New UX Patterns for AI Design

Work on designing AI-first assistant and agent experiences has been eye opening. AI UX is both fundamentally the same and widely different, especially for vertical use cases. There are clear and emerging patterns that will likely continue to scale: 1. Comfort will start with proactive intelligence and hyper personalization. The biggest expectation customers have of AI is that it’s smart and it knows them based on their data. Personalization will become a key entry point where a recommendation kicks off a “thread” of inquiry. Personalization should only get better with “memory”. Imagine a pattern where an assistant or an agent notifies you of an anamoly, advice that’s specific to your business, or an area to dig deeper into relative to peers. 2. There are two clear sets of UX patterns that will emerge: assistant-like experiences and transformative experiences. Assistant-like experiences will sound familiar by now. Agents will complete a task partially either based on input or automation and the user confirms their action. You see this today with experiences like deep search. Transformative experiences will often start by human request and will then become background experiences that are long running. Transformative experiences, in particular, will require associated patterns like audit trails, failure notifications, etc. 3. We will start designing for agents as much as we design for humans. Modularity and building in smaller chunks becomes even more important. With architecture like MCP, the way you think of the world in smaller tools becomes a default. Understanding the human JTBD will remain core but you’ll end up building experiences in pieces to enable agents to pick and choose what parts to execute in what permutation of user asks. 4. It’ll become even more important to design and document existing standard operating procedures. One way to think about this is a more enhanced more articulated version of a customer journey. You need to teach agents the way not just what you know. Service design will become an even more important field. 5. There will be even less tolerance for complexity. Anything that feels like paperwork, extra clicks, or filler copy will be unacceptable; the new baseline is instant, crystal‑clear, outcome‑focused guidance. No experience, no input, no setting should start from zero. Just to name a few. The underlying piece is that this will all depend on the culture design teams, in particular, embrace as part of this transition. What I often hear is that design teams are already leading the way in adoption of AI. The role of Design in a world where prototyping is far more rapid and tools evolve so quickly will become even more important. It’ll change in many ways (some of it is by going back to basics) but will remain super important nonetheless. Most of the above will sound familiar on the surface but there’s so much that changes in the details of how we work. Exciting times.

"AI will make traditional interfaces invisible!" I keep hearing this, but my deep dive into over 100 AI workflows in SaaS products and reading about Microsoft's commitment to human agency in AI patterns have convinced me otherwise. Here are the UI patterns I'm seeing: Traditional interfaces serve a crucial role in the consumption of AI products. Advanced prompt engineering can be packaged into user-friendly point-and-click UI, short-hand sentences, or step-by-step wizards for less tech-savvy users. I refer to these as Prompt Triggers. They come in 3 flavors: 1. User Prompting 2. Prompt Templates 3. Prompt Builders Let's unpack these patterns with real-life examples. 1. User Prompting - The free-form approach that allows users to type and send any text-based response to the system. Best when users need fewer limitations. Example: Adobe Photoshop uses a floating action bar for the user’s text prompt. 2. Prompt Templates - Pre-constructed text prompts triggered by a button or action in the UI. Best for enabling specific tasks, like text summarization. Example: Scribe uses an inline editing UI with a list of pre-constructed text prompts. 3. Prompt Builders - Guided wizards that help users build detailed prompts without writing them. Example: Gamma uses a guided wizard for building a detailed prompt for presentation generation. For more weekly UI design insights and inspiration for AI patterns, check out my library at Teardowns.ai.

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg