Generative engine optimization (GEO), sometimes called LLM engine optimization (LEO), is re-imagining how businesses get discovered in 2026. Consequently, businesses are reviewing their strategies and tactics to show up online where their clients go to look for things. So, are we seeing a seismic shift that will abolish SEO?
Part 1: Language Changes
The most basic concept in this conversation about GEO and SEO in e-commerce is a query, that is, a laconic request asked on Amazon or Google. Now, when shoppers look up ‘sneakers’ or ‘beer glasses’ (guess which one is my query?) The underlying assumption is that the search engine will ‘get it, i.e., define their intent.
As of 2025, search queries are somewhere at 3.4 words, according to Semrush reports. Likewise, a 2024 study by Sparktoro supports this 3-word search tendency and even talks about short-query culture. The phenomenon describes how people, having adapted to the internet search, use ‘keywordese’ phrasing to get their intent across. The mindset for googling is ‘brand + category + modifier’.
People used to be very economical with the keywords they provided. And Google was truly great at finding answers! Now that AI is taking over, the pattern is changing — people speak differently to the internet.
The 2025 AI Index Report by HAI covers the greatest achievements of AI and how the world adapts to it. Among its other curiosities, the report explains that developers encourage people to speak natural language to AI because there is a deliberate shift towards language as the primary, unified interface for many tasks. The broader change is moving from narrow, tool-specific UIs toward general-purpose systems that can understand, reason, and act across media from a single conversational data point.
Part 2: Business Impact
With that in mind, look at Amazon Rufus or Walmart Sparky. These e-com platforms incorporate AI straight into their shopping apps and search bars. Shopping is now made more intuitive and goal-oriented and natural language queries help deliver contextual, highly personalized product recommendations.
| Feature | Amazon Rufus | Walmart Sparky |
|—-|—-|—-|
| Launch & Access | 2024; integrated in main search bar (web/app), chat interface, microphone/voice input. | Mid-2025; app button (smiley face), expanding to web; voice/image inputs planned. |
| NLP Processing | Retrieval-augmented generation (RAG) + catalog/reviews; interprets activities, events, prices, handwritten lists to cart. | Retail LLMs for intent/context (location/history); synthesizes reviews, weather/event-based recs. |
| Query Examples | “Laptop for uni under £600?”, photo uploads → filtered comparisons + “Buy for Me”. | “Best laptop for art student” → carousels with pros/cons; multi-product how-tos. |
| Key Strengths | Personalization from cross-Amazon data (Kindle/Video); agentic (auto-cart, price alerts). | Goal-driven (occasions/weather); comparisons/review distillation; in-store tie-ins via Marty. |
| Follow-ups/Actions | Chat refinements; memory of user prefs; auto-buy options. | Real-time refinements; policy-bound shopping focus. |
The reason e-commerce titans pour billions into AI-powered search and AI-augmented shopping experiences is to overcome stagnant conversion rates from traditional keyword search where short queries lead to vague results and cart abandonment.
Now, Amazon and Walmart, although relying on traffic, have already built a reputation in the world of online-shopping: “95% of participants identified Amazon as their primary online shopping destination this year, reflecting a 5% increase from 2024”, Reuters reports. I imagine that even without that Amazon Rufus thing, I’d still be checking the platform out when shopping for something. How should small online marketplaces and even individual e-commerce brands adapt to GEO — and should they at all?
As ModernRetail writes, there is every reason for 2026 to be the year AI checkouts grab their firm share of $20.9 billion in sales, which roughly translates to 1.5% of US e-com). As much as 83% of shoppers used AI to do their holiday shopping, going in for buys straight from AI interfaces. ChatGPT has been most active in inking checkout deals with platforms like Etsy, Target and Instacart. Likewise, Perplexity kicked off its partnership with PayPal to enable the actual transactions within its chat interface.
The limitations, of course, are there for AI-powered checkouts.
- Shopify’s Instant Checkout, while available to SMBs as well as ‘big’ brands, prioritizes complete rich catalogs for recommendations, putting under-optimized small stores at a disadvantage.
- Target’s AI-powered shopping is built with Google Gemini’s Universal Commerce Protocol (UCP) where buyers can complete transactions directly in Gemini or Search AI Mode. However, Target is not a marketplace like Amazon and it has a centralized control over all of its SKUs.
So where do small brands compete with larger e-com fish? Out of all AI models and platforms, Google Gemini seems to be the most democratic for its players. While Perplexity and ChatGPT ‘rent out’ its AI powers to online shopping engines and platforms, Google’s Search AI Mode is available to all sellers, as long as their content ranks well in traditional Google Search.
Google’s AI Mode works through what they call a ‘fan-out’ technique. Essentially, when a shopper types in a question, Google doesn’t just run a single search — it breaks that query down into subtopics and searches multiple angles at once, digging deeper across the web than traditional search would. The beauty of this approach? It’s open to everyone. Unlike Amazon Rufus or Walmart Sparky, which only work with their own curated catalogs, Google’s AI Mode doesn’t care if you’re a massive retailer or a small Woocommerce store selling handmade candles. If your content ranks well in traditional Google Search and is structured clearly enough for AI systems to understand it, you can show up in AI-generated answers. So for small e-commerce brands, this is genuinely democratic. Your visibility isn’t gated by advertising budgets or exclusive partnerships. It is instead determined by whether your content is good, clear, and easy for AI to find.
Andrew Weston, the founder of Appear Online, breaks down what actually works for getting stores into AI search results. The game has shifted. It’s no longer about ranking first in Google — it’s about being cited by AI systems. And here’s what that means: your content needs to be structured in a way that AI can actually understand and pull from. Think clear headings, short paragraphs and relevant verified information. The messier your page is, the harder it is for AI to summarize it accurately. So what does this look like in practice? A furniture store that writes detailed product descriptions — measurements, materials, care instructions, customer reviews, context for how you’d use the piece — will beat out a competitor with thin, keyword-stuffed listings. Same goes for how-to content with step-by-step logic, comparison guides that break down trade-offs, or expert takes that build credibility. Basically, if your content is scannable, organized, and makes sense to a human reader, it’ll make sense to AI systems too. And that’s your edge over bigger competitors who might have built their sites around old SEO rules.
Part 3. What are your next steps?
Where do you start your GEO game to compete at scale? I really loved these guides by Charle, Pimberly, and BigCommerce. I’ve read all three of them and crushed them into a summary here, although I strongly recommend you read each of them separately.
The greatest relief is that SEO is not dying — it’s getting augmented by GEO. No need to rebuild your entire site overnight!
Step 1. Audit and fix your (hopefully) structured data
All three guides are adamant: AI engines are hungry for structure. BigCommerce breaks it down into specific schema types you need to prioritize: Product, Offer, AggregateRating, FAQPage, and ImageObject schemas. They’re the language AI speaks when deciding whether to cite your products.
Charle explains the importance: schema markup reduces ambiguity. When AI tries to figure out if your “waterproof hiking jacket” is actually waterproof or just water-resistant, clear schema markup gives it confidence. And confidence equals citations.
Step 2. Rewrite your product descriptions (yes, again) for conversational AI
Pimberly gets really practical here. Your product descriptions need to sound like how people actually talk to AI. Instead of ‘men’s red shoes’, try ‘lightweight red running shoes for everyday training’. The difference might seem small, but remember — people are asking ChatGPT and Gemini full questions now, not typing keywords.
As search queries get longer and more conversational, your product descriptions need to match that energy. Charle suggests thinking about every product page as a reliable source that AI can confidently reference:
- Clear explanations of what the product is and who it’s for
- Detailed attributes: materials, dimensions, care instructions
- Honest descriptions of how it differs from alternatives
- High-quality images with descriptive alt text
Step 3. Sync and centralize your product data
If your product information is inconsistent across channels, AI won’t trust you. Why? Because AI systems cross-reference information. If your pricing on your website doesn’t match what’s in your Google feed, that creates uncertainty. And when AI is uncertain, it skips you.
Pimberly suggests implementing a Product Information Management (PIM) system. This centralizes everything (product attributes, descriptions, metadata) and ensures it stays consistent everywhere. By the end of 2026, this kind of structured data won’t just be nice for GEO; it’ll be required for Digital Product Passport regulations in some markets.
Step 4. Build content that answers real questions
Don’t publish more content, publish clearer content. AI engines favor sources that answer questions simply and accurately. This means:
- Buying guides to direct decisions
- Product comparison pages to highlight differences
- FAQ sections that address high-intent queries your customers actually ask
- How-to content with scannable sections
Basically, write like you’re helping a friend make a smart purchase — because that’s exactly what AI is trying to do for users.
Step 5. Build citations, not just links
Charle argues that citations matter more than you think. The guide breaks down the difference—citations are references to your brand on external websites (e.g. reviews or industry guides). They don’t always need to be direct links. AI engines use them to confirm you’re real, credible, and consistently described.
High-quality citations beat massive quantities of low-quality links every time. Get mentioned in authoritative publications. Show up on trusted review sites. Maintain accurate information on retailer listings. These signals tell AI: “This brand is legit.”
Step 6. Start small, stay consistent
The beautiful thing about GEO is that even small retailers can compete. Why? Because AI doesn’t care about the size of your ad spend. It cares about the quality and structure of your data. Here’s a quick win for your GEO efforts:
- This week: validate structured data on your top 5 landing pages using Google’s Rich Results Test.
- This month: rewrite product descriptions for your top-selling listings using conversational, AI-friendly language.
- This quarter: audit product feeds for consistency and fill in missing attributes (especially GTINs, Global Trade Item Number).
- Ongoing: monitor how your brand appears in AI responses using tools like ChatGPT, Perplexity or dedicated software like Peec.ai.
Conclusion
So, was my observation about using a different language for Google versus GPT relevant? Absolutely. E-commerce is being reshaped as I’m writing this. But here’s the nuance that matters:
- It’s evolution, not revolution. The AI-powered search transformation is real and measurable, but it’s not replacing SEO overnight. Instead, GEO is augmenting SEO. Brands that understand this layered approach will win; those abandoning SEO altogether will fall behind.
- Your approach needs to change, but gradually. You don’t need to rewrite your entire website next week. Small, consistent improvements to your structured data, product descriptions, and content clarity compound over time. Start with your top 5 products. Fix your schema. Rewrite for conversational search. These aren’t revolutionary moves but smart incremental improvements.
- AI democratizes competition. Unlike the old SEO playbook, which rewarded patience and massive link-building budgets, GEO rewards clean data and trustworthy content. A small Woocommerce store with perfectly structured product information can compete against much larger retailers on Google’s Search AI Mode. Your visibility is determined by quality and clarity.
- Enjoy the ride. Unlike traditional SEO, which required years of groundwork, GEO moves faster. You can see real results from structured data improvements within months, not years. So don’t panic—but don’t procrastinate either. Your next steps are clear. Go get them.
