UX Design For Customer Support Tools

🔎 How To Redesign Complex Navigation: How We Restructured Intercom’s IA (https://lnkd.in/ezbHUYyU), a practical case study on how the Intercom team fixed the maze of features, settings, workflows and navigation labels. Neatly put together by Pranava Tandra. 🚫 Customers can’t use features they can’t discover. ✅ Simplifying is about bringing order to complexity. ✅ First, map out the flow of customers and their needs. ✅ Study how people navigate and where they get stuck. ✅ Spot recurring friction points that resonate across tasks. 🚫 Don’t group features based on how they are built. ✅ Group features based on how users think and work. ✅ Bring similar things together (e.g. Help, Knowledge). ✅ Establish dedicated hubs for key parts of the product. ✅ Relocate low-priority features to workflows/settings. 🤔 People don’t use products in predictable ways. 🤔 Users often struggle with cryptic icons and labels. ✅ Show labels in a collapsible nav drawer, not on hover. ✅ Use content testing to track if users understand icons. ✅ Allow users to pin/unpin items in their navigation drawer. One of the helpful ways to prioritize sections in navigation is by layering customer journeys on top of each other to identify most frequent areas of use. The busy “hubs” of user interactions typically require faster and easier access across the product. Instead of using AI or designer’s mental model to reorganize navigation, invite users and run a card sorting session with them. People are usually not very good at naming things, but very good at grouping and organizing them. And once you have a new navigation, test and refine it with tree testing. As Pranava writes, real people don’t use products in perfectly predictable ways. They come in with an infinite variety of needs, assumptions, and goals. Our job is to address friction points for their realities — by reducing confusion and maximizing clarity. Good IA work and UX research can do just that. [Useful resources in the comments ↓] #ux #IA

Tired of AI projects that don't deliver? Try this human-centred approach. From my research over the past couple of years, I’ve noticed a recurring pattern. We often treat AI as a technology experiment rather than an upgrade to how people actually work. That mindset can quietly limit a project’s success. To support better decisions, I’ve developed a human-centred AI readiness checklist based on that research. I hope it’s useful for your next initiative. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗮𝗻𝗱 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 𝗖𝗵𝗲𝗰𝗸 (𝗖𝗥𝗜𝗦𝗣-𝗗𝗠 𝗺𝗶𝗻𝗱𝘀𝗲𝘁) →Are we clear on the operational outcome and metric we are improving? ↳If we cannot say “this reduces X by Y%”, we are chasing tools, not performance. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗖𝗵𝗲𝗰𝗸 (𝗟𝗲𝗮𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴) →Which real human decisions are we supporting? ↳AI should strengthen judgment points like prioritisation or scheduling, not automate activity without purpose. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 (𝗟𝗲𝗮𝗻 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲) → Is the workflow stable enough to augment? ↳Automating instability scales, defects and frustrates the people doing the work. 𝗩𝗮𝗹𝘂𝗲 𝘃𝘀 𝗗𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗼𝗻 𝗖𝗵𝗲𝗰𝗸 (𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴) →Does the benefit outweigh frontline disruption? ↳Operational AI should improve flow, not create friction for teams. 𝗗𝗮𝘁𝗮 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 (𝗖𝗥𝗜𝗦𝗣-𝗗𝗠 𝗱𝗮𝘁𝗮 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴) →Does our data reflect lived operational reality? ↳Human trust collapses when AI runs on distorted inputs. 𝗛𝘂𝗺𝗮𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗖𝗵𝗲𝗰𝗸 (𝗛𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗲𝗿𝗲𝗱 𝗔𝗜 𝗱𝗲𝘀𝗶𝗴𝗻) →Where does AI advise, where do humans review, and where does automation act? ↳Clear boundaries protect autonomy and accountability. 𝗥𝗶𝘀𝗸 𝗮𝗻𝗱 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗖𝗵𝗲𝗰𝗸 (𝗡𝗜𝗦𝗧 𝗔𝗜 𝗿𝗶𝘀𝗸 𝗺𝗼𝗱𝗲𝗹) →Have we planned for failure, overrides, and fallback workflows? ↳Operations must remain safe and continuous when systems misfire. 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗖𝗵𝗲𝗰𝗸 (𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝗰𝗹𝗮𝗿𝗶𝘁𝘆) →Who owns outcomes, model behaviour, and data quality? ↳Human accountability must remain visible after launch. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 (𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴) →Will this support how people actually work? ↳Tools that slow teams are quietly abandoned. 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗧𝗿𝘂𝘀𝘁 𝗖𝗵𝗲𝗰𝗸 (𝗖𝗵𝗮𝗻𝗴𝗲 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲) →Are we designing for understanding, transparency, and behavioural adoption? ↳Trust grows when teams see AI improving their work, not replacing it. AI is an amplifier. It scales what we already have: good or bad ↳𝐆𝐚𝐫𝐛𝐚𝐠𝐞 𝐢𝐧. 𝐀𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 𝐠𝐚𝐫𝐛𝐚𝐠𝐞 𝐨𝐮𝐭.⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣The strongest AI initiatives aren’t just technology deployments. They are human-centred operating upgrades that happen to use AI. ♻️ Share if you found this useful. #AIinBusiness #HumanCenteredAI #Operations #Leadership #AIStrategy

When you're deploying AI agents for a CX function, having a good Knowledge Base is a non-negotiable. Why? When optimized, it can empower your AI agents to deliver fast, accurate responses. When neglected, it can leave customers frustrated and agents underperforming. If you want to make sure your help center actually HELPS, here are 5 strategies you can deploy: 1. Structure your content in a Q&A format with clear headings and concise instructions to make it easy for both customers and AI to find relevant information. 2. Use precise keywords. If you have membership tiers, explicitly say which tier you're talking about. 3. Update content regularly with release dates for new features and remove outdated articles. 4. Use visuals (carefully). Reference images and annotations can improve usability—just make sure you have the bandwidth to keep them accurate. 5. Make agents accessible by providing a clear link to the AI agent channels for when customers need help beyond the answers available to them. A lot of companies view help centers as a nice-to-have but the truth is, the ROI is massive. And if you're thinking of using (or already use) AI agents for your customer support, you need to keep it well maintained so the agents can: → Identify knowledge gaps → Make suggestions to make your documentation easier to understand When your help center is optimized, AI agents can perform at their best, which translates to happier customers and less workload for your team. Read the full article for more strategies we recommend—link in the comments! 👇

Most product failures aren’t engineering failures. They’re empathy failures. Teams ship what they think customers want… …and then wonder why adoption stalls, churn climbs, and the roadmap turns into a graveyard of “nice features.” Here’s the shift that changes everything: Customer-centric design isn’t a UX phase — it’s an operating system. It means building around real user needs, behaviors, and outcomes (not internal opinions). And in the last few years, AI has raised the bar: Customers expect relevance and ease (not generic journeys) Personalization is now table-stakes — but trust is fragile The winners will be the teams who pair speed with human-centered design The customer-centric loop (that actually works) 1) Learn deeply Talk to customers weekly. Mine tickets, reviews, churn reasons, behavior data. 2) Map reality Personas + journeys that expose friction, emotion, and drop-off points. 3) Design for outcomes Less effort. More clarity. Better defaults. Faster “time to value.” 4) Prototype + test fast Small tests beat big debates. 5) Measure + iterate Track experience and behavior (activation, retention, task success, effort). Where AI fits (and where it breaks) Use AI to accelerate: Synthesizing feedback Finding patterns Generating variations and prototypes But design AI like a relationship: Set expectations Provide controls (“undo,” preferences, corrections) Fail gracefully Escalate when confidence is low Customer-centric design is the advantage that compounds. Because when you build what people truly need, growth stops being a fight. Question: What’s one customer insight you learned recently that changed how you build? iQor we take customer centric design to the next level with InsightsIQ, hit me up with questions. #CustomerExperience #ProductManagement #UXDesign #ProductDesign #AI #HumanCenteredDesign #Leadership

Customer support is highly personalized, requiring empathy and nuanced understanding—qualities that many believe AI cannot replicate. As part of our course, AI in Business Applications, my team and I worked on a project that leverages Generative AI to enhance, not replace, the human aspect of customer support. By combining Large Language Models (LLMs) with human oversight, we created a scalable, efficient, and context-aware system tailored for support-heavy environments. ▶️The Reality of AI in Personalized Support AI tools like LLMs are not here to replace human agents but to complement them. However, skepticism remains due to the following limitations of LLMs: 1. Lack of Empathy: AI struggles to understand emotional nuances, which are often critical in support scenarios. 2. Generic Responses: LLMs may offer answers that lack the deep personalization customers expect. 3. Hallucinations: AI can occasionally generate inaccurate or misleading responses when context is unclear. 4. Complexity of Issues: AI might fall short in handling multi-layered or highly sensitive customer queries. 💡Our Solution: Human-AI Collaboration To address these challenges, we implemented a hybrid system that leverages AI’s efficiency and human agents’ empathy and expertise: Fine-Tuning for Accuracy: By training the AI on domain-specific data (e.g., product manuals, FAQs, past conversations), we ensured it could handle routine inquiries with precision. Retrieval-Augmented Generation (RAG): This framework enhances the AI’s reliability by pulling accurate, up-to-date information from a structured knowledge base before generating responses. Escalation to Human Agents: For personalized or emotionally charged cases, the AI seamlessly hands off the conversation to a human agent, ensuring customers feel heard and valued. 🎯How This Enhances Customer Support Efficiency: AI handles repetitive, straightforward queries, freeing human agents to focus on complex, high-value interactions. Scalability: With AI assisting in routine tasks, businesses can scale support operations without compromising quality. Empowered Human Agents: By providing agents with AI-curated insights, they can deliver faster, more informed, and empathetic solutions. Round-the-Clock Support: AI ensures customers receive instant responses to basic queries, even outside business hours. ⚖️A Balanced Approach The key takeaway? AI is not a replacement but a tool to enhance human capabilities. While it streamlines processes and improves efficiency, the human touch remains central in building trust and loyalty with customers. This project deepened my understanding of how AI can solve business challenges while respecting the personalized nature of customer support. By combining Generative AI with thoughtful design and human collaboration, we can create systems that are both powerful and people-centric. #AI #GenerativeAI #CustomerSupport #HumanAI #BusinessInnovation #HybridApproach #AIinBusiness

AI Virtual Assistants in Customer Support In today’s fast-paced digital world, exceptional customer support has become a cornerstone of business success. To meet the growing demand for 24/7 availability and personalized interactions, businesses are turning to AI-powered virtual assistants. These advanced systems are revolutionizing customer support by providing accurate, efficient, and empathetic solutions. Here’s a breakdown of the key components and insights shared: 1. Setting Up the Environment The first step involves configuring essential libraries and API keys, creating a robust foundation for developing the virtual assistant. By integrating OpenAI's capabilities with LangChain and ChromaDB, businesses can ensure their assistant is equipped with advanced language understanding and retrieval capabilities. 2. Creating the Knowledge Base A well-structured knowledge base is critical. The tutorial demonstrates how to prepare and organize customer support FAQs into a vectorized format, allowing the assistant to retrieve accurate responses efficiently. Using ChromaDB ensures seamless handling of document embeddings and vector searches. 3. Implementing Conversation Memory Context is key in delivering personalized and coherent responses. With LangChain's memory tools, the assistant retains chat history, enabling fluid interactions across multiple exchanges. 4. Designing the AI Assistant The tutorial provides a blueprint for creating a modular AI assistant. It highlights methods for retrieving context, generating responses, and ensuring smooth conversation flow. 5. Building a User Interface with Gradio Gradio offers a simple yet powerful platform for creating an interactive chat interface. This section explains how to design a user-friendly UI that enables real-time interaction, making the assistant accessible to customers across various devices. 6. Adding Feedback and Analytics Effective customer support relies on continuous improvement. By collecting feedback and analyzing customer interactions, businesses can identify areas for enhancement. 7. Enhancing with Sentiment Analysis Understanding customer sentiment adds a human touch to AI-driven support. The tutorial explores how sentiment analysis can adjust the tone of responses, providing empathetic interactions that resonate with users. ✅Key Takeaways and Opportunities for Enhancement The tutorial serves as a practical guide for businesses looking to implement AI in their customer support operations. By leveraging advanced tools and techniques, organizations can: - Improve response accuracy and efficiency - Deliver context-aware and personalized interactions - Collect actionable insights to refine their support strategies For detailed sample code, check out the Google Colab notebook(https://lnkd.in/gtA_mz-T) #ArtificialIntelligence #LangChain #OpenAI #ChromaDB #Gradio

Its time we move on from “Your ticket has been received” response. Most IT help desks were built for another era—reactive, slow, and siloed. But quietly, a transformation is underway. Enter the 𝘐𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘵 𝘏𝘦𝘭𝘱 𝘋𝘦𝘴𝘬 - Self-learning, context-aware systems that get smarter with every interaction. Here’s what’s changing: 🔹 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 (𝐍𝐋𝐏) Users talk to the help desk like they would to a colleague—no ticket codes, no dropdown menus. The system understands, interprets, and routes queries in real time. 🔹 𝐈𝐧𝐭𝐞𝐧𝐭 𝐑𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 AI can now identify the true purpose behind a request—whether resetting a password, provisioning software, or troubleshooting access—often without a human ever stepping in. 🔹 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐑𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 Routine issues are resolved instantly. Think: forgotten credentials, VPN errors, access rights—gone in seconds, not hours. 🔹 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐄𝐬𝐜𝐚𝐥𝐚𝐭𝐢𝐨𝐧𝐬 When a human does need to step in, the system has already collected the context, logs, and history, so L2 doesn’t start from scratch. The result? ✅ Shorter resolution times ✅ Higher end-user satisfaction ✅ Freed up IT teams to focus on innovation, not firefighting If you’re still measuring IT support by the number of tickets closed, you’re missing the bigger shift. Today, it's about how fast you can turn problems into insights—and insights into action. Is your help desk still stuck in 2015? #EnterpriseIT #ITSupport #AIAutomation

Pylon has long been on my radar as an industry trendsetter and thought leader. Scaling support is often an afterthought for many startups, so it’s refreshing to see a guide that assesses these challenges early on. That said, I’d like to add some clarifications regarding one critical area: managing knowledge effectively as you scale. First, while the piece rightly justifies the need for a Help Center, it describes it as a "giant FAQ". However, a true Help Center (or Knowledge Base) should be much more than that. It's not just about answering repetitive questions but about empowering users with a structured, searchable repository of comprehensive guides, troubleshooting tips, and best practices. This is the foundation of your customer self-service strategy. Here are a few points I believe could enhance the perspective: 1. Waiting until you're answering 20+ questions per day is a reactive approach. Instead, begin building your knowledge base as soon as patterns emerge in customer queries. It’s much easier to scale a well-laid foundation than to backfill a disorganized structure. 2. When multiple people contribute to documentation, style inconsistencies can creep in. Establishing a style guide early- covering tone, formatting, and terminology - ensures that the Help Center feels cohesive and professional, no matter who writes the content. 3. Articles need regular reviews and updates to stay relevant. As your product evolves, your Help Center should evolve too. Assign ownership to specific team members and create a review cadence to ensure nothing becomes outdated. 4. Meeting customers "where they are" with Slack or chat support is great, but a Help Center should be the go-to for common queries. A well-designed, user-friendly Help Center doesn’t just deflect support tickets - it enhances the customer experience by enabling them to find answers independently. 5. Invest in tagging, categorization, and analytics tools from the start. This will make scaling easier as your needs grow and support AI-driven search or predictive assistance when you reach Series B. Pylon’s guide is a fantastic resource and a great starting point for this conversation. Managing knowledge effectively is just as important as hiring the right team and choosing the right channels. I wonder how others are approaching knowledge management at the Series A stage. From my on-site experience discussing knowledge management at Web Summit, it seems this area is often overlooked. #KaterynaTracksUpdates

Why do customers keep emailing you instead of using your Help Center? It’s not laziness. And it’s not a “training problem.” A few months ago, I was reviewing metrics for a fast-growing SaaS client. Their Help Center was beautifully designed, packed with guides, FAQs and tutorials. They had invested heavily. Yet support tickets were spiking and CSMs were constantly firefighting instead of driving value. At first, everyone blamed adoption: “𝘊𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘫𝘶𝘴𝘵 𝘥𝘰𝘯’𝘵 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘳𝘦𝘢𝘥 𝘢𝘳𝘵𝘪𝘤𝘭𝘦𝘴.” “𝘛𝘩𝘦𝘺 𝘱𝘳𝘦𝘧𝘦𝘳 𝘵𝘢𝘭𝘬𝘪𝘯𝘨 𝘵𝘰 𝘴𝘰𝘮𝘦𝘰𝘯𝘦.” But when we dug deeper, the story was different. The Help Center existed but it didn’t deliver success the first time. Customers struggled to find what they needed, guessed which article applied and often failed before giving up. Every failed attempt became a support ticket and a missed opportunity for CS to drive adoption, engagement and outcomes. That’s when it clicked: 𝘈𝘥𝘰𝘱𝘵𝘪𝘰𝘯 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘥𝘳𝘪𝘷𝘦 𝘴𝘶𝘤𝘤𝘦𝘴𝘴. 𝘚𝘶𝘤𝘤𝘦𝘴𝘴 𝘥𝘳𝘪𝘷𝘦𝘴 𝘢𝘥𝘰𝘱𝘵𝘪𝘰𝘯. Here’s how we fixed it, with CS and Support aligned and subtle AI helping where it mattered most: 1️⃣ Guide customers through success during onboarding. CS teams solved real problems using the Help Center with customers, showing its impact immediately. 2️⃣ AI to recommend the right content. Instead of leaving customers to search, AI surfaced the most relevant articles in their workflow, turning friction into fast wins. 3️⃣ Embed success into the workflow. Proactively place Help Center links and resources exactly where customers need them; in emails, in-app prompts and task reminders. When guidance is built into the workflow, customers experience success naturally, without chasing answers. 4️⃣ Turn early wins into habits. Celebrate and highlight every time a customer solves a problem using the Help Center. Share success stories in onboarding calls, check-ins or internal team updates. When customers see results and recognize the value, using the Help Center becomes a habit, not a task. Within weeks, customers started using the Help Center naturally. CS could focus on driving adoption, adoption led to better engagement and engagement created measurable outcomes. The principle is simple and often overlooked: 𝘛𝘰𝘰𝘭𝘴 𝘥𝘰𝘯’𝘵 𝘨𝘦𝘵 𝘢𝘥𝘰𝘱𝘵𝘦𝘥. 𝘚𝘶𝘤𝘤𝘦𝘴𝘴 𝘨𝘦𝘵𝘴 𝘢𝘥𝘰𝘱𝘵𝘦𝘥. 𝘏𝘦𝘭𝘱 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘴𝘶𝘤𝘤𝘦𝘦𝘥 𝘰𝘯𝘤𝘦 𝘢𝘯𝘥 𝘵𝘩𝘦𝘺’𝘭𝘭 𝘬𝘦𝘦𝘱 𝘤𝘰𝘮𝘪𝘯𝘨 𝘣𝘢𝘤𝘬. How are your CS and Support teams working together to create first-time success for your customers?

In a post that recently went viral, Andrej Karpathy described using LLMs to build personal knowledge bases - you throw raw data into a directory, an LLM compiles it into a structured wiki, Q&A runs against it, and outputs feed back in to make it richer. He said "there is room here for an incredible new product." He's right. And the pattern he's describing applies to something most companies already struggle with: the help center. Think about what a help center actually is. It's a knowledge base compiled from scattered sources - docs, tickets, product pages, tribal knowledge - organized for customers to query. The compilation today is manual. A person writes every article, updates every screenshot, chases down every SME. And despite all that work, customers still can't find answers and file tickets anyway. Now apply Karpathy's pattern at enterprise scale. Ingest from connected sources - Notion, Zendesk, Confluence, your website. AI compiles articles, organizes collections, indexes everything. Customers ask questions and get direct answers grounded in compiled knowledge - not a list of links. When the AI synthesizes across sources to answer a question, that synthesis becomes a draft article. When a question can't be answered and a human resolves it, that resolution becomes a draft article. Human reviews. Approves. Publishes. The loop closes. Every customer interaction makes the knowledge base more complete. The help center compounds from use - exactly the way Karpathy describes, but for the customers hitting your support portal every day. This is what we've been building at Enjo. The compounding knowledge base for customer support.