User Experience Considerations for Chatbot Updates

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 👇

AI chatbots are everywhere, but are we designing them right? Lately, I’ve been using and researching lots of AI chatbots—especially as more clients request this feature. Many rely on design patterns borrowed from their predecessors and the giants, often without much reconsideration. While these patterns may seem like industry standards, they leave me, and likely others, feeling overwhelmed, confused, or even annoyed. Here are some examples: 1️⃣ The Blank Page Dilemma Whenever I see a chatbot interface with nothing but a search bar or “Type anything” prompt, I hesitate. It feels like staring at a blank page for an essay—endless possibilities but no guidance. ✅ What works better: Give users suggested actions, tailored to your product, to help them understand what’s possible. Focus your AI on specific, valuable use cases instead of trying to make it an all-knowing oracle. -- 2️⃣ The “✨ with AI” Hype Buttons like “Summarize with AI” or “Ask AI Anything” feel unnecessary. AI doesn’t need the sparkle anymore—it’s a commonplace part of the digital toolkit now. This idea really stuck with me after hearing Vitaly Friedman mention it in a fantastic talk on smart AI design patterns. ✅ What works better: Clear, functional labels like “Summarize” or “Ask anything” do the job better. They’re easier for users to understand at a glance. -- 3️⃣ “Prompt” Jargon The word “prompts” has always felt technical and unfamiliar. For many users, it’s not clear what that even means. ✅ What works better: Use friendlier language like “Here’s what you can try” or “Suggestions to get started.” Simple shifts like this can make AI feel less intimidating. -- The best chatbot interfaces meet their users where they are. As we design these complex features, we shouldn’t overlook our UX principles.

AI Chatbots: Houston, we have a problem! ...and #CustomerExperience is caught in the crossfire. The Forrester #CX Index saw a general drop in customer experience scores overall. Some of the blame was put on the proliferation of #AIChatbots. Don’t let ineffective AI Chatbots hurt your business. Learn how to fix it with these simple steps: 1. Evaluate the chatbot's performance ↳ Regularly check if it meets customer needs. ↳ Ineffective chatbots drive customers away. 2. Train your AI with real customer data ↳ Use real interactions for better responses. ↳ The more relevant the data, the better the chatbot. 3. Update the chatbot regularly ↳ Technology and customer needs change. ↳ Keep your chatbot updated to stay effective. 4. Offer a human fallback option ↳ Always have a human available if the bot fails. ↳ This ensures customer satisfaction. 5. Simplify the chatbot's tasks ↳ Focus on simple, repetitive tasks. ↳ Complex tasks should be handled by humans. 6. Test the chatbot with real users ↳ Get feedback from actual customers. ↳ Use this feedback to make improvements. 7. Ensure the chatbot understands context ↳ Context is key for accurate responses. ↳ Use advanced AI to improve context understanding. 8. Monitor and analyze interactions ↳ Keep track of how the chatbot performs. ↳ Use analytics to find and fix issues. 9. Personalize the chatbot experience ↳ Tailor responses to individual customers. ↳ Personalization increases customer satisfaction. 10. Keep the conversation natural ↳ Avoid robotic responses. ↳ Natural language processing can help. 11. Train staff on chatbot use ↳ Employees should know how to use and troubleshoot the bot. ↳ Proper training ensures smooth operation. 12. Set clear goals for the chatbot ↳ Define what you want the chatbot to achieve. ↳ Clear goals lead to better performance. Effective AI chatbots can boost customer experience. Follow these steps to ensure your chatbot helps, not hurts, your business.