Generative engine optimization: how to get your products into AI answers

Product discovery is shifting again. Not in the incremental way we’ve seen with algorithm updates, but more fundamentally, in how decisions actually get made.

Customers are not only searching; they’re asking.

They’re asking AI assistants what to buy, what’s best, and what’s worth their money.

And as well as returning a list of product links, these systems are also responding with a single, calculated answer.

That shift changes the rules… because there’s no page two, no long tail keywords to capture. Your product either shows up in the answer or it doesn’t even exist.

This is where generative engine optimization (GEO) comes in, and you’re probably already trying to figure it out – or at least you should be.

From ranking pages to inclusion

Traditionally, SEO has trained businesses to think in rankings: how to get pages ranked first on search engines for specific keywords, while focusing on click-through rates.

GEO asks a different question: does the model trust your product data enough to include it in the answer?

Search engines like Google have long acted as gatekeepers in SEO, crawling and indexing the web to rank pages based on relevance and authority.

AI systems take that a step further.

They don’t just point users somewhere but decide what matters and present it.

Why most product data isn’t optimized for AI systems

One key friction point for retailers is that most digital commerce catalogs weren’t built for machines. They were built for merchandising teams and human browsing behavior. 

Over time, inconsistencies build up: duplicate SKUs, vague product titles, outdated availability, broken links, conflicting descriptions across channels. 

For SEO that creates inefficiency but for GEO, it means exclusion. 

That’s because large language models don’t “browse” your site. They extract signals. If those signals are inconsistent or incomplete, the system just moves on. 

So the data needs fixing first.

Start with the foundations: make your data usable

Before anything else, brands need to treat their product catalog as structured data, not just content.

That means standardizing how products are named and described. It means removing duplication, maintaining accurate stock numbers, and ensuring every product has a single, canonical URL.

More importantly, it means implementing structured data properly.

Schema.org markup, specifically Product JSON-LD, gives machines a consistent way to interpret your catalog. Product name, brand, SKU, price, availability. These represent the baseline for inclusion. 

Technical hygiene matters just as much. Clean sitemaps. No broken internal links. No accidental noindex tags. These are the signals that tell both search engines and AI systems that your data can be trusted. 

Then comes two pipelines:

Pipeline one: make your data accessible to AI systems 

Some AI models learn by crawling the web and ingesting information into their knowledge base. 

So if your site isn’t accessible, your products won’t be learned. 

Many brands are unknowingly blocking AI crawlers through restrictive robots.txt files. This not only affects visibility but limits whether your products exist in future AI responses at all. 

The fix is to allow relevant AI user agents. But keep sensitive pages like checkout and admin areas restricted. Maintain a clean robots.txt file and reference your sitemap clearly. 

Some teams are also starting to experiment with llms.txt files to guide crawlers toward high-value content. 

The biggest challenge at the moment is feedback. 

Unlike Google Search Console, there’s no dashboard that confirms what data an AI model has ingested, so we’re working with limited visibility for now… until clearer reporting emerges.

Pipeline two: earn visibility in real-time AI answers

When a user asks a question, many AI systems perform a real-time search to build an answer. 

This is where SEO still carries weight. But it needs to connect more tightly with product data and brand presence. 

If your product pages don’t rank, they won’t be retrieved. If your data isn’t consistent across platforms, it won’t be aggregated correctly. If your brand doesn’t appear on trusted third-party sites, the model has fewer signals to rely on. 

In practice, that means maintaining strong SEO, submitting accurate product feeds through platforms like Google Merchant Center, and building presence across review sites, comparison platforms, and editorial roundups.

Your next step

If you want to understand how your products perform in AI-driven discovery, explore the full framework by downloading our guide: How to make your products discoverable in AI-driven search.

The guide breaks down the exact actions needed across both pipelines, from catalog structure to crawl accessibility and real-time visibility.