Create groundbreaking user experiences with Josh Clark and Veronika Kindred’s new book, Sentient Design: Crafting Intelligent Interfaces with AI

Artificial intelligence is impacting the way designers everywhere create experiences. But how can AI be used to benefit the user?

User experience designers, rejoice! In Sentient Design: Crafting Intelligent Interfaces with AI, Josh Clark and Veronika Kindred dive deep into this idea.

What is sentient design?

Sentient design describes the practice of creating digital experiences that crackle with awareness and agency, adapting to your users in the moment. These are intelligent interfaces: dashboards that design themselves, apps that manifest on demand, agents that just get it done, and much more.

Why write a book about sentient design?

Josh Clark and Veronika Kindred learned sentient design the hard way. But you don’t have to. Here’s what our authors have to say about it:

“Intelligent interfaces are emerging everywhere—digital whiteboards that turn sketches into applications, customer service agents that handle routine issues on their own, assistants that generate medical reports from a doctor’s spoken notes. Instead of using AI to wring savings by grinding out efficiencies, forward-looking teams are creating value through extraordinary new experiences.

This exploration has rippled across enterprise and startup alike, but the approach has been haphazard. There has been no map for this new territory, no uniform vocabulary, no framework for choosing what type of intelligent interface to create—or even what those types might be.

So, we started mapping. We named and identified new kinds of intelligent interfaces, and we organized their design patterns. Along the way, we discovered a broad expanse of experiences that goes far beyond chatbot clichés.

What began with a study of form led to something deeper: a practice for creating intelligent interfaces. This is Sentient Design. We created it because we needed it. We’re sharing it because we think you might need it too.”

P.S. To read chapter one of the book for free, go here »

How can I get started with sentient design?

First, you can pick up this book!

Josh and Veronika wrote this book as a guide to using AI as a design material. Because it’s not about tooling or replacing design tasks with AI. This book isn’t a framework filled with tips; it’s so much more. Sentient Design exists to give you (yes, you!) perspective and principles to create and deliver new products, with artificial intelligence in your toolkit.

Designers are at a crossroads right now. You can either stick with your tried-and-true tools, or embrace innovation and design experiences that truly come alive.

As Josh Clark said on the Rosenfeld Review podcast, “First we retool, then we reorganize to get our process around that. And then we invent.”

Listen to Josh and Veronika discuss what sentient design is, how it became so crucial, and why they wrote this book in this episode of the Rosenfeld Review podcast.

How is Sentient Design relevant to my design work?

Take it from John Maeda, author of How To Speak Machine and Laws of Simplicity and VP engineering at Microsoft CoreAI:

“I often say designers used to create obstacle courses for users, carefully crafted paths through menus and forms. Now, with AI, users just teleport to their goal. But the designer doesn’t disappear. The role transforms. You become creative director for systems that make real-time decisions, teaching principles rather than touching pixels.”

How does this impact my job? And what about when AI gets it wrong?

We get it; change is intense. That’s why Josh and Veronika answered questions just like these in the FAQ section of their book—available for free here!

Who would benefit from reading this book?

Sentient Design is for…

• Designers
• Product leaders
• Design-minded developers who want to create entirely new categories of experience with AI

This book helps you decide what to make and why it matters, giving you the patterns and process to conjure the next generation of AI-powered products.

Who are Josh Clark and Veronika Kindred?

Josh Clark is principal of Big Medium, a digital agency that helps complex organizations design for what’s next. Josh has over 30 years of experience in emerging technology, user experience, and design innovation. His projects include future-friendly interfaces for AI, connected devices, and websites for many of the world’s biggest companies.

 

Veronika Kindred is designer and researcher at Big Medium, where she defines and solves design problems alongside some of the world’s biggest companies. She travels internationally to lead Sentient Design workshops and speak to teams at startups and Fortune 100 companies alike.

 

 

Ready to dive in? Order your copy of Sentient Design: Crafting Intelligent Interfaces with AI today!

Last week in the Rosenverse: Migration and automation

Last week in the Rosenverse, we hosted two sessions: one with Mark Rettig about the desire to journey into a different kind of work, and one with Maria Rosala about how AI can assist with your qualitative analysis.

Log into the Rosenverse to watch the recordings.

See what you missed below.

 

The Urge to Migrate

“The urge to migrate comes from restlessness, a longing for change, not just because the habitat becomes colder.”

June 4: This session starts with a feeling. A kind of itch. Maybe the project is touching questions we weren’t trained to answer. The outcomes feel smaller than the problems. Work as usual feels… uncomfortable. Or maybe it’s better than that: seeing the forces at play in the world, there’s a desire to contribute beyond the scale of an organizational agenda. Something is pulling. Toward what, exactly, you can’t yet say. This session is for anyone who recognizes that feeling, even faintly.

Marc Rettig spent decades in corporate design before following what his late collaborator Hanna du Plessis called “”the urge to migrate””—the push of a container that no longer fits, and the pull of something not yet visible. In his case, that migration carried him out of the familiar territory of design-led problem-solving, into a different kind of work: long-term co-creation with communities, and the challenge of shifting deeply ingrained social patterns. In a series of first-person vignettes, he traces that journey: the first spark, the crossings, the companions, and what he found on the other side. Not as a how-to, but as a personal lens useful to anyone standing at the edge of something they cannot yet name. Watch the recording »

About the speaker:

Marc Rettig is a designer, educator, and part-time publisher. As principal of Fit Associates, he helps leaders and communities better see and create together. He also founded Okay Then, a production company amplifying voices that point to new ways of seeing, working, and being.

After a first career in software, Marc worked for two decades in corporate design research, strategic design, and interaction design. In 2009 he turned from business- and tech-focused design toward deeper social questions. This led to a fifteen-year immersion in group facilitation, emergent practices, and the invisible dynamics of self, relationship, and story that shape truly social design. Read more »

 

Q&A with our speaker, Mark Rettig

This Q&A was drawn from the Rosenverse Live session.

Q: How is AI changing the way people work?

A: AI is accelerating a shift that has happened many times before: technology reorganizes labor, changes which jobs survive, and pushes people toward new kinds of work.

Q: What kinds of jobs are hardest to automate?

A: The work that is hardest to automate is practice-based work, because it depends on trust, judgment, and direct human relationships.

Q: Why does community matter in the workplace?

A: Community matters because work is never just about output — it is also about belonging, accountability, and the human need to be connected to other people.

Q: What is the relationship between work and dignity?

A: Work is tied to identity and dignity, so when people lose jobs or lose meaningful roles, the impact is emotional and social as well as economic.

Q: Why are corporate jobs changing?

A: Corporate jobs are changing because the system that created them may be reaching its limits, especially as technology and social expectations reshape how value gets created.

 

AI-Assisted Qualitative Analysis: What to Automate and What to Own

“AI is a tool. It can be as powerful as the person who is wielding that tool.”

June 5: Qualitative analysis is about more than summarizing common trends in your data. It’s about prioritizing and explaining what’s important, and that requires getting familiar with your data and thinking hard about what you’re seeing. AI can support that process, but it can also short-circuit the thinking you need to do if you’re not careful. In this session, we look at what qualitative analysis actually involves, where AI genuinely helps, and where skipping the process costs you. You’ll come away with a practical sense of how to use AI as an accelerant and thought partner while maintaining control of the work. Watch the recording »

About the speaker:

Maria Rosala is the Director of Research at Nielsen Norman Group. With over a decade of experience leading research for digital products and services, she helps organizations understand user needs, reduce risk in decision-making, and build research practices that scale. At NN/G, Maria leads research efforts, consults for product teams across industries, and teaches UX professionals around the world.

Maria’s work frequently explores how emerging technologies are changing research practices. Through her research, writing, and the courses she develops and teaches, she has advanced practical approaches to discovery, qualitative data analysis, and research operations. Maria is known for transforming thorny research topics into clear, repeatable practices that teams can apply in the real world. Read more »

Q&A with our speaker, Maria Rosala

This Q&A was drawn from the Rosenverse Live session.

Q: What can AI automate in qualitative research?

A: AI can do a lot of the structured, repetitive work, like helping with coding, tallying, and other first-pass tasks that don’t require deep interpretation.

Q: What should researchers keep human?

A: Researchers should stay closely involved in the messy, rich parts of the data, because that’s where surprise, nuance, and important insights often appear.

Q: When is it safe to automate qualitative analysis?

A: Automation works best when the data is structured and the task is clear, but be much more cautious when working with messy, complex, or emotionally rich data.

Q: How does AI help with coding qualitative data?

A: AI can support the coding process by handling the first pass and reducing manual effort, but people still need to review, refine, and own the codes.

Q: What is the risk of relying too much on AI for research analysis?

A: One major risk is cognitive offloading, where you stop doing the harder thinking that qualitative analysis requires and lose some of the skill that makes the work valuable.

 

Catch up on last week’s recordings, and mark your calendar for upcoming events.

See you in the Rosenverse!

Read chapter 1 of Sentient Design by Josh Clark & Veronika Kindred

Create experiences that crackle with awareness and agency, adapting to your users in the moment. Sentient Design is the practice of crafting intelligent interfaces: dashboards that design themselves, apps that manifest on demand, agents that just get it done, and much more.

And Sentient Design: Crafting Intelligent Interfaces with AI by Josh Clark and Veronika Kindred gives designers and product leaders the practical framework and imaginative perspective to deliver extraordinary new products using AI as a design material.

Read chapter one for free below!

 

Chapter 1

It’s Paris, 1905. The cobblestone street bustles with energy in the warm autumn light. You weave your way through a sea of top hats and overcoats to cross the boulevard, dodging horse-drawn carriages and early automobiles. As you round a corner, a grand cathedral looms into view.

It’s a century-old painting brought to life, animated by an AI-powered platform called Mirage 2 (Figure 1.1). Give the system any image and text description, and Mirage 2 turns your prompt into a world to explore. It feels like walking around a 3D video game, but this isn’t the prerendered map of a traditional game engine.

FIGURE 1.1 Mirage 2 turns a fin de siècle painting by Eugène Galien-Laloue (top) into an immersive world that’s created as you explore it (bottom).

Instead, this world is generated frame by frame in response to your actions. What’s inside that cathedral? Nothing, not yet. The interior won’t exist until you step through those doors. In fact, the cathedral itself didn’t exist until you turned the corner to see what was there.

Mirage 2 is a “world model” that creates its universe on the fly—an experience invented as you encounter it, based on your specific actions, and never the same for anyone else.

What if any digital experience could be delivered this way? What if websites were invented as you explored them? What if you could manifest apps on demand to meet your immediate need? What if digital systems behaved as flexible environments instead of rigid tools?

These questions aren’t speculative. They describe a new category of digital experience: the intelligent interface.

Awareness and Agency

Intelligent interfaces have awareness and agency to adapt in response to user context. They bend their content, medium, and behavior—even the system’s very goals—to meet the moment. These interfaces adapt to you instead of forcing the reverse. The result enables a new kind of collaboration between system and user—many new kinds, in fact.

Intelligent interfaces include enterprise dashboards that reshape themselves, or digital whiteboards that turn rough sketches into working applications. They feature agents that go off on their own to work on your behalf, or AI collaborators that work alongside you as “just another user.” We’ve identified over a dozen new experience patterns for intelligent interfaces, and we’ll share them all starting in Chapter 3, “The Sentient Triangle.”

Even conservative business applications can be radically adaptive in this way. Salesforce is a buttoned-up enterprise platform heavy with data dashboards, but it’s made lighter through the selective use of adaptive interfaces. Machine intelligence assembles certain dashboards on the fly, selecting and arranging precisely the metrics and controls to anticipate user needs (Figure 1.2). The underlying data hasn’t changed—it’s still pulled from the same sturdy sources—but the presentation is invented in the moment. The system is empowered to make simple design decisions, choosing from a curated collection of components from the Salesforce design system: familiar elements like cards, tables, and charts.

FIGURE 1.2 Salesforce’s “generative canvas” feature builds dashboards on the fly, assembling content and UI widgets based on both contextual cues and explicit prompts.

Users get layouts uniquely tailored to their immediate need, instead of wading through static templates built through manual configuration. Those real-time screens can be built for explicit requests (“what’s the health of the Acme Inc account?”) or for implicit context. The system can notice an upcoming sales meeting on your calendar, for example, and compile a dashboard with that client’s pipeline, recent communications, and relevant market signals. Every new context yields a fresh, first-time-ever arrangement of data.

The system pulls information toward the user instead of requiring them to scramble through complex navigation.

For users, the benefits of intelligent interfaces compound quickly. Individualized experiences that adapt in the moment increase satisfaction and efficiency. They foster understanding of complex systems by providing the right information when you need it. They promise empowerment through the creativity of newly possible interactions. And perhaps most exciting, these experiences can enable inclusivity by adapting to individual capability, inviting new audiences into activities where they previously didn’t have the ability to participate.

For designers, creating intelligent interfaces is a thrilling and consequential challenge. How do you design an experience that has no fixed path? How do you make such a fluid experience feel grounded, reliable, and intuitive? And how do you apply this intelligence in ways that elevate (not replace) human judgment and agency?

These are the questions that define the next era of experience design, and they demand a new perspective and process.

This Is Sentient Design

Sentient Design is the practice of creating intelligent interfaces. It provides the framework and philosophy to craft those experiences responsibly and effectively.

This practice is exciting, delicate, and a little bit weird—but not as weird as full-blown consciousness. The “sentient” in Sentient Design describes something more modest but powerful in its simplicity: systems that can perceive context, interpret intent, and act with agency to make meaningful design decisions on the fly. The resulting experiences feel almost self-aware in their ability to respond to the user.

To turn those concepts into practice, Sentient Design delivers a pragmatic toolkit of design patterns and repeatable processes. The focus is on the interface: how to use AI as a designer delegate that can make real-time decisions in content, presentation, and interaction.

NOTE: SENTIENT DESIGN AT A GLANCE
Sentient Design applies perception, interpretation, and agency to deliver intelligent interfaces that are…

• Aware of user context and intent
• Radically adaptive across content, structure, behavior, and medium
• Emergent and conceived in real time
• Collaborative in shaping the experience with the user
• Individualized to moment and context, not to stale profiles
• Deferential to human judgment, especially in consequential moments
• Continuous across interaction modes

Sentient Design also provides a measured view of AI—what it’s good at and where you should steer clear. While intelligent interfaces offer exciting opportunities, they also bring plenty of new risks and responsibilities. Care is required with experiences conceived in real time by systems empowered to make design decisions. This doesn’t “just work” by plugging AI into the interface. As interfaces become more intelligent, designers have to become more mindful, too. Throughout the book, we’ll take careful stock of what can go wrong, how to prevent those problems—and how to soften the blow when they happen anyway.

Take heart, though; the fundamentals are familiar. Machine intelligence has animated everyday interfaces for decades, long enough that the basics no longer seem special.

You likely encounter hundreds of services every day that are aware and adaptive, delivering just-for-you experiences. Your favorite streaming service, for example, is aware of your preferences and history and adapts content and presentation to match. The result is a playlist to suit your tastes, or maybe just the moment. Likewise, your phone’s predictive keyboard is aware of what you’re typing and adapts suggested content in anticipation of what you’ll type next. Ho hum, right?

What’s new is the degree and posture of this intelligence.

Machine intelligence has traditionally been something felt more than seen, an undercurrent powering everything from social feeds and product recommendations to your car’s auto-braking. But now machine intelligence has broken the surface (Figure 1.3). It’s no longer hidden below the waterline of digital experiences. With AI assistants and other generative tools, users interact directly with the algorithm. In addition to powering underlying systems, machine intelligence now guides the experience—and often becomes the experience, an intelligent interface as creative collaborator.

Chat experiences are the most familiar example of this direct interaction. Instead of chugging away as the engine under the hood, the algorithm has emerged as conversational peer. This direct exchange with the model not only changes how users interact with machine intelligence, but it also changes the arc of the entire journey.

Every comment and response in a chat can take the conversation in a new and unpredictable direction. User and system take turns shaping the experience, a one-of-a-kind path that meanders wherever the participants take it. Like any genuine conversation, the exchange can’t be designed in advance; it is ephemeral, created for the moment.

Sentient Design explores what’s possible when any interface—not just chat—can be so open-ended and radically adaptive.

FIGURE 1.3 Machine intelligence has surfaced, moving from background systems to the direct interface for a growing number of experiences.

Beyond the Bot

Sentient Design goes beyond narrow visions of conversational interfaces. Intelligent interfaces are much more (and in some exciting ways, much less) than talking machines … or systems that can write, draw, make music, or otherwise do things that until recently seemed uniquely human. Any of those feats might be part of an intelligent interface, but they’re not essential to it.

Instead, the spark of machine intelligence can animate any interaction model. Sentient Design proposes embedding this intelligence into many, many experiences. That’s critically different from the reverse: embedding all experiences into a single intelligence. Some imagine a near-term future where all applications are swallowed up by a few super-smart systems that “do it all.” In that model, a centralized intelligence subsumes all software: ChatGPT takes over the world, a single interface to do everything.

Sentient Design instead envisions a world of distributed intelligence, with capabilities embedded in thousands or millions of software and hardware systems. Rather than a small set of “ask me anything” assistants, we anticipate a proliferation of intelligent interfaces that take countless forms, each aware and radically adaptive (Figure 1.4). In this future, machine intelligence lives inside all of them, just like electric motors power tons of everyday appliances and databases support nearly every piece of software.

FIGURE 1.4 The future isn’t one big brain, but countless sparks of intelligence lighting up everyday experiences.

What becomes possible when you can weave intelligence into any interface or feature? This provocation invites designers to explore interaction models well beyond text dialogue or even the generative interfaces we’ve already described.
This infusion of intelligence can be a light sprinkling that gives special talents to traditional controls or web forms. Or it might be a heaping portion of savvy that creates an entirely new interaction where the system itself plays an active and creative character in the plot. We’ll dig deep into the details of what this looks like throughout the book, but let’s take a quick tour, starting at the “light” end of that spectrum.

Casual Intelligence

Not every intelligent interface has to be revolutionary. Casual intelligence drizzles machine smarts onto everyday content and interface elements. Small interventions add up to interfaces that are quietly aware and adaptive, easing friction in routine interactions without introducing new paradigms.

Consider the possibilities for the old-school web form. Every time you add a question in the Google Forms survey tool, for example, you have to choose from a dozen answer formats to use: multiple choice, checklist, linear scale, and so on. It’s a necessary but heavy bit of friction. To ease the way, Google Forms sprinkles machine intelligence into that form field. As you type a question, the system suggests an answer type based on your phrasing. Start typing “How easy is it …,” and the default answer format updates to “Linear scale” (Figure 1.5).

FIGURE 1.5 Google Forms prompts you to select from 12 answer formats for every question. Machine intelligence eases the way by suggesting a default (here, linear scale) as you start typing the question.

Under the hood, machine learning categorizes that half-written question based on the billions of other question/answer pairs that Google Forms has processed. The interface doesn’t decide for you, but it tees up a smart default as an informed suggestion.

As operating systems make on-device models available to applications, casual intelligence becomes a “why wouldn’t we?” thing. iPhone apps, for example, can use the phone’s onboard models to do things like summarize content, propose content titles, and tag categories. All of this is local to the phone, making it private intelligence that comes free on the device.

But don’t feel limited to the humdrum. Casual intelligence can ease even complex and high-stakes domains like health care. You know that clipboard of long, redundant forms you fill out at the doctor’s office to describe your health and symptoms? The Ada Health app uses machine intelligence to slim that hefty one-size-fits-all questionnaire and turn it into a focused conversation about relevant symptoms. The app adapts its questions as you describe what you’re experiencing. It uses your description, history, and its own general medical knowledge to steer toward relevant topics, leaving off-topic stuff alone. The result is at once thorough and efficient, saving the patient time and outfitting the medical staff with the right details for a meaningful visit.

Sentient Design begins with commonplace human needs. Your journey will eventually take you to new, mind-bending interaction paradigms, but start with what you know. As you design the familiar interactions of your everyday practice, start asking yourself how the experience could be made better with a pinch of awareness and a dash of casual intelligence. Think of features that suggest, organize, or gently nudge users forward without demanding much thought or effort. Small improvements add up.

But casual intelligence is just the beginning. When you dial up the awareness and agency, something more transformative emerges.

Radically Adaptive Experiences

Radically adaptive experiences change content, structure, style, or behavior—sometimes all at once—to provide the right experience for the moment.

This is not exactly ye olde website. For designers and users alike, the risk of radically adaptive interfaces is that they turn into robot fever dreams without shape or destination. That’s where intentional design comes in: to conceive and apply thoughtful constraints and guardrails that keep the experience coherent and the user grounded.

The earlier Salesforce example provided a taste of this, creating dashboards on the fly by choosing from a small collection of available UI patterns. This is a conservative and reliable approach to the bespoke UI experience pattern, one of Sentient Design’s 14 experience patterns. Bespoke UIs compose their own layout in direct response to immediate context. This approach relies on a stable set of interface elements that can be remixed to meet the moment.

NOTE: IS THIS “GENERATIVE UI”?

Some people use the term generative UI to describe interfaces that assemble or rearrange their own layouts. That’s not wrong, but it’s overly broad. All of Sentient Design’s 14 experience patterns are generative in some way, assembling content, structure, or behavior on the fly. “Bespoke UI” refers to a specific expression of that idea: interfaces that generate their own structure and UI elements.”

The bespoke UI pattern can support more open-ended scenarios beyond dashboards. Google’s Gemini team developed a prototype experience that deploys a bespoke UI inside a chat context—but it doesn’t stay chat for long. The demo starts by asking for help planning a child’s birthday party. Instead of a text reply, the system responds with an interactive UI module—a purpose-built interface to explore party themes (see Figure 1.6). Familiar UI components like cards, forms, and sliders materialize to help you understand, browse, or select the content.

FIGURE 1.6 Google’s bespoke UI prototype conceives UI elements on the fly to replace traditional dialogue responses in chat.

Although the UI elements are familiar, the path is not fixed. Highlighting a word or phrase in the Google prototype triggers a contextual menu with relevant actions, allowing you to pivot the conversation based on any random word or element (Figure 1.7). This blends the flexibility of conversation with the grounding of familiar visual UI elements, creating an experience that is endlessly flexible yet intuitive.

This prototype eventually evolved into something even more ambitious, a feature called Dynamic View that spins entire custom web pages in response to questions you ask of the Gemini assistant. The presentation may be more complex—rich layouts instead of one-off components—but the principle is the same: the intelligent interface responds with the composed interface that’s best suited to the content.

FIGURE 1.7 Select any arbitrary word or element (“Cupcakes” in this example), and you can ask the system to use your selection as a base to veer off in a new direction—to get the recipe, for example.

Successful bespoke UI experiences rely on familiarity and a tightly constrained set of UI and interaction patterns. Design systems and content systems become more important than ever in this context, providing the coherent building blocks that keep these adaptive experiences from spinning out of control. The Gemini example succeeds because it has a limited stable of UI widgets in its design system, and the system was taught to match specific patterns to specific user intent. Experiences like this are open-ended in what they accept for input but disciplined in the patterns they produce.

The same principles and opportunities apply beyond graphical UI, too. Walkcast is an individualized podcast, applying the bespoke UI pattern to an audio format that is typically fixed and scripted. The app tells one-of-a-kind stories seeded by your physical location. While other apps have attempted something similar by finding and reading Wikipedia entries, Walkcast goes further and in new directions. The app weaves those location-based facts into weird and discursive stories that leap from local trivia to musings about nature, life goals, local personalities, and sometimes a few tall tales. There’s a fun fractured logic connecting those themes—it feels like going on a walk with an eccentric friend—and the more you walk, the more the story expands into adjacent topics. It’s an example of embracing some of the weird unpredictability of machine intelligence as an asset instead of a liability. It’s an experience that is unique to you, radically adaptive to your physical location.

All of these bespoke UI examples present and arrange content according to a fixed set of options. Things get even wilder when you let the user define (and bend) those rules to create the universe they want. You’ll explore the frontier of these most radically adaptive of experiences with the intelligent canvas in Chapter 6, “Chat Is Not All Talk”—an experience pattern that lets users create their own interfaces or even conjure their own applications.

This quick survey is just a teaser of Sentient Design’s broad spectrum of intelligent interfaces, from quietly helpful web forms to imagined-on-the-spot applications. These are the book-end extremes of a sprawling range of possibilities. In between, you’ll discover chat interfaces, focused tools for specific tasks, autonomous agents that coordinate with other systems, and copilots that quietly support your work with gentle suggestions. The full landscape is vast, and we’ll explore it systematically starting in Chapter 3.

Conceived and Delivered in Real Time

These intelligent interfaces create fundamentally emergent experiences that arise organically from interaction between user and system, often in ways that weren’t specifically designed or even imagined. Sometimes that collaboration is explicit (“make the game harder”) and sometimes implicit (the system notices you’re ready for a new challenge). It’s the same goal in both cases: to deliver meaningful change by providing exactly the right experience at precisely the right moment.

This is a big shift. Over the last few decades, the prevailing method to discover content and applications has evolved from search (“the thing I want must be out there”) to social curation (“these people always have things I like”) to algorithmic recommendation (“TikTok knows what I want better than I do”).

Now this new era of interaction design introduces manifestation. On-demand content, code, images, and music are already here. Describe the thing you want, and the answer or artifact appears (Figure 1.8). In fact, thanks to the awareness and anticipation of these systems, you may not even have to ask at all. The boundary between user and creator begins to dissolve.

FIGURE 1.8 As interfaces evolve from search to curation to manifestation, the boundary between finding and making software may dissolve.

This is powerfully flexible but also powerfully unpredictable. When unpredictable humans mix with the quirky logic of machine intel- ligence, you can’t anticipate every possibility. Yet it’s still possible to keep these experiences grounded within certain rules that the system must observe. When Mirage 2 invents an entire Paris city block the moment you turn a corner, it still follows the city’s architectural conventions, gravity still applies, and horses still canter like horses.

As the designer of the intelligent interface, you’re the one who establishes the sticky rules and guidelines, the physics of the universe you create for the system and its users.

JOSH’S TAKE: EPHEMERAL EXPERIENCES

The whole point of radically adaptive experiences is that they are conceived on demand. By the same logic, they don’t have to stick around when the demand passes. Get comfortable with the idea of ephemeral experiences: content, interfaces, or even entire applications that you spin up for one-time use and then set aside.

It sounds unsettling. Usability best practices emphasize consistency and predictability. Presentations and interactions that come and go sound like the exact opposite, an anti-pattern. But when ephemeral experiences are optimized for the current moment, they often feel more intuitive, not less. The right experience at the right time is, well, always right—even if it’s unique and temporary. Predictability and wayfinding still matter, but the path doesn’t have to be static, and it’s okay to let it fade from view until you need it again.

This shift to ephemeral experiences changes how to think about iteration and improvement. Instead of refining a single, persistent interface over time, the work becomes building systems that get better at understanding context and generating the appropriate response.

A New Role for Designers

Traditionally, it’s been the designer’s job to craft the ideal journey through the interface, the so-called happy path. You construct a well-lit road to success, paved with carefully chosen content and interactions completely under your control. That’s the way interface design has worked since the get-go.

Not anymore, or at least not entirely. With Sentient Design, the designer allows parts of the path to pave themselves, adapting to each traveler’s one-of-a-kind footsteps (Figure 1.9). When you deliver an intelligent interface with the awareness and agency to make design decisions on the spot, the happy path gives way to happy possibilities.

“We’ve moved from designing ‘waterslides,’ where we focused on minimizing friction and ensuring fluid flow, to ‘wave pools’ where there is no clear path and every user engages in a unique way,” says design strategist Alex Klein.

FIGURE 1.9 Like meandering conversations, intelligent interfaces often follow unpredictable trajectories that are defined by the “conversation” between the user and system.

If the interface is making real-time design decisions, then who’s the designer here? It might seem confusing: Is Sentient Design about the design of intelligent interfaces? Or is it the reverse: Intelligent interfaces that do design?

The answer is both. Sentient Design is a design practice for creating systems that can, in turn, design their own moment-to-moment experiences. Systems design becomes primary over tactical UI design.

As the designer creating the system, you become its creative director. You give the system the brief, the constraints, and the design patterns to use. What patterns can the system use, and when? How should it choose among them? What tone or manner should it adopt? The work is behavior design . . .not only for the user but for the system itself.

The interface then uses that foundation to compose the experience dynamically in response to each user and their context. It’s like teaching someone cooking fundamentals instead of handing them a recipe; you provide the principles and techniques and then step back to see what dishes they come up with. We’ll dig into specific examples in Chapter 6, and Chapter 12, “The Sentient Design Sprint.”

Just don’t get so lost in the machinery that you lose sight of the user. What puts the “intelligent” in intelligent interfaces is their awareness of context and their ability to interpret user intent. As the designer, your job is to provide the system with guidance for how to translate that interpretation into action in real time. This requires a fresher perspective of the user than traditional software design has—and also requires far less user surveillance than traditional personalization.

From Personalized to Individualized

Intelligent interfaces promise to go beyond coarsely personalized experiences to deeply individualized encounters that are tailored to the user in the precise moment. This shift recognizes a fundamental truth: people aren’t the static profiles that traditional personalization imagines. Sentient Design goes beyond remembering preferences or tracking behavior patterns; it responds to immediate context (Table 1.1).

How do you approach designing for this moment of one? Can traditional UX methods keep up? Thinking tools like personas and journey maps have long given designers representative views of user needs, motivations, and mental models. In a multiverse of individualized experiences, a handful of personas might suddenly seem too thin to cover the possibilities.

Give these stalwart tools their due. Understanding core user needs and mental models becomes more crucial when designing individualized experiences. Think of personas as guideposts for understanding contexts and mindsets—not guardrails for building rigid paths and interactions. Use these tools to identify the moments where flexibility can make the difference and understand the boundaries of acceptable adaptation.

Traditional design builds a fixed understanding of the user, a snapshot you reference as you design. But individualized experiences demand a fluid theory of the user that accounts for shifting contexts, changing needs, and the reality that today’s goals may contradict yesterday’s patterns. Start with an archetype but build from there.

The goal isn’t to capture every user preference or map every path; you just have to help AI develop broad cues for action. Even general- purpose large language models often have enough world knowledge to make good decisions based on limited context. World knowledge is the statistical shadow of human experience that these models have through their vast exposure to data. The models don’t have to know everything that you do because they have good instincts of what people in general will do. With a designer’s specific guidance about common personas—often combined with proprietary data—AI can use its world knowledge and contextual signals to illuminate immediate user needs, without invasive personal surveillance.

Respectful, Collaborative, and Deferential

Separating individualization from data exploitation underscores the careful respect that Sentient Design demands. The awareness and agency of intelligent interfaces make them powerful—which is precisely why thoughtful boundaries matter. Sentient Design is as much about what these systems don’t do as what they can do. Respectful experiences are collaborative and deferential, working with you rather than imposing on you.

THE PERSONALIZATION MYTH

Traditional personalization assumes that if you collect enough data to build a perfect profile, you can deliver a perfect experience. But profile- based personalization often fails because it prioritizes who you were over who you are right now. The “you” who googles “fix water leak” at 2 a.m. is very different from the person drafting a board presentation at 2 p.m. Reader, you contain multitudes.

The result is targeted ads or personalized feeds that reflect a lowest- common-denominator version of you, a muddy mix of your interests that rarely reflect your interests right now. You might want news in your feed in the morning, but by evening all you want is viral dance videos and babies tasting lemons for the first time.

Most companies don’t have the user base to gather meaningful profile data in the first place. “I’ve talked to people at pre-launch startups with fewer than 100 users who say, ‘We’re going to start on personalization,’” says Amanda Richardson, formerly Hotel Tonight’s data and strategy chief.

Either the profile is too slim, or the user has to slog through an obstacle course of manual preferences. Instead, general world knowledge paired with the user’s immediate context is often plenty to deliver tailored results that are perfect for the moment—no profile needed.

“Maybe my last booking was a luxe hotel. It was the weekend. It was my anniversary,” Richardson continues. “But if I show up in New York City for work and book a luxe, that expense report is getting rejected. Day of week matters. Time of day matters. If you’re booking at 11 o’clock at night, you really don’t care if they have a spa, right? All of those factors matter far more than the individual user.”

You don’t have to know a person’s whole history to build a strong theory of the user based on how everyone behaves in the specific context.

Our client, People Inc., demonstrates this with their D/Cipher ad platform, which serves super-relevant ads without any profile tracking at all. The company uses audience behavior (not individual behavior) to do the trick: people moving through the same content and context tend to have the same need. That 2 a.m. search about water leaks provides a strong enough clue without knowing the user’s history or demographics.

“This real-time reasoning is much more valuable than backward-looking personalization, which is always a day late and a dollar short,” says People’s Chief Innovation Officer Jonathan Roberts.

There’s an art to creating proactive systems that offer suggestions or act on your behalf, but that still leave you in charge. The casual intelligence of Google Forms is proactive, for example, when it quietly suggests the right answer format before you even finish your question. But it doesn’t make the decision for you, instead setting a new default value that’s waiting for you when you get to that /eld. (That’s copilot behavior; see Chapter 9, “Copilots and Uncharted Territory,” for more about copilots.)

As the designer, your job is to treat machine-generated results as signals rather than facts. When acting on an anticipated need, the system should defer to the user to make or redirect the decision. A mapping app that suggests an alternate route should propose the route, not impose it—and offer context for the driver to decide the pros and cons.

We’ll explore many patterns for establishing trust, transparency, and consent, but the point is not to make the user into a micromanager of everything the system does. Indeed, intelligent interfaces can and should quietly absorb and execute lots more complexity than traditional systems ever could. When a user tells an intelligent canvas to create an on-demand application, for example, they’re still initiating the action—but the system proactively handles all the complexity of creating that experience. The user leads with what they want, and the system has the intelligence to manifest it. The user explains the what, and the system figures out the how.

This careful blend of collaboration and deference affects not only manner but mode. Radically adaptive experiences are multimodal, shifting fluidly between GUI, voice, camera input, or physical interaction based on what makes sense in the moment. This affects the app’s interaction as well as the format of the content it delivers—do you need that report as long-form prose, presentation, or audio summary?

The point of interaction can change, too, leaping between devices or affecting an external system. When you receive a call while driving, your car answers—not the phone—and you speak into the car’s cockpit. When you park, the call shifts to your phone, and you just carry on. This is deferential interaction, the system bending to your context.

The most respectful interfaces are transparent about their reasoning, present uncertainty clearly, and offer paths to escape or override decisions. They make users more capable, not more dependent.

This is the relationship you establish as the designer and the contract at the heart of Sentient Design: Intelligent interfaces have the aware- ness and agency to make meaningful decisions, but they defer to human judgment at every consequential moment.

If this all sounds simultaneously exciting and overwhelming, take heart. While Sentient Design introduces new patterns and possibilities, it builds on familiar foundations. The principles of good user experience—clarity, consistency, sharp mental models, user focus and user control—remain as important as ever.

What does change is the material you work with and how you apply those principles in a more fluid context. Now that you’ve gotten a glimpse at the opportunities and challenges of what you can build, it’s time to understand the material itself: what machine intelligence is made of and what it can do.

 

Excerpt From Sentient Design: Crafting Intelligent Interfaces with AI by Josh Clark & Veronika Kindred. Rosenfeld Media, 2026.

Last week in the Rosenverse: Connection, community, and the future of work

Last week in the Rosenverse, we hosted a session with Liminal Thinking author Dave Gray. In this video, Dave explores how corporate jobs have divided us from community, and what we can do to foster connection in a deeply disconnected time.

Log into the Rosenverse to watch the recording.

See what you missed below.

Connection, Community, and the Future of Work

“If the corporate form collapses, we need to think about how to provide value to our actual communities, virtual or local.”

May 28: Corporate jobs have conditioned us to divide work life from community. The division of labor disconnects us from meaningful work, from customers, and from each other. Dave and Lou open a conversation about the questions we’re holding and what we are learning about connection, community, and the future of work. This is an informal session. We kick it off with a few thoughts and open the floor for discussion. Watch the recording »

About the speaker:

Dave Gray is known for his work bridging visual thinking, business, change and innovation. His books and methods make complexity manageable and creativity practical. He is the founder of the School of the Possible, a creative community of interesting people doing interesting things.

Dave is also the author of Liminal Thinking: Create the Change You Want by Changing the Way You Think (Rosenfeld Media 2016). Read more »

 

Q&A with our speaker

This Q&A was drawn from the Rosenverse Live session.

Q: How is AI changing the way people work?

A: AI is accelerating a shift that has happened many times before: technology reorganizes labor, changes which jobs survive, and pushes people toward new kinds of work.

Q: What kinds of jobs are hardest to automate?

A: The work that is hardest to automate is practice-based work, because it depends on trust, judgment, and direct human relationships.

Q: Why does community matter in the workplace?

A: Community matters because work is never just about output — it is also about belonging, accountability, and the human need to be connected to other people.

Q: What is the relationship between work and dignity?

A: Work is tied to identity and dignity, so when people lose jobs or lose meaningful roles, the impact is emotional and social as well as economic.

Q: Why are corporate jobs changing?

A: Corporate jobs are changing because the system that created them may be reaching its limits, especially as technology and social expectations reshape how value gets created.

 

Catch up on last week’s recordings, and mark your calendar for upcoming events.

See you in the Rosenverse!

 

From efficiency to imagination with Josh Clark and Veronika Kindred

AI is opening the door to a new era of design—but most teams are still focused on making their existing work faster rather than reimagining what’s possible. Lou talks with Josh Clark and Veronica Kindred about their new book, Sentient Design, and what it takes to design truly intelligent interfaces.

They introduce the idea of “practical magic”—starting with bold, even impossible wishes and then working backward to create real, deliverable experiences. Rather than defaulting to chatbots and efficiency gains, they argue that AI enables entirely new interaction models, from adaptive interfaces to agents that collaborate directly within products.

The conversation also explores how design systems and past UX practices lay the groundwork for this shift, while designers themselves must unlearn habits that limit creativity. Through their “sentient design sprint” and “minimum magical product” framework, Josh and Veronica offer a structured way to move from imagination to implementation.

At its core, this episode is a call for designers to reclaim their role as inventors—embracing AI not just as a tool, but as a new design material for creating more responsive, dynamic, and human-centered experiences.

What You’ll Learn from this Episode:

• Why AI is a new “design material,” not just a productivity tool
• How “practical magic” helps teams rethink what’s possible
• Why designers are stuck focusing on process instead of product innovation
• What adaptive, intelligent interfaces could look like in practice
• How the “sentient design sprint” turns ideas into real solutions
• What designers need to unlearn to work effectively with AI

Q&A with Josh Clark and Veronika Kindred

This Q&A has been drawn from the podcast episode.

Q: What is “sentient design” in UX and AI?

A: Sentient design is about creating interfaces that can respond, adapt, and participate in the user experience in real time. It doesn’t mean consciousness—it means systems with awareness and agency that can interpret user intent and adjust accordingly. Think of it as a shift from static interfaces to intelligent, adaptive ones. Instead of fixed layouts, you’re designing systems that can make decisions with the user, not just for them.

Why is AI a turning point for UX and product design?

A: AI introduces a completely new design material. For the first time, interfaces can understand intent—not just inputs. That changes everything about how we design interactions. It’s also the first major interaction shift since mobile. For the past decade, design innovation has focused on tools and process. Now we’re back to reinventing the product itself.

Can non-designers use sentient design principles?

A: Absolutely. AI lowers the barrier to entry because the interface is often language-based. Product managers, developers, and others can actively participate in design. In fact, the best results come from cross-functional teams. AI enables everyone to contribute in the same space, which makes collaboration more powerful.

What do designers need to “unlearn” in the age of AI?

A: Many established best practices don’t fully apply anymore. Designers need to let go of rigid patterns and be open to new interaction models. The biggest shift is moving away from immediate solutioning. Instead of jumping to familiar UI patterns, start with outcomes and possibilities.

What should UX and product teams do next?

A: Look beyond tools. Start thinking about new product possibilities enabled by AI, and embrace a beginner’s mindset. This is a new medium—success will come from curiosity, experimentation, and collaboration across disciplines.

About out guests

Josh Clark is principal of Big Medium, a digital agency that helps complex organizations design for what’s next. Josh has over 30 years of experience in emerging technology, user experience, and design innovation. His projects include future-friendly interfaces for AI, connected devices, and websites for many of the world’s biggest companies. He is co-author with Veronika Kindred of the book Sentient Design: Crafting Intelligent Interfaces with AI (Rosenfeld Media, 2026).

Josh speaks around the world about what’s next for digital interfaces. He has keynoted hundreds of events in over 25 countries, and has offered countless more private workshops and executive sessions. Josh is also author of several other books, including Designing for Touch (A Book Apart) and Tapworthy: Designing Great iPhone Apps (O’Reilly). Read more »

Veronika Kindred is designer and researcher at Big Medium, where she defines and solves design problems alongside some of the world’s biggest companies. She travels internationally to lead Sentient Design workshops and speak to teams at startups and Fortune 100 companies alike. She is co-author with Josh Clark of the book Sentient Design: Crafting Intelligent Interfaces with AI (Rosenfeld Media, 2026). Sentient Design describes the practice of creating digital experiences that crackle with awareness and agency, adapting to your users in the moment. These are intelligent interfaces: dashboards that design themselves, apps that manifest on demand, agents that just get it done, and much more.

Veronika holds degrees from New York University (Politics and Data Science) and the Fashion Institute of Technology (Photography). Her photographs have appeared in O, The Oprah Magazine and UNWomen.org. Her research has ranged from AI’s effect on user experience to mobile technology’s impact on African political engagement to the nuances of congressional climate hearings. Read more »

Quick Reference Guide:

0:13 – Meet Josh and Veronika and learn about their new Rosenfeld Media book
3:14 – Harnessing magical thinking with AI
9:19 – Moving beyond process, speed, and efficiency
13:11 – How designers can transition from tooling to inventing
16:53 – Exciting places the magic could take us
23:40 – 5 reasons to use the Rosenverse
25:52 – The Sentient Design Sprint
30:08 – The Sentient Triangle
33:36 – Is this applicable to non-traditional designers? Or what designers need to unlearn?
39:56 – Josh and Veronica’s gifts for listeners

Resources and Links from Today’s Episode:

Sentient Design: Crafting Intelligent Interfaces with AI by Josh Clark and Veronika Kindred

Designing with AI 2026 – June 9, 2026

Enchanted Objects: Innovation, Design, and the Future of Technology by David Rose

This is Running: A Celebration of the World of Running, Exploring the Culture, History, Brands, Races and People Behind It by Raziq Rauf

Want to step up your product management game? Read this.

Are you ready to master one of business’s most rewarding roles?

Julia Barham, product management extraordinaire, has the book for you.

The Product Management Playbook: Create, Ship, and Optimize Winning Products is your guide to becoming the product manager (PM) you’ve always wanted to be. And it’s officially on pre-order for 15% off list price.

What is product management?

Product management is not a one-size-fits-all career.

In fact, if you ask a dozen PMs how they would describe their job, you’ll likely get a d0zen different answers, from ‘orchestrator’ to ‘team quarterback.’ Yet, there is one thing that rings true for product managers: they act as a connective tissue behind the scenes of our favorite products.

Responsibilities of a Product Manager include:

• Translating market research, customer insights, and business goals into products that deliver value
• Establishing a product vision, strategy, and roadmap that defines success
• Guiding teams that build technology-enabled solutions
• Influence teams and maintain alignment across different groups
• Optimize in-market solutions with evidence-based decision-making
• Monitor data and synthesize insights into actionable improvements

Everything you need as a product manager

Current or aspiring product managers, product leaders, digital professionals or career changers who are considering product management…this book is especially for you.

Many product managers haven’t gotten through any specialized training. If you’re anything like Julia Barham, you were thrown into (or dove headfirst into) the deep end! Figuring things out by trial and error has its perks, but what about those looking for inspiration, or just starting out in their product management careers?

Julia Barham has written the guide for product managers that she wishes she had when starting this career path.

If you’re on a mission keep customers happy and businesses healthy, then join Julia as she guides you through high-impact problem-solving frameworks and methods that every PM needs.

What does The Product Management Playbook have that other PM books don’t?

If product management itself isn’t easily captured in one title, how can one book capture everything you need to know? The secret is this: Julia Barham uses her incredibly varied background to showcase frameworks and plans that account for the wide breadth of customers and businesses that PMs interact with.

Take it from John Cutler, writer and product management extraordinaire who wrote the foreword to The Product Management Playbook:

“[Julia] has led products at a subscription media company, built experimentation culture at a major bank, launched a digital-first insurance model from scratch, and scaled acquisition portfolios serving millions of customers. That range matters. It’s the reason the advice in this book holds up, even when your context doesn’t match the Silicon Valley default.”

The Product Management Playbook gets down to the goal of product management: creating, shipping, and optimizing winning products.

What else does this book include?

• 21 step-by-step product management methods
• Ready-to-use templates to use in everyday work for real-world impact
• A skills assessment test to baseline your core competencies
• Technologies for PMs working on AI-based products
• Thought leadership insights on what separates elite PMs from the rest
• Interview tips for aspiring product managers

Who is Julia Barham?

Julia Barham is a product executive, patented inventor, and veteran of the full product journey—from zero-to-one launches to scaling market-leading solutions across Fortune 100 companies. She has built inaugural product teams, led B2C and B2B products, and run digital experimentation engines that generated multimillion-dollar revenue gains.

Julia’s work has earned top-tier recognition for innovation and design, but her true passion is helping teams cut through corporate complexity and bridge the gap between ambitious strategy and daily execution—the space where most business leaders and product teams struggle. Read more »

Q&A with the author

Q: Why this book? How will it help me?

A: This book is meant to give you something you can use on Monday morning when a new problem hits your inbox. It keeps in mind the reality for everyday practitioners: managing a portfolio of features in different stages of maturity, trying to balance discovery and delivery simultaneously, working without durable design and data partners, and dealing with needy stakeholders.

Q: If I’m not a product manager, will I get anything out of this book?

A: Yes! This book uses the term PM as shorthand, but it’s written for anyone building technology-enabled solutions to meet customer needs and business goals. If that’s the work you do, this book is for you.

Q: How is AI changing the product manager role?

A: Yes, AI is reshaping and accelerating parts of the product development life cycle in powerful ways, but even AI product teams require human-in-the-loop checkpoints.

But if you’re working directly with artificial intelligence, there is an AI Product Manager’s Starter Kit included in this book!

When does The Product Management Playbook come out?

The Product Management Playbook: Create, Ship, and Optimize Winning Products will be released on July 14, 2026. Until then, it is available for pre-order at 15% off the list price.

Last week in the Rosenverse: From UXer to social entrepreneur

Last week in the Rosenverse, we hosted a session in which Dolly Parikh as a part of our Exit Interviews series. In it, Dolly discussed her transition from UX and product to social and environmental causes, and how others can make a similar change in their careers.

Log into the Rosenverse to watch the recording.

See what you missed below.

Exit Interview #7: Journey of a Social Entrepreneur

“UX mindset lets you see where the friction and gaps are to create value inside and outside organizations.” 

May 21: Dolly Parikh reflects on her transition from a long career in UX, Product, and Information Architecture to her current work as an Impact Strategist and Social Entrepreneur advancing sustainable development. She shares how Human?Centered Design, Systems Thinking, and Innovation Practices became the foundation for her shift into Ecosystem Conservation at The Nature Conservancy and her mentorship of Global Social Ventures. This session offers UX practitioners an inside look at how design skills translate beyond the tech industry and how they can be leveraged to drive regenerative, systems?level impact in the social and environmental sector. Watch the recording »

About the speaker:

As a social entrepreneur, Dolly Parikh focuses on impact, strategy, and innovation capacity building for sustainable development. At The Nature Conservancy, whose mission is to protect land, water, and ocean on which life thrives, she leverages Human-Centered Design, Systems Thinking, and Innovation methodologies to drive impact in ecosystem conservation. After spending a couple of decades as a Product, UX, and Information Architect in B2B and B2C Technology Companies, Dolly expanded her design and problem-solving practice for social good. Read more »

Q&A with our speaker

This Q&A was drawn from the Rosenverse Live session.

Q: How do UX skills translate into social impact work?

A: UX is fundamentally about understanding people, clarifying complex problems, and designing better experiences. Those same skills are powerful in the impact sector because they help teams communicate clearly, prioritize effectively, and build services that are easier for people to use.

Q: What is the biggest leverage point for design in this kind of work?

A: The biggest leverage comes from communication and storytelling. When you can frame a problem well and show why it matters, you make it easier for others to align around the solution and move the work forward.

Q: Why is AI especially useful in the impact sector?

A: AI can help close resource gaps when teams are underfunded, understaffed, or stretched thin. Used thoughtfully, it can support faster operations, better knowledge access, and more scalable service delivery.

Q: What makes transferable UX skills valuable outside tech?

A: Research, facilitation, synthesis, and design strategy are useful almost anywhere people need better decisions and better services. Those skills travel well because they are built around understanding behavior and improving human systems.

Q: How can mission-driven teams use AI responsibly?

A: AI should support people, not replace judgment. In the impact sector, it can help stretch limited resources, but it works best when teams stay focused on equity, context, and the actual needs of communities.

 

Catch up on last week’s recordings, and mark your calendar for upcoming events.

See you in the Rosenverse!

Your guide to keeping up with AI trends in user research

Get acquainted with AI or get left behind.

That seems to be the consensus these days across all industries. As artificial intelligence (AI) continues to boom, we want to explore how the technology is impacting the user experience (UX) space. According to Maze’s 2026 Future of User Research report, two out of three researchers are using AI at some point in their process. With such a significant number, it’s crucial to stay up-to-date on the latest and greatest in artificial intelligence.

But how can researchers keep up when everything seems to be moving at the speed of light?

Have no fear; we’ve compiled some of the top resources available to you in the Rosenverse to help keep up with AI trends in user research. So now you don’t have to worry about getting left behind. View all the resources in this post here.

The highlights

• AI is your research partner—not your replacement
• Understand where AI can help, and where it falls short
• Continuously discover how UXers are using AI

AI is your research partner—not your replacement

Since the first whispers of ChatGPT came onto the scene, we at Rosenfeld have kept an ear to the ground for how artificial intelligence has been reshaping the research experience.

In 2023, we hosted a panel moderated by Dr. Jamika D. Burge, How UX researchers can partner with (and not be replaced by) AI, in which panelists spoke to their own lived experiences and expanded on the most crucial actions researchers could take at this turning point in UX. Alexandra Jayeun Lee of Microsoft said that “learning prompt engineering will make us better researchers in the same way coding helped designers.”

Since the beginnings of the AI boom, companies everywhere have been seeking out ways to incorporate the newest technology—much to the behest of some employees. How can we implement AI into our work without it replacing us? Will AI take away jobs like predicted when it first exploded? (Spoiler: Not really). When it comes to user research, innovation involves utilizing AI in a way that is a research partner, rather than a shortcut or a replacement.

“Learning prompt engineering will make us better researchers in the same way coding helped designers.”
– Alexandra Jayeun Lee

When used as a tool, AI has the potential to do lots of heavy lifting and ease burdens of things such as transcription, pattern mapping, data organization, and more. There are many tools out there that additionally boast of insight generation, but it’s important to pair these tools with a human perspective, keeping an eye out for confirmation bias and feedback loops in AI-generated information.

By crafting the right prompts and using AI to its strengths, user researchers can develop a companion in their research, and a helpful, trainable one at that.

Watch: How UX researchers can partner with (and not be replaced by) AI

Understand where AI can help, and where it falls short

While artificial intelligence can make researchers’ lives easier, it has its limitations.

In Research That Scales, author Kate Towsey says to “Treat AI as you would any other tool: question where it will be best used, and how it will impact the culture, results, and value of research.”

Jake Burghardt, author of Stop Wasting Research, has dedicated a whole book to building research repositories and limiting research waste—even without the help of AI. Yet Jake advocates for appropriate AI use, citing the time it can save, as well as the support it can offer. At the same time, he cautions that there is no “push-button” technology that will magically solve the problem of research waste.

“When appropriate, adding AI-based features to research tooling can valuably support operations…AI in defined use cases can be a complement for researchers’ smarts, sometimes saving extensive manual efforts.”
– Jake Burghardt

This is a situation in which understanding AI’s benefits versus its limitations is critical. Artificial intelligence can be of great use to the user research process. In fact, User Interviews’ 2024 AI in UX Research report cites that 48% of surveyed participants cite AI’s speed as a benefit. And in Maze’s 2026 User Research Report, 63% cite improved turnaround time as a benefit. The evidence here is clear: AI can be a timesaver for UX research. But it’s not the answer to everything.

Build Better Products author Laura Klein presented her talk, Human vs. machine: Testing AI’s ability to synthesize and analyze research at Advancing Research 2026 this past March, as a way to demonstrate this exact conundrum. Here’s a peek at some of her findings:

• AI tools frequently produce insight-shaped outputs but often lack the rigor and accuracy of trained human researchers
• AI excels at finding semantic connections and grouping codes in large, already coded qualitative datasets quickly
• AI moderators cannot currently assess user behavior beyond spoken words, missing key usability observations like failed or inefficient tasks
• Contextual elements such as environmental interruptions are critical in research but are invisible to AI tools
• Integrating AI with organizational systems to pull in diverse data sources improves context but requires expert setup and is not yet simple

Read: Research That Scales by Kate Towsey

Read: Stop Wasting Research by Jake Burghardt

Watch: Human vs. machine: Testing AI’s ability to synthesize and analyze research

Continuously discover how UXers are using AI

What better way to keep up with trends than by hearing from the researchers on the front lines of this innovation?

There are many resources out there for user experience professionals, but the Rosenverse combines thousands of hours of conference and community videoconferences with years of podcast episodes, dozens of high-quality books, and a UX-specific chatbot. We may be biased, but we seriously cannot recommend it enough. Here’s our two favorite ways to stay up-to-date on AI trends:

Rosenverse Live

Did you know that Rosenfeld Media hosts free virtual events—around 100 per year?

We like the keep our finger on the pulse of all things UX, and right now that includes frank discussions about AI. Here are some of our recent Rosenverse Live sessions about artificial intelligence:

• The Handoff is Dead: Design-Led Engineering with AI Agents
• When AI Agents Meet Reality. Service Design Lessons from a Pilot
• Improving Democratized Research with CustomGPTs and Gems

Designing with AI

The Designing with AI conference is entering its third year as a standout in the UX space. Since 2023, Rosenfeld Media has been painstakingly crafting the annual event with the most poignant and relevant case studies and talks about AI.

Who better to learn about AI from than the designers and researchers on the ground using it?

If you’re Rosenverse Gold member, you can watch all past Designing with AI conference sessions. But you don’t have to miss out: Designing with AI 2026 is in just a few short weeks! Register now for #DwAI2026.

Watch: The Handoff is Dead: Design-Led Engineering with AI Agents

Watch: When AI Agents Meet Reality. Service Design Lessons from a Pilot

Watch: Improving Democratized Research with CustomGPTs and Gems

Attend: Designing with AI 2026

 

How are you staying up to date with the latest AI trends in user research? Do you believe AI is the next evolution of research, or do you have a more cynical take? Let us know!

You can access all resources listed in this post here »

 

The jagged mind: Staying human in an AI-smooth world with Paul Ford

AI may be built on language—but according to Paul Ford, we’re still struggling to find the right words to describe what it’s actually doing to our work and thinking. Lou and Paul explore how language shapes our ability to understand—and responsibly use—AI.

Drawing on his dual background in programming and writing, Paul shares a set of evolving “rules” for working with AI: don’t let it replace your thinking, be wary of its tendency to flatter, and build systems that help you verify and structure its output rather than blindly trusting it. He explains how he uses AI to accelerate prototyping and research while still preserving human judgment, creativity, and accountability.

The discussion also zooms out to the broader cultural moment. From skeptical college students to industry hype cycles, Paul argues that people are more discerning than we often assume—and that AI’s impact will play out in diverse, deeply human ways.

Paul will be the opening speaker at the upcoming Designing with AI conference, where he’ll expand on these ideas and introduce new language for navigating this rapidly evolving space.

His takeaway? We’re not at the end of history—we’re in a messy, fascinating transition, and the best we can do is stay curious, thoughtful, and engaged.

What you’ll learn from this episode

• Why shared language is critical for making sense of AI
• How Paul Ford approaches “rules” for using AI responsibly
• The risks of AI’s built-in flattery and “smooth” thinking
• Practical ways to use AI for prototyping without losing control
• Why verification systems matter more than trusting the model
• How younger generations actually view AI (less hype, more pragmatism)
• Why AI may be powerful—but not as historically radical as we think
• How to stay grounded and thoughtful amid rapid technological change

Q&A with Paul Ford

This Q&A is drawn from the podcast episode.

Q: The episode is called “The Jagged Mind.” What does that mean, and why does it matter for how we work with AI?

A: The image I keep coming back to is the difference between jagged and smooth. AI output tends toward smooth — it’s confident, fluent, well-structured, and immediately plausible. That smoothness is actually a kind of trap, because the jagged stuff — the weird hesitation, the half-formed idea, the counterintuitive hunch — is often where real thinking lives. Your own roughness isn’t a bug to be edited out. It’s evidence that something is actually happening upstairs.

When you outsource too much of your thinking to AI, you get smooth output but you risk losing the texture of your own mind. The goal isn’t to resist AI — it’s to stay jagged enough that you’re still genuinely contributing, still thinking your own thoughts, rather than just editing what a model handed you.

Q: You’ve talked about the importance of language for understanding AI. Why does that feel urgent right now?

A: Because the words we use to describe what AI is doing shape whether we can think clearly about it at all. Right now, the available language is mostly borrowed — from science fiction, from corporate marketing, from hype cycles. We say models “hallucinate,” we say they “understand,” we say they’re “thinking.” None of those words are quite right, and using them imprecisely leads to both overconfidence and misplaced fear.

Part of what I want to do — and what I’ll be expanding on at the Designing with AI conference — is develop better, more honest vocabulary for what these systems actually do and don’t do. You can’t navigate something responsibly if you don’t have language that lets you describe it accurately. That’s not a philosophical nicety. It has real consequences for how teams use these tools and what decisions they make.

Q: You’ve developed what you call “rules” for working with AI. Can you walk through them?

A: I want to be careful not to oversell these as rules, because I keep revising them — which is part of the point. The first is the most important: don’t let it replace your thinking. Use it to accelerate your thinking, to stress-test your ideas, to cover research ground faster. But the judgment, the synthesis, the thing you’re actually trying to figure out — that has to stay with you. The moment you hand that over, you’ve also handed over the accountability.

The second is to be genuinely wary of the flattery. These systems are natively inclined to tell you that your idea is good. They are almost constitutionally agreeable. If you’re a leader, that is an extremely dangerous quality to expose yourself to — you will hear a lot of “yes, and” when what you actually need is “wait, but.”
The third is to build systems around the output rather than trusting it directly. Verification structures, review layers, prompts that force the model to argue against its own previous answer. You want to narrow the risk before you let it run.

Q: You mentioned that AI’s flattery is dangerous especially for people in leadership. Can you say more about that?

A: When it gets into that weird social relationship where it’s telling you that was a good idea, that’s where my alarm bells go off. The native buttering-up quality of these technologies is genuinely dangerous, because of course you always want to hear it — especially when you’re a boss.

People in positions of authority are already somewhat insulated from honest feedback. Direct reports learn quickly what the boss likes to hear. And now you have a technology that has essentially been trained to be agreeable, to be helpful, to give you what you seem to want. That’s not a neutral tool. It can quietly reinforce your blind spots and confirm your assumptions without you ever noticing it’s happening.

The antidote is to be deliberate about using AI to challenge you, not just assist you. Ask it to find the flaws in your plan. Ask it to steelman the opposing view. Ask it to tell you what you’re probably missing. That takes discipline, but it’s the difference between AI as a thinking partner and AI as an expensive yes-man.

Q: How do you personally use AI for prototyping and research without losing control of the output?

A: The key move for me is front-loading the structure. Before I let a model generate anything significant, I put real effort into defining the constraints — what I’m trying to learn, what format I want, what I already believe, and crucially, what I want to verify independently afterward. You can really narrow your risk when you’re working with this stuff, and then you can let it go and see what it comes up with.

For prototyping, AI is extraordinary. You can go from an idea to something you can actually interact with and react to in a fraction of the time it used to take. That changes the creative and strategic process in ways that are genuinely exciting. But I’m always conscious that a prototype that looks polished isn’t the same as an idea that’s been validated. The speed is real; the judgment still has to come from somewhere else.

For research, I use it to cover ground quickly and surface things I didn’t know to look for — and then I go verify the things that matter. The model is a collaborator, not an oracle.

About our guest

Paul Ford is a multidisciplinary technology founder, writer, and product leader based in New York with 16+ years of experience building software-driven companies. He co-founded Aboard and Postlight, where he built a design-driven product studio and helped Postlight grow into a 100-person firm before its acquisition by NTT Data in 2022, then returned to focus on new product initiatives and climate-data storytelling. As a prolific writer and editor, he has contributed to WIRED, Harper’s, NPR, The Morning News, and New York Magazine, blending technical rigor with cultural insight. His ventures range from solo projects like Ftrain.com to community experiments like tilde.club, reflecting an enduring passion for hands-on creation and open communities. Read more »

Quick reference guide

0:11 – Meet Paul
5:30 – Can language keep up with technological change?
12:48 – Paul’s rules for professionals
18:11 – Where is the slippery slope? Paul weighs in.
22:23 – Paul reveals his gift for the audience
23:03 – 5 reasons to use the Rosenverse
25:18 – A story about some NY college students
29:21 – The anger and skepticism toward AI
35:18 – Wrapping up

Resources

Designing with AI conference (June 9-10, 2026)

Shell Game Podcast, by Evan Ratliff

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Frequently asked questions about Sentient Design

How is sentient design different from AI design?

AI design is the broader practice of designing products that use artificial intelligence. Sentient design is more specific: it focuses on interfaces that feel context-aware, adaptive, and behaviorally responsive to the user.

Why is sentient design relevant to UX?

It changes how UX teams think about flows, states, feedback, and trust. Instead of designing only what users click, teams also design how systems should respond, explain themselves, and adjust in real time.

What skills do designers need for sentient design?

Designers need systems thinking, content strategy, interaction design, research skills, and a strong understanding of AI limitations. They also need to design for uncertainty, not just ideal user journeys.

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