When AI systems open a page to answer a question, they mostly read the visible copy.
Structured data adds labels to the facts on that page: who you are, what your product does, and who wrote an article. Search engines use those labels to understand pages, but recent studies found that adding markup alone did not raise AI citations for pages already being cited, and that several AI systems ignored it when reading a page directly. Its value comes from pairing it with clear content, checking how bots and AI agents use your pages, and measuring what happens when people arrive.

Before a buyer books a demo, an AI assistant may already have researched the category, compared options, and narrowed the list. That answer can introduce your brand, leave it out, or shape the buyer's next step. For B2B SaaS teams, the practical question is how to make your pages easy to understand for the assistant doing the research and useful for the person making the decision.

We looked at what this shift means for brands in What ChatGPT Search means for your brand in 2026. This guide focuses on one input your team controls directly: structured data.

What are GEO and AEO, and where does structured data fit?

GEO (generative engine optimization) and AEO (answer engine optimization) are two names for the same goal: helping your content be found, understood, and represented accurately in AI-generated answers. We use the term AI Search for the whole channel.

If you want the background on how AI systems choose and use sources, read our posts on the two stages of GEO, citation selection and citation absorption, and on what actually works in generative engine visibility. This article picks up where they leave off, with the markup itself.

Is this just SEO?

There is real overlap. Useful, accessible, and credible content still matters. AI Search adds new questions: how answers describe your brand, which sources they cite, which pages bots and AI agents access, and what visitors do after they click.

Which structured data types matter for B2B SaaS?

Structured data is code, usually JSON-LD using the schema.org vocabulary, that labels the facts on a page. For a B2B SaaS site, the most useful types tend to be:

• Organization on every page, to state your company name, website, and official profiles

• SoftwareApplication on product pages, to describe what the product is and its category

• BlogPosting or Article on content, with a named author and publish and update dates

• FAQPage where the page shows real questions and answers

• BreadcrumbList to show how pages relate to each other

Markup should describe what is already visible on the page. If the copy is vague, the markup will be too.

What is the two-reader page framework?

Every important page on a B2B SaaS site has two readers: the AI agent researching on a buyer's behalf, and the person who makes the decision. The two-reader page framework is a four-step loop for serving both.

1. See. Start with a topic your customers care about. Look at how your brand appears in tracked AI answers, which sources are cited, and which of your pages bots and AI agents access.

2. Structure. State the important facts clearly in the copy, then label them with structured data. Use the same product names, plan names, integrations, and definitions everywhere.

3. Act. Prioritize the changes with the clearest link to your goal. That might mean fixing a page agents cannot access, answering a missing question, or correcting outdated information on a source that answers cite often.

4. Measure. Track the same topic over a defined period. Keep the stages separate: observed AI activity, the recommended action, the change you made, and the outcome you measured.

The loop repeats. What you measure tells you where to look next.

How do you put structured data to work, step by step?

1. Choose one priority topic. Pick a topic tied to revenue, such as a core use case or a comparison buyers make. List the pages that should answer it.

2. Check access first. Make sure the key facts appear in the page's HTML and that bots and AI agents can reach the page. Limy's Agent Monitor shows real, page-level, server-side activity from bots and AI agents, including requests to pages that fail or no longer exist. Remember that a bot request is not a human visit.

3. Write the answer before the markup. Open each section with a direct answer, then add detail. A section should make sense if an assistant reads it on its own.

4. Add JSON-LD. Start with Organization site-wide, then SoftwareApplication, BlogPosting, and FAQPage where they fit. Keep values in the markup identical to the visible copy.

5. Validate. Test the markup with the Schema Markup Validator and Google's Rich Results Test before publishing. Fix errors and warnings that affect the facts you care about.

6. Keep facts consistent off your site. AI answers can draw on review sites, comparison guides, partner listings, and community discussions. Limy's Source Analysis shows which sources recur for your tracked topics, so you can decide where accuracy matters most. For more on how those sources shape the way answers describe you, see how to monitor brand sentiment in AI.

7. Measure the outcome you can track. Look at sampled answers for the topic, citations and mentions, and bot activity. Then connect visits from AI Search to on-site actions such as demo requests, with pipeline and revenue where your tracking supports it.

A mention means an answer names your brand. A citation means it links to a source. Both are useful signals on the path to growth, not the outcome itself.

What does focused execution look like in practice?

Two public Limy case studies show the value of choosing a specific focus and measuring it over a set period. Neither result is about structured data alone, and neither predicts what another brand will see.

In Limy's Owl Labs case study , checkout events increased 143% and live demo requests increased 73% over 30 days after the team implemented 10 recommendations, including six across two priority topics. Checkout events are events, not completed purchases or revenue.

In Limy's Inter IL case study, the share of AI answers on a tracked priority investment topic rose from 1.4% to 10.3% between June 9 and July 9, 2026. That is an 8.9 percentage point increase in topic-level visibility, not a measure of customers, pipeline, or revenue.

Best practices and checklist

Ask these 8 questions before you publish a page you want AI Search to understand.

1. What question does this page answer, and does the first paragraph answer it directly?

2. Would each section make sense if an assistant read it without the rest of the page?

3. Are product names, plan names, integrations, and definitions identical in the copy, the markup, and your other pages?

4. Does the structured data describe only what a visitor can see on the page?

5. Can bots and AI agents reach the page and its key facts?

6. Which sources do AI answers cite for this topic, and is your information accurate there?

7. Does the page show a named author and an updated date that match the markup?

8. Which outcome will you measure, over what period, and what will count as progress?

What should you do next?

Structured data works best as part of a loop: see how AI Search represents you, structure your facts for both readers, act on the highest-priority gaps, and measure the outcomes you can track. Start with one topic and one set of pages, then expand from what you learn.

Limy helps brands turn AI Search into a growth channel for pipeline, customers, and revenue. If you want to see how your brand appears in tracked AI answers and which pages bots and AI agents access, Book a demo.