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I used to go to a lot of conferences. Then COVID happened, and I just... stopped. At first it felt like a win. No early trains. No hotel breakfasts. No lanyard. I could watch the talks online in my slippers. It took me a long time to notice what I'd actually lost. And it wasn't the talks. The best part of a conference was never on stageThink back to the last good conference you went to. I bet the thing you remember isn't a slide. It's a conversation. For me, it goes back to my first conference, @media 2005. At one point they did a sort of live blogroll, asking people who wrote well-known web standards blogs to stand up. They called out PPK (Peter-Paul Koch, whose blog I read every single day) and the guy sitting next to me stood up. The poor man spent the rest of the break being peppered with questions by me. I don't remember a single talk from that day. I remember that conversation. That's what conferences gave us: the chance to turn to the person next to us and ask. Those conversations did something your colleagues can't. Your colleagues are great, if you have them. But they work in the same organization, under the same pressures, with the same blind spots. They can't tell you how things work somewhere else. And a lot of in-house UX people don't have colleagues doing what they do anyway. They're a team of one, or a team of a few. Conferences and local meetups gave us that outside view almost by accident. Now most of us don't go. Budgets got cut. Travel got harder to justify. Meetups quietly fizzled out. And nothing really replaced them. And then the job changed under our feetWhat really worries me is that this is happening at exactly the moment we need each other most. AI has changed how we work, and it's still changing. Someone senior wants everything faster. We're relearning research, design and content in real time. And there's almost no established best practice yet, because it's all so new. I'll be honest. I don't know if I'm doing it right. I'm experimenting with new ways of working all the time, and some of them are brilliant and some are a waste of an afternoon. Take prototyping. I've stopped using Figma for it and now build prototypes in code with Claude instead. It made sense at the time. But Figma has changed a lot since then, and I honestly don't know if I made the right call. I don't have time to try every new way of working. What I'd really like is to ask someone who stuck with Figma how it's going for them. And I don't have anyone to ask. I suspect you're in the same boat. That's a strange position for a whole profession to be in. Everyone reinventing the same wheel, alone, at speed. What I've learned from running a communitySince July 2024 I've run Agency Academy, a community for agency owners and freelancers. We meet on a call every week and chat in Slack between calls. What surprised me is where the useful advice comes from. It's rarely me. It's another member who solved the exact problem last month. Unsurprisingly, for a room full of agency owners and freelancers, the topic that comes up most is winning new work. I thought I had a good handle on that in my own business. Then members started sharing what was working for them. Regular meetups with old clients. Hyper-specific SEO landing pages. Ideas I'd have dismissed as a waste of time if I'd read them in a blog post. But I didn't read them in a blog post. I heard them from people in the same position as me, who'd tried them and could tell me exactly how it went. That's very hard to ignore. That's the hallway conversation, except it happens every week, not once a year. So I'm starting one for in-house peopleIt's called UX Insiders, and I'm launching it in partnership with Smashing Magazine. It's for designers, researchers, UX leads and anyone championing UX inside an organization. Universities, charities, government and companies of every size. Here's how it works: A weekly call every Tuesday, 3:30 to 4:30pm UK time. Bring a problem, a question or a win.A private Slack for the questions that can't wait until Tuesday.Every call recorded with a summary, so you can catch up when you can't make it.It's in-house only, so people can talk openly about their organization without worrying who's listening. You apply first, just so I can check you work in-house. It takes about a minute. Founder places cost £49 a month billed annually (£588 a year) + VAT, for the first 25 members. That's less than most conference tickets before you've booked a train or a hotel, and it lasts all year. It can usually come out of the same training budget too, and you can pay by invoice. Nothing is charged until we launch on Tuesday 5 January. If fewer than 15 people sign up by Friday 11 December, it doesn't go ahead and nobody pays a penny. Apply for a founder place Run an agency or freelance?Then Agency Academy is your place. Same format: a weekly call every Thursday and a Slack full of people who get it. Members even pass work to each other, which has a habit of paying for the membership. Since I'm offering founder pricing on UX Insiders, it only seemed fair to do something for Agency Academy too. Until the end of October, you can join for £59 a month + VAT, or £49 a month + VAT if you pay annually (£588 a year). That's down from £79 a month, and you keep that rate for as long as you stay. Join Agency Academy at the October price Either way, find your peopleEven if neither of these is for you, please don't do this job alone. Find a meetup. Start a Slack with three people you trust. Book that conference. The answers we're all looking for right now aren't in a book yet and aren’t being answered by AI . They're in each other's heads. And if you're on the fence about UX Insiders, hit reply and tell me what's holding you back. I read every reply, and your questions will shape how this thing works.

We are doing it all wrong… again. I'm getting serious déjà vu at the moment as I watch organizations attempt to rollout out AI, and to be honest, it is frustrating the shit out of me. Back in 2014, I wrote a book called Digital Adaptation where I talked about the shortcomings in how organizations were approaching digital transformation. This was the time when organizations were finally waking up to the fact that their website wasn't just another brochure and that digital had become essential to all elements of their operation. Digital transformation was never really a technology problem. The technology was the easy part, and most of it was already sitting there waiting to be used. The hard part was that users had changed how they behaved, and organizations were being dragged along behind them, trying to keep up with expectations set by Amazon and Google rather than by anyone in the boardroom. Catching up meant changing culture, processes, and the way people worked, which turns out to be a lot harder than buying a new content management system. AI adoption is the same problem all over again. Yes, there is a shiny new technology involved. But, the thing that will decide whether it works in your organization is people. Not just your users, but your staff too. We appointed a chief electricity officer once, tooIn the book I compared digital teams to the "chief electricity officer" that some companies appointed when electricity first arrived. It made sense for about five minutes, until everyone realized electricity was something every part of the business used and putting one person in charge of it was a bit daft. Today organizations are busy appointing Chief AI Officers and setting up central AI committees. I understand the impulse. It feels responsible, and it gives the board someone to point at when things go wrong. But, AI is going to end up woven into almost every job in the building, and no central team understands the daily work of finance, customer service, and the warehouse well enough to decide how AI should fit into each of them. IT built it, so staff ignored itWe saw what happens when that distance exists with digital. IT designed systems for people they had never met, and when those systems did not work, staff routed around them using their own off-the-books workarounds. The same thing is happening with AI, except the workaround is now a personal ChatGPT account on a phone under the desk. The CEO has had a revelation. Everyone else hasn't.However, not everything is the same. When I wrote the book, it was really aimed at helping digital teams convince management that change was required. Now the direction has flipped. Often it is the CEO who has become the convert, and the rest of the organization is lagging behind. I spoke to the CEO of a cargo airline recently who had binned a reporting project that had been "in production" forever without ever actually being finished. That was because, within days of trying AI, the CEO had working dashboards built with Claude. That is a nice illustration of another point from the book, by the way. Big projects that try to specify everything up front rarely finish, and building, testing, and iterating beats them almost every time. Understandably, that CEO is now evangelical. The problem is that a dashboard for the CEO means nothing to the person processing shipping paperwork. Everyone needs their own revelation about their own role, and you cannot hand somebody a revelation in an all-staff email. Why your staff are right to be nervousThis is where AI differs from digital, and where I see most rollouts fall apart, because your staff are nervous about AI, and understandably so. Is this about replacing me?AI makes every individual employee wonder whether the CEO's enthusiasm is about replacing them. When leadership announces an AI initiative, a lot of staff hear "we are working out how many of you we need." Now, you can reassure them as much as you want, but they have read the same headlines you have. What's in it for me?Then there is the question of what is in it for them. If staff are still going to be sitting at their desks for eight hours but are now expected to produce more, the honest answer is nothing. They take on the learning curve and the risk, and the organization keeps the benefit. I would be reluctant too. Decades of habit don't vanish overnightWe also underestimate what we are asking when we tell people to give up ways of working they have built over decades. That procedural knowledge is a big part of how people see themselves as good at their jobs, so being told a tool can now do it is hardly a neutral message. They are already using it anywayAnd on top of all that, with some people, adoption is already happening without management permission. Plenty of staff are using AI right now to make their lives easier. With these people, the job is less about getting approval and more about catching up with what they already do and making it safe. So what do you actually do about it?Start with the people who already careFirst, start with the enthusiasts rather than the resisters. Every organization has a handful of people who are already experimenting. Bring them together, give them space to share what is working, and let their combined enthusiasm do the heavy lifting. In the book I described an internal conference where staff showed colleagues what they had built, and it did more for adoption than any memo from the top. That works just as well for AI. Make it safe to mess upSecond, make it safe to experiment. That was a big theme in the book, and it matters even more now. Lots of people are afraid to try something different in case they get it wrong, and if your organization has a culture of fear, no amount of memos or shiny new tools will persuade them to play. Playing with new tools is how we learn, so people need to know that a failed experiment will not count against them. Give people time to playFeeling safe is only half of it, though. People also need the time and space to experiment. Most of the staff I meet are so stretched that trying something new feels like a luxury they cannot afford. So, if you want people to explore AI, you have to give them the hours to do it rather than expecting it to happen somewhere between back-to-back meetings. Aim it at the boring bitsThird, frame AI around the parts of the job people hate. Nobody is emotionally attached to reformatting reports or chasing timesheets. At that same airline, a technical writer was convinced AI would replace her, and she became one of its biggest advocates the moment it made the tedious parts of her work faster. Skip the one-size-fits-all trainingFourth, be wary of generic training. I have seen organizations run one-size-fits-all AI sessions aimed at their most resistant staff, and the result was exactly what you would expect. People typed in vague requests, got vague answers, and left convinced AI was rubbish. Garbage in, garbage out. Interfaces matter more than you thinkFifth, pay attention to the user interface. My wife ignored AI entirely until she used Grokbot, which presented it as a team of helpers. Same technology, completely different reaction, which is a reminder that adoption is a design problem as much as anything. Sort out governance (without a committee)Sixth, sort out governance rather than forming another committee. Lock down only the data that genuinely needs protecting, with permissions set at the data layer, and then trust your staff to do the right thing with everything above that. Most people want to do a good job and have no interest in leaking customer data. What they need is a lightweight governance framework that sets out a few clear principles and who is responsible for what, and the responsibility matrix from the book applies almost unchanged here. Don't lock it down until it's uselessFinally, do not lock things down so tightly that AI becomes useless. Yes, sensitive information needs protecting. But, I keep seeing organizations where AI is so restricted that nobody can do anything of real value with it. I am working with a UX team at the moment who want to use AI to build a user research repository, and their Copilot license is so restrictive it will not let them. They are not allowed to bring in any other AI tools without going through a complicated committee process, so the whole idea is stuck, despite management telling them they should embrace AI. Over-locked AI gives you roughly the same result as no AI, except you are still paying for the licenses. So, lock down the data that genuinely needs it, then make it easy for people to get on with everything else, because overdo the governance and you kill creativity and adoption along with the risk. Make sure UX is in the roomIf you work in UX, you have an incredibly important role to fulfill here. I am not suggesting you should be the one leading AI adoption. There are HR issues, technology issues, and strategy questions that sit firmly with senior management, and none of those are ours to own. But, UX should very much be in the room and part of the conversation. After all, UX was born out of human-computer interaction, and AI is human-computer interaction in its purest form. We are now having actual conversations with machines, which is about as close to our core discipline as it gets. We also understand behavior, motivation, emotion, and resistance to change better than most people in an organization, and those are exactly the things that will decide whether AI adoption works. In the book I asked web teams to lead digital change. This time I am asking UX people to make sure they have a voice in it, because I am not convinced anyone else will speak up for how staff actually feel about all this. I am curious whether you are seeing the same patterns where you work. If enough of you are, this might turn into a series, and maybe one day another book, assuming I can stay focused long enough to finish one. I'm thinking

As we all know, one of the key characteristics of somebody working in any user focused field is a fascination with people. How they think, what motivates them, how they make decisions. But that fascination should not just be focused on others, we should apply it to ourselves too. It is easy to go through life oblivious to what we are doing and why we are doing it. But as we become more self-aware, we also begin to understand more how others are likely to respond too. As an early adopter of any new technology, I have learned that what I go through when wrapping my head around a new tool is likely to be the experience of others slightly further down the road. This is why I have been particularly interested in my own use of AI, especially relating to using AI to inform my decision making. The contradiction in how I use AII have noticed a contradiction in my behavior when using AI and I suspect it is one that many of us share. When I ask AI about a subject I know little about, I have a tendency to accept its recommendations and responses without question. Meanwhile, if I talk to it about user experience design or conversion optimization, I am much more critical of its responses. I recognize its limitations such as its lack of context, its inability to pick up on subtle signals, and its fixation on one particular approach to the problem. Equally, I am much more aware of its proclivity to agree with me and to be overly confident in its responses. The conclusion is obvious. If I am not impressed by AI's responses on a subject I know deeply, I should not place too much trust in its responses on a subject I know little about. There is no reason to assume it knows more about a subject I am unfamiliar with than a subject I am an expert in. Where AI falls short in the real worldFurthermore, this shows that in truth, AI's real world knowledge is still fairly limited. Sure, it might excel at every benchmark under the sun, but when it comes to the complexities of real world human decision making, it is relatively poor. It doesn't know when to challenge and when to encourage, or when to ask for more information and when to just take action. It cannot make judgment calls or handle internal politics. It is great at presenting options, but it lacks judgment in its recommendations. It can write a set of compelling cases for a subject, but it has no idea which will sway the decision maker. But it has taken me, and I suspect all of us, time to identify the edges of what AI can and cannot do. This has been true of every new technology we have ever invented as a species. We are always exploring the edges of what is possible. Why many people are having a rough time right nowSo, why do I raise this? Well, it is to encourage you that things will get better, because at the moment many people are not having a good time. Take for example a member of the Agency Academy who was telling us about a meeting she had recently where the client was validating everything she said with Claude. Or others who have had their work picked apart by an AI. Then, of course, there is the naive belief of some executives that AI allows them to avoid recruiting or even to lay people off. Heck, I am even feeling the impact myself. The amount of coaching I have been doing has plummeted because of AI. Why hire a coach when you can just ask AI? Why I am not worriedBut I am not really worried about that. I am not worried, because I know it will pass, and it already is beginning to. Like us early adopters, who have begun to identify the edges of what AI can do, everybody will eventually come to the same understanding. In time, people will begin to trust the judgment of AI less than they trust the experts. They will realize that AI is most effective when it is used by those experts. The desktop publishing lessonApologies if you have heard me tell this story before, but when I was at university, desktop publishing became a thing. There was panic among those of us studying graphic design because suddenly anybody could create a flyer, brochure or poster. Why hire a graphic designer when you can just do it yourself? And indeed, for a while it was tough, but in time people realized that although they had an amazing new tool, it didn't replace an expert. After one too many posters with Comic Sans and clip art, people realized that they needed a graphic designer. Be patient, and watch your own hypocrisySo I would encourage you to be patient as people work out the limitations of AI, and to be aware of your own hypocrisy. You cannot get annoyed at people for using AI rather than trusting you, when you then use AI in the same way in other fields rather than asking an expert. Don't get me wrong, people won't stop using AI, and I am not expecting my coaching levels to ever return to what they were before. But I am confident that in time people will realize that AI doesn't replace human judgment, understand the complexity of a work environment, or tell you when you are the problem, which trust me, I will do.

Most of my clients come to me with a problem. These can be wide ranging from “we are seeing large churn in our app” to “we cannot get stakeholders to sign off on vital UX work”. They are sometimes expressed as a user need such as “we need to make it easier for users to do X” or as a business need such as “we need to increase the number of users who sign up for our demo”. Or sometimes just a scream of frustration! Whatever the case, the ability to diagnose the root causes of these problems and address them is a core part of my role and, I would argue, the role of anybody who is working in digital. Whether you are a product manager, UX designer, developer, marketer or business owner, the ability to troubleshoot these kinds of challenges is essential to being successful in your role. Fortunately, no matter the trouble that needs ‘shooting’, there is an approach you can use to resolve the challenge. Admittedly, it takes practice, experience and nuances that are far beyond what I can cover here. However, hopefully I can at least point you in the right direction. So, let’s take a real challenge that I often encounter and work through the process I use to troubleshoot and resolve it. Our example challengeThere are so many possible challenges I could use, but let’s pick one I hear all the time from marketing teams. They are running ad campaigns and yet they are seeing low conversion rates. They want me to help them understand why users are not acting and work out how to fix it. Well, whatever the challenge is, the first step is to diagnose the problem. Diagnosing the problemObviously that is easier said than done. But, normally I start by brainstorming a list of possible causes. For example, in this case, I might consider the following: The ads are targeting the wrong audience.The ad copy is not compelling enough.The landing page is not converting.Conversions are not being properly tracked.The post landing page experience is flawed in some way.The list could go on, but you get the idea. Narrow the fieldNext we need to identify which of the possible causes is the most likely to be the primary issue. Of course, it could be a combination of a few, so we need to bear that in mind. Problems are rarely black and white. We can do this with good old fashioned research. We might look at the analytics data, carry out user testing or run a survey. The idea is to find supporting evidence that the problem is real and that it is the right one. Once we are fairly confident that we have the right diagnosis, we need to zoom in on exactly the nature of the problem. Get specific about the problemLet’s say for example that the problem turns out to be the landing page experience. Ads are well targeted, but the conversion rate on the landing page is low. Well, what exactly is the problem with the landing page experience? Is it the design, the copy, the call to action or something else? Identifying this might be done through years of experience, research or user testing. However we do it, we need a specific diagnosis before we proceed. Now, at this point, most people will jump into fixing the problem, but that would be a mistake. Before doing that we need to ask, why that problem exists in the first place. Understanding why the problem existsI mean think about it. Is there a marketer on the planet who doesn’t know that a campaign will underperform if the landing page experience is poor? It’s easy to assume that the cause is incompetence, but that is rarely the case. Often there are underlying issues that have prevented the problem being addressed. For example, the solution might be to have customized landing pages for each campaign. But there are often barriers that prevent this. Barriers like: A lack of design and development capacity.No template that can be used.An overly restrictive template that cannot be customized for a specific campaign.A lack of time to produce multiple landing pages.Technological limitations.Legal or compliance issues.Again, I could go on. **Identifying these blockers is the most important part of troubleshooting. **Giving them a new beautiful landing page will not fix the problem if they cannot use it. Your solution has to be practical within the constraints of the real world or you need to at least be able to make a compelling case for why these barriers have to be addressed. Addressing the barriersThis is where you need to get creative. Ideally you want to circumvent the barriers, rather than try to overcome them. This will prove far more successful and faster to do. For example, it is not going to be easy to get more design and development capacity or replace the tech stack. Circumventing the barriersInstead, you need to look for workarounds that will get people 80% of the way there, even if it is not perfect. In the case of our landing pages, that might be to create a landing page playbook that teaches AI and marketers how to easily create high converting landing pages in minutes. A playbook that takes into account the constraints within which the organization operates. In fact, this is such a common scenario I have a packaged approach to do exactly this. This is so much more effective than some report telling people everything that is wrong or a prototype showing best practice, because they don’t address the underlying barriers. Of course, you cannot always circumvent the barriers. Sometimes they need to be overcome. Overcoming the barriersI came across this scenario recently. I recommended to a large charity that they needed customized landing pages for their fundraising campaigns. Unfortunately, their tech stack just didn’t allow for it without significant development investment and it just wasn’t a priority. My job therefore was to make it a priority. I had to build them a business case that showed this was a job that should be done and done soon. Step one was to gather evidence that the limitations of their tech stack were causing the organization real damage. We did that by running a test campaign with a custom landing page that did not use their tech stack. This was easy enough to spin up with the help of AI, despite still plugging into the existing tech stack for the actual donation. We tracked the number of people entering the donation flow and compared it to the numbers from previous campaigns. The uplift was significant. Despite poor tracking of average donation amounts, and lifetime value of donors, we were able to make some estimates about the financial impact of the improved landing page. Or put another way, we were able to estimate how much the barrier (existing tech stack) was costing the organization for each and every campaign they ran. These figures, alongside our proof of concept test, could be rolled into a business case alongside a small ask that the fundraising team be allowed to create landing pages separately from the existing tech stack, until resources were available to fix the issue. Notice I didn’t suggest the whole tech stack should be replaced. Neither did I demand development resources should be allocated immediately. Instead I suggested a small, temporary solution that would allow the organization to recover the money lost to the tech stack. Checking you actually fixed anythingSo, you have a solution that survives contact with the real world. But, that is not the end of the job. Yes, this is the point where everybody wants to move on, and I understand why. The fix has shipped, the pressure is off, and there is always another problem waiting. But, if nobody goes back to look at the numbers, you have no way of knowing whether you diagnosed the problem correctly or simply got lucky. You also throw away the evidence that would have made your next business case much easier to win. So, agree up front what you are going to measure and when you are going to look at it again. In our landing page example that might be the conversion rate per campaign, the point where people abandon the page, or the number of campaign pages the team manages to produce in a month. Pick 1 or 2 numbers, write down where they sit today, and put a date in the diary to revisit them. If the numbers have not moved, you have not solved the problem, you have only relocated it. The questions, in orderIf you take nothing else from this, take the order of the questions. What do we think the problem is?What evidence do we have that it is the real problem?What exactly about it is broken?Why has nobody fixed it already?Can we go around that barrier, or do we have to knock it down?How will we know whether it worked?Yes, most of us can answer question one in our sleep. But, the value sits in questions four and five, and those are the two it is easy to skip on the way to a nice slide deck of recommendations. A slower way of working, and worth itI am not going to pretend this is quicker than firing over a list of improvements. It isn’t. It involves awkward conversations about why the obvious fix has been sitting undone for 3 years, and those conversations are rarely fun. But, advice that nobody can act on is just an expensive opinion, and I have written more of those than I care to admit. It is also difficult when you work in-house. There are politics and hierarchies to navigate. That is where getting some outside help can make a difference. They sit outside of those structures and tend to be seen as more impartial. So if you are struggling with a barrier that needs to be circumvented or a problem that you just cannot solve, give me a shout.

I've sat through plenty of meetings where someone made a genuinely good case for UX work and got precisely nowhere. On more than one occasion in the past that someone was me. I have found myself standing in front of a slide covered in personas while the finance director quietly lost the will to live. The work was solid, my arguments were rubbish. But over the years I have learned. You see, most in-house teams lose this argument because they make the case for UX in the language of UX, to an audience that has never once been rewarded for caring about it. So senior managers nod politely, agree that it all sounds very interesting, and then fund the project that turned up with a number attached to it. So I want to walk through how you build a business case for UX work based on my years of mistakes. I am not necessarily talking about a formal document with a cover sheet and an approvals matrix, but any argument solid enough to get the work you need signed off. Nobody cares about UX as much as you do And why would they? You don't care about health and safety, and you'd glaze over inside 30 seconds if the accounts team started walking you through their reconciliation process, however lovely their diagram. Everybody has their own patch and defends their own patch, and UX happens to be yours, which means the translating is your job rather than theirs. That means selling UX rarely involves talking about users at all, which I admit feels like a small betrayal of everything we bang on about at conferences. Yes, talking about users is how we do the work. But, talking about business risk and opportunity is how you get permission to do the work in the first place. Risk for the comfortable, opportunity for the hungry A good business case leads with the risks and opportunities your proposal addresses, and the framing you pick depends enormously on the mood of the organization you happen to work for. If you're in a big, established, comfortable business, the risk of doing nothing is your strongest card, because these organizations have plenty to lose and a deep institutional fear of losing it. Talk about customers drifting to competitors with a better signup process, or support costs climbing because the website can't answer a simple question. If the business is newer and hungrier, flip it around and sell the upside, because nobody in a fast-growing company is lying awake worrying about protecting what they already have. There the conversation is about the sales you could win, the markets you could reach, and the growth currently leaking out of a checkout nobody has looked at properly. Work out who you actually need to convince Before you write a word, get clear on whose signature you need, what that specific person is measured on, and how your proposal makes their year easier. Business goals are useful, but personal goals are what get budget released, and the person approving your work has targets, a boss, and a nagging worry of their own. The emphasis shifts depending on where you work, and in my experience it breaks down roughly like this: Commercial businesses. Pick either customer acquisition or retention and build the case around one of them rather than gesturing at both. Government and public sector. Focus on reducing risk, avoiding the sort of failure that ends up in the press, and improving the profile of the service. Charities and non-profits. Talk about fundraising, supporter engagement, and building the profile of the cause. But, cost savings work almost everywhere, and they're badly underused by UX people who would rather talk about delight. Fewer support calls, less staff time spent on manual workarounds, and fewer expensive rebuilds are all things a CFO understands without any translation from you. Use numbers, even wobbly ones Wherever you can, tie your case to hard figures, and don't let the fact that they're estimates stop you. Be honest that they're estimates, explain the assumptions behind them, and offer to do a more detailed analysis if the decision hinges on it. A rough number invites a conversation, while no number invites a polite no. If the maths makes you nervous, I built an ROI calculator that does the heavy lifting for you, so you can walk in with something more persuasive than a strong feeling. Beyond the numbers, get people excited about what's possible, because approval is an emotional decision dressed up in a spreadsheet. Vibe code a rough prototype, mock up the improved journey, or build something shiny that stakeholders can see and immediately want. I've watched a scrappy 2 day prototype do more for a business case than 40 pages of analysis ever managed. What goes into the case Strip away the formatting and every decent business case answers the same handful of questions: The diagnosis. What's the problem or the opportunity, described in business terms rather than UX ones. The work. What you're proposing to actually do. The objective. What success looks like, and how you'll know you got there. The evidence. What research, data, or experience supports your case. The practicalities. What needs to happen, in what order. The resources. What you need in people, time, and money. The first step. One small, well defined thing you can start on now. Ask for less than you want Those last two points are where most business cases die, usually because they ask for too much in one go. Focus on reallocating resources you already have rather than requesting new money, especially at the start, because moving a designer onto a project for 3 weeks is a much smaller decision than approving a budget line. Don't ask anyone to sign off on the whole plan either. Build momentum with a small first step, whether that's a prototype, a limited pilot, or a focused piece of research, and make sure it's something you can deliver quickly so people see progress while they still remember agreeing to it. Then define, up front, what results would justify committing to the rest, so the next conversation becomes a matter of pointing at evidence everyone already agreed would be convincing. None of this is as satisfying as being handed budget because the work is obviously the right thing to do, and I spent years waiting for that to happen. It never did, so I learned to talk about money instead, and the UX work finally started getting approved. If you've got a case to make and no idea where to start with the figures, I can actually help with the process. You can learn more here.

I spent a good chunk of my career insisting that proper UX work should only be done by proper UX people, and I told myself that was about protecting quality, when honestly a fair amount of it was about protecting my own job description. I would sit in a meeting, watch a product owner sketch a screen on a whiteboard, and feel a small internal wince, as if they had wandered into my kitchen and started rearranging the crockery. It felt professional at the time. It was territorial, and it has not aged well. These days I find myself telling clients something that would have horrified younger me. If you want better digital products, the answer probably isn't sending more work through your UX team. It's changing what you employ them to do. Everybody is designing now, whether you sanctioned it or not You have almost certainly seen this happening inside your own organization. Somebody without design in their job title describes an idea to an AI tool and comes back a few hours later with a clickable prototype, realistic content, passable copy, and a flow that mostly hangs together. Developers are generating interface options before the ticket is even refined. Marketers are building and testing their own landing pages. Product owners are turning a rough thought into something demonstrable over a lunch break. Some of that work is genuinely good. Some of it is a small mountain of plausible looking rubbish that nobody has the expertise to spot. All of it is happening whether the design team blesses it or not, and it happens fast, which means it usually arrives before anyone thinks to involve them. Cutting the design team is the obvious response and the expensive one I understand the temptation. The team is expensive, the tooling has made production cheap, and somebody senior is asking what the return on all that research actually is. So the headcount that leaves doesn't get replaced, the budget line gets trimmed, and the design team's seat at the table quietly becomes an invitation to comment on decisions after they have been made. What you lose in that trade is judgment, and it shows up about two quarters later. Every team invents its own version of the same pattern, so the product starts to feel like it was assembled from three different companies. Accessibility problems accumulate because generated interfaces look fine and fail quietly. Decisions get made on assumption rather than evidence, so you build things nobody wanted and only find out after launch. Rework becomes the largest hidden line in your delivery costs, and nobody attributes it to the design cut that caused it. The organizations getting this right aren't the ones with the biggest design teams. They are the ones who moved their design people upstream, away from producing every screen and toward setting the conditions in which everybody else produces decent ones. What that structure actually looks like The version of this role that earns its keep looks less like a traditional designer and more like a conductor. Rather than routing all design work through a small team and watching a queue form, you fund that team to build the tools, standards, and guidance everyone else needs. Quality gets protected through what you hand people, rather than through gatekeeping that colleagues will route around anyway. In practice that means investing in a handful of assets. A design system with real usage guidance**, so a developer building a screen at 4pm on a Friday makes a reasonable decision without asking permission. Playbooks for the work people keep repeating**, like a landing page playbook that walks a marketer through structure, evidence, and calls to action without them inventing it from scratch each time. A research repository anybody can query**, tagged and maintained, so the research you already paid for keeps earning its money long after the readout deck has been forgotten. Functional personas that stakeholders can interrogate**, built around what customers are trying to get done rather than their age and job title, and useful enough to settle an argument in a meeting. Standards for briefing AI well, because the difference between useful output and confident nonsense sits almost entirely in the brief, and your design people are better placed than anyone to teach that. Alongside those assets, the team offers services rather than delivery. Open office hours for anyone about to build something, quick audits of work in progress, coaching for the team that keeps getting it wrong, training for the people who want to get it right. They still take on the genuinely hard, high risk design problems, but they stop being the only route to a wireframe. What has to change on your side None of this survives contact with the existing performance conversation, because most design teams are still measured on throughput. If you judge them on how many screens and tickets they got through, they will keep behaving like a production line and the queue will reappear within a month. Measure adoption of the design system instead, along with reuse of existing research, and the quality of what non-designers are shipping without help. The hiring mix shifts too. You need fewer people whose main strength is producing polished interfaces, and more who can think about systems, standards, research operations, and how to influence colleagues who don't report to them. Give them the authority to set standards that hold across teams, and get them into decisions early enough to shape what gets built rather than tidy it afterward. Somewhere sensible to start Ask whoever runs design for you what people keep coming to them for, week after week. Not what they think colleagues should want, the requests that genuinely keep landing. Then fund turning one of those into something the rest of the business can use without them. One playbook, one documented pattern, one persona people can question. See what it does to the queue, and do the next one. The uncomfortable part is that a team working this way looks less busy for a while, and busy has been our proxy for valuable for about two decades. That freed up time is the whole point, because it's where the strategic work finally happens. There is considerably more to it than I can fit in an email, which is why I have put together a free course on exactly this. Fourteen short lessons on shifting from doing the work to directing it, with the workshop slides thrown in. You can sign up here, and it's worth forwarding to whoever leads design for you. If you think I've got any of this wrong, hit reply and tell me, because I'm still working out how much of it I have.

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. Of course, when everything is important, nothing is. You end up with a homepage trying to please 14 departments, navigation labels negotiated by committee, and an interface that gives the refund policy the same prominence as whatever users came to do in the first place. Which is a slightly peculiar way to design something for humans. This is why I’ve relied on top task analysis for years. Find the few things that deserve attention 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. That distinction is useful because people can want many things in theory. But when they have to choose, a much smaller set usually rises to the top. Those top tasks give you an evidence-based foundation for decisions such as: What should appear prominently on a landing page Which features deserve attention in an app How a website’s information architecture should be organized What information belongs in a dashboard Which stakeholder requests can safely sit further down the list It won’t make the political conversations completely disappear, sadly. But “our users ranked this above that” is a considerably stronger position than “I feel this button should be bigger.” Why a normal survey isn’t enough A traditional survey can collect what people say they want, but the result is often another long list. You’ve discovered 47 things your audience cares about and somehow made the original problem worse. Well done, everyone. 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. The approach can work for all sorts of digital products. On an ecommerce site, tasks might include checking delivery charges, tracking an order, or arranging a return. On a marketing site, they might be understanding pricing, comparing options, or finding evidence that a product works. In an application, they might be the handful of features people use every day. Once you know what sits at the top, you can design around those priorities and fit the smaller tasks around them. I’ve built a free app to make this easier Although top task analysis is valuable, running one has traditionally involved a slightly awkward collection of survey tools, spreadsheets, and manual cleanup. I’ve spent enough of my life staring at those spreadsheets, so I built a dedicated Top Task Analysis app instead. It is 100% free, and I intend to keep it that way. You can create a survey, add a few sample tasks, and share it with your audience. Participants select their 5 most important tasks, add missing options for others to choose, and then prioritize their selections. The admin area shows which tasks matter most, and you can compare the priorities of different audience groups. You can also rename, merge, or remove responses as you clean up the results, rather than performing spreadsheet surgery and hoping you haven’t accidentally deleted Western Europe. If you’d like to understand the process before creating a survey, I’ve also written a step-by-step guide to running a top task analysis. It covers gathering tasks, recruiting participants, analyzing the results, and using what you learn. The difficult bit remains reassuringly human 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. That is a good thing. We already have enough tools claiming to replace judgment while producing dashboards nobody reads. But top task analysis gives that judgment somewhere solid to start. It replaces a surprising amount of guesswork with direct evidence about what your audience came to do, and it makes prioritization conversations much easier to have.Try the free Top Task Analysis app A small Product Hunt-shaped favor 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 I’d genuinely value your first impressions, so if you have a moment, please leave a comment on Product Hunt. In particular, I’d love to know: Whether you can see a situation where you’d use it How you’d like to use it Whether there are any features you think are missing And once you’ve had a chance to try it properly, ongoing feedback is equally welcome. If you run a survey and discover something confusing, missing, or mildly irritating, please let me know. I built this to make a useful research approach easier to adopt, so real-world grumbling is far more useful than polite applause.

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. The slow slide into copying 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. It's not only about being one step behind 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. But they're bigger than us, surely they know what they're doing? 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. You are not your 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. The real reason we copy 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. Do your own research instead 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. A brand is not a mood board 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. The web is an afterthought, and it shows 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. What this actually costs you 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. The bit that tends to land 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. What to do about it 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: A semantic differential survey, to see whether the brand actually communicates the values you think it does rather than the ones you hope it does. Accessibility and readability testing, to see whether it genuinely works on a screen rather than just in a presentation. 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. Don't win the argument. Sidestep it. 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. The questions worth exploring 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. How much control do they want? 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. How do they want to see it? 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. How do they want to interact with it? 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. Let the users build your case 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. A grudging defense of quick wins 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. What quick wins actually do well 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. Why we end up here (and yes, that includes me) Clients call us in too late 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. Stakeholders are responding to terrible incentives 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. Agencies and consultants are complicit 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 slow, accumulating cost The trouble with an all-quick-wins strategy is that the damage compounds out of view. The easy wins run out 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. The structural issues never get touched 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." UX debt accumulates quietly 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. What I'm trying to do instead 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. 1. Treat quick wins as proof of concepts 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. 2. Be honest about the ceiling 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. 3. Bank the trust and plan the bigger work 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. 4. Challenge the metrics 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. The honest version 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. The numbers nobody is acting on This is strange when you stop and think about it. The economics of retention have been well known for years. Acquiring a new customer typically costs around five times more than keeping an existing one. Cross-selling or upselling to an existing customer costs roughly 24% of what it takes to win the same revenue from a new one. You don't need to convince someone who's already bought from you. You just have to not screw it up. Retention falls between the cracks 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. Acquisition belongs to marketing. Onboarding sometimes sits with product. Support lives in customer success. Renewals end up with sales. Retention falls into the gaps between all of them, which is a polite way of saying it falls on the floor. A real opportunity for UX 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. Why retention is a UX problem in disguise 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. They couldn't find what they needed. The product didn't deliver what they expected. Support was a maze. The onboarding fizzled out before the value clicked. 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. Register for free What this looks like in practice A few starting points. 1. Change your KPIs 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: Churn rate Repeat purchase rate Lifetime customer value 2. Audit the post-purchase experience 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: Onboarding The first month of use The renewal flow The upgrade prompts 3. Get involved in cross-functional work 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. A flag worth planting 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. The problem nobody talks about 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. The conductor problem 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. The design leader parallel 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. AI burnout is already real 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. Where to start 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. The expectation gap is real 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. Why waiting is a bad idea 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. What a solid audit covers A UX maturity audit should cover five areas. Not exhaustively, but enough to give you a real picture. Strategy and leadership. Does UX have a seat at the table? Is there genuine sponsorship from someone with budget and influence, or is UX being practiced in a corner while real decisions happen elsewhere? Culture and capability. How widely does the organization understand what UX actually involves? Are there training pathways and career development? Or is it just a job title a few people happen to have? Research and design processes. Is UX practice consistent, or does it depend entirely on who’s available? Are designers and researchers involved early, or called in after the big decisions are already made? Outcomes and measurement. Can the team point to specific improvements in user outcomes? Are there agreed definitions of what success looks like, and is anyone actually tracking it? Cross-functional integration. Is UX embedded across teams, or sitting in its own silo waiting for people to come to it? None of these are particularly complicated questions. The hard part is being honest about the answers. The difference between a real audit and a survey 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. Where to start 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. The playbook was the best answer we had 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. What AI changes about this picture 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. What an organizational AI skills library actually looks like 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 writing user stories that capture real intent rather than implementation detail. conducting a heuristic review of an interface. analyzing the results of an A/B test without drawing confident conclusions from a sample size of 40. assessing whether a proposed feature maps to an actual user need or is just something that sounded good in a meeting. 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. The compounding effect 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. ---- 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. ---- Where to start 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.