Is Your Website Built to Be Cited by AI? The Audit

The pressure to respond to AI has led many marketing teams to add generative tools to the content workflow, automate a few processes, and call it progress. That’s useful work, but it doesn’t answer a more fundamental question about the website itself: whether AI tools like ChatGPT, Perplexity, and Google’s AI Overviews can find your content, understand it, and cite it back to the people asking them questions.

Creating content with AI is a production question. Being findable by AI is a structural question about how the site is built, and it’s the one that tends to get skipped. An honest AI readiness assessment is meant to surface that gap. What follows is a practical framework built around three pillars: schema markup, content structure, and internal linking.

The Biggest Myth About AI Readiness

The most common misconception about AI readiness is that it’s about the authoring experience. Teams invest in content generation tools, translation workflows, and editorial automation, all of which help the people inside the organization. What often gets overlooked is the external-facing side: whether the content those tools produce can be extracted and cited by AI systems once it’s published.

That’s a structural problem, and it shows up in specific places. More than half of all websites have no structured data at all, which means AI tools have to guess at what a page is about instead of reading it directly. High-value content often lives inside a single rich-text field with no labeled pieces for a model to pull from. Internal linking between related pages is weak or inconsistent, so the connections between a program, the classes inside it, and the people who teach it are invisible to anything trying to map them.

Thankfully, none of these are exotic problems. They’re the kind of thing a basic audit surfaces quickly, which is where the three pillars come in.

Audit Your Schema Markup First

Schema markup tells AI what your content means rather than just what it says. It’s the difference between a page that reads “Dr. Sarah Chen, Director of Admissions” and a page that explicitly labels that string as a Person with a job title, an organization, and a verified profile link. The first version leaves the model guessing. The second version hands it a fact it can cite with confidence.

Without schema, AI systems fill in the blanks probabilistically, and they often skip pages that are harder to parse in favour of sources that made the work easier. Google itself recommends structured data for AI search, which is about as clear a signal as the industry gets. Schema is also one of the cheapest items on this list to implement, and a basic rollout on the pages that matter most can usually be done inside a single sprint.

How to Check If Your Site Has Any Schema at All

You don’t need a specialist to run this check. Three tools handle most of what you need:

• Screaming Frog for a full crawl that shows every page and flags whether it contains structured data
• Google’s Rich Results Test for a quick single-page check
• org validator for detailed debugging when something looks off

Run the crawl, then spot-check the pages that carry the most weight: the homepage, the top service or program pages, and the highest-traffic articles. If nothing comes back, that’s the answer, and it’s a common one.

The Minimum Schema Every Site Should Have

For a basic implementation that makes a site legible to AI, four schema types do most of the work:

Author attribution is worth taking seriously here. AI models use signals like credentials, linked profiles, and connections to professional associations when deciding whether a source is safe to cite, which is why a practical guide to schema markup treats Person markup as part of the foundation rather than a finishing touch.

Check Whether Your Content Is Structured or Trapped

The second pillar is what’s sometimes called the blob body field problem, and it shows up in almost every content audit. Open a high-value page inside the CMS to see how it’s built, and the entire thing, from the hero headline down to the final call to action, lives inside one rich-text field. To an AI model trying to parse the page, the whole thing reads as one undifferentiated paragraph of HTML.

When all page content lives inside a single rich-text field, AI systems struggle to identify relationships between pieces of information like pricing, outcomes, instructors, or prerequisites. Structured content solves this by separating content into labeled fields that can be independently understood, reused, and surfaced across channels.

What Structured Content Looks Like

Structured content splits a page into labeled fields that each hold one meaningful piece of information. A program page, for instance, might have separate fields for title, summary, duration, cost, outcomes, prerequisites, and instructor. Each field maps to something an AI tool can identify and pull independently, so the content can adapt to new contexts without being rewritten, which is what makes structured content stay flexible across channels.

The AI benefit is the obvious one, but the operational benefits tend to be even larger in practice. Editors work faster because they’re filling in labeled fields instead of wrestling with formatting inside a wall of text. Pages stay visually consistent because the template handles the heavy lifting. And the same content becomes reusable across contexts, so one program summary can feed the program page, a comparison table, a search result card, and a syndication feed without anyone rewriting anything.

How to Spot Unstructured Pages on Your Site

The fastest way to run this check is to open your top five traffic pages inside the CMS and look at the edit screen. A single WYSIWYG field containing everything is a blob. Multiple labeled fields with specific purposes are structured content. Counting how many of your highest-value pages fall into each category is a reasonable first pass at an AI website audit, and the results usually point directly to the next piece of work.

Look at How Your Pages Connect to Each Other

The third pillar is internal linking, and it matters more for AI than it did for traditional SEO. AI tools build their understanding of a brand by crawling the relationships between pages, so a strong internal link structure teaches the model that an admissions page connects to programs, programs connect to faculty, and faculty connect to student outcomes. A weak one leaves the model looking at a collection of loosely related pages without a clear story tying them together.

Large enterprise sites often suffer from fragmented taxonomies, inconsistent tagging, and broken internal links, making it difficult for crawlers to understand how content relates.

The cumulative effect is a site that reads as a confusing mess to any crawler trying to make sense of it, whether that crawler belongs to Google or to an AI assistant.

How to Audit Your Entity Relationships

Start with the most important entity on the site, whether that’s a flagship program, a headline service, or a core product offering, and map out every page that should connect to it. Then check whether those connections exist in the form of real internal links, and whether the anchor text describes the relationship in natural language rather than generic phrases like “click here” or “learn more.”

Let’s use an example from higher education.

A graduate program page should link to the individual courses inside it, each course should link to the instructor who teaches it, and each instructor’s bio should link back to the program and to any relevant student outcomes. That web of connections is the beginning of a knowledge graph, and it’s what gives AI systems the context to explain your organization at the level of relationships rather than individual pages.

Drupal makes this kind of work considerably easier once a content model supports how to connect related content through entity references.

Build a Phased Roadmap You Can Realistically Follow

The temptation when tackling something like this is to try everything at once, which usually means nothing gets finished. A phased plan works better, and for teams that want to know how to make a website discoverable by AI without overwhelming the roadmap, breaking the work into time horizons is the most reliable approach.

First 30 days: quick wins

• Implement basic schema on the homepage and top ten pages
• Audit the three highest-intent pages for the blob body field problem
• Set up GA4 tracking for AI referral traffic so there’s a baseline to measure against

Next 90 days: structural improvements

• Restructure content models for the most important content types
• Build internal linking between key entities on priority pages
• Expand schema coverage across article, product, or program pages

Longer-term: capacity building

• Map out full knowledge graph relationships for the most critical entities
• Make CMS-level improvements that keep ongoing audits easy for editors to run
• Treat the CMS like a product, with a steady loop of publish, measure, and improve, rather than a publishing tool that ships once and gets left alone

How Drupal Makes This Work Out of the Box

Most of this audit applies regardless of the CMS underneath the site, and the principles translate to any platform that takes content seriously. Drupal happens to be built in a way that makes each piece of the audit easier to act on, which is part of why it holds up well for complex, content-heavy projects.

Drupal supports many of the structural requirements behind AI readiness out of the box. Structured fields, entity references, and Schema.org integrations make it easier to organize content in ways AI systems can understand and connect.

Modules like Schema Metatag help automate structured data implementation, while Drupal’s content modeling capabilities make internal relationships between programs, people, services, and content easier to maintain over time.

Where to Go From Here

A good audit comes back to three questions:

• Does the site use schema markup, and does it cover the pages that matter?
• Is the content structured into labeled fields, or trapped in a single body field?
• Does the internal linking teach AI tools how the key entities on the site connect to each other?

Honest answers to those three questions point directly to the next piece of work.   For a deeper walkthrough, the ImageX team recently ran a Search Engine Journal webinar called Drupal AI Audit: How CMOs Can Assess Whether Their Stack Is Built for What’s Coming, covering citation readiness, content structure, and a technical roadmap for Drupal AI.   View now at your own pace.

In the SEO world, when we talk about how to structure content for AI search, we often default to structured data – Schema.org, JSON-LD, rich results, knowledge graph eligibility – the whole shooting match.

While that layer of markup is still useful in many scenarios, this isn’t another article about how to wrap your content in tags.

Structuring content isn’t the same as structured data

Instead, we’re going deeper into something more fundamental and arguably more important in the age of generative AI: How your content is actually structured on the page and how that influences what large language models (LLMs) extract, understand, and surface in AI-powered search results.

Structured data is optional. Structured writing and formatting are not.

If you want your content to show up in AI Overviews, Perplexity summaries, ChatGPT citations, or any of the increasingly common “direct answer” features driven by LLMs, the architecture of your content matters: Headings. Paragraphs. Lists. Order. Clarity. Consistency.

In this article, I’m unpacking how LLMs interpret content — and what you can do to make sure your message is not just crawled, but understood.

How LLMs Actually Interpret Web Content

Let’s start with the basics.

Unlike traditional search engine crawlers that rely heavily on markup, metadata, and link structures, LLMs interpret content differently.

They don’t scan a page the way a bot does. They ingest it, break it into tokens, and analyze the relationships between words, sentences, and concepts using attention mechanisms.

They’re not looking for a <meta> tag or a JSON-LD snippet to tell them what a page is about. They’re looking for semantic clarity: Does this content express a clear idea? Is it coherent? Does it answer a question directly?

LLMs like GPT-4 or Gemini analyze:

• The order in which information is presented.
• The hierarchy of concepts (which is why headings still matter).
• Formatting cues like bullet points, tables, bolded summaries.
• Redundancy and reinforcement, which help models determine what’s most important.

This is why poorly structured content – even if it’s keyword-rich and marked up with schema – can fail to show up in AI summaries, while a clear, well-formatted blog post without a single line of JSON-LD might get cited or paraphrased directly.

Why Structure Matters More Than Ever In AI Search

Traditional search was about ranking; AI search is about representation.

When a language model generates a response to a query, it’s pulling from many sources – often sentence by sentence, paragraph by paragraph.

It’s not retrieving a whole page and showing it. It’s building a new answer based on what it can understand.

What gets understood most reliably?

Content that is:

• Segmented logically, so each part expresses one idea.
• Consistent in tone and terminology.
• Presented in a format that lends itself to quick parsing (think FAQs, how-to steps, definition-style intros).
• Written with clarity, not cleverness.

AI search engines don’t need schema to pull a step-by-step answer from a blog post.

But, they do need you to label your steps clearly, keep them together, and not bury them in long-winded prose or interrupt them with calls to action, pop-ups, or unrelated tangents.

Clean structure is now a ranking factor – not in the traditional SEO sense, but in the AI citation economy we’re entering.

What LLMs Look For When Parsing Content

Here’s what I’ve observed (both anecdotally and through testing across tools like Perplexity, ChatGPT Browse, Bing Copilot, and Google’s AI Overviews):

• Clear Headings And Subheadings: LLMs use heading structure to understand hierarchy. Pages with proper H1–H2–H3 nesting are easier to parse than walls of text or div-heavy templates.
• Short, Focused Paragraphs: Long paragraphs bury the lede. LLMs favor self-contained thoughts. Think one idea per paragraph.
• Structured Formats (Lists, Tables, FAQs): If you want to get quoted, make it easy to lift your content. Bullets, tables, and Q&A formats are goldmines for answer engines.
• Defined Topic Scope At The Top: Put your TL;DR early. Don’t make the model (or the user) scroll through 600 words of brand story before getting to the meat.
• Semantic Cues In The Body: Words like “in summary,” “the most important,” “step 1,” and “common mistake” help LLMs identify relevance and structure. There’s a reason so much AI-generated content uses those “giveaway” phrases. It’s not because the model is lazy or formulaic. It’s because it actually knows how to structure information in a way that’s clear, digestible, and effective, which, frankly, is more than can be said for a lot of human writers.

A Real-World Example: Why My Own Article Didn’t Show Up

In December 2024, I wrote a piece about the relevance of schema in AI-first search.

It was structured for clarity, timeliness, and was highly relevant to this conversation, but didn’t show up in my research queries for this article (the one you are presently reading). The reason? I didn’t use the term “LLM” in the title or slug.

All of the articles returned in my search had “LLM” in the title. Mine said “AI Search” but didn’t mention LLMs explicitly.

You might assume that a large language model would understand “AI search” and “LLMs” are conceptually related – and it probably does – but understanding that two things are related and choosing what to return based on the prompt are two different things.

Where does the model get its retrieval logic? From the prompt. It interprets your question literally.

If you say, “Show me articles about LLMs using schema,” it will surface content that directly includes “LLMs” and “schema” – not necessarily content that’s adjacent, related, or semantically similar, especially when it has plenty to choose from that contains the words in the query (a.k.a. the prompt).

So, even though LLMs are smarter than traditional crawlers, retrieval is still rooted in surface-level cues.

This might sound suspiciously like keyword research still matters – and yes, it absolutely does. Not because LLMs are dumb, but because search behavior (even AI search) still depends on how humans phrase things.

The retrieval layer – the layer that decides what’s eligible to be summarized or cited – is still driven by surface-level language cues.

What Research Tells Us About Retrieval

Even recent academic work supports this layered view of retrieval.

A 2023 research paper by Doostmohammadi et al. found that simpler, keyword-matching techniques, like a method called BM25, often led to better results than approaches focused solely on semantic understanding.

The improvement was measured through a drop in perplexity, which tells us how confident or uncertain a language model is when predicting the next word.

In plain terms: Even in systems designed to be smart, clear and literal phrasing still made the answers better.

So, the lesson isn’t just to use the language they’ve been trained to recognize. The real lesson is: If you want your content to be found, understand how AI search works as a system – a chain of prompts, retrieval, and synthesis. Plus, make sure you’re aligned at the retrieval layer.

This isn’t about the limits of AI comprehension. It’s about the precision of retrieval.

Language models are incredibly capable of interpreting nuanced content, but when they’re acting as search agents, they still rely on the specificity of the queries they’re given.

That makes terminology, not just structure, a key part of being found.

How To Structure Content For AI Search

If you want to increase your odds of being cited, summarized, or quoted by AI-driven search engines, it’s time to think less like a writer and more like an information architect – and structure content for AI search accordingly.

That doesn’t mean sacrificing voice or insight, but it does mean presenting ideas in a format that makes them easy to extract, interpret, and reassemble.

Core Techniques For Structuring AI-Friendly Content

Here are some of the most effective structural tactics I recommend:

Use A Logical Heading Hierarchy

Structure your pages with a single clear H1 that sets the context, followed by H2s and H3s that nest logically beneath it.

LLMs, like human readers, rely on this hierarchy to understand the flow and relationship between concepts.

If every heading on your page is an H1, you’re signaling that everything is equally important, which means nothing stands out.

Good heading structure is not just semantic hygiene; it’s a blueprint for comprehension.

Keep Paragraphs Short And Self-Contained

Every paragraph should communicate one idea clearly.

Walls of text don’t just intimidate human readers; they also increase the likelihood that an AI model will extract the wrong part of the answer or skip your content altogether.

This is closely tied to readability metrics like the Flesch Reading Ease score, which rewards shorter sentences and simpler phrasing.

While it may pain those of us who enjoy a good, long, meandering sentence (myself included), clarity and segmentation help both humans and LLMs follow your train of thought without derailing.

Use Lists, Tables, And Predictable Formats

If your content can be turned into a step-by-step guide, numbered list, comparison table, or bulleted breakdown, do it. AI summarizers love structure, so do users.

Frontload Key Insights

Don’t save your best advice or most important definitions for the end.

LLMs tend to prioritize what appears early in the content. Give your thesis, definition, or takeaway up top, then expand on it.

Use Semantic Cues

Signal structure with phrasing like “Step 1,” “In summary,” “Key takeaway,” “Most common mistake,” and “To compare.”

These phrases help LLMs (and readers) identify the role each passage plays.

Avoid Noise

Interruptive pop-ups, modal windows, endless calls-to-action (CTAs), and disjointed carousels can pollute your content.

Even if the user closes them, they’re often still present in the Document Object Model (DOM), and they dilute what the LLM sees.

Think of your content like a transcript: What would it sound like if read aloud? If it’s hard to follow in that format, it might be hard for an LLM to follow, too.

The Role Of Schema: Still Useful, But Not A Magic Bullet

Let’s be clear: Structured data still has value. It helps search engines understand content, populate rich results, and disambiguate similar topics.

However, LLMs don’t require it to understand your content.

If your site is a semantic dumpster fire, schema might save you, but wouldn’t it be better to avoid building a dumpster fire in the first place?

Schema is a helpful boost, not a magic bullet. Prioritize clear structure and communication first, and use markup to reinforce – not rescue – your content.

How Schema Still Supports AI Understanding

That said, Google has recently confirmed at Search Central Live in Madrid that its LLM (Gemini), which powers AI Overviews, does leverage structured data to help understand content more effectively.

In fact, at the event, John Mueller recommends to use structured data because it gives models clearer signals about intent and structure.

That doesn’t contradict the point; it reinforces it. If your content isn’t already structured and understandable, schema can help fill the gaps. It’s a crutch, not a cure.

Schema is a helpful boost, but not a substitute, for structure and clarity.

In AI-driven search environments, we’re seeing content without any structured data show up in citations and summaries because the core content was well-organized, well-written, and easily parsed.

In short:

• Use schema when it helps clarify the intent or context.
• Don’t rely on it to fix bad content or a disorganized layout.
• Prioritize content quality and layout before markup.

The future of content visibility is built on how well you communicate, not just how well you tag.

Conclusion: Structure For Meaning, Not Just For Machines

Optimizing for LLMs doesn’t mean chasing new tools or hacks. It means doubling down on what good communication has always required: clarity, coherence, and structure.

If you want to stay competitive, you’ll need to structure content for AI search just as carefully as you structure it for human readers.

The best-performing content in AI search isn’t necessarily the most optimized. It’s the most understandable. That means:

• Anticipating how content will be interpreted, not just indexed.
• Giving AI the framework it needs to extract your ideas.
• Structuring pages for comprehension, not just compliance.
• Anticipating and using the language your audience uses, because LLMs respond literally to prompts and retrieval depends on those exact terms being present.

As search shifts from links to language, we’re entering a new era of content design. One where meaning rises to the top, and the brands that structure for comprehension will rise right along with it.

More Resources:

• Why Now’s The Time To Adopt Schema Markup
• SEOs Are Recommending Structured Data For AI Search… Why?
• SEO In The Age Of AI

Featured Image: Igor Link/Shutterstock