GEO vs SEO vs AEO: what changes for retailers?

A customer needs a washing machine that fits a shallow space, costs less than €600 and can arrive next week. They might search Google for suitable models, ask an AI assistant to compare them, or ask it to arrange the purchase.

Your store could appear in the search and the comparison, then lose the order because delivery takes three weeks. Each step needs different information.

Search engine optimization (SEO), answer engine optimization (AEO) and generative engine optimization (GEO) help people find and choose products. Agentic commerce adds the ability to carry out shopping tasks within the customer's instructions.

The work shares much of the same product data. What you fix depends on where the customer gets stuck.

SEO vs GEO vs AEO: what is the difference?

The boundaries overlap. An AI assistant can use your installation guide in a direct answer and in a longer comparison. Both depend on it finding and understanding the information.

SEO remains part of that work. Google says its established SEO practices apply to AI Overviews and AI Mode. The GEO research paper studies visibility within generated responses, rather than a page's position in a list of search links.

A shopper who sees your store in an answer may visit it, or get enough information to stop there. Track the citation, but keep it separate from visits and sales.

AEO vs GEO: where the work overlaps

On a product page, AEO and GEO often ask for the same work: answer the questions someone needs to settle before buying, with facts they can check.

"Compact" tells a customer very little about whether a washing machine will fit. They need its depth in centimeters, whether that includes the door, and how much room to leave for hoses.

Those details let the shopper check the fit and give an assistant useful material for an answer or comparison. Writing a longer description won't help if it still leaves out the measurements.

Google requires pages to be indexed and eligible for a search snippet to appear as supporting links in its AI features. It also says no special AI markup is needed. An FAQ can answer a buying question, but its presence doesn't guarantee a citation.

Across a large catalog, the hard part is getting those details right for every product. Our SEO and GEO work for product catalogs starts with the available facts and turns them into content customers can use to decide.

Agentic commerce: from answering to taking action

Now the customer asks: "Find a washing machine under €600 that fits this space and can arrive next week. Show me the total before I approve."

The assistant has to check the exact model and dimensions, then confirm current price, stock and delivery to that address. If the customer approves, it needs a way to proceed with the order.

That is what the customer delegates in agentic commerce: part of the shopping task, with limits on what the agent can do.

An agent might use commerce APIs, tools a website exposes, or browser interactions. WebMCP's early preview, for example, describes how websites can expose structured tools for agents. Agents don't all use the same route to a purchase.

Product feeds supply information along the way. OpenAI's product schema has separate controls for search, checkout and ads. Our ChatGPT product feed guide explains the required fields and the limits of each use. A feed alone doesn't establish a working checkout.

The catalog also determines what an agent can check. In our analysis of Claude Commerce Agents, the demo backend ranks products using text, then applies filters to specific fields. A measurement in the description can help a product rank without being available to the filter. That finding applies to the reference implementation; other search backends can behave differently.

For the washing machine, the description explains why it might suit a small kitchen. The depth field lets a system check the customer's limit, provided the unit and measurement are clear.

Two waves of change in ecommerce

One change concerns how people choose: they can use search results, AI answers and conversational comparisons to research a purchase. SEO, AEO and GEO all belong here.

The other concerns how much of the task they delegate. As an assistant takes on more of the purchase, it needs access to stock, delivery and payment systems. The Universal Commerce Protocol provides specifications and reference implementations for discovery, checkout and later stages of the order.

Companies are shipping these flows and revising them. In February 2026, Google announced checkout for US shoppers buying from Etsy and Wayfair in AI Mode and Gemini. In March, OpenAI said its initial Instant Checkout lacked the flexibility it wanted, and put more emphasis on product discovery while merchants used their own checkout experiences.

I wouldn't use those announcements to predict when delegated shopping will become a material share of a retailer's sales. We still need to see how customers use it and how reliably purchases work in each market.

With physical products, a suitable model may be out of stock or unavailable for delivery next week. Shipping could push the total over €600. The agent has to resolve those conditions before placing the order.

The two changes can develop together. Someone may delegate a routine replacement and still want to browse before buying something unfamiliar. I would improve today's pages using product facts the store can also share with shopping agents. Our agentic commerce readiness page covers the catalog and feed work involved.

The diagram shows two kinds of change. Its dates and relative heights are illustrative, not adoption data or a forecast.

Where retailers should start: one category, real buying questions

Choose a category your team knows well and write down ten questions that affect the purchase. For washing machines, start with fit, capacity and installation.

Follow each question through the product record, the page and the feed or search tool. Check where the information disappears or changes meaning. Does "depth" include the door in every system?

Test each part of the purchase:

• For discovery, check that search engines can reach the page and that it addresses relevant searches. Follow queries, clicks and conversions.
• For answers, ask a fixed set of buying questions. Check accuracy and the sources cited, then repeat the tests. One favorable AI response is weak evidence.
• For actions, test whether the shopping flow respects the customer's constraints, checks current price and delivery, and completes the steps the customer allowed. Record where it fails.

Then repair the data behind those failures. Confirm measurements against supplier evidence, normalize units and keep variants distinct. Leave unknown values for review. This catalog foundation work lets several channels use the same facts.

Use Google's product structured data guidance to check the information you expose to search. Test shopping integrations separately: valid markup cannot tell you whether an order will succeed.

For site access, WebMCP and agent interactions, Your ecommerce has an AI agent score covers the technical checks.

I would do this before commissioning another batch of AI-written descriptions. Follow one buying question through the store, find the first missing or unreliable fact, and give someone responsibility for fixing it.

Book a demo and bring a sample feed from that category.