User Experience Innovation

AI is killing the UX Design role as we know it. Designers who adapt will evolve into Strategic Experience Architects who will be in high demand. While traditional designers are "pixel-pushing," a new set of designers is emerging.  They're using AI to fast-track design ideas and turning prototypes into working code. A lot of what UX designers are doing manually today is exactly what AI tools are getting good at: • Rapid wireframing concepts • UI component creation • Basic user research • Persona development • Usability testing automation The ability to automate some UX tasks is already here. We have to assume that the technology will only advance quickly. I recently spoke with several Product Managers who are already replacing basic UX tasks with AI tools. When PMs can generate, iterate, and validate designs using AI, what happens to the traditional UX role? Simple products and startups will streamline. PMs with AI will be able to handle the basics. We're already seeing this shift. However, there's a big opportunity here as well. AI has a critical blind spot: it can't grasp the nuanced psychology of human behavior. It can't navigate complex stakeholder dynamics. It can't translate business objectives into meaningful user experiences. This is where the evolution happens. The future belongs to Strategic Experience Architects who: ✦ Define the right problems to solve ✦ Extract insights from human complexity ✦ Align teams around user value ✦ Guide AI with human context The market is splitting: → Basic products: UX roles blend into other roles on the team → Complex enterprises: Strategic UX roles become critical Fortunately, most valuable products are complex and human-centered. Want to stay relevant? Here's what to consider. 1. Master AI design tools   But don't just use them, learn to orchestrate them 2. Evolve from maker to strategist   Your value is in thinking, not in pushing pixels (AI will eventually handle this) 3. Develop business intelligence   Connect user needs to revenue 4. Study human psychology    This is your moat against AI 5. Learn systems thinking Focus on developing repeatable systems in your daily work The UX industry isn't dead, but it is transforming. -- ♻️ Share if you think this will help others ➕ Follow Jason Moccia for more insights on AI and Product Design

Lyft knew they had a problem. Only 5.6% of its users are over 65, and those users are 57% more likely to miss the ride they ordered. So, Lyft created Silver – a special app version for seniors. But why create a separate app when these improvements would benefit all users? The curb-cut effect is real. Features designed for wheelchair users ended up helping parents with strollers, travelers with luggage, and delivery workers with carts. The features in Lyft's senior-friendly app wouldn't only benefit older riders: 💡The 1.4x larger font option? Great for bright sunlight, rough rides. 💡Simplified interface? Less cognitive load for all of us. 💡Live help operators? Great for anyone when there's a problem. 💡Select preference for easy entry/exit vehicles? Not everyone likes pickup trucks. What started as an accommodation should became a universal improvement. The most powerful insight? Designing for seniors forced Lyft to prioritize what truly matters: simplicity and ease of use. Will they leverage this for all their users? The next time someone suggests adding another button to your interface or feature to your product, consider this approach instead: sometimes the most innovative design is the one that works for everyone. Rather than creating separate "accessible" versions, what if we just built our core products to be usable by all? This is the paradox of inclusive design - what works better for some almost always works better for all. What "accessibility" feature have you encountered that actually made life better for all users? #UniversalDesign #ProductThinking #CustomerExperience

Most teams pick metrics that sound smart… But under the hood, they’re just noisy, slow, misleading, or biased. But today, I'm giving you a framework to avoid that trap. It’s called STEDII and it’s how to choose metrics you can actually trust: — ONE: S — Sensitivity Your metric should be able to detect small but meaningful changes Most good features don’t move numbers by 50%. They move them by 2–5%. If your metric can’t pick up those subtle shifts , you’ll miss real wins. Rule of thumb: - Basic metrics detect 10% changes - Good ones detect 5% - Great ones? 2% The better your metric, the smaller the lift it can detect. But that also means needing more users and better experimental design. — TWO: T — Trustworthiness Ever launch a clearly better feature… but the metric goes down? Happens all the time. Users find what they need faster → Time on site drops Checkout becomes smoother → Session length declines A good metric should reflect actual product value, not just surface-level activity. If metrics move in the opposite direction of user experience, they’re not trustworthy. — THREE: E — Efficiency In experimentation, speed of learning = speed of shipping. Some metrics take months to show signal (LTV, retention curves). Others like Day 2 retention or funnel completion give you insight within days. If your team is waiting weeks to know whether something worked, you're already behind. Use CUPED or proxy metrics to speed up testing windows without sacrificing signal. — FOUR: D — Debuggability A number that moves is nice. A number you can explain why something worked? That’s gold. Break down conversion into funnel steps. Segment by user type, device, geography. A 5% drop means nothing if you don’t know whether it’s: → A mobile bug → A pricing issue → Or just one country behaving differently Debuggability turns your metrics into actual insight. — FIVE: I — Interpretability Your whole team should know what your metric means... And what to do when it changes. If your metric looks like this: Engagement Score = (0.3×PageViews + 0.2×Clicks - 0.1×Bounces + 0.25×ReturnRate)^0.5 You’re not driving action. You’re driving confusion. Keep it simple: Conversion drops → Check checkout flow Bounce rate spikes → Review messaging or speed Retention dips → Fix the week-one experience — SIX: I — Inclusivity Averages lie. Segments tell the truth. A metric that’s “up 5%” could still be hiding this: → Power users: +30% → New users (60% of base): -5% → Mobile users: -10% Look for Simpson’s Paradox. Make sure your “win” isn’t actually a loss for the majority. — To learn all the details, check out my deep dive with Ronny Kohavi, the legend himself: https://lnkd.in/eDWT5bDN

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

Too often, I’ve been in a meeting where everyone agreed collaboration was essential—yet when it came to execution, things stalled. Silos persisted, friction rose, and progress felt painfully slow. A recent Harvard Business Review article highlights a frustrating truth: even the best-intentioned leaders struggle to work across functions. Why? Because traditional leadership development focuses on vertical leadership (managing teams) rather than lateral leadership (influencing peers across the business). The best cross-functional leaders operate differently. They don’t just lead their teams—they master LATERAL AGILITY: the ability to move side to side, collaborate effectively, and drive results without authority. The article suggests three strategies on how to do this: (1) Think Enterprise-First. Instead of fighting for their department, top leaders prioritize company-wide success. They ask: “What does the business need from our collaboration?” rather than “How does this benefit my team?” (2) Use "Paradoxical Questions" to Avoid Stalemates. Instead of arguing over priorities, they find a way to win together by asking: “How can we achieve my objective AND help you meet yours?” This shifts the conversation from turf battles to solutions. (3) “Make Purple” Instead of Pushing a Plan. One leader in the article put it best: “I bring red, you bring blue, and together we create purple.” The best collaborators don’t show up with a fully baked plan—they co-create with others to build trust and alignment. In my research, I’ve found that curiosity is so helpful in breaking down silos. Leaders who ask more questions—genuinely, not just performatively—build deeper trust, uncover hidden constraints, and unlock creative solutions. - Instead of assuming resistance, ask: “What constraints are you facing?” - Instead of pushing a plan, ask: “How might we build this together?” - Instead of guarding your function’s priorities, ask: “What’s the bigger picture we’re missing?” Great collaboration isn’t about power—it’s about perspective. And the leaders who master it create workplaces where innovation thrives. Which of these strategies resonates with you most? #collaboration #leadership #learning #skills https://lnkd.in/esC4cfjS

🔥 Hot take: Apps, as we know them, are about to change fundamentally. The best UI may soon be no UI. We’re moving from: ➡️ 10-step flows → single-trigger actions ➡️ Structured navigation → fluid intent recognition ➡️ Cluttered apps → ambient, invisible design At least half the content and complexity we see in today’s apps will disappear, and what remains will become radically simpler, faster, and smarter. Yet most designers are still following the old playbook. Let's add a new tab, more buttons, new row of content, coachmarks. That worked for decades when interfaces were the only way for us to communicate with computers because systems couldn’t understand us. We had to translate human intent into UI interactions—menus, forms, taps. But LLMs do understand us. They have memory, context, and predictive intelligence. They anticipate what we want before we even say it. The interface itself is NOW being replaced. So… why are we still building like we’re in 2015? Today, finding something to watch means opening Netflix, browsing for 15 minutes, maybe giving up altogether. Endless rows. Paralyzing choice. But imagine an AI-powered experience: ➡️ It knows your mood (based on your calendar, meetings, voice, even biometrics). ➡️ It filters based on time, tone, and your recent viewing habits. ➡️ It offers one perfect recommendation. ➡️ You say “Yes” or “Something else.” That’s it. No scroll, no search, no stress. The entire UX is compressed into one smart suggestion. Netflix are you working on that? Can't wait for that day. 👆 What’s your take on where product design is heading in the age of AI? Would love to hear your perspective. #newUI #future

In customer experience (CX), the closed-loop feedback (CLF) model has been a cornerstone for over two decades, originally designed to ensure responsiveness and adaptation. It's time for a change. With the advent of artificial intelligence, it's clear that merely adapting this model isn't enough. It's old tapes. It needs to evolve. Here's what's next: Real-time Interaction Management: Traditional CLF reacts to feedback after the fact. And, traditionally, closing the "inner loop" requires a human to follow up. AI turns this on its head. Imagine a system that adjusts the customer journey in real-time based on predictive analytics, reducing friction points before they affect the customer experience. Large Action Models: We all know that AI can dive deep into data lakes to instantly identify patterns and root causes of customer dissatisfaction. This rapid analysis allows companies to not only close the feedback loop faster, but also implement more effective solutions. This will come in the evolution of Large Language Models, or LLMs, to LAMs, or Large Action Models. Continuous Learning Systems: AI transforms CLF from a loop that ends into continuous cycle of improvement. These systems learn from each interaction, constantly updating and refining strategies to enhance the customer experience. This means that the feedback loop is ever-evolving, driven by AI's ability to adapt to new information and complex variables, seamlessly. CX leaders have to embrace AI's potential to redefine our foundational practices. It's time to innovate beyond the traditional CLF and leverage AI to deliver personalized experiences, and at scale. How are you thinking about adaptive, predictive, and personalized CX strategies? Your answer can't be to hire more people to close more loops. #customerexperience #ai #journeymanagement #survey #CLF

Ensuring collaboration is central to a product's success during the UX strategy phase begins with uncertainty about where to start. ➡️ It's important to start by integrating resources and knowledge from various areas of expertise. Here's a combined approach on my experience to get a successful results and great user satisfaction rate 1️⃣ Get Smart Early in the Process: Involvement: Bring in PMs, Engineers, Designers, Researchers, and key stakeholders early to gain insights. Understanding: Focus on the "4W's" (Who, What, When, Where), technical impact, and project scope.
 2️⃣ Learn and Explore: Understanding Customer Needs: Identify customer pain points and their actual needs. Analysis and Metrics: Make assumptions, conduct competitive analysis, and define success metrics and current statistics.
 3️⃣ Define Problem: Validation and Conceptualization: Validate the problem, draft high-level concepts, and define hypotheses for testing.
 4️⃣ Design: Concept Creation: Develop low-fidelity (low-fi) concepts and involve researchers for testing. Collaboration: Show concepts to Tech and PMs, and address technical challenges.
 5️⃣ Re-iterate: Feedback and Refinement: Fix the main journey (happy path), take internal and external feedback, and implement changes. Testing: Conduct another round of testing.
 6️⃣ Hand off to Development: Finalization and QA: Design the final prototype, perform QA testing, and ensure all workflows are correct. Cross-Platform Check: Ensure designs are optimized for all viewports. Approval: Get sign-off from all parties before handing over to development.
 7️⃣ Launch and Monitor: Post-Launch Feedback: After launching, gather feedback through success metrics and third-party tools. Client and User Feedback: Seek feedback from real clients and conduct user interviews. Refinement: Address major feedback issues, prioritize, and monitor. Useful Resources ✅ Ux Vision — A vision is an aspirational view of the experience users will have with your product, service, or organization in the future. https://lnkd.in/gPPY-zPJ https://lnkd.in/g8Rc9pzp ✅ Outcome over Outputs — Work towards purposeful outcomes (problems solved, needs addressed, and real benefits) leads to better results. https://lnkd.in/gAFX_Wxw ✅ OKR in UX — Define objectives and measurable key results to guide and track UX work. https://lnkd.in/gDYvreN2 ✅ UX Goal Analytics — Focus on UX goals to drive analytics measurement plans, rather than tracking superficial metrics. https://lnkd.in/g3QmZqBd #UxStrategy #TransitionToUx #UxCoach #BeAvailable

Your research findings are useless if they don't drive decisions. After watching countless brilliant insights disappear into the void, I developed 5 practical templates I use to transform research into action: 1. Decision-Driven Journey Map Standard journey maps look nice but often collect dust. My Decision-Driven Journey Map directly connects user pain points to specific product decisions with clear ownership. Key components: - User journey stages with actions - Pain points with severity ratings (1-5) - Required product decisions for each pain - Decision owner assignment - Implementation timeline This structure creates immediate accountability and turns abstract user problems into concrete action items. 2. Stakeholder Belief Audit Workshop Many product decisions happen based on untested assumptions. This workshop template helps you document and systematically test stakeholder beliefs about users. The four-step process: - Document stakeholder beliefs + confidence level - Prioritize which beliefs to test (impact vs. confidence) - Select appropriate testing methods - Create an action plan with owners and timelines When stakeholders participate in this process, they're far more likely to act on the results. 3. Insight-Action Workshop Guide Research without decisions is just expensive trivia. This workshop template provides a structured 90-minute framework to turn insights into product decisions. Workshop flow: - Research recap (15min) - Insight mapping (15min) - Decision matrix (15min) - Action planning (30min) - Wrap-up and commitments (15min) The decision matrix helps prioritize actions based on user value and implementation effort, ensuring resources are allocated effectively. 4. Five-Minute Video Insights Stakeholders rarely read full research reports. These bite-sized video templates drive decisions better than documents by making insights impossible to ignore. Video structure: - 30 sec: Key finding - 3 min: Supporting user clips - 1 min: Implications - 30 sec: Recommended next steps Pro tip: Create a library of these videos organized by product area for easy reference during planning sessions. 5. Progressive Disclosure Testing Protocol Standard usability testing tries to cover too much. This protocol focuses on how users process information over time to reveal deeper UX issues. Testing phases: - First 5-second impression - Initial scanning behavior - First meaningful action - Information discovery pattern - Task completion approach This approach reveals how users actually build mental models of your product, leading to more impactful interface decisions. Stop letting your hard-earned research insights collect dust. I’m dropping the first 3 templates below, & I’d love to hear which decision-making hurdle is currently blocking your research from making an impact! (The data in the templates is just an example, let me know in the comments or message me if you’d like the blank versions).

There's more to accessibility than assistive technologies (AT), such as screen readers and alternative inputs. Companies that want to create inclusive and accessible experiences need to think beyond AT. Examples: - Processes - Motion - Verification options - Communication options - Alerts - Colors - Text formatting Processes refer to interactions with customers. Tech support entered a chat with me. They asked for a phone number in case we got disconnected. I explained I'm deaf and that's not a good option. I suggested they offer choices of an email address or a phone number. The next time I contacted them, they gave me a choice of providing an email address or a phone number. Such a simple change made a huge difference. Another example. I was watching a video from a company that wanted my feedback on captions. I could barely tolerate the video with a lot of fast motion. It's a problem for many folks with and without disabilities. Some have an animated GIF in their email signatures. Every time I open the email, repetitive motion plays endlessly. I can't stop it. Though I have reduced motion turned on, it won't work for this. It also makes the email file size bigger, which affects performance. If the animated GIF is important, then you can work around it by creating a YouTube or Vimeo video and linking to it. It gives people control. Verification options refer to calling someone to verify their identity. Often, the only option is a phone call. Texting needs to be an option. Emailing needs to be an option as not everyone has a phone. It's OK to require verification as long as we have choices. Communication options refer to giving us choices in how we communicate. Often, a company's contact information only lists a phone number. We need another option. Many folks don't like phone calls, not just those who are deaf or hard of hearing. Why can't we sit next to each other and text each other in a noisy room? I've had an entire conversation on an airplane using pen and paper. Sometimes the initial communication may be accessible, but it changes later. For example, I chatted with support. They said they needed to escalate the ticket to a team that only does phone calls. Sometimes, tech support will suggest I have someone call for me. I'm a capable adult. Besides the person who helps me shouldn't have access to my private information. It's a privacy issue. Alerts are how we get notified. Offer options. Android and iPhones do a great job of offering many custom notification options. I still run into hotel rooms with no visual fire alarm. What other ways do we need to consider accessibility aside from keyboards, switches, other inputs, assistive devices, and assistive technologies? Color contrast and text formatting make or break the experience. 🔔 Tap the profile bell 👉 Follow #MerylMots for more ✉️ Want to work together? Contact me. #Accessibility Image: Chase verification form with a choice of being texted or called.