In my previous article, I explored how AI can help us create functional personas more efficiently. We looked at building personas that focus on what users are trying to accomplish rather than demographic profiles that look good on posters but rarely change design decisions.

But creating personas is only half the battle. The bigger challenge is getting those insights into the hands of people who need them, at the moment they need them.

Every day, people across your organization make decisions that affect user experience. Product teams decide which features to prioritize. Marketing teams craft campaigns. Finance teams design invoicing processes. Customer support teams write response templates. All of these decisions shape how users experience your product or service.

And most of them happen without any input from actual users.

The Problem With How We Share User Research

You do the research. You create the personas. You write the reports. You give the presentations. You even make fancy infographics. And then what happens?

The research sits in a shared drive somewhere, slowly gathering digital dust. The personas get referenced in kickoff meetings and then forgotten. The reports get skimmed once and never opened again.

When a product manager is deciding whether to add a new feature, they probably do not dig through last year’s research repository. When the finance team is redesigning the invoice email, they almost certainly do not consult the user personas. They make their best guess and move on.

This is not a criticism of those teams. They are busy. They have deadlines. And honestly, even if they wanted to consult the research, they probably would not know where to find it or how to interpret it for their specific question.

The knowledge stays locked inside the heads of the UX team, who cannot possibly be present for every decision being made across the organization.

What If Users Could Actually Speak?

What if, instead of creating static documents that people need to find and interpret, we could give stakeholders a way to consult all of your user personas at once?

Imagine a marketing manager working on a new campaign. Instead of trying to remember what the personas said about messaging preferences, they could simply ask: “I’m thinking about leading with a discount offer in this email. What would our users think?”

And the AI, drawing on all your research data and personas, could respond with a consolidated view: how each persona would likely react, where they agree, where they differ, and a set of recommendations based on their collective perspectives. One question, synthesized insight across your entire user base.

This is not science fiction. With AI, we can build exactly this kind of system. We can take all of that scattered research (the surveys, the interviews, the support tickets, the analytics, the personas themselves) and turn it into an interactive resource that anyone can query for multi-perspective feedback.

Building the User Research Repository

The foundation of this approach is a centralized repository of everything you know about your users. Think of it as a single source of truth that AI can access and draw from.

If you have been doing user research for any length of time, you probably have more data than you realize. It is just scattered across different tools and formats:

• Survey results sitting in your survey platform,
• Interview transcripts in Google Docs,
• Customer support tickets in your helpdesk system,
• Analytics data in various dashboards,
• Social media mentions and reviews,
• Old personas from previous projects,
• Usability test recordings and notes.

The first step is gathering all of this into one place. It does not need to be perfectly organized. AI is remarkably good at making sense of messy inputs.

If you are starting from scratch and do not have much existing research, you can use AI deep research tools to establish a baseline.

These tools can scan the web for discussions about your product category, competitor reviews, and common questions people ask. This gives you something to work with while you build out your primary research.

Creating Interactive Personas

Once you have your repository, the next step is creating personas that the AI can consult on behalf of stakeholders. This builds directly on the functional persona approach I outlined in my previous article, with one key difference: these personas become lenses through which the AI analyzes questions, not just reference documents.

The process works like this:

1. Feed your research repository to an AI tool.
2. Ask it to identify distinct user segments based on goals, tasks, and friction points.
3. Have it generate detailed personas for each segment.
4. Configure the AI to consult all personas when stakeholders ask questions, providing consolidated feedback.

Here is where this approach diverges significantly from traditional personas. Because the AI is the primary consumer of these persona documents, they do not need to be scannable or fit on a single page. Traditional personas are constrained by human readability: you have to distill everything down to bullet points and key quotes that someone can absorb at a glance. But AI has no such limitation.

This means your personas can be considerably more detailed. You can include lengthy behavioral observations, contradictory data points, and nuanced context that would never survive the editing process for a traditional persona poster. The AI can hold all of this complexity and draw on it when answering questions.

You can also create different lenses or perspectives within each persona, tailored to specific business functions. Your “Weekend Warrior” persona might have a marketing lens (messaging preferences, channel habits, campaign responses), a product lens (feature priorities, usability patterns, upgrade triggers), and a support lens (common questions, frustration points, resolution preferences). When a marketing manager asks a question, the AI draws on the marketing-relevant information. When a product manager asks, it pulls from the product lens. Same persona, different depth depending on who is asking.

The personas should still include all the functional elements we discussed before: goals and tasks, questions and objections, pain points, touchpoints, and service gaps. But now these elements become the basis for how the AI evaluates questions from each persona’s perspective, synthesizing their views into actionable recommendations.

Implementation Options

You can set this up with varying levels of sophistication depending on your resources and needs.

The Simple Approach

Most AI platforms now offer project or workspace features that let you upload reference documents. In ChatGPT, these are called Projects. Claude has a similar feature. Copilot and Gemini call them Spaces or Gems.

To get started, create a dedicated project and upload your key research documents and personas. Then write clear instructions telling the AI to consult all personas when responding to questions. Something like:

You are helping stakeholders understand our users. When asked questions, consult all of the user personas in this project and provide: (1) a brief summary of how each persona would likely respond, (2) an overview highlighting where they agree and where they differ, and (3) recommendations based on their collective perspectives. Draw on all the research documents to inform your analysis. If the research does not fully cover a topic, search social platforms like Reddit, Twitter, and relevant forums to see how people matching these personas discuss similar issues. If you are still unsure about something, say so honestly and suggest what additional research might help.

This approach has some limitations. There are caps on how many files you can upload, so you might need to prioritize your most important research or consolidate your personas into a single comprehensive document.

The More Sophisticated Approach

For larger organizations or more ongoing use, a tool like Notion offers advantages because it can hold your entire research repository and has AI capabilities built in. You can create databases for different types of research, link them together, and then use the AI to query across everything.

The benefit here is that the AI has access to much more context. When a stakeholder asks a question, it can draw on surveys, support tickets, interview transcripts, and analytics data all at once. This makes for richer, more nuanced responses.

What This Does Not Replace

I should be clear about the limitations.

Virtual personas are not a substitute for talking to real users. They are a way to make existing research more accessible and actionable.

“

There are several scenarios where you still need primary research:

• When launching something genuinely new that your existing research does not cover;
• When you need to validate specific designs or prototypes;
• When your repository data is getting stale;
• When stakeholders need to hear directly from real humans to build empathy.

In fact, you can configure the AI to recognize these situations. When someone asks a question that goes beyond what the research can answer, the AI can respond with something like: “I do not have enough information to answer that confidently. This might be a good question for a quick user interview or survey.”

And when you do conduct new research, that data feeds back into the repository. The personas evolve over time as your understanding deepens. This is much better than the traditional approach, where personas get created once and then slowly drift out of date.

The Organizational Shift

If this approach catches on in your organization, something interesting happens.

The UX team’s role shifts from being the gatekeepers of user knowledge to being the curators and maintainers of the repository.

“

Instead of spending time creating reports that may or may not get read, you spend time ensuring the repository stays current and that the AI is configured to give helpful responses.

Research communication changes from push (presentations, reports, emails) to pull (stakeholders asking questions when they need answers). User-centered thinking becomes distributed across the organization rather than concentrated in one team.

This does not make UX researchers less valuable. If anything, it makes them more valuable because their work now has a wider reach and greater impact. But it does change the nature of the work.

Getting Started

If you want to try this approach, start small. If you need a primer on functional personas before diving in, I have written a detailed guide to creating them. Pick one project or team and set up a simple implementation using ChatGPT Projects or a similar tool. Gather whatever research you have (even if it feels incomplete), create one or two personas, and see how stakeholders respond.

Pay attention to what questions they ask. These will tell you where your research has gaps and what additional data would be most valuable.

As you refine the approach, you can expand to more teams and more sophisticated tooling. But the core principle stays the same: take all that scattered user knowledge and give it a voice that anyone in your organization can hear.

In my previous article, I argued that we should move from demographic personas to functional personas that focus on what users are trying to do. Now I am suggesting we take the next step: from static personas to interactive ones that can actually participate in the conversations where decisions get made.

Because every day, across your organization, people are making decisions that affect your users. And your users deserve a seat at the table, even if it is a virtual one.

Further Reading On SmashingMag

• “A Closer Look At Personas: What They Are And How They Work | 1”, Shlomo Goltz
• “How To Improve Your Design Process With Data-Based Personas”, Tim Noetzel
• “How To Make Your UX Research Hard To Ignore”, Vitaly Friedman
• “How To Build Strong Customer Relationships For User Research”, Renaissance Rachel

(yk)

So we design and ship a shiny new feature. How do we know if it’s working? How do we measure and track its impact? There is no shortage in UX metrics, but what if we wanted to establish a simple, repeatable, meaningful UX metric — specifically for our features? Well, let’s see how to do just that.

I first heard about the TARS framework from Adrian H. Raudschl’s wonderful article on “How To Measure Impact of Features”. Here, Adrian highlighted how his team tracks and decides which features to focus on — and then maps them against each other in a 2×2 quadrants matrix.

It turned out to be a very useful framework to visualize the impact of UX work through the lens of business metrics.

Let’s see how it works.

1. Target Audience (%)

We start by quantifying the target audience by exploring what percentage of a product’s users have the specific problem that a feature aims to solve. We can study existing or similar features that try to solve similar problems, and how many users engage with them.

Target audience isn’t the same as feature usage though. As Adrian noted, if we know that an existing Export Button feature is used by 5% of all users, it doesn’t mean that the target audience is 5%. More users might have the problem that the export feature is trying to solve, but they can’t find it.

Question we ask: “What percentage of all our product’s users have that specific problem that a new feature aims to solve?”

2. A = Adoption (%)

Next, we measure how well we are “acquiring” our target audience. For that, we track how many users actually engage successfully with that feature over a specific period of time.

We don’t focus on CTRs or session duration there, but rather if users meaningfully engage with it. For example, if anything signals that they found it valuable, such as sharing the export URL, the number of exported files, or the usage of filters and settings.

High feature adoption (>60%) suggests that the problem was impactful. Low adoption (<20%) might imply that the problem has simple workarounds that people have relied upon. Changing habits takes time, too, and so low adoption in the beginning is expected.

Sometimes, low feature adoption has nothing to do with the feature itself, but rather where it sits in the UI. Users might never discover it if it’s hidden or if it has a confusing label. It must be obvious enough for people to stumble upon it.

Low adoption doesn’t always equal failure. If a problem only affects 10% of users, hitting 50–75% adoption within that specific niche means the feature is a success.

Question we ask: “What percentage of active target users actually use the feature to solve that problem?”

3. Retention (%)

Next, we study whether a feature is actually used repeatedly. We measure the frequency of use, or specifically, how many users who engaged with the feature actually keep using it over time. Typically, it’s a strong signal for meaningful impact.

If a feature has >50% retention rate (avg.), we can be quite confident that it has a high strategic importance. A 25–35% retention rate signals medium strategic significance, and retention of 10–20% is then low strategic importance.

Question we ask: “Of all the users who meaningfully adopted a feature, how many came back to use it again?”

4. Satisfaction Score (CES)

Finally, we measure the level of satisfaction that users have with that feature that we’ve shipped. We don’t ask everyone — we ask only “retained” users. It helps us spot hidden troubles that might not be reflected in the retention score.

Once users actually used a feature multiple times, we ask them how easy it was to solve a problem after they used that feature — between “much more difficult” and “much easier than expected”. We know how we want to score.

Using TARS For Feature Strategy

Once we start measuring with TARS, we can calculate an S÷T score — the percentage of Satisfied Users ÷ Target Users. It gives us a sense of how well a feature is performing for our intended target audience. Once we do that for every feature, we can map all features across 4 quadrants in a 2×2 matrix.

Overperforming features are worth paying attention to: they have low retention but high satisfaction. It might simply be features that users don’t have to use frequently, but when they do, it’s extremely effective.

Liability features have high retention but low satisfaction, so perhaps we need to work on them to improve them. And then we can also identify core features and project features — and have a conversation with designers, PMs, and engineers on what we should work on next.

Conversion Rate Is Not a UX Metric

TARS doesn’t cover conversion rate, and for a good reason. As Fabian Lenz noted, conversion is often considered to be the ultimate indicator of success — yet in practice it’s always very difficult to present a clear connection between smaller design initiatives and big conversion goals.

The truth is that almost everybody on the team is working towards better conversion. An uptick might be connected to many different initiatives — from sales and marketing to web performance boost to seasonal effects to UX initiatives.

UX can, of course, improve conversion, but it’s not really a UX metric. Often, people simply can’t choose the product they are using. And often a desired business outcome comes out of necessity and struggle, rather than trust and appreciation.

High Conversion Despite Bad UX

As Fabian writes, high conversion rate can happen despite poor UX, because:

• Strong brand power pulls people in,
• Aggressive but effective urgency tactics,
• Prices are extremely attractive,
• Marketing performs brilliantly,
• Historical customer loyalty,
• Users simply have no alternative.

Low Conversion Despite Great UX

At the same time, a low conversion rate can occur despite great UX, because:

• Offers aren’t relevant to the audience,
• Users don’t trust the brand,
• Poor business model or high risk of failure,
• Marketing doesn’t reach the right audience,
• External factors (price, timing, competition).

An improved conversion is the positive outcome of UX initiatives. But good UX work typically improves task completion, reduces time on task, minimizes errors, and avoids decision paralysis. And there are plenty of actionable design metrics we could use to track UX and drive sustainable success.

Wrapping Up

Product metrics alone don’t always provide an accurate view of how well a product performs. Sales might perform well, but users might be extremely inefficient and frustrated. Yet the churn is low because users can’t choose the tool they are using.

We need UX metrics to understand and improve user experience. What I love most about TARS is that it’s a neat way to connect customers’ usage and customers’ experience with relevant product metrics. Personally, I would extend TARS with UX-focused metrics and KPIs as well — depending on the needs of the project.

Huge thanks to Adrian H. Raudaschl for putting it together. And if you are interested in metrics, I highly recommend you follow him for practical and useful guides all around just that!

Meet “How To Measure UX And Design Impact”

You can find more details on UX Strategy in 🪴 Measure UX & Design Impact (8h), a practical guide for designers and UX leads to measure and show your UX impact on business. Use the code 🎟 IMPACT to save 20% off today. Jump to the details.

Useful Resources

• “How To Measure UX and Design Impact”, by yours truly
• “Business Thinking For Designers”, by Ryan Rumsey
• “ROI of Design Project
• “How the Right UX Metrics Show Game-Changing Value”, by Jared Spool
• “Research Sample Size Calculators”

Further Reading

• “Designing For Stress And Emergency”, Vitaly Friedman
• “AI In UX: Achieve More With Less”, Paul Boag
• “The Accessibility Problem With Authentication Methods Like CAPTCHA”, Eleanor Hecks
• “From Prompt To Partner: Designing Your Custom AI Assistant”, Lyndon Cerejo

(yk)

This article is sponsored by Accessible UX Research

Smashing Library expands again! We’re so happy to announce our newest book, Accessible UX Research, is now available for download in eBook formats. Michele A. Williams takes us for a deep dive into the real world of UX testing, and provides a road map for including users with different abilities and needs in every phase of testing.

But the truth is, you don’t need to be conducting UX testing or even be a UX professional to get a lot out of this book. Michele gives in-depth descriptions of the assistive technology we should all be familiar with, in addition to disability etiquette, common pitfalls when creating accessible prototypes, and so much more. You’ll refer to this book again and again in your daily work.

This is also your last chance to get your printed copy at our discounted presale price. We expect printed copies to start shipping in early 2026. We know you’ll love this book, but don’t just take our word for it — we asked a few industry experts to check out Accessible UX Research too:

“Accessible UX Research stands as a vital and necessary resource. In addressing disability at the User Experience Research layer, it helps to set an equal and equitable tone for products and features that resonates through the rest of the creation process. The book provides a solid framework for all aspects of conducting research efforts, including not only process considerations, but also importantly the mindset required to approach the work.

This is the book I wish I had when I was first getting started with my accessibility journey. It is a gift, and I feel so fortunate that Michele has chosen to share it with us all.”

Eric Bailey, Accessibility Advocate

“User research in accessibility is non-negotiable for actually meeting users’ needs, and this book is a critical piece in the puzzle of actually doing and integrating that research into accessibility work day to day.”

Devon Pershing, Author of The Accessibility Operations Guidebook

“Our decisions as developers and designers are often based on recommendations, assumptions, and biases. Usually, this doesn’t work, because checking off lists or working solely from our own perspective can never truly represent the depth of human experience. Michele’s book provides you with the strategies you need to conduct UX research with diverse groups of people, challenge your assumptions, and create truly great products.”

Manuel Matuzović, Author of the Web Accessibility Cookbook

“This book is a vital resource on inclusive research. Michele Williams expertly breaks down key concepts, guiding readers through disability models, language, and etiquette. A strong focus on real-world application equips readers to conduct impactful, inclusive research sessions. By emphasizing diverse perspectives and proactive inclusion, the book makes a compelling case for accessibility as a core principle rather than an afterthought. It is a must-read for researchers, product-makers, and advocates!”

Anna E. Cook, Accessibility and Inclusive Design Specialist

About The Book

The book isn’t a checklist for you to complete as a part of your accessibility work. It’s a practical guide to inclusive UX research, from start to finish. If you’ve ever felt unsure how to include disabled participants, or worried about “getting it wrong,” this book is for you. You’ll get clear, practical strategies to make your research more inclusive, effective, and reliable.

Inside, you’ll learn how to:

• Plan research that includes disabled participants from the start,
• Recruit participants with disabilities,
• Facilitate sessions that work for a range of access needs,
• Ask better questions and avoid unintentionally biased research methods,
• Build trust and confidence in your team around accessibility and inclusion.

The book also challenges common assumptions about disability and urges readers to rethink what inclusion really means in UX research and beyond. Let’s move beyond compliance and start doing research that reflects the full diversity of your users. Whether you’re in industry or academia, this book gives you the tools — and the mindset — to make it happen.

High-quality hardcover, 320 pages. Written by Dr. Michele A. Williams. Cover art by Espen Brunborg. Print edition shipping early 2026. eBook now available for download. Download a free sample (PDF, 2.3MB) and reserve your print copy at the presale price.

Contents

1. Disability mindset: For inclusive research to succeed, we must first confront our mindset about disability, typically influenced by ableism.
2. Diversity of disability: Accessibility is not solely about blind screen reader users; disability categories help us unpack and process the diversity of disabled users.
3. Disability in the stages of UX research: Disabled participants can and should be part of every research phase — formative, prototype, and summative.
4. Recruiting disabled participants: Recruiting disabled participants is not always easy, but that simply means we need to learn strategies on where to look.
5. Designing your research: While our goal is to influence accessible products, our research execution must also be accessible.
6. Facilitating an accessible study: Preparation and communication with your participants can ensure your study logistics run smoothly.
7. Analyzing and reporting with accuracy and impact: How you communicate your findings is just as important as gathering them in the first place — so prepare to be a storyteller, educator, and advocate.
8. Disability in the UX research field: Inclusion isn’t just for research participants, it’s important for our colleagues as well, as explained by blind UX Researcher Dr. Cynthia Bennett.

Who This Book Is For

Whether a UX professional who conducts research in industry or academia, or more broadly part of an engineering, product, or design function, you’ll want to read this book if…

1. You have been tasked to improve accessibility of your product, but need to know where to start to facilitate this successfully.
2. You want to establish a culture for accessibility in your company, but not sure how to make it work.
3. You want to move from WCAG/EAA compliance to established accessibility practices and inclusion in research practices and beyond.
4. You want to improve your overall accessibility knowledge and be viewed as an Accessibility Specialist for your organization.

About the Author

Dr. Michele A. Williams is owner of M.A.W. Consulting, LLC - Making Accessibility Work. Her 20+ years of experience include influencing top tech companies as a Senior User Experience (UX) Researcher and Accessibility Specialist and obtaining a PhD in Human-Centered Computing focused on accessibility. An international speaker, published academic author, and patented inventor, she is passionate about educating and advising on technology that does not exclude disabled users.

Technical Details

• ISBN: 978-3-910835-03-0 (print)
• Quality hardcover, stitched binding, ribbon page marker.
• Free worldwide airmail shipping from Germany early 2026. We are currently unable to ship printed books to the United States due to customs clearance issues. If you have any questions, please contact us any time.
• eBook now available for download as PDF, ePUB, and Amazon Kindle.
• Order the eBook for instant download now.
• Reserve your print copy at the presale price.

Community Matters ❤️

Producing a book takes quite a bit of time, and we couldn’t pull it off without the support of our wonderful community. A huge shout-out to Smashing Members for the kind, ongoing support. The eBook is and always will be free for Smashing Members. Plus, Members get a friendly discount when purchasing their printed copy. Just sayin’! ;-)

More Smashing Books & Goodies

Promoting best practices and providing you with practical tips to master your daily coding and design challenges has always been (and will be) at the core of everything we do at Smashing.

In the past few years, we were very lucky to have worked together with some talented, caring people from the web community to publish their wealth of experience as printed books that stand the test of time. Trine, Heather, and Steven are three of these people. Have you checked out their books already?

(cm, il)

Your senior management is excited about AI. They’ve read the articles, attended the webinars, and seen the demos. They’re convinced that AI will transform your organization, boost productivity, and give you a competitive edge.

Meanwhile, you’re sitting in your UX role wondering what this means for your team, your workflow, and your users. You might even be worried about your job security.

The problem is that the conversation about how AI gets implemented is happening right now, and if you’re not part of it, someone else will decide how it affects your work. That someone probably doesn’t understand user experience, research practices, or the subtle ways poor implementation can damage the very outcomes management hopes to achieve.

You have a choice. You can wait for directives to come down from above, or you can take control of the conversation and lead the AI strategy for your practice.

Why UX Professionals Must Own the AI Conversation

Management sees AI as efficiency gains, cost savings, competitive advantage, and innovation all wrapped up in one buzzword-friendly package. They’re not wrong to be excited. The technology is genuinely impressive and can deliver real value.

But without UX input, AI implementations often fail users in predictable ways:

• They automate tasks without understanding the judgment calls those tasks require.
• They optimize for speed while destroying the quality that made your work valuable.

Your expertise positions you perfectly to guide implementation. You understand users, workflows, quality standards, and the gap between what looks impressive in a demo and what actually works in practice.

Use AI Momentum to Advance Your Priorities

Management’s enthusiasm for AI creates an opportunity to advance priorities you’ve been fighting for unsuccessfully. When management is willing to invest in AI, you can connect those long-standing needs to the AI initiative. Position user research as essential for training AI systems on real user needs. Frame usability testing as the validation method that ensures AI-generated solutions actually work.

How AI gets implemented will shape your team’s roles, your users’ experiences, and your organization’s capability to deliver quality digital products.

Your Role Isn’t Disappearing (It’s Evolving)

Yes, AI will automate some of the tasks you currently do. But someone needs to decide which tasks get automated, how they get automated, what guardrails to put in place, and how automated processes fit around real humans doing complex work.

That someone should be you.

Think about what you already do. When you conduct user research, AI might help you transcribe interviews or identify themes. But you’re the one who knows which participant hesitated before answering, which feedback contradicts what you observed in their behavior, and which insights matter most for your specific product and users.

When you design interfaces, AI might generate layout variations or suggest components from your design system. But you’re the one who understands the constraints of your technical platform, the political realities of getting designs approved, and the edge cases that will break a clever solution.

Your future value comes from the work you’re already doing:

• Seeing the full picture.
  You understand how this feature connects to that workflow, how this user segment differs from that one, and why the technically correct solution won’t work in your organization’s reality.
• Making judgment calls.
  You decide when to follow the design system and when to break it, when user feedback reflects a real problem versus a feature request from one vocal user, and when to push back on stakeholders versus find a compromise.
• Connecting the dots.
  You translate between technical constraints and user needs, between business goals and design principles, between what stakeholders ask for and what will actually solve their problem.

AI will keep getting better at individual tasks. But you’re the person who decides which solution actually works for your specific context. The people who will struggle are those doing simple, repeatable work without understanding why. Your value is in understanding context, making judgment calls, and connecting solutions to real problems.

Step 1: Understand Management’s AI Motivations

Before you can lead the conversation, you need to understand what’s driving it. Management is responding to real pressures: cost reduction, competitive pressure, productivity gains, and board expectations.

Speak their language.
When you talk to management about AI, frame everything in terms of ROI, risk mitigation, and competitive advantage. “This approach will protect our quality standards” is less compelling than “This approach reduces the risk of damaging our conversion rate while we test AI capabilities.”

Separate hype from reality.
Take time to research what AI capabilities actually exist versus what’s hype. Read case studies, try tools yourself, and talk to peers about what’s actually working.

Identify real pain points.
AI might legitimately address in your organization. Maybe your team spends hours formatting research findings, or accessibility testing creates bottlenecks. These are the problems worth solving.

Step 2: Audit Your Current State and Opportunities

Map your team’s work. Where does time actually go? Look at the past quarter and categorize how your team spent their hours.

Identify high-volume, repeatable tasks versus high-judgment work.
Repeatable tasks are candidates for automation. High-judgment work is where you add irreplaceable value.

Also, identify what you’ve wanted to do but couldn’t get approved.
This is your opportunity list. Maybe you’ve wanted quarterly usability tests, but only get budget annually. Write these down separately. You’ll connect them to your AI strategy in the next step.

Spot opportunities where AI could genuinely help:

• Research synthesis:
  AI can help organize and categorize findings.
• Analyzing user behavior data:
  AI can process analytics and session recordings to surface patterns you might miss.
• Rapid prototyping:
  AI can quickly generate testable prototypes, speeding up your test cycles.

Step 3: Define AI Principles for Your UX Practice

Before you start forming your strategy, establish principles that will guide every decision.

Set non-negotiables.
User privacy, accessibility, and human oversight of significant decisions. Write these down and get agreement from leadership before you pilot anything.

Define criteria for AI use.
AI is good at pattern recognition, summarization, and generating variations. AI is poor at understanding context, making ethical judgments, and knowing when rules should be broken.

Define success metrics beyond efficiency.
Yes, you want to save time. But you also need to measure quality, user satisfaction, and team capability. Build a balanced scorecard that captures what actually matters.

Create guardrails.
Maybe every AI-generated interface needs human review before it ships. These guardrails prevent the obvious disasters and give you space to learn safely.

Step 4: Build Your AI-in-UX Strategy

Now you’re ready to build the actual strategy you’ll pitch to leadership. Start small with pilot projects that have a clear scope and evaluation criteria.

Connect to business outcomes management cares about.
Don’t pitch “using AI for research synthesis.” Pitch “reducing time from research to insights by 40%, enabling faster product decisions.”

Piggyback your existing priorities on AI momentum.
Remember that opportunity list from Step 2? Now you connect those long-standing needs to your AI strategy. If you’ve wanted more frequent usability testing, explain that AI implementations need continuous validation to catch problems before they scale. AI implementations genuinely benefit from good research practices. You’re simply using management’s enthusiasm for AI as the vehicle to finally get resources for practices that should have been funded all along.

Define roles clearly.
Where do humans lead? Where does AI assist? Where won’t you automate? Management needs to understand that some work requires human judgment and should never be fully automated.

Plan for capability building.
Your team will need training and new skills. Budget time and resources for this.

Address risks honestly.
AI could generate biased recommendations, miss important context, or produce work that looks good but doesn’t actually function. For each risk, explain how you’ll detect it and what you’ll do to mitigate it.

Step 5: Pitch the Strategy to Leadership

Frame your strategy as de-risking management’s AI ambitions, not blocking them. You’re showing them how to implement AI successfully while avoiding the obvious pitfalls.

Lead with outcomes and ROI they care about.
Put the business case up front.

Bundle your wish list into the AI strategy.
When you present your strategy, include those capabilities you’ve wanted but couldn’t get approved before. Don’t present them as separate requests. Integrate them as essential components. “To validate AI-generated designs, we’ll need to increase our testing frequency from annual to quarterly” sounds much more reasonable than “Can we please do more testing?” You’re explaining what’s required for their AI investment to succeed.

Show quick wins alongside a longer-term vision.
Identify one or two pilots that can show value within 30-60 days. Then show them how those pilots build toward bigger changes over the next year.

Ask for what you need.
Be specific. You need a budget for tools, time for pilots, access to data, and support for team training.

Step 6: Implement and Demonstrate Value

Run your pilots with clear before-and-after metrics. Measure everything: time saved, quality maintained, user satisfaction, team confidence.

Document wins and learning.
Failures are useful too. If a pilot doesn’t work out, document why and what you learned.

Share progress in management’s language. Monthly updates should focus on business outcomes, not technical details. “We’ve reduced research synthesis time by 35% while maintaining quality scores” is the right level of detail.

Build internal advocates by solving real problems.
When your AI pilots make someone’s job easier, you create advocates who will support broader adoption.

Iterate based on what works in your specific context. Not every AI application will fit your organization. Pay attention to what’s actually working and double down on that.

Taking Initiative Beats Waiting

AI adoption is happening. The question isn’t whether your organization will use AI, but whether you’ll shape how it gets implemented.

Your UX expertise is exactly what’s needed to implement AI successfully. You understand users, quality, and the gap between impressive demos and useful reality.

Take one practical first step this week.
Schedule 30 minutes to map one AI opportunity in your practice. Pick one area where AI might help, think through how you’d pilot it safely, and sketch out what success would look like.

Then start the conversation with your manager. You might be surprised how receptive they are to someone stepping up to lead this.

You know how to understand user needs, test solutions, measure outcomes, and iterate based on evidence. Those skills don’t change just because AI is involved. You’re applying your existing expertise to a new tool.

“

Your role isn’t disappearing. It’s evolving into something more strategic, more valuable, and more secure. But only if you take the initiative to shape that evolution yourself.

Further Reading On SmashingMag

• “Designing With AI, Not Around It: Practical Advanced Techniques For Product Design Use Cases”, Ilia Kanazin & Marina Chernyshova
• “Beyond The Hype: What AI Can Really Do For Product Design”, Nikita Samutin
• “A Week In The Life Of An AI-Augmented Designer”, Lyndon Cerejo
• “Functional Personas With AI: A Lean, Practical Workflow”, Paul Boag

(yk, il)

In my last piece, we established a foundational truth: for users to adopt and rely on AI, they must trust it. We talked about trust being a multifaceted construct, built on perceptions of an AI’s Ability, Benevolence, Integrity, and Predictability. But what happens when an AI, in its silent, algorithmic wisdom, makes a decision that leaves a user confused, frustrated, or even hurt? A mortgage application is denied, a favorite song is suddenly absent from a playlist, and a qualified resume is rejected before a human ever sees it. In these moments, ability and predictability are shattered, and benevolence feels a world away.

Our conversation now must evolve from the why of trust to the how of transparency. The field of Explainable AI (XAI), which focuses on developing methods to make AI outputs understandable to humans, has emerged to address this, but it’s often framed as a purely technical challenge for data scientists. I argue it’s a critical design challenge for products relying on AI. It’s our job as UX professionals to bridge the gap between algorithmic decision-making and human understanding.

This article provides practical, actionable guidance on how to research and design for explainability. We’ll move beyond the buzzwords and into the mockups, translating complex XAI concepts into concrete design patterns you can start using today.

De-mystifying XAI: Core Concepts For UX Practitioners

XAI is about answering the user’s question: “Why?” Why was I shown this ad? Why is this movie recommended to me? Why was my request denied? Think of it as the AI showing its work on a math problem. Without it, you just have an answer, and you’re forced to take it on faith. In showing the steps, you build comprehension and trust. You also allow for your work to be double-checked and verified by the very humans it impacts.

Feature Importance And Counterfactuals

There are a number of techniques we can use to clarify or explain what is happening with AI. While methods range from providing the entire logic of a decision tree to generating natural language summaries of an output, two of the most practical and impactful types of information UX practitioners can introduce into an experience are feature importance (Figure 1) and counterfactuals. These are often the most straightforward for users to understand and the most actionable for designers to implement.

Feature Importance

This explainability method answers, “What were the most important factors the AI considered?” It’s about identifying the top 2-3 variables that had the biggest impact on the outcome. It’s the headline, not the whole story.

Example: Imagine an AI that predicts whether a customer will churn (cancel their service). Feature importance might reveal that “number of support calls in the last month” and “recent price increases” were the two most important factors in determining if a customer was likely to churn.

Counterfactuals

This powerful method answers, “What would I need to change to get a different outcome?” This is crucial because it gives users a sense of agency. It transforms a frustrating “no” into an actionable “not yet.”

Example: Imagine a loan application system that uses AI. A user is denied a loan. Instead of just seeing “Application Denied,” a counterfactual explanation would also share, “If your credit score were 50 points higher, or if your debt-to-income ratio were 10% lower, your loan would have been approved.” This gives Sarah clear, actionable steps she can take to potentially get a loan in the future.

Using Model Data To Enhance The Explanation

Although technical specifics are often handled by data scientists, it’s helpful for UX practitioners to know that tools like LIME (Local Interpretable Model-agnostic Explanations) which explains individual predictions by approximating the model locally, and SHAP (SHapley Additive exPlanations) which uses a game theory approach to explain the output of any machine learning model are commonly used to extract these “why” insights from complex models. These libraries essentially help break down an AI’s decision to show which inputs were most influential for a given outcome.

When done properly, the data underlying an AI tool’s decision can be used to tell a powerful story. Let’s walk through feature importance and counterfactuals and show how the data science behind the decision can be utilized to enhance the user’s experience.

Now let’s cover feature importance with the assistance of Local Explanations (e.g., LIME) data: This approach answers, “Why did the AI make this specific recommendation for me, right now?” Instead of a general explanation of how the model works, it provides a focused reason for a single, specific instance. It’s personal and contextual.

Example: Imagine an AI-powered music recommendation system like Spotify. A local explanation would answer, “Why did the system recommend this specific song by Adele to you right now?” The explanation might be: “Because you recently listened to several other emotional ballads and songs by female vocalists.”

Finally, let’s cover the inclusion of Value-based Explanations (e.g. Shapley Additive Explanations (SHAP) data to an explanation of a decision: This is a more nuanced version of feature importance that answers, “How did each factor push the decision one way or the other?” It helps visualize what mattered, and whether its influence was positive or negative.

Example: Imagine a bank uses an AI model to decide whether to approve a loan application.

Feature Importance: The model output might show that the applicant’s credit score, income, and debt-to-income ratio were the most important factors in its decision. This answers what mattered.

Feature Importance with Value-Based Explanations (SHAP): SHAP values would take feature importance further based on elements of the model.

• For an approved loan, SHAP might show that a high credit score significantly pushed the decision towards approval (positive influence), while a slightly higher-than-average debt-to-income ratio pulled it slightly away (negative influence), but not enough to deny the loan.
• For a denied loan, SHAP could reveal that a low income and a high number of recent credit inquiries strongly pushed the decision towards denial, even if the credit score was decent.

This helps the loan officer explain to the applicant beyond what was considered, to how each factor contributed to the final “yes” or “no” decision.

It’s crucial to recognize that the ability to provide good explanations often starts much earlier in the development cycle. Data scientists and engineers play a pivotal role by intentionally structuring models and data pipelines in ways that inherently support explainability, rather than trying to bolt it on as an afterthought.

Research and design teams can foster this by initiating early conversations with data scientists and engineers about user needs for understanding, contributing to the development of explainability metrics, and collaboratively prototyping explanations to ensure they are both accurate and user-friendly.

XAI And Ethical AI: Unpacking Bias And Responsibility

Beyond building trust, XAI plays a critical role in addressing the profound ethical implications of AI*, particularly concerning algorithmic bias. Explainability techniques, such as analyzing SHAP values, can reveal if a model’s decisions are disproportionately influenced by sensitive attributes like race, gender, or socioeconomic status, even if these factors were not explicitly used as direct inputs.

For instance, if a loan approval model consistently assigns negative SHAP values to applicants from a certain demographic, it signals a potential bias that needs investigation, empowering teams to surface and mitigate such unfair outcomes.

The power of XAI also comes with the potential for “explainability washing.” Just as “greenwashing” misleads consumers about environmental practices, explainability washing can occur when explanations are designed to obscure, rather than illuminate, problematic algorithmic behavior or inherent biases. This could manifest as overly simplistic explanations that omit critical influencing factors, or explanations that strategically frame results to appear more neutral or fair than they truly are. It underscores the ethical responsibility of UX practitioners to design explanations that are genuinely transparent and verifiable.

UX professionals, in collaboration with data scientists and ethicists, hold a crucial responsibility in communicating the why of a decision, and also the limitations and potential biases of the underlying AI model. This involves setting realistic user expectations about AI accuracy, identifying where the model might be less reliable, and providing clear channels for recourse or feedback when users perceive unfair or incorrect outcomes. Proactively addressing these ethical dimensions will allow us to build AI systems that are truly just and trustworthy.

From Methods To Mockups: Practical XAI Design Patterns

Knowing the concepts is one thing; designing them is another. Here’s how we can translate these XAI methods into intuitive design patterns.

Pattern 1: The “Because” Statement (for Feature Importance)

This is the simplest and often most effective pattern. It’s a direct, plain-language statement that surfaces the primary reason for an AI’s action.

• Heuristic: Be direct and concise. Lead with the single most impactful reason. Avoid jargon at all costs.

Example: Imagine a music streaming service. Instead of just presenting a “Discover Weekly” playlist, you add a small line of microcopy.

Song Recommendation: “Velvet Morning”
Because you listen to “The Fuzz” and other psychedelic rock.

Pattern 2: The “What-If” Interactive (for Counterfactuals)

Counterfactuals are inherently about empowerment. The best way to represent them is by giving users interactive tools to explore possibilities themselves. This is perfect for financial, health, or other goal-oriented applications.

• Heuristic: Make explanations interactive and empowering. Let users see the cause and effect of their choices.

Example: A loan application interface. After a denial, instead of a dead end, the user gets a tool to determine how various scenarios (what-ifs) might play out (See Figure 1).

Pattern 3: The Highlight Reel (For Local Explanations)

When an AI performs an action on a user’s content (like summarizing a document or identifying faces in photos), the explanation should be visually linked to the source.

• Heuristic: Use visual cues like highlighting, outlines, or annotations to connect the explanation directly to the interface element it’s explaining.

Example: An AI tool that summarizes long articles.

AI-Generated Summary Point:
Initial research showed a market gap for sustainable products.

Source in Document:
“...Our Q2 analysis of market trends conclusively demonstrated that no major competitor was effectively serving the eco-conscious consumer, revealing a significant market gap for sustainable products...”

Pattern 4: The Push-and-Pull Visual (for Value-based Explanations)

For more complex decisions, users might need to understand the interplay of factors. Simple data visualizations can make this clear without being overwhelming.

• Heuristic: Use simple, color-coded data visualizations (like bar charts) to show the factors that positively and negatively influenced a decision.

Example: An AI screening a candidate’s profile for a job.

Why this candidate is a 75% match:

Factors pushing the score up:

• 5+ Years UX Research Experience
• Proficient in Python

Factors pushing the score down:

• No experience with B2B SaaS

Learning and using these design patterns in the UX of your AI product will help increase the explainability. You can also use additional techniques that I’m not covering in-depth here. This includes the following:

• Natural language explanations: Translating an AI’s technical output into simple, conversational human language that non-experts can easily understand.
• Contextual explanations: Providing a rationale for an AI’s output at the specific moment and location, it is most relevant to the user’s task.
• Relevant visualizations: Using charts, graphs, or heatmaps to visually represent an AI’s decision-making process, making complex data intuitive and easier for users to grasp.

A Note For the Front End: Translating these explainability outputs into seamless user experiences also presents its own set of technical considerations. Front-end developers often grapple with API design to efficiently retrieve explanation data, and performance implications (like the real-time generation of explanations for every user interaction) need careful planning to avoid latency.

Some Real-world Examples

UPS Capital’s DeliveryDefense

UPS uses AI to assign a “delivery confidence score” to addresses to predict the likelihood of a package being stolen. Their DeliveryDefense software analyzes historical data on location, loss frequency, and other factors. If an address has a low score, the system can proactively reroute the package to a secure UPS Access Point, providing an explanation for the decision (e.g., “Package rerouted to a secure location due to a history of theft”). This system demonstrates how XAI can be used for risk mitigation and building customer trust through transparency.

Autonomous Vehicles

These vehicles of the future will need to effectively use XAI to help their vehicles make safe, explainable decisions. When a self-driving car brakes suddenly, the system can provide a real-time explanation for its action, for example, by identifying a pedestrian stepping into the road. This is not only crucial for passenger comfort and trust but is a regulatory requirement to prove the safety and accountability of the AI system.

IBM Watson Health (and its challenges)

While often cited as a general example of AI in healthcare, it’s also a valuable case study for the importance of XAI. The failure of its Watson for Oncology project highlights what can go wrong when explanations are not clear, or when the underlying data is biased or not localized. The system’s recommendations were sometimes inconsistent with local clinical practices because they were based on U.S.-centric guidelines. This serves as a cautionary tale on the need for robust, context-aware explainability.

The UX Researcher’s Role: Pinpointing And Validating Explanations

Our design solutions are only effective if they address the right user questions at the right time. An explanation that answers a question the user doesn’t have is just noise. This is where UX research becomes the critical connective tissue in an XAI strategy, ensuring that we explain the what and how that actually matters to our users. The researcher’s role is twofold: first, to inform the strategy by identifying where explanations are needed, and second, to validate the designs that deliver those explanations.

Informing the XAI Strategy (What to Explain)

Before we can design a single explanation, we must understand the user’s mental model of the AI system. What do they believe it’s doing? Where are the gaps between their understanding and the system’s reality? This is the foundational work of a UX researcher.

Mental Model Interviews: Unpacking User Perceptions Of AI Systems

Through deep, semi-structured interviews, UX practitioners can gain invaluable insights into how users perceive and understand AI systems. These sessions are designed to encourage users to literally draw or describe their internal “mental model” of how they believe the AI works. This often involves asking open-ended questions that prompt users to explain the system’s logic, its inputs, and its outputs, as well as the relationships between these elements.

These interviews are powerful because they frequently reveal profound misconceptions and assumptions that users hold about AI. For example, a user interacting with a recommendation engine might confidently assert that the system is based purely on their past viewing history. They might not realize that the algorithm also incorporates a multitude of other factors, such as the time of day they are browsing, the current trending items across the platform, or even the viewing habits of similar users.

Uncovering this gap between a user’s mental model and the actual underlying AI logic is critically important. It tells us precisely what specific information we need to communicate to users to help them build a more accurate and robust mental model of the system. This, in turn, is a fundamental step in fostering trust. When users understand, even at a high level, how an AI arrives at its conclusions or recommendations, they are more likely to trust its outputs and rely on its functionality.

AI Journey Mapping: A Deep Dive Into User Trust And Explainability

By meticulously mapping the user’s journey with an AI-powered feature, we gain invaluable insights into the precise moments where confusion, frustration, or even profound distrust emerge. This uncovers critical junctures where the user’s mental model of how the AI operates clashes with its actual behavior.

Consider a music streaming service: Does the user’s trust plummet when a playlist recommendation feels “random,” lacking any discernible connection to their past listening habits or stated preferences? This perceived randomness is a direct challenge to the user’s expectation of intelligent curation and a breach of the implicit promise that the AI understands their taste. Similarly, in a photo management application, do users experience significant frustration when an AI photo-tagging feature consistently misidentifies a cherished family member? This error is more than a technical glitch; it strikes at the heart of accuracy, personalization, and even emotional connection.

These pain points are vivid signals indicating precisely where a well-placed, clear, and concise explanation is necessary. Such explanations serve as crucial repair mechanisms, mending a breach of trust that, if left unaddressed, can lead to user abandonment.

The power of AI journey mapping lies in its ability to move us beyond simply explaining the final output of an AI system. While understanding what the AI produced is important, it’s often insufficient. Instead, this process compels us to focus on explaining the process at critical moments. This means addressing:

• Why a particular output was generated: Was it due to specific input data? A particular model architecture?
• What factors influenced the AI’s decision: Were certain features weighted more heavily?
• How the AI arrived at its conclusion: Can we offer a simplified, analogous explanation of its internal workings?
• What assumptions the AI made: Were there implicit understandings of the user’s intent or data that need to be surfaced?
• What the limitations of the AI are: Clearly communicating what the AI cannot do, or where its accuracy might waver, builds realistic expectations.

AI journey mapping transforms the abstract concept of XAI into a practical, actionable framework for UX practitioners. It enables us to move beyond theoretical discussions of explainability and instead pinpoint the exact moments where user trust is at stake, providing the necessary insights to build AI experiences that are powerful, transparent, understandable, and trustworthy.

Ultimately, research is how we uncover the unknowns. Your team might be debating how to explain why a loan was denied, but research might reveal that users are far more concerned with understanding how their data was used in the first place. Without research, we are simply guessing what our users are wondering.

Collaborating On The Design (How to Explain Your AI)

Once research has identified what to explain, the collaborative loop with design begins. Designers can prototype the patterns we discussed earlier—the “Because” statement, the interactive sliders—and researchers can put those designs in front of users to see if they hold up.

Targeted Usability & Comprehension Testing: We can design research studies that specifically test the XAI components. We don’t just ask, “*Is this easy to use?*” We ask, “*After seeing this, can you tell me in your own words why the system recommended this product?*” or “*Show me what you would do to see if you could get a different result.*” The goal here is to measure comprehension and actionability, alongside usability.

Measuring Trust Itself: We can use simple surveys and rating scales before and after an explanation is shown. For instance, we can ask a user on a 5-point scale, “*How much do you trust this recommendation?*” before they see the “Because” statement, and then ask them again afterward. This provides quantitative data on whether our explanations are actually moving the needle on trust.

This process creates a powerful, iterative loop. Research findings inform the initial design. That design is then tested, and the new findings are fed back to the design team for refinement. Maybe the “Because” statement was too jargony, or the “What-If” slider was more confusing than empowering. Through this collaborative validation, we ensure that the final explanations are technically accurate, genuinely understandable, useful, and trust-building for the people using the product.

The Goldilocks Zone Of Explanation

A critical word of caution: it is possible to over-explain. As in the fairy tale, where Goldilocks sought the porridge that was ‘just right’, the goal of a good explanation is to provide the right amount of detail—not too much and not too little. Bombarding a user with every variable in a model will lead to cognitive overload and can actually decrease trust. The goal is not to make the user a data scientist.

One solution is progressive disclosure.

1. Start with the simple. Lead with a concise “Because” statement. For most users, this will be enough.
2. Offer a path to detail. Provide a clear, low-friction link like “Learn More” or “See how this was determined.”
3. Reveal the complexity. Behind that link, you can offer the interactive sliders, the visualizations, or a more detailed list of contributing factors.

This layered approach respects user attention and expertise, providing just the right amount of information for their needs. Let’s imagine you’re using a smart home device that recommends optimal heating based on various factors.

Start with the simple: “*Your home is currently heated to 72 degrees, which is the optimal temperature for energy savings and comfort.*”

Offer a path to detail: Below that, a small link or button: “Why is 72 degrees optimal?“

Reveal the complexity: Clicking that link could open a new screen showing:

• Interactive sliders for outside temperature, humidity, and your preferred comfort level, demonstrating how these adjust the recommended temperature.
• A visualization of energy consumption at different temperatures.
• A list of contributing factors like “Time of day,” “Current outside temperature,” “Historical energy usage,” and “Occupancy sensors.”

It’s effective to combine multiple XAI methods and this Goldilocks Zone of Explanation pattern, which advocates for progressive disclosure, implicitly encourages this. You might start with a simple “Because” statement (Pattern 1) for immediate comprehension, and then offer a “Learn More” link that reveals a “What-If” Interactive (Pattern 2) or a “Push-and-Pull Visual” (Pattern 4) for deeper exploration.

For instance, a loan application system could initially state the primary reason for denial (feature importance), then allow the user to interact with a “What-If” tool to see how changes to their income or debt would alter the outcome (counterfactuals), and finally, provide a detailed “Push-and-Pull” chart (value-based explanation) to illustrate the positive and negative contributions of all factors. This layered approach allows users to access the level of detail they need, when they need it, preventing cognitive overload while still providing comprehensive transparency.

Determining which XAI tools and methods to use is primarily a function of thorough UX research. Mental model interviews and AI journey mapping are crucial for pinpointing user needs and pain points related to AI understanding and trust. Mental model interviews help uncover user misconceptions about how the AI works, indicating areas where fundamental explanations (like feature importance or local explanations) are needed. AI journey mapping, on the other hand, identifies critical moments of confusion or distrust in the user’s interaction with the AI, signaling where more granular or interactive explanations (like counterfactuals or value-based explanations) would be most beneficial to rebuild trust and provide agency.

Ultimately, the best way to choose a technique is to let user research guide your decisions, ensuring that the explanations you design directly address actual user questions and concerns, rather than simply offering technical details for their own sake.

XAI for Deep Reasoning Agents

Some of the newest AI systems, known as deep reasoning agents, produce an explicit “chain of thought” for every complex task. They do not merely cite sources; they show the logical, step-by-step path they took to arrive at a conclusion. While this transparency provides valuable context, a play-by-play that spans several paragraphs can feel overwhelming to a user simply trying to complete a task.

The principles of XAI, especially the Goldilocks Zone of Explanation, apply directly here. We can curate the journey, using progressive disclosure to show only the final conclusion and the most salient step in the thought process first. Users can then opt in to see the full, detailed, multi-step reasoning when they need to double-check the logic or find a specific fact. This approach respects user attention while preserving the agent’s full transparency.

Next Steps: Empowering Your XAI Journey

Explainability is a fundamental pillar for building trustworthy and effective AI products. For the advanced practitioner looking to drive this change within their organization, the journey extends beyond design patterns into advocacy and continuous learning.

To deepen your understanding and practical application, consider exploring resources like the AI Explainability 360 (AIX360) toolkit from IBM Research or Google’s What-If Tool, which offer interactive ways to explore model behavior and explanations. Engaging with communities like the Responsible AI Forum or specific research groups focused on human-centered AI can provide invaluable insights and collaboration opportunities.

Finally, be an advocate for XAI within your own organization. Frame explainability as a strategic investment. Consider a brief pitch to your leadership or cross-functional teams:

“By investing in XAI, we’ll go beyond building trust; we’ll accelerate user adoption, reduce support costs by empowering users with understanding, and mitigate significant ethical and regulatory risks by exposing potential biases. This is good design and smart business.”

Your voice, grounded in practical understanding, is crucial in bringing AI out of the black box and into a collaborative partnership with users.

(yk)

The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) has become ingrained in internet browsing since personal computers gained momentum in the consumer electronics market. For nearly as long as people have been going online, web developers have sought ways to block spam bots.

The CAPTCHA service distinguishes between human and bot activity to keep bots out. Unfortunately, its methods are less than precise. In trying to protect humans, developers have made much of the web inaccessible to people with disabilities.

Authentication methods, such as CAPTCHA, typically use image classification, puzzles, audio samples, or click-based tests to determine whether the user is human. While the types of challenges are well-documented, their logic is not public knowledge. People can only guess what it takes to “prove” they are human.

What Is CAPTCHA?

A CAPTCHA is a reverse Turing test that takes the form of a challenge-response test. For example, if it instructs users to “select all images with stairs,” they must pick the stairs out from railings, driveways, and crosswalks. Alternatively, they may be asked to enter the text they see, add the sum of dice faces, or complete a sliding puzzle.

Image-based CAPTCHAs are responsible for the most frustrating shared experiences internet users have — deciding whether to select a square when only a small sliver of the object in question is in it.

Regardless of the method, a computer or algorithm ultimately determines whether the test-taker is human or machine. This authentication service has spawned many offshoots, including reCAPTCHA and hCAPTCHA. It has even led to the creation of entire companies, such as GeeTest and Arkose Labs. The Google-owned automated system reCAPTCHA requires users to click a checkbox labeled “I’m not a robot” for authentication. It runs an adaptive analysis in the background to assign a risk score. hCAPTCHA is an image-classification-based alternative.

Other authentication methods include multi-factor authentication (MFA), QR codes, temporary personal identification numbers (PINs), and biometrics. They do not follow the challenge-response formula, but serve fundamentally similar purposes.

These offshoots are intended to be better than the original, but they often fail to meet modern accessibility standards. Take hCaptcha, for instance, which uses a cookie to let you bypass the challenge-response test entirely. It’s a great idea in theory, but it doesn’t work in practice.

You’re supposed to receive a one-time code via email that you send to a specific number over SMS. Users report receiving endless error messages, forcing them to complete the standard text CAPTCHA. This is only available if the site explicitly enabled it during configuration. If it is not set up, you must complete an image challenge that does not recognize screen readers.

Even when the initial process works, subsequent authentication relies on a third-party cross-site cookie, which most browsers block automatically. Also, the code expires after a short period, so you have to redo the entire process if it takes you too long to move on to the next step.

Why Do Teams Use CAPTCHA And Similar Authentication Methods?

CAPTCHA is common because it is easy to set up. Developers can program it to appear, and it conducts the test automatically. This way, they can focus on more important matters while still preventing spam, fraud, and abuse. These tools are supposed to make it easier for humans to use the internet safely, but they often keep real people from logging in.

These tests result in a poor user experience overall. One study found users wasted over 819 million hours on over 512 billion reCAPTCHA v2 sessions as of 2023. Despite it all, bots prevail. Machine learning models can solve text-based CAPTCHA within fractions of a second with over 97% accuracy.

A 2024 study on Google’s reCAPTCHA v2 — which is still widely used despite the rollout of reCAPTCHA v3 — found bots can solve image classification CAPTCHA with up to 100% accuracy, depending on the object they are tasked with identifying. The researchers used a free, open-source model, which means that bad actors could easily replicate their work.

Why Should Web Developers Stop Using CAPTCHA?

Authentication methods like CAPTCHA have an accessibility problem. Machine learning advances forced these services to grow increasingly complex. Even still, they are not foolproof. Bots get it right more than people do. Research shows they can complete reCAPTCHA within 17.5 seconds, achieving 85% accuracy. Humans take longer and are less accurate.

Many people fail CAPTCHA tests and have no idea what they did wrong. For example, a prompt instructing users to “select all squares with traffic lights” seems simple enough, but it gets complicated if a sliver of the pole is in another square. Should they select that box, or is that what an algorithm would do?

Although bot capabilities have grown by magnitudes, humans have remained the same. As tests get progressively more difficult, they feel less inclined to attempt them. One survey shows nearly 59% of people will stop using a product after several bad experiences. If authentication is too cumbersome or complex, they might stop using the website entirely.

People can fail these tests for various reasons, including technical ones. If they block third-party cookies, have a local proxy running, or have not updated their browser in a while, they may keep failing, regardless of how many times they try.

Authentication Issues With CAPTCHA

Due to the reasons mentioned above, most types of CAPTCHA are inherently inaccessible. This is especially true for people with disabilities, as these challenge-response tests were not designed with their needs in mind. Some of the common issues include the following:

Issues Related To Visuals And Screen Reader Use

Screen readers cannot read standard visual CAPTCHAs, such as the distorted text test, since the jumbled, contorted words are not machine-readable. The image classification and sliding puzzle methods are similarly inaccessible.

In one WebAIM survey conducted from 2023 to 2024, screen reader users agreed CAPTCHA was the most problematic item, ranking it above ambiguous links, unexpected screen changes, missing alt text, inaccessible search, and lack of keyboard accessibility. Its spot at the top has remained largely unchanged for over a decade, illustrating its history of inaccessibility.

Issues Related To Hearing and Audio Processing

Audio CAPTCHAs are relatively uncommon because web development best practices advise against autoplay audio and emphasize the importance of user controls. However, audio CAPTCHAs still exist. People who are hard of hearing or deaf may encounter a barrier when attempting these tests. Even with assistive technology, the intentional audio distortion and background noise make these samples challenging for individuals with auditory processing disorders to comprehend.

Issues Related To Motor And Dexterity

Tests requiring motor and dexterity skills can be challenging for those with motor impairments or physical disabilities. For example, someone with a hand tremor may find the sliding puzzles difficult. Also, the image classification tests that load more images until none that fit the criteria are left may pose a challenge.

Issues Related To Cognition And Language

As CAPTCHAs become increasingly complex, some developers are turning to tests that require a combination of creative and critical thinking. Those that require users to solve a math problem or complete a puzzle can be challenging for people with dyslexia, dyscalculia, visual processing disorders, or cognitive impairments.

Why Assistive Technology Won’t Bridge The Gap

CAPTCHAs are intentionally designed for humans to interpret and solve, so assistive technology like screen readers and hands-free controls may be of little help. ReCAPTCHA in particular poses an issue because it analyzes background activity. If it flags the accessibility devices as bots, it will serve a potentially inaccessible CAPTCHA.

Even if this technology could bridge the gap, web developers shouldn’t expect it to. Industry standards dictate that they should follow universal design principles to make their websites as accessible and functional as possible.

CAPTCHA’s accessibility issues could be forgiven if it were an effective security tool, but it is far from foolproof since bots get it right more than humans do. Why keep using a method that is ineffective and creates barriers for people with disabilities?

“

There are better alternatives.

Principles For Accessible Authentication

The idea that humans should consistently outperform algorithms is outdated. Better authentication methods exist, such as multifactor authentication (MFA). The two-factor authentication market will be worth an estimated $26.7 billion by 2027, underscoring its popularity. This tool is more effective than a CAPTCHA because it prevents unauthorized access, even with legitimate credentials.

Ensure your MFA technique is accessible. Instead of having website visitors transcribe complex codes, you should send push notifications or SMS messages. Rely on the verification code autofill to automatically capture and enter the code. Alternatively, you can introduce a “remember this device” feature to skip authentication on trusted devices.

Apple’s two-factor authentication approach is designed this way. A trusted device automatically displays a six-digit verification code, so they do not have to search for it. When prompted, iPhone users can tap the suggestion that appears above their mobile keyboard for autofill.

Single sign-on is another option. This session and user authentication service allows people to log in to multiple websites or applications with a single set of login credentials, minimizing the need for repeated identity verification.

One-time-use “magic links” are an excellent alternative to reCAPTCHA and temporary PINs. Rather than remembering a code or solving a puzzle, the user clicks on a button. Avoid imposing deadlines because, according to WCAG Success Criterion 2.2.3, users should not face time limits since those with disabilities may need more time to complete specific actions.

Alternatively, you could use Cloudflare Turnstile. It authenticates without showing a CAPTCHA, and most people never even have to check a box or hit a button. The software works by issuing a small JavaScript challenge behind the scenes to automatically differentiate between bots and humans. Cloudflare Turnstile can be embedded into any website, making it an excellent alternative to standard classification tasks.

Testing And Evaluation Of Accessible Authentication Designs

Testing and evaluating your accessible alternative authentication methods is essential. Many designs look good on paper but do not work in practice. If possible, gather feedback from actual users. An open beta may be an effective way to maximize visibility.

Remember, general accessibility considerations do not only apply to people with disabilities. They also include those who are neurodivergent, lack access to a mobile device, or use assistive technology. Ensure your alternative designs consider these individuals.

Realistically, you cannot create a perfect system since everyone is unique. Many people struggle to follow multistep processes, solve equations, process complex instructions, or remember passcodes. While universal web design principles can improve flexibility, no single solution can meet everyone’s needs.

Regardless of the authentication technique you use, you should present users with multiple authentication options upfront. They know their capabilities best, so let them decide what to use instead of trying to over-engineer a solution that works for every edge case.

“

Address The Accessibility Problem With Design Changes

A person with hand tremors may be unable to complete a sliding puzzle, while someone with an audio processing disorder may have trouble with distorted audio samples. However, you cannot simply replace CAPTCHAs with alternatives because they are often equally inaccessible.

QR codes, for example, may be difficult to scan for those with reduced fine motor control. People who are visually impaired may struggle to find it on the screen. Similarly, biometrics can pose an issue for people with facial deformities or a limited range of motion. Addressing the accessibility problem requires creative thinking.

You can start by visiting the Web Accessibility Initiative’s accessibility tutorials for developers to better understand universal design. Although these tutorials focus more on content than authentication, you can still use them to your advantage. The W3C Group Draft Note on the Inaccessibility of CAPTCHA provides more relevant guidance.

Getting started is as easy as researching best practices. Understanding the basics is essential because there is no universal solution for accessible web design. If you want to optimize accessibility, consider sourcing feedback from the people who actually visit your website.

Further Reading

• “The CAPTCHA: Perspectives and Challenges,” Darko Brodić and Alessia Amelio
• “Designing Accessible Text Over Images: Best Practices, Techniques, And Resources,” Hannah Milan
• “In Search Of The Best CAPTCHA,” David Bushell
• “WCAG 3.0’s Proposed Scoring Model: A Shift in Accessibility Evaluation,” Mikhail Prosmitskiy

(yk)

No design exists in isolation. As designers, we often imagine specific situations in which people will use our product. It might be indeed quite common — but there will also be other — urgent, frustrating, stressful situations. And they are the ones that we rarely account for.

So how do we account for such situations? How can we help people use our products while coping with stress — without adding to their cognitive load? Let’s take a closer look.

Study Where Your Product Fits Into People’s Lives

When designing digital products, sometimes we get a bit too attached to our shiny new features and flows — often forgetting the messy reality in which these features and flows have to neatly fit. And often it means 10s of other products, 100s of other tabs, and 1000s of other emails.

If your customers have to use a slightly older machine, with a smallish 22” screen and a lot of background noise, they might use your product differently than you might have imagined, e.g., splitting the screen into halves to see both views at the same time (as displayed above).

Chances are high that our customers will use our product while doing something else, often with very little motivation, very little patience, plenty of urgent (and way more important) problems, and an unhealthy dose of stress. And that’s where our product must do its job well.

What Is Stress?

What exactly do we mean when we talk about “stress”? As H Locke noted, stress is the body’s response to a situation it cannot handle. There is a mismatch between what people can control, their own skills, and the challenge in front of them.

If the situation seems unmanageable and the goal they want to achieve moves further away, it creates an enormous sense of failing. It can be extremely frustrating and demotivating.

Some failures have a local scope, but many have a far-reaching impact. Many people can’t choose the products they have to use for work, so when a tool fails repeatedly, causes frustration, or is unreliable, it affects the worker, the work, the colleagues, and processes within the organization. Fragility has a high cost — and so does frustration.

How Stress Influences User Interactions

It’s not a big surprise: stress disrupts attention, memory, cognition, and decision-making. It makes it difficult to prioritize and draw logical conclusions. In times of stress, we rely on fast, intuitive judgments, not reasoning. Typically, it leads to instinctive responses based on established habits.

When users are in an emergency, they experience cognitive tunneling — it’s a state when their peripheral vision narrows, reading comprehension drops, fine motor skills deteriorate, and patience drops sharply. Under pressure, people often make decisions hastily, while others get entirely paralyzed. Either way is a likely path to mistakes — often irreversible ones and often without time for extensive deliberations.

Ideally, these decisions would be made way ahead of time — and then suggested when needed. But in practice, it’s not always possible. As it turns out, a good way to help people deal with stress is by providing order around how they manage it.

Single-Tasking Instead Of Multi-Tasking

People can’t really multi-task, especially in very stressful situations or emergencies. Especially with a big chunk of work in front of them, people need some order to make progress, reliably. That’s why simpler pages usually work better than one big complex page.

Order means giving users a clear plan of action to complete a task. No distractions, no unnecessary navigation. We ask simple questions and prompt simple actions, one after another, one thing at a time.

An example of the plan is the Task List Pattern, invented by fine folks at Gov.uk. We break a task into a sequence of sub-tasks, describe them with actionable labels, assign statuses, and track progress.

To support accuracy, we revise default settings, values, presets, and actions. Also, the order of actions and buttons matters, so we put high-priority things first to make them easier to find. Then we add built-in safeguards (e.g., Undo feature) to prevent irreversible errors.

Supporting In Emergencies

The most effective help during emergencies is to help people deal with the situation in a well-defined and effective way. That means being prepared for and designing an emergency mode, e.g., to activate instant alerts on emergency contacts, distribute pre-assigned tasks, and establish a line of communication.

Rediplan App by Australian Red Cross is an emergency plan companion that encourages citizens to prepare their documents and belongings with a few checklists and actions — including key contracts, meeting places, and medical information, all in one place.

Just Enough Friction

Not all stress is equally harmful, though. As Krystal Higgins points out, if there is not enough friction when onboarding new users and the experience is too passive or users are hand-held even through the most basic tasks, you risk that they won’t realize the personal value they gain from the experience and, ultimately, lose interest.

Design And Test For Stress Cases

Stress cases aren’t edge cases. We can’t predict the emotional state in which a user comes to our site or uses our product. A person looking for specific information on a hospital website or visiting a debt management website, for example, is most likely already stressed. Now, if the interface is overwhelming, it will only add to their cognitive load.

Stress-testing your product is critical to prevent this from happening. It’s useful to set up an annual day to stress test your product and refine emergency responses. It could be as simple as running content testing, or running tests in a real, noisy, busy environment where users actually work — at peak times.

And in case of emergencies, we need to check if fallbacks work as expected and if the current UX of the product helps people manage failures and exceptional situations well enough.

Wrapping Up

Emergencies will happen eventually — it’s just a matter of time. With good design, we can help mitigate risk and control damage, and make it hard to make irreversible mistakes. At its heart, that’s what good UX is exceptionally good at.

Key Takeaways

People can’t multitask, especially in very stressful situations.

• Stress disrupts attention, memory, cognition, decision-making.
• Also, it’s difficult to prioritize and draw logical conclusions.
• Under stress, we rely on fast, intuitive judgments — not reasoning.
• It leads to instinctive responses based on established habits.

Goal: Design flows that support focus and high accuracy.

• Start with better default settings, values, presets, and actions.
• High-priority first: order of actions and buttons matters.
• Break complex tasks down into a series of simple steps (10s–30s each).
• Add built-in safeguards to prevent irreversible errors (Undo).

Shift users to single-tasking: ask for one thing at a time.

• Simpler pages might work better than one complex page.
• Suggest a step-by-step plan of action to follow along.
• Consider, design, and test flows for emergency responses ahead of time.
• Add emergency mode for instant alerts and task assignments.

Meet “How To Measure UX And Design Impact”

You can find more details on UX Strategy in 🪴 Measure UX & Design Impact (8h), a practical guide for designers and UX leads to measure and show your UX impact on business. Use the code 🎟 IMPACT to save 20% off today. Jump to the details.

Useful Resources

• “Designing The SOS Emergency System”, by Ritik Jayy
• “Designing For Crisis”, by Eric Meyer
• “Designing For Stressed Out Users” (Series), by H Locke
• Designing For Stress (Podcast), by Katie Swindler
• Designing For Edge Cases and Exceptions, by yours truly
• Design For Real Life, by Sara Wachter-Boettcher, Eric Mayer
• “Optimal Stress Levels For Onboarding, by Krystal Higgins

Further Reading

• “How To Minimize The Environmental Impact Of Your Website”, James Chudley
• “AI In UX: Achieve More With Less”, Paul Boag
• “How To Make Your UX Research Hard To Ignore”, Vitaly Friedman
• “From Prompt To Partner: Designing Your Custom AI Assistant,” Lyndon Cerejo

(yk)

For years, “UX strategy” felt like a confusing, ambiguous, and overloaded term to me. To me, it was some sort of a roadmap or a “grand vision”, with a few business decisions attached to it. And looking back now, I realize that I was wrong all along.

UX Strategy isn’t a goal; it’s a journey towards that goal. A journey connecting where UX is today with a desired future state of UX. And as such, it guides our actions and decisions, things we do and don’t do. And its goal is very simple: to maximize our chances of success while considering risks, bottlenecks and anything that might endanger the project.

Let’s explore the components of UX strategy, and how it works with product strategy and business strategy to deliver user value and meet business goals.

Strategy vs. Goals vs. Plans

When we speak about strategy, we often speak about planning and goals — but they are actually quite different. While strategy answers “what” we’re doing and “why”, planning is about “how” and “when” we’ll get it done. And the goal is merely a desired outcome of that entire journey.

• Goals establish a desired future outcome,
• That outcome typically represents a problem to solve,
• Strategy shows a high-level solution for that problem,
• Plan is a detailed set of low-level steps for getting the solution done.

A strong strategy requires making conscious, and oftentimes tough, decisions about what we will do — and just as importantly, what we will not do, and why.

Business Strategy

UX strategy doesn’t live in isolation. It must inform and support product strategy and be aligned with business strategy. All these terms are often slightly confusing and overloaded, so let’s clear it up.

At the highest level, business strategy is about the distinct choices executives make to set the company apart from its competitors. They shape the company’s positioning, objectives, and (most importantly!) competitive advantage.

Typically, this advantage is achieved in two ways: through lower prices (cost leadership) or through differentiation. The latter part isn’t about being different, but rather being perceived differently by the target audience. And that’s exactly where UX impact steps in.

In short, business strategy is:

• A top-line vision, basis for core offers,
• Shapes positioning, goals, competitive advantage,
• Must always adapt to the market to keep a competitive advantage.

Product Strategy

Product strategy is how a high-level business direction is translated into a unique positioning of a product. It defines what the product is, who its users are, and how it will contribute to the business’s goals. It’s also how we bring a product to market, drive growth, and achieve product-market fit.

In short, product strategy is:

• Unique positioning and value of a product,
• How to establish and keep a product in the marketplace,
• How to keep competitive advantage of the product.

UX Strategy

UX strategy is about shaping and delivering product value through UX. Good UX strategy always stems from UX research and answers to business needs. It established what to focus on, what our high-value actions are, how we’ll measure success, and — quite importantly — what risks we need to mitigate.

Most importantly, it’s not a fixed plan or a set of deliverables; it’s a guide that informs our actions, but also must be prepared to change when things change.

In short, UX strategy is:

• How we shape and deliver product value through UX,
• Priorities, focus + why, actions, metrics, risks,
• Isn’t a roadmap, intention or deliverables.

Six Key Components of UX Strategy

The impact of good UX typically lives in differentiation mentioned above. Again, it’s not about how “different” our experience is, but the unique perceived value that users associate with it. And that value is a matter of a clear, frictionless, accessible, fast, and reliable experience wrapped into the product.

I always try to include 6 key components in any strategic UX work so we don’t end up following a wrong assumption that won’t bring any impact:

1. Target goal
   The desired, improved future state of UX.
2. User segments
   Primary users that we are considering.
3. Priorities
   What we will and, crucially, what we will not do, and why.
4. High-value actions
   How we drive value and meet user and business needs.
5. Feasibility
   Realistic assessment of people, processes, and resources.
6. Risks
   Bottlenecks, blockers, legacy constraints, big unknowns.

It’s worth noting that it’s always dangerous to be designing a product with everybody in mind. As Jamie Levy noted, by being very broad too early, we often reduce the impact of our design and messaging. It’s typically better to start with a specific, well-defined user segment and then expand, rather than the other way around.

Practical Example (by Alin Buda)

UX strategy doesn’t have to be a big 40-page long PDF report or a Keynote presentation. A while back, Alin Buda kindly left a comment on one of my LinkedIn posts, giving a great example of what a concise UX strategy could look like:

UX Strategy (for Q4)

Our UX strategy is to focus on high-friction workflows for expert users, not casual usability improvements. Why? Because retention in this space is driven by power-user efficiency, and that aligns with our growth model.

To succeed, we’ll design workflow accelerators and decision-support tools that will reduce time-on-task. As a part of it, we’ll need to redesign legacy flows in the Crux system. We won’t prioritize UI refinements or onboarding tours, because it doesn’t move the needle in this context.

What I like most about this example is just how concise and clear it is. Getting to this level of clarity takes quite a bit of time, but it creates a very precise overview of what we do, what we don’t do, what we focus on, and how we drive value.

Wrapping Up

The best path to make a strong case with senior leadership is to frame your UX work as a direct contributor to differentiation. This isn’t just about making things look different; it’s about enhancing the perceived value.

A good strategy ties UX improvements to measurable business outcomes. It doesn’t speak about design patterns, consistency, or neatly organized components. Instead, it speaks the language of product and business strategy: OKRs, costs, revenue, business metrics, and objectives.

Design can succeed without a strategy. In the wise words of Sun Tzu, strategy without tactics is the slowest route to victory. And tactics without strategy are the noise before defeat.

Meet “How To Measure UX And Design Impact”

You can find more details on UX Strategy in 🪴 Measure UX & Design Impact (8h), a practical guide for designers and UX leads to measure and show your UX impact on business. Use the code 🎟 IMPACT to save 20% off today. Jump to the details.

Useful Resources

• “UX Strategy: Definition and Components”, Sarah Gibbons, Anna Kaley
• “UX Strategy: Study Guide”, Sarah Gibbons, Anna Kaley
• What Goes Into a Proactive UX Strategy (video), Jared Spool
• “How To Develop An Effective UX Strategy”, Chloé Garnham
• The Little Book Of Strategy (free PDF), Peter Bihr
• “Enterprise UX Strategy”, Cassandra Naji
• “UX Strategy Guide” + Blueprint (Template), Alex Souza
• Product Strategy Playbooks
• UX Strategy, Jaime Levy

(yk)

I have made a lot of mistakes with AI over the past couple of years. I have wasted hours trying to get it to do things it simply cannot do. I have fed it terrible prompts and received terrible output. And I have definitely spent more time fighting with it than I care to admit.

But I have also discovered that when you stop treating AI like magic and start treating it like what it actually is (a very enthusiastic intern with zero life experience), things start to make more sense.

Let me share what I have learned from working with AI on real client projects across user research, design, development, and content creation.

How To Work With AI

Here is the mental model that has been most helpful for me. Treat AI like an intern with zero experience.

An intern fresh out of university has lots of enthusiasm and qualifications, but no real-world experience. You would not trust them to do anything unsupervised. You would explain tasks in detail. You would expect to review their work multiple times. You would give feedback and ask them to try again.

This is exactly how you should work with AI.

The Basics Of Prompting

I am not going to pretend to be an expert. I have just spent way too much time playing with this stuff because I like anything shiny and new. But here is what works for me.

• Define the role.
  Start with something like “Act as a user researcher” or “Act as a copywriter.” This gives the AI context for how to respond.
• Break it into steps.
  Do not just say “Analyze these interview transcripts.” Instead, say “I want you to complete the following steps. One, identify recurring themes. Two, look for questions users are trying to answer. Three, note any objections that come up. Four, output a summary of each.”
• Define success.
  Tell it what good looks like. “I am looking for a report that gives a clear indication of recurring themes and questions in a format I can send to stakeholders. Do not use research terminology because they will not understand it.”
• Make it think.
  Tell it to think deeply about its approach before responding. Get it to create a way to test for success (known as a rubric) and iterate on its work until it passes that test.

Here is a real prompt I use for online research:

Act as a user researcher. I would like you to carry out deep research online into [brand name]. In particular, I would like you to focus on what people are saying about the brand, what the overall sentiment is, what questions people have, and what objections people mention. The goal is to create a detailed report that helps me better understand the brand perception.

Think deeply about your approach before carrying out the research. Create a rubric for the report to ensure it is as useful as possible. Keep iterating until the report scores extremely high on the rubric. Only then, output the report.

That second paragraph (the bit about thinking deeply and creating a rubric), I basically copy and paste into everything now. It is a universal way to get better output.

Learn When To Trust It

You should never fully trust AI. Just like you would never fully trust an intern you have only just met.

To begin with, double-check absolutely everything. Over time, you will get a sense of when it is losing its way. You will spot the patterns. You will know when to start a fresh conversation because the current one has gone off the rails.

But even after months of working with it daily, I still check its work. I still challenge it. I still make it cite sources and explain its reasoning.

The key is that even with all that checking, it is still faster than doing it yourself. Much faster.

Using AI For User Research

This is where AI has genuinely transformed my work. I use it constantly for five main things.

Online Research

I love AI for this. I can ask it to go and research a brand online. What people are saying about it, what questions they have, what they like, and what frustrates them. Then do the same for competitors and compare.

This would have taken me days of trawling through social media and review sites. Now it takes minutes.

I recently did this for an e-commerce client. I wanted to understand what annoyed people about the brand and what they loved. I got detailed insights that shaped the entire conversion optimization strategy. All from one prompt.

Analyzing Interviews And Surveys

I used to avoid open-ended questions in surveys. They were such a pain to review. Now I use them all the time because AI can analyze hundreds of text responses in seconds.

For interviews, I upload the transcripts and ask it to identify recurring themes, questions, and requests. I always get it to quote directly from the transcripts so I can verify it is not making things up.

The quality is good. Really good. As long as you give it clear instructions about what you want.

Making Sense Of Data

I am terrible with spreadsheets. Put me in front of a person and I can understand them. Put me in front of data, and my eyes glaze over.

AI has changed that. I upload spreadsheets to ChatGPT and just ask questions. “What patterns do you see?” “Can you reformat this?” “Show me this data in a different way.”

Microsoft Clarity now has Copilot built in, so you can ask it questions about your analytics data. Triple Whale does the same for e-commerce sites. These tools are game changers if you struggle with data like I do.

Research Projects

This is probably my favorite technique. In ChatGPT and Claude, you can create projects. In other tools, they are called spaces. Think of them as self-contained folders where everything you put in is available to every conversation in that project.

When I start working with a new client, I create a project and throw everything in. Old user research. Personas. Survey results. Interview transcripts. Documentation. Background information. Site copy. Anything I can find.

Then I give it custom instructions. Here is one I use for my own business:

Act as a business consultant and marketing strategy expert with good copywriting skills. Your role is to help me define the future of my UX consultant business and better articulate it, especially via my website. When I ask for your help, ask questions to improve your answers and challenge my assumptions where appropriate.

I have even uploaded a virtual board of advisors (people I wish I had on my board) and asked AI to research how they think and respond as they would.

Now I have this project that knows everything about my business. I can ask it questions. Get it to review my work. Challenge my thinking. It is like having a co-worker who never gets tired and has a perfect memory.

I do this for every client project now. It is invaluable.

Creating Personas

AI has reinvigorated my interest in personas. I had lost heart in them a bit. They took too long to create, and clients always said they already had marketing personas and did not want to pay to do them again.

Now I can create what I call functional personas. Personas that are actually useful to people who work in UX. Not marketing fluff about what brands people like, but real information about what questions they have and what tasks they are trying to complete.

I upload all my research to a project and say:

Act as a user researcher. Create a persona for [audience type]. For this persona, research the following information: questions they have, tasks they want to complete, goals, states of mind, influences, and success metrics. It is vital that all six criteria are addressed in depth and with equal vigor.

The output is really good. Detailed. Useful. Based on actual data rather than pulled out of thin air.

Here is my challenge to anyone who thinks AI-generated personas are somehow fake. What makes you think your personas are so much better? Every persona is a story of a hypothetical user. You make judgment calls when you create personas, too. At least AI can process far more information than you can and is brilliant at pattern recognition.

My only concern is that relying too heavily on AI could disconnect us from real users. We still need to talk to people. We still need that empathy. But as a tool to synthesize research and create reference points? It is excellent.

“

Using AI For Design And Development

Let me start with a warning. AI is not production-ready. Not yet. Not for the kind of client work I do, anyway.

Three reasons why:

1. It is slow if you want something specific or complicated.
2. It can be frustrating because it gets close but not quite there.
3. And the quality is often subpar. Unpolished code, questionable design choices, that kind of thing.

But that does not mean it is not useful. It absolutely is. Just not for final production work.

Functional Prototypes

If you are not too concerned with matching a specific design, AI can quickly prototype functionality in ways that are hard to match in Figma. Because Figma is terrible at prototyping functionality. You cannot even create an active form field in a Figma prototype. It’s the biggest thing people do online other than click links — and you cannot test it.

Tools like Relume and Bolt can create quick functional mockups that show roughly how things work. They are great for non-designers who just need to throw together a prototype quickly. For designers, they can be useful for showing developers how you want something to work.

But you can spend ages getting them to put a hamburger menu on the right side of the screen. So use them for quick iteration, not pixel-perfect design.

Small Coding Tasks

I use AI constantly for small, low-risk coding work. I am not a developer anymore. I used to be, back when dinosaurs roamed the earth, but not for years.

AI lets me create the little tools I need. A calculator that calculates the ROI of my UX work. An app for running top task analysis. Bits of JavaScript for hiding elements on a page. WordPress plugins for updating dates automatically.

Just before running my workshop on this topic, I needed a tool to create calendar invites for multiple events. All the online services wanted £16 a month. I asked ChatGPT to build me one. One prompt. It worked. It looked rubbish, but I did not care. It did what I needed.

If you are a developer, you should absolutely be using tools like Cursor by now. They are invaluable for pair programming with AI. But if you are not a developer, just stick with Claude or Bolt for quick throwaway tools.

Reviewing Existing Services

There are some great tools for getting quick feedback on existing websites when budget and time are tight.

If you need to conduct a UX audit, Wevo Pulse is an excellent starting point. It automatically reviews a website based on personas and provides visual attention heatmaps, friction scores, and specific improvement recommendations. It generates insights in minutes rather than days.

Now, let me be clear. This does not replace having an experienced person conduct a proper UX audit. You still need that human expertise to understand context, make judgment calls, and spot issues that AI might miss. But as a starting point to identify obvious problems quickly? It is a great tool. Particularly when budget or time constraints mean a full audit is not on the table.

For e-commerce sites, Baymard has UX Ray, which analyzes flaws based on their massive database of user research.

Checking Your Designs

Attention Insight has taken thousands of hours of eye-tracking studies and trained AI on it to predict where people will look on a page. It has about 90 to 96 percent accuracy.

You upload a screenshot of your design, and it shows you where attention is going. Then you can play around with your imagery and layout to guide attention to the right place.

It is great for dealing with stakeholders who say, “People won’t see that.” You can prove they will. Or equally, when stakeholders try to crowd the interface with too much stuff, you can show them attention shooting everywhere.

I use this constantly. Here is a real example from a pet insurance company. They had photos of a dog, cat, and rabbit for different types of advice. The dog was far from the camera. The cat was looking directly at the camera, pulling all the attention. The rabbit was half off-frame. Most attention went to the cat’s face.

I redesigned it using AI-generated images, where I could control exactly where each animal looked. Dog looking at the camera. Cat looking right. Rabbit looking left. All the attention drawn into the center. Made a massive difference.

Creating The Perfect Image

I use AI all the time for creating images that do a specific job. My preferred tools are Midjourney and Gemini.

I like Midjourney because, visually, it creates stunning imagery. You can dial in the tone and style you want. The downside is that it is not great at following specific instructions.

So I produce an image in Midjourney that is close, then upload it to Gemini. Gemini is not as good at visual style, but it is much better at following instructions. “Make the guy reach here” or “Add glasses to this person.” I can get pretty much exactly what I want.

The other thing I love about Midjourney is that you can upload a photograph and say, “Replicate this style.” This keeps consistency across a website. I have a master image I use as a reference for all my site imagery to keep the style consistent.

Using AI For Content

Most clients give you terrible copy. Our job is to improve the user experience or conversion rate, and anything we do gets utterly undermined by bad copy.

I have completely stopped asking clients for copy since AI came along. Here is my process.

Build Everything Around Questions

Once I have my information architecture, I get AI to generate a massive list of questions users will ask. Then I run a top task analysis where people vote on which questions matter most.

I assign those questions to pages on the site. Every page gets a list of the questions it needs to answer.

Get Bullet Point Answers From Stakeholders

I spin up the content management system with a really basic theme. Just HTML with very basic formatting. I go through every page and assign the questions.

Then I go to my clients and say: “I do not want you to write copy. Just go through every page and bullet point answers to the questions. If the answer exists on the old site, copy and paste some text or link to it. But just bullet points.”

That is their job done. Pretty much.

Let AI Draft The Copy

Now I take control. I feed ChatGPT the questions and bullet points and say:

Act as an online copywriter. Write copy for a webpage that answers the question [question]. Use the following bullet points to answer that question: [bullet points]. Use the following guidelines: Aim for a ninth-grade reading level or below. Sentences should be short. Use plain language. Avoid jargon. Refer to the reader as you. Refer to the writer as us. Ensure the tone is friendly, approachable, and reassuring. The goal is to [goal]. Think deeply about your approach. Create a rubric and iterate until the copy is excellent. Only then, output it.

I often upload a full style guide as well, with details about how I want it to be written.

The output is genuinely good. As a first draft, it is excellent. Far better than what most stakeholders would give me.

Stakeholders Review And Provide Feedback

That goes into the website, and stakeholders can comment on it. Once I get their feedback, I take the original copy and all their comments back into ChatGPT and say, “Rewrite using these comments.”

Job done.

The great thing about this approach is that even if stakeholders make loads of changes, they are making changes to a good foundation. The overall quality still comes out better than if they started with a blank sheet.

It also makes things go smoother because you are not criticizing their content, where they get defensive. They are criticizing AI content.

Tools That Help

If your stakeholders are still giving you content, Hemingway Editor is brilliant. Copy and paste text in, and it tells you how readable and scannable it is. It highlights long sentences and jargon. You can use this to prove to clients that their content is not good web copy.

If you pay for the pro version, you get AI tools that will rewrite the copy to be more readable. It is excellent.

What This Means for You

Let me be clear about something. None of this is perfect. AI makes mistakes. It hallucinates. It produces bland output if you do not push it hard enough. It requires constant checking and challenging.

But here is what I know from two years of using this stuff daily. It has made me faster. It has made me better. It has freed me up to do more strategic thinking and less grunt work.

A report that would have taken me five days now takes three hours. That is not an exaggeration. That is real.

Overall, AI probably gives me a 25 to 33 percent increase in what I can do. That is significant.

Your value as a UX professional lies in your ideas, your questions, and your thinking. Not your ability to use Figma. Not your ability to manually review transcripts. Not your ability to write reports from scratch.

“

AI cannot innovate. It cannot make creative leaps. It cannot know whether its output is good. It cannot understand what it is like to be human.

That is where you come in. That is where you will always come in.

Start small. Do not try to learn everything at once. Just ask yourself throughout your day: Could I do this with AI? Try it. See what happens. Double-check everything. Learn what works and what does not.

Treat it like an enthusiastic intern with zero life experience. Give it clear instructions. Check its work. Make it try again. Challenge it. Push it further.

And remember, it is not going to take your job. It is going to change it. For the better, I think. As long as we learn to work with it rather than against it.

(yk)

In the early days of my career, I believed that nothing wins an argument more effectively than strong and unbiased research. Surely facts speak for themselves, I thought.

If I just get enough data, just enough evidence, just enough clarity on where users struggle — well, once I have it all and I present it all, it alone will surely change people’s minds, hearts, and beliefs. And, most importantly, it will help everyone see, understand, and perhaps even appreciate and commit to what needs to be done.

Well, it’s not quite like that. In fact, the stronger and louder the data, the more likely it is to be questioned. And there is a good reason for that, which is often left between the lines.

Research Amplifies Internal Flaws

Throughout the years, I’ve often seen data speaking volumes about where the business is failing, where customers are struggling, where the team is faltering — and where an urgent turnaround is necessary. It was right there, in plain sight: clear, loud, and obvious.

But because it’s so clear, it reflects back, often amplifying all the sharp edges and all the cut corners in all the wrong places. It reflects internal flaws, wrong assumptions, and failing projects — some of them signed off years ago, with secured budgets, big promotions, and approved headcounts. Questioning them means questioning authority, and often it’s a tough path to take.

As it turns out, strong data is very, very good at raising uncomfortable truths that most companies don’t really want to acknowledge. That’s why, at times, research is deemed “unnecessary,” or why we don’t get access to users, or why loud voices always win big arguments.

So even if data is presented with a lot of eagerness, gravity, and passion in that big meeting, it will get questioned, doubted, and explained away. Not because of its flaws, but because of hope, reluctance to change, and layers of internal politics.

This shows up most vividly in situations when someone raises concerns about the validity and accuracy of research. Frankly, it’s not that somebody is wrong and somebody is right. Both parties just happen to be right in a different way.

What To Do When Data Disagrees

We’ve all heard that data always tells a story. However, it’s never just a single story. People are complex, and pointing out a specific truth about them just by looking at numbers is rarely enough.

When data disagrees, it doesn’t mean that either is wrong. It’s just that different perspectives reveal different parts of a whole story that isn’t completed yet.

In digital products, most stories have 2 sides:

• Quantitative data ← What/When: behavior patterns at scale.
• Qualitative data ← Why/How: user needs and motivations.
• ↳ Quant usually comes from analytics, surveys, and experiments.
• ↳ Qual comes from tests, observations, and open-ended surveys.

Risk-averse teams overestimate the weight of big numbers in quantitative research. Users exaggerate the frequency and severity of issues that are critical for them. As Archana Shah noted, designers get carried away by users’ confident responses and often overestimate what people say and do.

And so, eventually, data coming from different teams paints a different picture. And when it happens, we need to reconcile and triangulate. With the former, we track what’s missing, omitted, or overlooked. With the latter, we cross-validate data — e.g., finding pairings of qual/quant streams of data, then clustering them together to see what’s there and what’s missing, and exploring from there.

And even with all of it in place and data conflicts resolved, we still need to do one more thing to make a strong argument: we need to tell a damn good story.

Facts Don’t Win Arguments, Stories Do

Research isn’t everything. Facts don’t win arguments — powerful stories do. But a story that starts with a spreadsheet isn’t always inspiring or effective. Perhaps it brings a problem into the spotlight, but it doesn’t lead to a resolution.

The very first thing I try to do in that big boardroom meeting is to emphasize what unites us — shared goals, principles, and commitments that are relevant to the topic at hand. Then, I show how new data confirms or confronts our commitments, with specific problems we believe we need to address.

When a question about the quality of data comes in, I need to show that it has been reconciled and triangulated already and discussed with other teams as well.

A good story has a poignant ending. People need to see an alternative future to trust and accept the data — and a clear and safe path forward to commit to it. So I always try to present options and solutions that we believe will drive change and explain our decision-making behind that.

They also need to believe that this distant future is within reach, and that they can pull it off, albeit under a tough timeline or with limited resources.

And: a good story also presents a viable, compelling, shared goal that people can rally around and commit to. Ideally, it’s something that has a direct benefit for them and their teams.

These are the ingredients of the story that I always try to keep in my mind when working on that big presentation. And in fact, data is a starting point, but it does need a story wrapped around it to be effective.

Wrapping Up

There is nothing more disappointing than finding a real problem that real people struggle with and facing the harsh reality of research not being trusted or valued.

We’ve all been there before. The best thing you can do is to be prepared: have strong data to back you up, include both quantitative and qualitative research — preferably with video clips from real customers — but also paint a viable future which seems within reach.

And sometimes nothing changes until something breaks. And at times, there isn’t much you can do about it unless you are prepared when it happens.

“Data doesn’t change minds, and facts don’t settle fights. Having answers isn’t the same as learning, and it for sure isn’t the same as making evidence-based decisions.”

— Erika Hall

Meet “How To Measure UX And Design Impact”

You can find more details on UX Research in Measure UX & Design Impact (8h), a practical guide for designers and UX leads to measure and show your UX impact on business. Use the code 🎟 IMPACT to save 20% off today. Jump to the details.

Useful Resources

• “How to Present Research So Stakeholders Sit Up and Take Action”, by Nikki Anderson
• “What To Do When Data Disagrees”, by Subhasree Chatterjee, Archana Shah, Sanket Shukl, and Jason Bressler
• “Mixed-Method UX Research”, by Raschin Fatemi
• “A Step-by-Step Framework For Mixed-Method Research”, by Jeremy Williams
• “The Ultimate Guide To Mixed Methods”, by Ben Wiedmaier
• Survey Design Cheatsheet, by yours truly
• Useful Calculators For UX Research, by yours truly
• Beyond Measure, by Erika Hall

Useful Books

• Just Enough Research, by Erika Hall
• Designing Surveys That Work, by Caroline Jarrett
• Designing Quality Survey Questions, by Sheila B. Robinson

(yk)

In Part 1 of this series, we explored the “lopsided horse” problem born from mockup-centric design and demonstrated how the seductive promise of vibe coding often leads to structural flaws. The main question remains:

How might we close the gap between our design intent and a live prototype, so that we can iterate on real functionality from day one, without getting caught in the ambiguity trap?

In other words, we need a way to build prototypes that are both fast to create and founded on a clear, unambiguous blueprint.

The answer is a more disciplined process I call Intent Prototyping (kudos to Marco Kotrotsos, who coined Intent-Oriented Programming). This method embraces the power of AI-assisted coding but rejects ambiguity, putting the designer’s explicit intent at the very center of the process. It receives a holistic expression of intent (sketches for screen layouts, conceptual model description, boxes-and-arrows for user flows) and uses it to generate a live, testable prototype.

This method solves the concerns we’ve discussed in Part 1 in the best way possible:

• Unlike static mockups, the prototype is fully interactive and can be easily populated with a large amount of realistic data. This lets us test the system’s underlying logic as well as its surface.
• Unlike a vibe-coded prototype, it is built from a stable, unambiguous specification. This prevents the conceptual model failures and design debt that happen when things are unclear. The engineering team doesn’t need to reverse-engineer a black box or become “code archaeologists” to guess at the designer’s vision, as they receive not only a live prototype but also a clearly documented design intent behind it.

This combination makes the method especially suited for designing complex enterprise applications. It allows us to test the system’s most critical point of failure, its underlying structure, at a speed and flexibility that was previously impossible. Furthermore, the process is built for iteration. You can explore as many directions as you want simply by changing the intent and evolving the design based on what you learn from user testing.

My Workflow

To illustrate this process in action, let’s walk through a case study. It’s the very same example I’ve used to illustrate the vibe coding trap: a simple tool to track tests to validate product ideas. You can find the complete project, including all the source code and documentation files discussed below, in this GitHub repository.

Step 1: Expressing An Intent

Imagine we’ve already done proper research, and having mused on the defined problem, I begin to form a vague idea of what the solution might look like. I need to capture this idea immediately, so I quickly sketch it out:

In this example, I used Excalidraw, but the tool doesn’t really matter. Note that we deliberately keep it rough, as visual details are not something we need to focus on at this stage. And we are not going to be stuck here: we want to make a leap from this initial sketch directly to a live prototype that we can put in front of potential users. Polishing those sketches would not bring us any closer to achieving our goal.

What we need to move forward is to add to those sketches just enough details so that they may serve as a sufficient input for a junior frontend developer (or, in our case, an AI assistant). This requires explaining the following:

• Navigational paths (clicking here takes you to).
• Interaction details that can’t be shown in a static picture (e.g., non-scrollable areas, adaptive layout, drag-and-drop behavior).
• What parts might make sense to build as reusable components.
• Which components from the design system (I’m using Ant Design Library) should be used.
• Any other comments that help understand how this thing should work (while sketches illustrate how it should look).

Having added all those details, we end up with such an annotated sketch:

As you see, this sketch covers both the Visualization and Flow aspects. You may ask, what about the Conceptual Model? Without that part, the expression of our intent will not be complete. One way would be to add it somewhere in the margins of the sketch (for example, as a UML Class Diagram), and I would do so in the case of a more complex application, where the model cannot be simply derived from the UI. But in our case, we can save effort and ask an LLM to generate a comprehensive description of the conceptual model based on the sketch.

For tasks of this sort, the LLM of my choice is Gemini 2.5 Pro. What is important is that this is a multimodal model that can accept not only text but also images as input (GPT-5 and Claude-4 also fit that criteria). I use Google AI Studio, as it gives me enough control and visibility into what’s happening:

Note: All the prompts that I use here and below can be found in the Appendices. The prompts are not custom-tailored to any particular project; they are supposed to be reused as they are.

As a result, Gemini gives us a description and the following diagram:

The diagram might look technical, but I believe that a clear understanding of all objects, their attributes, and relationships between them is key to good design. That’s why I consider the Conceptual Model to be an essential part of expressing intent, along with the Flow and Visualization.

As a result of this step, our intent is fully expressed in two files: Sketch.png and Model.md. This will be our durable source of truth.

Step 2: Preparing A Spec And A Plan

The purpose of this step is to create a comprehensive technical specification and a step-by-step plan. Most of the work here is done by AI; you just need to keep an eye on it.

I separate the Data Access Layer and the UI layer, and create specifications for them using two different prompts (see Appendices 2 and 3). The output of the first prompt (the Data Access Layer spec) serves as an input for the second one. Note that, as an additional input, we give the guidelines tailored for prototyping needs (see Appendices 8, 9, and 10). They are not specific to this project. The technical approach encoded in those guidelines is out of the scope of this article.

As a result, Gemini provides us with content for DAL.md and UI.md. Although in most cases this result is quite reliable enough, you might want to scrutinize the output. You don’t need to be a real programmer to make sense of it, but some level of programming literacy would be really helpful. However, even if you don’t have such skills, don’t get discouraged. The good news is that if you don’t understand something, you always know who to ask. Do it in Google AI Studio before refreshing the context window. If you believe you’ve spotted a problem, let Gemini know, and it will either fix it or explain why the suggested approach is actually better.

It’s important to remember that by their nature, LLMs are not deterministic and, to put it simply, can be forgetful about small details, especially when it comes to details in sketches. Fortunately, you don’t have to be an expert to notice that the “Delete” button, which is in the upper right corner of the sketch, is not mentioned in the spec.

Don’t get me wrong: Gemini does a stellar job most of the time, but there are still times when it slips up. Just let it know about the problems you’ve spotted, and everything will be fixed.

Once we have Sketch.png, Model.md, DAL.md, UI.md, and we have reviewed the specs, we can grab a coffee. We deserve it: our technical design documentation is complete. It will serve as a stable foundation for building the actual thing, without deviating from our original intent, and ensuring that all components fit together perfectly, and all layers are stacked correctly.

One last thing we can do before moving on to the next steps is to prepare a step-by-step plan. We split that plan into two parts: one for the Data Access Layer and another for the UI. You can find prompts I use to create such a plan in Appendices 4 and 5.

Step 3: Executing The Plan

To start building the actual thing, we need to switch to another category of AI tools. Up until this point, we have relied on Generative AI. It excels at creating new content (in our case, specifications and plans) based on a single prompt. I’m using Google Gemini 2.5 Pro in Google AI Studio, but other similar tools may also fit such one-off tasks: ChatGPT, Claude, Grok, and DeepSeek.

However, at this step, this wouldn’t be enough. Building a prototype based on specs and according to a plan requires an AI that can read context from multiple files, execute a sequence of tasks, and maintain coherence. A simple generative AI can’t do this. It would be like asking a person to build a house by only ever showing them a single brick. What we need is an agentic AI that can be given the full house blueprint and a project plan, and then get to work building the foundation, framing the walls, and adding the roof in the correct sequence.

My coding agent of choice is Google Gemini CLI, simply because Gemini 2.5 Pro serves me well, and I don’t think we need any middleman like Cursor or Windsurf (which would use Claude, Gemini, or GPT under the hood anyway). If I used Claude, my choice would be Claude Code, but since I’m sticking with Gemini, Gemini CLI it is. But if you prefer Cursor or Windsurf, I believe you can apply the same process with your favourite tool.

Before tasking the agent, we need to create a basic template for our React application. I won’t go into this here. You can find plenty of tutorials on how to scaffold an empty React project using Vite.

Then we put all our files into that project:

Once the basic template with all our files is ready, we open Terminal, go to the folder where our project resides, and type “gemini”:

And we send the prompt to build the Data Access Layer (see Appendix 6). That prompt implies step-by-step execution, so upon completion of each step, I send the following:

Thank you! Now, please move to the next task.
Remember that you must not make assumptions based on common patterns; always verify them with the actual data from the spec. 
After each task, stop so that I can test it. Don’t move to the next task before I tell you to do so.

As the last task in the plan, the agent builds a special page where we can test all the capabilities of our Data Access Layer, so that we can manually test it. It may look like this:

It doesn’t look fancy, to say the least, but it allows us to ensure that the Data Access Layer works correctly before we proceed with building the final UI.

And finally, we clear the Gemini CLI context window to give it more headspace and send the prompt to build the UI (see Appendix 7). This prompt also implies step-by-step execution. Upon completion of each step, we test how it works and how it looks, following the “Manual Testing Plan” from UI-plan.md. I have to say that despite the fact that the sketch has been uploaded to the model context and, in general, Gemini tries to follow it, attention to visual detail is not one of its strengths (yet). Usually, a few additional nudges are needed at each step to improve the look and feel:

Once I’m happy with the result of a step, I ask Gemini to move on:

Thank you! Now, please move to the next task.
Make sure you build the UI according to the sketch; this is very important. Remember that you must not make assumptions based on common patterns; always verify them with the actual data from the spec and the sketch.  
After each task, stop so that I can test it. Don’t move to the next task before I tell you to do so.

Before long, the result looks like this, and in every detail it works exactly as we intended:

The prototype is up and running and looking nice. Does it mean that we are done with our work? Surely not, the most fascinating part is just beginning.

Step 4: Learning And Iterating

It’s time to put the prototype in front of potential users and learn more about whether this solution relieves their pain or not.

And as soon as we learn something new, we iterate. We adjust or extend the sketches and the conceptual model, based on that new input, we update the specifications, create plans to make changes according to the new specifications, and execute those plans. In other words, for every iteration, we repeat the steps I’ve just walked you through.

Is This Workflow Too Heavy?

This four-step workflow may create an impression of a somewhat heavy process that requires too much thinking upfront and doesn’t really facilitate creativity. But before jumping to that conclusion, consider the following:

• In practice, only the first step requires real effort, as well as learning in the last step. AI does most of the work in between; you just need to keep an eye on it.
• Individual iterations don’t need to be big. You can start with a Walking Skeleton: the bare minimum implementation of the thing you have in mind, and add more substance in subsequent iterations. You are welcome to change your mind about the overall direction in between iterations.
• And last but not least, maybe the idea of “think before you do” is not something you need to run away from. A clear and unambiguous statement of intent can prevent many unnecessary mistakes and save a lot of effort down the road.

Intent Prototyping Vs. Other Methods

There is no method that fits all situations, and Intent Prototyping is not an exception. Like any specialized tool, it has a specific purpose. The most effective teams are not those who master a single method, but those who understand which approach to use to mitigate the most significant risk at each stage. The table below gives you a way to make this choice clearer. It puts Intent Prototyping next to other common methods and tools and explains each one in terms of the primary goal it helps achieve and the specific risks it is best suited to mitigate.

The key takeaway is that each method is a specialized tool for mitigating a specific type of risk. For example, Figma de-risks the visual presentation. ProtoPie de-risks the feel of an interaction. Intent Prototyping is in a unique position to tackle the most foundational risk in complex applications: building on a flawed or incoherent conceptual model.

Bringing It All Together

The era of the “lopsided horse” design, sleek on the surface but structurally unsound, is a direct result of the trade-off between fidelity and flexibility. This trade-off has led to a process filled with redundant effort and misplaced focus. Intent Prototyping, powered by modern AI, eliminates that conflict. It’s not just a shortcut to building faster — it’s a fundamental shift in how we design. By putting a clear, unambiguous intent at the heart of the process, it lets us get rid of the redundant work and focus on architecting a sound and robust system.

There are three major benefits to this renewed focus. First, by going straight to live, interactive prototypes, we shift our validation efforts from the surface to the deep, testing the system’s actual logic with users from day one. Second, the very act of documenting the design intent makes us clear about our ideas, ensuring that we fully understand the system’s underlying logic. Finally, this documented intent becomes a durable source of truth, eliminating the ambiguous handoffs and the redundant, error-prone work of having engineers reverse-engineer a designer’s vision from a black box.

Ultimately, Intent Prototyping changes the object of our work. It allows us to move beyond creating pictures of a product and empowers us to become architects of blueprints for a system. With the help of AI, we can finally make the live prototype the primary canvas for ideation, not just a high-effort afterthought.

Appendices

You can find the full Intent Prototyping Starter Kit, which includes all those prompts and guidelines, as well as the example from this article and a minimal boilerplate project, in this GitHub repository.

(yk)

In “A Week In The Life Of An AI-Augmented Designer”, Kate stumbled her way through an AI-augmented sprint (coffee was chugged, mistakes were made). In “Prompting Is A Design Act”, we introduced WIRE+FRAME, a framework to structure prompts like designers structure creative briefs. Now we’ll take the next step: packaging those structured prompts into AI assistants you can design, reuse, and share.

AI assistants go by different names: CustomGPTs (ChatGPT), Agents (Copilot), and Gems (Gemini). But they all serve the same function — allowing you to customize the default AI model for your unique needs. If we carry over our smart intern analogy, think of these as interns trained to assist you with specific tasks, eliminating the need for repeated instructions or information, and who can support not just you, but your entire team.

Why Build Your Own Assistant?

If you’ve ever copied and pasted the same mega-prompt for the n^th time, you’ve experienced the pain. An AI assistant turns a one-off “great prompt” into a dependable teammate. And if you’ve used any of the publicly available AI Assistants, you’ve realized quickly that they’re usually generic and not tailored for your use.

Public AI assistants are great for inspiration, but nothing beats an assistant that solves a repeated problem for you and your team, in your voice, with your context and constraints baked in. Instead of reinventing the wheel by writing new prompts each time, or repeatedly copy-pasting your structured prompts every time, or spending cycles trying to make a public AI Assistant work the way you need it to, your own AI Assistant allows you and others to easily get better, repeatable, consistent results faster.

Benefits Of Reusing Prompts, Even Your Own

Some of the benefits of building your own AI Assistant over writing or reusing your prompts include:

• Focused on a real repeating problem
  A good AI Assistant isn’t a general-purpose “do everything” bot that you need to keep tweaking. It focuses on a single, recurring problem that takes a long time to complete manually and often results in varying quality depending on who’s doing it (e.g., analyzing customer feedback).
• Customized for your context
  Most large language models (LLMs, such as ChatGPT) are designed to be everything to everyone. An AI Assistant changes that by allowing you to customize it to automatically work like you want it to, instead of a generic AI.
• Consistency at scale
  You can use the WIRE+FRAME prompt framework to create structured, reusable prompts. An AI Assistant is the next logical step: instead of copy-pasting that fine-tuned prompt and sharing contextual information and examples each time, you can bake it into the assistant itself, allowing you and others achieve the same consistent results every time.
• Codifying expertise
  Every time you turn a great prompt into an AI Assistant, you’re essentially bottling your expertise. Your assistant becomes a living design guide that outlasts projects (and even job changes).
• Faster ramp-up for teammates
  Instead of new designers starting from a blank slate, they can use pre-tuned assistants. Think of it as knowledge transfer without the long onboarding lecture.
  

Reasons For Your Own AI Assistant Instead Of Public AI Assistants

Public AI assistants are like stock templates. While they serve a specific purpose compared to the generic AI platform, and are useful starting points, if you want something tailored to your needs and team, you should really build your own.

A few reasons for building your AI Assistant instead of using a public assistant someone else created include:

• Fit: Public assistants are built for the masses. Your work has quirks, tone, and processes they’ll never quite match.
• Trust & Security: You don’t control what instructions or hidden guardrails someone else baked in. With your own assistant, you know exactly what it will (and won’t) do.
• Evolution: An AI Assistant you design and build can grow with your team. You can update files, tweak prompts, and maintain a changelog — things a public bot won’t do for you.

Your own AI Assistants allow you to take your successful ways of interacting with AI and make them repeatable and shareable. And while they are tailored to your and your team’s way of working, remember that they are still based on generic AI models, so the usual AI disclaimers apply:

Don’t share anything you wouldn’t want screenshotted in the next company all-hands. Keep it safe, private, and user-respecting. A shared AI Assistant can potentially reveal its inner workings or data.

Note: We will be building an AI assistant using ChatGPT, aka a CustomGPT, but you can try the same process with any decent LLM sidekick. As of publication, a paid account is required to create CustomGPTs, but once created, they can be shared and used by anyone, regardless of whether they have a paid or free account. Similar limitations apply to the other platforms. Just remember that outputs can vary depending on the LLM model used, the model’s training, mood, and flair for creative hallucinations.

When Not to Build An AI Assistant (Yet)

An AI Assistant is great when the same audience has the same problem often. When the fit isn’t there, the risk is high; you should skip building an AI Assistant for now, as explained below:

• One-off or rare tasks
  If it won’t be reused at least monthly, I’d recommend keeping it as a saved WIRE+FRAME prompt. For example, something for a one-time audit or creating placeholder content for a specific screen.
• Sensitive or regulated data
  If you need to build in personally identifiable information (PII), health, finance, legal, or trade secrets, err on the side of not building an AI Assistant. Even if the AI platform promises not to use your data, I’d strongly suggest using redaction or an approved enterprise tool with necessary safeguards in place (company-approved enterprise versions of Microsoft Copilot, for instance).
• Heavy orchestration or logic
  Multi-step workflows, API calls, database writes, and approvals go beyond the scope of an AI Assistant into Agentic territory (as of now). I’d recommend not trying to build an AI Assistant for these cases.
• Real-time information
  AI Assistants may not be able to access real-time data like prices, live metrics, or breaking news. If you need these, you can upload near-real-time data (as we do below) or connect with data sources that you or your company controls, rather than relying on the open web.
• High-stakes outputs
  For cases related to compliance, legal, medical, or any other area requiring auditability, consider implementing process guardrails and training to keep humans in the loop for proper review and accountability.
• No measurable win
  If you can’t name a success metric (such as time saved, first-draft quality, or fewer re-dos), I’d recommend keeping it as a saved WIRE+FRAME prompt.

Just because these are signs that you should not build your AI Assistant now, doesn’t mean you shouldn’t ever. Revisit this decision when you notice that you’re starting to repeatedly use the same prompt weekly, multiple teammates ask for it, or manual time copy-pasting and refining start exceeding ~15 minutes. Those are some signs that an AI Assistant will pay back quickly.

In a nutshell, build an AI Assistant when you can name the problem, the audience, frequency, and the win. The rest of this article shows how to turn your successful WIRE+FRAME prompt into a CustomGPT that you and your team can actually use. No advanced knowledge, coding skills, or hacks needed.

As Always, Start with the User

This should go without saying to UX professionals, but it’s worth a reminder: if you’re building an AI assistant for anyone besides yourself, start with the user and their needs before you build anything.

• Who will use this assistant?
• What’s the specific pain or task they struggle with today?
• What language, tone, and examples will feel natural to them?

Building without doing this first is a sure way to end up with clever assistants nobody actually wants to use. Think of it like any other product: before you build features, you understand your audience. The same rule applies here, even more so, because AI assistants are only as helpful as they are useful and usable.

From Prompt To Assistant

You’ve already done the heavy lifting with WIRE+FRAME. Now you’re just turning that refined and reliable prompt into a CustomGPT you can reuse and share. You can use MATCH as a checklist to go from a great prompt to a useful AI assistant.

• M: Map your prompt
  Port your successful WIRE+FRAME prompt into the AI assistant.
• A: Add knowledge and training
  Ground the assistant in your world. Upload knowledge files, examples, or guides that make it uniquely yours.
• T: Tailor for audience
  Make it feel natural to the people who will use it. Give it the right capabilities, but also adjust its settings, tone, examples, and conversation starters so they land with your audience.
• C: Check, test, and refine
  Test the preview with different inputs and refine until you get the results you expect.
• H: Hand off and maintain
  Set sharing options and permissions, share the link, and maintain it.

A few weeks ago, we invited readers to share their ideas for AI assistants they wished they had. The top contenders were:

• Prototype Prodigy: Transform rough ideas into prototypes and export them into Figma to refine.
• Critique Coach: Review wireframes or mockups and point out accessibility and usability gaps.

But the favorite was an AI assistant to turn tons of customer feedback into actionable insights. Readers replied with variations of: “An assistant that can quickly sort through piles of survey responses, app reviews, or open-ended comments and turn them into themes we can act on.”

And that’s the one we will build in this article — say hello to Insight Interpreter.

Walkthrough: Insight Interpreter

Having lots of customer feedback is a nice problem to have. Companies actively seek out customer feedback through surveys and studies (solicited), but also receive feedback that may not have been asked for through social media or public reviews (unsolicited). This is a goldmine of information, but it can be messy and overwhelming trying to make sense of it all, and it’s nobody’s idea of fun. Here’s where an AI assistant like the Insight Interpreter can help. We’ll turn the example prompt created using the WIRE+FRAME framework in Prompting Is A Design Act into a CustomGPT.

When you start building a CustomGPT by visiting https://chat.openai.com/gpts/editor, you’ll see two paths:

• Conversational interface
  Vibe-chat your way — it’s easy and quick, but similar to unstructured prompts, your inputs get baked in a little messily, so you may end up with vague or inconsistent instructions.
• Configure interface
  The structured form where you type instructions, upload files, and toggle capabilities. Less instant gratification, less winging it, but more control. This is the option you’ll want for assistants you plan to share or depend on regularly.

The good news is that MATCH works for both. In conversational mode, you can use it as a mental checklist, and we’ll walk through using it in configure mode as a more formal checklist in this article.

M: Map Your Prompt

Paste your full WIRE+FRAME prompt into the Instructions section exactly as written. As a refresher, I’ve included the mapping and snippets of the detailed prompt from before:

• Who & What: The AI persona and the core deliverable (“…senior UX researcher and customer insights analyst… specialize in synthesizing qualitative data from diverse sources…”).
• Input Context: Background or data scope to frame the task (“…analyzing customer feedback uploaded from sources such as…”).
• Rules & Constraints: Boundaries (“…do not fabricate pain points, representative quotes, journey stages, or patterns…”).
• Expected Output: Format and fields of the deliverable (“…a structured list of themes. For each theme, include…”).
• Flow: Explicit, ordered sub-tasks (“Recommended flow of tasks: Step 1…”).
• Reference Voice: Tone, mood, or reference (“…concise, pattern-driven, and objective…”).
• Ask for Clarification: Ask questions if unclear (“…if data is missing or unclear, ask before continuing…”).
• Memory: Memory to recall earlier definitions (“Unless explicitly instructed otherwise, keep using this process…”).
• Evaluate & Iterate: Have the AI self-critique outputs (“…critically evaluate…suggest improvements…”).

If you’re building Copilot Agents or Gemini Gems instead of CustomGPTs, you still paste your WIRE+FRAME prompt into their respective Instructions sections.

A: Add Knowledge And Training

In the knowledge section, upload up to 20 files, clearly labeled, that will help the CustomGPT respond effectively. Keep files small and versioned: reviews_Q2_2025.csv beats latestfile_final2.csv. For this prompt for analyzing customer feedback, generating themes organized by customer journey, rating them by severity and effort, files could include:

• Taxonomy of themes;
• Instructions on parsing uploaded data;
• Examples of real UX research reports using this structure;
• Scoring guidelines for severity and effort, e.g., what makes something a 3 vs. a 5 in severity;
• Customer journey map stages;
• Customer feedback file templates (not actual data).

An example of a file to help it parse uploaded data is shown below:

T: Tailor For Audience

• Audience tailoring
  If you are building this for others, your prompt should have addressed tone in the “Reference Voice” section. If you didn’t, do it now, so the CustomGPT can be tailored to the tone and expertise level of users who will use it. In addition, use the Conversation starters section to add a few examples or common prompts for users to start using the CustomGPT, again, worded for your users. For instance, we could use “Analyze feedback from the attached file” for our Insights Interpreter to make it more self-explanatory for anyone, instead of “Analyze data,” which may be good enough if you were using it alone. For my Designerly Curiosity GPT, assuming that users may not know what it could do, I use “What are the types of curiosity?” and “Give me a micro-practice to spark curiosity”.
• Functional tailoring
  Fill in the CustomGPT name, icon, description, and capabilities.
  • Name: Pick one that will make it clear what the CustomGPT does. Let’s use “Insights Interpreter — Customer Feedback Analyzer”. If needed, you can also add a version number. This name will show up in the sidebar when people use it or pin it, so make the first part memorable and easily identifiable.
  • Icon: Upload an image or generate one. Keep it simple so it can be easily recognized in a smaller dimension when people pin it in their sidebar.
  • Description: A brief, yet clear description of what the CustomGPT can do. If you plan to list it in the GPT store, this will help people decide if they should pick yours over something similar.
  • Recommended Model: If your CustomGPT needs the capabilities of a particular model (e.g., needs GPT-5 thinking for detailed analysis), select it. In most cases, you can safely leave it up to the user or select the most common model.
  • Capabilities: Turn off anything you won’t need. We’ll turn off “Web Search” to allow the CustomGPT to focus only on uploaded data, without expanding the search online, and we will turn on “Code Interpreter & Data Analysis” to allow it to understand and process uploaded files. “Canvas” allows users to work on a shared canvas with the GPT to edit writing tasks; “Image generation” - if the CustomGPT needs to create images.
  • Actions: Making third-party APIs available to the CustomGPT, advanced functionality we don’t need.
  • Additional Settings: Sneakily hidden and opted in by default, I opt out of training OpenAI’s models.

C: Check, Test & Refine

Do one last visual check to make sure you’ve filled in all applicable fields and the basics are in place: is the concept sharp and clear (not a do-everything bot)? Are the roles, goals, and tone clear? Do we have the right assets (docs, guides) to support it? Is the flow simple enough that others can get started easily? Once those boxes are checked, move into testing.

Use the Preview panel to verify that your CustomGPT performs as well, or better, than your original WIRE+FRAME prompt, and that it works for your intended audience. Try a few representative inputs and compare the results to what you expected. If something worked before but doesn’t now, check whether new instructions or knowledge files are overriding it.

When things don’t look right, here are quick debugging fixes:

• Generic answers?
  Tighten Input Context or update the knowledge files.
• Hallucinations?
  Revisit your Rules section. Turn off web browsing if you don’t need external data.
• Wrong tone?
  Strengthen Reference Voice or swap in clearer examples.
• Inconsistent?
  Test across models in preview and set the most reliable one as “Recommended.”

H: Hand Off And Maintain

When your CustomGPT is ready, you can publish it via the “Create” option. Select the appropriate access option:

• Only me: Private use. Perfect if you’re still experimenting or keeping it personal.
• Anyone with the link: Exactly what it means. Shareable but not searchable. Great for pilots with a team or small group. Just remember that links can be reshared, so treat them as semi-public.
• GPT Store: Fully public. Your assistant is listed and findable by anyone browsing the store. (This is the option we’ll use.)
• Business workspace (if you’re on GPT Business): Share with others within your business account only — the easiest way to keep it in-house and controlled.

But hand off doesn’t end with hitting publish, you should maintain it to keep it relevant and useful:

• Collect feedback: Ask teammates what worked, what didn’t, and what they had to fix manually.
• Iterate: Apply changes directly or duplicate the GPT if you want multiple versions in play. You can find all your CustomGPTs at: https://chatgpt.com/gpts/mine
• Track changes: Keep a simple changelog (date, version, updates) for traceability.
• Refresh knowledge: Update knowledge files and examples on a regular cadence so answers don’t go stale.

And that’s it! Our Insights Interpreter is now live!

Since we used the WIRE+FRAME prompt from the previous article to create the Insights Interpreter CustomGPT, I compared the outputs:

The results are similar, with slight differences, and that’s expected. If you compare the results carefully, the themes, issues, journey stages, frequency, severity, and estimated effort match with some differences in wording of the theme, issue summary, and problem statement. The opportunities and quotes have more visible differences. Most of it is because of the CustomGPT knowledge and training files, including instructions, examples, and guardrails, now live as always-on guidance.

Keep in mind that in reality, Generative AI is by nature generative, so outputs will vary. Even with the same data, you won’t get identical wording every time. In addition, underlying models and their capabilities rapidly change. If you want to keep things as consistent as possible, recommend a model (though people can change it), track versions of your data, and compare for structure, priorities, and evidence rather than exact wording.

While I’d love for you to use Insights Interpreter, I strongly recommend taking 15 minutes to follow the steps above and create your own. That is exactly what you or your team needs — including the tone, context, output formats, and get the real AI Assistant you need!

Inspiration For Other AI Assistants

We just built the Insight Interpreter and mentioned two contenders: Critique Coach and Prototype Prodigy. Here are a few other realistic uses that can spark ideas for your own AI Assistant:

• Workshop Wizard: Generates workshop agendas, produces icebreaker questions, and follows up survey drafts.
• Research Roundup Buddy: Summarizes raw transcripts into key themes, then creates highlight reels (quotes + visuals) for team share-outs.
• Persona Refresher: Updates stale personas with the latest customer feedback, then rewrites them in different tones (boardroom formal vs. design-team casual).
• Content Checker: Proofs copy for tone, accessibility, and reading level before it ever hits your site.
• Trend Tamer: Scans competitor reviews and identifies emerging patterns you can act on before they reach your roadmap.
• Microcopy Provocateur: Tests alternate copy options by injecting different tones (sassy, calm, ironic, nurturing) and role-playing how users might react, especially useful for error states or Call to Actions.
• Ethical UX Debater: Challenges your design decisions and deceptive designs by simulating the voice of an ethics board or concerned user.

The best AI Assistants come from carefully inspecting your workflow and looking for areas where AI can augment your work regularly and repetitively. Then follow the steps above to build a team of customized AI assistants.

Ask Me Anything About Assistants

• What are some limitations of a CustomGPT?
  Right now, the best parallels for AI are a very smart intern with access to a lot of information. CustomGPTs are still running on LLM models that are basically trained on a lot of information and programmed to predictively generate responses based on that data, including possible bias, misinformation, or incomplete information. Keeping that in mind, you can make that intern provide better and more relevant results by using your uploads as onboarding docs, your guardrails as a job description, and your updates as retraining.
• Can I copy someone else’s public CustomGPT and tweak it?
  Not directly, but if you get inspired by another CustomGPT, you can look at how it’s framed and rebuild your own using WIRE+FRAME & MATCH. That way, you make it your own and have full control of the instructions, files, and updates. But you can do that with Google’s equivalent — Gemini Gems. Shared Gems behave similarly to shared Google Docs, so once shared, any Gem instructions and files that you have uploaded can be viewed by any user with access to the Gem. Any user with edit access to the Gem can also update and delete the Gem.
• How private are my uploaded files?
  The files you upload are stored and used to answer prompts to your CustomGPT. If your CustomGPT is not private or you didn’t disable the hidden setting to allow CustomGPT conversations to improve the model, that data could be referenced. Don’t upload sensitive, confidential, or personal data you wouldn’t want circulating. Enterprise accounts do have some protections, so check with your company.
• How many files can I upload, and does size matter?
  Limits vary by platform, but smaller, specific files usually perform better than giant docs. Think “chapter” instead of “entire book.” At the time of publishing, CustomGPTs allow up to 20 files, Copilot Agents up to 200 (if you need anywhere near that many, chances are your agent is not focused enough), and Gemini Gems up to 10.
• What’s the difference between a CustomGPT and a Project?
  A CustomGPT is a focused assistant, like an intern trained to do one role well (like “Insight Interpreter”). A Project is more like a workspace where you can group multiple prompts, files, and conversations together for a broader effort. CustomGPTs are specialists. Projects are containers. If you want something reusable, shareable, and role-specific, go to CustomGPT. If you want to organize broader work with multiple tools and outputs, and shared knowledge, Projects are the better fit.

From Reading To Building

In this AI x Design series, we’ve gone from messy prompting (“A Week In The Life Of An AI-Augmented Designer”) to a structured prompt framework, WIRE+FRAME (“Prompting Is A Design Act”). And now, in this article, your very own reusable AI sidekick.

CustomGPTs don’t replace designers but augment them. The real magic isn’t in the tool itself, but in how you design and manage it. You can use public CustomGPTs for inspiration, but the ones that truly fit your workflow are the ones you design yourself. They extend your craft, codify your expertise, and give your team leverage that generic AI models can’t.

Build one this week. Even better, today. Train it, share it, stress-test it, and refine it into an AI assistant that can augment your team.

(yk)

There is a spectrum of opinions on how dramatically all creative professions will be changed by the coming wave of agentic AI, from the very skeptical to the wildly optimistic and even apocalyptic. I think that even if you are on the “skeptical” end of the spectrum, it makes sense to explore ways this new technology can help with your everyday work. As for my everyday work, I’ve been doing UX and product design for about 25 years now, and I’m always keen to learn new tricks and share them with colleagues. Right now, I’m interested in AI-assisted prototyping, and I’m here to share my thoughts on how it can change the process of designing digital products.

To set your expectations up front: this exploration focuses on a specific part of the product design lifecycle. Many people know about the Double Diamond framework, which shows the path from problem to solution. However, I think it’s the Triple Diamond model that makes an important point for our needs. It explicitly separates the solution space into two phases: Solution Discovery (ideating and validating the right concept) and Solution Delivery (engineering the validated concept into a final product). This article is focused squarely on that middle diamond: Solution Discovery.

How AI can help with the preceding (Problem Discovery) and the following (Solution Delivery) stages is out of the scope of this article. Problem Discovery is less about prototyping and more about research, and while I believe AI can revolutionize the research process as well, I’ll leave that to people more knowledgeable in the field. As for Solution Delivery, it is more about engineering optimization. There’s no doubt that software engineering in the AI era is undergoing dramatic changes, but I’m not an engineer — I’m a designer, so let me focus on my “sweet spot”.

And my “sweet spot” has a specific flavor: designing enterprise applications. In this world, the main challenge is taming complexity: dealing with complicated data models and guiding users through non-linear workflows. This background has had a big impact on my approach to design, putting a lot of emphasis on the underlying logic and structure. This article explores the potential of AI through this lens.

I’ll start by outlining the typical artifacts designers create during Solution Discovery. Then, I’ll examine the problems with how this part of the process often plays out in practice. Finally, we’ll explore whether AI-powered prototyping can offer a better approach, and if so, whether it aligns with what people call “vibe coding,” or calls for a more deliberate and disciplined way of working.

What We Create During Solution Discovery

The Solution Discovery phase begins with the key output from the preceding research: a well-defined problem and a core hypothesis for a solution. This is our starting point. The artifacts we create from here are all aimed at turning that initial hypothesis into a tangible, testable concept.

Traditionally, at this stage, designers can produce artifacts of different kinds, progressively increasing fidelity: from napkin sketches, boxes-and-arrows, and conceptual diagrams to hi-fi mockups, then to interactive prototypes, and in some cases even live prototypes. Artifacts of lower fidelity allow fast iteration and enable the exploration of many alternatives, while artifacts of higher fidelity help to understand, explain, and validate the concept in all its details.

It’s important to think holistically, considering different aspects of the solution. I would highlight three dimensions:

1. Conceptual model: Objects, relations, attributes, actions;
2. Visualization: Screens, from rough sketches to hi-fi mockups;
3. Flow: From the very high-level user journeys to more detailed ones.

One can argue that those are layers rather than dimensions, and each of them builds on the previous ones (for example, according to Semantic IxD by Daniel Rosenberg), but I see them more as different facets of the same thing, so the design process through them is not necessarily linear: you may need to switch from one perspective to another many times.

This is how different types of design artifacts map to these dimensions:

As Solution Discovery progresses, designers move from the left part of this map to the right, from low-fidelity to high-fidelity, from ideating to validating, from diverging to converging.

Note that at the beginning of the process, different dimensions are supported by artifacts of different types (boxes-and-arrows, sketches, class diagrams, etc.), and only closer to the end can you build a live prototype that encompasses all three dimensions: conceptual model, visualization, and flow.

This progression shows a classic trade-off, like the difference between a pencil drawing and an oil painting. The drawing lets you explore ideas in the most flexible way, whereas the painting has a lot of detail and overall looks much more realistic, but is hard to adjust. Similarly, as we go towards artifacts that integrate all three dimensions at higher fidelity, our ability to iterate quickly and explore divergent ideas goes down. This inverse relationship has long been an accepted, almost unchallenged, limitation of the design process.

The Problem With The Mockup-Centric Approach

Faced with this difficult trade-off, often teams opt for the easiest way out. On the one hand, they need to show that they are making progress and create things that appear detailed. On the other hand, they rarely can afford to build interactive or live prototypes. This leads them to over-invest in one type of artifact that seems to offer the best of both worlds. As a result, the neatly organized “bento box” of design artifacts we saw previously gets shrunk down to just one compartment: creating static high-fidelity mockups.

This choice is understandable, as several forces push designers in this direction. Stakeholders are always eager to see nice pictures, while artifacts representing user flows and conceptual models receive much less attention and priority. They are too high-level and hardly usable for validation, and usually, not everyone can understand them.

On the other side of the fidelity spectrum, interactive prototypes require too much effort to create and maintain, and creating live prototypes in code used to require special skills (and again, effort). And even when teams make this investment, they do so at the end of Solution Discovery, during the convergence stage, when it is often too late to experiment with fundamentally different ideas. With so much effort already sunk, there is little appetite to go back to the drawing board.

It’s no surprise, then, that many teams default to the perceived safety of static mockups, seeing them as a middle ground between the roughness of the sketches and the overwhelming complexity and fragility that prototypes can have.

As a result, validation with users doesn’t provide enough confidence that the solution will actually solve the problem, and teams are forced to make a leap of faith to start building. To make matters worse, they do so without a clear understanding of the conceptual model, the user flows, and the interactions, because from the very beginning, designers’ attention has been heavily skewed toward visualization.

The result is often a design artifact that resembles the famous “horse drawing” meme: beautifully rendered in the parts everyone sees first (the mockups), but dangerously underdeveloped in its underlying structure (the conceptual model and flows).

While this is a familiar problem across the industry, its severity depends on the nature of the project. If your core challenge is to optimize a well-understood, linear flow (like many B2C products), a mockup-centric approach can be perfectly adequate. The risks are contained, and the “lopsided horse” problem is unlikely to be fatal.

However, it’s different for the systems I specialize in: complex applications defined by intricate data models and non-linear, interconnected user flows. Here, the biggest risks are not on the surface but in the underlying structure, and a lack of attention to the latter would be a recipe for disaster.

Transforming The Design Process

This situation makes me wonder:

How might we close the gap between our design intent and a live prototype, so that we can iterate on real functionality from day one?

“

If we were able to answer this question, we would:

• Learn faster.
  By going straight from intent to a testable artifact, we cut the feedback loop from weeks to days.
• Gain more confidence.
  Users interact with real logic, which gives us more proof that the idea works.
• Enforce conceptual clarity.
  A live prototype cannot hide a flawed or ambiguous conceptual model.
• Establish a clear and lasting source of truth.
  A live prototype, combined with a clearly documented design intent, provides the engineering team with an unambiguous specification.

Of course, the desire for such a process is not new. This vision of a truly prototype-driven workflow is especially compelling for enterprise applications, where the benefits of faster learning and forced conceptual clarity are the best defense against costly structural flaws. But this ideal was still out of reach because prototyping in code took so much work and specialized talents. Now, the rise of powerful AI coding assistants changes this equation in a big way.

The Seductive Promise Of “Vibe Coding”

And the answer seems to be obvious: vibe coding!

“Vibe coding is an artificial intelligence-assisted software development style popularized by Andrej Karpathy in early 2025. It describes a fast, improvisational, collaborative approach to creating software where the developer and a large language model (LLM) tuned for coding is acting rather like pair programmers in a conversational loop.”

— Wikipedia

The original tweet by Andrej Karpathy:

The allure of this approach is undeniable. If you are not a developer, you are bound to feel awe when you describe a solution in plain language, and moments later, you can interact with it. This seems to be the ultimate fulfillment of our goal: a direct, frictionless path from an idea to a live prototype. But is this method reliable enough to build our new design process around it?

The Trap: A Process Without A Blueprint

Vibe coding mixes up a description of the UI with a description of the system itself, resulting in a prototype based on changing assumptions rather than a clear, solid model.

The pitfall of vibe coding is that it encourages us to express our intent in the most ambiguous way possible: by having a conversation.

“

This is like hiring a builder and telling them what to do one sentence at a time without ever presenting them a blueprint. They could make a wall that looks great, but you can’t be sure that it can hold weight.

I’ll give you one example illustrating problems you may face if you try to jump over the chasm between your idea and a live prototype relying on pure vibe coding in the spirit of Andrej Karpathy’s tweet. Imagine I want to prototype a solution to keep track of tests to validate product ideas. I open my vibe coding tool of choice (I intentionally don’t disclose its name, as I believe they all are awesome yet prone to similar pitfalls) and start with the following prompt:

I need an app to track tests. For every test, I need to fill out the following data:
- Hypothesis (we believe that...) 
- Experiment (to verify that, we will...)
- When (a single date, or a period) 
- Status (New/Planned/In Progress/Proven/Disproven)

And in a minute or so, I get a working prototype:

Inspired by success, I go further:

Please add the ability to specify a product idea for every test. Also, I want to filter tests by product ideas and see how many tests each product idea has in each status.

And the result is still pretty good:

But then I want to extend the functionality related to product ideas:

Okay, one more thing. For every product idea, I want to assess the impact score, the confidence score, and the ease score, and get the overall ICE score. Perhaps I need a separate page focused on the product idea, with all the relevant information and related tests.

And from this point on, the results are getting more and more confusing.

The flow of creating tests hasn’t changed much. I can still create a bunch of tests, and they seem to be organized by product ideas. But when I click “Product Ideas” in the top navigation, I see nothing:

I need to create my ideas from scratch, and they are not connected to the tests I created before:

Moreover, when I go back to “Tests”, I see that they are all gone. Clearly something went wrong, and my AI assistant confirms that:

No, this is not expected behavior — it’s a bug! The issue is that tests are being stored in two separate places (local state in the Index page and App state), so tests created on the main page don’t sync with the product ideas page.

Sure, eventually it fixed that bug, but note that we encountered this just on the third step, when we asked to slightly extend the functionality of a very simple app. The more layers of complexity we add, the more roadblocks of this sort we are bound to face.

Also note that this specific problem of a not fully thought-out relationship between two entities (product ideas and tests) is not isolated at the technical level, and therefore, it didn’t go away once the technical bug was fixed. The underlying conceptual model is still broken, and it manifests in the UI as well.

For example, you can still create “orphan” tests that are not connected to any item from the “Product Ideas” page. As a result, you may end up with different numbers of ideas and tests on different pages of the app:

Let’s diagnose what really happened here. The AI’s response that this is a “bug” is only half the story. The true root cause is a conceptual model failure. My prompts never explicitly defined the relationship between product ideas and tests. The AI was forced to guess, which led to the broken experience. For a simple demo, this might be a fixable annoyance. But for a data-heavy enterprise application, this kind of structural ambiguity is fatal. It demonstrates the fundamental weakness of building without a blueprint, which is precisely what vibe coding encourages.

Don’t take this as a criticism of vibe coding tools. They are creating real magic. However, the fundamental truth about “garbage in, garbage out” is still valid. If you don’t express your intent clearly enough, chances are the result won’t fulfill your expectations.

Another problem worth mentioning is that even if you wrestle it into a state that works, the artifact is a black box that can hardly serve as reliable specifications for the final product. The initial meaning is lost in the conversation, and all that’s left is the end result. This makes the development team “code archaeologists,” who have to figure out what the designer was thinking by reverse-engineering the AI’s code, which is frequently very complicated. Any speed gained at the start is lost right away because of this friction and uncertainty.

From Fast Magic To A Solid Foundation

Pure vibe coding, for all its allure, encourages building without a blueprint. As we’ve seen, this results in structural ambiguity, which is not acceptable when designing complex applications. We are left with a seemingly quick but fragile process that creates a black box that is difficult to iterate on and even more so to hand off.

This leads us back to our main question: how might we close the gap between our design intent and a live prototype, so that we can iterate on real functionality from day one, without getting caught in the ambiguity trap? The answer lies in a more methodical, disciplined, and therefore trustworthy process.

In Part 2 of this series, “A Practical Guide to Building with Clarity”, I will outline the entire workflow for Intent Prototyping. This method places the explicit intent of the designer at the forefront of the process while embracing the potential of AI-assisted coding.

Thank you for reading, and I look forward to seeing you in Part 2.

(yk)

Misuse and misplaced trust of AI is becoming an unfortunate common event. For example, lawyers trying to leverage the power of generative AI for research submit court filings citing multiple compelling legal precedents. The problem? The AI had confidently, eloquently, and completely fabricated the cases cited. The resulting sanctions and public embarrassment can become a viral cautionary tale, shared across social media as a stark example of AI’s fallibility.

This goes beyond a technical glitch; it’s a catastrophic failure of trust in AI tools in an industry where accuracy and trust are critical. The trust issue here is twofold — the law firms are submitting briefs in which they have blindly over-trusted the AI tool to return accurate information. The subsequent fallout can lead to a strong distrust in AI tools, to the point where platforms featuring AI might not be considered for use until trust is reestablished.

Issues with trusting AI aren’t limited to the legal field. We are seeing the impact of fictional AI-generated information in critical fields such as healthcare and education. On a more personal scale, many of us have had the experience of asking Siri or Alexa to perform a task, only to have it done incorrectly or not at all, for no apparent reason. I’m guilty of sending more than one out-of-context hands-free text to an unsuspecting contact after Siri mistakenly pulls up a completely different name than the one I’d requested.

With digital products moving to incorporate generative and agentic AI at an increasingly frequent rate, trust has become the invisible user interface. When it works, our interactions are seamless and powerful. When it breaks, the entire experience collapses, with potentially devastating consequences. As UX professionals, we’re on the front lines of a new twist on a common challenge. How do we build products that users can rely on? And how do we even begin to measure something as ephemeral as trust in AI?

Trust isn’t a mystical quality. It is a psychological construct built on predictable factors. I won’t dive deep into academic literature on trust in this article. However, it is important to understand that trust is a concept that can be understood, measured, and designed for. This article will provide a practical guide for UX researchers and designers. We will briefly explore the psychological anatomy of trust, offer concrete methods for measuring it, and provide actionable strategies for designing more trustworthy and ethical AI systems.

The Anatomy of Trust: A Psychological Framework for AI

To build trust, we must first understand its components. Think of trust like a four-legged stool. If any one leg is weak, the whole thing becomes unstable. Based on classic psychological models, we can adapt these “legs” for the AI context.

1. Ability (or Competence)

This is the most straightforward pillar: Does the AI have the skills to perform its function accurately and effectively? If a weather app is consistently wrong, you stop trusting it. If an AI legal assistant creates fictitious cases, it has failed the basic test of ability. This is the functional, foundational layer of trust.

2. Benevolence

This moves from function to intent. Does the user believe the AI is acting in their best interest? A GPS that suggests a toll-free route even if it’s a few minutes longer might be perceived as benevolent. Conversely, an AI that aggressively pushes sponsored products feels self-serving, eroding this sense of benevolence. This is where user fears, such as concerns about job displacement, directly challenge trust—the user starts to believe the AI is not on their side.

3. Integrity

Does AI operate on predictable and ethical principles? This is about transparency, fairness, and honesty. An AI that clearly states how it uses personal data demonstrates integrity. A system that quietly changes its terms of service or uses dark patterns to get users to agree to something violates integrity. An AI job recruiting tool that has subtle yet extremely harmful social biases, existing in the algorithm, violates integrity.

4. Predictability & Reliability

Can the user form a stable and accurate mental model of how the AI will behave? Unpredictability, even if the outcomes are occasionally good, creates anxiety. A user needs to know, roughly, what to expect. An AI that gives a radically different answer to the same question asked twice is unpredictable and, therefore, hard to trust.

The Trust Spectrum: The Goal of a Well-Calibrated Relationship

Our goal as UX professionals shouldn’t be to maximize trust at all costs. An employee who blindly trusts every email they receive is a security risk. Likewise, a user who blindly trusts every AI output can be led into dangerous situations, such as the legal briefs referenced at the beginning of this article. The goal is well-calibrated trust.

Think of it as a spectrum where the upper-mid level is the ideal state for a truly trustworthy product to achieve:

• Active Distrust
  The user believes the AI is incompetent or malicious. They will avoid it or actively work against it.
• Suspicion & Scrutiny
  The user interacts cautiously, constantly verifying the AI’s outputs. This is a common and often healthy state for users of new AI.
• Calibrated Trust (The Ideal State)
  This is the sweet spot. The user has an accurate understanding of the AI’s capabilities—its strengths and, crucially, its weaknesses. They know when to rely on it and when to be skeptical.
• Over-trust & Automation Bias
  The user unquestioningly accepts the AI’s outputs. This is where users follow flawed AI navigation into a field or accept a fictional legal brief as fact.

Our job is to design experiences that guide users away from the dangerous poles of Active Distrust and Over-trust and toward that healthy, realistic middle ground of Calibrated Trust.

“

The Researcher’s Toolkit: How to Measure Trust In AI

Trust feels abstract, but it leaves measurable fingerprints. Academics in the social sciences have done much to define both what trust looks like and how it might be measured. As researchers, we can capture these signals through a mix of qualitative, quantitative, and behavioral methods.

Qualitative Probes: Listening For The Language Of Trust

During interviews and usability tests, go beyond “Was that easy to use?” and listen for the underlying psychology. Here are some questions you can start using tomorrow:

• To measure Ability:
  “Tell me about a time this tool’s performance surprised you, either positively or negatively.”
• To measure Benevolence:
  “Do you feel this system is on your side? What gives you that impression?”
• To measure Integrity:
  “If this AI made a mistake, how would you expect it to handle it? What would be a fair response?”
• To measure Predictability:
  “Before you clicked that button, what did you expect the AI to do? How closely did it match your expectation?”

Investigating Existential Fears (The Job Displacement Scenario)

One of the most potent challenges to an AI’s Benevolence is the fear of job displacement. When a participant expresses this, it is a critical research finding. It requires a specific, ethical probing technique.

Imagine a participant says, “Wow, it does that part of my job pretty well. I guess I should be worried.”

An untrained researcher might get defensive or dismiss the comment. An ethical, trained researcher validates and explores:

“Thank you for sharing that; it’s a vital perspective, and it’s exactly the kind of feedback we need to hear. Can you tell me more about what aspects of this tool make you feel that way? In an ideal world, how would a tool like this work with you to make your job better, not to replace it?”

This approach respects the participant, validates their concern, and reframes the feedback into an actionable insight about designing a collaborative, augmenting tool rather than a replacement. Similarly, your findings should reflect the concern users expressed about replacement. We shouldn’t pretend this fear doesn’t exist, nor should we pretend that every AI feature is being implemented with pure intention. Users know better than that, and we should be prepared to argue on their behalf for how the technology might best co-exist within their roles.

Quantitative Measures: Putting A Number On Confidence

You can quantify trust without needing a data science degree. After a user completes a task with an AI, supplement your standard usability questions with a few simple Likert-scale items:

• “The AI’s suggestion was reliable.” (1-7, Strongly Disagree to Strongly Agree)
• “I am confident in the AI’s output.” (1-7)
• “I understood why the AI made that recommendation.” (1-7)
• “The AI responded in a way that I expected.” (1-7)
• “The AI provided consistent responses over time.” (1-7)

Over time, these metrics can track how trust is changing as your product evolves.

Note: If you want to go beyond these simple questions that I’ve made up, there are numerous scales (measurements) of trust in technology that exist in academic literature. It might be an interesting endeavor to measure some relevant psychographic and demographic characteristics of your users and see how that correlates with trust in AI/your product. Table 1 at the end of the article contains four examples of current scales you might consider using to measure trust. You can decide which is best for your application, or you might pull some of the items from any of the scales if you aren’t looking to publish your findings in an academic journal, yet want to use items that have been subjected to some level of empirical scrutiny.

Behavioral Metrics: Observing What Users Do, Not Just What They Say

People’s true feelings are often revealed in their actions. You can use behaviors that reflect the specific context of use for your product. Here are a few general metrics that might apply to most AI tools that give insight into users’ behavior and the trust they place in your tool.

• Correction Rate
  How often do users manually edit, undo, or ignore the AI’s output? A high correction rate is a powerful signal of low trust in its Ability.
• Verification Behavior
  Do users switch to Google or open another application to double-check the AI’s work? This indicates they don’t trust it as a standalone source of truth. It can also potentially be positive that they are calibrating their trust in the system when they use it up front.
• Disengagement
  Do users turn the AI feature off? Do they stop using it entirely after one bad experience? This is the ultimate behavioral vote of no confidence.

Designing For Trust: From Principles to Pixels

Once you’ve researched and measured trust, you can begin to design for it. This means translating psychological principles into tangible interface elements and user flows.

Designing for Competence and Predictability

• Set Clear Expectations
  Use onboarding, tooltips, and empty states to honestly communicate what the AI is good at and where it might struggle. A simple “I’m still learning about [topic X], so please double-check my answers” can work wonders.
• Show Confidence Levels
  Instead of just giving an answer, have the AI signal its own uncertainty. A weather app that says “70% chance of rain” is more trustworthy than one that just says “It will rain” and is wrong. An AI could say, “I’m 85% confident in this summary,” or highlight sentences it’s less sure about.

The Role of Explainability (XAI) and Transparency

Explainability isn’t about showing users the code. It’s about providing a useful, human-understandable rationale for a decision.

Instead of:
“Here is your recommendation.”

Try:
“Because you frequently read articles about UX research methods, I’m recommending this new piece on measuring trust in AI.”

This addition transforms AI from an opaque oracle to a transparent logical partner.

Many of the popular AI tools (e.g., ChatGPT and Gemini) show the thinking that went into the response they provide to a user. Figure 3 shows the steps Gemini went through to provide me with a non-response when I asked it to help me generate the masterpiece displayed above in Figure 2. While this might be more information than most users care to see, it provides a useful resource for a user to audit how the response came to be, and it has provided me with instructions on how I might proceed to address my task.

Figure 4 shows an example of a scorecard OpenAI makes available as an attempt to increase users’ trust. These scorecards are available for each ChatGPT model and go into the specifics of how the models perform as it relates to key areas such as hallucinations, health-based conversations, and much more. In reading the scorecards closely, you will see that no AI model is perfect in any area. The user must remain in a trust but verify mode to make the relationship between human reality and AI work in a way that avoids potential catastrophe. There should never be blind trust in an LLM.

Designing For Trust Repair (Graceful Error Handling) And Not Knowing an Answer

Your AI will make mistakes.

Trust is not determined by the absence of errors, but by how those errors are handled.

• Acknowledge Errors Humbly.
  When the AI is wrong, it should be able to state that clearly. “My apologies, I misunderstood that request. Could you please rephrase it?” is far better than silence or a nonsensical answer.
• Provide an Easy Path to Correction.
  Make feedback mechanisms (like thumbs up/down or a correction box) obvious. More importantly, show that the feedback is being used. A “Thank you, I’m learning from your correction” can help rebuild trust after a failure. As long as this is true.

Likewise, your AI can’t know everything. You should acknowledge this to your users.

UX practitioners should work with the product team to ensure that honesty about limitations is a core product principle.

“

This can include the following:

• Establish User-Centric Metrics: Instead of only measuring engagement or task completion, UXers can work with product managers to define and track metrics like:
  • Hallucination Rate: The frequency with which the AI provides verifiably false information.
  • Successful Fallback Rate: How often the AI correctly identifies its inability to answer and provides a helpful, honest alternative.
• Prioritize the “I Don’t Know” Experience: UXers should frame the “I don’t know” response not as an error state, but as a critical feature. They must lobby for the engineering and content resources needed to design a high-quality, helpful fallback experience.

UX Writing And Trust

All of these considerations highlight the critical role of UX writing in the development of trustworthy AI. UX writers are the architects of the AI’s voice and tone, ensuring that its communication is clear, honest, and empathetic. They translate complex technical processes into user-friendly explanations, craft helpful error messages, and design conversational flows that build confidence and rapport. Without thoughtful UX writing, even the most technologically advanced AI can feel opaque and untrustworthy.

The words and phrases an AI uses are its primary interface with users. UX writers are uniquely positioned to shape this interaction, ensuring that every tooltip, prompt, and response contributes to a positive and trust-building experience. Their expertise in human-centered language and design is indispensable for creating AI systems that not only perform well but also earn and maintain the trust of their users.

A few key areas for UX writers to focus on when writing for AI include:

• Prioritize Transparency
  Clearly communicate the AI’s capabilities and limitations, especially when it’s still learning or if its responses are generated rather than factual. Use phrases that indicate the AI’s nature, such as “As an AI, I can…” or “This is a generated response.”
• Design for Explainability
  When the AI provides a recommendation, decision, or complex output, strive to explain the reasoning behind it in an understandable way. This builds trust by showing the user how the AI arrived at its conclusion.
• Emphasize User Control
  Empower users by providing clear ways to provide feedback, correct errors, or opt out of certain AI features. This reinforces the idea that the user is in control and the AI is a tool to assist them.

The Ethical Tightrope: The Researcher’s Responsibility

As the people responsible for understanding and advocating for users, we walk an ethical tightrope. Our work comes with profound responsibilities.

The Danger Of “Trustwashing”

We must draw a hard line between designing for calibrated trust and designing to manipulate users into trusting a flawed, biased, or harmful system. For example, if an AI system designed for loan approvals consistently discriminates against certain demographics but presents a user interface that implies fairness and transparency, this would be an instance of trustwashing.

Another example of trustwashing would be if an AI medical diagnostic tool occasionally misdiagnoses conditions, but the user interface makes it seem infallible. To avoid trustwashing, the system should clearly communicate the potential for error and the need for human oversight.

Our goal must be to create genuinely trustworthy systems, not just the perception of trust. Using these principles to lull users into a false sense of security is a betrayal of our professional ethics.

To avoid and prevent trustwashing, researchers and UX teams should:

• Prioritize genuine transparency.
  Clearly communicate the limitations, biases, and uncertainties of AI systems. Don’t overstate capabilities or obscure potential risks.
• Conduct rigorous, independent evaluations.
  Go beyond internal testing and seek external validation of system performance, fairness, and robustness.
• Engage with diverse stakeholders.
  Involve users, ethics experts, and impacted communities in the design, development, and evaluation processes to identify potential harms and build genuine trust.
• Be accountable for outcomes.
  Take responsibility for the societal impact of AI systems, even if unintended. Establish mechanisms for redress and continuous improvement.
• Be accountable for outcomes.
  Establish clear and accessible mechanisms for redress when harm occurs, ensuring that individuals and communities affected by AI decisions have avenues for recourse and compensation.
• Educate the public.
  Help users understand how AI works, its limitations, and what to look for when evaluating AI products.
• Advocate for ethical guidelines and regulations.
  Support the development and implementation of industry standards and policies that promote responsible AI development and prevent deceptive practices.
• Be wary of marketing hype.
  Critically assess claims made about AI systems, especially those that emphasize “trustworthiness” without clear evidence or detailed explanations.
• Publish negative findings.
  Don’t shy away from reporting challenges, failures, or ethical dilemmas encountered during research. Transparency about limitations is crucial for building long-term trust.
• Focus on user empowerment.
  Design systems that give users control, agency, and understanding rather than just passively accepting AI outputs.

The Duty To Advocate

When our research uncovers deep-seated distrust or potential harm — like the fear of job displacement — our job has only just begun. We have an ethical duty to advocate for that user. In my experience directing research teams, I’ve seen that the hardest part of our job is often carrying these uncomfortable truths into rooms where decisions are made. We must champion these findings and advocate for design and strategy shifts that prioritize user well-being, even when it challenges the product roadmap.

I personally try to approach presenting this information as an opportunity for growth and improvement, rather than a negative challenge.

For example, instead of stating “Users don’t trust our AI because they fear job displacement,” I might frame it as “Addressing user concerns about job displacement presents a significant opportunity to build deeper trust and long-term loyalty by demonstrating our commitment to responsible AI development and exploring features that enhance human capabilities rather than replace them.” This reframing can shift the conversation from a defensive posture to a proactive, problem-solving mindset, encouraging collaboration and innovative solutions that ultimately benefit both the user and the business.

It’s no secret that one of the more appealing areas for businesses to use AI is in workforce reduction. In reality, there will be many cases where businesses look to cut 10–20% of a particular job family due to the perceived efficiency gains of AI. However, giving users the opportunity to shape the product may steer it in a direction that makes them feel safer than if they do not provide feedback. We should not attempt to convince users they are wrong if they are distrustful of AI. We should appreciate that they are willing to provide feedback, creating an experience that is informed by the human experts who have long been doing the task being automated.

Conclusion: Building Our Digital Future On A Foundation Of Trust

The rise of AI is not the first major technological shift our field has faced. However, it presents one of the most significant psychological challenges of our current time. Building products that are not just usable but also responsible, humane, and trustworthy is our obligation as UX professionals.

Trust is not a soft metric. It is the fundamental currency of any successful human-technology relationship. By understanding its psychological roots, measuring it with rigor, and designing for it with intent and integrity, we can move from creating “intelligent” products to building a future where users can place their confidence in the tools they use every day. A trust that is earned and deserved.

Table 1: Published Academic Scales Measuring Trust In Automated Systems

Appendix A: Trust-Building Tactics Checklist

To design for calibrated trust, consider implementing the following tactics, organized by the four pillars of trust:

1. Ability (Competence) & Predictability

• ✅ Set Clear Expectations: Use onboarding, tooltips, and empty states to honestly communicate the AI’s strengths and weaknesses.
• ✅ Show Confidence Levels: Display the AI’s uncertainty (e.g., “70% chance,” “85% confident”) or highlight less certain parts of its output.
• ✅ Provide Explainability (XAI): Offer useful, human-understandable rationales for the AI’s decisions or recommendations (e.g., “Because you frequently read X, I’m recommending Y”).
• ✅ Design for Graceful Error Handling:
  • ✅ Acknowledge errors humbly (e.g., “My apologies, I misunderstood that request.”).
  • ✅ Provide easy paths to correction (e. ] g., prominent feedback mechanisms like thumbs up/down).
  • ✅ Show that feedback is being used (e.g., “Thank you, I’m learning from your correction”).
• ✅ Design for “I Don’t Know” Responses:
  • ✅ Acknowledge limitations honestly.
  • ✅ Prioritize a high-quality, helpful fallback experience when the AI cannot answer.
• ✅ Prioritize Transparency: Clearly communicate the AI’s capabilities and limitations, especially if responses are generated.

2. Benevolence

• ✅ Address Existential Fears: When users express concerns (e.g., job displacement), validate their concerns and reframe the feedback into actionable insights about collaborative tools.
• ✅ Prioritize User Well-being: Advocate for design and strategy shifts that prioritize user well-being, even if it challenges the product roadmap.
• ✅ Emphasize User Control: Provide clear ways for users to give feedback, correct errors, or opt out of AI features.

3. Integrity

• ✅ Adhere to Ethical Principles: Ensure the AI operates on predictable, ethical principles, demonstrating fairness and honesty.
• ✅ Prioritize Genuine Transparency: Clearly communicate the limitations, biases, and uncertainties of AI systems; avoid overstating capabilities or obscuring risks.
• ✅ Conduct Rigorous, Independent Evaluations: Seek external validation of system performance, fairness, and robustness to mitigate bias.
• ✅ Engage Diverse Stakeholders: Involve users, ethics experts, and impacted communities in the design and evaluation processes.
• ✅ Be Accountable for Outcomes: Establish clear mechanisms for redress and continuous improvement for societal impacts, even if unintended.
• ✅ Educate the Public: Help users understand how AI works, its limitations, and how to evaluate AI products.
• ✅ Advocate for Ethical Guidelines: Support the development and implementation of industry standards and policies that promote responsible AI.
• ✅ Be Wary of Marketing Hype: Critically assess claims about AI “trustworthiness” and demand verifiable data.
• ✅ Publish Negative Findings: Be transparent about challenges, failures, or ethical dilemmas encountered during research.

4. Predictability & Reliability

• ✅ Set Clear Expectations: Use onboarding, tooltips, and empty states to honestly communicate what the AI is good at and where it might struggle.
• ✅ Show Confidence Levels: Instead of just giving an answer, have the AI signal its own uncertainty.
• ✅ Provide Explainability (XAI) and Transparency: Offer a useful, human-understandable rationale for AI decisions.
• ✅ Design for Graceful Error Handling: Acknowledge errors humbly and provide easy paths to correction.
• ✅ Prioritize the “I Don’t Know” Experience: Frame “I don’t know” as a feature and design a high-quality fallback experience.
• ✅ Prioritize Transparency (UX Writing): Clearly communicate the AI’s capabilities and limitations, especially when it’s still learning or if responses are generated.
• ✅ Design for Explainability (UX Writing): Explain the reasoning behind AI recommendations, decisions, or complex outputs.

(yk)

Climate change is the single biggest health threat to humanity, accelerated by human activities such as the burning of fossil fuels, which generate greenhouse gases that trap the sun’s heat.

The average temperature of the earth’s surface is now 1.2°C warmer than it was in the late 1800’s, and projected to more than double by the end of the century.

The consequences of climate change include intense droughts, water shortages, severe fires, melting polar ice, catastrophic storms, and declining biodiversity.

The Internet Is A Significant Part Of The Problem

Shockingly, the internet is responsible for higher global greenhouse emissions than the aviation industry, and is projected to be responsible for 14% of all global greenhouse gas emissions by 2040.

If the internet were a country, it would be the 4th largest polluter in the world and represents the largest coal-powered machine on the planet.

But how can something digital like the internet produce harmful emissions?

Internet emissions come from powering the infrastructure that drives the internet, such as the vast data centres and data transmission networks that consume huge amounts of electricity.

Internet emissions also come from the global manufacturing, distribution, and usage of the estimated 30.5 billion devices (phones, laptops, etc.) that we use to access the internet.

Unsurprisingly, internet related emissions are increasing, given that 60% of the world’s population spend, on average, 40% of their waking hours online.

We Must Urgently Reduce The Environmental Impact Of The Internet

As responsible digital professionals, we must act quickly to minimise the environmental impact of our work.

It is encouraging to see the UK government encourage action by adding “Minimise environmental impact” to their best practice design principles, but there is still too much talking and not enough corrective action taking place within our industry.

The reality of many tightly constrained, fast-paced, and commercially driven web projects is that minimising environmental impact is far from the agenda.

So how can we make the environment more of a priority and talk about it in ways that stakeholders will listen to?

A eureka moment on a recent web optimisation project gave me an idea.

My Eureka Moment

I led a project to optimise the mobile performance of www.talktofrank.com, a government drug advice website that aims to keep everyone safe from harm.

Mobile performance is critically important for the success of this service to ensure that users with older mobile devices and those using slower network connections can still access the information they need.

Our work to minimise page weights focused on purely technical changes that our developer made following recommendations from tools such as Google Lighthouse that reduced the size of the webpages of a key user journey by up to 80%. This resulted in pages downloading up to 30% faster and the carbon footprint of the journey being reduced by 80%.

We hadn’t set out to reduce the carbon footprint, but seeing these results led to my eureka moment.

I realised that by minimising page weights, you improve performance (which is a win for users and service owners) and also consume less energy (due to needing to transfer and store less data), creating additional benefits for the planet — so everyone wins.

This felt like a breakthrough because business, user, and environmental requirements are often at odds with one another. By focussing on minimising websites to be as simple, lightweight and easy to use as possible you get benefits that extend beyond the triple bottom line of people, planet and profit to include performance and purpose.

So why is ‘minimising’ such a great digital sustainability strategy?

• Profit
  Website providers win because their website becomes more efficient and more likely to meet its intended outcomes, and a lighter site should also lead to lower hosting bills.
• People
  People win because they get to use a website that downloads faster, is quick and easy to use because it’s been intentionally designed to be as simple as possible, enabling them to complete their tasks with the minimum amount of effort and mental energy.
• Performance
  Lightweight webpages download faster so perform better for users, particularly those on older devices and on slower network connections.
• Planet
  The planet wins because the amount of energy (and associated emissions) that is required to deliver the website is reduced.
• Purpose
  We know that we do our best work when we feel a sense of purpose. It is hugely gratifying as a digital professional to know that our work is doing good in the world and contributing to making things better for people and the environment.

In order to prioritise the environment, we need to be able to speak confidently in a language that will resonate with the business and ensure that any investment in time and resources yields the widest range of benefits possible.

So even if you feel that the environment is a very low priority on your projects, focusing on minimising page weights to improve performance (which is generally high on the agenda) presents the perfect trojan horse for an environmental agenda (should you need one).

Doing the right thing isn’t always easy, but we’ve done it before when managing to prioritise issues such as usability, accessibility, and inclusion on digital projects.

Many of the things that make websites easier to use, more accessible, and more effective also help to minimise their environmental impact, so the things you need to do will feel familiar and achievable, so don’t worry about it all being another new thing to learn about!

So this all makes sense in theory, but what’s the master plan to use when putting it into practice?

The Masterplan

The masterplan for creating websites that have minimal environmental impact is to focus on offering the maximum value from the minimum input of energy.

It’s an adaptation of Buckminister Fuller’s ‘Dymaxion’ principle, which is one of his many progressive and groundbreaking sustainability strategies for living and surviving on a planet with finite resources.

Inputs of energy include both the electrical energy that is required to operate websites and also the mental energy that is required to use them.

You can achieve this by minimising websites to their core content, features, and functionality, ensuring that everything can be justified from the perspective of meeting a business or user need. This means that anything that isn’t adding a proportional amount of value to the amount of energy it requires to provide it should be removed.

So that’s the masterplan, but how do you put it into practice?

Decarbonise Your Highest Value User Journeys

I’ve developed a new approach called ‘Decarbonising User Journeys’ that will help you to minimise the environmental impact of your website and maximise its performance.

Note: The approach deliberately focuses on optimising key user journeys and not entire websites to keep things manageable and to make it easier to get started.

The secret here is to start small, demonstrate improvements, and then scale.

The approach consists of five simple steps:

1. Identify your highest value user journey,
2. Benchmark your user journey,
3. Set targets,
4. Decarbonise your user journey,
5. Track and share your progress.

Here’s how it works.

Step 1: Identify Your Highest Value User Journey

Your highest value user journey might be the one that your users value the most, the one that brings you the highest revenue, or the one that is fundamental to the success of your organisation.

You could also focus on a user journey that you know is performing particularly badly and has the potential to deliver significant business and user benefits if improved.

You may have lots of important user journeys, and it’s fine to decarbonise multiple journeys in parallel if you have the resources, but I’d recommend starting with one first to keep things simple.

To bring this to life, let’s consider a hypothetical example of a premiership football club trying to decarbonise its online ticket-buying journey that receives high levels of traffic and is responsible for a significant proportion of its weekly income.

Step 2: Benchmark Your User Journey

Once you’ve selected your user journey, you need to benchmark it in terms of how well it meets user needs, the value it offers your organisation, and its carbon footprint.

It is vital that you understand the job it needs to do and how well it is doing it before you start to decarbonise it. There is no point in removing elements of the journey in an effort to reduce its carbon footprint, for example, if you compromise its ability to meet a key user or business need.

You can benchmark how well your user journey is meeting user needs by conducting user research alongside analysing existing customer feedback. Interviews with business stakeholders will help you to understand the value that your journey is providing the organisation and how well business needs are being met.

You can benchmark the carbon footprint and performance of your user journey using online tools such as Cardamon, Ecograder, Website Carbon Calculator, Google Lighthouse, and Bioscore. Make sure you have your analytics data to hand to help get the most accurate estimate of your footprint.

To use these tools, simply add the URL of each page of your journey, and they will give you a range of information such as page weight, energy rating, and carbon emissions. Google Lighthouse works slightly differently via a browser plugin and generates a really useful and detailed performance report as opposed to giving you a carbon rating.

A great way to bring your benchmarking scores to life is to visualise them in a similar way to how you would present a customer journey map or service blueprint.

This example focuses on just communicating the carbon footprint of the user journey, but you can also add more swimlanes to communicate how well the journey is performing from a user and business perspective, too, adding user pain points, quotes, and business metrics where appropriate.

I’ve found that adding the energy efficiency ratings is really effective because it’s an approach that people recognise from their household appliances. This adds a useful context to just showing the weights (such as grams or kilograms) of CO2, which are generally meaningless to people.

Within my benchmarking reports, I also add a set of benchmarking data for every page within the user journey. This gives your stakeholders a more detailed breakdown and a simple summary alongside a snapshot of the benchmarked page.

Your benchmarking activities will give you a really clear picture of where remedial work is required from an environmental, user, and business point of view.

In our football user journey example, it’s clear that the ‘News’ and ‘Tickets’ pages need some attention to reduce their carbon footprint, so they would be a sensible priority for decarbonising.

Step 3: Set Targets

Use your benchmarking results to help you set targets to aim for, such as a carbon budget, energy efficiency, maximum page weight, and minimum Google Lighthouse performance targets for each individual page, in addition to your existing UX metrics and business KPIs.

There is no right or wrong way to set targets. Choose what you think feels achievable and viable for your business, and you’ll only learn how reasonable and achievable they are when you begin to decarbonise your user journeys.

Setting targets is important because it gives you something to aim for and keeps you focused and accountable. The quantitative nature of this work is great because it gives you the ability to quickly demonstrate the positive impact of your work, making it easier to justify the time and resources you are dedicating to it.

Step 4: Decarbonise Your User Journey

Your objective now is to decarbonise your user journey by minimising page weights, improving your Lighthouse performance rating, and minimising pages so that they meet both user and business needs in the most efficient, simple, and effective way possible.

It’s up to you how you approach this depending on the resources and skills that you have, you can focus on specific pages or addressing a specific problem area such as heavyweight images or videos across the entire user journey.

Here’s a list of activities that will all help to reduce the carbon footprint of your user journey:

• Work through the recommendations in the ‘diagnostics’ section of your Google Lighthouse report to help optimise page performance.
• Switch to a green hosting provider if you are not already using one. Use the Green Web Directory to help you choose one.
• Work through the W3C Web Sustainability Guidelines, implementing the most relevant guidelines to your specific user journey.
• Remove anything that is not adding any user or business value.
• Reduce the amount of information on your webpages to make them easier to read and less overwhelming for people.
• Replace content with a lighter-weight alternative (such as swapping a video for text) if the lighter-weight alternative provides the same value.
• Optimise assets such as photos, videos, and code to reduce file sizes.
• Remove any barriers to accessing your website and any distractions that are getting in the way.
• Re-use familiar components and design patterns to make your websites quicker and easier to use.
• Write simply and clearly in plain English to help people get the most value from your website and to help them avoid making mistakes that waste time and energy to resolve.
• Fix any usability issues you identified during your benchmarking to ensure that your website is as easy to use and useful as possible.
• Ensure your user journey is as accessible as possible so the widest possible audience can benefit from using it, offsetting the environmental cost of providing the website.

Step 5: Track And Share Your Progress

As you decarbonise your user journeys, use the benchmarking tools from step 2 to track your progress against the targets you set in step 3 and share your progress as part of your wider sustainability reporting initiatives.

All being well at this point, you will have the numbers to demonstrate how the performance of your user journey has improved and also how you have managed to reduce its carbon footprint.

Share these results with the business as soon as you have them to help you secure the resources to continue the work and initiate similar work on other high-value user journeys.

You should also start to communicate your progress with your users.

It’s important that they are made aware of the carbon footprint of their digital activity and empowered to make informed choices about the environmental impact of the websites that they use.

Ideally, every website should communicate the emissions generated from viewing their pages to help people make these informed choices and also to encourage website providers to minimise their emissions if they are being displayed publicly.

Often, people will have no choice but to use a specific website to complete a specific task, so it is the responsibility of the website provider to ensure the environmental impact of using their website is as small as possible.

You can also help to raise awareness of the environmental impact of websites and what you are doing to minimise your own impact by publishing a digital sustainability statement, such as Unilever’s, as shown below.

A good digital sustainability statement should acknowledge the environmental impact of your website, what you have done to reduce it, and what you plan to do next to minimise it further.

As an industry, we should normalise publishing digital sustainability statements in the same way that accessibility statements have become a standard addition to website footers.

“

Useful Decarbonising Principles

Keep these principles in mind to help you decarbonise your user journeys:

• More doing and less talking.
  Start decarbonising your user journeys as soon as possible to accelerate your learning and positive change.
• Start small.
  Starting small by decarbonising an individual journey makes it easier to get started and generates results to demonstrate value faster.
• Aim to do more with less.
  Minimise what you offer to ensure you are providing the maximum amount of value for the energy you are consuming.
• Make your website as useful and as easy to use as possible.
  Useful websites can justify the energy they consume to provide them, ensuring they are net positive in terms of doing more good than harm.
• Focus on progress over perfection.
  Websites are never finished or perfect but they can always be improved, every small improvement you make will make a difference.

Start Decarbonising Your User Journeys Today

Decarbonising user journeys shouldn’t be done as a one-off, reserved for the next time that you decide to redesign or replatform your website; it should happen on a continual basis as part of your broader digital sustainability strategy.

We know that websites are never finished and that the best websites continually improve as both user and business needs change. I’d like to encourage people to adopt the same mindset when it comes to minimising the environmental impact of their websites.

Decarbonising will happen most effectively when digital professionals challenge themselves on a daily basis to ‘minimise’ the things they are working on.

“

This avoids building ‘carbon debt’ that consists of compounding technical and design debt within our websites, which is always harder to retrospectively remove than avoid in the first place.

By taking a pragmatic approach, such as optimising high-value user journeys and aligning with business metrics such as performance, we stand the best possible chance of making digital sustainability a priority.

You’ll have noticed that, other than using website carbon calculator tools, this approach doesn’t require any skills that don’t already exist within typical digital teams today. This is great because it means you’ve already got the skills that you need to do this important work.

I would encourage everyone to raise the issue of the environmental impact of the internet in their next team meeting and to try this decarbonising approach to create better outcomes for people, profit, performance, purpose, and the planet.

Good luck!

(yk)