How to optimise your owned content for AI search

Why AI search changes the content game

Search has fundamentally changed. The days of writing for Google and hoping keywords do the heavy lifting are over. Large language models like ChatGPT, Copilot, Perplexity, and Claude now act as discovery engines - answering questions directly and shaping the buyer journey before anyone visits your site.

The numbers are hard to ignore. Gartner predicts search engine volume will drop by 25% by 2026 as buyers lean on AI assistants, and 94% of B2B buying groups already use generative AI tools for research before they speak to a sales rep. Buyers are forming their consideration sets during AI-driven research. If you're not cited, you're not considered - which means fewer opportunities, longer sales cycles, and missed revenue.

The fix is not to abandon SEO. It's to make your content structured, credible, and context-rich enough for AI systems to trust and surface - while staying genuinely useful for the human on the other end. This guide walks through how.

Writing for humans and AI

Your content now serves two audiences: the human reader and the AI models that summarise information for them. Google still rewards quality, usefulness, and originality, so a human-first approach stays essential. The shift is in how you format it.

• Write for clarity and accessibility. Use plain language and concise sentences - aim for under 20 words - to minimise ambiguity for both readers and models.
• Adopt a neutral, authoritative voice. Be straightforward, clear, and accurate. The goal is to be a quotable, credible source, not a sales pitch.
• Write for snippets. Assume each section will be pulled into an AI answer without its surrounding context. Every section should make sense on its own.
• Use complete, declarative sentences. Instead of "helps grow pipeline," write "lead generation campaigns increase qualified opportunities."
• Provide inline context. Define terms and add examples within the sentence - for instance, "content syndication, such as distributing whitepapers through trusted B2B networks, expands reach to mid-funnel prospects."

Two things to avoid: overly creative language (skip the metaphors - "this campaign produced a 20% increase in MQLs" beats "this campaign was a rocket ship"), and dropping transitional words. Connectors like because, therefore, and for example create the logical flow models rely on.

Optimising for LLMs vs. Google - a balancing act

The good news: most practices that improve LLM visibility align with established SEO - structured content, strong E-E-A-T signals, and solid technical foundations all still matter. But some changes made to win citations can quietly hurt your Google rankings. Watch for three traps: over-compressing content (short answer blocks that sacrifice the depth Google rewards for comprehensiveness), neglecting SEO fundamentals (site performance, crawlability, canonicals, thin content), and radical site changes (altering URL structure or removing pages without redirects). Your ultimate audience is human - any major change for LLMs must include standard SEO hygiene.

Structure: headings, answer blocks, and lists

Clear structure is the single biggest lever for AI extraction. A logical layout acts as a roadmap that guides both search engines and AI to the most relevant part of your page.

Headings and hierarchy

Your H1 should communicate a full idea, so readers immediately understand the page's focus. Use H2s and H3s to break content into smaller sub-topics, and move from broad to specific in a predictable order (H1 → H2 → H3) so models understand how the page is organised.

• Make H1s complete questions or answers. "How B2B teams generate and qualify leads today" beats a bare label like "Lead generation."
• Avoid vague headings. Replace "Overview" or "Best practices" with descriptive titles that tell the reader exactly what follows.
• Maintain a logical narrative. Headings should read like a story the reader can follow, not a list of loosely related terms.

Here's what a clean hierarchy looks like in practice - note how every level nests logically beneath the one above it:

<H1> Call to Action
  <H2> What is a CTA? Examples, key benefits, and measurement strategies
  <H2> Definition: what is a call to action (CTA)?
  <H2> CTA examples: different types of calls to action
    <H3> Different types of CTA formats and designs
      <H4> Buttons
      <H4> Contextual links
      <H4> Banner and video ads
      <H4> Pop-ups
      <H4> Slide-ins or carousel ads
  <H2> Popular types of CTA copy
  <H2> What are the best practices for writing a call to action
    <H3> 1. Be brief, specific, and actionable
    <H3> 2. Create a sense of urgency
    <H3> 3. Focus on the target audience's goals
    <H3> 4. Make sure the CTA button meets the campaign's objectives
    <H3> 5. Consider the CTA's surrounding marketing copy
  <H2> Key CTA benefits
    <H3> Increased leads and conversions
    <H3> Improved customer engagement
  <H2> How to measure and test CTA success
  <H2> CTAs help to boost online marketing success

Answer-block formatting - don't bury the lead

To boost visibility in AI answers, start each major section with a 30–80 word "answer block" that directly addresses the question in its H1 or H2. Follow it with deeper explanation, examples, and sources - this balances concise answers with the comprehensiveness Google still expects. An optional label like "Quick answer:" signals to AI that the block is a direct answer. It's the same logic as the classic SEO pillar approach: give the definitive answer first, then let the rest of the page break it down.

Enhancements for AI extraction

• Use structured lists and tables. Models extract bullets, tables, and numbered lists far more reliably than dense paragraphs.
• Number your steps. For how-to content, format instructions as "Step 1, Step 2…".
• Define key concepts explicitly. For example: "AI search visibility refers to how often a brand's content appears in LLM-generated answers."
• Add a real FAQ. Use prompt-style questions that mirror how buyers actually talk to AI.
• Show freshness. Display a "last updated" date near the title, revisit high-value content every 6–12 months, add a "2025 update" note to evergreen guides, and update the FAQ schema whenever you revise.

Internal linking

Internal links help both people and AI systems understand how your content fits together - and give search and LLMs stronger signals about what each page covers.

• Use descriptive, keyword-rich anchors. "See our LLM visibility measurement framework" works far better than "learn more."
• Link related entity clusters so connected topics reinforce each other within a theme.
• Maintain a hub-and-spoke structure - a pillar page links out to supporting subtopics, and those link back.
• Limit link density to roughly three to five meaningful internal links per 1,000 words.
• Keep crawl depth shallow - key pillar pages should be no more than two clicks from the homepage.

The technical side of AI search

Schema and structured data

Schema markup helps search engines and LLMs understand the context of your content. In testing, it's one of the highest-impact tactics available.

• Implement FAQPage or QAPage schema - currently the highest-impact markup for LLM discoverability.
• Pair it with Article schema - include author, dateModified, headline, and "about" to signal credibility and freshness.
• Add Organization or Product schema with sameAs links to trusted profiles (your LinkedIn page, Wikipedia entry) to reinforce entity trust.
• Keep schema and on-page content consistent. AI crawlers increasingly check that your markup matches what's visible.

Accessibility and semantic markup

Clear, accessible structure helps both users and AI interpret your content - and the same practices improve crawl depth and indexability. Use semantic HTML elements like <article>, <section>, and <aside> to define meaning; keep all important text accessible rather than script-generated; and apply <strong> and <em> for meaning, not styling, since crawlers read them as semantic signals.

Metadata and URLs

Titles, descriptions, and slugs give search systems and AI models their earliest signal about what a page covers. Natural language here helps models match your content to real queries.

• Write titles in natural language that reflect how users ask questions.
• Use full, natural-language slugs that read like prompt-style phrases - avoid abbreviations or internal shorthand, and match the URL to the H1.
• Treat the meta description as a direct answer to the page's primary query, 140–160 characters, using connector words like how, what, and why.

Visual and media elements

Visuals do more in AI search than most teams realise - captions, filenames, and surrounding text help models confirm a page's focus. Use relevant, captioned media; write descriptive alt text ("diagram showing the stages of the digital marketing funnel"); and use keyword-rich filenames like stages-of-the-marketing-funnel.png. For video, embed YouTube with transcripts and VideoObject schema, and optimise titles and descriptions so the metadata clearly reflects the content.

The content optimisation checklist

A quick reference you can run any page against before it ships:

AI search is reshaping how buyers discover information - and it rewards content that's clearer, more structured, and genuinely useful. The teams that make these adjustments now will be the ones cited when it matters.

Frequently asked questions

What is answer engine optimisation (AEO)?

Answer engine optimisation is the practice of structuring your content so large language models such as ChatGPT, Perplexity, Claude, and Google AI Overviews can trust, extract, and cite it in their generated answers. It builds on traditional SEO but prioritises clear structure, direct answers, and machine-readable signals like schema.

How is writing for AI different from writing for SEO?

The fundamentals overlap - structure, E-E-A-T signals, and a solid technical foundation matter for both. The difference is that AI engines pull sections out of context, so each section must stand on its own, lead with the answer, and use plain, declarative language. You still write for a human first; you just format so a model can extract a clean, quotable answer.

What is an answer block and how long should it be?

An answer block is a 30–80 word summary at the start of a major section that directly answers the question in the H1 or H2 above it. Follow it with deeper explanation, examples, and sources. An optional label such as "Quick answer:" tells AI systems the block is a direct answer.

Which schema types matter most for AI search?

FAQPage or QAPage schema is currently the highest-impact markup for AI discoverability. Pair it with Article schema (author, dateModified, headline, about) for credibility and freshness, and Organization or Product schema with sameAs links to trusted profiles to reinforce entity trust. Always make sure your schema matches the visible content.

How should I structure URLs and metadata for AI search?

Use full, natural-language slugs that read like the prompts people type, and match the URL to the topic in the H1. Write the meta description as a short, direct answer to the page's primary question - 140 to 160 characters - using connector words like how, what, and why.

How often should I update content for AI search?

Review high-value, evergreen content every 6–12 months. Add fresh examples and metrics, display a visible "last updated" date near the title, and update the FAQ schema so the metadata matches the revised content. AI engines favour recent, clearly-dated sources.