UX research can take so much of the guesswork out of the design process! But it’s easy to forget just how different people are and how their needs and preferences can vary. We can’t predict the needs of every user, but we shouldn’t expect different people using the product in roughly the same way. That’s how we end up with an incomplete, inaccurate, or simply wrong picture of our customers.

There is no shortage of accessibility checklists and guidelines. But accessibility isn’t a checklist. It doesn’t happen by accident. It’s a dedicated effort to include and consider and understand different needs of different users to make sure everyone can use our products successfully. That’s why we’ve teamed up with Michele A. Williams on a shiny new book around just that.

Meet Accessible UX Research, your guide to making UX research more inclusive of participants with different needs — from planning and recruiting to facilitation, asking better questions, avoiding bias, and building trust. Pre-order the book.

Please note that 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.

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. Written by Dr. Michele A. Williams. Cover art by Espen Brunborg. Print edition shipping early 2026. eBook available for download in December 2025. Pre-order the book.

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.

Testimonials

“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

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 available for download as PDF, ePUB, and Amazon Kindle in December 2025.
• Pre-order the book.

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 as soon as it’s out. 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. Addy, Heather, and Steven are three of these people. Have you checked out their books already?

(as, cm)

Hey, I’m Mat, but “Wilto” works too — I’m here to teach you JavaScript. Well, not here-here; technically, I’m over at Piccalil.li’s JavaScript for Everyone course to teach you JavaScript. The following is an excerpt from the Iterables and Iterators module: the lesson on Iterators.

Iterators are one of JavaScript’s more linguistically confusing topics, sailing easily over what is already a pretty high bar. There are iterables — array, Set, Map, and string — all of which follow the iterable protocol. To follow said protocol, an object must implement the iterable interface. In practice, that means that the object needs to include a [Symbol.iterator]() method somewhere in its prototype chain. Iterable protocol is one of two iteration protocols. The other iteration protocol is the iterator protocol.

See what I mean about this being linguistically fraught? Iterables implement the iterable iteration interface, and iterators implement the iterator iteration interface! If you can say that five times fast, then you’ve pretty much got the gist of it; easy-peasy, right?

No, listen, by the time you reach the end of this lesson, I promise it won’t be half as confusing as it might sound, especially with the context you’ll have from the lessons that precede it.

An iterable object follows the iterable protocol, which just means that the object has a conventional method for making iterators. The elements that it contains can be looped over with for…of.

An iterator object follows the iterator protocol, and the elements it contains can be accessed sequentially, one at a time.

To reiterate — a play on words for which I do not forgive myself, nor expect you to forgive me — an iterator object follows iterator protocol, and the elements it contains can be accessed sequentially, one at a time. Iterator protocol defines a standard way to produce a sequence of values, and optionally return a value once all possible values have been generated.

In order to follow the iterator protocol, an object has to — you guessed it — implement the iterator interface. In practice, that once again means that a certain method has to be available somewhere on the object’s prototype chain. In this case, it’s the next() method that advances through the elements it contains, one at a time, and returns an object each time that method is called.

In order to meet the iterator interface criteria, the returned object must contain two properties with specific keys: one with the key value, representing the value of the current element, and one with the key done, a Boolean value that tells us if the iterator has advanced beyond the final element in the data structure. That’s not an awkward phrasing the editorial team let slip through: the value of that done property is true only when a call to next() results in an attempt to access an element beyond the final element in the iterator, not upon accessing the final element in the iterator. Again, a lot in print, but it’ll make more sense when you see it in action.

You’ve seen an example of a built-in iterator before, albeit briefly:

const theMap = new Map([ [ "aKey", "A value." ] ]);

console.log( theMap.keys() );
// Result: Map Iterator { constructor: Iterator() }

That’s right: while a Map object itself is an iterable, Map’s built-in methods keys(), values(), and entries() all return Iterator objects. You’ll also remember that I looped through those using forEach (a relatively recent addition to the language). Used that way, an iterator is indistinguishable from an iterable:

const theMap = new Map([ [ "key", "value " ] ]);

theMap.keys().forEach( thing => {
  console.log( thing );
});
// Result: key

All iterators are iterable; they all implement the iterable interface:

const theMap = new Map([ [ "key", "value " ] ]);

theMap.keys()[ Symbol.iterator ];
// Result: function Symbol.iterator()

And if you’re angry about the increasing blurriness of the line between iterators and iterables, wait until you get a load of this “top ten anime betrayals” video candidate: I’m going to demonstrate how to interact with an iterator by using an array.

“BOO,” you surely cry, having been so betrayed by one of your oldest and most indexed friends. “Array is an iterable, not an iterator!” You are both right to yell at me in general, and right about array in specific — an array is an iterable, not an iterator. In fact, while all iterators are iterable, none of the built-in iterables are iterators.

However, when you call that [ Symbol.iterator ]() method — the one that defines an object as an iterable — it returns an iterator object created from an iterable data structure:

const theIterable = [ true, false ];
const theIterator = theIterable[ Symbol.iterator ]();

theIterable;
// Result: Array [ true, false ]

theIterator;
// Result: Array Iterator { constructor: Iterator() }

The same goes for Set, Map, and — yes — even strings:

const theIterable = "A string."
const theIterator = theIterable[ Symbol.iterator ]();

theIterator;
// Result: String Iterator { constructor: Iterator() }

What we’re doing here manually — creating an iterator from an iterable using %Symbol.iterator% — is precisely how iterable objects work internally, and why they have to implement %Symbol.iterator% in order to be iterables. Any time you loop through an array, you’re actually looping through an iterator created from that Array. All built-in iterators are iterable. All built-in iterables can be used to create iterators.

Alternately — preferably, even, since it doesn’t require you to graze up against %Symbol.iterator% directly — you can use the built-in Iterator.from() method to create an iterator object from any iterable:

const theIterator = Iterator.from([ true, false ]);

theIterator;
// Result: Array Iterator { constructor: Iterator() }

You remember how I mentioned that an iterator has to provide a next() method (that returns a very specific Object)? Calling that next() method steps through the elements that the iterator contains one at a time, with each call returning an instance of that Object:

const theIterator = Iterator.from([ 1, 2, 3 ]);

theIterator.next();
// Result: Object { value: 1, done: false }

theIterator.next();
// Result: Object { value: 2, done: false }

theIterator.next();
// Result: Object { value: 3, done: false }

theIterator.next();
// Result: Object { value: undefined, done: true }

You can think of this as a more controlled form of traversal than the traditional “wind it up and watch it go” for loops you’re probably used to — a method of accessing elements one step at a time, as-needed. Granted, you don’t have to step through an iterator in this way, since they have their very own Iterator.forEach method, which works exactly like you would expect — to a point:

const theIterator = Iterator.from([ true, false ]);

theIterator.forEach( element => console.log( element ) );
/* Result:
true
false
*/

But there’s another big difference between iterables and iterators that we haven’t touched on yet, and for my money, it actually goes a long way toward making linguistic sense of the two. You might need to humor me for a little bit here, though.

See, an iterable object is an object that is iterable. No, listen, stay with me: you can iterate over an Array, and when you’re done doing so, you can still iterate over that Array. It is, by definition, an object that can be iterated over; it is the essential nature of an iterable to be iterable:

const theIterable = [ 1, 2 ];

theIterable.forEach( el => {
  console.log( el );
});
/* Result:
1
2
*/

theIterable.forEach( el => {
  console.log( el );
});
/* Result:
1
2
*/

In a way, an iterator object represents the singular act of iteration. Internal to an iterable, it is the mechanism by which the iterable is iterated over, each time that iteration is performed. As a stand-alone iterator object — whether you step through it using the next method or loop over its elements using forEach — once iterated over, that iterator is past tense; it is iterated. Because they maintain an internal state, the essential nature of an iterator is to be iterated over, singular:

const theIterator = Iterator.from([ 1, 2 ]);

theIterator.next();
// Result: Object { value: 1, done: false }

theIterator.next();
// Result: Object { value: 2, done: false }

theIterator.next();
// Result: Object { value: undefined, done: true }

theIterator.forEach( el => console.log( el ) );
// Result: undefined

That makes for neat work when you’re using the Iterator constructor’s built-in methods to, say, filter or extract part of an Iterator object:

const theIterator = Iterator.from([ "First", "Second", "Third" ]);

// Take the first two values from `theIterator`:
theIterator.take( 2 ).forEach( el => {
  console.log( el );
});
/* Result:
"First"
"Second"
*/

// theIterator now only contains anything left over after the above operation is complete:
theIterator.next();
// Result: Object { value: "Third", done: false }

Once you reach the end of an iterator, the act of iterating over it is complete. Iterated. Past-tense.

And so too is your time in this lesson, you might be relieved to hear. I know this was kind of a rough one, but the good news is: this course is iterable, not an iterator. This step in your iteration through it — this lesson — may be over, but the essential nature of this course is that you can iterate through it again. Don’t worry about committing all of this to memory right now — you can come back and revisit this lesson anytime.

Conclusion

I stand by what I wrote there, unsurprising as that probably is: this lesson is a tricky one, but listen, you got this. JavaScript for Everyone is designed to take you inside JavaScript’s head. Once you’ve started seeing how the gears mesh — seen the fingerprints left behind by the people who built the language, and the good, bad, and sometimes baffling decisions that went into that — no itera-, whether -ble or -tor will be able to stand in your way.

My goal is to teach you the deep magic — the how and the why of JavaScript, using the syntaxes you’re most likely to encounter in your day-to-day work, at your pace and on your terms. If you’re new to the language, you’ll walk away from this course with a foundational understanding of JavaScript worth hundreds of hours of trial-and-error. If you’re a junior developer, you’ll finish this course with a depth of knowledge to rival any senior.

I hope to see you there.

(gg, 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.

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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)