From SEO to GEO: Why your e-commerce business needs this shift now

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AI agents are quietly replacing traditional search. By 2027, experts predict "Google Zero", a world where Google stops sending traffic to websites because AI answers everything directly (1). Meanwhile, agentic AI in retail is growing rapidly with significant investment from major retailers (2), while 41% of Gen Z already uses AI assistants for shopping decisions (3), and 93% of e-commerce companies call AI agents "essential" for competition (3).

The AI agents in ecommerce market is exploding from $3.6 billion in 2024 to an expected $282.6 billion by 2034 (4). This isn't some distant future. ChatGPT Shopping and Google AI Mode are changing how people buy right now (5, 6).

When searching for headphones, you type in "best wireless headphones 2025". Then, based on the results, you gradually become more specific as you understand the differences between brands and the importance of certain features (e.g. anti-drop) as you find results that are increasingly in line with your requirements.

An AI agent makes it easier for you to process more complex requests, because that's how we've learned to know them, and with them we process requests such as: "I need headphones for training that don't fall out, cost less than €200 and have good battery life".

See the difference? With AI agents, we have learned that with a single request we can already solve problems specific to our needs, without having to try and test with an active search like on Google. This completely changes the way we need to structure content, optimise product pages and reach customers.

The new discovery patterns are already here:

• Comparative queries dominate AI recommendations. Instead of visiting multiple sites to compare products, AI agents synthesize everything instantly and present complete comparisons. For example, when someone asks "Which laptop is better for video editing, MacBook Pro or Dell XPS 15?" an AI agent immediately presents a side-by-side analysis covering processing power, graphics capabilities, battery life, and price points from multiple retailer sources;

• Contextual recommendations are getting scary-good. AI agents analyze user context deeper than traditional search ever could, creating hyper-personalized suggestions that make generic "best of" lists obsolete. Consider this: instead of showing generic "best running shoes" an AI agent might recommend specific models after learning you're a beginner runner, weigh 180 pounds, have flat feet, run on pavement, and have a $150 budget;

• Problem-solution matching shifts focus from features to outcomes. Customers describe problems and expect product solutions, not feature lists. When someone says "My back hurts after working at my desk all day" the AI agent suggests ergonomic chairs, standing desk converters, and lumbar support cushions rather than listing technical specifications about office furniture;

• Attribute-based filtering becomes the primary selection method. When customers ask for specific product combinations, AI agents need structured data to filter effectively. For instance, a request like "Show me wireless headphones under $200 with noise cancellation and at least 20-hour battery life" requires the AI to instantly filter inventory based on those exact technical specifications across multiple brands and retailers.

Traditional SEO taught us to think like Google's algorithm.

GEO requires thinking like an AI shopping assistant.

The shift nobody saw coming

Three things that actually work

Here's what most e-commerce sites do wrong:

"Looking for the best wireless earbuds? Our comprehensive guide covers top picks for 2025, including detailed reviews, comparisons, and buying advice to help you make the right choice."

Here's what works:

"Sony WH-1000XM5: Best noise-canceling headphones for travel ($299). 30-hour battery, superior ANC, comfortable for long flights. Beats AirPods Max in comfort, exceeds Bose in battery life."

Notice how the second version immediately tells AI agents what the product is, who it's for, and how it compares. That's the clarity AI agents need to make confident recommendations.

1) Make your content AI-friendly

AI agents want answers fast. Lead with the solution in your first sentence, then support with details

You need schema markup for product specifications with detailed pricing and availability, customer reviews with ratings and testimonials, brand authority markers, frequently asked questions, and usage instructions (7, 8). Think of schema markup as teaching AI agents your product's language. When you do this right, you're making it easy for AI to understand and recommend your products confidently.

2) Use schema markup that AI actually reads

Here's what most businesses miss: AI agents prioritize structured data over regular text when making recommendations (7).

Experience signals include first-hand product testing and real customer testimonials. AI agents can tell when you've actually used products versus copying manufacturer descriptions.

Expertise indicators involve verified credentials in your product category, industry certifications, and technical depth that demonstrates genuine understanding. Authority markers encompass media mentions, thought leadership content, and expert recognition. Trust factors include transparent pricing, clear policies, secure payment processing, and accessible customer service.

3) Build authority AI agents trust

AI algorithms evaluate Experience, Expertise, Authoritativeness, and Trustworthiness more sophisticatedly than traditional search engines (7, 8).

Your action plan

Start here (months 1-3)

First, audit how visible your content is to AI systems right now. Most businesses discover they're essentially invisible to AI agents despite ranking well in traditional search.Focus on your top-performing products first:

1. Add comprehensive schema markup;

2. Restructure descriptions so they answer specific customer problems;

3. Add FAQ sections using natural language questions;

4. Create comparison charts for your main product categories that clearly show competitive advantages.

Expert tip: If you don't know where to start with optimisation to be found by AI, remember that you can get help from... AI 😶‍🌫️

Scale up (months 4-12)

Deploy GEO strategies across all product categories (9, 10, 11). Set up advanced schema markup for inventory, pricing, and availability so AI agents always have current information about what you're selling.

Build content around user intent rather than just keywords. Think about different ways customers describe problems your products solve, then create content addressing each scenario. And then you can start thinking to:

1. Partner with emerging AI shopping platforms. For example, integrate your product catalog with ChatGPT Shopping's API, list your products on Google's AI-powered shopping results, or join Amazon's Rufus recommendations program. Smaller platforms like Perplexity Shopping and Claude's shopping features are also accepting early partners.

2. Optimize for voice search and conversational commerce. Create product descriptions that answer spoken questions like "What's the best coffee maker for small kitchens?" instead of just "Coffee Makers - Kitchen Appliances" Add FAQ sections with natural language like "How long does shipping take?" and "Is this dishwasher safe?" that match how people actually talk to AI assistants.

3. Set up systems that keep AI agents updated with your latest inventory and pricing. Implement automated product feeds through Google Merchant Center, connect your inventory management system to social commerce APIs, or use tools like Shopify's AI integrations that automatically sync stock levels and price changes across platforms where AI agents source product information.

Why speed matters more than ever

First-mover advantage in GEO is pronounced because AI agents learn and reinforce preferences over time (12). Businesses establishing AI visibility now build cumulative authority that becomes increasingly difficult for competitors to overcome.

We're seeing unique opportunity convergence that won't last forever. AI e-commerce market growth hits 54.7% annually (4), underlying technologies like ChatGPT Shopping are mature enough to rely on (5, 6), consumer adoption accelerates with 74% seeing AI improvements (3), and competitive gaps remain bridgeable (12).

This resembles early Google SEO days when businesses investing in optimization early gained lasting advantages. The same dynamic happens now with AI agents, but the window is smaller because technology advances faster.

The bottom line

SEO to GEO transformation isn't distant future planning. OpenAI's ChatGPT Shopping and Google's AI Mode are changing consumer discovery today (5, 6).

Start your audit now. Add schema markup. Restructure key content conversationally. Monitor AI mentions. Keep optimizing as AI systems evolve rapidly.

Businesses acting now establish competitive advantages defining the next e-commerce decade. Those waiting risk permanent catch-up in a first-mover-takes-all game.

AI agents aren't experimental anymore. They're commercial reality reshaping trillion-dollar markets. The question isn't whether to optimize for AI discovery, but whether you can afford not to.

Sources

1. Xponent21 - AI Agents Are Taking Over: Do You Have an AI SEO Strategy?

2. eMarketer - 5 key stats on the rise of agentic AI in retail

3. SellersCommerce - AI In ECommerce Statistics (2025)

4. Scoop Market - AI Agents in eCommerce Market Growth to USD 282.6 n By 2034

5. OpenAI - Search | OpenAI

6. PYMNTS - OpenAI Seeks Piece of ChatGPT-Driven eCommerce Sales

7. CMSWire - How Schema Markup Drives Success in AI-Powered Search

8. Convert - The Complete Guide to Optimizing Your Content For AI Search

9. Salsify - How To Do Generative Engine Optimization (GEO) for Ecommerce

10. Foundation Inc. - What's Generative Engine Optimization (GEO) & How To Do It

11. Search Engine Land - What is generative engine optimization (GEO)?

12. McKinsey & Company -Seizing the agentic AI advantage