Intelligent User Flow Design

What's your approach to designing user flows? ✏️ -Understand the User and Goals: Start by gaining a deep understanding of the target users, their needs, and their goals. Conduct user research, interviews, and surveys to gather insights into their behaviors, pain points, and motivations. Define User Personas: Create user personas to represent different segments of your target audience. Personas help humanize the users and guide the design process to meet their specific needs. -Map the User Journey: Outline the entire user journey from the initial touchpoint to the final goal. This involves understanding the various stages users go through when interacting with your product and identifying potential entry and exit points. Identify Key User Tasks: Identify the primary tasks users want to accomplish within your product. Focus on the core functionality and prioritize these tasks in the user flow. Create a Flowchart: Visualize the user flow by creating a flowchart. Use arrows to show the sequence of steps users will take to complete their tasks. Consider different scenarios and decision points they might encounter. Keep it Simple and Intuitive: Aim for simplicity and clarity in the user flow. Minimize the number of steps required to achieve a task and avoid unnecessary complexity that could confuse users. Consistency across Platforms: If your product is available on multiple platforms (e.g., web, mobile), ensure a consistent user flow across all of them. Users should feel comfortable and familiar with the flow, regardless of the device they are using. Anticipate User Errors: Design the user flow with the anticipation of user errors or confusion. Provide clear error messages and guidance to help users recover quickly. User Testing and Iteration: Test the user flow with real users through usability testing sessions. Analyze the feedback and data to identify pain points and areas of improvement. Iterate and refine the user flow based on the insights gained. Collaborate with the Team: Involve stakeholders, designers, developers, and other team members in the user flow design process. Collaborative efforts lead to a more comprehensive and well-rounded user experience. Consider Edge Cases: Take into account edge cases and less common scenarios in your user flow design. This ensures that your product is accessible and usable for all users, regardless of their specific circumstances. Accessibility and Inclusivity: Design with accessibility and inclusivity in mind. Ensure that the user flow is usable by people with disabilities and diverse backgrounds.

Intelligent interfaces make real-time design choices. For designers, sharing design decisions with robots can be… uncomfortable. But delegation ≠ abdication. The new work for designers is to give context and guidance to help the system make good choices. I made a guide, demo, and video for designers (link in the comments) about how to do this and keep the results on the rails. Done right, the result is a radically adaptive experience that responds to user context and intent. Layouts that rearrange themselves. Forms that choose smart defaults. Chat that “speaks” with well chosen GUI elements instead of text. It’s easier and more reliable than you might expect. The guide includes a simple, directional pattern library for giving the LLM its marching orders. For designers, sketching in simple plain-language system prompts becomes part of the design process, at least as important as drawing interfaces in your design tool. Instead of designing every interaction, you’re designing the *physics* of your application’s tiny universe. You define the behavior and constraints for making design decisions in the interface. It’s design system work for real-time decision-making. The basic recipe for wiring interface to intent: 1. Provide a constrained set of UI outputs. 2. Map those outputs to intent (“use this pattern to address that intent”). 3. Ask the LLM to understand intent and choose the right UI or action. It used to be really, really hard for systems to determine user intent from natural language or other cues. Now LLMs just get it. They grasp underlying semantics, they get slang, they can infer from context. LLMs may hallucinate facts, but they’re brilliant at interpreting intent and the shape of the expected response. This makes them a powerful and reliable partner for interpreting user meaning and delivering an appropriate interface. Check out the demo and give it a try yourself. Start writing; the interface is listening. Link in the comments (because you know, LinkedIn).

☂️ Designing For Edge Cases and Exceptions. Practical design guidelines to prevent dead-ends, lock-outs and other UX failures ↓ 🚫 People are never edge cases; “average” users don’t exist. ✅ Exceptions will occur eventually, it’s just a matter of time. ✅ To prevent failure, we need to explore unhappy paths early. ✅ Design full UI stack: blank, loading, partial, error, ideal states. ✅ Design defaults deliberately to prevent slips and mistakes. ✅ Start by designing the core flow, then scrutinize every part of it. ✅ Allow users to override validators, or add an option manually. ✅ Design for incompatibility: contradicting filters, prefs, settings. 🚫 Avoid generic error messages: they are often main blockers. ✅ Suggest presets, templates, starter kits for quick recovery. ✅ Design extreme scales: extra long/short, wide/tall, offline/slow. ✅ Design irreversible actions, e.g. Delete, Forget, Cancel, Exit. ✅ Allow users to undo critical actions for some period of time. ✅ Design a recovery UX due to delays, lock-outs, missing data. ✅ Accessibility is a reliable way to ensure design resilience. Good design paves happy paths for everyone, but also casts a wide safety net when things go sideways. I love to explore unhappy paths by setting up a dedicated design review to discover exceptions proactively. It can be helpful to also ask AI tooling to come up with alternate scenarios. Once we start discussing exceptions, we start thinking outside of the box. We have to actively challenge generic expectations, stereotypes and assumptions that we as designers typically embed in our work, often unconsciously. And to me, that’s one of the most valuable assets of such discussions. And: whenever possible, flag any mentions of average users in your design discussions. Such people don’t exist, and often it’s merely an aggregated average of assumptions and hunches. Nothing stress tests your UX better then testing it in realistic conditions with realistic data sets with real people. Useful resources: How To Fix A Bad User Interface, by Scott Hurff https://lnkd.in/ecj6PGPU How To Design Edge Cases, by Tanner Christensen https://lnkd.in/ecs3kr8z How To Find Edge Cases In UX, by Edward Chechique https://lnkd.in/e2pfqqen Just About Everyone Is an Edge Case, by Kevin Ferris https://lnkd.in/eDdUVHyj Edge Cases In UX, by Krisztina Szerovay https://lnkd.in/eM2Xynba Recommended books: – Design For Real Life, by Sara Wachter-Boettcher, Eric Meyer – The End of Average, by Todd Rose – Think Like a UX Researcher, by David Travis, Philip Hodgson – Mismatch: How Inclusion Shapes Design, by Kat Holmes #ux #design

Just observed a subtle yet impactful design choice in super.money's payment flow that deserves attention from Product Managers & UX Designers. As soon as the user starts entering the payment amount on Supermoney, the system instantly calculates and displays the cashback amount right below the amount field. Exactly 4 design principles at play while building such impactful flow ↴ ✅ Immediate Feedback - Nielsen's Usability Heuristics The cashback value of ₹3.15 appears instantly as the user starts typing the payment amount. This eliminates uncertainty & reinforces the incentive in real-time, keeping users engaged. ✅ Contextual Nudges for Positive Behavior By surfacing the cashback dynamically within the same flow, Supermoney nudges users toward completing the transaction with a sense of added value. It's subtle, non-intrusive, yet effective behavioral design. ✅ Progressive Disclosure Instead of overwhelming users with details upfront, Supermoney reveals relevant information, like cashback, precisely when the user is ready to make a decision, keeping the interface clean and focused. ✅ Goal Alignment This interaction aligns perfectly with both user goals (maximizing value) and business goals (increasing transaction conversion). ✅ Emotional Reinforcement Micro-moments like these, where users feel they're "earning" something, drive positive emotions, fostering loyalty and repeat usage. It's a thoughtful design :) #UXDesign #ProductManagement #MobileUX #UserExperience #DesignThinking #ProductManager #ProductDesign #UIUX #Design 

During meetings with stakeholders, we often hear about 𝒓𝒆𝒅𝒖𝒄𝒊𝒏𝒈 𝒃𝒐𝒖𝒏𝒄𝒆 𝒓𝒂𝒕𝒆𝒔, 𝒊𝒏𝒄𝒓𝒆𝒂𝒔𝒊𝒏𝒈 𝒓𝒆𝒕𝒆𝒏𝒕𝒊𝒐𝒏, 𝒂𝒏𝒅 𝒐𝒑𝒕𝒊𝒎𝒊𝒛𝒊𝒏𝒈 𝒄𝒐𝒏𝒗𝒆𝒓𝒔𝒊𝒐𝒏 𝒇𝒖𝒏𝒏𝒆𝒍𝒔. If you're feeling confused and overwhelmed about how to do all of this, you're not alone. Here's something for those new to the world of metric-driven design. Trust me, your designs can make a real difference :) 𝗙𝗶𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗴𝘀 𝗳𝗶𝗿𝘀𝘁, 𝗴𝗲𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲𝗿𝘀 𝗔𝗡𝗗 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 → Talk to real users. Understand their pain points. But also, grab coffee with the marketing team. Learn what those metrics mean. You'd be surprised how often a simple chat can clarify things. 𝗠𝗮𝗽 𝗼𝘂𝘁 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗳𝗹𝗼𝘄 → Sketch it out, literally. Where are users dropping off? Where are they getting stuck? This visual approach can reveal problems you might miss otherwise and which screens you need to tackle. 𝗞𝗲𝗲𝗽 𝗶𝘁 𝘀𝗶𝗺𝗽𝗹𝗲, 𝘀𝘁𝘂𝗽𝗶𝗱 (𝗞𝗜𝗦𝗦)→ We've all heard this before, but it's true. A clean, intuitive interface can work wonders for conversion rates. If a user can't figure out what to do in 5 seconds, you might need to simplify. 𝗕𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗱𝗲𝘀𝗶𝗴𝗻 → Trust isn't built by security badges alone. It's about creating an overall feeling of reliability. Clear communication, consistent branding, and transparency go a long way. 𝗠𝗮𝗸𝗲 𝗶𝘁 𝗲𝗻𝗴𝗮𝗴𝗶𝗻𝗴 → Transform mundane tasks into engaging experiences. Progress bars, thoughtful micro-animations, or even well-placed humor can keep users moving forward instead of bouncing off. Remember, engaged users are more likely to convert and return, directly impacting your key metrics. 𝗧𝗲𝘀𝘁, 𝗹𝗲𝗮𝗿𝗻, 𝗿𝗲𝗽𝗲𝗮𝘁 → Set up usability tests to validate your design decisions. Start small - even minor changes in copy or button placement can yield significant results. The key is to keep iterating based on real data, not assumptions. This approach improves your metrics and also sharpens your design intuition over time. 𝗗𝗼𝗻'𝘁 𝗿𝗲𝗶𝗻𝘃𝗲𝗻𝘁 𝘁𝗵𝗲 𝘄𝗵𝗲𝗲𝗹 → While it's tempting to create something totally new, users often prefer familiar patterns. Research industry standards and find data around successful interaction models, then adapt them to address your specific challenges. This approach combines fresh ideas with proven conventions, enhancing user comfort and adoption. Metric-driven design isn't about sacrificing creativity for numbers. It's about using data to inform and elevate your design decisions. By bridging the gap between user needs and business goals.

Design for AI-native products changes the role of designers from building blueprints to shaping ecosystems. Traditional product design is like drafting a blueprint: predictable, linear, and structured. Designing for AI products, however, feels more like cultivating an ecosystem. It’s unpredictable and dynamic, requiring designers to embrace ambiguity. So how is designing for AI-native products different? 1. Designing for probabilities, not certainties: Traditional design assumes predictable outcomes. With AI, outputs vary based on data and context, so designers must create patterns for feedback and error handling that feels intuitive. 2. Design systems, not flows: AI products adapt over time, requiring modular systems that can handle continuous changes and scale. 3. Designing feedback loops: Users collaborate with AI to refine outcomes, making iteration cycles intuitive and efficient. Personalization features, like custom rules or GPT configurations, adds depth. 4. Evaluation criteria: AI needs evaluation frameworks based on to measure and improve accuracy and relevancy over time. This should be grounded in user needs and goals. 5. Considering the cost: Running AI has real costs, so designers must understand and optimize to balance user needs with business constraints. But some things remain the same. → User-centricity is timeless: Understanding user needs and pain points is still foundational. → Non-AI foundations matter: Onboarding, settings, IA, etc. remain critical for good product design. → Design systems are still your best friend: A strong design system saves time and ensures consistency, especially with AI’s unpredictability. Designing for AI-native products redefines what’s possible by combining innovation with empathy. I’m thrilled for the experimental patterns that will shape the future of design.

7 ways to seamlessly integrate AI into your users journey 1. The core purpose of AI directly shapes the user’s journey. 
 Conduct user research to identify key pain points or tasks users want AI to solve. ↳ if the startup’s AI helps automate content creation, what’s the user’s biggest friction in the current workflow? 2. Where will the AI interact with users within the product flow? Map out where AI should intervene in the user journey. For instance, ↳ does it act as an assistant (suggesting actions)
 ↳ a decision-maker (making recommendations)
 ↳ a tool (executing commands) 3. Simplify feedback loops help build trust and comprehension
 Focus on how users will receive AI feedback. ↳ What kind of feedback does the user need to understand why the AI made a recommendation? 4. Build a modular, responsive interface that scales with AI’s complexity. Visual elements should adapt easily to different screen sizes, user behaviors, and data volume. ↳ if the AI recommends personalized content, how will it handle hundreds or thousands of users while maintaining accuracy?
 
 5. Use layers of transparency At first glance, provide a simple explanation, and offer deeper insights for users who want more detailed information. Visual cues like "Why?" buttons can help. For more on how layered feedback can improve UX, check out my post here 
https://lnkd.in/eABK5XiT 6. Leverage Emotion Detection patterns that shift the tone of feedback or assistance. ↳ when the system detects confusion, the interface could shift to a more supportive tone, offering simpler explanations or encouraging the user to ask for help. For tips on emotion detection, check this https://lnkd.in/ekVC6-HN 7. Prototype different AI patterns ⤷ such as proactive learning prompts ⤷ goal-based suggestions ⤷ confidence estimation based on the business goals and user needs Run usability tests focusing on how users interact with AI features. ↳ Track metrics like user engagement, completion rates, and satisfaction with AI recommendations. Check out the visual breakdown below 👇 How are you integrating AI into your product flows? #aiux #scalability #designsystems #uxdesign #startups

How AI will change design systems 🔥👇 I shared my thinking in a talk at the Into Design System Conference yesterday. Here's a snapshot: Design systems are evolving into intelligent experience systems. Now, it’s about intent, context, and adaptability. ↪️ Foundations Start with what you already have: → Design tokens → Themes → Components This is your base system. ⚙️ Interaction Blueprints Add agentic flows. We design for: → Adaptive layouts → Flows that shift with user behavior → Document agentic patterns that respond intelligently UX becomes responsive to psychology, not just screen size. ↪️ Context Engine Now we add intelligence: → Intent detection → Personalization → Platform, locale, brand adaptation It understands who the user is and what they need. ↪️ Multimodality We need experience across: → Touch, visual, audio, voice, gesture → Haptic, text, ambient, motion One intent, expressed in the best way for the context. 📚 Logic & Governance (the brain behind it all) Everything connects here. → Internal docs → Design principles → Decision rules → Knowledge and context This is how the system stays aligned with your standards (at scale). When multimodal interfaces meet agentic intelligence, they can:  ✅ Understand intent, context  ✅ Adapt response modality in real-time  ✅ Proactively assist users  ✅ Seamlessly switch modalities  ✅ Logic & Governance ensures it all follows your rules  ✅ Detect changes Design systems must evolve. We need context-aware, governed, adaptive systems that become a central part of how we build products. 🙌 More to follow. 😊 What do you think? #designsystem #AI #productdesign #designstrategy #ux

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.

Ai UX/UI design workflow :: User stories + narrative wireframes (ChatGPT) > visual wireframes (Relume) > UI concepts (Midjourney) > responsive UI concepts with VDL & components library (Figma). Liftoff. 🚀 This micro-workflow was the last piece of the puzzle to complete a 100% Ai-fueled XD workflow, from Objective Statement to responsive UI visual design concepts. (I wanna stress this is still about *conceptualization*, even if I can make functional prototypes with the end result.) I just couldn't find a smooth, lightning fast transition that bridged the gap from Chat user stories to responsive UI visual designs populated with Midjourney UI imagery at the level of quality that I would expect of myself without Ai. Welp, it's a fully armed & operational battle station now 🌑 🤩 The Ai UX/UI workflow: + Objective Statement + Initial Insights + Trends + Query Quilting + Competitive Landscape + Ideal Customer Profiles + Enhanced User Personas + Conversational Personality + Day in the Life + Persona Imagery with Midjourney + Interview Script + Speech Patterns with Memory + Ai Voice Interviews + Interview Analysis + Empathy Maps + Interview Summaries + Goals, Needs, & Challenges Analysis + Competitor Analysis + Analogs Research + Market Sizing Analysis + Market Opportunity Analysis + Worst Possible Idea + Provocations + How Might We.... + Free Association + Feature Ideation + Cross-Pollination Ideation & Analysis + Desirability Analysis + Impact/Effort Matrix + Feature Formulation + Feature Selection + User Goals + User Tasks + User Stories + Digital Architecture + Design Language Specs + Sitemaps + Narrative wireframing** + Ai wireframing** + Visual Design Language Visualization + UI Visual Design Conceptualization + Context-Enhanced Conceptualization + Design Concept Synthesis** **This is where the weak point in the Death Star was! No mas. Stefan Navarrete Lee O'Connor Yep, this all started with your help! 🙏🏼 //// Hello. I'm Greg. (Gregory Joseph to my mom.) I've been a designer for 25 years. I've always been fascinated by artificial intelligence, and I've spent the last decade exploring the usage of Ai for creative purposes, including earning multiple certificates in machine learning and prompt engineering. ✏❤️🤖 I'm a certified design + artificial intelligence supergeek. 🤓 I provide next-gen Ai services for individual designers & design teams, including classes, training, projects, workshops, & expert hourly consulting. Explore my little corner of the universe at Superunknown Studios 🚀 🌖 🛸 https://lnkd.in/guKi4C_Y I talk about design & wildlife & Ai things. #generativeai #midjourney #relume #designai #chatgpt #uxdesign #uidesign #designwithai