AI For Enhancing User Experience

🔮 Design Patterns For AI Interfaces (https://lnkd.in/dyyMKuU9), a practical overview with emerging AI UI patterns, layout considerations and real-life examples — along with interaction patterns and limitations. Neatly put together by Sharang Sharma. One of the major shifts is the move away from traditional “chat-alike” AI interfaces. As Luke Wroblewski wrote, when agents can use multiple tools, call other agents and run in the background, users orchestrate AI work — there’s a lot less chatting back and forth. In fact, chatbot widgets are rarely an experience paradigm that people truly enjoy and can fall in love with. Mostly because the burden of articulating intent efficiently lies on the user. It can be done (and we’ve learned to do that), but it takes an incredible amount of time and articulation to give AI enough meaningful context for it to produce meaningful insights. As it turned out, AI is much better at generating prompt based on user’s context to then feed it into itself. So we see more task-oriented UIs, semantic spreadsheets and infinite canvases — with AI proactively asking questions with predefined options, or where AI suggests presets and templates to get started. Or where AI agents collect context autonomously, and emphasize the work, the plan, the tasks — the outcome, instead of the chat input. All of it are examples of great User-First, AI-Second experiences. Not experiences circling around AI features, but experiences that truly amplify value for users by sprinkling a bit of AI in places where it delivers real value to real users. And that’s what makes truly great products — with AI or without. ✤ Useful Design Patterns Catalogs: Shape of AI: Design Patterns, by Emily Campbell 👍 https://shapeof.ai/ AI UX Patterns, by Luke Bennis 👍 https://lnkd.in/dF9AZeKZ Design Patterns For Trust With AI, via Sarah Gold 👍 https://lnkd.in/etZ7mm2Y AI Guidebook Design Patterns, by Google https://lnkd.in/dTAHuZxh ✤ Useful resources: Usable Chat Interfaces to AI Models, by Luke Wroblewski https://lnkd.in/d-Ssb5G7 The Receding Role of AI Chat, by Luke Wroblewski https://lnkd.in/d8xcujMC Agent Management Interface Patterns, by Luke Wroblewski https://lnkd.in/dp2H9-HQ Designing for AI Engineers, by Eve Weinberg https://lnkd.in/dWHstucP #ux #ai #design

AI is rewriting one of banks’ most fundamental assumptions: customer loyalty — and with it their entire value creation model. For decades, banks treated loyalty as a product-driven outcome: • Measured by cross-sell ratios and account counts, under the unchallenged assumption that product quantity equals loyalty depth. • What no one asked was what lay beneath — how often those products were used, how central the bank truly was to a customer’s financial life, and how much value each relationship created. 𝗔𝗜 𝗶𝘀 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗶𝗻𝘀𝗶𝗱𝗲 𝗼𝘂𝘁: • It amplifies a shift already underway — from products to experiences — but one banks had mostly failed to embrace. • It turns customer relationships into continuous engagement. AI allows banks to move beyond static account ownership toward an ongoing, data-driven dialogue — where every interaction updates understanding and deepens relevance over time. • Growth no longer runs on campaigns but on intelligence. AI turns static marketing into a self-learning loop — where goals, propositions, and interactions evolve in real time as every customer action feeds new insight and response. 𝗕𝘂𝘁 𝗵𝗼𝘄 𝗰𝗮𝗻 𝗯𝗮𝗻𝗸𝘀 𝗺𝗮𝗸𝗲 𝘁𝗵𝗶𝘀 𝘀𝗵𝗶𝗳𝘁? As loyalty becomes dynamic, banks need platforms that connect intelligence and experience in real time — systems that don’t just analyse behaviour but act instantly. One of the best examples of how to actually do this is what Backbase showcased at its recent Engage Europe flagship event in London. Two things stand out: 𝗙𝗿𝗼𝗺 𝗳𝘂𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁: • Unified growth loop: Backbase connects data and engagement across acquisition, activation, expansion, and retention — making every interaction feed the next. Example: a new card activation triggers a personalised onboarding journey. • Data-driven expansion: AI identifies when customers are ready for deeper engagement — such as suggesting a savings goal when deposits rise. • Predictive retention: Early signs of inactivity, like fewer app logins, prompt timely action before churn. • Actionable visibility: A single dashboard ties engagement to ROI, revealing which actions truly drive lifetime value. 𝗙𝗿𝗼𝗺 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀: • Backbase’s AI Orchestrator links customer data, product logic, and engagement channels into one adaptive system that learns and responds in real time. • When a customer’s spending shifts — for example, higher grocery and fuel payments and a consistently high balance before payday — the Orchestrator detects and responds instantly, prompting a personalised savings plan and cashback card offer, delivered seamlessly in-app. • Over time, engagement deepens, deposits and card usage rise, and churn risk falls — showing that with the right systems, loyalty becomes measurable and manageable. Opinions: my own, Graphic source: Backbase   𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

The initial gold rush of building AI applications is rapidly maturing into a structured engineering discipline. While early prototypes could be built with a simple API wrapper, production-grade AI requires a sophisticated, resilient, and scalable architecture. Here is an analysis of the core components: 𝟭. 𝗧𝗵𝗲 𝗡𝗲𝘄 "𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗖𝗼𝗿𝗲": The Brain, Nervous System, and Memory At the heart of this stack lies a trinity of components that differentiate AI applications from traditional software:  • Model Layer (The Brain): This is the engine of reasoning and generation (OpenAI, Llama, Claude). The choice here dictates the application's core capabilities, cost, and performance.  • Orchestration & Agents (The Nervous System): Frameworks like LangChain, CrewAI, and Semantic Kernel are not just "glue code." They are the operational logic layer that translates user intent into complex, multi-step workflows, tool usage, and function calls. This is where you bestow agency upon the LLM.  • Vector Databases (The Memory): Serving as the AI's long-term memory, vector databases (Pinecone, Weaviate, Chroma) are critical for implementing effective Retrieval-Augmented Generation (RAG). They enable the model to access and reason over proprietary, real-time data, mitigating hallucinations and providing contextually rich responses. 𝟮. 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗚𝗿𝗮𝗱𝗲 𝗦𝗰𝗮𝗳𝗳𝗼𝗹𝗱𝗶𝗻𝗴: Scalability and Reliability The intelligence core cannot operate in a vacuum. It is supported by established software engineering best practices that ensure the application is robust, scalable, and user-friendly:  • Frontend & Backend: These familiar layers (React, FastAPI, Spring Boot) remain the backbone of user interaction and business logic. The key challenge is designing seamless UIs for non-deterministic outputs and architecting backends that can handle asynchronous, long-running agent tasks.  • Cloud & CI/CD: The principles of DevOps are more critical than ever. Infrastructure-as-Code (Terraform), containerization (Kubernetes), and automated pipelines (GitHub Actions) are essential for managing the complexity of these multi-component systems and ensuring reproducible deployments. 𝟯. 𝗧𝗵𝗲 𝗟𝗮𝘀𝘁 𝗠𝗶𝗹𝗲: Governance, Safety, and Data Integrity. The most mature AI teams are now focusing heavily on this operational frontier:  • Monitoring & Guardrails: In a world of non-deterministic models, you cannot simply monitor for HTTP 500 errors. Tools like Guardrails AI, Trulens, and Llamaguard are emerging to evaluate output quality, prevent prompt injections, enforce brand safety, and control runaway operational costs.  • Data Infrastructure: The performance of any RAG system is contingent on the quality of the data it retrieves. Robust data pipelines (Airflow, Spark, Prefect) are crucial for ingesting, cleaning, chunking, and embedding massive volumes of unstructured data into the vector databases that feed the models.

🚀 I Stopped Designing Alone. I Started Designing With AI. And honestly? It changed my entire UX process. Over the past few months, I’ve been integrating AI Figma plugins directly into my real-world client projects,not as shortcuts, but as thinking partners. Here’s how I actually use them in real projects 👇 1. UX Pilot: My Rapid Prototyping Engine When I receive a PRD or rough client requirements, I don’t jump straight into polished UI. I prompt UX Pilot to: • Generate quick wireframes • Create possible user flows • Explore multiple layout structures This helps me validate direction in hours instead of days. I never ship AI output directly, I refine it with business logic and user behavior insights. 2. Clueify: My Pre-User-Test Check Before showing designs to stakeholders, I run an AI usability audit. It helps me analyze: • Visual hierarchy • CTA focus • Cognitive overload • Attention flow It’s like doing a “silent usability test” before real users ever see it. 3. Stark: Accessibility Is Not Optional Real-world products serve real people. I use Stark to: • Check contrast ratios • Simulate visual impairments • Ensure WCAG compliance Accessibility isn’t a feature. It’s responsibility. 4. Octopus.do: I Structure Before Screens In large projects (especially SaaS dashboards), structure matters more than UI. Before designing anything, I: • Map the entire sitemap • Validate navigation depth • Align user journeys Because messy structure = messy experience. 5. Magician: Fast Ideation Mode When brainstorming: • Placeholder content • Icon ideas • Micro-interactions • Empty states Magician speeds up exploration so I can focus on strategy. 6. MagiCopy: UX Writing That Converts Good UI means nothing without clear communication. I use it to: • Generate button variations • Test tone (friendly vs professional) • Improve clarity Then I humanize it with brand voice. 7. Uizard: From Sketch to Prototype Sometimes clients send hand-drawn ideas. Instead of rebuilding from scratch: I convert sketches → editable wireframes → interactive prototypes. Faster iteration. Faster validation. 💡 My Personal Approach AI doesn’t replace UX thinking. It accelerates it. In real projects, I follow this rule: - AI for speed. - Human for strategy. - Users for validation. The result? • Faster delivery • Better alignment with stakeholders • More time spent on problem-solving • Less time on repetitive tasks And most importantly, better user experiences. If you’re a designer still afraid AI will replace you… It won’t. But designers who use AI effectively? They will replace those who don’t. Let’s build smarter. 💜 Whats your way of design? Comment below👇 UX Pilot AI Clueify #UXDesign #UIDesign #Figma #AIinDesign #ProductDesign #UXResearch #DesignProcess #Accessibility #SaaSDesign #UserExperience #DesignThinking #Prototyping #UXWriting #FutureOfDesign #designtools #uiux

There's one use case for AI agents not being talked about enough: volatile or seasonal industries. Think about what crypto, fintech, travel, and even retail have in common. Their surges in volume (some random, some not) and customer inquiries make it extremely challenging for traditional CX systems to keep up. But where legacy systems struggle, AI systems step up. Here's how: 1. Scalability When inquiry volumes spike, AI agents can handle the influx without missing a beat. There are no delays from hiring surplus human agents to handle more volume, making AI agents both cost- and process-efficient. 2. Consistency Whether it's 1K or 1M customer inquiries, AI agents guarantee the same level of accuracy and precision every time. Humans need downtime, AI doesn't. 3. Prioritization Customer inquiries come with varying degrees of complexity. While AI agents take care of the low-hanging fruit and repeatable tasks, human agents can focus on the high-touch cases that demand personal attention. Take Coinbase’s customer support, for example. They handle $226B in quarterly trading volume in 100+ countries. Their margin of error is slim, and CX mistakes could cost billions. Instead of leaning on human CX alone, they use AI agents to: • Handle thousands of messages per hour • Reduced customer service handling time • Improve search relevance for their help center The enterprises we work with at Decagon experience the same benefits using AI customer service agents—scalable support, no gaps in performance, and higher customer satisfaction. Just because your industry is volatile doesn't mean your CX should be.

Exciting AI + accessibility news for the blind community! Be My Eyes has partnered with OpenAI/ChatGPT to create a groundbreaking accessibility tool that uses AI. Users can point their phone at the scenery in front of them, and the phone will provide a visual description and speak back to them in real time for tasks such as hailing down a taxi, reading a menu, or describing a monument. This could be a gamechanger for many blind people, enhancing independence and making the world more accessible for them. As a deafblind woman, it excites me to see a new accessibility tool emerging. This innovation holds great promise, and I’m eager to witness how it empowers the blind community by offering real-time descriptions of their surroundings. Imagine the freedom and confidence this could instill in daily life for blind people, from navigating new places to simply enjoying the beauty of nature. However, blindness varies widely, so this tool might be more suitable for some people than for others. For example, there are still limitations for the deafblind community. As blindness is a spectrum, many blind people still have remaining vision. If they're deafblind like me, they need captions to have full access when receiving auditory information. I'm curious about what blind users will think of the tool once they start to adopt it. While this is a fantastic advancement, there’s always need for continued improvements and iteration. I also care deeply about preventing the harmful impacts of AI so I hope that this is also being thought about. Accessibility technology is crucial for the disability community. It not only enhances our ability to engage with the world but also promotes independence and equity. What are your thoughts on this new development? P.S. Here’s a cool video on it: https://lnkd.in/etfHehCh #Accessibility #AI #DisabilityInclusion

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFjXBrPe

“Let me explain the issue again…I was saying…” Does this sound familiar? We’ve all been there: stuck on the phone or chat, explaining the same problem to a new support agent for the third, fourth, or fifth time, feeling unheard. But customer service isn’t just about solving problems. It’s about making people feel heard. Yet, far too often, support interactions feel robotic, cold, and disconnected. You’re bounced between departments. Asked to repeat yourself again and again. Given a ticket number instead of a real solution. And the worst part? No one seems to remember your last conversation. This isn’t just inefficient; it’s deeply frustrating and exhausting, and it shows a lack of empathy. Customer service must go beyond transactions. It should tap into attentive empathy, truly listening to customers, acknowledging their frustrations and cognitive empathy, and offering relevant solutions based on past interactions and emotional context. So how do we do that at scale? OpenAI’s latest update is a step in that direction. ChatGPT can now remember past conversations across sessions. This simple upgrade unlocks a smarter, more empathetic future for customer service. Imagine this: • Your support agent already knows what you’ve been through • They pick up right where you left off • They tailor responses to your preferences and pain points This is what modern, emotionally intelligent service should feel like. And the data speaks volumes: 🔹 76% of customers say repeating themselves is their #1 frustration 🔹 81% prefer brands that personalize the experience With AI memory in play, customer service teams can now: • Offer personalized support journeys • Reduce friction in every interaction • Proactively engage based on past pain points • Build long-term trust through seamless continuity For businesses, this means: • Smarter, AI-powered systems that improve with every touchpoint • Consistent journeys that feel human even when powered by machines • Stronger retention through empathy-led engagement If you’re a forward-thinking company, here’s what to do: • Invest in AI tools with conversational memory • Redesign support flows to feel continuous, not fragmented • Train agents to collaborate with AI as empathy amplifiers • Prioritize data transparency and privacy to build lasting trust Because when customers feel understood, they don’t just stay, they advocate. #AI #ChatGPT #customerexperience #CX #KSA #SaudiArabia

Giving users clear insight into how AI systems think is a smart business strategy that builds loyalty, reduces friction, and keeps people from feeling like they’re at the mercy of a mysterious black box. Explainable AI (XAI) enhances the transparency of AI decision-making, which is vital for customer trust—especially in sectors like finance or healthcare, where stakes are high. Tools like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) break down complex algorithms into interpretable outputs, helping users understand not just the “what” but the “why” behind decisions. Interactive dashboards translate this data into visual forms that are easier to digest, while personalized explanations align AI insights with individual user needs, reducing confusion and resistance. This approach supports more responsible deployment of AI and encourages wider adoption across industries. #AI #ExplainableAI #XAI #ArtificialIntelligence #DigitalTransformation #EthicalAI