Developing Adaptive User Pathways

If you know me at all, you know I've spent years building AI-powered products and converting legacy systems into adaptive experiences. And I keep seeing the same pattern: talented designers asking me "what even is adaptive UI?" because nobody's explaining it in practical, buildable terms. Your interface is frozen in time. Same buttons, same layout, same experience for everyone. Meanwhile, your users are all completely different. Adaptive UI fixes this. WHAT IS ADAPTIVE UI? (aka, responsive, generative, dynamic or intelligent UI) Your interface watches how people behave, learns their patterns, and redesigns itself in real-time to fit them. Some shoppers know exactly what they want (fast checkout). Others need to research everything (reviews, specs). Some are visual (show me photos). Others are price-sensitive (where's the sale?). Static UI forces everyone through the same experience. Adaptive UI generates a personalized interface based on actual behavior. This isn't just showing different content. The entire interface regenerates around each user's workflow. HOW IT WORKS Two components: The Observer: Watches behavior What do they click? Where do they hesitate? What patterns emerge? The Generator: Creates personalized layouts Rearranges content hierarchy Shows/hides relevant features Adjusts buttons and placement Rewrites microcopy for skill level The loop: Observe → Learn → Predict → Generate → Repeat BEST USE CASES E-commerce: Financial services: SaaS tools: Healthcare: Adaptive UI wins where users are doing something complex, high-stakes, or repeated frequently. HOW YOU BUILD IT You're not coding this yourself. But you ARE designing the system. Step 1: Map behavioral signals Watch sessions. List patterns: clicks size chart 3x = fit anxiety Step 2: Define 3-5 behavioral profiles Not demographics. Behavioral patterns like "Confident Buyer," "Anxious Researcher" Step 3: Design variants in Figma One product page becomes five variants (one per profile) Step 4: Write adaptation rules IF [signal] THEN [interface change] BECAUSE [user need] Step 5: Hand off to engineering They build: event tracking, profile detection, conditional rendering THE REALITY The full build involves cold start problems, filter bubbles, spatial memory, ethical guardrails, mobile constraints, accessibility. But understand this: You're not designing screens anymore. You're designing systems that generate screens. Static interfaces aren't wrong. They're just frozen. And if you're still designing for that mythical "average user," you're designing for someone who doesn't exist. The companies winning in 5 years won't have the prettiest static sites. They'll have interfaces that learn and adapt in real-time. Drop a comment if you're looking to learn more on this subject 💡

𝗬𝗼𝘂𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝗺𝗮𝗽 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘄𝗿𝗼𝗻𝗴. 𝗬𝗼𝘂 𝗷𝘂𝘀𝘁 𝗵𝗮𝘃𝗲𝗻’𝘁 𝗻𝗼𝘁𝗶𝗰𝗲𝗱 𝘆𝗲𝘁. Most businesses map their customer journey once, file it away, and wonder why conversion rates flatline. The journey your customer takes in 2026 is not the one you mapped in your last planning session. It shifts by channel, by device, by intent signal, sometimes within a single session. That is the problem with traditional journey mapping. It is static in a world that never stops moving. An AI-first approach changes the architecture entirely. Instead of mapping a journey based on assumptions and quarterly reviews, you build a system that reads behavioural signals in real time and adapts the experience to match where each customer actually is, not where you assumed they would be. Here is how the framework breaks down: 𝗦𝘁𝗮𝗴𝗲 𝗼𝗻𝗲 𝗶𝘀 𝗦𝗶𝗴𝗻𝗮𝗹 𝗖𝗮𝗽𝘁𝘂𝗿𝗲. AI tools monitor micro-behaviours, scroll depth, time on page, content interactions, return visit patterns, and translate them into intent scores. You stop guessing. You start knowing. 𝗦𝘁𝗮𝗴𝗲 𝘁𝘄𝗼 𝗶𝘀 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻. Forget broad personas. AI clusters users by live behavioural patterns, not demographic labels. 𝚃̲𝚑̲𝚎̲ ̲𝚗̲𝚞̲𝚛̲𝚜̲𝚎̲ ̲𝚋̲𝚛̲𝚘̲𝚠̲𝚜̲𝚒̲𝚗̲𝚐̲ ̲𝚊̲𝚝̲ ̲𝟸̲𝚊̲𝚖̲ ̲𝚑̲𝚊̲𝚜̲ ̲𝚍̲𝚒̲𝚏̲𝚏̲𝚎̲𝚛̲𝚎̲𝚗̲𝚝̲ ̲𝚒̲𝚗̲𝚝̲𝚎̲𝚗̲𝚝̲ ̲𝚝̲𝚘̲ ̲𝚝̲𝚑̲𝚎̲ ̲𝚜̲𝚊̲𝚖̲𝚎̲ ̲𝚗̲𝚞̲𝚛̲𝚜̲𝚎̲ ̲𝚋̲𝚛̲𝚘̲𝚠̲𝚜̲𝚒̲𝚗̲𝚐̲ ̲𝚊̲𝚝̲ ̲𝚕̲𝚞̲𝚗̲𝚌̲𝚑̲.̲ The system sees that. Static segmentation does not. 𝗦𝘁𝗮𝗴𝗲 𝘁𝗵𝗿𝗲𝗲 𝗶𝘀 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗧𝗼𝘂𝗰𝗵𝗽𝗼𝗶𝗻𝘁 𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗶𝗻𝗴. Once you know who someone is in this moment, you deliver the next touchpoint that fits that moment. Not the next step in your pre-built funnel. The right step, for this person, right now. 𝗦𝘁𝗮𝗴𝗲 𝗳𝗼𝘂𝗿 𝗶𝘀 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗟𝗼𝗼𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴. Every interaction feeds back into the model. What converted, what stalled, what caused drop-off, all of it becomes training data that sharpens the next cycle. The adding up effect here is significant. Recent research shows that businesses using AI-driven personalisation across their customer journey see conversion rate improvements of up to 35% within the first six months of implementation. 𝘛𝘩𝘢𝘵 𝘪𝘴 𝘯𝘰𝘵 𝘮𝘢𝘳𝘨𝘪𝘯𝘢𝘭 𝘨𝘢𝘪𝘯 𝘵𝘦𝘳𝘳𝘪𝘵𝘰𝘳𝘺. That is a structural advantage. The infographic below maps each stage visually, showing how AI signals and feedback loops drive performance across the journey. If there is interest I could do an overlay of data inputs that power the framework at each layer.

"We've identified all these user needs and created a roadmap to address them, but it's just a long list of disconnected features. We keep shipping, but we're not moving metrics or able to pivot quickly when we learn new things about our users." While starting with user problems is a great first step, translating them directly into specific feature solutions often leads to rigid roadmaps and narrow implementation paths that limit your team's ability to adapt as you learn more. There's a more strategic approach: Capability-Driven Roadmaps What's the difference? ↳ A feature is a specific solution to a specific user problem ↳ A capability is the power to address a class of user problems in multiple ways For example: 🔦 Users need control over their experience → Instead of "add dark mode," think "build preference management capabilities" 📚 Users need relevant content discovery → Instead of "add homepage recommendations," think "develop personalization capabilities"  💸 Users need seamless payment options → Instead of "implement Apple Pay," think "expand payment processing capabilities" Capabilities create more business value: 1️⃣ Adds optionality to your roadmap Each capability you build opens multiple paths to value, allowing you to respond quickly to market changes. When your experiment fails (and some will), you can pivot to a new implementation without rebuilding foundations. 2️⃣ Ties directly to your business outcomes Great capabilities are outcome multipliers. A robust recommendation capability doesn't just power one feature — it enables personalization across your entire product, directly impacting engagement, conversion, and retention metrics. 3️⃣ Compounds in unexpected ways Capabilities combine in unexpected ways. The intersection of your identity, personalization, and communication capabilities might enable an entirely new product experience you hadn't initially envisioned. 4️⃣ Improves time to market A capability-focused approach doesn't mean slow platform projects with distant payoffs. These capabilities can be built incrementally, with each providing immediate business value while expanding future possibilities. Ensure each capability: ↳ Addresses persistent user needs, not just one-off requests ↳ Connects directly to current business priorities ↳ Delivers at least one immediate, measurable win ↳ Starts with user problems, not technology solutions The most successful product teams start with user problems, but they don't just build one-off features — they systematically develop capabilities that address fundamental user needs while creating sustainable competitive advantage and immediate business impact. #productmanagement #productstrategy #leadership -------- 👋 Hi, I'm Nathan Broslawsky. Follow me here and subscribe to my newsletter above for more insights on leadership, product, and technology. ♻️ If you found this useful and think others might as well, please repost for reach!

If you blank out the logos on most cybersecurity products, they all start to look the same. Same dashboards. Same graphs. Same complex menu structures. So I experimented with an idea in v0 👉 What if the same cyber product looked different depending on the person using it? The idea comes from a very real challenge in cybersecurity product management: Multiple personas, each with different goals and a different definition of “value.” I call this the multi-persona problem. A product can cater to 5 different personas and not deliver for any of them. This causes a user to context-switch out of the product and costs one thing PMs can't afford to lose: Their attention. One solution? Adaptive interfaces. - A product that looks simple and educational for someone new to cybersecurity. - Practical and configuration-focused for someone setting it up. - Outcome-driven for an executive. I played with this idea by designing an adaptive UI/UX for Nmap. (The image shows what it would look like for an L1 analyst) With access to deep research, rapid prototyping and code assistants, these type of experiments are now within reach. Substack: https://lnkd.in/gbb8cYyN

Is The Linear User Journey Dead? 🚨 For decades, product development revolved around the traditional, linear user journey. This wasn’t just a handy framework—it shaped UX design, marketing strategies, sales processes, and customer success programs as we know them. But AI is rewriting the rules. Let’s quickly revisit the old model: 1️⃣ Awareness – Users discover your product through marketing or word-of-mouth. Example: A colleague recommends Asana. 2️⃣ Consideration – Users research alternatives, read reviews, attend demos. Example: Comparing Asana vs. Monday.com vs. Trello. 3️⃣ Purchase – They decide based on pricing tiers or free trials. Example: Starting with Asana’s free plan. 4️⃣ Onboarding – Users follow tutorials, guided tours, and training. Example: Setting up their first project in Asana. 5️⃣ Adoption & Expansion – Over time, they discover advanced features and upgrade. Example: Implementing Asana’s custom fields and automations. It’s a clean framework—but AI may be crushing the foundations. Why? The traditional model relied on three big assumptions: - Sequential Learning – Users needed to follow a step-by-step path. - Feature Discovery – Exploration was slow and manual. - Value Realization – Value only came after mastering the product. Now AI has flipped this script. Welcome to the Era of Adaptive User Journeys Products are now smarter than their users—and that’s a game-changer for how we build, market, and grow them. Here’s what defines this new paradigm: 1️⃣ Contextual Intelligence Products instantly adapt to your expertise level. 👉 Example: Notion AI knows if you’re a power user or a novice within minutes. 2️⃣ Predictive Adaptation Features surface organically based on your context. 👉 Example: Figma’s AI suggests design patterns tailored to your project. 3️⃣ Compounding Value Value grows as the system learns—not just the user. 👉 Example: GitHub Copilot refines suggestions based on your coding style. 4️⃣ Non-Linear Progression Users can dive straight into advanced use cases. 👉 Example: ChatGPT enables complex workflows from day one, while scaffolding understanding as needed. The takeaway? Product development is no longer about guiding users through a funnel—it’s about creating systems that learn faster than the users themselves. So ask yourself: Is your product ready for the shift? If not, the clock’s ticking. ⏰