Every website and app I’ve worked on has eventually developed the same problem. It accumulates content, features, navigation options, and stakeholder requests until everything is apparently important.

Top task analysis identifies the tasks, questions, and features your audience values most. Rather than asking people whether they like a particular idea, it forces them to prioritize what matters.

Top task analysis adds prioritization. Participants choose the 5 tasks that matter most to them and then rank those choices. That gives you a clearer picture of relative importance rather than a pile of individually reasonable requests.

It is 100% free, and I intend to keep it that way.

The app will collect and organize the evidence, but it won’t decide what your interface should become. You still need to interpret the results, balance competing needs, and make sensible design choices.

I’m also launching the app on Product Hunt today, complete with a short video showing how it works. If you have a moment, please take a look at the launch and let me know what you think there.

View the app on Product Hunt

One of the most dangerous mistakes I see people make with their websites is fixating on what the competition is doing.

Don't get me wrong. Competitive analysis is a valuable part of any digital strategy, but there's a fine line between being aware of the competition's strengths and weaknesses and letting them dictate your own direction.

That's the danger here. Very quickly you can go from a healthy interest in your competition to essentially copying them every step. And if you fall into that trap, the inevitable result is that you're always going to be one step behind them.

Not that anybody ever sets out to copy their competition, but it's insidious and it can happen without you even noticing. It starts out with a competitive review and then quickly moves to "Well, none of our competitors do that," or "Everybody has this kind of information architecture, so we should too," and before you know it, you've created a clone of your competitors.

But this isn't just the worry of being one step behind your competition. There are two other considerations here as well.

First, you quickly find that in some sectors all the companies end up looking the same, as they all copy one another, and so your site does nothing to stand out from the crowd, making it largely redundant.

Secondly, and in my opinion more importantly, there's a false premise behind the desire to copy the competition. That's the belief that your competitors somehow know how to do things properly and that we should learn from them. There's an assumption that they've done their research, that they've made good decisions, which in my experience is simply not true.

Now I know what you might be thinking. "Our competition is much bigger than us. They've got more resources and more time, so surely they're making good decisions based on real data, and we can learn from that." However, that's rarely the case. I've worked with many large enterprise organizations, and to be frank they're just as disorganized, inefficient and over-stretched as their much smaller competitors.

And then, of course, there's the fact that you are not your competitors. Although you may be very similar, every organization is unique and needs to approach the market in a different way. If your competitors really are bigger than you, then you're not going to have the same success adopting their tactics.

However, in many ways, all of this is a distraction from the real reason that most people want to copy their competition. That's the fact that you don't get in trouble for doing what your competitors have done. They give you a point of reference that you can point at and say, "Well, they did this and it obviously worked for them." Of course, the chances are that's not actually true, and the very feature you're copying could well be underperforming for your competitors. Nevertheless, you have a justification for your actions.

But copying the competition is not the only justification you can use. Far better is to carry out your own research into user needs and behavior and base your decisions on that. Even if that research is just desk research, carried out online using the help of AI deep research tools like Perplexity.

And if you really need to see a particular approach working in the real world, then I'd encourage you to look outside of your sector, where there are ample opportunities for you to find ideas that could be truly innovative in your own.

So the next time you find yourself tempted to reject an idea because a competitor hasn't adopted that approach, or to implement a feature you've seen on a competitor's website, I'd encourage you to think twice. Because the best that approach can ever deliver is mediocrity.

I have had enough. I've spent a good chunk of my career nodding politely while a brand guideline told me to do something daft. Excessive use of all caps, color combinations that are unreadable and a complete lack of visual hierarchy in typography. I have seen all kinds of horrors and for a long time I went along with it, because questioning the brand felt a bit like questioning someone's child.

Lately, though, I've run out of patience.

I keep meeting people who use their brand as a reason not to change anything. The logo is sacred. The colors are locked. The fonts came down from a mountain on stone tablets. So the brand ends up quietly undermining the business completely untouched, because nobody wants to open that particular Pandora's box.

Don't get me wrong, I think branding matters enormously. Get your visual identity right, and it can shape whether an organization sinks or swims. I'm not one of those people who thinks design is decoration you sprinkle on at the end.

But a good brand has to earn its keep. It has to work in the real world, on a real phone, for a real person squinting at the screen in bright sunlight. Looking pretty isn't the job. Looking pretty while also being legible, scannable, and accessible is what matters.

This is where a lot of brands fall down. Somebody picked a color palette in a quiet studio on a lovely big monitor. It looked gorgeous. Then it hit the website, where it has to convince a distracted human to actually do something, and it fell apart.

Most branding is now seen online. That's where the majority of people meet your brand. Yet most branding agencies I come across still don't approach the work with a digital-first mindset.

Oh sure, they'll tell you they know about digital. They might even include the odd mock-up for a website or a social media platform. But they're not specialists in usability or conversion, and the gaps in their knowledge shine through. And they do zero testing, despite having access to all the same testing tools the rest of us in UX use every day.

They design for the brochure, the business card, and the launch presentation. The website is treated as one more place to paste the logo. So you get text baked into images, type that's beautiful at poster size and unreadable on mobile, and a palette that looks confident on a wall and washed out on a screen.

It's a slightly depressing way to build something that lives mostly online.

I've had two clients recently where the visual branding was genuinely not fit for purpose.

In one case it failed on accessibility. It flunked the legal tests, sure, but it also let people down in a simpler way. They just couldn't read it easily.

In the other, the brand was all over the place. It may well have started life as a decent identity, but years of inconsistent use had distorted it beyond recognition. Different rules on every page, no hierarchy, and nothing to guide the eye.

The symptoms are always familiar. There are walls of capital letters, which have been shown to cut readability by as much as 20%. There are color combinations that make body copy hard work. There's no typographic hierarchy, so headlines and sections blur together. And there are no visual containers, so the whole page becomes a soup of elements with nothing pulling your attention anywhere.

None of these feels like a disaster on its own. Stack them up and you get a page that quietly repels the very people you spent a fortune attracting.

Clients don't always take my word for this, which is fair enough. So recently I ran two versions of a homepage through an AI attention tool. The current brand-compliant design, and a tweaked version of mine.

The numbers were uncomfortable for the brand identity and the existing website. Clarity and focus both climbed by around 20% in the new version. Predicted engagement with the main call to action jumped by more than 80%.

Same content, same offer. The only real difference was loosening the grip of a brand that was fighting the reader instead of helping them.

That's the conversation I would encourage you to have. Not "is the brand sacred," but "is the brand costing us more than it benefits us." At some point you have to decide which matters more. Honoring a set of brand assets that were never designed for the web, or actually converting the people who land on your site.

You don't need to burn the brand to the ground. That's rarely the answer, and it's rarely on the table anyway.

Start small. Look at where the brand and basic readability are openly at war. Look for restrictive layouts, poor image choice, low-contrast text, and headlines you can't tell apart from the body copy. Fix those first, and you'll usually claw back most of the benefit without getting into a complete rebranding exercise.

While you're at it, do the testing those branding agencies never bother with. You don't need a big research budget for it. A couple of cheap, practical checks will tell you most of what you need to know:

Then push for one slightly braver conversation. Ask whether the brand was ever really designed for the screen, or just retrofitted onto it. If the honest answer is the second one, you've got a case for a proper digital-first refresh rather than another round of polishing something that doesn't work.

And if you're the one clutching the brand guidelines like a holy relic, I'd gently suggest having a word with yourself. I've been that person. The brand is meant to serve the business, not the other way around. The moment it starts costing you customers, it's stopped doing its job, however nice it looks in a style guide.

I have lost count of the conversations that start the same way at the moment. Someone with a budget and a deadline leans in and says the business needs AI. Where, exactly? Everywhere. In the product, on the site, in the onboarding flow.

It's not that they've no idea what they want. They usually arrive with something in mind. The trouble is it's a half-baked solution to a problem that may or may not exist.

Push back and you're the difficult one, the blocker, the person who doesn't get it. Go along with it and you build a chatbot nobody asked for. Neither ending is much fun.

So I stopped arguing. Now I do something that works far better. I send the whole thing to the users.

When someone hands me a shaky AI idea, I don't tell them it's shaky. That's a quick way to make an enemy and lose. Instead I say something like this.

"That's a really interesting idea. I think there's something in it. Let me go away and test it with a few users so we build it in the right way."

Often that's enough. You sound keen, not obstructive, and the stakeholder feels heard. You've quietly moved the decision out of a meeting room, where the loudest voice wins, and handed it to the only people whose opinion really counts.

But sometimes they won't budge. They're so sure they've got it right that testing feels like a waste of time. When that happens, I fall back on one of two tactics.

The first is to ask questions. I throw a lot of very specific ones at them about how the thing should work. What happens in this case? What about that one? Before long they start to struggle, and that's the moment to step in. It will be quicker to ask a few users than to guess our way through all of this.

The second is to talk about risk. If we build this without testing, there's a real chance we go down the wrong path and waste the budget. So I ask whether they're happy to own that risk. In my experience, nobody ever is. The moment they hesitate, you've got your user research.

If the idea is hollow, the users will tell you, and you get to be just as surprised as your client. No bruised egos. Just evidence. And if the idea is solid, even better. You now know it's worth building.

But validation isn't a thumbs up or a thumbs down. The real prize is what you learn in those conversations. The same questions that tell you whether to build also tell you how to build.

When you sit down with users, you're not just asking "would you use this?" You're working out the shape of the thing. Three questions matter most.

Ask people how much say they want over what the AI does on their behalf.

Some will want to set it and forget it. They'd happily never see it. For them, the best answer is an invisible solution. No interface, no buttons, no chat window. The AI gets on with the work in the background and the problem quietly goes away.

Others will want their hands on the wheel. They don't trust a black box making choices for them, and fair enough. The moment people want control, your invisible solution becomes a visible one, and you've got an interface to design. You only know which camp they're in because you asked.

If it does need to be visible, the default everyone reaches for is text. That's usually the client or stakeholder talking, not the user, and it's rarely a deliberate choice. They land on text because it's familiar, not because it gets the point across best. This is exactly where asking the user opens things up.

Ask users what they're actually trying to understand. Often a chart, a simple dashboard, or a quick visual does in a glance what a paragraph fumbles. AI is getting genuinely good at generating that sort of thing on the fly, so there's no reason to settle for a wall of prose when a graph would land faster.

There's one last thing to ask, which is how people want to interact with it. Most will expect a conversation. Type a question, wait, read the answer, type again.

For plenty of tasks that's slower and more irritating than the alternatives. A few form fields can beat a back-and-forth with a bot that keeps asking you to clarify. A dashboard can feel like the most natural way in the world to poke at data. Ask users how they'd rather do it, and many will tell you the chat box was never the point.

Notice what's happened here. You haven't had a single argument about whether AI belongs in the product. You've turned a turf war into a research question, and come back with answers nobody can wave away.

Maybe the project dies because users don't care. Maybe it lives, but as a quiet background helper rather than the chatbot your client pictured. Either way, the decision was made by the people who'll live with it, not by whoever was most confident in the room.

That's a far stronger place to design from. And it's a much easier life than being the one who's forever saying no.

Here is roughly how every conversion rate optimization project I take on begins.

We get through introductions, I sketch out an approach, everyone nods politely, and then, usually about forty minutes in, someone leans forward and asks the question. The quick wins question. The "what can we do this quarter" question. The "what's the easy thing we can ship before the board meeting" question.

I always nod sympathetically. I always say yes, of course, there are some quick wins we can target. I always deliver them. And for a long time I told myself I was being responsive to client needs, which is the polite consultant phrase for "I know what they want to buy and I'm cheerfully selling it to them."

But after enough years of this, I've started to notice that the clients who fixate on quick wins don't actually win much. The ones who do best treat quick wins as the opening move and then get on with the actual work.

So, awkwardly, here we are.

I should be careful here, because it would be very easy to read what follows as "quick wins are bad and you should feel bad for wanting them." That isn't quite the argument.

Early in an engagement, a few well-chosen tests genuinely earn their keep. They build trust with stakeholders who've spent years being told that CRO is a black art performed by people who own too many ergonomic chairs. They prove that experimentation actually moves the numbers, which is how you get budget approval for anything bigger. They drag a team through the discipline of hypothesis, test, learn, iterate, which a surprising number of teams have not actually done before. And they cough up early data you can wave at finance when you eventually ask to look at the difficult stuff.

That is a perfectly reasonable amount of value. The trouble starts when "a few quick wins to get us going" quietly becomes the entire strategy, and we all agree, very politely, to pretend that's fine.

There's a timing problem sitting underneath all of this, and it's worth naming first. By the time a company calls someone like me in, the conversion rate has usually been quietly underperforming for a year or more. People will tolerate a slow leak for ages and then panic the moment it becomes a flood. Of course they want quick wins at that point. They want the bleeding to stop, and they want it to stop yesterday.

Which is rational, in its way. But it biases the whole engagement before it's even started. We're not having a calm conversation about long-term value. We're triaging.

It's tempting to roll one's eyes at stakeholders for being short-sighted, but honestly, they're not being stupid. The problem is that their incentives are just appalling.

Quarterly bonuses reward this quarter's number. Senior leadership wants to see green arrows every month. Championing a structural fix that takes nine months to land is a career risk in a way that "we lifted click-through by three percent" simply isn't. Small experiments feel politically safe. Big bets feel like the kind of thing that ends up in a LinkedIn post about your unexpected career pivot.

And while I'm cheerfully pointing fingers, some of them point straight back at me. Agencies and consultants are part of the problem. We are, in fact, a substantial part of the problem.

Our business model rewards short engagements, monthly reports stuffed with reassuring green ticks, and the constant low-grade panic of needing to demonstrate value inside ninety days. We are structurally set up to find things to optimize. We are not structurally set up to walk into a steering committee and say, "Look, your returns process is the actual reason your customers leave. None of us can fix that with a button test. Sorry about that."

The trouble with an all-quick-wins strategy is that the damage compounds out of view.

For a start, the easy stuff gets used up. Most pages have already had their obvious tests run, so what's left tends to move the needle less and less. Diminishing returns are a real thing in CRO, and I'm always slightly amazed we don't talk about them more, given how much of our work rests on the cheerful assumption that they don't apply to us.

Meanwhile, the bigger problems never get looked at. Refund policies, product photography, page weight, customer service quality, the post-purchase experience. These are the things that actually move lifetime value, and they sit serenely untouched while we hold a fourth meeting about whether the button should say "Buy now" or "Shop now."

But the cost I find most uncomfortable is the slow accumulation of UX debt. Take any homepage that's been A/B tested for eighteen months and look at what's actually there. Urgency timers. Exit-intent popups. Social proof badges. Micro-copy nudges. A polite little chatbot that won't go away. Each test won in isolation. The cumulative effect is a confused, faintly manipulative mess that erodes the trust we are theoretically there to build.

Nobody owns the whole picture, because nobody's job is the whole picture. Which is, when you think about it, a slightly concerning way to run the customer experience.

A free workshop on AI user personas

Speaking of doing the structural work rather than chasing quick wins. I'm running a free online workshop with Smashing on using AI to build user personas that actually inform decisions, rather than the laminated nonsense most teams end up with.

Sign up here.

I am not suggesting anyone walk into a stakeholder meeting and declare that quick wins are dead. That's a great way to lose friends and influence nobody.

In practice, I've found a few framings work better.

A homepage test isn't a strategy. It's a small piece of evidence that experimentation works, which is the foundation for asking to test something harder. Once you have that, you can start asking the bigger questions. Slowly. Politely. Ideally with biscuits.

Be honest, early and often, about the limited ceiling of small tests. Stakeholders can handle the news that a button-color test won't double revenue. They are grown adults. What they cannot handle, quite reasonably, is being sold a quiet fantasy and then watching it underdeliver in a steering committee six months later.

Use the trust that quick wins buy you to map out the bigger projects and actually agree timelines for them. People find it surprisingly easy to commit to a meaningful piece of work three or six months out. The trick is to get the commitment while the goodwill is fresh, not after the quick wins have run dry.

This is the hardest one. Take aim at the metrics that produced the quick-win mindset in the first place. Lifetime value, repeat purchase rate, referrals. These are the metrics that reward long-term thinking.

I want to be honest about this one. Lifetime value is easy to name and a complete nightmare to track cleanly. You will need a rough proxy, like a six- or twelve-month value estimate, rather than the perfect formula nobody has ever actually built. The goal isn't perfection. It's getting one metric in the room that points the conversation somewhere other than this quarter.

If I'm being really honest about my own work, the engagements I'm proudest of are not the ones where I delivered a thick deck of green-ticked tests. They are the ones where, twelve months in, the client and I sat down and noticed we'd actually changed something structural. The returns flow. The way support tickets feed back into product. The post-purchase experience. The boring, expensive, slow stuff.

None of that came from a quick win. But quick wins bought the trust that bought the room to do the real work, which is, I think, the only honest case for them.

That's the version of CRO I'm trying to do more of. Not quick wins versus big bets. Just quick wins in their proper place, which is at the start of a much longer, much more interesting conversation.

Most of the organizations I work with are obsessed with the top of the funnel. Ads, SEO, social media, the next campaign, the next traffic spike. The marketing team has dashboards full of acquisition metrics, and the design team usually gets drafted in to support that effort. New landing pages, better hero sections, smoother sign-up flows.

That's all fine as far as it goes. I've written an entire email course on campaign landing pages because I genuinely believe most of them are leaking conversions like a colander. But it does mean something important keeps getting ignored. Most organizations have no cohesive strategy at all for retention and upselling. They pour effort into getting the customer through the door, then more or less forget about them once they're inside.

This is strange when you stop and think about it. The economics of retention have been well known for years.

You don't need to convince someone who's already bought from you. You just have to not screw it up.

So why does retention keep slipping through? In my experience, it's because nobody really owns it. Every other part of the customer journey has a clear home.

Retention falls into the gaps between all of them, which is a polite way of saying it falls on the floor.

This is where I think UX has a genuine opportunity. Not just to help with retention, but to own it. To plant our flag and say this is our patch.

I know that sounds like more work for a profession that's already stretched thin. But hear me out. UX has a chronic problem with how it's perceived inside organizations. We're seen as the people who make screens look nice. Helpful, but not strategic. The reason for that perception is partly our own fault. We've spent years talking about users when senior leaders are thinking about revenue. We've reported back on usability scores when the board is looking at MRR and churn.

Nobody at the top of an organization wakes up worrying about whether the user's mental model matches the interface. They worry about lifetime customer value. They worry about monthly recurring revenue. They worry, sometimes very loudly, about churn going in the wrong direction.

And yet plenty of businesses worry about those numbers without ever actively tracking them. Nobody is responsible for measuring them, so they sit in the background as a vague anxiety rather than a managed metric. If the UX team picked up that responsibility, and started tying our work to those numbers, our standing inside the business would change dramatically.

We'd stop being the screen-prettifying team and start being the team that protects revenue. That's a very different conversation to have with a CFO.

The other reason retention is such a good fit for UX is that the levers are largely ours already. Customers usually leave because something in the experience disappointed them.

Every one of those is a UX problem dressed up as a business problem.

The same goes for upselling. Customers buy more from companies that have nurtured them properly, where the experience has built trust over time. You can't bolt that on with a clever email campaign three months in. It has to be designed.

Free workshop: Giving Your Users a Voice in Every Decision with AI

Tuesday, 9 June 2026. One hour live, plus Q&A with me.

Most personas die quietly in a shared drive. I'll show you how to build AI-powered personas that focus on what users are actually trying to do, and how to make them available on demand so anyone in your organization can consult them at the moment decisions get made.

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A few starting points.

If you're still reporting on task completion rates and System Usability Scale scores, you're speaking a language the business doesn't really care about. Pick one or two retention metrics and put them at the top of your dashboard. Any of these work:

Most organizations have spent years polishing what happens before the credit card comes out, and almost no time at all on what happens afterwards. That's where the easy wins tend to be:

Retention sits across teams, so if you wait for someone to invite you to the retention conversation, you'll be waiting a long time. Volunteer for the onboarding redesign. Sit in on the customer success reviews. Make yourself useful where the conversations are actually happening.

Acquisition will always be glamorous. There's a reason it gets the budget and the attention. But it's also crowded. Marketers, performance specialists, growth teams, ad platforms, they all already own a piece of it. Retention is sitting there, largely unclaimed, and it happens to be where most of the long-term revenue actually comes from.

That feels like a reasonable place for UX to plant its flag.

There’s a scene in the Steve Jobs biopic where Steve Wozniak asks Jobs what he actually does. Wozniak understood his own role clearly: he was an engineer. He wrote code. He built things. But Jobs? Jobs described himself as the conductor of an orchestra.

I’ve been thinking about that exchange a lot lately, because I think it captures exactly where we’re all heading. AI isn’t turning us into supercharged doers. It’s turning us into conductors, and that requires a completely different mindset.

I’ve been coaching a number of people on integrating AI into their workflows recently, and I keep running into the same pattern. The people who aren’t getting time savings from AI aren’t failing because they don’t understand what it can do. They’re not failing because they lack access to the right tools. They’re failing because they’re fundamentally disorganized.

AI is only as useful as the foundation it’s built on. If your work processes are messy, your context is scattered, and your task management is a loose collection of mental notes and sticky tabs, AI can’t do much for you. It needs structure to work from.

I hear this complaint constantly: “AI has been mis-sold to me. I’m not saving any time.” But it hasn’t been mis-sold. It’s just that AI can only deliver on its promise if there’s an organized workflow underneath it. Build that first, and the time savings follow.

That’s why I’ve written before about building AI playbooks and developing proper AI skills. These aren’t nice-to-haves. They’re the infrastructure that lets AI actually work.

But here’s the deeper shift, the one that’s genuinely harder to adapt to.

When you’re doing tactical work, you’re usually focused on one or two tasks at a time. You go deep, you finish a thing, you move on. It’s cognitively manageable.

A conductor doesn’t work like that. A conductor holds the entire orchestra in mind simultaneously: what the strings are doing, where the brass comes in, what the percussion is building toward. They’re not playing any of the instruments. They’re managing the relationships between all of them.

In a world of AI agents, we’re going to be managing multiple projects running in parallel, all moving faster than any human team would. We’re task-switching constantly. We’re accountable for outputs we didn’t directly produce. And we have to resist the urge to dive in and do the work ourselves, because that’s precisely where we get bogged down.

This isn’t a new challenge, as it happens. Design leaders face exactly this transition when they move from senior practitioner to managing a team.

I’ve watched a lot of talented designers struggle with that shift. They get promoted because they’re brilliant at the work, and then they spend the next year quietly sneaking back into Figma because they can’t let go of doing. They micromanage their reports. They redesign things that were already fine. They can’t operate at the level of abstraction that leadership requires.

Working with AI agents is going to feel very similar. The temptation to wrestle with the AI until it produces exactly the output you had in your head, rather than accepting a good result and moving on, is going to be real. Learning to let go of that control is a skill in itself.

The good news is that unlike a team of designers, you can’t upset an AI agent by micromanaging it. But you can waste enormous amounts of time doing it, and that defeats the whole point.

There’s one more aspect of this I want to flag, because I don’t think it gets talked about enough.

When you’re managing a team of agents all moving at AI speed, the cognitive load is significant. You’re context-switching constantly across multiple workstreams. Things are completing faster than you can review them. It’s relentless in a way that managing a human team simply isn’t.

This is what’s increasingly being called AI burnout. Learning to pace yourself, to batch your reviews, to build in breathing room: these are the organizational skills that will separate people who thrive in an AI-augmented world from those who burn out in it.

If I had to distill this to one practical thing: start building the habits of a manager now, before the agents fully take over.

Get organized. Build the infrastructure that AI needs to work from. Practice delegating, even to imperfect tools, rather than doing everything yourself. Work on your ability to hold multiple projects in your head without losing the thread on any of them.

If you want help working through that transition, I offer coaching specifically for this. It’s something I’m increasingly focused on, because I think it’s one of the most valuable things I can help people with right now.

I’m also running a workshop with Smashing Magazine in July. Modern UX Practitioner covers a lot of this ground in a more structured way, if that’s more your style.

The shift from doer to conductor is coming whether we prepare for it or not. The people who handle it best will be the ones who start thinking like managers now.

Something uncomfortable is happening in organizations right now. UX teams are being quietly reassessed. AI has disrupted the field, leadership expectations have gone unmet, and there’s a growing sense that UX hasn’t delivered what it promised. The conversations are happening, but often not with the people who actually do UX work.

If you’re in a UX role, decisions about your team’s future might be forming in rooms you’re not in.

That’s the situation I’ve been thinking about lately, and it’s why I want to talk about UX maturity audits. Not as a defensive measure or a tick-box exercise, but as a genuinely useful tool for getting ahead of a conversation that’s already underway.

A lot of the cynicism toward UX right now traces back to one thing: overselling. Leadership was told UX would deliver a hundredfold return on every dollar spent. That figure gets thrown around a lot, and someone took it seriously enough to hire one UX person and wait for the magic to happen.

It didn’t.

That disappointment is partly our industry’s fault, though it’s not something we often admit openly. We’ve marketed UX with promises that assume a level of organizational change nobody warned leadership they’d have to make. Hiring one person doesn’t transform an organization into a user-centric one. It never did. There’s a certain naivety in the idea that a single hire will magically produce amazing experiences, without understanding the breadth of change required for an organization to truly become user-focused. But plenty of people implied it would.

The result is a leadership team that feels, not unreasonably, like they were sold something that didn’t arrive.

The natural response to this situation is to keep your head down and hope things settle. Understandable, but a mistake.

If leadership is already souring on UX, the absence of any structured conversation about what UX is actually delivering gives that skepticism room to grow unchallenged. Decisions start getting made. Quietly, and without much input from the people who understand what’s actually happening.

A proactive UX maturity audit changes that dynamic. Instead of waiting to be judged, you’re shaping the conversation. You’re the one bringing evidence, framing the questions, and defining what success looks like. That’s a considerably better position to be in.

And it’s not just damage control. Even mature, well-functioning UX teams benefit from this kind of review. There’s always a next stage. Whether it’s wider adoption, better integration with product teams, or moving toward something more democratized, an audit helps you see where you are and decide where to go.

A UX maturity audit should cover five areas. Not exhaustively, but enough to give you a real picture.

None of these are particularly complicated questions. The hard part is being honest about the answers.

An audit that just collects opinions tells you what people think, which is interesting but not necessarily accurate. A good audit looks for evidence.

That means checking whether research plans actually exist. Whether findings get used or disappear into a folder. Whether design systems are maintained or quietly falling apart. Whether the team can point to specific recent changes that improved user outcomes rather than just shipped features.

But the more revealing question is often why these things aren’t happening, because the answer usually points straight to the organizational problems that stop UX from gaining traction in the first place. A missing research plan isn’t just an admin gap. It’s often a signal that no one with authority has made space for it, or that the team has learned it wouldn’t be taken seriously anyway.

The questions worth asking aren’t simply “how good is our UX?” They’re “how well is UX supported here? How consistently is it practiced? What would move us forward?”

This shifts the audit from a performance review to a diagnostic tool. Diagnostics are much easier to have productive conversations about.

It’s worth being honest about one thing before you dive in: this isn’t something you can do half-heartedly. A UX maturity audit that gets treated as a side project, or squeezed into the gaps between real work, tends to produce polite summaries that nobody acts on. It needs management buy-in from the outset, not as an afterthought once the findings are ready.

There’s also a strong argument for bringing in someone external to run it. Not because your internal team lacks the ability, but because independence matters here. People will say different things to an outsider. And an external reviewer is less likely to be seen as someone with a stake in the outcome, which means their conclusions carry more weight when they land on a senior leader’s desk.

The right person for this isn’t someone who will sit in judgment of the UX team’s output. The question isn’t whether the work is good. The question is whether the organization has created the conditions for good work to be possible. That’s a different kind of assessment, and it requires someone who understands enough about how UX actually functions to read the environment accurately rather than just counting deliverables.

The question isn’t whether the work is good. The question is whether the organization has created the conditions for good work to be possible.

Given where things are right now, that feels like a fairly important prerequisite.

I've spent a lot of years arguing that most organizations have the wrong mental model of what a UX team is for.

In the vast majority of organizations, UX is dramatically underinvested. You have one UX person, or at most a small team, supporting an organization with dozens of developers, product managers, and business analysts. Or a small digital team made up of a variety of disciplines and generalists, supposed to raise the quality of every digital touchpoint across an organization of several thousand.

In that environment, expecting UX to own and shape the entire user experience is not a strategy. It is wishful thinking dressed up as one.

The only approach that actually makes sense is democratization. Instead of trying to do everything yourselves, your job is to spread the capability: set the standards, train people, and give everyone who touches digital the knowledge and tools to apply UX best practice on their own.

I've written about this for years, and most UX professionals I talk to agree with the principle. The problem has always been the execution.

For the past decade or so, the most sensible response to this challenge has been the digital playbook.

A playbook, in this context, is a collection of policies, principles, standard operating procedures, and training material that documents how the organization should approach digital work. Done well, it does several things at once: it educates people who don't have a UX background, it standardizes how work gets done, and it gives the UX or digital team something to point at when a stakeholder wants to skip testing or cram twelve things onto a homepage.

The UK Government Digital Service manual is probably the best public example of this. Comprehensive, well-structured, and genuinely useful. It also took a significant amount of work to produce, and presumably even more work to get people to actually use.

The UK Government Digital Service Manual is probably the best example of a digital playbook.

That last part is the problem with most playbooks. They ask a lot of the people you want to reach. If a product manager wants to run a quick survey to inform a decision, they now need to find the right section of the playbook, absorb methodology they've never thought about before, learn to apply it to their specific situation, and avoid the dozen ways this kind of thing typically goes wrong. That is a reasonable request if surveys are their job. It is a significant ask if they have three other priorities and a deadline on Friday.

The playbook shifts the burden of UX knowledge from the UX team onto everyone else. In theory, fine. In practice, people are busy, and busy people take shortcuts.

I say this having spent years advocating for playbooks, so make of that what you will.

I've been building out a library of AI skills for my own consulting practice over the past year or so, and somewhere along the way I realized these are doing the same job as a playbook, just in a radically different form.

An AI skill, if you haven't come across the term, is a reusable standard operating procedure that an AI can follow on demand. You write it once, document the process in enough detail that an AI can apply it reliably, and from that point on anyone can use it without needing to understand the underlying methodology.

This is what makes them interesting at an organizational level. A well-designed AI skills library doesn't ask your product manager to read the playbook before running a survey. It lets them say, "I need to design a survey to find out why users are dropping off at checkout," and have an AI walk them through the process, applying your organization's standards as it goes. The best practice is embedded in the skill. The person using it doesn't need to have absorbed it first.

That is a qualitatively different proposition from anything a static playbook can offer.

The specific skills worth building will vary depending on the organization. But for a UX or digital team trying to extend their influence, the candidates tend to cluster around the tasks that non-specialists most often get wrong.

Survey design is an obvious one. Writing questions that don't inadvertently bias the answers is harder than it looks, and most people who aren't researchers have no idea how their phrasing is leading respondents astray. A skill that guides someone through question design, flags leading language, and checks for common structural problems would save a lot of quietly-useless survey data from being collected.

Prototype testing is another. The basics of a usability test, what to observe, what to ask, how to avoid putting words in a participant's mouth, are genuinely learnable. The problem is that someone needs to learn them before running the test, not during it.

You could build skills for

Each of these represents expertise that currently lives in the heads of a few specialists and gets applied only when those specialists have capacity and are directly involved. An AI skills library changes that dynamic. The expertise is no longer gated by headcount or availability. It is available whenever someone in the organization needs it, in a form they can actually use.

Building a skills library at an organizational level is different from building one for yourself. You're not just creating tools that save you time. You're creating tools that let anyone in the organization apply a consistent standard, without needing to be a specialist first. The UX team's influence is no longer bounded by their headcount.

And this is still relatively early days. Most organizations haven't started thinking about AI skills at that level. The teams that build these libraries now will have a head start that gets harder to close over time.

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Running an agency or working freelance? This is worth thinking about for your own practice too. A well-built skills library makes your service more consistent, helps you bring junior team members up to speed faster, and gives clients a reason to see you as something more than a pair of hands.

If this is the kind of thing you'd like to work through alongside other freelancers and agency owners, my Agency Academy is probably the right place. It's a group coaching community where we get into exactly these kinds of challenges. £28 a month, cancel whenever.

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If you're thinking about this for your own organization, the most practical starting point is to identify the five tasks that non-specialists most often get wrong, and that cause the most friction or quality problems when they do.

For each one, document how it should actually be done, in as much detail as you can. What does the process involve? What are the most common errors? What does good output look like? Then work with an AI to turn that documentation into a skill, test it against real examples, and refine it based on what it gets wrong. The goal isn't perfection on the first pass. The goal is something good enough to use, that improves each time you use it.

That is a manageable starting point, and one that tends to produce visible results quickly enough that people want to keep going.

If you're working through this and would like a thinking partner, or if your organization is seriously considering building out an AI skills library and wants some help thinking through what that looks like, I'd genuinely enjoy that conversation. Book some time here and let's explore it.

TL;DR: AI skills are reusable, chainable instructions that tell AI exactly how to complete a specific task your way. Building your own library of them now gives you a compounding advantage that will only grow over time. This post explains what they are, why they matter, and how to start building yours.