User Flows And Pathways

🤖 How To Design Better AI Experiences. With practical guidelines on how to add AI when it can help users, and avoid it when it doesn’t ↓ Many articles discuss AI capabilities, yet most of the time the issue is that these capabilities either feel like a patch for a broken experience, or they don't meet user needs at all. Good AI experiences start like every good digital product by understanding user needs first. 🚫 AI isn’t helpful if it doesn’t match existing user needs. 🤔 AI chatbots are slow, often expose underlying UX debt. ✅ First, we revisit key user journeys for key user segments. ✅ We examine slowdowns, pain points, repetition, errors. ✅ We track accuracy, failure rates, frustrations, drop-offs. ✅ We also study critical success moments that users rely on. ✅ Next, we ideate how AI features can support these needs. ↳ e.g. Estimate, Compare, Discover, Identify, Generate, Act. ✅ Bring data scientists, engineers, PMs to review/prioritize. 🤔 High accuracy > 90% is hard to achieve and rarely viable. ✅ Design input UX, output UX, refinement UX, failure UX. ✅ Add prompt presets/templates to speed up interaction. ✅ Embed new AI features into existing workflows/journeys. ✅ Pre-test if customers understand and use new features. ✅ Test accuracy + success rates for users (before/after). As designers, we often set unrealistic expectations of what AI can deliver. AI can’t magically resolve accumulated UX debt or fix broken information architecture. If anything, it visibly amplifies existing inconsistencies, fragile user flows and poor metadata. Many AI features that we envision simply can’t be built as they require near-perfect AI performance to be useful in real-world scenarios. AI can’t be as reliable as software usually should be, so most AI products don’t make it to the market. They solve the wrong problem, and do so unreliably. As a result, AI features often feel like a crutch for an utterly broken product. AI chatbots impose the burden of properly articulating intent and refining queries to end customers. And we often focus so much on AI that we almost intentionally avoid much-needed human review out of the loop. Good AI-products start by understanding user needs, and sparkling a bit of AI where it helps people — recover from errors, reduce repetition, avoid mistakes, auto-correct imported files, auto-fill data, find insights. AI features shouldn’t feel disconnected from the actual user flow. Perhaps the best AI in 2025 is “quiet” — without any sparkles or chatbots. It just sits behind a humble button or runs in the background, doing the tedious job that users had to slowly do in the past. It shines when it fixes actual problems that it has, not when it screams for attention that it doesn’t deserve. Useful resources: AI Design Patterns, by Emily Campbell https://www.shapeof.ai AI Product-Market-Fit Gap, by Arvind Narayanan, Sayash Kapoor https://lnkd.in/duEja695 [continues in comments ↓]

I stopped using Claude as a chatbot. I started using it as my Product Design team. 🚀 Most designers use AI for generating copy, rewriting text, or creating random UI ideas. That's only scratching the surface. While working on an AI Interview Engine project, I realized Claude can contribute to almost every stage of product design from problem discovery to developer handoff. Today, my workflow looks very different. How I use Claude to ship AI-powered products 🌱1. Discover & Research - User pain points - Competitor analysis - Market research - Interview questions - Research synthesis Instead of spending hours organizing notes, Claude helps me identify patterns and opportunities faster. 💡 2. Product Thinking & Strategy - PRDs - Feature prioritization - User journeys - Edge cases - Success metrics This is where Claude becomes powerful. Not because it gives answers. Because it helps me ask better questions. 🎯 3. UX Flows & Information Architecture - User flows - Task flows - Journey maps - States and scenarios - Error handling Many UX problems appear before a single screen is designed. 🎨 4. Claude Design + UI Creation - Screen concepts - UX critiques - Design system recommendations - Interaction ideas - Accessibility checks Claude helps me explore more possibilities before committing to a direction. ⚡ 5. Claude Code: This changed my workflow completely. I use it for: - Frontend prototypes - Design system implementation - UX validation - Product simulations - Documentation generation Seeing ideas come alive in code helps uncover issues much earlier. 🌻6. Validation & Iteration - Heuristic reviews - UX audits - Edge case testing - Scenario generation - Accessibility review The goal is not to validate designs. The goal is to validate decisions. 🚀 7. Handoff & Delivery - Functional requirements - Developer documentation - Acceptance criteria - Component behavior - Interaction specifications Developers get more clarity and fewer assumptions. The biggest lesson? AI didn't replace my design process. It amplified it. The more product thinking, judgment, and decision-making I bring, the better the output becomes. That's why I believe the future belongs to designers who can combine: 🧠 Product Thinking 🤖 AI Leverage 🎨 Design Craft 📈 Business Understanding Not just screen design. What part of your design process are you currently using Claude for? 👇 I'd love to learn from your workflow too. #uxdesign #productdesign #uidesign #claudeai #claudecode #artificialintelligence #designsystems #uxresearch #figma #designleadership #aidesign #productdesigner #userexperience #designthinking #aitools #aiindesign #ai #aidesigntools #designercommunity # #juniordesigners #learning #linkedin #creator #uiux

I redesigned my entire UX/UI process with AI. It’s not about “use ChatGPT to brainstorm.” I mean, I rebuilt the whole pipeline. From product idea to prototype. What used to take months? Now gets done in days. Here’s what it looks like step-by-step: 1. Instant User Flows I drop rough product ideas into ChatGPT. (It's not the public one; it's a custom GPT trained on how I think.) It gives me: - Sitemap - User journey - Logic flows All in less time than it takes to make coffee. 2. Wireframes Without Drawing I stopped sketching. I describe the layout in plain English, and Magician does the rest. "Hero. CTA. Testimonials." Boom. Wireframe. No more dragging boxes like it’s 2015. 3. AI-Built Design System Spacing? Typography? Button styles? I just describe the vibe. Tools like Relume and Uizard take that and build me a full design system. This used to take WEEKS. Now it’s done before lunch. 4. Smarter Figma Time Now everything moves to Figma. But I don’t waste time pixel-pushing. AI plugins handle: - spacing - responsiveness - and accessibility. I just make the ideas click. 5. Prototyping = Auto-On Final step? Auto-connect flows with Figma’s AI tools. Clickable. Shareable. Client-ready. Dev-approved. No extra buttons. No guesswork. Here’s the real punchline: AI didn’t replace my work. It replaced the boring parts, so I can focus on design thinking. It’s not about working faster. It’s about designing smarter. We’re not in 2015 anymore. Let’s build like it’s 2030. What part of your UX workflow do you still do manually? Curious to hear.

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

Yesterday, everyone saw Claude Design. Today, I see designers… slightly anxious. Let’s clear one thing AI didn’t suddenly become a “designer.” It became a very fast executor. And speed always creates panic before it creates clarity. Here’s the truth most people are missing AI can generate screens. But it still doesn’t understand - Why does this flow exist - What trade-offs the business is making - How trust is built across a journey - When not to show something That’s system-level thinking. And that’s still deeply human. The shift is NOT from designer to AI The shift is from pixel designer to system designer What designers need to learn (now, not later) - Think in systems, not screens Stop designing pages. Start designing - states - flows - decision logic - failure scenarios Because AI can draw UI… but it can’t design a living system. - Govern every phase of the experience Think like this: - Input , What data is captured? - Processing , How AI interprets it? - Output, What is shown (and what is hidden)? - Feedback, How does the system learn or adapt? This is AI UX governance. Not just design — responsibility. Design for dynamic UI, not static components Traditional design systems = buttons, cards, colors. But AI systems are - context-aware - adaptive - unpredictable So your role becomes - defining boundaries - setting rules - designing safe variations Not fixed screens. - Own the “judgment layer” AI can generate 10 options. Only you can decide - which one aligns with user trust - which one fits business goals - which one should NOT exist That’s your leverage. So relax. You’re not being replaced. You’re being upgraded. Designers who stay in execution will feel pressure. Designers who move to systems + governance + decision design will lead. And in the end, decision > execution

A few months ago, this wasn’t even part of my hiring process. Now it’s one of the first things I look at. Recently, I interviewed two designers for the same role. Both had strong portfolios. Both understood modern UI. Both could use Figma well. But one question changed the entire conversation: “How do you use AI in your design workflow?” One designer said: “I use ChatGPT sometimes for content ideas.” The other designer showed me how they use AI to: turn rough client briefs into structured UX flows generate multiple user journey ideas in minutes speed up UX writing organize research findings improve accessibility checks explore layout directions faster before moving into UI And honestly… The gap was impossible to ignore. Not because AI made them more creative. ↳ But because it made them more efficient. That’s the shift happening right now in design. AI is no longer just a tool designers casually experiment with. It’s becoming part of the workflow. Especially after tools like Claude started changing how designers think about execution, ideation, and speed. After 18 years in UX and leading a design agency, here’s what I’m noticing: The designers growing the fastest right now are not necessarily the ones with the flashiest visuals. They’re the ones who know: what to automate what to simplify and where human thinking still matters most So if you’re a designer trying to stay ahead, start here: Step 1: Use AI before opening Figma Most designers still jump straight into UI. Instead, ask AI: “Act as a UX strategist. Help me plan the structure for a [project type].” Ask for: user pain points user flows feature suggestions onboarding ideas information architecture You’ll start designing with more clarity from the beginning. Step 2: Use AI to speed up UX thinking AI shouldn’t replace your process. ↳ It should remove friction from it. Ask: “Review this landing page structure and identify: possible UX issues confusing sections weak hierarchy drop-off risks” You’ll save hours of manual review. Step 3: Use AI as a design reviewer This part is underrated. Upload your screen and ask: “Act as a senior UX reviewer. Give me honest feedback on: usability accessibility hierarchy CTA clarity cognitive load” Sometimes AI catches things your own eyes miss after staring at a screen too long. That’s where the industry is heading. Not toward “AI replacing designers.” But toward designers who know how to combine: ✓ design thinking ✓ human empathy ✓ and AI efficiency Because clients are starting to expect faster thinking, faster iteration, and smarter workflows. And AI is now part of that expectation. Are designers adapting fast enough? (If this resonated, repost it ♻️)

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

One of the challenges with introducing AI into users’ workflows and decision support systems is how people work. 🧠 𝐖𝐡𝐞𝐧 𝐚𝐧𝐚𝐥𝐲𝐳𝐢𝐧𝐠 𝐡𝐨𝐰 𝐩𝐞𝐨𝐩𝐥𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦 𝐰𝐨𝐫𝐤 𝐭𝐚𝐬𝐤𝐬, 𝐈'𝐯𝐞 𝐪𝐮𝐢𝐜𝐤𝐥𝐲 𝐫𝐞𝐚𝐥𝐢𝐳𝐞𝐝 𝐨𝐧𝐞 𝐬𝐢𝐳𝐞 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐟𝐢𝐭 𝐚𝐥𝐥. A key challenge…The gap between descriptive task analysis (how work is 𝒂𝒄𝒕𝒖𝒂𝒍𝒍𝒚 done) and prescriptive task analysis (how work 𝒔𝒉𝒐𝒖𝒍𝒅 be done). 👩💻 Descriptive analyses reveal what I like to call the “𝐦𝐞𝐬𝐬𝐲 𝐦𝐢𝐝𝐝𝐥𝐞.” The real-world intricacies of user behavior, shortcuts, and workarounds. 📋 Prescriptive analyses focus on ideal workflows, often shaped by organizational goals or best practices. Both are essential. But the mismatch can lead to: ❌ Misaligned AI functionality that ignores real user needs. ❌ Disruption of workflows, causing frustration rather than efficiency. 🎯 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐟𝐨𝐫 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐫𝐬 1️⃣ 𝐁𝐥𝐞𝐧𝐝 𝐭𝐡𝐞 𝐭𝐰𝐨 𝐭𝐡𝐞𝐧 𝐭𝐞𝐬𝐭: Use descriptive insights to ground prescriptive goals in reality. Let actual user behavior guide refinement. Test AI tools in live settings and continuously adjust for unexpected user behaviors. It’s not one-and-done. You have to iterate. 2️⃣ 𝐌𝐚𝐩 𝐭𝐚𝐬𝐤 𝐭𝐲𝐩𝐞 𝐚𝐧𝐝 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: Identify tasks AI can augment (data analysis, recommendations) versus tasks it might automate (routine, repetitive steps). Where AI is a partner in decision making, incorporate explainability into its design. Help users understand the recommendations. Support user trust. 3️⃣ 𝐔𝐬𝐞𝐫-𝐜𝐞𝐧𝐭𝐞𝐫𝐞𝐝 𝐀𝐈 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Create adaptive AI that learns from user input and gets better over time. 𝐓𝐚𝐬𝐤 𝐚𝐧𝐚𝐥𝐲𝐬𝐞𝐬 𝐚𝐫𝐞 𝐚 𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐟𝐢𝐫𝐬𝐭 𝐬𝐭𝐞𝐩 𝐢𝐧 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭𝐧𝐞𝐫, 𝐧𝐨𝐭 𝐚 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Have you accounted for task analyses in your AI implementation strategy that help avoid costly rework and churn? #AI #taskanalysis #humanfactors #humancentereddesign #workflowoptimization #industrialengineering #systemsdesign

Here’s the secret to AI-first products: If your AI isn’t where your users already work, it’s just a cool tool they’ll never adopt. Too many teams build standalone apps for developer convenience, only to see low adoption because they disrupt user workflows. Want to create AI that feels like a co-pilot, not a detour? Too many teams treat AI like an add-on instead of designing around how people actually work. If you want your tool to stick, start by testing where and how users will reach for it—not just which feature they like. 1. Watch before you wireframe Shadow your users for days. Note which apps they open first, what data they reference, where they pause. When you map their natural workflow, you can slot your AI into it—rather than forcing them onto a new path. 2. Make the channel your core hypothesis Is the right interface a sidebar in your CRM, a chatbot in Teams, a Slack app, or a push notification on mobile? Instead of asking “is lead-scoring useful?”, test “will sales reps use this inside their CRM?” Show partners quick sketches in each context and see which one they instinctively click. 3. Decouple logic from presentation Build one robust AI engine that powers a chat widget, a browser extension or a simple web view. When someone asks for a new capability, ask “What decision are you making?” and “Where do you need to make it?” You avoid duplicate work and can adapt fast to new platforms. 4. Capture data as part of the flow The best way to train your model is to let users work as usual. If your AI suggests optimal campaign parameters, log every tweak automatically. Don’t make marketers export logs or fill out extra forms—that creates gaps and biases your training set. 5. Earn trust through real-time dialogue In a conversational UI, let the AI ask clarifying questions (“I see you’re about to launch the summer campaign—should we include last quarter’s top keywords?”) and explain its suggestions inline (“These three segments drove 18% more conversions last month”). Then package the output in a ready-to-send summary or email draft. 6. Shift from one-off tasks to continuous value If your tool only fires during project kick-off, users will forget it. Surface a lightweight insight each week—like an alert when support ticket volume spikes or when a key metric drifts. Those small, correct nudges build confidence and prime users for the big recommendations they’ll need later. Validate your assumptions about channel, data capture, trust and engagement before you write a line of production code. When your AI lives inside the tools people already use, it becomes part of their daily routine—and that’s when it becomes indispensable. The Big Takeaway: AI-first products must be invisible, conversational, and proactive, living inside users’ existing tools. Don’t build a standalone app for control—tackle the engineering to embed your AI where it belongs. That’s how you build a platform, not a feature.