Trends in AI Distribution and User Experience

Everything we know about online brand discovery is about to break. People aren’t browsing pages anymore. They’re asking AI. And that means the entire digital value chain is being rebuilt. Users are no longer starting with a Google search, clicking on links, and browsing websites. Increasingly, they’re turning to AI tools like ChatGPT, Perplexity, and Gemini to get direct answers, summaries, and recommendations. No links. No websites. No page one rankings. That shift is forcing a rethink of how visibility works online - and where SEO (Search Engine Optimization) fits. Traditionally, SEO has been about helping businesses appear higher in Google results. That meant optimizing websites to match search terms, earn backlinks, load quickly, and convert well once the user arrived. But that model depends on one thing: people clicking on search results. AI tools don’t work that way. When users ask ChatGPT for help - “compare project management tools,” “what’s the best CRM for startups,” “find me a cheaper alternative to X” - they’re not browsing. They’re expecting a direct, summarized answer in the chat itself. That’s where the biggest shift is happening. Data from Profound shows how user intent is evolving in AI environments: 1. Generative intent now leads at 37.5%. These are prompts where users ask AI to create or do something directly: write an email, summarize a document, recommend a product. 2. Informational intent - traditionally the most common in Google - is down to 32%. These are questions looking for facts or explanations. 3. Navigational intent - looking for a specific website - has collapsed from 32% in traditional search to 2% in AI. In chat, people don’t say “take me to X.com.” 4. Transactional intent has jumped 9x (to over 6%). That includes prompts like “buy running shoes,” “find deals on laptops,” or “compare prices.” 12% of prompts are conversational: things like “thanks,” “make it shorter,” or “can you add a joke?” - which play a subtle but growing role in shaping how AI interprets tone, preferences, and even brands. Why does this matter? Because all of this happens before a user visits a website - if they visit at all. In this new model, there’s no clear click path. No landing page. No bounce rate. That makes most current marketing KPIs and tools largely obsolete. A new wave of startups is helping brands adapt - decoding how AI models reference products and content. The focus has shifted from rankings to being included in AI-generated responses. The move from SEO to “AI visibility” is early, but accelerating. The question now isn’t: “How do I get more search traffic?” but “What do AI systems say about the brand - and is it even part of the answer?” Because soon, it won’t just be users asking. It will be AI agents deciding - on their / our behalf. Are you ready? Opinions: my own, Graphic source: Profound 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

Something feels off in UX right now 🥺 My 2026 UX Design predictions Over the last months, I’ve had the same conversations again and again. With designers. With teams. With leaders. “Should I learn AI?” “Is my role still relevant?” “Am I falling behind… or overreacting?” Here’s what I’m seeing for UX in 2026 👇 1️⃣ Interfaces are dissolving With Generative UI, interfaces are created on demand. No more fixed screens. UX shifts from designing flows to defining systems, constraints, and intent. 2️⃣ UX research is scaling radically AI can analyze thousands of open-ended responses, run deep research, and surface patterns in minutes. The role of designers and researchers is changing: less execution, more sense-making, validation, and decision-making. 3️⃣ Designing for AI is no longer optional AI products learn from users. That makes them powerful and incredibly confusing if UX is missing. Trust, explainability, recovery UX, and mental models are now core design work. “Just type something” is not a UX strategy. 4️⃣ AI agents change how systems behave AI doesn’t just respond anymore. It plans and acts on behalf of users. This breaks traditional UX patterns. The key question becomes: When can a system act on its own and when must it stop? 5️⃣ Vibe coding removes technical barriers Ideas turn into prototypes and products at near-zero cost. The real bottleneck is no longer code, but judgment. UX shifts even further toward problem framing, direction, and quality control. 6️⃣ Roles matter less. Ownership matters more. Job titles lose their edge. Execution becomes cheap. What matters is who takes responsibility, who makes decisions, and who asks the uncomfortable questions. 7️⃣ Personalization is entering uncomfortable territory AI systems are building long-term memory about users. Helpful at first. Creepy the moment people realize what the system knows. UX must make data use visible, adjustable, and understandable. Not more data. More choice. My biggest takeaway: 2026 is not about designing faster. It’s about deciding better. UX is moving away from interface design toward intent, systems thinking, and responsibility. If you work in UX and ignore AI, you won’t be replaced. You’ll slowly become irrelevant. If you stay curious, critical, and intentional about how you use AI, this is one of the biggest opportunities UX has ever had. P.S.: I also recorded a podcast episode (link in the comments) where I go much deeper into these shifts, with concrete examples and reflections from my own work. If you want the longer version beyond a LinkedIn post, feel free to give it a listen.

The Future of Design Systems is Here: From UI Libraries to AI-Powered Experience Systems Gone are the days when design systems were just static UI component libraries. As highlighted in recent articles (sources below), we're witnessing a pivotal shift towards "Experience Systems" – intelligent frameworks that serve not only human users but also increasingly sophisticated AI agents. Key takeaways that every design system leader should consider: 1️⃣ Beyond Components: Designing for Intent & Context Our systems are becoming "intelligent experience systems," integrating "intent, context, and adaptability." We are beyond device responsiveness and are now in context-aware adaptation: user experience level (beginners get guidance, experts get streamlined flows) and business priorities (different features surface based on user segments). Also consider multimodal expression: one user intent can be expressed through whatever interaction method works best for the situation - voice for hands-free use, visual for detailed tasks, haptic feedback on mobile, or gestures on emerging platforms. 2️⃣ AI Agents are Your New Users AI agents are becoming primary consumers of our design systems, but most aren’t ready. When agents encounter components named “InfoBox” or “CardBase,” they’re clueless about purpose or behavior. The Problem: Most design systems name components based on how they look, not what they do. So you might have: - `BlueCard` (describes color/style) - `InfoBox` (vague purpose) - `CardBase` (just tells you it’s a card) Why This Breaks for AI: When an AI agent needs to build an interface, it doesn’t know what these components actually do. “BlueCard” could be anything - a promotional banner, an error message, a feature callout. The AI has to guess. The Solution - Treat Components Like API Endpoints: - `BlueCard` → `FeatureHighlight` (now AI knows this highlights features) - `InfoBox` → `ErrorNotification` or `SuccessMessage` (specific purpose) - Generic props → Semantic props (`color="red"` → `appearance="danger"`) 3️⃣ ROI: Prove Your Business Value It's no longer enough to just build components. As Josh Cusick emphasizes, "you can only build so many components before your team becomes a production shop." Focus on quantifiable value through workflow automation. Automating tasks like icon-to-code pipelines can save hundreds of thousands of dollars annually. Design systems are becoming "operating systems for design orgs." What are your thoughts on this evolution? How are you preparing your design system for the age of AI agents? #YourDesignSystemIsAnAPI #DesignSystems #AI #ExperienceDesign #ProductDesign #UX #Automation #FutureOfDesign #AgenticAI #TechEvolution Sources: Cusick, Josh. “The future of design systems", Beyond the Pixel, May 6, 2025. Kavcic, Romina. “Experience Systems”, The Design System Guide, May 30, 2025 Trueman, Murphy. “Your next design system user is an agent”, Design Systems Collective, June 3, 2025

A new study released today by OpenAI and Harvard economists draws on anonymized data from over 700 million weekly ChatGPT users worldwide. It offers the first large-scale, privacy-preserving look at how people actually rely on generative AI for sophisticated reasoning and decision support. Five findings leap out at me: ⭐ Decision support is exploding. Almost half of all messages, and now more than half, are people asking for guidance, advice, or analysis. The real economic value lies here: AI as a thinking partner. ⭐ Workplace reasoning is front and center. Among work-related messages, 56% involve “doing” tasks, and nearly three-quarters of those are writing tasks where the model is helping to solve problems or craft strategy, not just generate boilerplate. ⭐ These tasks match the core of knowledge work. Over 45% of all messages map to O*NET work activities such as “Getting Information,” “Interpreting Information,” and “Making Decisions & Solving Problems.” ⭐ Quality rises with complexity. Interactions in which people ask the model to reason or advise consistently rank highest in user satisfaction. ⭐ AI is becoming a teacher. Roughly 10% of all messages are tutoring or teaching requests, a striking signal that people already trust AI to explain and guide. And for those driving enterprise transformation, the same research adds a powerful call to action: ⚡ ChatGPT adoption has reached 10% of the world’s adult population, with users sending 2.5 billion messages daily, one of the fastest technology diffusions in history. ⚡ Even as personal use grows, absolute work-related usage has more than tripled in a year, proving that employees already incorporate AI into their daily jobs, often before formal corporate programs. ⚡ The highest-value interactions, decision support, strategic writing, and problem-solving are precisely the activities that define knowledge-intensive industries. For enterprises and their advisors, this is more than a trend; it’s an urgent signal. The next competitive edge isn’t just automating routine tasks. It’s embedding AI as a true co-pilot for human judgment, from strategic planning and R&D to regulated decision environments. If you’re shaping an AI strategy today, these data points make the case clear: your teams and your customers are already treating AI as a reasoning partner. The question isn’t whether they will... It’s whether your enterprise is ready to design for it and become truly AI-first. Read the paper here: http://bit.ly/4na2eeA

AI weekly brief is back! Everyone keeps asking what changed in AI going into 2026. Not what launched. Not what demoed well. What fundamentally shifted. Here’s the real change: AI is no longer being optimized for intelligence. It’s being optimized for execution under constraints. Speed is beating depth. Predictability is beating novelty. Systems are beating standalone models. Distribution is tightening. AI is consolidating into a few operating surfaces instead of infinite tools. Agents are being valued for doing work, not sounding smart. Revenue, throughput, and reliability matter more than benchmarks. Security is no longer theoretical. Prompt injection, guardrails, and human-in-the-loop design are now baseline requirements. Regulation and infrastructure are shaping architecture. Energy, compute, and compliance are no longer external problems. Retail is the canary in the coal mine. If AI doesn’t integrate across fragmented systems, it fails in production. I believe 2026 is the year AI stops being a feature and becomes business infrastructure. The winners won’t have the “best model.” They’ll have systems that integrate, execute, stay secure, and survive regulation. AI hype collapses the moment it touches real operations. That’s not a problem. That’s progress. If you’re building in AI this year, ask yourself one question: Is this designed for demos — or for reality? #ai #artificialintelligence #tech #technology

I always share a post each year talking about my predictions in technology. Here are my general technology trends for 2025. 🔺 Wider Adoption of Generative AI 🔹 Domain-specific models: We’ll see more specialized generators trained on targeted data (e.g., legal, medical, scientific) that can produce highly accurate and context-specific content. 🔹 Hybrid approaches: Enterprises will use generative AI alongside rule-based or traditional ML methods to achieve more reliable outcomes, minimizing hallucinations and biases. 🔺 Rise of Multimodal Systems 🔹 Unified AI experiences: Instead of siloed text, image, audio, and video models, we’ll see integrated systems that seamlessly handle multiple data types. This leads to richer applications, from next-gen customer support to advanced robotics. 🔹 Context-aware processing: AI will better understand real-world context, combining visual, audio, and textual cues to offer smarter responses and predictions. 🔺 Advances in Explainability and Trust 🔹 Regulatory frameworks: With stricter AI regulations on the horizon, model explainability and audibility will become core requirements, especially in finance, healthcare, and government. 🔹 AI “nutrition labels”: Standardized ways of conveying model biases, training datasets, and reliability will help build user trust and improve transparency. 🔺 Edge and On-Device AI 🔹 Lower latency, better privacy: More powerful AI models will run directly on phones, wearables, and IoT devices, reducing dependence on the cloud for tasks like speech recognition, image processing, and anomaly detection. 🔹 Specialized hardware: Continued investment in AI accelerators, TPUs, and neuromorphic chips will enable high-performance AI at the edge. 🔺 Human-AI Teaming and Augmented Decision-Making 🔹 Decision intelligence platforms: AI will shift from purely providing recommendations to working interactively with humans to explore complex problems—reducing cognitive load, but keeping humans in the loop. 🔹 Collaborative coding and content creation: AI co-pilots will expand from code generation and text drafting to more sophisticated collaboration, shaping design, research, and strategic planning. 🔺 Rapid Growth of AI as a Service (AIaaS) 🔹 “No-code” and “low-code” tools: Tools that allow non-technical users to deploy custom AI solutions will proliferate, lowering barriers to entry and accelerating adoption across industries. 🔺 Emphasis on Ethical and Responsible AI 🔹 Bias mitigation: Tools and techniques to detect and reduce bias will grow more advanced, spurred by public scrutiny and regulatory demands. 🔹 Standards for accountability: Organizations will create ethics boards and formal guidelines to ensure AI alignment with corporate values and social responsibility. 🔺 Quantum Computing Experiments 🔹 Hybrid quantum-classical models: Though still early-stage, breakthroughs in quantum hardware could lead to specialized quantum-assisted AI algorithms.

🚀 The Future of AI: DeepSeek and the Shift from Hardware to Software-Driven Innovation 🌐 The AI landscape is undergoing a seismic shift, and DeepSeek's groundbreaking AI model is at the forefront of this transformation. Traditionally, advancements in artificial intelligence relied heavily on brute-force hardware strategies—amassing GPUs, servers, and computational resources. But DeepSeek has redefined the game by prioritizing software-driven optimization over hardware dependency. This paradigm shift could fundamentally alter how we approach AI development, creating ripples across both enterprise and consumer applications. Why DeepSeek Matters: DeepSeek's latest model, DeepSeek-R1, has set new benchmarks in efficiency and performance. By leveraging techniques like custom communication schemes between chips, memory optimization, and reinforcement learning, it achieves superior results with significantly lower computational power. This approach not only democratizes access to cutting-edge AI but also challenges the dominance of hardware-centric players like Nvidia. For enterprises, this means cost-effective AI solutions; for consumers, it promises smarter and more accessible applications. Upcoming Innovations: Enterprise and Consumer Impact 1. Enterprise-Level Breakthroughs: - Hyper-Specialized AI Models: Businesses are moving toward domain-specific AI tailored for industries like healthcare (precision diagnostics), finance (fraud detection), and retail (personalized customer engagement). DeepSeek's cost-efficient models align perfectly with this trend. - Agentic AI Systems: Multi-agent systems capable of autonomous decision-making are set to revolutionize workflows. Imagine AI agents that proactively manage supply chains or customer inquiries with minimal human intervention. 2. Consumer-Centric Advancements: - Unified Memory Architecture (UMA): Apple's Unified Memory technology exemplifies how hardware-software integration can enhance user experiences. By allowing CPUs and GPUs to share a single memory pool, UMA boosts performance for tasks like video editing and gaming while reducing energy consumption. - AI-Powered Devices: From smartwatches to laptops, CES 2025 showcased innovations like Samsung’s Vision AI for real-time translation and LG’s AI-enhanced OLED screens. These developments highlight how software-optimized AI is reshaping consumer electronics. The Bigger Picture: DeepSeek's rise signals a broader shift in the tech world—one where software innovation takes precedence over raw hardware power. The message is clear: The future of AI isn't about who has the most GPUs; it's about who can do more with less. And with pioneers like DeepSeek leading the charge, the possibilities are endless. Let’s embrace this new chapter in AI innovation together. What are your thoughts on this software-first revolution? Let’s discuss! 💬 #AI #DeepSeek #Innovation #UnifiedMemory #FutureTech

The AI landscape is evolving at an electrifying pace. It feels like just yesterday, in 2023, we marveled at AI's ability to write basic code or generate simple marketing copy. Now, AI is transforming industries, redefining workflows, and pushing the boundaries of what's possible. As we enter 2025, it's clear that AI is no longer a futuristic concept but a powerful force shaping our present and future. We've seen a surge of companies successfully move their AI prototypes into production. 𝐁𝐮𝐭 𝐭𝐡𝐞 𝐣𝐨𝐮𝐫𝐧𝐞𝐲 𝐡𝐚𝐬 𝐣𝐮𝐬𝐭 𝐛𝐞𝐠𝐮𝐧. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐟𝐨𝐮𝐫 𝐤𝐞𝐲 𝐭𝐫𝐞𝐧𝐝𝐬 𝐭𝐡𝐚𝐭 𝐰𝐢𝐥𝐥 𝐝𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐀𝐈 𝐥𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞 𝐢𝐧 2025: 1. 𝐓𝐡𝐞 𝐑𝐢𝐬𝐞 𝐨𝐟 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐀𝐈 AI is becoming increasingly adept at processing and integrating diverse data sources like images, video, code, and audio, alongside text. This multimodal capability is unlocking new possibilities for richer, more personalized experiences. Imagine searching for information using a combination of text, images, and voice commands, or interacting with AI-powered chatbots that understand and respond to your visual cues. We're already seeing this in action with companies like WPP, which is leveraging multimodal AI to empower creatives, and Mercedes-Benz, which is integrating it into its MBUX Virtual Assistant to create a highly personalized in-car experience. 2. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐅𝐫𝐨𝐦 𝐏𝐫𝐨𝐦𝐢𝐬𝐞 𝐭𝐨 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 AI agents, which act as an abstraction for grounding, reasoning, and augmentation tasks, are crucial for converting AI models into real-world value. Organizations are increasingly using AI agents to scale the experimentation and deployment of AI across their workflows. Banco BV and Deloitte are leading the way in utilizing agent platforms like Google Agentspace to connect data sources, foster rapid experimentation, and uncover hidden insights. 3. 𝐓𝐡𝐞 𝐘𝐞𝐚𝐫 𝐨𝐟 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 With AI proving its value, organizations are shifting their focus to optimizing its performance and maximizing ROI. This involves fine-tuning models, optimizing infrastructure, and ensuring long-term relevance and effectiveness. Companies like LG AI Research are already achieving significant reductions in processing time and operating costs through optimization efforts. 4. 𝐃𝐞𝐦𝐨𝐜𝐫𝐚𝐭𝐢𝐳𝐢𝐧𝐠 𝐀𝐈 𝐀𝐜𝐜𝐞𝐬𝐬 The rise of generative AI is breaking down traditional silos and democratizing access to AI tools. This empowers a wider range of users to participate in AI-driven innovation, fostering collaboration and accelerating the creation of novel customer experiences. These are just a few of the exciting AI trends I foresee shaping 2025 and beyond. I'm eager to hear your thoughts and learn about the innovative ways you're using AI in your own work. Follow Omkar Sawant for more! #AI #ArtificialIntelligence #GenerativeAI #MultimodalAI #AIAgents #Optimization #Innovation #TechTrends #FutureofWork #GoogleAI

The landscape of AI in 2024 has seen significant shifts, with advancements that will shape industries and daily life for years to come. Multimodal AI, which integrates text, audio, and visuals into cohesive models, emerged as a powerful tool despite refining its accuracy. Simultaneously, small language models (SLMs) began to gain momentum, offering solid performance on smaller devices like wearables and smartphones. Additionally, the rise of customizable generative AI has signaled a move away from generic solutions towards tailored applications, indicating that the future of AI is moving towards personalization and efficiency. 2025, AI is expected to become indispensable in everyday life and business operations. Key trends point to the shift from cloud-based systems to edge AI, where devices like smartphones and wearables will process data locally, bringing AI’s benefits to personal devices. Autonomous AI agents will be central to this transformation, managing tasks across industries, from supply chains to customer service. Creative AI tools are also set to expand, revolutionizing sectors like entertainment and marketing by making content creation easier, faster, and more accessible than ever before. However, as #AI becomes a crucial part of our lives, there is an urgent need for widespread AI literacy. The technology is no longer just for tech experts; everyone needs to understand how AI works and how it will impact their fields. By 2025, AI will not just be a tool but a collaborator, influencing everything from business processes to healthcare. As AI adoption continues to rise, those who are prepared will stay competitive and thrive in an AI-driven world. It’s time for businesses and individuals alike to embrace this shift and ensure they have the knowledge to leverage AI effectively.

From GenAI to GenUI We’re witnessing a shift as significant as the leap from MS-DOS to graphical user interfaces. The AI era marks our latest upgrade in how we interact with technology. For decades, we designed for workflows and specific actions. Everything was deterministic. Behind every interface sat a flowchart, with logic carefully coded. The backend made decisions, and the frontend rendered them. This model worked because we could predict every path a user might take. With agents, this paradigm breaks down. Text alone isn’t sufficient anymore. Chat works for conversation, but interaction demands something more. We need to engage with agents, not just talk to them. Reasoning state and intent become critical factors in the exchange. LLMs can now generate UI, and this capability feels like natural progression. Model Context Protocol enables mini-apps to emerge on the fly, no longer bound by deterministic rules. This opens the door to genuine hyper-personalization. We’ve moved from designing screens to designing for outcomes. Agents now dynamically assemble workflows based on intent, available data, and accessible tools. The fact that agents can create interfaces without traditional designers and developers is revolutionary. We can finally shift from UI-centric thinking to truly user-centered experience design. This fundamentally transforms the designer’s role. We’re no longer pixel pushers or interface assemblers. The work of arranging buttons, spacing elements, and crafting individual screens can now be handled by agents. Instead, designers become architects of experience, defining the principles, guardrails, and intent that shape how agents respond. We set the boundaries of possibility, orchestrate the logic of interaction, and ensure coherence across dynamic, personalized experiences. Our canvas expands from static screens to adaptive systems. We design the intelligence behind the interface, the relationships between user needs and agent capabilities, the quality standards that govern generated UIs. We curate outcomes rather than outputs. The ability to adapt, reorganize, and respond to both user intent and application context is transformative. With reasoning and action combined, agents can generate dynamic artifacts that enable interaction, not merely conversation. What a time to be alive as a designer! 🫶 #ai