User Experience Innovation

Invisible UX is coming 🔥 And it’s going to change how we design products, forever. For decades, UX design has been about guiding users through an experience. We’ve done that with visible interfaces: Menus. Buttons. Cards. Sliders. We’ve obsessed over layouts, states, and transitions. But with AI, a new kind of interface is emerging: One that’s invisible. One that’s driven by intent, not interaction. Think about it: You used to: → Open Spotify → Scroll through genres → Click into “Focus” → Pick a playlist Now you just say: “Play deep focus music.” No menus. No tapping. No UI. Just intent → output. You used to: → Search on Airbnb → Pick dates, guests, filters → Scroll through 50+ listings Now we’re entering a world where you guide with words: “Find me a cabin near Oslo with a sauna, available next weekend.” So the best UX becomes barely visible. Why does this matter? Because traditional UX gives users options. AI-native UX gives users outcomes. Old UX: “Here are 12 ways to get what you want.” New UX: “Just tell me what you want & we’ll handle the rest.” And this goes way beyond voice or chat. It’s about reducing friction. Designing systems that understand intent. Respond instantly. And get out of the way. The UI isn’t disappearing. It’s mainly dissolving into the background. So what should designers do? Rethink your role. Going forward you’ll not just lay out screens. You’ll design interactions without interfaces. That means: → Understanding how people express goals → Guiding model behavior through prompt architecture → Creating invisible guardrails for trust, speed, and clarity You are basically designing for understanding. The future of UX won’t be seen. It will be felt. Welcome to the age of invisible UX. Ready for it?

🔬 How To Run UX Research In B2B and Enterprise. Practical techniques of what you can do in strict environments, often without access to users. 🚫 Things you typically can’t do 1. Stakeholder interviews ← unavailable 2. Competitor analysis ← not public 3. Data analysis ← no data collected yet 4. Usability sessions ← no users yet 5. Recruit users for testing ← expensive 6. Interview potential users ← IP concerns 7. Concept testing, prototypes ← NDA 8. Usability testing ← IP concerns 9. Sentiment analysis ← no media presence 10. Surveys ← no users to send to 11. Get support logs ← no security clearance 12. Study help desk tickets ← no clearance 13. Use research tools ← no procurement yet ✅ Things you typically can do 1. Focus on requirements + task analysis 2. Study existing workflows, processes 3. Study job postings to map roles/tasks 4. Scrap frequent pain points, challenges 5. Use Google Trends for related search queries 6. Scrap insights to build a service blueprint 7. Find and study people with similar tasks 8. Shadow people performing similar tasks 9. Interview colleagues closest to business 10. Test with customer success, domain experts 11. Build an internal UX testing lab 12. Build trust and confidence first In B2B, people buying a product are not always the same people who will use it. As B2B designers, we have to design at least 2 different types of experiences: the customer’s UX (of the supplier) and employee’s UX (of end users of the product). In customer’s UX, we typically work within a highly specialized domain, along with legacy-ridden systems and strict compliance and security regulations. You might not speak with the stakeholder, but rather company representatives — who regulate the flow of data they share to manage confidentiality, IP and risk. In employee’s UX, it doesn’t look much brighter. We can rarely speak with users, and if we do, often there is only a handful of them. Due to security clearance limitations, we don’t get access to help desk tickers or support logs — and there are rarely any similar public products we could study. As H Locke rightfully noted, if we shed the light strongly enough from many sources, we might end up getting a glimpse of the truth. Scout everything to see what you can find. Find people who are the closest to your customers and to your users. Map the domain and workflows in service blueprints and . Most importantly: start small and build a strong relationship first. In B2B and Enterprise, most actors are incredibly protective and cautious, often carefully manoeuvring compliance regulations and layers of internal politics. No stones will be moved unless there is a strong mutual trust from both sides. It can be frustrating, but also remarkably impactful. B2B relationships are often long-term relationships for years to come, allowing you to make huge impact for people who can’t choose what they use and desperately need your help to do their work better. [continues in comments ↓] #ux #b2b

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

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Have you ever spent endless hours on a project just to end up realising that a more straightforward method would have been more effective? This common mistake, referred to as over-engineering, can cause needless complexity and inefficiency when developing new products. Understanding Over-engineering > Over-engineering happens when a solution gets more difficult than it needs to be, usually by adding features or functionalities that do not directly meet the needs of customers. > This can lead to higher costs, longer development cycles, and less user-friendly products. Real-World Example: The Juicero The Juicero, a high-tech juicing machine, was released in 2016. It cost $700 and was designed to squeeze proprietary juice packets with considerable force. Later on, though, it was found that the costly machine was not essential because the same juice bags could be squeezed by hand. The company was eventually shut down as a result of the public outcry following this disclosure. My Own Story: The Overly Complex Website I was in a team early in my career that was assigned with creating a company website. We included the newest interactive elements and design trends in an effort to wow. Feedback received after the launch, however, indicated that visitors found the website overwhelming and challenging to use. In our pursuit of innovation, we had failed to realise the website's main purpose, which is to provide easily comprehensible information. I learnt the importance of simplicity and user-centred design from this experience. Useful Tips to Prevent Over-Engineering 1. Pay attention to the essential needs: Focus on key features that meet user needs and clearly explain the issue you're trying to solve. Don't include features that aren't directly useful. 2. Adopt Incremental Development: Begin with an MVP that satisfies the fundamental specifications. By using this method, you may get user input and decide on new features with knowledge. 3. Put Simplicity First: Use the KISS philosophy, which stands for "Keep It Simple, Stupid." Simpler designs are frequently easier to use and more efficient. 4. Verify Assumptions: Talk to users to learn about their wants and needs. This guarantees that the things you create will actually be useful to them. 5. Promote Open Communication: Create an environment where team members are at ease sharing thoughts and possible difficulties. Over-engineering tendencies can be recognised and avoided with the support of this collaborative environment. Have any of your initiatives involved over-engineering? How did you respond to it? Post your thoughts and experiences in the comments section below!

I am constantly thinking about how to foster innovation in my product organization. Building teams that are experts at execution is the easy part—when there’s a clear problem, product orgs are great at coming up with smart solutions. But it’s impossible to optimize your way into innovation. You can’t only rely on incremental improvement to keep growing. You need to come up with new problem spaces, rather than just finding better solutions to the same old problems. So, how do we come up with those new spaces? Here are a few things I’m trying at Duolingo: 1. Innovation needs a high-energy environment, and a slow process will kill a great idea. So I always ask myself: Can we remove some of the organizational barriers here? Do managers from seven different teams really need to say yes on every project? Seeking consensus across the company—rather than just keeping everyone informed—can be a major deterrent to innovation. 2. Similarly, beware of defaulting to “following up.” If product meetings are on a weekly cadence, every time you do this, you are allocating seven days to a task that might only need two. We try to avoid this and promote a sense of urgency, which is essential for innovative ideas to turn into successes. 3. Figure out the right incentive. Most product orgs reward team members whose ideas have measurable business impact, which works in most contexts. But once you’ve found product-market fit, it is often easiest to generate impact through smaller wins. So, naturally, if your org tends to only reward impact, you have effectively incentivized constant optimization of existing features instead of innovation. In the short term things will look great, but over time your product becomes stale. I try to show my teams that we value and reward bigger ideas. If someone sticks their neck out on a new concept, we should highlight that—even if it didn’t pan out. Big swings should be celebrated, even if we didn’t win, because there are valuable learnings there. 4. Look for innovative thinkers with a history of zero-to-one feature work. There are lots of amazing product managers out there, but not many focus on new problem domains. If a PM has created something new from scratch and done it well, that’s a good sign. An even better sign: if they show excitement about and gravitate toward that kind of work. If that sounds like you—if you’re a product manager who wants to think big picture and try out big ideas in a fast-paced environment with a stellar mission—we want you on our team. We’re hiring a Director of Product Management: https://lnkd.in/dQnWqmDZ #productthoughts #innovation #productmanagement #zerotoone

You don’t have to log in to the workstation — because your phone 𝗜𝗦 the workstation. That’s the headline that knocked me sideways at this year’s UGM. Epic is famously tight-lipped and private, but one of the things they do really well is listen, and understand healthcare's problems. I’ll often think "I wish Epic did X" — and a year later, they've added it. So UGM usually feels like déjà vu: things I’ve wanted, and now they're delivering. Useful, but not always surprising to me. 𝘛𝘩𝘪𝘴 𝘰𝘯𝘦 𝘴𝘶𝘳𝘱𝘳𝘪𝘴𝘦𝘥 𝘮𝘦. Demo’d inside Epic’s “Hospital Room of the Future,” the concept is so simple it feels obvious in hindsight (which is a sign of great innovation): 1️⃣ Take your Android phone (they said Apple support is in the works). 2️⃣ Plug it into a monitor with a keyboard and mouse — but no computer. 3️⃣ Full Epic Hyperspace appears on the monitor. You’re logged in. It’s your exact session. It's not running on the cloud. The phone isn’t just a phone. It’s THE workstation. No generic logins. No typing passwords with sweaty gloves. No hijacking someone else’s session. You plug in, you chart, you unplug, you go. This is what real innovation looks like: not just shiny tech, but reimagined workflows. And that’s rare for companies this big. It’s built for nursing right now. But I want it for the ER, too. (If anyone knows the tech behind this, I'd love to hear details)

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.

AI is dramatically reshaping business models. This framework is the foundation of my new LinkedIn Learning course "AI-Driven Business Model Innovation". See below for a brief summary of the 6 domains of AI’s impact on value creation, together with the major driving forces and the capabilities required as business models rapidly evolve. Link to the course - free for LinkedIn subscribers - in comments. DRIVING FORCES 🧠 Driving Forces of AI Evolution We’re at a structural shift in business. AI capabilities are accelerating, costs are falling, and data is becoming a strategic asset. These forces are reshaping the foundations of value creation — demanding that leaders rethink not just what their business does, but how it evolves. SIX DOMAINS OF AI-DRIVEN BUSINESS MODEL INNOVATION ⚙️ Scalable Efficiency AI enables organizations to operate at a new scale — automating tasks, streamlining decisions, and amplifying productivity. This isn’t just about cost-cutting and efficiency — it’s augmenting talent for higher-value work and building systems that continuously learn and improve. 🎁 Enhanced Value Propositions AI enhances what you offer — and how it’s experienced. From smart, adaptive products to deeply personalized services, it allows you to deliver more relevance, utility, and meaning to every customer. The frontier of value lies in customer responsiveness and learning at scale. 💞 Shifting Customer Relationships AI transforms how we engage with customers — not just improving service, but enabling co-creation, building trust, and responding to individual needs in real time. The most successful companies will be those that become embedded in customers’ lives through intelligent, trusted relationships. 🏗️ Redesigning Organizations Organizations must evolve from static hierarchies to adaptive systems that blend human and AI capabilities. This means rethinking workflows, decision-making, and structures to be more fluid, responsive, and innovation-driven. AI is not a bolt-on — it enables dramatic reconfiguration of value creation. 🧑💻 The AI Agent Economy AI agents are becoming participants in the economy — acting on behalf of users, negotiating, coordinating, and executing tasks. This shift calls for new strategies, where businesses design for agents as well as humans, and where trust and interoperability become core to competitive advantage. 🌐 AI in Platforms and Ecosystems The most powerful business models today are built around data-rich ecosystems. AI turns data into action, unlocking new platform value and shared innovation. Success increasingly depends on how well you participate in — or build — dynamic, intelligent ecosystems. CAPABILITIES 🚀 Capabilities for AI Evolution Thriving in this landscape requires more than tools. It demands vision, adaptability, experimentation, and the ability to work across boundaries — human, organizational, and technical. These capabilities are the foundation of tomorrow's business models and success.

It is maddening. Why are we still hearing investors speak of the B2C marketplace playbook when they speak to our B2B marketplace founders? At Foundamental, across our construction marketplace portfolio, we’ve seen this up close and the mentality is sticky. Startups chasing automated UX (at all costs) can often miss what really matters in construction: human touchpoints. In B2C, removing friction makes sense: small AOVs, high volumes, standardized product, low risk when failing one transaction. Investors learnt to look for network effects as THE moat. In AEC? You’re in the opposite game. Large AOVs. Lower frequency. Massive consequence of failure. That means one thing: Track record becomes one of your moats, and often the earliest one. What we have noticed: Successful B2B marketplaces actually amplify (not remove) high-value human touchpoints. The distinction is critical - these aren't just any interactions, but specifically those that reduce financial and operational risk for both sides of the marketplace. Think of it like injecting a sales engineer into every transaction, whom your customer can ask contextual questions that apply to their current project or problem. That’s what great B2B marketplaces that we have seen do. One of our portfolio companies conducts monthly volume forecasting sessions with customers. They agree on price bands tied to forecasted volumes. If customers stay within bands, prices remain stable. That level of mutual commitment? No UI can replicate it today, because customers attach credibility to a person, their jargon, their insight. And that commitment does drive predictability, as well. And that, in turn, drives surprising outcomes: The ability to capture significant category market share in fragmented AEC sub-segments, sometimes 20-30% or even more. Another firmly held belief from consumer marketplaces is the myth of supply liquidity. It does not apply the same way to B2B supply. In fact, most successful B2B marketplaces in construction have done the opposite: Concentrate supply and guarantee high utilization. So there are counterintuitive insights from our experience: Track record often becomes your defensibility moat. Concentrating supply rather than making it liquid is the entry way. Apart from what this means for founders of B2B marketplaces, what I love about this is that it demonstrates the awesome quirkiness of our AEC sector. We get to build truly unique companies on unique opportunities with unique ingredients. Pretty awesome. Have you observed similar patterns in B2B contexts? Or found cases where the opposite holds true? #B2BMarketplaces #ConstructionTech #VentureCapital #AECS #B2C