From SEO to GEO: Why your e-commerce business needs to make this switch

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AI agents are quietly replacing traditional search. By 2027, experts predict ‘Google Zero’ – a world in which Google stops sending traffic to websites because AI answers everything directly (1). Meanwhile, AI agents in retail are growing rapidly, with significant investment from major retailers (2), whilst 41% of Gen Z already use AI assistants for purchasing decisions (3), and 93% of e-commerce companies describe AI agents as “essential” for competitiveness (3).

The market for AI agents in e-commerce is set to explode from $3.6 billion in 2024 to a projected $282.6 billion by 2034 (4). This is not a distant future. ChatGPT Shopping and Google AI Mode are changing the way people shop right now (5, 6).

When you’re looking for headphones, you type in “best wireless headphones 2026”. Then, based on the results, you gradually narrow your search as you understand the differences between brands and the importance of certain features (such as anti-slip) whilst finding results that increasingly match your needs.

An AI agent makes it easier for you to formulate more complex queries, because that’s how we’ve come to know them, and with them we formulate queries such as: “I need workout headphones that won’t fall off, cost less than €200 and have good battery life”.

See the difference? With AI agents, we’ve learnt that a single query can already solve problems specific to our needs, without having to trial and test through active searches like on Google. This completely changes the way we need to structure content, optimise product pages and reach customers.

The new discovery models are already here:

• Comparative queries dominate recommendations: rather than visiting multiple sites to compare products, AI agents instantly summarise everything and present comprehensive comparisons. For example, when someone asks, “Which laptop is better for video editing, the MacBook Pro or the Dell XPS 15?”, an AI agent immediately presents a comparative analysis covering processing power, graphics capabilities, battery life and price ranges from multiple retailer sources;

• Contextual recommendations are becoming frighteningly accurate. AI agents analyse the user’s context more deeply than traditional search ever could, creating hyper-personalised suggestions that render generic ‘best of’ lists obsolete. Consider this: instead of showing generic “best running shoes”, an AI agent might recommend specific models after learning that you are a beginner runner, weigh 80 kilos, have flat feet, run on tarmac and have a budget of €150;

• The problem-solution approach shifts the focus from features to results. Customers describe problems and expect products that offer them a solution, not lists of features. If you try asking: “My back hurts after working at a desk all day”, the AI agent suggests ergonomic chairs, standing desk adapters and lumbar support cushions rather than listing technical specifications for 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 example, a request such as “Show me wireless headphones under €200 with noise cancellation and at least 20 hours of battery life” requires the AI to instantly filter the inventory based on those precise technical specifications across multiple brands and retailers.

Traditional SEO has taught us to think like Google’s algorithm

GEO requires us to think like an AI shopping assistant

The unexpected change

Three tips for your online shop

Here’s what’s not right:

“Looking for the best wireless earphones? Our comprehensive guide covers the top picks for 2026, including detailed reviews, comparisons and buying advice to help you make the right choice”

Here’s what works:

“Sony WH-1000XM5: Best noise-cancelling headphones for travel (€299). 30-hour battery life, superior ANC, comfortable for long flights. Beats the AirPods Max in comfort, outperforms Bose in battery life”

Note how the second version immediately tells AI agents what the product is, who it’s recommended for, and some comparison models. This is the clarity AI agents need to make recommendations with confidence, so focus on the essentials first, and then you can consider adding other secondary information.

1) Make your content AI-friendly

AI agents want quick answers. Start with the solution in the first sentence, then back it up with details

You need a schema markup for product specifications, including detailed prices and availability, customer reviews with ratings and testimonials, indicators of brand authority, frequently asked questions, and instructions for use (7, 8). Think of the schema markup as an identity card for your products; AI agents use it to get to know them. When you do this correctly, you’re making it easy for AI to understand and recommend your products.

2) Use a markup scheme that AI can read

Here’s what most companies overlook: AI agents prioritise structured data over plain text when making recommendations (7).

To build this network of signals, you need to conduct real-world product testing (experience) and provide authentic customer testimonials (reliability). AI agents can tell when you’ve actually tried a product, distinguishing this from simply copying the manufacturer’s descriptions.

Expertise is demonstrated through verifiable credentials in your sector, professional certifications and in-depth technical knowledge that shows true mastery of the subject. Authority is established through media citations, thought leadership content and recognition from industry experts. Trust is built through clear pricing, transparent policies, secure payments and easily accessible customer service.

3) Build authority and trust for AI agents

Artificial intelligence algorithms assess experience, expertise, authoritativeness and trustworthiness (EEAT) in a more sophisticated way than traditional search engines (7, 8).

The next steps

Start here (1–3 months)

First of all, check how visible your content is to AI systems at the moment. Most companies find that they are virtually invisible to AI agents, even though they rank well in traditional search results.

Focus first on your best-performing products:

1. Check that the product markup is complete and well-structured;

2. Ensure that product descriptions address specific customer concerns;

3. Add an FAQ section using natural language questions and answers that people ask about those products;

4. Create comparison tables that clearly highlight the competitive advantages over the best-known products in that product category.

Tip: if you’re not sure where to start with optimising your content for AI, remember that you can ask the AI for help 😶‍🌫️

Climbing (4–12 months)

Implement GEO strategies across all product categories (9, 10, 11). Set up a schema markup with inventory, prices and availability so that AI agents always have up-to-date information on what you’re selling.

Create content based on user intent rather than just keywords. Think about the different ways customers describe their problems, and from there present your product as the solution, creating content that addresses various scenarios. And then you can start thinking about:

1. Partnering with emerging AI shopping platforms. For example, integrate your product catalogue with ChatGPT Shopping’s APIs, list your products in Google’s AI-powered shopping results, or join Amazon’s Rufus recommendations programme. Even smaller platforms such as Perplexity Shopping and Claude are accepting merchant partners so that products can be sold directly from their platforms, without going via the website.

2. Optimise for voice search and conversational selling. Create product descriptions that answer spoken questions such as ‘What’s the best coffee machine if I have a small kitchen?’ rather than simply describing the product as ‘Coffee machines – Kitchen appliances’. Add FAQ sections using natural language such as “How long does delivery take?” and “Is it dishwasher-safe?”, which match how people actually interact with AI assistants.

3. Set up systems to keep AI agents up to date with your latest stock levels and prices. Implement automated product feeds via Google Merchant Center, or use tools such as Shopify’s AI integrations that automatically synchronise stock levels and price changes across all platforms from which AI agents source product information.

Why speed is more important than ever

The advantage of being an early mover in GEO is significant because AI agents learn and reinforce preferences over time (12). Companies that establish visibility now build up cumulative authority that becomes increasingly difficult for competitors to surpass.

We are witnessing a unique convergence of opportunities that will not last forever. The current trend, from 2022 to 2025, shows annual market growth of 54.7% for AI e-commerce (4), and the underlying technologies, such as ChatGPT Shopping, are mature enough to be relied upon (5, 6). Consumer adoption is accelerating, with 74% seeing improvements in AI (3), and competitive gaps can still be bridged (12).

This is reminiscent of the early days of Google SEO, when companies that invested in optimisation early on gained lasting advantages. The same dynamic is now playing out with AI agents, but the window of opportunity for adoption is narrower because the technology is advancing faster.

Conclusions

The shift from SEO to GEO isn’t something to be planned for the distant future. OpenAI’s ChatGPT Shopping and Google’s AI Mode are set to transform the way consumers search today (5, 6).

Don’t waste time – make sure you embrace the change, rather than being swept away by it. Review your product descriptions, ensuring the content is clear, to the point and written in a conversational style. Monitor mentions of AI and the traffic it drives to your site, as there is daily talk of an imminent revolution, and we can already see the consequences

Companies that act now are establishing competitive advantages that will define the next decade of e-commerce. If you wait any longer, you risk having to join a race where the first to arrive takes it all.

AI agents are no longer experimental, and we need to accept that. They are a commercial reality that is reshaping markets worth trillions. The question is not whether to optimise 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