Is Your Content AI-Ready? How Generative Search Changes the Rules

Introduction: What’s Changing?

Today, many people don’t click links — a March 2025 SEMrush study found that AI Overviews appeared in 13.14% of all Google searches, with 88.1% of these being informational queries. These searches are more likely to result in zero-click behaviour, where the user gets what they need on the results page and doesn't need to click through to your site. (SEMrush, 2025).

Informational queries are searches where the user is looking for knowledge or answers, rather than intending to make a purchase or visit a specific site. Zero-click behaviour occurs when the user's query is answered directly in the search results, eliminating the need to click through to a webpage. So you won't even know it is happening.

Your content is no longer ranked by relevance; it’s read, interpreted, and reused by machines. That means how you write, structure, and tag your pages now affects whether your voice appears in the answers people see.

The rules of digital visibility are shifting. SEO has long rewarded keyword precision, backlinks, and crawlability. However, with the rise of tools like ChatGPT, Claude, Gemini, and Perplexity, LLMs are actively embedding content, chunking it into semantically meaningful units, retrieving what aligns best with a user’s prompt, and rephrasing it to answer that query.

Rather than replacing SEO, this generative approach complements it. GEO (Generative Engine Optimisation) and SEO now coexist, each with different emphases depending on how content is surfaced and consumed.

With AI overviews now present on most searches in the coveted position 0 (top of page), it is important to embrace them when thinking about your content so that you can gain the benefit of having your brand and content surfaced, even if it doesn’t lead to a click through to your site.

From Keywords to Concepts: The LLM Retrieval Model

Search engines like Google use crawling and indexing to map literal word matches. Large language models (LLMs) are AI-trained on vast amounts of text to understand and generate human-like language. They work differently:

LLMs convert content into tokens — small units of meaning — and represent their relationships in a high-dimensional semantic space.

LLMs then chunk content into segments, usually 300–800 tokens in length, and retrieve the one most semantically aligned with a user’s query. From this, they generate a response using a process called retrieval-augmented generation (RAG).

This means the better your content is structured and thematically coherent, the more likely it is to be surfaced and reused effectively by LLMs.

Understanding and working with natural language

Richer, more natural language helps LLMs understand intent and context. For example, while a search engine might match “running shoes for trail” to a product category page, an LLM could interpret “Which running shoes deal with sloppy mud the best?” as a request for expert comparison, user experience, or product durability — even if those exact words don’t appear in your copy.

This richer phrasing increases the semantic weight of a chunk, improving its chances of being surfaced and understood as a complete thought.

Anticipating these natural language queries makes your content more retrievable and relevant in AI-powered environments, especially if your content is narrative or experiential rather than informational.

LLMs are especially good at interpreting nuance, tone, and intent — but only when your content expresses those elements clearly and unambiguously. We explore the internal workings of LLMs — how they listen, reason, and write — in our companion article: “Large Language Models: What Do They Really Do?”

Quotability: Clarity, Context, and Chunking

LLMs like Perplexity and Claude prefer to quote and summarise content that is:

• Clear: front-loaded with insight
• Self-contained: understandable without external context
• Well-structured: separated by topic or theme

This preference means your content needs:

• Punchy statements and summary sentences
• Semantic sectioning (each subheading = one idea)
• Purposeful phrasing that can be lifted and reused

Think of each chunk as a quote candidate, not just part of a long scroll. Suppose LLMs are looking to retrieve and construct a response to a question. In that case, a well-articulated piece of content within a page is often a better response than a link to the page itself.

A recent analysis found that 42.5% of search results appear with AI Overviews. Search Engine Journal (2024) showed that the display of AI overviews strongly correlates with declining click-through rates for informational queries, with many users receiving answers directly on the search page instead of clicking through to a website.

Having quotable content that LLMs can repurpose increases the likelihood of the LLM surfacing your content. For strategies on protecting your ideas and ensuring your content is cited accurately, see our companion article: Safeguarding Your Content in an LLM World. Note that generative tools may occasionally misquote or paraphrase inaccurately — clarity helps reduce the risk of misrepresentation.

AI-Readable = AI-Retrievable: Content Hygiene Essentials

LLMs won’t surface even the most brilliant content if it’s not machine-readable:

• Use semantic HTML (<h1>, <section>, <article>, etc.) to mark up your structure. A well-structured page might look like <header><nav><article><footer>, which tells both browsers and machines what each section is for. In contrast, <div><div><div> provides no such meaning, making it harder for machines to infer context or importance.
• These tags help humans and machines understand the hierarchy and intent of your content. Semantic HTML helps convey the meaning of content and improves accessibility, SEO, and LLM retrievability.
• While semantic markup has long been considered table stakes for well-constructed websites, it’s always worth reviewing, mainly as AI systems increasingly rely on this structure to interpret and retrieve content effectively. See (Mozilla Developer Network, 2024) or (Wikidata, 2024) for more background.
• Write in short, focused paragraphs with logical flow. This approach helps LLMs and will make your content more skimmable for human readers.
• Avoid overloaded or decorative markup. Tags should convey meaning, not style. For example, an emphasis tag <em> vs an italic <i> tag.
• Ensure your site’s accessibility is solid (alt text, ARIA, contrast, etc.). ARIA (Accessible Rich Internet Applications) provides attributes that enhance accessibility for users relying on assistive technologies. See the WAI-ARIA Authoring Practices for guidance on proper implementation.

Content Freshness and Recrawlability

While live web data increasingly power LLMs, not all AI tools operate in real-time. Some models train on static snapshots of the web, which means your content might not be current.

To improve visibility:

• Keep timestamps accurate with a visible “last updated” date.
• Republishing or meaningfully updating older content can trigger re-indexing.
• Use structured data to indicate modification dates and content type.

Remember: tools like Perplexity or Gemini use both retrieval and generation, and often favour recently published or frequently cited sources.

Technical Foundations: Schema and Metadata

Structured data helps LLMs and AI-enhanced search understand what your content is about:

• Implement Article, FAQPage, and HowTo schema.org markup.
• Include visible signals like author, last updated, and topic tags.
• Use sameAs links to connect your brand or authors to known entities (e.g., Wikidata).
• Add internal linking between conceptually related content chunks.

Understanding Result Types: Snippets vs Summaries

Before you assess whether your content is quote-ready, it’s helpful to understand the different types of enhanced search results it might appear in.

• ‍Featured snippets extract a concise answer from a single webpage, displaying itprominently in search. They typically include a source link and are driven byclear structure and markup.‍
• Rich snippets enhance the appearance of search listings by adding details suchas star ratings, pricing, or author info based on structured data.‍
• AI Overviews, powered by large language models (LLMs), synthesise informationfrom multiple sources to generate original summaries. These don’t alwaysattribute quotes to a single source and rely less on schema markup.

Traditional featured and rich snippets rely heavily on structured data and HTML tags — meaning you can influence them with schema.org markup and on-page formatting. AI summaries, however, are different.

Instead of extracting pre-defined content blocks, LLMs interpret and synthesise information across multiple sources. That makes them harder to influence directly:

• There’s no guaranteed ‘slot’ for your content to fill.
• Attribution is inconsistent and often absent.
• Content may be paraphrased, condensed, or even distorted depending on how clearly it's written.

Schema still helps provide structure and context, but with AI-generated overviews, clarity, quotability, and thematic consistency matter even more than markup. You’re optimising not just for ranking — but for accurate reuse.

Audit Your Content for LLM Visibility

If you’re concerned about how your content could be distorted or decontextualised by AI tools, we explore mitigation strategies in our follow-up article: Safeguarding Your Content in an LLM World.

Ask yourself:

• Are my articles structured with proper HTML?
• Is each section no longer than 400–800 words with a distinct theme?
• Have I defined key terms and concepts for AI interpretation?
• Is my schema valid, and are there citations or entity links?

Then test:

• Ask ChatGPT, Claude, or Gemini to summarise or answer a question from your content.
• See what they quote, distort, or ignore. The difference between what LLMs surface versus what search engines highlight as featured or rich snippets reveals how your content performs under different retrieval models. It’s incredibly revealing.

Conclusion: Start Here, Then Go Deeper

This article gives a top-level overview of how generative AI changes the digital landscape. You may not be seeing it in your traffic just yet, but the likelihood is that you will. Being AI-ready means thinking differently about structure, meaning, and format.

As large language models continue to power more of what we see in search, chat interfaces, summarisation tools, and intelligent assistants, they will increasingly surface, quote, and contextualise content without a traditional click.

Generative results are on track to become the default, not the exception. That shift deeply affects how your content is structured, cited, and consumed. Now is the time to prepare for that impact and ensure your business can show its best face, whatever happens.

References

Mozilla Developer Network (2024) Semantics. Available at: https://developer.mozilla.org/en-US/docs/Glossary/Semantics (Accessed: 5 May 2025).

Wikidata (2024) Semantic HTML. Available at: https://www.wikidata.org/wiki/Q28913915 (Accessed: 5 May 2025).

SEMrush (2025) AI Overviews study: Impact on search visibility and zero-click behaviour. Available at: https://www.semrush.com/blog/semrush-ai-overviews-study/ (Accessed: 5 May 2025).

The HOTH (2024) AI Overviews vs. Featured Snippets: What's the Difference? Available at: https://www.thehoth.com/blog/ai-overviews-vs-featured-snippets/?utm_source=chatgpt.com (Accessed: 5 May 2025).

Fast Company (2024) Are featured snippets still important? How AI overview impacts SEO strategy. Available at: https://www.fastcompany.com/91229697/are-featured-snippets-still-important-how-ai-overview-impacts-seo-strategy?utm_source=chatgpt.com (Accessed: 5 May 2025).

Stan Ventures (2024) How AI Overviews (AI Featured Snippets) Are Transforming Search Results. Available at: https://www.stanventures.com/news/how-ai-overviews-ai-featured-snippets-are-transforming-search-results-1796/?utm_source=chatgpt.com (Accessed: 5 May 2025).

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