『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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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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  • Designing Beyond the Chatbot

    Designing Beyond the Chatbot
    2026/07/21
    AI is changing far more than the speed at which designers produce work. In this episode, we talk with Josh Clark and Veronika Kindred about their book Sentient Design, how intelligent interfaces can respond to people in the moment, and why designers need to understand the character of AI before they can use it well. --- Use the code SENTIENT-BOAG to get 20% off the book through Aug 31 at rosenfeldmedia.com. --- Designing With AI as a Material Josh and Veronika describe AI as a design material, much as paint, paper, code, or the web itself can be materials. Every material has a grain. It has qualities that make some things easy and other things awkward, unreliable, or downright foolish. Designers get better results when they understand those qualities rather than forcing the material to behave like something familiar. Large language models are probabilistic. They can interpret intent, adapt tone, change formats, and produce many plausible variations, but they may also give different answers to the same question and present shaky information with alarming confidence. That makes them poor choices for some deterministic tasks, especially when a single correct answer matters. Asking one to count letters or provide an exact food-safety temperature without verification rather misses the point of what the material does well. Designers need enough experience with AI to make an informed choice about when to use it and when to leave it alone. Refusing to engage with it leaves that decision to ignorance, which has rarely been a dependable design system, despite Paul's suspiciously successful career testing the theory! The comparison with the early web runs throughout the conversation. Print designers initially approached websites with expectations shaped by paper, while the people who learned HTML and understood the new medium found different possibilities. AI creates a similar shift. Its rough edges can feel threatening, particularly when companies use it to cut costs or flatten skilled work into production, but those edges also point toward forms of interaction that were difficult or impossible before. Moving Beyond the Chatbot Chat has become the default AI interface, partly because our culture has spent decades imagining intelligent machines as talking machines. It can be useful because both the input and output remain open, but a blank text box also makes the user do a great deal of work. People must know what to ask, how to phrase it, and how to judge the resulting wall of text. Sentient Design describes 4 broader postures for intelligent experiences: Tools accept an input and return a controlled, precise output. Shazam is a familiar example.Chat uses a turn-based exchange, although those turns can involve images, interface components, or shared artifacts rather than paragraphs of text.Agents receive a goal, plan and perform the work, then return with a result. They still need direction, oversight, and review.Copilots remain quietly present, notice context, and offer assistance when useful, much like spellcheck waiting behind the scenes. These postures allow teams to choose an interaction that fits the task. A conversational box might suit exploration, while a focused tool is better for a clear transaction. An agent can handle delegated work, while a copilot can notice opportunities without demanding constant management. Josh and Veronika also share examples of AI taking part inside an existing interface. Salesforce's Generative Canvas assembles relevant components using trusted customer and calendar data. Miro's sidekicks can enter a canvas with their own cursors, while Pointer participates in a Google Doc as an editor, using the collaboration patterns people already understand. The interesting design question is how intelligence participates in an experience, not where to bolt on another chat window. Defensive Design for Uncertain Systems Trust becomes a design problem when systems are probabilistic. A generic disclaimer saying that AI can make mistakes does very little for someone deciding whether to believe a specific answer. Confidence scores often fare no better because most people have no useful way to interpret a claim such as “73% likely.” Defensive design communicates uncertainty through language, interface, and context. A system can present an answer as a signal rather than an unquestionable fact, show nearby possibilities, reveal where information came from, and give the user sensible points for review or intervention. Veronika describes “spaghetti scenarios,” borrowed from weather forecasting, where several possible paths are shown together. A search interface can do something similar by presenting adjacent questions and contrasting answers. This helps people see how wording, assumptions, and context affect the result. Tone matters too. Human beings constantly signal confidence through phrasing, body language, and shared cultural habits. AI systems tend to speak with the polished ...
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    1 時間 9 分
  • From Doer to Director, Getting Value From AI

    From Doer to Director, Getting Value From AI
    2026/06/16
    This month we dig into whether Claude Design is any good, why so many people feel like AI is costing them time rather than saving it, and what it really means to stop being a doer and start being a director. Along the way we wander into the loss of craft, the ethics of AI, and a joke so niche it needs its own history lesson. App of the Month Claude Design is the tool that grabbed our attention this month. It builds out designs for you, and it is genuinely impressive. We used it to rebuild the website for a small UK charity that funds children's education in India, going from nothing to a finished static HTML site in around eight hours, with Claude Design handling the design and Claude Code doing the build. Beyond the standard twenty pounds a month subscription, it cost roughly fifty quid in extra credits, which for a small organization is a no-brainer. Claude design and code together allowed Paul to create a fully working website in less than 8 hours. It turns out it does more than websites. It builds presentations too, and exports them to PowerPoint or PDF for offline editing. We put together a fifty three slide deck for a client in about two hours, work that would normally have eaten the best part of four days. Here is what we liked. It works with design systems, you can import one from Figma, you can make manual edits without burning tokens, and you can select elements visually to tweak them. The things that hold it back are that you can't export back to Figma, there's no easy publish button, and the usage allowance vanishes in what feels like five minutes flat. When you hit the wall it cheerfully suggests you try again on Sunday, which is no use when you're mid project and have already forgotten what you were doing. One word of warning. If you don't guide it heavily, Claude Design has tells, like a recurring decorative bar under the hero section that serves no real purpose. Then again, every designer has a style you can spot, so we're not convinced that's the criticism people think it is. From Doer to Director A lot of people tell us AI isn't saving them time, it's costing them more of it. That confused us at first. How can a tool that turns four days of slide work into two hours possibly slow anyone down? The more we coached people through it, the clearer the answer became, and it has very little to do with the tools. It comes down to how organized you already are. If you're not fundamentally efficient in how you work, and especially if you've never had to delegate to other people, AI exposes that straight away. The people struggling most are the ones who still want to be doers. They want to be in the code, pushing pixels in Figma, or typing every word themselves. To get real value from AI you have to shift from that doer mindset to a director one. Be the conductor, not the violinist It reminded us of the moment in the Steve Jobs biopic where Wozniak asks Jobs what he actually does, given that Woz writes the code and builds the hardware. Jobs answers that he conducts the orchestra. Woz is the finest violinist in the room, but someone has to bring all the players together. That conductor role is exactly the shift most of us need to make. Running agents in parallel A real example from this month. Working on a client presentation, we had three things running at once. Notion AI was drafting the outline in one window. Claude Design was studying the client's website to build a matching design system in another. A third agent was drafting video transcripts for a separate project entirely. Three workstreams all moving at the same time, where you would once have plodded through them one after another. That is a genuinely hard skill to build. The people best placed for it are those with management experience, because they're used to handing work off and holding several threads in their head at once. If you've never worked that way, it can feel distressing, and there's even a name for where it leads, which our reader of the month gets into. The micromanaging trap There's a design leadership parallel too. Talented designers get promoted, then can't resist sneaking back into Figma to do the work themselves. The same thing happens with AI. The agent produces something perfectly good, but it isn't quite what was in your head, so you fiddle and fiddle and fiddle, burning the very time you were meant to save. The upside is that you can't hurt an AI's feelings, so just say "no, that's not it" and move on. Get organized first The fix is unglamorous. Get organized before the agents fully take over. Build the digital playbooks, SOPs and policies we keep banging on about, so the AI already knows how you work and gets it right first time.Keep your knowledge in one place it can reference, so you're not repeating yourself endlessly.Run a task system it can see, and learn markdown while you're at it. It takes ten minutes and AI loves it. Tool or output, where's the joy? We didn't agree on all of this. Marcus prefers using ...
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    52 分
  • AI Can Fix Your Broken Research Repository

    AI Can Fix Your Broken Research Repository
    2026/05/19
    This week, Paul and Marcus dig into why traditional user research repositories fail almost everyone in an organization, and how AI is quietly changing the game. There's also an App of the Month pick that's a little too on-the-nose, some pointed Google bashing, and a sheep-based punchline. AI-Powered User Research Repositories The pattern in most organizations is depressingly familiar: user research gets done, a PowerPoint gets presented to stakeholders, everyone nods along or ignores it entirely, and then the research disappears. It might prompt some short-term action, but the knowledge evaporates. Nobody references it again six months later. The traditional solution has been to build a research repository: a central place to store everything from interviews and surveys to usability tests and diary studies. The problem is that these repositories almost always become what Paul generously describes as "dumping grounds." Dense folder structures, difficult navigation, and search tools that require you to already know what you're looking for make them practically unusable for anyone outside the UX team. And who ends up using them? Other UX professionals, the people who already understand the research anyway. Everyone else ignores them. AI changes this in three meaningful ways. First, it makes the initial build far less painful. You can throw everything at it, PDFs, old PowerPoints, interview transcripts, survey exports, and AI will structure and organize that material into something coherent. What used to be a daunting, months-long project becomes manageable. Second, it makes the repository accessible to people who aren't UX specialists. Instead of requiring a precise search query, a conversational interface lets anyone ask vague, natural questions. A product manager can ask "what do our users think about the checkout process?" and get a synthesized answer drawn from five different studies they never knew existed. That's a genuinely different kind of value. Third, and this is the part Paul finds most compelling, it can identify gaps in your research. When someone asks the repository a question and there's no relevant research to draw on, a well-configured AI won't fabricate an answer. It flags the gap and notifies the UX team that this is an area worth investigating. Over time, the questions people ask become a demand-driven research roadmap, shaped by what people in the organization actually need to know rather than what the UX team assumes they need. Marcus pushed back on the reliability question, which is fair given AI's well-documented habit of confidently inventing things. Paul's response: proper setup matters enormously. You instruct the AI explicitly not to fabricate, you add a quality gate that checks answers before they're returned, and you can even have it verify claims against source material. Even with pessimistic assumptions, say one in ten answers being wrong, that's still more useful than having nothing at all. And the failure mode is reassuring: if the AI can't find relevant research, it defaults to generic best practice rather than making something specific up about your users. Paul then connected this to something he's discussed before: AI-powered virtual personas. The repository feeds the persona generation. AI analyzes the accumulated research and builds queryable personas from it. Unlike static persona documents that go stale almost immediately, these update as new research is added. And here's the detail Paul is clearly delighted by: put a QR code on your printed persona posters. Scan it, and you're now having a conversation with a virtual version of that persona. Marcus had recently written about the value of physical personas on walls as simple reminders of who you're designing for, and this neatly bridges the physical and digital. The upshot: organizations that invest in an AI-powered research repository end up with something that prevents duplicate research, makes user insights accessible to everyone, identifies gaps in what's known, and gives the whole organization a quick way to gut-check decisions against actual user data. The reason more organizations aren't doing this, Paul notes with characteristic subtlety, is that UX teams are too small and too busy. "Hire me to do it" being the conclusion he arrived at, live on air. App of the Month Notion Paul's pick this month is Notion, which he acknowledges he's almost certainly recommended before, given that he runs his entire business on it and describes its potential failure as roughly equivalent to his own. The recommendation here is specific though: Notion as the platform for building AI-powered user research repositories. Two things make it well-suited for this. First, structural flexibility: you can organize a repository however your organization needs, and bring in almost any format of research artifact. Second, Notion has a powerful built-in AI agent that can reference, search, and synthesize across everything stored in it. ...
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    51 分
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