How AI Transforms Product Discovery

Great conversation with Tracy Poulliot at the Walmart Growth Forum, where one theme came through clearly: AI is fundamentally changing how customers discover products.   We’re moving from keywords search to highly personalized, intent-driven, multimodal conversational discovery, where systems interpret, curate, and recommend in real time. For sellers, that shift raises the bar. Success in this new model isn’t just about assortment or price; it’s about data quality and depth.   Rich, structured, and accurate product content is quickly becoming the foundation for visibility in AI-driven experiences. If your data isn’t complete, contextual, and trustworthy, it simply won’t surface the way you expect. Sellers who invest in better content, stronger signals, and continuous optimization will be best positioned to win in this environment.

Rufus is an AI designed to revolutionize product discovery through natural language understanding, inference, and multimedia optimization. Here's how it works and how sellers can use it to boost their sales. Rufus changes the rules of product discovery by focusing on context, not just keywords. Instead of matching queries like "desk lamp" to products with the same exact words, Rufus identifies noun phrases and their relationships. For example: 1. A shopper asks: "What lamp is best for reading in bed?" 2. Rufus identifies key phrases like “reading lamp” and “bedside.” 3. It ranks products semantically, recommending items with phrases like “adjustable bedside reading lamp with eye-friendly light.” This ensures shoppers see relevant, high-quality products tailored to their needs. Key Features  1. Noun Phrase Optimization (NPO): Rufus focuses on detailed, descriptive phrases. Sellers should build product titles and descriptions differently: ▪️ Instead of: "Table Lamp" ▪️ Use: "Vintage Brass Table Lamp with Adjustable Arm for Home Office." 2. Visual Label Tagging (VLT): Rufus reads images as well as text. Adding overlays like “Energy Efficient | 6 Brightness Levels” directly on product images can increase discoverability. 3. Semantic Understanding: Rufus connects implied customer needs to product benefits. For example, it knows “easy-to-clean” is relevant for a query like “pet-friendly couch.” 4. Q&A Enhancement: Rufus thrives on clear answers to common customer questions. Example: Q: “Does it fit a queen-size mattress?” A: “Yes, our bed frame is designed for all queen-size mattresses up to 12 inches thick.” 5. Inference Optimization: Rufus maps product features to inferred benefits. A product labeled “durable non-stick pan” might also be shown for “easy-to-clean cookware.” Steps Sellers Need to Take 1. Optimize Product Titles with Rich Noun Phrases ▪️ Use descriptors like material, design, and purpose. Example: “Professional Chef Knife Set with German Steel Blades”. 2. Enhance Images with Text ▪️ Include labels like “Anti-Fog Coating | Shatterproof Design” directly on images. ▪️ Ensure images demonstrate key features clearly 3. Leverage FAQs ▪️ Anticipate shopper questions and weave them into your listings. Example: Q: “How do I clean this air fryer?” A: “Wipe with a damp cloth or place removable parts in the dishwasher.” 4. Use Semantic Context in Descriptions ▪️ Avoid keyword stuffing; write naturally. Example: “This ergonomic office chair supports your back during long hours at your desk, making it perfect for work-from-home setups.” 5. Update Content Regularly ▪️ Monitor trends in customer queries and adapt your listings accordingly. If shoppers search for “eco-friendly packaging,” ensure your products highlight those features. 6. Incorporate Click Training Data Insights ▪️ Analyze which features customers click on most and highlight them in your product content. Amazon’s Rufus thrives on detailed, customer-centric content.

Consumers are changing the way they discover beauty products, and AI is at the centre of this shift. Large language models (LLMs) like ChatGPT, Google Gemini, and Microsoft Copilot are turning hours of online research into seconds 🤖💡 Beauty enthusiasts and influencers are now asking these platforms highly specific prompts to generate personalised skincare routines, complete with product recommendations and detailed usage instructions 🧴✨ The impact on consumer behaviour is striking. People are beginning to think of LLMs not just as tools, but as trusted advisors and expert guides 🧠. This hyper-personalised, on-demand discovery is reshaping the decision-making journey, creating a world where brand visibility depends on being cited by AI, not just ranking on Google. Brands are taking notice. Shopify, which has partnered with ChatGPT, Gemini, and Copilot, has reported 15-fold growth in AI-driven searches 🚀, prompting companies to invest heavily in Generative Engine Optimisation (GEO). This includes: 📄 Expanding blog content to capture long-tail prompts 🏆 Pitching for inclusion in “best of” product lists 🔬 Structuring ingredient lists and website content so AI can read and verify claims backed by clinical trials 👀 Analysing prompt volume to understand exactly what consumers are asking, giving brands a direct window into demand Brands must start thinking like AI, asking: How can my content be interpreted and recommended by LLMs? How do I appear in the AI-generated routines and recommendations consumers are now trusting? The brands seeing the biggest gains are those who combine authority, transparency, and AI-readiness. By ensuring that their content is structured, credible, and tailored for LLMs, these companies are effectively surfing the wave of AI-driven discovery 🌊 Source: The Business of Fashion

Skating to where the puck is going has always been tough for brands & retailers. Humans are messy & we change our minds often.  Now, with the surge of AI tools like ChatGPT & Perplexity, building a consumer-obsessed business has become more challenging. In 2025, nearly 40% of consumers (and the majority of #GenZ) let AI agents decide what products they see and buy, flipping the script on traditional brand-owned shopping channels. Are you ready for the age when your customer belongs to the agent, not your website? We’ve entered the era of infinite channels and the always-on shopper. Commerce is being driven by a moshpit of AI agents that mediate many customer journeys—from product search to purchase—largely outside brands’ direct control. Most retailers are still building for the past, optimizing search tools that only matter when shoppers find their way to your digital properties (.com, app, etc.). But that traffic is evaporating. The old model is dying fast, replaced by a new reality where AI agents decide what customers see, buy, and from whom. This is your wake-up call. AI is rapidly rewriting the rules of commerce. Traditional traffic pipelines have dried up. “Traffic” today is made up of both humans and agents, flowing through an infinite number of channels. If your products aren’t ready to be discovered, chosen, and purchased by AI agents—are you even trying? 😁 For Brands and Retailers: ➡️ Owning the customer journey is tougher than ever. Your fight for site clicks is obsolete. Products must now be surfaced in AI-powered results and agentic checkout experiences on 3rd-party platforms. ➡️ SEO is losing relevance. Optimize for AI discovery: create solution-focused and rich product data (not just keywords) optimized for AI agents, not just search engines. ➡️ Personalization is table stakes. AI agents understand customer context and needs better than ever, offering hyper-targeted product suggestions and streamlining the shopping experience more effectively than traditional tools. Brands that adapt to AI-driven shopping will see higher conversion rates & be positioned to capture this increase in sales volume. Data Points: 1️⃣ 39% of shoppers (and over 50% of #GenZ) already use AI agents (like ChatGPT, Perplexity, Amazon’s Buy for Me, Google AI Mode) for product discovery. (🙏🏼 Salesforce)  2️⃣ Nearly 3 in 5 consumers have replaced traditional search engines with gen AI for product recommendations, led by Millennials & Gen Z. (🙏🏼 Capgemini) The race is on to build the future! I’m thrilled to see Cimulate AI led by my buddy John Andrews, Profound led by my future buddies Dylan Babbs & James Cadwallader, & Scot Wingo led by ReFiBuy.ai tackling this head on. For my brand & retailer community, talk to me: Are your products ready to be chosen by the customer of the future - a moshpit of AI agents?

By July 2025, traffic from generative AI to retail sites in the U.S. surged 4,700%. Consumers landing from ChatGPT, Claude, and Perplexity links are:   ➡️ Spending 32% more time on site ➡️ Viewing 10% more pages ➡️ Bouncing 27% less often   And Adobe’s latest analysis is clear: AI isn’t just shaping how products are described. It’s shaping where shoppers go, what they trust, and how they decide.   We’re not in a “future of shopping” moment. We’re in a present where AI is becoming a distribution channel.   These are snapshots of how discovery, trust, and conversion are being redefined.   💡 The AI shopper isn’t searching the way we think. They’re not typing keywords and browsing pages of results. They’re asking, and trusting, an answer.   But that means sellers need to rethink how they structure their catalogs and content:   🔹 AI is not SEO 2.0 It doesn’t rank results, it interprets and recommends. Your data isn’t about keyword stuffing, it’s about credibility and clarity when summarized by a model.   🔹 Trust is compressed Instead of spreading traffic across ten blue links, AI concentrates attention on one or two recommendations. Being the “chosen” option is the new top of search.   🔹 Catalog = Language Listings are no longer just for human eyes. They’re a language that models parse to decide what to highlight or ignore. If your attributes aren’t structured, you’re invisible.   🔹 The invisible margin In AI-driven answers, the model might push a multipack, bundle, or alternative configuration. If your catalog isn’t built to support those, you hand that margin to someone else.   🔹 The cultural flip Shopping used to begin with an intentional search. Now, AI starts the search for the shopper. Discovery is becoming mediated, and the assistant is often the first filter between you and the buyer.   The world doesn’t just need better ad campaigns. It needs sellers who understand how AI “reads” a listing and how that interpretation changes sales velocity and margins.   That’s the strategic gap: sellers optimizing only for Amazon search will miss the parallel channel AI is building across retail.   The takeaway: Generative AI isn’t just a productivity tool. It’s fast becoming a demand driver. If your catalog isn’t ready to be “read” by AI, you’ll be invisible in the very places shaping the next wave of retail traffic.   #AmazonSellers #AmazonAI #GenerativeAI #MarketplaceGrowth

Your customers stopped typing keywords years ago. They're having conversations with machines now. And your product pages are still written for robots from 2019. I see this disconnect everywhere in e-commerce. Sellers optimize for search algorithms that barely exist anymore. Meanwhile, shoppers ask AI assistants natural questions like real humans. "Find me a water bottle that keeps drinks cold for camping trips." Not "insulated stainless steel 32oz BPA-free bottle." This changes everything about product optimization. 1. Write for conversations, not keyword searches. Your product descriptions should answer the questions people actually ask. Not the phrases they used to type into search boxes. AI tools scan your content looking for clear, helpful information. They reward pages that communicate naturally. 2. Test your content with the same tools your customers use. Take your product images and descriptions to any AI chat platform. Ask it to explain what your product does and who it's for. If the response doesn't match your target customer perfectly, you found the problem. 3. Answer questions before they're asked. People want to know compatibility, durability, and real-world performance. Build this information directly into your content. Don't make them hunt through bullet points or reviews. 4. Focus on specific customer segments over broad appeal. Generic product pages used to work when algorithms were simple. Now AI tools excel at matching specific needs with specific solutions. The more targeted your messaging, the better you'll perform. 5. Make every element work together. Your title, images, descriptions, and features should tell one cohesive story. AI scans everything looking for consistency and clarity. Mixed messages confuse both algorithms and customers. 6. Optimize for cross-platform discovery. Customers might find your product through ChatGPT, Perplexity, or voice assistants. Then click directly to your Amazon page. Your listing needs to convert visitors who never saw search results. The biggest shift in e-commerce optimization isn't technical. It's philosophical. Stop thinking like a search engine and start thinking like a helpful sales assistant. Because that's exactly what AI has become for your customers. At GigaBrands.ai, we help Amazon brands optimize for AI-driven discovery and natural language search patterns. Ready to future-proof your product pages for the conversation economy? Book a strategy call from the link in my bio. P.S. The brands that adapt to conversational commerce first will dominate the next decade of e-commerce.

🚨 ChatGPT didn’t launch a feature, it quietly rewrote product discovery Everyone saw “Shopping Research.” Few realized what actually changed. ChatGPT no longer answers shopper questions. It evaluates products (like a mini-CMO, data analyst, and category expert fused into one). And the criteria it uses go far beyond keywords: → Structured product attributes (ingredients, claims, certifications, specs) → Comparative reasoning (cost per use, value, performance trade-offs) → Expert & scientific references → Real-world performance signals (verified reviews, sentiment patterns, defect patterns) → Availability, shipping, returns, stock status → Brand authority & trust signals → Safety constraints & compliance indicators This isn’t “search” anymore. This is AI-powered decision architecture (and most brands are not even remotely prepared). What many teams still get wrong: ❌ If your product data isn’t structured, the model can’t use it. ❌ If your authority signals are weak, the model won’t select you. ❌ If your content isn’t unified and consistent, it won’t trust you. ❌ If the model doesn’t recognize your brand, no ad budget can buy visibility. Your competition is no longer winning shelf space or search placement. They’re winning inside the model’s ranking logic (a logic no marketplace publicly explains). At HatchEcom, we’ve spent years studying this shift. We reverse-engineered ChatGPT’s Shopping Research behavior and mapped the signals the model uses: → Constraints → Product metadata → Performance indicators → Review-derived evidence → Safety weighting → Brand authority → Multi-product comparative scoring We paired that with the backend logic we’ve been developing since 2022 to understand how AI surfaces products across Amazon, Shopify, Walmart, and now LLM-driven ecosystems. Growth planning for 2026 must include this reality. AI will increasingly decide which products matter (not search engines, not ads, not shelf space). If your brand doesn’t align to the model’s logic, you’re invisible. If you want the breakdown, I’m happy to share the full document. Just comment “RELEVANCY” and I’ll send it over. G #growthstrategy #aivisibility #openai #chatgptshopping #ecommercegrowth #retailinnovation #aicommerce #productdata #digitalstrategy #llmoptimization

Clicks are no longer the competition. Inclusion in AI-generated answers is. During a recent test with a generative search tool for a niche B2B service, the output was simple. A clean summary. A few companies mentioned. None of them were top-ranked in traditional search, and one had no visible paid presence. What stood out was not visibility, but clarity. This reflects a broader shift. AI now shapes discovery before a buyer ever reaches a website or speaks to sales. It interprets, filters, and presents brands based on how well their information is structured and understood. If that structure is weak, the brand does not just rank lower. It gets excluded. Publishing more content does not solve this. Structured clarity does. This week’s newsletter explores why semantic consistency, knowledge frameworks, and disciplined metadata are becoming real advantages. It also unpacks why volume without cohesion is starting to work against teams, not for them. For teams responsible for growth, brand, or go-to-market strategy, this shift is already in motion. The full piece dives deeper: AI Systems Mediate All Discovery.

Amazon just launched "Interests" - their new AI-powered shopping feature. But what does it really mean for brands and sellers? While some are predicting doomsday scenarios for SEO and traditional product discovery, let's take a breath and focus on what really matters: 🎯 This is evolution, not revolution. Search behavior has been changing for years - this is just the next step. Here's what smart brands should focus on: 1. Create authentic, problem-solution focused listings Your products need to speak naturally to customer needs - not just stuff keywords. This has ALWAYS been best practice. 2. Embrace user-generated content Reviews and customer feedback aren't just social proof - they're valuable signals about how real people use and value your products. 3. Build emotional connections Your brand needs to stand for something. AI can match features, but it can't create brand loyalty. 💡 Key Takeaway: Instead of chasing algorithm changes, focus on timeless principles: Clear communication, authentic customer connections, and strong brand building. The tools for discovery may change, but great products that solve real problems will always find their audience. Read the official release notes here: https://lnkd.in/gxCnUg_C What are your thoughts on this new feature? How is your brand adapting to AI-driven discovery?