AI-Driven Strategies For User Experience Design

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

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

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.

Forget what you know about UI. (here comes outcome-oriented UI) A new paradigm is emerging in UI design. Now where user goals trump traditional UI elements. Thanks to AI and generative UI principles. Outcome-oriented design will revolutionize how we create digital experiences. 5 ways to implement Outcome-oriented UI design: 1. GOAL-BASED NAVIGATION: Ditch traditional menus for AI-powered, goal-oriented navigation. Example: A banking app that presents options based on the user's financial goals (e.g., "Save for a house," "Reduce debt") rather than generic account categories. 2. ADAPTIVE WORKFLOWS: Create interfaces that morph to match the user's current objective. Example: A video editing tool that simplifies or expands its interface based on whether the user is making a quick social media clip or a professional-grade film. 3. PREDICTIVE TASK COMPLETION: Leverage AI to anticipate and streamline user tasks. Example: A project management platform that automatically generates and populates task lists based on team goals, past projects, and current deadlines. 4. CONTEXTUAL INFORMATION HIERARCHY: Dynamically adjust info prominence based on user context and goals. Example: An e-commerce site that prioritizes different product descriptions (e.g., sustainability, price, delivery time) based on each user's shopping priorities and behavior. 5. INTELLIGENT FORM OPTIMIZATION: Design forms that adapt to user goals and known information. Example: A travel booking system that only asks for relevant information based on the type of trip (business vs. leisure) and automatically fills in known preferences. ................................................................................. Outcome-oriented UI design focuses on what users want to achieve, not how they navigate an interface. Designers embracing this approach will create more intuitive, efficient, and personalized digital experiences. The future of UI isn't about buttons and menus – it's about understanding and facilitating user goals.