, and connections she’s making across our digital product portfolio. She uses plain language, bringing others who don’t share her business design discipline into her thinking.

Let the learner try

The best teachers I’ve observed share one hard-earned skill: they show restraint, letting learners grapple and find their feet with just the right amount of support. In The Pitt, when a new patient arrives in critical condition, the attending physician, Dr. Robby, turns to a resident and asks, “What’s your plan?” He doesn’t jump in, even when the clock is ticking. As a viewer, you feel the pressure that the resident is under and wonder, “Is he trusting a beginner with too much?” Turns out, this is an important part of the learning process.

This is the “do one” part of Halsted’s model, and it’s harder than it sounds. We know from decades of research that learning happens when we reach the edges of what we know how to do, and it happens even faster with scaffolding: a gradual release of responsibility from teacher to student. Instead of just telling residents to do the procedure on their own, Dr. Robby asks them questions, nudges them to consider alternatives, and fills in information they miss if it will have consequences for the patient. He lets them try first, then steps in when they get stuck. The scaffolding makes the risk survivable but not invisible. 

As a firstborn, Virgo, recovering perfectionist, I’ll be the first to admit that it is hard to use suggesting mode when you can just use editing mode. But the shortcut has real implications for my colleagues and the work. While I may get a cleaner draft, we both skip the interesting part that could help someone else grow, and probably get the draft to a better place than I could alone.

How might we demonstrate restraint and let learners try?

• Provide feedback in the margins: Suggest, don’t edit. Offer comments over rewrites. The extra friction is the point—it forces a conversation about the choice you would have made, instead of making it.
• Design it now: My colleague Bri Patawaran in our San Francisco studio is a huge fan of design-it-now moments to get a team out of swirl and uplift viewpoints that haven’t been heard. She asks, “What would you do if you had to deliver this today?” Everyone goes heads-down and then presents.
• Ask, don’t answer: When your team is stuck or a direct report brings you a challenge, resist the urge to solve it. Ask, “What's your first take?” and stay in question-asking mode as long as you can to help them explore their own thinking, giving you a chance to see their problem-solving skills in the process. 

Tell the story

The story we tell about what just happened is how we absorb what we’ve learned. On The Pitt, residents present each case to the attending after their patient is discharged. They walk through what the patient came in with, the diagnoses they considered and ruled out, the call they made, and the result. It’s a small ritual with an outsized effect—the resident consolidates what they learned by teaching it, and everyone within earshot learns alongside them. 

Reflection doesn’t have to be formal. Christopher Myers, a researcher at Johns Hopkins, studied medical transport teams—paramedics and nurses who fly in helicopters to accident scenes and rush patients back to the hospital. On any given day, they have no idea what they’ll encounter, and no single person can accumulate enough experience to be ready for all of it. What Myers found is that the crews built shared expertise through vicarious learning: telling each other stories, informally, between calls. These stories—casual, unscheduled, and recounted in whatever time they had—were how the whole team became more prepared together.

There are plenty of ways to build reflection into your work. The harder—and more interesting—work is building it into your culture. 

Here are a few practices we use across IDEO to encourage reflective storytelling.

• Ask “What are you working on?” The deceptively simple five-word question is a reflective invitation dressed up as small talk. Ask it in hallways, on Slack, at the start of one-on-ones. Half the time people just answer. The other half, they tell you something they didn’t realize they’d learned.
• Never skip a retro: It’s easy to nix a project post-mortem when you’re running short of time. But “I liked…,” “I learned…,” “I lacked…,” and “I longed for…,” is an easy and effective feedback framework to prompt everyone to share their ah-has and takeaways from the work. 
• Host a “Wrap Party”: Once a month, we host a virtual IDEO-wide “Wrap Party” where teams share three behind-the-scenes stories about recently completed work, giving everyone an opportunity to learn and get inspired together.

At one point while watching The Pitt, I said to my husband, “Wow, if I’d seen this when I was younger, I might have wanted to become a doctor.” (He gently reminded me that I have to avert my eyes every time I see blood.) Though what we see on screen may seem aspirational, none of the lessons The Pitt shares about teaching and learning on the job is exclusive to an emergency room. Observability, restraint, and storytelling are choices—small ones, mostly—that we can put into practice every day wherever we work. None of them require a shift change.

Curious about how to improve ways of working at your organization? Get in touch.

“The best teachers I’ve observed share one hard-earned skill: they show restraint, letting learners grapple and find their feet with just the right amount of support.”

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Imagine an engine that generates alternative endings for stories, a 2x2 visual tool for choosing which films to watch,  or a digital game of telephone that transforms a poem into something altogether different, like a location on a map. For Khyati Trehan, a Design Lead at Google Creative Lab, these are the weird, improbable, delightful explorations that AI is catalyzing.

Trehan’s lifelong curiosity for making began at Mirambika, a progressive school in New Delhi, the city where she was born and raised. “When you give children complete freedom, they choose to learn,” she says. That love of learning took her from the National Institute of Design to a globetrotting creative career spanning interning at a type foundry, creating AR experiences for Snapchat Spectacles and Instagram, producing 3D editorial illustrations for The New Yorker, The New York Times, and WIRED, working as a communication designer at IDEO’s former Munich studio, and “one crazy Oscars’ project the year of Will Smith and Chris Rock.” 

She’s now at Google Creative Lab in New York, working on projects that “humanize complex technology and remind people why they love Google.” She collaborates with a diverse team of designers, writers, and technologists, united by a desire for creative freedom and a novel approach to problem-solving.

‍I spoke to Trehan recently about how AI enhances her creativity, her evolving mindset as a designer who embraces coding, and the importance of fostering play and experimentation in organizations to promote AI adoption.

‍Ed White (EW): Do you remember when you first started using AI?‍

Khyati Trehan (KT): I got early access to DALL-E before it launched in 2021. Ironically, I remember liking the blurry loading states more than the final images. The results never quite stuck because nothing that came from my prompts felt like…me. There wasn’t much control over the output. I was looking for tools that would feed my creative process, and “one-shot AI”  didn’t do it for me.

An interest in using LLMs to turn natural language into software code happened more recently. My friend Pedro Sanches, a brilliant creative technologist, designer, and Creative Lab alumnus, came over for tea and shared some sketches he’d made using what he called “coding sans coding.” (This was before “vibe coding” was a term.) I was much more excited by that approach because it felt like something I could incorporate into my practice by building tools to help me explore new places without them feeling completely unfamiliar.

Those early experiments provided initial insights into my personal philosophy on how and when I use AI. My goal has never been to rely on AI to do all the work; instead, it’s about exploring how AI can help me supercharge and advance existing ideas.

EW: How is AI changing your team and Google as an organization?

KT: As the boundaries around our disciplines blur, the number of hybrid creatives is growing. Writers are making films, graphic designers are building writing tools, animators are engaging in creative coding, and developers are designing interfaces. There’s still a distinct difference between the vision and “flavor” of what I might design and develop as a graphic designer and what a creative technologist might create using the new capabilities that AI unlocks for both of us. Our individual experiences, values, core strengths, skills, and knowledge still define what makes us unique, even when we have the same tools at our disposal. 

EW: What are the things you and your team are learning, as designers, about using this technology?

KT: I think we’re realizing that regardless of how you look at the creative process, the shape of it remains the same. We still start by playing and experimenting to explore the edges of technology. We engineer every aspect of the applications we create, ensuring that we consider people’s needs. In fact, we now spend even more time and energy focusing on what matters to people and asking ourselves: “When we can make anything, what do we choose to make?”

While engineers are trained to focus on efficiency and optimization—and form the backbone of Google—creatives, when given the opportunity to lead research and gain early insights, instinctively seek out emotion and play. We naturally discover the right metaphors and interfaces that make complex systems clear.

EW: What’s worrying you about AI and design, and why?

KT: With every big shift, it’s wise to be cautiously optimistic. I often reflect on how we studied design in school: manually painting a color wheel and using our judgment to create the right shade of orange that would sit between red and yellow, even when the Blend Tool existed in Illustrator. Putting time and effort into these exercises sharpened our skills and helped us develop a strong foundation, which remains useful regardless of the tools we use. We shouldn’t forget that. You can use AI as a crutch, or you can use it to unlock or supercharge your existing skills, expertise, and ideas. To me, that’s the distinction between an effective use of AI and slop.

EW: What are the really concrete ways AI has changed your craft as a designer at Google? 

KT: The tools you use change the way you think. When I was learning 3D modeling and texturing years ago, it quite literally unlocked a new dimension in my graphic design practice. I’d surprise myself with the ideas that came to me and what I was capable of with this new ability.

This holds true for using AI. I’ve added surfaces like Gemini Canvas, AI Studio, and Flow to my toolkit to choreograph Google’s models and APIs. Mindset-wise, it’s made a lot of us hybrids. Now that we can build things, I find that in meetings, designers show more often than they tell.

I still design using traditional tools, except now, once I translate the visual and the flow into a clickable prototype, I can iterate in the same environment, and the work becomes more lived-in. 

EW: How has that changed you as a creative, and why?

KT: It’s definitely a leap. It’s like the difference between learning about qualitative interviews versus being in the room yourself. You can understand both objectively, but with the latter, you feel more connected to the learnings. For example, I’ve designed loading states plenty of times, but now that I’m closer to the front end, I’m bringing so much more of my design flavor and delight to them. I feel more comfortable taking risks and taking departures from what’s deemed standard in the space. 

EW: What’s an example of that?

KT: I’ve been designing digital experiences for very personal, everyday, specific, and idiosyncratic needs. For example, I got my hands on Ted Chiang’s Exhalation, a collection of short stories, and fell in love with them. I’ve been inventing alternate endings for some of them and extending his beautiful worldbuilding.

This led to the creation of Story Arc Engine, a narrative-building tool that allows users to deconstruct stories using a five-part narrative arc. By tweaking one part of the arc, users can see how a change in the plot affects the rest of the story and generate new narratives based on their own plot ideas.

EW: How else does AI change what you’re designing? 

KT: Because I’m now building both the final output and the intermediary tools that help me get there, I share both: the final product and the tool I vibe-coded to create it. This means that others can use the tools I make, often in unexpected ways. For example, someone once used Story Arc Engine to draft their next career move, hiding a sabbatical in the narrative. 

EW: What other examples of these types of projects have you been working on?

KT: I’ve recently made several tools for myself. Around the World in Good News is a digital newspaper that explores uplifting historical events and stories of human achievement from across the globe and throughout time. Another project, 2x2 Anything, emerged from my desire to make more informed decisions for movie night. It's a concept-mapping experiment where you define two conceptual axes, set the context, and click anywhere on the coordinate map to generate a fitting concept or summon an existing result. My most recent sketch, Machine Telephone, is a playful game in which you enter an input, pass it sequentially through different media and models, and observe how context shifts, translates, or gets misunderstood over time. For example, you might see a poem translated to a specific location on the map or a song transformed into a spherical material. Now that mediums feel less siloed, and those that remain siloed are easier to learn about, my first instinct is no longer to dismiss an idea just because it initially seems unfeasible.

EW: What advice would you give leaders who want their orgs to use AI effectively?

KT: An urgent tone from execs and leadership, coupled with a top-down mandate and a lack of concrete guidance, isn’t helpful. It only leads to confusion and stress, and, ironically, slows down the actual work

Instead, explicitly give people permission to play. Play is a powerful tool, especially when you’re faced with ambiguity. Give people access to a variety of tools and functions, and offer learning resources. Let people stumble upon new paths, and figure out which tedious parts of their process AI can take off their plates. 

EW: How do you think AI will transform your industry over the next five years?

KT: AI is already transforming industries by narrowing the gap between different disciplines. When the mechanics of creation are no longer a bottleneck, and the effort it takes to make things decreases, where does our time and attention go? It comes down to the core of why we create: our taste, the sum total of our experiences, our irrationality, our perspective, and our vision. Maybe we’ll just learn more about what makes us uniquely human along the way.

“Design in the Age of AI” is a series of conversations with designers and makers from across industries and disciplines, building the future with AI, today.

“My goal has never been to rely on AI to do all the work; instead, it’s about exploring how AI can help me supercharge and advance existing ideas."

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.