D4V is a Tokyo-based early stage Venture Capital in partnership with IDEO. Two years ago, D4V invested in healthcare startup Ubie, which develops a service called AI Monshin Ubie. The digital service was created with the goal of reducing the burden on healthcare professionals working long hours, under harsh conditions. AI Monshin Ubie automates a number of patient registration and documentation processes through digital tools, enabling doctors to spend less of their time on manual paperwork, and to dedicate their valuable skills and attention to the patients themselves. Currently, the service is installed in 200 medical institutions in 41 prefectures across Japan.

With AI Monshin Ubie, patients fill out preliminary forms on a tablet, rather than paper, entering information about their health history and current symptoms directly into their digital medical record. As patients fill out the questionnaire, the Ubie AI generates questions in real-time according to patients’ responses, which results in more appropriate and accurate data. The patient’s responses are automatically formatted for the doctor to review prior to and during the consultation. As a result, doctor consultation times and facility operation times are drastically reduced.

With the Coronavirus raising fears of a possible health system breakdown, Ubie has made improvements to anticipate and possibly prevent in-hospital infections. Until recently, Ubie’s services have been primarily used by large hospitals; but in light of the global health crisis, Ubie approached D4V—who partners with IDEO—to help rethink the onboarding experience and quickly facilitate the introduction of AI Monshin Ubie in regional clinics across Japan. D4V and IDEO have been working with Ubie for many years to collaborate in various areas, including UX, rebranding, international research, and more. Therefore, they had a foundation of knowledge from which to build these new features.

Typically, AI Monshin Ubie has been introduced to new hospital clients through direct, in-depth instructional visits from Ubie sales staff. This approach has worked well for rolling out the product to Japan’s 8,000 large hospitals; but for the 100,000 smaller clinics, Ubie needed a more efficient onboarding method to enable staff to get set up smoothly and independently.

During research, D4V and IDEO team members engaged with many different clinic staff members, including full-time doctors, part-time doctors, nurses and receptionists. The researchers found that staff tend to react skeptically to new tools and services, with a common preconception that they will add to their workload. One clinic director’s first impression of AI Monshin Ubie was, “It looks complicated.” But once he actually used the service, he realized that it was very user-friendly.

The team quickly understood that it was important to remove preconceptions by clearly communicating Ubie’s benefits and ease of use. They created a two-minute animation and designed three separate guidebooks. The animation adopted a friendly tone and expressed empathy for doctors, staff members and patients, highlighting how Ubie can bring benefits to each of them. The animation showed how Ubie fits into the existing workflow and explained complex systems in a straightforward and accessible way.

WATCH: A short video clip explains the benefit of their product and service to avoid the users’ concerns in advance.

The guidebook for doctors and directors explains how Ubie’s AI smooths the patient consultation experience and greatly improves the quality of medical care. It also introduces concrete how-tos around set-up, implementation, and ideal workflow. FAQs were also included, addressing concerns about whether or not elderly people would be able to use it.

Ubie business development leader Kenji Yamada says, “D4V and IDEO have a deep understanding not only of design, but of our entire business. They provide inspiration by bringing experience and knowledge that comes from working with other industries and clients outside of healthcare. We appreciate that they can share the knowledge that they’ve obtained with our business.”

Read the Japanese-language version of the Ubie case study here.
When You Feel Unwell, Ask Ubie: Check your symptoms for free
Learn more about Ubie: Global Company Website

In the emergency room, an intervention that’s administered too slowly could cost a patient their life. With COVID-19 fueling a surge in acute respiratory conditions, intubation—a procedure that involves inserting a tube through the mouth or nose and into the airway—is more common than ever. For patients struggling to breathe, the tube can be connected to a ventilator that pumps oxygen to the lungs, keeping them alive.

While a laser-focus on the present is critical for successful patient outcomes, hospitals and physicians often don’t have time to look ahead and anticipate the future of the operating room experience. As technology rapidly improves, there is a ripe opportunity and pressing need to evolve medical equipment to be more innovative and intuitive. Verathon Medical, a global medical device manufacturer, is supporting health care providers at the cutting-edge of care.

Verathon approached IDEO to redesign the existing product system for laryngoscopies and bronchoscopies—procedures that examine parts of the throat through intubation. During these examinations, a tube with a camera embedded in the tip is inserted through the patient’s nose or mouth. The doctor can then see inside the throat on a video monitor, and therefore more easily and quickly intubate and perform the exam.

To create a groundbreaking device, the team observed over a dozen laryngoscopies, bronchoscopies, and intubations. They noticed that many anesthesiologists relied on fixed equipment during the procedures. For example, some had grown accustomed to looking over their shoulder—away from the patient—to view the monitor.

During interviews, physicians and residents expressed difficulties with the current technology, sharing that the small screens aren’t conducive to team viewing or student instruction, and they must often use a separate computer to record patient notes. Others reported frustrating workflows, needing to constantly divert their attention and move their hands between the patient, prep tray, monitor, sterile bin, and disposal bin. These inconveniences and inefficiencies are not only cumbersome, but can increase the risk of infection and fatality.

The team relied on these insights to start designing a next-generation product. Through sketching, storyboarding, and building in real-time alongside doctors and clinic staff, designers quickly teased out additional findings and validated concepts.

This work led to the creation of the GlideScope Core: an all-in-one, portable product system that supports both video laryngoscopies and bronchoscopies. The base of the device—a wheeled cart—has built-in storage units for tools and a disposal bin, offering more mobility and accessibility for medical professionals.

The product’s tagline, “see more, do more,” points to the improvements made to the video system, including a large 10- or 15-inch high-definition touch screen monitor. Displayed images can be enlarged and rotated 180 degrees for improved visualization, team viewing, resident training, and classroom demonstration. To help doctors keep both the patient and monitor in their line of sight, the detachable screen can be secured to an adjustable arm that can be positioned directly over the patient. Additionally, the monitor’s patient annotation feature gives staff access to more of what they need in one device.

The product has been especially valuable for providers and patients during the coronavirus pandemic, when it’s imperative for doctors to keep their distance from patients’ mouths and noses. The GlideScope Core’s movable, detachable, and large, high-definition monitor makes it easier to take this precaution and intubate more quickly. This usefulness is reflected in the 5-fold increase in cart-based device orders for Verathon. While most providers typically order anywhere from 1 to 20 devices for airway procedures, one hospital system requested 600 GlideScope Core systems to meet current intubation demands.

Through modern technology and a focus on human needs, the GlideScope Core supports improved patient outcomes and creates a more seamless operating experience for medical professionals. The product underlines the key but often overlooked fact that often, in order to ensure the healthcare industry is patient-centered, medical device manufacturers and hospitals must also focus on improving the experience of providers.

Healthcare is notoriously difficult to change. Rules and regulations present complex barriers; health practitioners and organizations often have entrenched ways of working; and lives are literally on the line. Despite these significant barriers, the leaders at Newton-Wellesley Hospital (NWH) boldly committed to improving patient care through a new approach.

NWH is a community teaching medical center in Newton, Massachusetts. Because of its proximity to Boston—a city known for world-class healthcare institutions—NWH needed to differentiate itself. The hospital's new president, Dr. Michael R. Jaff, reached out to IDEO for help: "I knew that providing an unparalleled patient experience would set us apart from our competitors." But, Dr. Jaff’s particular approach was unique. He and Dr. Jodi Larson, Chief Quality and Experience Offer (CQXO), saw an opportunity to drive better patient outcomes through cultural and organizational change.

"You cannot learn any more than when you get to listen to your potential patients say why they would never walk into your place." —Dr. Michael R. Jaff

IDEO partnered with NWH for a year of work together, kicked off by defining the vision for NWH and ramping up to in-hospital prototyping and the launch of several new initiatives. A pilot project with the Women's Imaging Center (WIC) focused on solving departmental and employee challenges to provide a better experience for women getting mammograms.

Fueled by this potential, the WIC team redesigned key work processes and created a set of experience principles that inspired new service prototypes. Guided by principles like "Express mutual gratitude" and "Honor every voice," the WIC team tested ways for patients to acknowledge outstanding caregivers and created tools to gather better feedback from patients and adjust quickly. The experience of designing better services inspired the WIC staff to become ambassadors for the power of design thinking across other departments inside NWH and they continue to build on the work through new prototypes and experiments.

Next, IDEO partnered with NWH’s ambulatory surgery group. They took an analogous research trip to Boston’s Logan airport to see with fresh eyes how other high-touch customer service organizations provide customer value in a complex setting. Insights from the trip were applied through live prototyping of several new experiences inside the hospital, including a pre-surgery visit summary and pre-operation care carts.

Analogous research helped us realize that we can learn from other industries and incorporate some (cool) best practices if we allow ourselves to get over the ‘that would never work in the hospital’ mentality. - Dr. Jodi Larson

One of the most impactful prototypes was Care Modes, a tool to empower the NWH care teams to tailor their approach and interaction with patients based on the patients’ unique needs, beyond simply their clinical requirements. Four distinct approaches were identified for engaging a patient before surgery—Explain, Partner, Embrace, and Encourage. The Care Mode is determined based on the provider's understanding of a patient's degree of self-advocacy and their confidence that surgery is the best solution to their issue.

Historically, care providers have been trained to deliver consistent care above all else. However, Care Modes powerfully acknowledge that patients respond differently to different types of care. The Care Modes empower the team—from surgeon to nurse to anesthesiologist—with a clear framework for how to interact with and provide care for patients so they feel most comfortable. Based on the prototype's success with patients and impact on the staff culture, NWH has incorporated Care Modes into EPIC, the hospital's electronic medical record.

The importance of being patient-centered has been central to the healthcare conversation over the past thirty years; but new explorations reveal that there’s far more potential for innovation and improvement when the provider experience is considered in tandem with the patient’s. Each enables the other, and design for both together yields stronger solutions and better outcomes. IDEO's work with NWH demonstrates this vital shift in user-centered healthcare—one can't design services and experiences for patients without designing for their providers’ culture as well. When these go hand-in-hand, meaningful institutional change in healthcare is more than possible.

The mission of Chicago's Museum of Science and Industry (MSI) is to inspire children to achieve their full potential in the fields of science, technology, medicine, and engineering. Leaders at MSI realized they needed to drive day-to-day operations and maintain historic exhibits while focusing on where to go next. To prepare for its next decade, MSI worked with IDEO to create a new business focus to support the museum’s aspirations for the future. The plan would allow MSI to respond to the demands of the marketplace and raise the funds to get there.

At the beginning of the project, MSI laid out its priorities: improve equity of access, enhance the museum experience, and create a more innovative culture. To dive into each of these areas, IDEO conducted immersive research with families, teachers, donors, and staff.

IDEO brought together 60-plus donors, board members, and staff members to engage in a design thinking exercise to unpack and address the tensions that come with choosing a new strategic trajectory. Instead of having a standard ballot vote, the IDEO team encouraged participants to get up and vote with their feet, helping the group of stakeholders physically align on the museum’s new direction.

At the end of the research and prototyping phases, MSI and IDEO developed a vision called Provoking Minds, Inspiring Futures, creating six detailed design briefs to equip MSI teams to bring initiatives forward. The briefs laid out everything from recommending a new Department of Next, dedicated to ensuring that the future is at the heart of everything the MSI does, to MSI Runway, a place for experimental, small-scale, and limited-run exhibits. The ideas were brought to life with an interactive exhibit, built quickly with everyday materials like plywood and circuit boards, to demonstrate that creating an inspiring vision of the future doesn’t have to be an expensive endeavor.

The team also focused on insights that highlighted accessibility issues some families brought up early in the design process—travel across the city via public transportation can be lengthy, and the cost of admission to special exhibits could be prohibitive. To address these concerns, IDEO came up with the Mission Accessible brief: Inspired by casino buses, MSI bus stop signs would be posted in various low-income neighborhoods, signaling a way to take kids and families to the museum for free.

During the project, the IDEO team identified some low-hanging fruit, like rethinking ticketing and improving the student field trip experience, and helped the museum create cross-functional innovation teams to apply design thinking skills towards solving those challenges. They researched, brainstormed, and pushed forward new concepts, meeting weekly to share and build on ideas. At the end of the four week period, each team prototyped their ideas—new outdoor programming, software tools for visiting tour groups—and pitched them to each other.

In 2019, two years after the conclusion of the project, the collaboration paid off. With its new strategic plan and additional resources, the museum has renewed its commitment to serving youth, and MSI’s team, including donors, are aligned on a collective vision of the future. MSI raised more funds than in any previous year, including the single largest gift in its history—$125 million, nearly half of its $380 million fundraising goal—that will help ensure the years ahead.

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.”

‍

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."

When we prototype, we embrace what we call the three Rs: rough, rapid, and right. We keep prototypes rough, so that the people we design with feel comfortable providing honest feedback. We move rapidly, getting in as many reps as we can to open up creative possibilities, learn from each iteration, and avoid falling in love with the first ideas that pop into our heads. And we work very hard to get it right, creating lo-fi designs that test for just one variable at a time, so that, for example, a beautiful design doesn’t distract from a less-than-ideal experience. As AI makes it easier to generate polished renders with minimal effort, we see more people celebrating the ease and speed of design. But beautiful images aren’t design. In fact, they can seduce us into thinking an idea is fully baked, stopping us from asking the most important questions, the ones that uncover the insights that help us truly meet human needs. 

For decades, the three Rs have kept us grounded, reminding us why we prototype and ensuring that we get the most out of the design process. They apply to everything we design—from physical products to AI-enabled systems and digital tools. One of our most memorable prototypes was for a digital product for Sesame Workshop. It involved a simple cardboard cutout of an iPhone and a bit of role-play. One colleague pretended to be a preschooler, standing in front of the board, pointing to elements on the improvised screen. Another stood behind the board, impersonating a furry monster that introduced a new dance move whenever our “preschooler” tapped the invisible screen. It did exactly what a prototype is meant to do: It drew people in, sparked a discussion with our partners at Sesame Workshop, invited valuable feedback, and could be easily adjusted and improved, giving us numerous opportunities to make it into something kids and parents loved. The video we recorded became so popular that professors still show it in design schools today.

As we integrate AI tools into our prototyping process, it’s crucial that we use them thoughtfully and intentionally, so we don’t lose the methods that have enabled us to create category-defining innovations that meet real human needs. We have to maintain the three Rs to expose knowledge gaps, invite real feedback, build belief, and give us confidence that a concept is worth developing and investing in. They help us make space for surprising turns, illuminate opportunities for joy in the design process, and even let us embody and fully engage with our ideas. Here’s a deeper dive into why each R matters, and how to integrate them while embracing AI tools.

Rough: A strategic incompleteness

Early prototypes aren’t finished or even pretty. That’s on purpose—it gives the people we test with permission to dive in and co-create with us. It also keeps us from becoming overly attached to our initial ideas, making it easier to pivot if we see a better way forward. 

When our toy inventors created the first prototype of the Aerobie Rocket Football, for example, it featured a small pedestal glued to the bottom of a foam football. The goal was to create a football that could stand upright, allowing kids to practice place-kicking without someone holding the ball. The pedestal was clunky and awkward, so we replaced it with fins. Nothing fancy—just four fins cut from a sheet of foam then hot-glued to the ball. It was better, but still incomplete. The next iteration included a ball with two curved parting lines running from the nose to the tail. Taking advantage of this feature, we attached the fins along the ball’s curve, creating a helix. The design compromised stability, and the ball immediately toppled when we tried to stand it up. But when we picked up the ball and threw it, it flew through the air with a perfect spiral. We had accidentally made something better than what we were aiming for—a toy so successful that it’s been selling for decades.

How can you intentionally introduce roughness into your prototypes when your tools automatically default to high fidelity? One approach IDEOers use is to roll them back to earlier versions, such as Midjourney 1.0, where figures are rendered with multiple fingers and other noticeable flaws that indicate a work in progress. You can also ask AI to create sketch-level or monochrome outputs rather than full-color rendered ones, leaving things visibly incomplete, annotate prototypes with open questions, or present multiple competing directions side by side, ensuring that no single option feels like the answer. The goal is to signal to collaborators and users that their input is still welcome and that the design has room to evolve.

Rapid: More cycles, questions, and opportunities to learn

We don’t want to spend too much time on a single round of prototypes. The goal is to iterate as quickly as possible, giving us multiple opportunities to share our ideas with users and to grow and morph our designs into something that resonates functionally and emotionally. By addressing challenges and responding to feedback, we reduce risk and build confidence with each iteration.

For example, when we prototyped a new voting booth for Los Angeles County, we wanted to incorporate tactile and audio interfaces for individuals with visual impairments. Instead of fully coding the experience and pre-recording audio clips to respond to testers’ actions, we hired a voice actor and improviser. He was hidden in an adjacent room with one of our designers, who had taped a flowchart to the wall. When our “voter” tapped the keypad to navigate the voting process, the designer would point to a phrase on the flowchart, prompting the voice actor/improviser to read it into the microphone. The audio was then relayed to the voter via headphones. Throughout the research session, our designer could make last-minute adjustments and explore different paths on the flowchart, enabling him to quickly test which scenarios provided the best experience for our voters. The flexibility of the prototype—combined with the voice actor’s improvisational skills—allowed us to modify not only the flow of the interaction, but also the wording, pacing, tone of voice, and more, on the fly.

There’s no doubt that AI accelerates output, but that’s only valuable if the time saved goes back into more cycles, more questions, and more variations. How do you make sure AI’s speed translates into more iterations rather than locking in your design too early? Show your prototype to various stakeholders to solicit their feedback. Listen closely and incorporate what you’re hearing. If you’re working on a digital design, for example, and the people you’re testing with don’t like the size or placement of a button, or how an interaction works, consider how you might alter the UI. Does moving the button over a tad solve the problem, or is there something else that isn’t working? With AI, you can make changes and test them almost immediately, even if you don’t have a coding background. However, be sure to keep your updates focused on the feedback you’re hearing rather than on testing a completely new design. 

Right: Testing one variable at a time

“Right” means building a prototype early in the process to answer a specific question, rather than showcasing the entire vision. When too many variables are included in one prototype, it can be hard to discern what is working and what is not. Do people love it because the ergonomic shape feels great in their hand or because they like the color or texture you chose? Or do they dislike it because of how it functions mechanically or digitally? Splitting these attributes apart allows you to learn about each element separately before bringing them all together into a cohesive prototype in the later stages of refinement.

An example of this is Flipslide, an electronic game that we designed and licensed to Moose Games. The toy was inspired by the fidget craze, which includes items like fidget spinners, chew beads, pop-its, and the resurgence of the Rubik’s Cube. Recognizing this trend and wanting to create a challenging gameplay experience without a screen, we came up with a game concept in which pieces flip, slide, and click into place as players manipulate them to match tiles that light up and change color. We built our first prototype to test the movement of our design and understand what it would take to create a mechanical toy with shifting components. The initial model was made of foam core and rubber bands. Next, we wanted to create a fidget toy that felt satisfying to hold and transform, so we made a prototype with more bulk, incorporating machined plastic parts held together with tension springs that provided just the right amount of resistance when moving the pieces around. By refining this model, we created a prototype that felt particularly satisfying to hold and manipulate. It also made a fantastic clicking sound as pieces fell into place. 

After we solved the mechanical elements, we moved on to gameplay. We started by taping a stack of 2 x 2-inch laminated color grids onto the front. Each grid had a different color pattern, representing rounds of gameplay. To complete a round, rectangular wings had to be rotated, flipped, and moved into place to match the colors on the grid. Once the colors matched, we’d yell “ding!” and pull off a grid to represent the next round, initiating another flurry of rotating, flipping, and sliding to match the new color scheme. Satisfied that the gameplay felt fulfilling, we added electronics to bring sound effects, music, pacing, and more advanced gameplay into the test. Soon after Flipslide hit the market, we had an award-winning game and fans around the world.

Don’t get caught "boiling the ocean”—using AI to address every question in one prototype. Instead, build discrete prototypes for discrete questions. Before generating anything, ask, “What am I trying to learn? Who needs to weigh in?” AI is exceptional at producing artifacts, but you need to direct it to give you artifacts that teach you something. You want to create prototypes that you can bring into research and put in front of users to understand what resonates with them and why. That’s easier to do when you know what you’re testing for.

Maintaining joy and discovery

It’s easy to use AI tools to manage a creative process. Some are even designed to do parts of it for us. But as designers, it’s crucial to invest in our own learning by doing. When we get our hands dirty, we learn to recognize what’s right, engage our imaginations and deep thinking, and energize ourselves and our teams. That practice is how we achieve great outcomes, for ourselves and our clients. It’s also how we keep it human—putting ourselves in the shoes of the people who will live with these designs every day and experiencing them ourselves.

There’s no question that AI is a game-changer for designers, and one that we’re happy to embrace. But it’s crucial to use these tools to support the best of design, rather than replacing the methods that have led to some of the world’s most important innovations. Building prototypes that enable you to co-design with users, test assumptions, and iterate with new learning leads to better results. Experiment with different tools, including the latest that AI has to offer. Once you understand each tool’s strengths and limitations, you’ll better understand what combination you need to engage not only others, but also yourself. Because ultimately, prototypes and great design will always need human creativity, imagination, and judgment. And the three Rs are a great way to get there.

“For decades, the three Rs have kept us grounded, reminding us why we prototype and ensuring that we get the most out of the design process.”

As part of the London Leadership team, I head-up IDEO’s Health Portfolio for Europe, working with leaders and their teams to design new strategies and experiences that enable people to live healthier lives and enjoy better futures.

I have had the great privilege of working with some of the most influential companies in the world—Novartis, Sanofi, Roche, NHS, CalMHSA, HP, and Amazon to name a few.

Over the last 15 years, my journey through consulting and venture leadership has been rooted in a deep commitment to human-centered design.

I believe in design as a powerful tool for change, allowing us to create groundbreaking experiences that address the biggest, most complex challenges of today.

Originally from England, I began my journey at IDEO in California, which expanded my worldview, emphasizing the importance of diversity and cross-cultural perspectives in driving innovation.

My passion for helping creative companies thrive led me to IDEO, where I oversee financial planning, strategy, and operational efficiency.

In this role, collaboration is at the heart of what I do—building strong partnerships between finance and client teams to drive results and strengthen relationships.

Before joining IDEO, I served as CFO/COO at Sylvain, Sub Rosa, Aruliden, Redscout, and ?What If! Innovation Partners. Earlier in my career, I held senior finance and operations roles at New Line Cinema, Napster, and Universal Music Group/PolyGram Records. I’m a CPA and based in New York City, where I live with my family.

As Managing Director of IDEO London, I lead a creative powerhouse fueled by the collective energy of our diverse makers, visionary client partners, and extensive local network.

IDEO’s human-centred approach to innovation is more crucial than ever. Today’s defining business challenges—climate change, inequality, emerging technologies—all have human behaviour at heart. Addressing them requires profound understanding of people’s needs and radically redesigning the systems and services shaping our world.

‍

I remain in the work, leading change and strategy programmes, and helping leaders set inspiring visions, make confident choices, and cultivate teams delivering transformational human-centric solutions at scale. This hands-on, iterative approach makes big ideas tangible for customers and clients through real-world prototyping and testing, setting the stage for growth.

My work has focused on hospitality, retail, and technology. Prior to joining IDEO, I founded a sportswear brand and was a brand strategy consultant in consumer goods. I hold an MBA from INSEAD.

With more than two decades of experience in brand and communication design, I bring a deep understanding of brand strategy, identity, expression, user experience, and communications to my work. As a pragmatic aesthete, I combine creativity with problem-solving, mentoring, and leading multi-disciplinary teams to create human-centered designs.

Throughout my career, I have played a key role in shaping, strengthening, and transforming some of the world's leading brands across a range of industries, including tech, CPG, health, food, and financial services. I have worked with small businesses, cultural institutions, and Fortune 100 corporations, using my broad set of design skills to develop brands and consumer experiences that resonate with audiences.

I help companies articulate their purpose, vision, and values through strategic development and organizational transformation, and I thrive solving complex problems and mentoring teams through the creative process.

In addition to my work at IDEO, I have served on the boards of AIGA Chicago and AIGA Minnesota. My award-winning work has been recognized by The One Show, AIGA 365, Communication Arts, Print, Graphis, Rebrand 100, and was featured in Rick Valicenti's CHGO DSGN exhibition at the Chicago Cultural Center.

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

‍

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

‍

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

‍

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

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

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

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