User Experience Trends in Chatbot Development

Work on designing AI-first assistant and agent experiences has been eye opening. AI UX is both fundamentally the same and widely different, especially for vertical use cases. There are clear and emerging patterns that will likely continue to scale: 1. Comfort will start with proactive intelligence and hyper personalization. The biggest expectation customers have of AI is that it’s smart and it knows them based on their data. Personalization will become a key entry point where a recommendation kicks off a “thread” of inquiry. Personalization should only get better with “memory”. Imagine a pattern where an assistant or an agent notifies you of an anamoly, advice that’s specific to your business, or an area to dig deeper into relative to peers. 2. There are two clear sets of UX patterns that will emerge: assistant-like experiences and transformative experiences. Assistant-like experiences will sound familiar by now. Agents will complete a task partially either based on input or automation and the user confirms their action. You see this today with experiences like deep search. Transformative experiences will often start by human request and will then become background experiences that are long running. Transformative experiences, in particular, will require associated patterns like audit trails, failure notifications, etc. 3. We will start designing for agents as much as we design for humans. Modularity and building in smaller chunks becomes even more important. With architecture like MCP, the way you think of the world in smaller tools becomes a default. Understanding the human JTBD will remain core but you’ll end up building experiences in pieces to enable agents to pick and choose what parts to execute in what permutation of user asks. 4. It’ll become even more important to design and document existing standard operating procedures. One way to think about this is a more enhanced more articulated version of a customer journey. You need to teach agents the way not just what you know. Service design will become an even more important field. 5. There will be even less tolerance for complexity. Anything that feels like paperwork, extra clicks, or filler copy will be unacceptable; the new baseline is instant, crystal‑clear, outcome‑focused guidance. No experience, no input, no setting should start from zero. Just to name a few. The underlying piece is that this will all depend on the culture design teams, in particular, embrace as part of this transition. What I often hear is that design teams are already leading the way in adoption of AI. The role of Design in a world where prototyping is far more rapid and tools evolve so quickly will become even more important. It’ll change in many ways (some of it is by going back to basics) but will remain super important nonetheless. Most of the above will sound familiar on the surface but there’s so much that changes in the details of how we work. Exciting times.

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.

Your User Interfaces are becoming totally outdated with AI: 1. The old rules of UI design are fading. One-size-fits-all is out the window. There was a time when clean layouts, intuitive buttons, and predictable flows defined a great UI. That’s not enough anymore. AI has flipped the script. Interfaces are now dynamic, personalized, and proactive. Users don’t just interact with your page; your page needs to anticipate what users may need. 2. Static designs and rigid UX metrics? They’re not keeping up. We used to obsess over click-through rates, time-on-page, or task completion speed. But those don’t capture the full picture when AI-driven UIs adapt in real-time. How do you measure success of a chatbot that predicts your next question or a dashboard that reshapes itself based on your habits? Your traditional metrics probably miss the magic. 3. The new frontier: intent-driven, adaptive interfaces. Why force users to navigate when AI can meet them where they are? Think: - Contextual suggestions (e.g., auto-filling forms based on behavior) - Predictive layouts (e.g., surfacing tools before you know you need them) - Conversational UI (e.g., voice or text that feels human, not robotic) The data’s there: behavioral patterns, historical inputs, even sentiment analysis. You must use all of it to make this happen. 4. I challenge you to map your UI’s “AI maturity curve.” Take your product’s interface and plot it: (a) Static—basic and unchanging (b) Responsive—adjusts to screen or clicks (c) Personalized—tailors to user history (d) Predictive—anticipates needs (e) Autonomous—acts on behalf of the user Where are you now? Where’s the gap to the next stage? 5. What matters is delivering value before users ask for it. UI design used to be about ease and reduced friction. But now it’s about foresight. Designers and product teams need to orchestrate *everything* that shapes the experience: AI models, data pipelines, and yes, even the guardrails to keep it ethical and uncreepy. 6. Next-gen AI tools are accelerating this shift. From generative design (think AI mocking up wireframes) to natural language processing (making voice UIs seamless), the tech is here. The catch? Integration’s still messy. Tying AI outputs to real-time UI updates takes work. Experiment with small pilots, measure user delight (not just efficiency), and scale what sticks.