『Boagworld: UX, Design Leadership, Marketing & Conversion Optimization』のカバーアート

Boagworld: UX, Design Leadership, Marketing & Conversion Optimization

Boagworld: UX, Design Leadership, Marketing & Conversion Optimization

著者： Paul Boag Marcus Lillington
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今ならプレミアムプランが3カ月 月額99円

2026年5月12日まで。4か月目以降は月額1,500円で自動更新します。

概要

 Boagworld: The podcast where digital best practices meets a terrible sense of humor! Join us for a relaxed chat about all things digital design. We dish out practical advice and industry insights, all wrapped up in friendly conversation. Whether you're looking to improve your user experience, boost your conversion or be a better design lead, we've got something for you. With over 400 episodes, we're like the cool grandads of web design podcasts – experienced, slightly inappropriate, but always entertaining. So grab a drink, get comfy, and join us for an entertaining journey through the life of a digital professional.Boagworks Ltd 経済学
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  • AI Is Showing UI Designers the Door

    AI Is Showing UI Designers the Door
    2026/04/21
    So this month Marcus and I get into a slightly uncomfortable question. If AI can knock out decent interfaces from a text prompt, where does that leave the people whose day job is opening Figma and making screens look nice? We start with Google Stitch, which has been getting a lot of attention lately. Then we zoom out into something I have become mildly obsessed with, which is building AI skills. Not prompt snippets, but reusable, documented processes that let you get consistent work out of AI without drowning it in context. App of the Month This month’s tool is Google Stitch (v2), Google’s AI UI generator. You describe what you want, it produces an interface, and you can do some light manual tweaking. It is not a full replacement for Figma. The editing controls are basic. The bigger story is what it represents. We are now at the point where a decent, usable UI can be generated fast enough that the real value shifts from "can you draw the screens" to "can you judge what good looks like." That is where experience, and yes, taste, starts to matter. If you want to compare approaches, I mentioned Figr again, which I still prefer for the quality of what it produces. Are UI Designers Becoming Vinyl? The question Stitch raises is not "can AI design interfaces". It clearly can. The question is what happens to the job market when "good enough" becomes cheap, fast, and widely available. I found myself telling 2 different clients recently that they could probably skip hiring a UI designer. They had tight budgets, tight timelines, and already had solid brand guidelines or a design system. In those situations, I could push the work through AI, iterate it a bit, and get something perfectly serviceable. That line of advice made me feel a bit grubby. Not because it was wrong for those clients, but because it hints at a bigger shift. My worry is that UI design becomes like vinyl records. Most people will not need it. A small number will care deeply and pay for it. The middle ground shrinks. Marcus made the important caveat here. Some designers will still be in demand because they bring something AI cannot easily fake. A distinctive visual style. Creative judgment. Brand thinking. The ability to make something feel like it came from a real point of view, not a model averaging the internet. We also talked about where UI designers can expand their value, because "I make pretty screens" is not a great long-term career plan. Broaden into UX and problem solving. Look past the interface and into the business problem, user needs, and research.Own the stuff between screens. AI still tends to think screen by screen. Humans are better at flows, journeys, and the messy reality of how people actually get from A to B.Lean into information architecture. For websites especially, the structure and content model matter as much as the visual design. We used a music analogy that will probably annoy some people, which makes it perfect. AI tools can generate "background" output that is fine for low-stakes use. They will not replace great musicians. But they will reduce the number of gigs available. AI Skills As a Career Asset After we finished terrifying UI designers, we moved on to something more useful. I think a lot of roles are going to need an AI toolkit. Not a handful of clever prompts, but a proper library of reusable skills. When I say "AI skills," I mean documented processes that an AI can follow reliably. Think SOPs you can run repeatedly, not prompt snippets you copy and paste. I now have around 60 skills in my library, and it is growing constantly. Outside of the Boagworld website, it might be the most valuable business asset I have. The reason is consistency and context management. AI can produce terrible output when you dump too much information on it at once. Skills let you break work into focused chunks and chain them. We talked about 3 levels of skills: Company-level skills Standard processes that keep things consistent. Proposals. Expense claims. Holiday booking. The sort of stuff that should not depend on one person remembering every step. Team or discipline skills For example, UX teams can create skills for personas, journey mapping, surveys, and top task analysis. That helps remove bottlenecks and lets colleagues do decent work without reinventing the wheel. Individual skills This is where it gets interesting for your career. These are the skills that capture how you do something, including all the weird little bits you have learned over the years. A key point here is that the value is not only in having the skill. It is in creating it. Writing down a process forces you to surface assumptions and explain what "good" looks like. We also got into AI agents. If you describe your skills well, an agent can chain them to complete bigger jobs. I gave a sales example where a meeting transcript can be turned into a CRM entry, follow-up tasks, company research, and a draft proposal with very little manual effort. That is ...
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    53 分
  • Website Rebuilds, AI Tools, and UX in 2026

    Website Rebuilds, AI Tools, and UX in 2026
    2026/03/17
    This month, Paul and Marcus get into a tool that has made Paul cancel his Figma subscription, walk through how Paul has completely changed the way he approaches website rebuilds thanks to AI, and round things off with the latest thinking from Nielsen Norman Group on where UX is heading in 2026. App of the Week: figr.design Paul has been road-testing AI design tools as part of a workshop he ran on AI and UI, and after going through dozens of them, one stood out: figr.design. What makes it work where others fall short? A few things. It lets you feed in a significant amount of context upfront, things like style guides, design systems, and personas, which means the output is far more tailored than the generic average you often get from AI design tools. Iteration is also genuinely fast. You can queue up a whole list of changes and it processes them all in one go, rather than making you wait between each tweak. The prototypes it produces are more realistic than what you would typically get out of Figma. Text fields you can actually type in, accordion states that open and close, button states, fully responsive layouts. Not exactly revolutionary in theory, but refreshingly functional in practice. Export to Figma is available when you need it. The main limitation is that you cannot manually adjust elements yourself. Everything goes through the conversational interface. Paul has also been looking at a tool called Inspector, which runs locally and connects to the Claude API so you pay as you go rather than a flat monthly token allocation. It has been a bit fiddly to set up but worth keeping an eye on. For anyone regularly using Figma for wireframing and prototyping, it is worth giving figr.design a proper look. The shift Paul describes, from hunching over Figma to leaning back and having a conversation with the tool, is a fairly good summary of where this kind of work is heading. Rebuilding a Website in 2026 Paul has fundamentally changed how he approaches website rebuilds, and the shift is largely down to AI making a genuinely hard problem, getting good content onto a website, a lot easier. The old problem Website rebuilds have traditionally meant migrating existing content into a new design. Which sounds fine until you remember that most of that content was written by subject matter experts who know their field but have never thought about writing for the web. The result is pages that lecture rather than help, that bury the things users actually want to know, and that rarely arrive on time, because the content phase is almost always where projects stall. Why things are different now AI has changed three things meaningfully. First, generating content is no longer the enormous manual effort it used to be.Second, doing the research that informs good content, finding out what users actually ask, worry about, and need, is much simpler with tools like Perplexity.Third, AI-powered search engines are pushing toward a more question-oriented approach to content anyway, which makes getting this right more important than it used to be. How Paul works now Here is the process Paul walks through for a rebuild project. 1. Online research Using Perplexity, Paul researches the audience. For a well-known client, he'll ask specifically about them. For a smaller or niche client, he looks at the sector. He is looking for the questions people are asking, the tasks they are trying to complete, their objections, goals, and pain points. This takes about 10 minutes. 2. Personas The research output goes into AI, which identifies patterns and segments it into a set of personas. A couple of hours of back and forth to get these right. 3. Company overview Paul records his kickoff meeting with the client and points AI at the transcript. Out comes a clean summary of what the company does, its products and services, and how it talks about itself. An hour for the meeting, plus 10 minutes for the summary creation. 4. Top task analysis and information architecture If time and budget allow, Paul runs a formal top task analysis, collecting and prioritizing the questions users most want answered. For card sorting, he uses UX Metrics. If there is no time for that, AI brainstorms the top tasks from the personas and company overview. Either way, those tasks get fed into an AI-generated information architecture. 5. Building out the IA Paul builds the IA in the CMS or in Notion, assigning the relevant tasks and questions to each page. Stakeholders can see the structure and understand what each page is there to do before a word of copy is written. 6. Getting stakeholders to contribute Rather than asking stakeholders to write content (a recipe for delays), Paul asks them to do two simpler things for each page: bullet-point answers to the questions assigned to that page, and any other talking points they want included. Bullets only. No pressure to write. 7. Writing the content with AI This is where it all comes together. Paul sets up an AI project with four ...
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    1 時間
  • From Agency Work to Product Success

    From Agency Work to Product Success
    2026/02/17
    This episode we're joined by Stu Green, a product designer, agency founder, and serial app builder who's sold not one but two successful SaaS products.We dig into the realities of building your own product versus running an agency, the role AI plays in modern product development, and whether the flood of AI-built apps is a threat or an opportunity for professionals.Plus, we check out Bleet, an app that turns your meeting transcripts into social media content, and Paul shares how AI-powered personas are changing the way he approaches user research.App of the Week: BleetYou know you should be posting on LinkedIn. You've told yourself that every week for the past 6 months. But then you sit down, stare at the blank post box, and realize you have absolutely no idea what to write about. So you close the tab and promise yourself you'll do it tomorrow. You won't.Bleet is an app built by Stu Green (and collaborator Nick) that solves this by mining the conversations you're already having. It takes your meeting recordings and transcripts, extracts the key topics using AI, and helps you turn them into social media posts. And the thing that sets it apart from just asking ChatGPT to write something for you is that it pulls your actual words and phrases from the conversation, piecing them together into posts that genuinely sound like you rather than generic AI slop.How It WorksYou connect your meeting recordings or transcripts (or even just speak a thought into the app), and Bleet will surface a list of topics you covered. From there, you pick the ones you want to post about and hit "create." You can dial in how much creative liberty the AI takes, from near-verbatim to lightly polished.So you sit down for 10 minutes once a week, pick a handful of topics, schedule them up, and you're done. A single meeting can generate enough content for almost a week of daily posts.What About Client Confidentiality?The number one concern people raise is about sharing sensitive client information. Bleet strips out client names, specific people, and identifiable details. It focuses on the general topic and the ideas discussed, not the specifics of who said what in which meeting. And of course, you review everything before it goes anywhere, so if something feels too close to the bone, you just skip it or edit it.Topic of the Week: Building Products vs. Running AgenciesStu Green has lived both lives. He's run agencies, built products from scratch, and sold 2 SaaS businesses. So what's the difference between building for clients and building for yourself? Quite a lot, as it turns out.Start by Solving Your Own ProblemBoth of Stu's successful apps, a project management tool and HourStack (a time management app), started the same way: he needed something that didn't exist. The project management tool grew out of running his own consultancy. HourStack came from juggling small children and fragmented work hours, and wanting a way to visualize and stack little blocks of productive time.If you're genuinely your own best customer, there's a good chance others like you exist. And if even 2 or 5 or 10 of them show up, you've got the start of something real.The Myth of "I One-Shotted This"AI has made it dramatically easier to build apps, but Stu is refreshingly honest about the gap between a demo and a product. Sure, he cloned entire apps in a single prompt and it looked great. But behind that impressive facade? Hours of iteration, hosting setup, video infrastructure, S3 servers, and a stack of decisions that require real product-building experience.The people posting "I built this in one shot" on X are technically telling the truth, but they're showing you the Hollywood set, not the house behind the door. Getting from prototype to something you can actually charge money for still takes professional knowledge. You need to know what questions to ask, which answers are good, and when you're being led down a rabbit hole.Two Tiers of AI ToolsPaul and Stu landed on a useful mental model: there are essentially 2 categories of AI building tools.Tools for everyone: Platforms like Lovable or Figma Make that let anyone create a basic app or prototype. Great for personal use, proof of concepts, and quick experiments.Tools for professionals: Things like Cursor and Claude Code that enhance a developer's ability to build production-quality software faster and better, but still require real expertise to use well.Think of it like desktop publishing in the '90s. When it arrived, everyone panicked that graphic designers were finished. Instead, regular people made terrible flyers with Comic Sans, and the professionals used the same tools to produce better work, faster. AI-built apps are following the same pattern.The 3-Stage Development ModelPaul offered a framework for thinking about where AI fits in the build process:Prototype and proof of concept: Anyone can do this with AI tools. Great for validating ideas quickly and cheaply.The production build: This still needs...
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    1 時間
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