Integrating Chatbots In Ecommerce

🔮 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

👀 SEPHORA just launched its own app inside ChatGPT. And this is more interesting than it sounds... Starting in the US, customers can now type something like "help me find a foundation for dry skin" and get curated recommendations powered by their Beauty Insider profile, directly inside ChatGPT. No app switch, no search bar. What's really happening here? The interface is moving away from #ecommerce as a destination, toward e-commerce embedded in #conversation. The search box is being replaced by the chat window. And Sephora (historically one of the sharpest digital innovators in #retail) is betting on that shift early. A few things caught my attention in the announcement: ➡️ OpenAI's Head of ChatGPT personally endorsed the move, calling it a new model for beauty discovery ➡️ Sephora's Global CDO explicitly mentioned global expansion as a goal (Europe, watch this space) ➡️ Future updates will allow checkout directly inside the app (!), closing the loop from discovery to purchase without leaving the conversation With 80M+ active Beauty Insider members worldwide, Sephora has the data flywheel to make personalization actually work here. It might be a new distribution strategy. The question for European markets: GDPR constraints on linking loyalty profiles to third-party AI interfaces will make the rollout here more complex. (But not impossible.) Conversational commerce was a buzzword for years. Sephora just made it a product. Worth watching closely, especially if you're in retail, beauty, or building the next generation of customer experience. (And let's see if that future "checkout directly in the app" will really work) 😉

Walmart x ChatGPT Shopping just changed and most CPG manufacturers have not noticed yet. Walmart and OpenAI confirmed a full integration that places Walmart’s entire catalog inside ChatGPT. Consumers can now discover products, plan meals, restock essentials and complete purchases without ever leaving a conversation with an AI. For decades, the battle for the shopper happened at the shelf, then it moved to the search bar, now it is moving to the AI conversation and the brands that show up in a ChatGPT response are not determined by shelf placement or trade spend. They are determined by data, relevance signals and how well a brand’s digital catalog is structured for AI. What makes this alliance interesting is what each side was solving for Walmart needed to close the ecommerce gap with Amazon, which built its own AI assistant Rufus rather than partnering externally. OpenAI needed a commerce engine with real transaction capability and a supply chain that could actually fulfill orders. The result is a new kind of distribution partnership, not retailer and manufacturer. A retailer and AI, co-owning the discovery moment. Why this matters for CPG The shopper journey is being restructured from the top. When someone asks ChatGPT what specific product to buy, the answer will not come from a planogram, will come from an algorithm trained on digital signals most manufacturers are not yet optimizing for. PepsiCo, Nestlé, Kraft Heinz, Mondelēz International, General Mills, Conagra Brands, Danone, Unilever and Kellanova all have massive shelf presence at Walmart. But shelf presence doesn’t automatically go into AI recommendation presence, that’s a different capability. Target has already signed a similar deal with OpenAI. Google launched its own AI shopping experience with Walmart through Gemini. Amazon is building its own closed ecosystem with Rufus. The race to own the AI-mediated shopping moment is accelerating faster than most brand teams are moving. The Better Peer take The manufacturers that win in the next 5 years will not just have great products, they will have great digital product architecture, clean data, rich catalog content, structured ingredient and benefit information that AI systems can parse and recommend. AB InBev, Diageo, Constellation Brands, Keurig Dr Pepper Inc., Hormel Foods, Grupo Bimbo, The Kerry Group, LLC, Associated British Foods plc, Arla Foods, Mars, The Hershey Company, Post Holdings, TreeHouse Foods. and McCormick & Company are all sitting on massive brand portfolios with varying degrees of digital catalog maturity. The gap between the most digitally structured and the least is about to become a competitive advantage that trade spend cannot fix. The question is: if an AI is recommending products to your shopper right now, does your brand show up. And if it does, is it the version you actually want to push?. #CPG #TheBetterPeer #CPGConsulting #StrategicAlliances #RetailStrategy #BrandStrategy #FoodAndBeverage

A Dubai client messaged me at 2 AM their time. "Our chatbot is responding to customers in German. We're an Arabic + English business." This wasn't a bug. This was a ₹1.8 lakh AI chatbot built by an agency. Trained on "advanced NLP." Integrated with "enterprise-grade AI." Promised to "handle customer conversations in 12 languages." Sounded cutting-edge, right? Except: => It couldn't handle simple size queries. => It gave wrong product recommendations. => It confused "exchange" with "refund." And apparently, it decided to learn German on its own. The owner told me: "We're manually checking every response before it goes out. What's the point?" We replaced it with a simple decision-tree bot. No AI. No machine learning. No fancy NLP. Just: => 8 predefined conversation flows => Clear buttons for common questions => Human handoff for anything complex => Works in 2 languages (Arabic + English) Built in 10 days. Results in 60 days: → Handles 73% of queries without human intervention → Zero wrong responses → Zero unexpected language switches → Customer satisfaction actually went UP Here's what I learned: AI is not the answer to everything. Sometimes the "dumb" solution that works beats the "smart" solution that fails. Your customers don't care if you're using GPT-5 or a simple menu. They care if they get the right answer. Fast. Accurate. Consistent. That's it. Stop chasing impressive tech. Start chasing reliable outcomes. Have you ever replaced "smart" tech with something simpler and got better results? #AIAutomation #Chatbots #WhatsAppBusiness #BusinessAutomation #StartupLessons #DubaiBusiness #EcommerceTech #Entrepreneurship #CustomerService #TechSimplicity #SmallBusiness #AITools #n8n #StartupGrowth #WorkflowAutomation

By now, most of us use AI tools daily. As an experience designer, here is my observation: the shift from task-based to intent-based design is fundamentally changing our discipline. The Interface Paradox Look at any conversational AI, ChatGPT, Claude, Grok, Gemini and more. They’re nearly identical. A text input field. A waiting state. An output response. Yet we have clear preferences. We favor one over another. Why? It’s not the visual design. It’s the quality of output. This is the critical insight: in AI-driven experiences, we’re no longer designing for tasks. We’re designing for intent and outcome. The GUI elements between input and output are minimal, almost invisible. What matters is relevance and accuracy. The Responsibility Gap Users rarely acknowledge poor prompts. When results disappoint, they blame the tool. “This AI sucks.” Never “My prompt sucked.” This is human nature, user psychology 101. The user is never wrong, the system always is. Whether deterministic or non-deterministic, we designers must account for this. We build padding around human error and input quality issues because that’s our job. The New Design Imperative Stop obsessing over visual representation. Start obsessing over output quality. In the age of AI, the experience isn’t what users see between input and output. It’s what they get as a result. That’s where differentiation lives. That’s where user experience is won or lost. #ai #design

Retailers shift from Google to AI agents – what this means for FMCG brands A silent shift is underway in digital commerce — and FMCG brands should take note. In August 2025, ChatGPT drove 20% of referral traffic to Walmart and Etsy Shop, with Target at ~15% and eBay at 10%. Just a month earlier, these numbers were significantly lower. While referral traffic is still under 5% of total visits, the growth velocity is clear. Consumers are replacing search with conversation. Instead of using Google, users now ask ChatGPT: - “Which toothpaste is best for sensitive teeth?” - “Top healthy snacks for kids?” - “Why is Swiss Cheese so good and where can I buy it?” - “Best laundry detergent for cold wash?” This behavioral shift matters. AI agents filter and surface product recommendations based on trust, brand recognition, and relevance — not just ad spend. For FMCG producers, the implications are clear: – Visibility is no longer guaranteed by shelf space or SEO. – If your brand isn’t part of AI agents’ product surfaces, you’re invisible. – Retailer data access policies now shape your discoverability. Retailers like Walmart (420 million SKUs) and Target are gaining exposure by remaining open to AI crawlers. Amazon, however, has blocked many bots — causing its ChatGPT-driven traffic to drop 18% in August. This evolving ecosystem affects how FMCG brands are discovered, recommended, and ultimately purchased. And unlike paid search, where placement is auctioned, AI-driven recommendation engines operate in more opaque, model-based hierarchies. Key facts: – 2.5 billion daily ChatGPT prompts – ~50 million daily shopping-related queries – 60% of US shoppers have used genAI for shopping (Omnisend, Aug 2025) As OpenAI and others move toward affiliate fees and embedded checkout, FMCG brands must act now — ensuring their products are correctly indexed, accurately represented, and promoted within retailer ecosystems that are embracing AI traffic. The next shelf is conversational. And it's already stocked. #retail #ecommerce #fmcg #omnichannel #ai #chatgpt #openai #shoppingagents #digitalcommerce #referraltraffic #amazon #walmart #etsy #target #ebay #rufus #retailtech #consumertrends #searchvschat #affiliate #onlineshopping #generativeai #shoppingbots #conversion #usa #northamerica #martech #digitalmarketing #adtech #aiincommerce #futureofshopping #platformeconomy #brandvisibility #fmcgmarketing

ChatGPT eCommerce drop: Part 3 (foundational Q&A) Q: Why should eCommerce leaders pay attention to ChatGPT’s shopping assistant? The way consumers discover and decide what to buy is fundamentally shifting, from keyword search to conversation. If your product content isn’t optimized for AI discovery, you're lagging. Q: How is this different from Google search or traditional marketplace discovery? Old-school search engines return a list of links or paid ads. ChatGPT returns curated, context-rich product suggestions with images, pricing, reviews, and direct buy links. Difference is that AI models understand intent, not just keywords. Instead of “best sneakers,” a user may ask, “What’s a comfortable walking shoe for traveling through Europe in the summer?” ChatGPT understands that nuance and recommends accordingly. Q: What powers ChatGPT’s product recommendations? It’s a mix of structured product data and contextual intent signals. Product metadata (titles, descriptions, tags, inventory) Real-world reviews with specific use cases or outcomes Signals of trust (brand credibility, availability, content quality) Integrations with platforms like Shopify and product feed partners The AI model then uses this data to recommend products that match the why, not just the what. Q: So what changes for brands now that AI is in the shopping flow? Discovery is an earned visibility game. You can’t just outbid, you have to out-relevance. Generic content doesn’t work; rich context wins. Volume of reviews matters less; specificity and clarity matter more. The brands showing up in ChatGPT’s results are the ones with deep, well-structured content and high-context product storytelling. Q: What are the key elements brands should focus on to stay visible in AI-driven shopping? Priorities: 1. Structured Data Implement schema markup across product pages. Use tools like Shopify’s native integrations to feed product info cleanly. 2. Contextual Product Descriptions Who is this for? What does it solve? What makes it different? 3. High-Context Reviews Prompt users to share how and why they used a product. 4. Review Accessibility Make reviews public, crawlable, and visible next to your products. 5. Feed Accuracy Keep product data synced: availability, pricing, variants, and descriptions. Outdated info will kill your ranking in AI. AI models favor reviews that mention specific use cases, emotions, and product outcomes. A single thoughtful review like “Perfect for marathon runners with flat feet” now outranks 50 vague 5-star ratings. I’m excited for this AI eCommerce era. More to come from The Other Group #ai #ecommerce #commerce

Customer service chatbots: most overhyped use case for Gen AI? 🤖 Customer service chatbots are often the first application that comes to mind when people think of #GenAI. After all, what could be better than an AI that understands customer needs and responds helpfully, 24/7? However, as exciting as the promise is, we must be realistic about the challenges involved in developing and operating customer facing chatbots: 1. Fine-tuning a large language model (LLM) and / or leveraging retrieval augmented generation (RAG) requires high-quality, labelled, and organised customer service data. Most companies have yet to assemble such datasets. 📚 2. Serving GenAI chatbots at scale can be costly, especially if conversations aren’t volume restricted and / or limited to specific topics. Without guardrails, customers can use the chatbot for any conversation. 😱 3. LLM security vulnerabilities like prompt injection and model poisoning are major concerns for deploying customer facing chatbots. ☠️ 4. LLMs can produce different outputs for similar prompts. Minimising variability requires human oversight and providing customers with templated prompts, thereby limiting the user experience. 📊 5. Similarly, closed source LLMs change over time, resulting in different outputs for the same prompts. Lack of internal control / governance over such changes makes it hard to anticipate new behaviours. 👽 6. In heavily regulated industries like financial services and healthcare, Gen AI chatbots must walk a fine line between assisting customers and providing financial or health advice, which only certified professionals should give. 👩⚕️ 7. And what if the customer loses out because of a chatbot? Who is accountable - the customer, the company, or the AI provider? This and other questions are yet to be addressed by governments and regulators. In the UK, FCA's Consumer Duty will likely make the company accountable for customer losses caused by AI. 🏛️ Should companies abandon hope of using Gen AI in customer service? Not at all! But the better use cases in 2024 will be low(er) stakes applications like content generation and search, FAQs or virtual assistants, augmenting human agents rather than fully automating customer interactions. What are your experiences implementing Gen AI chatbots? Are you optimistic or pessimistic about Gen AI for customer service? #GenerativeAI #Chatbot #AI #AIforGood Image: Petr Vaclav & Playground v2, “Chatborg”, 2024

Did you hear about Target and Shopping Inside of ChatGPT? AI Platforms Are Becoming Primary Commerce Channels 🦄 We're witnessing the shift from AI-assisted shopping to AI-native commerce, where major retailers are embedding full transactional experiences directly within AI platforms rather than using AI to drive traffic to traditional e-commerce sites. Target announced a ChatGPT integration which follows Walmart's similar OpenAI partnership. This is in addition to Amazon developing in-house AI shopping with Rufus, and Etsy and Shopify integrating with OpenAI's Instant Checkout. This represents a fundamental platform shift. Retailers are racing to establish commerce presence inside AI environments rather than trying to bring customers back to their own digital properties. ‼️ So What: This signals the potential "unbundling" of traditional e-commerce. Instead of browsing websites, consumers may increasingly shop through conversational AI that can access multiple retailers seamlessly. I think that these movements will dis-intermediate traditional e-commerce platforms and websites, similar to how social media changed content discovery. The retailers who establish early AI-native commerce capabilities may capture disproportionate market share, while those who remain website-dependent risk becoming invisible in AI-mediated shopping. 🏇 Do What: Evaluate your commerce strategy through an "AI-first" lens. Don't just ask "how can AI improve our website" instead ask "how do we sell when customers never visit our website?" Consider how your product discovery and purchase processes need to change for conversational rather than visual shopping experiences.

Your customers stopped Googling. You just don't know it yet. This week, something fundamental shifted in how people discover products. ChatGPT launched Shopping Research. Not another chatbot feature. A complete replacement for how consumers research purchases. Here's what actually happened: Instead of opening 12 browser tabs and comparing specs across Reddit threads, review sites, and product pages— People now just ask ChatGPT: "Best running shoes for flat feet under $150." And it does everything: Asks clarifying questions like a smart salesperson. Scans product pages across the internet. Compares specs, prices, reviews, availability. Delivers a personalized buyer's guide in 30 seconds. What used to take 90 minutes of research happens instantly. Why this matters for your brand: Product discovery is moving from search engines to AI. Your customers won't type "best moisturizer for oily skin" into Google. They'll ask ChatGPT. And if your product isn't optimized for AI discovery If your metadata isn't structured If your product descriptions aren't AI-readable You're invisible. This is what I call Prompt Commerce. The shift from search-page rankings to AI shelf space. From SEO optimization to prompt optimization. From competing for Google's algorithm to competing for AI recommendation. The trajectory is clear: Step 1: AI Research (this week) Step 2: Instant Checkout (months away) Step 3: Agentic Commerce (inevitable) Soon: "Buy me the best white linen shirt under $80." AI: "Done. What's your size?" The brands that understand this now will own the next decade. The brands that wait will watch their traffic disappear and won't understand why. This isn't theoretical. It's happening. And most consumer brands aren't ready. Want to understand how AI is reshaping commerce in your category? I break down what these shifts actually mean every week in AI Fashion Briefing. No hype. Just strategic clarity for brand leaders. [Link in comments]