Boagworld: UX, Design Leadership, Marketing & Conversion Optimization

This episode we're joined by Stu Green, a product designer, agency founder, and serial app builder who's sold not one but two successful SaaS products.

We dig into the realities of building your own product versus running an agency, the role AI plays in modern product development, and whether the flood of AI-built apps is a threat or an opportunity for professionals.

Plus, we check out Bleet, an app that turns your meeting transcripts into social media content, and Paul shares how AI-powered personas are changing the way he approaches user research.

You know you should be posting on LinkedIn. You've told yourself that every week for the past 6 months. But then you sit down, stare at the blank post box, and realize you have absolutely no idea what to write about. So you close the tab and promise yourself you'll do it tomorrow. You won't.

Bleet is an app built by Stu Green (and collaborator Nick) that solves this by mining the conversations you're already having. It takes your meeting recordings and transcripts, extracts the key topics using AI, and helps you turn them into social media posts. And the thing that sets it apart from just asking ChatGPT to write something for you is that it pulls your actual words and phrases from the conversation, piecing them together into posts that genuinely sound like you rather than generic AI slop.

You connect your meeting recordings or transcripts (or even just speak a thought into the app), and Bleet will surface a list of topics you covered. From there, you pick the ones you want to post about and hit "create." You can dial in how much creative liberty the AI takes, from near-verbatim to lightly polished.

So you sit down for 10 minutes once a week, pick a handful of topics, schedule them up, and you're done. A single meeting can generate enough content for almost a week of daily posts.

The number one concern people raise is about sharing sensitive client information. Bleet strips out client names, specific people, and identifiable details. It focuses on the general topic and the ideas discussed, not the specifics of who said what in which meeting. And of course, you review everything before it goes anywhere, so if something feels too close to the bone, you just skip it or edit it.

Stu Green has lived both lives. He's run agencies, built products from scratch, and sold 2 SaaS businesses. So what's the difference between building for clients and building for yourself? Quite a lot, as it turns out.

Both of Stu's successful apps, a project management tool and HourStack (a time management app), started the same way: he needed something that didn't exist. The project management tool grew out of running his own consultancy. HourStack came from juggling small children and fragmented work hours, and wanting a way to visualize and stack little blocks of productive time.

If you're genuinely your own best customer, there's a good chance others like you exist. And if even 2 or 5 or 10 of them show up, you've got the start of something real.

AI has made it dramatically easier to build apps, but Stu is refreshingly honest about the gap between a demo and a product. Sure, he cloned entire apps in a single prompt and it looked great. But behind that impressive facade? Hours of iteration, hosting setup, video infrastructure, S3 servers, and a stack of decisions that require real product-building experience.

The people posting "I built this in one shot" on X are technically telling the truth, but they're showing you the Hollywood set, not the house behind the door. Getting from prototype to something you can actually charge money for still takes professional knowledge. You need to know what questions to ask, which answers are good, and when you're being led down a rabbit hole.

Paul and Stu landed on a useful mental model: there are essentially 2 categories of AI building tools.

1. Tools for everyone: Platforms like Lovable or Figma Make that let anyone create a basic app or prototype. Great for personal use, proof of concepts, and quick experiments.
2. Tools for professionals: Things like Cursor and Claude Code that enhance a developer's ability to build production-quality software faster and better, but still require real expertise to use well.

Think of it like desktop publishing in the '90s. When it arrived, everyone panicked that graphic designers were finished. Instead, regular people made terrible flyers with Comic Sans, and the professionals used the same tools to produce better work, faster. AI-built apps are following the same pattern.

Paul offered a framework for thinking about where AI fits in the build process:

1. Prototype and proof of concept: Anyone can do this with AI tools. Great for validating ideas quickly and cheaply.
2. The production build: This still needs a professional. Security, scalability, accessibility, solid architecture: these are non-negotiable if people are paying to use your product.
3. Post-launch iteration: Once a professional has laid a strong foundation, less technical people can step back in and make tweaks and improvements with AI assistance, because they're working within a well-built structure.

Stu floated an interesting agency model: instead of charging a client the full upfront cost to build their app, what if you took partial ownership? The client pays a smaller retainer and upfront fee, you build and host the product, and you share in the revenue. If the app takes off, everyone wins. If it doesn't, your exposure is limited.

The key is picking partners carefully. They need to bring the marketing and audience side of the equation, because your job is the infrastructure and development. It's a model that silverorange, a Canadian agency, used successfully with e-commerce clients years ago, and it still holds up.

Stu sold both his apps when they hit what he calls "the plateau," that point where growth flattens and your churn rate starts catching up with new customer acquisition. At that stage, you either invest heavily to push through (hiring, scaling infrastructure, customer success teams) or you sell to someone who wants a product with proven recurring revenue.

For Stu, as a creative who'd rather build new things than manage database consultants and customer support, selling was the obvious choice. He used brokers both times, people who handle the paperwork, the letter of intent, and protect both sides of the deal. They take a cut, but they also sent chocolates, so it all evens out.

With everyone building apps now, how do you pick the ones worth pursuing? Stu's answer is to not go it alone. Find partners who are excited enough about the idea to invest their time and audience. If you pitch an idea and nobody wants in, that's useful information. If someone does, you've got both validation and a distribution channel on day one.

He tested this with an AI running coach concept, reaching out to local running coaches in Jacksonville. When they responded with polite indifference, he moved on rather than sinking months into a product nobody was asking for.

Paul shared his latest obsession: using AI to breathe new life into user personas. He's written 2 articles for Smashing Magazine that walk through the process:

1. Functional Personas With AI: A Lean, Practical Workflow: How to build genuinely useful personas that focus on what people are trying to do, not just demographic data.
2. AI In UX: Achieve More With Less: Broader lessons from using AI across user research, design, development, and content creation.

The approach: take all your research (surveys, interviews, call logs, analytics) plus deep online research from tools like Perplexity, feed it into AI, and generate highly detailed personas, far more detailed than the traditional single-page variety. Then load those personas into a project in ChatGPT, Claude, or Gemini, with instructions to answer questions from the persona's perspective.

The result is something you can consult in every meeting, on every decision. A product team can upload photos of next season's lineup and ask "what would our audience think?" A web team can test wireframes against the personas. Real user research still matters, of course, but this approach makes research-informed thinking available at a frequency and scale that traditional methods never could.

"I tried to steal spaghetti from the shop, but the female guard saw me and I couldn't get pasta."

Courtesy of comedian Masai Graham. And yes, it's exactly as bad as you think.

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In this episode, we kick off 2026 with a candid look at where the UX industry stands and where it's heading. We dig into a thought-provoking article from Nielsen Norman Group, share our hopes (and fears) for the year ahead, and explore a fantastic design pattern catalog focused on building user trust. Plus, we discuss why generalists might just be the unicorns the industry needs right now.

We spent a good chunk of this episode discussing an article from the Nielsen Norman Group that, while technically published in early 2025, remains just as relevant today. Written by Kate Morin, Sarah Gibbons, and others at NNGroup, it tackles the challenges facing our industry head-on.

Let's not sugarcoat it. It's been a tough time for UX professionals. Layoffs have hit hard, particularly in the US, and there's a palpable sense of doom and gloom floating around LinkedIn and other professional spaces. We've seen this before, though. We set up Headscape right in the middle of the dot-com bust, after being laid off ourselves. It wasn't fun, but times like these have a way of separating the wheat from the chaff.

Economic downturns tend to clear out people who jumped into UX because they saw easy opportunities, leaving behind those with genuine understanding and passion for the work. And despite all the negativity online, the World Economic Forum actually ranked UX design as one of the 8th fastest-growing industries. So the discipline itself isn't dying. There's just been a mismatch between the number of people entering the field and the reality of what the market can absorb.

Some people are suggesting we rebrand UX to "product design" or "experience design" to solve our problems. We don't think that's the answer. The word "design" does carry some baggage. In many business minds, it's seen as a luxury rather than a business-critical function. So when budgets get tight, "design" gets cut while "conversion optimization" and "customer retention" survive. That's a perception problem, not a naming problem.

The real issue is that there are too many low-quality UX practitioners who've been churned out through bootcamps. They've been taught a process to follow, and they follow it come what may. That's not their fault; they were taught that way. But six months of bootcamp doesn't prepare you for the messy, contextual reality of actual UX work.

The negativity around AI on LinkedIn has been phenomenal lately. There's anger about "AI slop" and a general feeling that it's no good for anything. Paul posted about using AI to help create personas and do online research, and got absolutely slated for it.

AI is just a tool. Like any tool, if you use it badly, you get bad results. If you use it well, it can be genuinely helpful. The good news is that we're finally moving past the "AI for AI's sake" phase. We're starting to see thoughtful integration of AI into products and services, AI that actually solves real user needs.

Every technology goes through the same cycle. Remember video recorders? First, we were just amazed the technology worked at all. Big analog buttons, you started recording and stopped recording, and that was it. Then manufacturers added more and more features until the things became unusable with their tiny buttons and complicated preset systems. Then someone invented a code you could enter from the Radio Times to set recording times automatically. And finally, Sky came along with "press a button and it records." AI is going through that exact same evolution right now.

Templates, processes, production-line UX: that stuff is really struggling, and it will continue to struggle. AI can do that now. You're not going to make money or build a career by blindly following the double diamond and churning out deliverables.

What you need going forward are distinctly human skills: critical thinking, taste, knowing whether something is heading in the right direction, and navigating messy organizational dynamics. Those are the skills that matter. Soft skills like relationship building, facilitation, and empathy are going to be far more valuable than whether you can use Figma.

UX is messy. You can't box it up the same way on every project. Templates and checklists are great starting points, but they're not a substitute for thinking. Context is everything.

There's no such thing as best practice. When someone from Google or Facebook says you need a 6-week discovery phase with facilitated usability testing of at least 6 people, and sure, that probably worked great for their situation, with their team, their product, and their stakeholders. But it doesn't mean it's right for your startup or your client with a third of the budget and massive internal politics.

If you've been taught a linear process, shift your mindset. Don't have a process. Have a toolkit of techniques you use as and when appropriate. You don't always need a discovery phase; sometimes a quick phone call is enough. You don't always need journey mapping; sometimes that's just not appropriate.

Be careful that all these AI-powered conveniences don't cost you your connection with actual users. It's tempting to just run surveys, do unfacilitated remote testing, or let AI do online research. But you don't build real empathy that way.

When you sit down to write copy or design an interface, you want to be able to picture the person in your head. You want to feel who they are, what they'd say, what they'd struggle with. The more levels of abstraction between you and your users, the harder that becomes. Even if it's just talking to one or two customers, make sure you're seeing them as real people.

For years, we've been told to specialize. But now? We need to become more comfortable wearing multiple hats. You might be doing wireframing, user research, strategy work, and training, all in the same week. You might need to understand adjacent fields like marketing, business strategy, data modeling, or product management.

AI can help extend your capabilities. Maybe you know a bit about accessibility or SEO, but not enough to do a full audit. With AI's help, you can now be better in those areas. Still not as good as a specialist, but better than you would have been alone.

Stop focusing so much on outputs (wireframes, reports) and start focusing on outcomes. Elevate your thinking from tactical to strategic.

If you want to dig deeper into this, check out Paul's free email course. It's 30+ emails on thinking more strategically and holistically about UX.

We stumbled across a brilliant resource from an agency called IF. They've created a design patterns catalog with a particular emphasis on building trust through transparency, user control, and thoughtful approaches to consent and data sharing.

This is increasingly important, especially as AI becomes more prevalent. It's not about slapping a testimonial on a page and calling it done. It's about baking trust into the experience itself. The catalog is beautifully illustrated and well-explained, making it a great scannable reference.

Paul found this while working on Bleet, a tool that automatically extracts advice from your recorded meetings and turns it into social media content. The trust challenge there is obvious: you're uploading client meetings with confidential information, so finding patterns for building that trust was essential.

"I dropped a tub of margarine on my foot two weeks ago. I can't believe it's not better."

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In this episode, we welcome back Andrew Millar from the University of Dundee to discuss the current state of higher education, vibe coding platforms for non-developers, and the importance of community-driven conferences like Scottish Web Folk.

This week we're looking at Bolt.new, a vibe coding platform designed specifically for non-developers. Unlike tools like Cursor that are built for developers to pair program with AI, Bolt is aimed at people like marketers, designers, and small business owners who want to create functional applications without ever touching code.

Paul has been using Bolt to build practical tools for his own business, including a custom top task analysis app, WordPress plugins, JavaScript extensions, and CSS animations. The platform handles everything from the database to publishing and hosting, making it genuinely accessible for non-technical users.

However, we'd caution against treating these tools as production-ready for enterprise use. They're excellent for prototyping, internal tools, and small-scale applications, but they likely won't pass rigorous quality control in larger organizations. Think of them like desktop publishing was in the early days. They democratize creation but don't eliminate the need for professional expertise.

For production-ready code, the real value comes when developers use AI pair programming tools where they can review, understand, and quality-check the output. The future likely involves professionals using these tools to increase productivity rather than replacing expertise entirely.

Andrew Millar, who runs the digital team at Dundee University, joins us to paint an honest picture of the current higher education landscape. It's not pretty, but his candid insights offer valuable lessons for anyone navigating organizational crisis, whether in universities or elsewhere.

Higher education has always claimed poverty, but the structural problems have become impossible to ignore. Universities face two fundamental financial challenges: funding per student hasn't kept pace with inflation over the past decade, and research grants typically only cover around 80% of actual costs, leaving institutions to make up the difference.

International students became the solution to plug this gap. They could be charged higher fees and effectively cross-subsidized teaching for domestic students and research activities. This worked until a perfect storm hit: COVID disruptions, international conflicts, hostile government rhetoric toward international students, and for Dundee specifically, the Nigerian economy's collapse, which dramatically reduced one of their key international markets.

Dundee found themselves with a 30 million pound deficit. Within a year, the principal resigned, the entire executive changed, the Scottish government stepped in with emergency funding, and 500 staff members have left from a workforce of around 3,000.

Andrew outlined three distinct phases organizations go through during financial crisis, and his framework offers practical guidance for anyone facing similar situations.

Phase 1: Cut, Cut, Cut

When crisis hits, budgets get slashed, often multiple times. Andrew recommends categorizing everything into three buckets: what's absolutely critical to keep the lights on, what will hurt but won't cause lasting harm, and what's easy to eliminate. This is actually an opportunity to clear out legacy systems and processes that nobody uses but somehow persist.

The challenge is that during this phase, people aren't open to change or new ways of working. They just want to see the existing stuff cut. Don't waste energy trying to introduce innovations here. Focus on strategic pruning.

Phase 2: The Great Spaghetti Flying Contest

This is where everyone becomes an expert on how to solve the crisis. Phrases like "we should at least try it" and "isn't it good to test ideas?" fly around constantly. The problem is that these are the exact phrases digital teams have been using for years to encourage experimentation, now thrown back at them by people with competing priorities.

Governance structures become critical here. You can clarify requests (ensuring they're truly worth pursuing), compromise on scope, or clog them up in committees until priorities become clearer. When your escalation paths have collapsed, as they did at Dundee when leadership departed, you're left justifying decisions without backup.

The key insight: never say "computer says no" via email. Have conversations. Explain your reasoning. When people understand the constraints, they typically accept them. Email refusals just get escalated to whoever shouts loudest.

Phase 3: The Big Squeeze

With less money, fewer people, less institutional knowledge, and no clear strategy, this phase is when things get really difficult. But paradoxically, it's also when people become more open to change. They've accepted that old ways aren't working and are more receptive to credible, evidence-based proposals for doing things differently.

Andrew's team has evolved significantly since their original digital transformation work. They reduced the number of people managing the corporate website from 350 to about 20 while maintaining quality. Now they're moving toward a hub-and-spoke model, with centralized governance but distributed execution.

The ideal version of this model, which IBM pioneered, has people embedded in individual departments but reporting into the central digital function. This creates healthy tension, since they need to keep their central manager happy while also serving their local colleagues. It maintains standards while building subject matter expertise across the organization.

One emerging priority is what Andrew calls "generative engine optimization," ensuring content is structured so AI tools can accurately surface and represent it. As more users get information through AI intermediaries without ever visiting your website, getting this right becomes critical.

The conference that inspired this episode, Scottish Web Folk, emerged partly out of necessity. When travel budgets got cut, Dundee created their own event. It's now grown to over 150 attendees with strong sponsor support, all while maintaining its community-first ethos.

The conference bans sales pitches from sponsors, limiting them to 30 seconds of self-promotion. Instead, it emphasizes knowledge sharing between suppliers and institutions. This approach keeps sponsors coming back because they recognize that embedding themselves in the community pays long-term dividends.

For any digital team, hosting events like this builds internal credibility and external relationships simultaneously. It positions you as thought leaders within your organization while creating the networks that sustain careers and enable collaboration across institutional boundaries.

"I started dating a zookeeper, but it turned out she was a cheetah."

That's a wrap for this episode. See you in the new year!

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If you run an e-commerce site or work on digital products, this conversation is packed with research-backed insights that could transform your conversion rates.

Before we get into our main discussion, we want to highlight a couple of tools that caught our attention recently.

We talked about this last week, but it deserves another mention. UX-Ray from Baymard Institute is an extraordinary tool built on 150,000 hours (soon to be 200,000 hours) of e-commerce research. You can scan your site or a competitor's URL, and it analyzes it against Baymard's research database, providing specific recommendations for improvement.

What makes UX-Ray remarkable is its accuracy. Baymard spent almost $100,000 just setting up a test structure with manually conducted UX audits of 50 different e-commerce sites across nearly 500 UX parameters. They then compared these line by line to how UX-Ray performed, achieving a 95% accuracy rate when compared to human experts. That accuracy is crucial because if a third of your recommendations are actually harmful to conversions, you end up wasting more time weeding those out than you saved.

Currently, UX-Ray assesses 40 different UX characteristics. They could assess 80 parameters if they dropped the accuracy to 70%, but they chose quality over quantity. Each recommendation links back to detailed guides explaining the research behind the suggestion.

For anyone working in e-commerce, particularly if you're trying to compete with larger players, this tool is worth exploring. There's also a free Baymard Figma plugin that lets you annotate your designs with research-backed insights, which is brilliant for justifying design decisions to stakeholders.

We also came across Snap this week, which offers AI-driven nonfacilitated testing. The tool claims to use AI personas that go around your site completing tasks and speaking out loud, mimicking user behavior.

These kinds of tools do our heads in a bit. On one hand, we're incredibly nervous about them because they could just be making things up. There's also the concern that they remove us from interacting with real users, and you don't build empathy with an AI persona the way you do with real people. But on the other hand, the pragmatic part of us recognizes that many organizations never get to do testing because management always says there's no time or money. Tools like this might enable people who would otherwise never test at all.

At the end of the day, it comes down to accuracy and methodology. Before using any such tool, you should ask them to document their accuracy rate and show you that documentation. That will tell you how much salt to take their output with.

Our main conversation this month is with Christian Holst, Research Director and Co-Founder of Baymard Institute. We've been following Baymard's work for years, and having Christian on the show gave us a chance to dig into what nearly 200,000 hours of e-commerce research has taught them about conversion optimization.

Christian shared the story of how Baymard started about 15 years ago. His co-founder Jamie was working as a lead front-end developer at a medium-sized agency, and he noticed something frustrating about design decision meetings. When the agency prepared three different design variations, the decision often came down to who could argue most passionately (usually the designer who created that version), the boss getting impatient and just picking one, or the client simply choosing their favorite.

Rarely did anyone say they had large-scale user experience data to prove which design would actually work better. They realized they could solve this problem by testing general user behavior across sites and looking for patterns that transcend individual websites. If they threw out the site-specific data and only looked for patterns across sites, they could uncover what are general user behaviors for specific UI components and patterns.

It started with just checkout flows. It wasn't even clear they would ever move beyond that. But now, 15 years later, Baymard has a team of around 60 people, with 35 working full-time on conducting new research or maintaining existing research.

One important point Christian emphasized is that Baymard's research isn't meant to replace your own internal testing. You should always do your own data collection and usability testing. The point of having a large database of user behavior and test-based best practices is that when you're redesigning something, you have maybe 100 micro decisions to make. You can't run internal tests for every single one of those decisions.

Even Fortune 500 companies that have the budget don't have the time to wait for results on every micro decision. So what happens is you collect research on the two or three big things that are site-specific or unique to your brand or customer demographic. But all the generic stuff (how to design an expand and collapse feature, how the quantity field should work, how the phone field should be designed in a checkout flow) these are extremely standardized UI components where users have standardized expectations.

You shouldn't squander your internal test resources on testing things that are completely generic. That's where pre-made research comes in. It removes 97 of the micro decisions so you can focus your resources on what's unique and important to your brand.

We asked Christian what kills conversion the most on e-commerce sites. While it depends on each site's specific issues, there are some concrete things Baymard has consistently seen sites fail at that are surprisingly easy to fix.

In countries where you have an order review step (where users review the whole order before pressing "place order"), there's a really dangerous trap. The order review step and the order confirmation step look very similar in users' minds. Both are textual pages that appear after entering credit card data. Both show a summary of information.

In testing, Baymard consistently sees some users misinterpret the order review step for a confirmation step. This is a critical error because these users will exit the page thinking they've completed their order. They don't even realize the abandonment occurred. It's the worst type of checkout abandonment that can happen.

A very simple trick is to take the "place order" button that you usually have at the bottom of the page and duplicate it so there's also one at the top of the page. One audit client did this and got a $10 million return on investment from just duplicating that button. It won't affect 10% of users, but if it prevents one out of 200 users from abandoning, that's half a percent of all your site revenue you've recovered.

Christian called this "the least sexy but most important topic" in checkout flows. The general error recovery experience in checkout flows has improved over the 15 years Baymard has been researching, but it's still way too poor.

When a validation error occurs, users struggle with three things:

1. Understanding that an error actually happened
2. Understanding where the error is
3. Understanding how to resolve it

Best practices for error recovery:

• Provide visual styling for each field that's wrong
• Have a description at the top of the page that outlines all errors
• Use conditional logic: if there's only one error, scroll them to that field. If there are multiple errors, scroll to the top where they can see the overview

Baymard sees users who fix one error, resubmit, and then get frustrated when the page reloads with another error they didn't see. They sometimes conclude the page is broken. When Baymard surveys users, 6% say they've abandoned a checkout flow in the past quarter due to perceived technical errors. Most of these aren't actual technical errors, the page is just extremely complicated to use.

Instead of saying "phone number is invalid," tell users exactly why. Your technical system knows exactly which validation rule was triggered. If the phone number is wrong because it includes a special character, tell them: "Special characters cannot be used. You don't need to include the country code." If it's too long or too short, say that specifically. This helps users recover faster.

Ideally, much of this should be fixed in the backend. Postcodes are a great example, some people put a space in UK postcodes, some don't. Some write it all uppercase, some use mixed case. Why isn't this fixed in the backend? There should be something tidying it up and dropping it into the database in the correct format.

One area where Baymard has seen genuine improvements is around product data and product imagery. Most sites took a long time to figure out that the content on the product details page is crucial to user experience.

When users land on a product details page, 90-95% of what they do as a first action is look at the image. But they also use images for tasks where it's a terrible idea. For instance, if trying to figure out whether a speaker has the right connection, instead of going to the specification sheet, they look for images showing the speaker from the back to see the connections. If they can't see it, they conclude it doesn't have the connection and abandon that product.

Users are extremely visually driven, even trying to use images to solve problems where it's a poor strategy. Sites need really good imagery from multiple angles, detailed videos showing what goes on visually, and proper product descriptions.

We asked Christian about building trust beyond the lazy approach of just shoving social proof and awards on the site. His insights were revealing:

Social Proof On and Off-Site

Social proof is important both on your page and off it. If people are in doubt whether to trust you, they won't trust your version of whether they should trust you. They'll go offsite to check reviews. Responding to negative reviews is crucial because it helps explain or set context. Users often seek out negative reviews more than positive ones to do due diligence. They understand not every product is perfect for every user, but they want to know if the shortcomings are relevant to them.

Return Policies and Professional Design

Clear and generous return policies help build trust. But there's also the "aesthetic usability effect", having a well-worked design without complications builds trust and credibility. Sites that look too dated will degrade trust. If something looks like it was made in the nineties, users may question if it's too unprofessional.

Simply having a site that's not too complicated to use also builds trust. If users get completely stuck, they may conclude it's too unprofessional or wonder if there's something wrong with the business.

These effects depend quite a bit on whether people know the brand. It changes dramatically if it's a large known brand versus a completely unknown small site with new users.

We couldn't resist asking Christian about AI's impact on e-commerce. There are similarities to when voice applications came out five or six years ago. Everyone said we'd order everything with our voice, but that didn't really happen. This time may be different, but it won't go as fast as people think, at least not for all purchases.

There are some commodity items and household staples you just want restocked when they run out. Those are well suited for AI purchasing, the same type of products you'd buy on subscription today. But many purchases require users to be in control.

Where AI is already changing things massively is not in the complete purchase but in research and product discovery. Which digital camera should I buy? Which one is best for my requirements? This has always been an offsite experience. Users typically have multiple e-commerce sites, review sites, blogs, and social media open when researching purchases. That part is changing rapidly with AI.

But going from winnowing down millions of products to a few options, then having AI auto-purchase one of them, will take quite a while before users are that confident. It may even be generational, people our age may never fully trust it even when it becomes trustworthy, while the next generation growing up with competent AI will develop different habits.

What really strikes us about e-commerce optimization is how it's death by a thousand cuts. It's not that one of these things will wreck your conversion rate, but collectively they cause real problems. When you're dealing with an entire e-commerce site, there are so many little things that it's impossible to plan for all of them upfront. You will miss things.

That's why post-launch optimization is crucial. There will always be things that need improving, and that ongoing work can span years. It's a big job, but the research and tools that organizations like Baymard provide make it far more manageable than trying to figure everything out from scratch.

And now, as always, Marcus leaves us with his joke of the week:

"My dad suggested I register for a donor card. He's a man after my own heart."

That's actually quite good, Marcus. We'll allow it.

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Welcome to Episode 27 of the Boagworld Show, where we dive into a side of web work that doesn't get nearly enough attention. This month, we're exploring life as a freelancer working with small businesses. We're joined by Paul Edwards, a fellow member of the Agency Academy who has spent two decades serving clients that don't have massive budgets or sprawling marketing teams. If you've ever wondered how best practice advice translates to the real world of limited resources and high stakes, this conversation is for you.

Before we get into our main conversation, we need to talk about an extraordinary tool that just launched. Baymard UX-Ray is built on the Baymard Institute's 150,000 hours of ecommerce research. If you're not familiar with Baymard, they've been conducting rigorous usability research for years, building an enormous repository of what actually works in ecommerce design.

What makes UX-Ray remarkable is how it applies all that research. You can input your own site or a competitor's URL, and the tool scans it against Baymard's research database. It then provides specific recommendations for improvement, each one linked back to detailed guides explaining the research behind the suggestion.

Now, we'll be honest. Tools like this can feel a bit depressing when you first encounter them. Another thing that AI can do that used to be our job, right? But the reality is more nuanced. You still need expertise to ask the right questions, to know when to ignore advice that doesn't fit your situation, and to implement recommendations effectively. What UX-Ray really does is democratize access to quality research, allowing smaller teams and solo practitioners to benefit from insights that would otherwise require a massive research budget.

For anyone working in ecommerce, particularly if you're trying to compete with larger players, this tool is worth exploring.

Our main conversation this month centers on something we don't discuss enough in the UX and web design community. Most of the advice you read online, most of the case studies and best practice articles, come from people working with large organizations. We're guilty of this too. Between the two of us, we've worked with clients like Doctors Without Borders, GlaxoSmithKline, and major universities. That shapes our perspective in ways we don't always recognize.

Paul Edwards brings a different lens. He's spent 20 years as a freelancer, and while he's worked with organizations of varying sizes, the common thread through his client list isn't scale. It's circumstance. His clients typically have small or nonexistent marketing teams. They're often time-poor and lack technical expertise. Most importantly, they have skin in the game in a way that corporate clients rarely do.

Paul's freelance journey started dramatically. On November 5, 2005, he had a tantrum at his job as a commercial manager for a civil engineering company and quit on the spot. No savings, no business plan, no real idea what he was doing. He just knew he'd been teaching himself web design with Dreamweaver and Fireworks, and he thought maybe he could make a go of it.

What followed was the classic freelancer trajectory. He worked his friends and family network, which led him into academia and international development work. He found himself building sites for projects funded by the Bill and Melinda Gates Foundation, DFID, and the World Bank. These weren't necessarily well-funded projects despite the prestigious funders, but they gave him experience working with agencies across Europe and projects in Africa focused on critical issues like hygiene and sanitation.

When you're working with a small business owner, the stakes are fundamentally different. As Paul put it, the number of clicks their campaign generates directly affects how much money they take home at the end of the month and the security of their family. That changes everything about the relationship.

This isn't to say working with large organizations is easy or that the work doesn't matter. But in a corporation, success and failure are distributed across many people and many factors. When you're working with someone who owns their business, your work has an immediate, visible impact on their livelihood. The opportunity cost of failure is enormous. The credit for success is also more direct, which can be incredibly motivating.

Paul's business has evolved toward more retainer and time bank arrangements over project work. This shift happened gradually but has been transformative. For clients, it guarantees access to his expertise when they need it. For Paul, it provides income stability. But there's another benefit that often gets overlooked. When you have long-term retainer clients, especially small ones with staff turnover, you become a point of continuity in their organization.

One of Paul's retainer clients had a marketing department of two people. Both left within a year. Paul was literally the only person who understood the history of their digital presence, their past campaigns, and their strategic direction. That kind of institutional knowledge is incredibly valuable, and it's something freelancers can uniquely provide.

We had to ask about budget because it's the elephant in every room. When you're working with smaller clients, you simply have fewer resources to work with. So how do you adapt all the best practice advice that assumes you have time for extensive user research, iterative testing, and comprehensive documentation?

Paul's answer was illuminating. He doesn't find himself frustrated by advice that doesn't apply to his situation. He just doesn't apply it. As a generalist, he's always picked and chosen what's relevant, learning what he needs for each specific job and disregarding the rest. He can't let his head explode trying to take in everything, so he focuses ruthlessly on what matters for the work at hand.

The reality is that best practice often needs to be adapted regardless of client size. A lot of what gets labeled as essential process work serves organizational needs as much as user needs. In a large organization, you might conduct extensive research partly to align compliance, get legal on board, and protect your client contact from political fallout. In a small business where you're talking directly to the decision maker, you can move faster and iterate post-launch.

That doesn't mean cutting corners on things that matter. Paul still does discovery and research work, but he structures it differently. Rather than one large project with research baked in, he often does pre-project discovery as separate billable work. This allows him to flex the scope based on what the client has in-house, what they lack, and what will actually move the needle for them.

One of the most valuable parts of our conversation was Paul's approach to client selection. He's learned through hard experience that taking on a client who isn't a good fit costs far more in stress and lost time than the revenue is worth. Every single time he's taken someone on when his gut said no, it's been worse than if he hadn't brought that money in.

So Paul has developed a risk scoring process. He researches Companies House filings and financial accounts. He Googles potential clients thoroughly. He makes sure to be himself from the first conversation, explaining that he's blunt and tends to say what he thinks. Some people say they want that but really don't, and it's better to discover the mismatch early.

When things do go wrong, which is rare after 20 years, Paul offboards as quickly and graciously as possible. He sees it as partly his fault for misjudging the fit, so he tries not to burn bridges. He'll help them find someone else to work with and exits professionally.

We wondered whether this kind of risk management is more necessary when working with smaller organizations. After all, you know Oxford University will eventually pay their bills, even if slowly. Paul's experience is that payment risk exists at all scales, but small businesses can have more volatile finances. However, most of his clients pay within 48 hours, which is remarkable. The key is that by moving toward retainer and time bank models where time is paid upfront, a lot of payment anxiety simply disappears.

Our conversation kept circling back to the value of being a generalist, and how AI is amplifying that advantage. Paul described AI as helping him get out of his own way. If he knows 90 percent of what's needed to help a client but lacks that final 10 percent, he used to decline the work. The opportunity cost of getting it wrong felt too high. Now, AI helps him bridge that last 10 percent with confidence.

He shared a perfect example. A trade business client, selling into the architectural sector, wanted help with their Google Ads campaign. Paul had dabbled in PPC but wasn't an expert. The client was willing to pay him to learn, which was fortunate, and AI supported that learning process. It helped him analyze the massive amounts of data that PPC campaigns generate, identify trends, and fill knowledge gaps. The result was a completely new campaign with much lower spend, a huge increase in relevant clicks, and better funnel positioning. The client was so pleased they sent him a Christmas hamper, a first in 20 years.

This is what the return of the generalist looks like. AI isn't replacing expertise. It's allowing people with broad knowledge and good judgment to tackle problems that previously required specialists. You still need to know enough to ask good questions, to recognize when something feels off, and to verify AI's suggestions. But you can now say yes to opportunities that would have been too risky before.

Near the end of our conversation, Paul made an observation that stuck with us. While he learns constantly from working with small businesses, he thinks there's value flowing the other way too. People working with large organizations, like us, often miss things that become obvious when the stakes are personal and immediate.

When you work with a business owner who's putting their family's financial security on the line, you can't hide behind process or best practice. You have to deliver real value. You have to be adaptable. You have to become genuinely invested in their success because they're so clearly invested themselves. That kind of clarity and accountability can be harder to find in large organizational work, where responsibility is diffuse and success has many parents.

Each month, we share a few articles, videos, and resources that caught our attention and sparked interesting conversations about the state of our industry.

Following up on last month's discussion about AI-generated personas, Paul has now written a comprehensive guide for Smashing Magazine. The article walks through his method for creating functional personas using AI, explaining when this approach makes sense and how to implement it effectively. If you've been curious about whether AI-generated personas can actually be useful, this piece answers that question with practical examples.

Nielsen Norman Group has posted a video arguing for a terminology shift from "user experience design" to "experience design." Their reasoning is that UX has developed a reputation problem. People think they know what it means, but they're often wrong, associating it primarily with visual interface design.

We have mixed feelings about this. The problem isn't really the word "user." It's the word "design." When most people hear design, they think of visual design and interface work, not the broader strategic and research work that UX encompasses. Changing to "experience design" doesn't solve that fundamental misunderstanding.

That said, the video makes interesting points about the return of the generalist, which aligns with much of our conversation this month. As tools like AI make specialist knowledge more accessible, there's growing value in people who can work across disciplines and see the bigger picture.

A perfectionist walks into a bar. Apparently it wasn't set high enough.

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You know, those sneaky little tricks sites use to funnel you into doing things you never intended, like paying for insurance you didn’t want or scrolling until your thumb falls off.

We talked about why this stuff isn’t just bad manners, but also an accessibility issue, and how to push back when your boss is shouting about conversion rates. We also wandered off into personas, because what’s a Boagworld Show without a tangent or two?

This week app is Be My Eyes. It’s designed to support blind and low-vision users by letting them connect with volunteers (or increasingly, AI) who can describe what’s in front of them. It’s practical, humane, and a great reminder that sometimes technology really does make life easier. Unlike my dishwasher, which still beeps at me like I’m trying to launch a nuclear missile.

This is where we rolled up our sleeves and got into the meat of it. What actually counts as deceptive design, why it’s more than just “bad UX,” and why the accessibility crowd are getting involved.

There’s no single definition everyone agrees on, but the gist is: if you’re deliberately steering or trapping users into something they didn’t intend or need (and especially if it lines your company’s pockets) it’s deceptive. That’s different from an anti-pattern, which is just poor design born of ignorance.

Deceptive patterns catch everyone out eventually, but they’re especially cruel to people with cognitive disabilities, attention difficulties, or those relying on assistive tech.

If you’ve ever been stuck doomscrolling until you realized it’s not lunchtime but bedtime, you’ll know the feeling. The difference is, for some users, the consequences can be more than just a lost afternoon. That’s why accessibility guidelines are starting to take these patterns seriously.

If you’re keen to see where this work is going, have a poke at these:

• WCAG 3 Working Draft
• W3C User Stories
• Proposed Personas Draft

Of course, it’s rarely moustache-twirling villains plotting this stuff. Most of the time it’s teams chasing KPIs (sales, clicks, engagement) and nudging too far. That’s how you get:

• The big shiny green “Buy with insurance” button, while the “Buy without” option is hiding in grey.
• Cheaper plans buried three clicks down, so the expensive ones look like the only choice.
• The friendly phone call that turns into a hard sell for extended warranties.

On paper the numbers look great. Meanwhile, refunds, complaints, and customer churn quietly tick upward. But hey, at least the dashboard looks good, right?

AI has the potential to make things better (look at how Be My Eyes uses it) but it also risks making things worse. More chatbots standing between you and an actual human being, for instance.

At the moment we haven’t seen a tidal wave of AI-driven trickery, but the ingredients are all there. Somewhere in Silicon Valley, there’s probably a twenty-something rubbing his hands and plotting.

Telling your boss “this is unethical” might get you a polite nod. Showing them how deceptive patterns increase refunds, tank repeat purchases, and hike up customer support costs? That’s when people start listening. Always lead with the business case, because sadly “doing the right thing” isn’t enough in most boardrooms.

Offer alternatives that still meet goals but don’t annoy users. Equal-weight buttons. Clear language. Confirmations before adding sneaky extras. And if management still insists, put your concerns in an email so there’s a record. Nobody likes receiving an email that basically says, “I warned you.”

While we’re at it, let’s talk personas. Most marketing personas are about as useful as a chocolate teapot. They’re built around demographics and stereotypes. King Charles and Ozzy Osbourne would end up in the same persona (same age, same country, both live in castles). Clearly useless.

Instead, think functional personas. Base them on needs, tasks, objections, and accessibility requirements.

You don’t need a “disabled persona.” Just make sure some of your personas have traits like dyslexia, ADHD, low vision, or anxiety about being conned. That way, you’ve got a ready-made reason to say, “This won’t work for Priya, who relies on a screen reader.”

Deception feels like a shortcut. It isn’t. It costs you in trust, support overhead, and long-term loyalty. Treat deceptive design as an accessibility barrier, argue with data, and keep users in your personas. That way you’ll serve both your customers and your company—and maybe sleep better at night.

In this week’s show we also highlighted two cracking resources:

Deceptive Design

A collection of manipulative patterns with real examples. Perfect for calling out “that thing the boss wants us to try.”

Deceptive Patterns and FAST by Todd Libby

Slides from Todd’s talk. Great for showing stakeholders that you’re not just making it up as you go along.

We’ll wrap up with Marcus’s groaner of the week:

“I told a joke on a Zoom meeting and nobody laughed. Turns out I am not even remotely funny.”

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By now, you’ve probably seen how powerful it can be to stop doing all the UX work yourself. Acting as a consultant and guide lets you touch far more projects. But that shift only works if your colleagues have the knowledge and resources they need.

That’s where a UX playbook comes in.

Think of it as your team’s reference manual. A central hub that gathers everything you’ve been building (principles, policies, templates, and tools) into one accessible place. When someone asks how to run a survey or plan a usability test, you don’t have to reinvent the wheel. You just point them to the playbook.

A UX playbook isn’t just documentation. It’s a lever of influence.

• It empowers colleagues to act on their own, without waiting for you to step in.
• It standardizes quality, ensuring consistent best practice across projects.
• It defuses conflict, since you can refer stakeholders to shared rules instead of relying on personal opinion.
• It builds credibility, both inside and outside your organization, by showing you’ve codified your approach.

Look at the UK Government Digital Service. When they launched their Service Manual, it didn’t just help civil servants build better services. It also established GDS as a leader across the public sector. Other organizations referenced it. Their reputation grew. And that external influence reinforced their internal authority.

That’s the kind of multiplier effect a playbook can unlock.

If you’re running a freelance practice or an agency, a playbook can be just as valuable, maybe even more so. But instead of being internal, it becomes a client-facing asset.

Imagine showing up to a pitch with your own playbook: a polished resource that outlines your approach to user research, testing, and design. It reassures clients that you have a clear methodology, not just a portfolio of past projects. It also helps set expectations about how you’ll work together, making tricky conversations about process and scope much easier.

Better yet, a playbook positions you as more than a pair of hands. It shows you’re a strategic partner with a repeatable system that clients can trust. You could even publish a slimmed-down version publicly, which acts as both marketing collateral and a credibility booster.

So whether you’re in-house or independent, the principle holds: codifying your standards and practices into a playbook makes you look professional, scales your influence, and reduces the time you spend re-explaining the basics.

There’s no single formula. Your playbook should reflect the challenges and questions that keep coming up in your organization. But here are some areas worth considering:

• The Role of UX: A page that frames why UX matters, sets expectations of your team, and positions you as a strategic partner.
• **Guiding Principles:** Short, memorable statements like "We design with evidence, not assumptions." These act as a compass for decision-making.
• Project Planning Guidance: Clear steps for how to integrate UX into a digital project, from defining user stories to selecting research methods.
• Prioritization Policy: A transparent way of ranking projects so you're not stuck working on "whoever shouts loudest."
• How Projects Run: A simple outline of your process (discovery, design, testing, iteration) so colleagues know what to expect.
• Ongoing Management: Policies around content maintenance, accessibility, and retirement so digital products don't rot.
• People and Roles: An overview of the skills involved in UX, to raise awareness of the complexity and collaboration required.
• Research and Testing Resources: Step-by-step guides, templates, and educational materials that help colleagues conduct basic user interviews, surveys, and usability tests independently.
• Governance and Compliance: Accessibility, privacy, or security policies that your organization needs to observe.
• Technology Considerations: Hosting, analytics, backups—practical guidance to remind colleagues of the details that matter.

Don’t worry about tackling all this at once. At first, each section might only be a single page. Over time, you can build them out into a richer resource.

The biggest mistake I see is trying to write the “definitive” playbook straight away. That’s overwhelming, and it rarely gets finished.

Instead, start small. Publish your principles. Add a couple of checklists or templates. Collect some common questions you get from stakeholders and answer them. Then keep iterating.

A few other tips:

• Assign ownership. One person should be responsible for maintaining the playbook, even if they draw in contributions from others.
• Make it engaging. Write it in plain language. Use visuals and examples. Keep it scannable.
• Keep it visible. Don’t bury it on the intranet where documents go to die. Make it a living site that demonstrates the best practices it promotes.
• Position it as a help, not a rulebook. If colleagues find it useful, they’ll return to it. If it feels like bureaucracy, they’ll ignore it.

Take one resource you’ve already created (maybe your design principles, a usability testing guide, or a research checklist) and publish it in a shareable format. That’s the seed of your playbook. Once it’s live, add to it bit by bit.

A digital playbook is one of the most powerful tools you can create. It strengthens your credibility, empowers others, and allows you to scale your impact without burning out.

In our next lesson, we’ll look at how to turn resources into real behavior change. Because giving people tools is one thing. Getting them to actually use them is another.

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One of the most important policies you can ever set for your UX team is how you prioritize work. Without it, you risk becoming a firefighter running from one blaze to another, driven by who shouts loudest or whose deadline is closest. That’s no way to deliver meaningful user experience.

Most of us are outnumbered. There will always be more requests than we can handle. The only way to keep your head above water is to establish a clear, fair, and transparent prioritization process. That’s where digital triage and a scoring system come in.

Many UX teams I encounter work on a “first come, first served” basis. Or worse, they work on whatever task has the loudest advocate or the scariest deadline. None of these methods are fair or effective. They waste energy on low-value projects and leave your most important work sidelined.

You need a way to make sure your time goes into projects that matter most. That means having two lines of defense: digital triage and a prioritization backlog.

Triage is your first filter. When a request lands on your desk, don’t dive straight in. Pause and ask a few key questions:

• Business alignment: Does this support core business objectives? If your company’s main goal this quarter is customer retention, a flashy microsite for a one-off campaign probably doesn’t make the cut.
• Audience: Does it affect a primary audience group or just a fringe one? Improving onboarding for new customers has more weight than polishing a tool used by a handful of internal staff.
• User need: Is this solving a real, pressing problem for users, or is it just someone’s nice-to-have idea?
• Feasibility: Is it realistic with the resources available, or will it swallow months of effort for limited gain?

If a request fails on most of these, it doesn’t mean it disappears forever. It just doesn’t deserve your attention right now. Triage is about protecting your limited capacity from being drained by low-impact work.

When a job comes in, score it immediately. This scoring system is your triage method and determines where each request sits in your backlog. I use four simple criteria, each ranked 1 to 5:

• Business alignment: 5 if it's central to strategy, 1 if it's unrelated.
• Effort required: 5 if trivial, 1 if it's huge.
• User group impact: 5 if it affects your core audience, 1 if it barely touches anyone.
• User need: 5 if it addresses a critical need, 1 if it's minor.

Add up the scores, and you've got a clear view of where each project belongs in your prioritized backlog.

As new jobs come in, they are assessed and then slotted into the appropriate place in the backlog.

Say marketing asks for a new landing page. You score it like this:

• Business alignment: 4 (supports acquisition, a current business goal)
• Effort required: 3 (will take some design and dev time, but manageable)
• User group impact: 2 (only affects one segment, not core users)
• User need: 3 (helps users, but not a burning problem)

That gives a total of 12 out of 20. Useful, but not top priority. It slots into your backlog beneath projects with higher scores.

The beauty of this system is that you’re not saying “no.” You’re simply placing requests in order. Lower-value work naturally slides to the bottom of the pile.

Keep your backlog visible. Maintain separate lists if you handle both major projects and small “business as usual” work.

I recommend most digital teams are split into two work streams. One focuses on “business as usual” (optimization), the other on larger, future focused projects (innovation.

Whenever a new request comes in, score it and slot it in transparently. This takes the politics out of the process. People can see for themselves why their project sits where it does.

Over time, you’ll find the backlog itself becomes a communication tool. It helps you show leadership how much demand there is and how you’re focusing on the projects that deliver the most value.

Of course, not everyone will like where their project lands. Here’s how to handle it and some of the common objections you’ll hear:

• Urgent queue-jumpers: Make it policy that deadlines are agreed with you upfront. If someone comes late in the process, they may need to go to an external supplier. A common objection here is: “But what if everything feels urgent?” The truth is, not everything can be urgent. If everything is top priority, nothing really is. Triage forces tough but necessary trade-offs.
• Disagreements over scoring: Define an escalation path. If stakeholders challenge your scoring, who makes the final call? Having this agreed in advance avoids endless debates.
• Some worry: “Doesn’t scoring everything slow us down?” In practice, scoring is quick, just minutes of work that save weeks of wasted effort on the wrong priorities.
• Stakeholders ignoring the backlog: Digital Triage needs to be approved as an organizational policy, not your personal system. When leadership endorses it, people tend to fall inline, especially when you don’t back down.
• Leadership overrides: When senior managers bypass the system, don’t resist. Instead, invite them to refine the scoring criteria so they better reflect leadership’s priorities. Often this nudges them back toward consistency.
• Perception of bureaucracy: Some will say, “Isn’t this just bureaucracy?” Not if you keep it simple. A lightweight scoring system and transparent backlog is far less bureaucratic than endless meetings arguing over priorities.

The great thing about this approach is that it prevents you from being perceived as the bottleneck or the “bad guy.” As I said in the last lesson, policies are not personal. You are just implementing a policy equally to all and working within the resources you have been given.

This approach makes your workload transparent, fair, and defensible. It reduces politics and ensures your energy goes into projects with the biggest impact on both users and the business. Most importantly, it shifts you from reactive firefighting to proactive leadership.

This system has another hidden benefit for UX professionals. You too can submit projects to be scored alongside everyone else's requests.

Because of your knowledge and experience, these strategic UX initiatives will typically rank well when scored against business objectives and user needs. This means all that strategic work you've always wanted to do (like user research, design system improvements, or accessibility audits) won't keep getting pushed to the bottom of the pile in favor of tactical requests.

Next time, we're going to talk about bringing all of these policies and procedures, alongside training material, together into a digital playbook.

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In this episode, we look at why trust is key to good UX, especially with scams, deepfakes, and AI blurring the line between helpful and deceptive. We also ask if emotion-reading apps are helpful or just unsettling, and explore the tricky process of turning services into products. Plus, we discuss a framework from Nielsen Norman Group, tackle a listener's question on productization, and end with Marcus's joke.

Check out Emotion Sense Pro—a Chrome extension that analyzes micro‑expressions and emotional tone in real time during Google Meet calls, while keeping all data safely on your device. It's privacy-first, insightful, and a bit unsettling. But if you're moderating user tests, hosting webinars, or running interviews, it gives a useful look into unseen emotional cues.

This week's topic dives into why trust is absolutely essential in today's digital landscape. Here's a summary of what was discussed, but we encourage you to listen to the whole show for more detailed insights.

We're convinced trust isn't optional, it's foundational. Amid a haze of misinformation, broken customer promises, slick AI-generated content, and user fatigue, building trust isn't just ethical, it's strategic.

Trust isn't automatic anymore. Big brands used to get the benefit of the doubt. Now users are skeptical. Scams and data breaches have made people cautious. Small problems like unfamiliar checkout pages, strange wording, or awkward user flows make people suspicious.

Keep your visuals and interface consistent so users don't have to work hard. When people get confused, they put their guard up. Think about clicking through to a payment page with no familiar branding. That tiny moment can kill trust. Messages like "Only 3 left in stock" can seem manipulative if users don't trust you yet.

Talking about "the company" instead of "we" creates distance. Use normal conversation with "you" and "we" instead of "students" or "customers." Skip the marketing language. And remember that if your photos don't show people like your users, they might leave without saying why.

Here are concrete steps that showcase trust-building in real-world scenarios. Implementing these practices can transform how users perceive and interact with your digital experiences:

• Audit for trust breakpoints. Look for spots where your UI might confuse users.
• Loop in legal early. This stops compliance from ruining your tone with last-minute jargon.
• Test trust directly. Ask "Would you feel comfortable sharing your data here?" during testing.
• Use authentic social proof. Link testimonials to sources, use third-party reviews. Even better? Simple, unpolished video testimonials.
• Prioritize clarity over cleverness. Skip the buzzwords.
• Make human support obvious. This is one of the strongest trust signals you can offer.

Trust runs through every part of your experience. Get it right and it becomes your biggest advantage.

This week's read is "Hierarchy of Trust: The 5 Experiential Levels of Commitment" by Nielsen Norman Group. They outline a trust pyramid:

1. Baseline trust. Can the site meet my needs?
2. Interest & preference. Is this better than alternatives?
3. Trust with personal info. Worth registering?
4. Trust with sensitive data. Can I trust you with payments?
5. Long-term commitment. Will I come back?

Main point? Don't ask for level-3 or level-4 commitments before earning levels 1 and 2. Users leave when you push for sign-ups or newsletter pop-ups too early. Build trust in stages.

"Is productizing my services a good idea, and if so, how should I approach it?

It depends. Productisation can add clarity but might limit your value by putting your service in a rigid box. We find it works better to focus on outcomes rather than fixed processes.

If you do want to productise:

• Focus on the outcome, not the deliverable. Example: "Conversion rate strategy" not "5 interviews and wireframes."
• Stay flexible. Your process should change as the project develops.
• Don't use fixed pricing that punishes change.
• Think about your service's value, not just features.

Most of us will get further with a custom toolkit and clear outcomes than a one-size-fits-all "product."

“I removed the shell from my racing snail. I thought it would make it faster, but if anything, it’s more sluggish.”

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In this episode, we chat with Sarah Zama from the University of Oxford about how she's helping to influence UX across one of the most complex and decentralized organizations in the world.

We explore how she built a UX center of excellence almost from scratch, how the team is transforming culture through coaching and community, and what it takes to push UX forward in a challenging environment. There's also a digression into Apple's questionable design choices, a fantastic app recommendation, and of course, Marcus' joke.

This week’s app recommendation is Zuko Form Analytics. It’s an incredibly helpful tool for anyone involved in conversion rate optimization or form design.

Zuko tracks detailed interactions with every field in a form—like how long someone spends in a field, where they drop off, and what fields trigger abandonment.

You get session-level insights, and it all works via a simple JavaScript snippet. There's a free tier to get started (up to 1,000 sessions), and pricing starts around £40/month for 5,000 tracked sessions. It’s the kind of tool we wish we’d known about sooner.

We were thrilled to be joined by Sarah Zama, UX Lead at the University of Oxford, to discuss a journey we’ve had the privilege of being part of: building a UX center of excellence in one of the most decentralized institutions in the world.

Paul originally worked with a small team at Oxford to create the business case for a UX team, ultimately recommending a center of excellence model rather than a centralized tactical team.

Why? Because hiring enough UXers to match developer headcount across such a massive organization was never going to be viable. Instead, a small, strategic team could focus on enabling others.

Sarah took that vision and ran with it. She started with a written plan—not just a strategy that collects dust but a living, practical document with measurable outcomes. She quickly assembled a lean team, brought in an existing accessibility lead, and even secured a six-month secondee to help with projects and spread good UX practice further into the organization.

The Oxford UX team doesn’t do UX for people. Instead, they help others do UX better. Through consulting, coaching, training, and providing reusable assets (like a design system), the team makes itself useful across a broad landscape without getting dragged into execution.

This consultative model includes:

• Workshops to support high-profile projects
• Guest training sessions with external speakers
• Custom-built resources tailored to Oxford’s context
• Supportive relationships with departments already doing good UX

They’ve also cleverly leveraged accessibility requirements as a wedge to introduce better UX thinking, combining compliance with best practices to gain traction.

Perhaps most impressively, Sarah and her team have focused on growing a UX culture through grassroots advocacy. They’ve built a UX Champions network that now includes over 150 people from across the university. This community shares knowledge, resources, and a passion for improving user experience, even when UX isn’t in their job title.

It’s a smart way to scale. By empowering individuals and embedding UX thinking across departments, Sarah's team extends its reach far beyond what any centralized team could manage.

Sarah admits the biggest challenge is visibility. Getting buy-in across such a large institution takes time and constant communication. There’s also the frustration that people still perceive UX as a cost or blocker rather than an enabler of success.

But the wins are meaningful. A growing, skilled team. A network of passionate advocates. And projects where UX clearly moved the needle. Sarah credits much of the team’s progress to strong collaboration, openness to learning, and sheer persistence. It’s a long game, but one that’s already paying off.

You can follow Sarah’s team and explore their resources at staff.admin.ox.ac.uk/ux. They welcome feedback, iteration, and anyone who wants to borrow from their growing UX playbook.

This episode’s recommended read is The Leadership Dilemma, an article Paul wrote for Smashing Magazine. It reflects on the exact challenges Oxford faced: how do you scale UX influence when your team is too small to do all the work? The article walks through a strategic approach to UX leadership that empowers others, shifts the organizational mindset, and creates lasting change.

If you’re trying to build UX maturity in a large or slow-moving organization, this is worth your time.

This week’s question wasn’t submitted via email but came up naturally during the show: "What does a typical week look like for a small UX team in a large organization?"

Sarah’s answer? There’s no such thing as a typical week. Her team works on everything from:

• Running UX training
• Providing design and accessibility consultations
• Participating in project meetings
• Developing shared resources like design systems

They also embed temporarily into project teams to upskill staff, run workshops, and seed best practices. Some team members even take secondments into other departments to help spread UX thinking more deeply.

All of this reflects their consultative, empowering model. It’s not about building everything themselves but enabling others to build better.

And finally, Marcus graced us with this gem:

"When I was young, I thought rich people owned Bose music systems and the rest of us had Sony products. Turns out they were just stereotypes."

We’ll let you groan in your own time.

Thanks for reading, and we’ll be back soon with another episode!

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