Trends in AI Distribution and User Experience

I’ve been a PM in the Generative AI field for quite some time now, and here are some of the trends I’m noticing—trends that are shaping how we build and scale AI products. Product management in Generative AI isn’t just about building features—it’s about shaping intelligence itself. Traditional PM frameworks don’t fully apply when your product learns, evolves, and creates on its own. So, how do we build and scale GenAI products? Here are some key insights from the trenches: 1️⃣ Problem-Solution Fit > Model Capabilities Many AI products fail because they start with “What can this model do?” instead of “What real problem can AI solve?” ✅ Example: Canva’s Magic Write focuses on speeding up content creation, not just showcasing LLM capabilities. 📌 Takeaway: AI should seamlessly fit into a user’s workflow rather than forcing users to adapt to AI. 2️⃣ The Interface is the Product In GenAI, design is crucial. If the AI’s responses feel unpredictable, slow, or overwhelming, users won’t trust it. ✅ Example: ChatGPT’s regenerate response button gives users control, reducing frustration when AI misfires. 📌 Takeaway: Transparency, control, and iteration in UI/UX build trust in AI-powered products. 3️⃣ Data is Your Moat In traditional SaaS, features create the moat. In GenAI, high-quality proprietary data is the differentiator. ✅ Example: OpenAI partnered with Reddit for richer conversational data—this improves response quality & context awareness. 📌 Takeaway: The best AI products don’t just use bigger models—they use better, domain-specific data. Data is the new GOLD. 4️⃣ Expect (and Design for) Hallucinations AI will make mistakes. The question is: how do you handle them? ✅ Example: Microsoft Copilot cites sources in responses, making it easier for users to verify information. 📌 Takeaway: Users don’t need AI to be perfect, but they need ways to fact-check & correct it. 5️⃣ AI Adoption is Behavioral, Not Just Technical AI adoption isn’t just about performance—it’s about human psychology. Users need to trust, understand, and feel comfortable using AI. ✅ Example: Adobe Firefly emphasizes “commercially safe AI”—addressing creators’ concerns about copyright and ownership. 📌 Takeaway: The best AI products solve not just technical, but emotional friction points. 🔮 What’s Next for AI Product Management? As multimodal AI (text, image, video, code) and personalization improve, I believe PMs will need to: ✅ Build AI experiences that feel human & intuitive ✅ Design feedback loops for continuous learning ✅ Prioritize safety & transparency for long-term trust 💡 What’s the biggest challenge you see in AI product management today? Let’s discuss! 👇 #GenerativeAI #ArtificialIntelligence #ProductManagement #AIPM #TechTrends #AIProductManagement #MachineLearning #Innovation #FutureOfWork #AITrends #UXDesign #DataDriven #ProductStrategy #DeepLearning

In the world of AI, most products today lean towards pull-based experiences like you ask a question, and the system responds. These experiences feel intuitive, empowering users to be in control. But while they create a solid foundation for usability, the real wow factor emerges when AI shifts to push-based use cases. Imagine AI anticipating your needs: suggesting edits to your document as you write, proposing new paragraphs to enhance clarity, or even offering tailored deals across portals as you browse a product attached to your shopping list, unlike standard recommendations. Push-based AI doesn’t wait to be called upon, but it’s there, actively delivering value in real time. This proactive intelligence becomes feasible with agentic AI systems across systems. These agents not only automate tasks but also enhance user workflows by making smart decisions on their behalf. For instance, writing an ad copy becomes seamless when AI not only generates ideas but also conducts market research, optimizes for SEO, and aligns with the latest trends that too are all in the background. It’s no longer about searching for insights but having them delivered at the right moment. The value is in timing and relevance, making AI feel more like a trusted assistant than a tool. This shift from pull to push in AI is why agentic systems are gaining so much momentum. It’s not just a race for computing power; rather, it’s a race for attention. By meeting users where they are and anticipating their needs, AI applications can elevate user experiences and redefine expectations. The future of AI isn’t just about solving problems when asked; it’s about solving problems before you even realize they exist. #ExperienceFromTheField #WrittenByHuman #EditedByAI

🌟 What’s Next for AI Design: Themes for 2025 🌟 As we enter 2025, the landscape of AI design is evolving rapidly, with emerging trends reshaping how we build and interact with technology. Here are some key trends I’m particularly excited about: ���� 1. Interfaces That Adapt to User Needs We’re moving from static UIs to interfaces that dynamically adapt to context, personalization, and real-time inputs. This means simpler, cleaner, and more intuitive UX that delivers exactly what users need when they need it. 🛠️ Examples: Jordan Singer's work at Mainframe and Beam by @Toby Bream (https://beem.computer/) showcase the future of adaptive design. 🔹 2. Reimagining Data Organization Traditional data structuring feels ancient today. AI is helping us rethink how unstructured data is reorganized and delivered intuitively, in formats tailored to our needs. 💡 Check out @MatthewWsiu's explorations on this (https://lnkd.in/gFADJkXS) 🔹 3. Fluid Media AI is democratizing media creation - transforming text into videos, sketches into 3D models, and more. These capabilities open up a world of immersive, creative possibilities. 🎨 There are many advanced models out there, but here is a classic example I worked on a while back that transforms sketches into animated characters (https://lnkd.in/gPYA7xfP) 🔹 4. Multimodal Interactions Gone are the days of singular inputs. Multimodal AI systems combine voice, visuals, text, and beyond to create richer, more engaging user experiences. Claude artefacts are a good example! 🔹 5. Human-AI Connections AI isn’t just a tool - it’s becoming a partner for advice, journaling, task management, and more. Designing safe, meaningful interactions is key to ensuring this shift feels natural and intuitive. 🤖 e.g. I’ve been using apps like Rosebud (https://www.rosebud.app/) that probably know me better than some of my friends! 🔹 6. Immersive Experiences Adaptive interfaces, fluid media, and multimodal capabilities make immersive experiences more accessible than ever. 🌐 Rooms by Things, Inc. has recently launched some fun examples of this (https://lnkd.in/grcnyRcy) 🔹 7. Empowering Anyone to Build Anything The lines between designer, PM, and engineer are blurring. Tools like Cursor are empowering everyone to create AI apps, breaking down traditional silos. 🚀 Dreamcut.ai by Meng To is a great example of the creative potential unlocked by AI. 🔹 8. AI-First Interaction Patterns As AI capabilities grow, we must develop new design patterns to handle these challenges. For those interested in diving deeper, check out my course (https://lnkd.in/gcVgP3My). The next cohort starts in February, and we’ll explore these trends and more! As a reminder, these are just some themes I'm personally excited about and I'm sure I've missed many. Are there other themes you're excited about? Please share them in the comments!

UX metrics are key to meeting user needs and business goals. We've been thinking a lot about how using numbers to represent user needs can bring more clarity to design, product, and marketing teams. A new challenge is emerging with the rise of AI, both in meeting personal user needs through AI agents and handling company automation. This adds a new layer of complexity, as we need to evaluate how well user needs are being met when robots and automated systems interact with designs. There’s a mix of user and robot needs. An AI agent starts to cloud the user's needs as a robot is a proxy for a user's end goal. Here’s how we’re thinking about UX metrics: → Businesses rely on data to shape their strategies, and UX metrics provide a way to measure user behavior, preferences, and satisfaction. This reduces guesswork and enables informed decisions based on measurable outcomes. → UX metrics connect user needs with business goals by tying user experience to performance indicators like retention, conversion, and loyalty. They show the value of improving the user experience, justifying investments in user-centered design. AI changes these problems. As tech changes faster, we need to see how users adapt. Metrics like "comprehension" and "error rate" reveal usability issues and highlight areas for improvement. → As AI and personalized technology advance, user expectations become more complex. Metrics like "comprehension" and "desirability" reveal gaps between users' expectations and experience, ensuring a user-centric design approach. → Personal AI agents and automation demand tailored experiences for millions of users. Metrics like usefulness and satisfaction help evaluate how well systems address diverse needs at scale, even when users don’t directly interact with a product or app. → As AI and automation grow, metrics like trust and credibility are key to ensuring these systems are reliable, secure, and user-friendly. UX metrics help balance efficiency and trust, aligning technologies with user needs and ethical considerations. Staying ahead of the curve: Companies with strong user experience outperform competitors. UX metrics allow businesses to refine products quickly and stay ahead of market trends. → Continuous monitoring of UX metrics provides early warnings of problems, such as a rising "drop-off rate" or falling "satisfaction score." This helps organizations address issues before they affect user adoption or satisfaction, especially in automated systems where triggers can reveal experience flaws. → Continuous discovery requires constant feedback and iteration. Metrics like "frequency" and "intent" offer ongoing insights into user interactions, enabling improvements that keep up with changing user needs and industry trends. It's exciting. There are a lot of new challenges to improving user experience. How are you thinking about these issues? #productdesign #productdiscovery #userresearch #uxresearch