SEO for AI search: build an AI search strategy

SEO for AI search is quickly becoming a core part of how brands are discovered online. AI-driven search has moved from novelty to default behaviour faster than almost any previous shift in digital marketing. Users are no longer just typing short keywords into Google and scanning a list of links. They are asking full questions, requesting comparisons, validating decisions and expecting a clear, confident answer.

AI platforms now sit between brands and their audiences. These systems synthesise information from multiple sources and decide what gets cited, summarised or ignored entirely. Visibility is no longer just about ranking highly – it is about being trusted enough to be referenced.

What is SEO’s place in AI search?

SEO still matters in AI-driven search results, but it has changed shape. The core foundations of SEO still apply: crawlable sites, structured content, topical authority and strong brand signals. What has shifted is how content is consumed and surfaced. Instead of being clicked, content is increasingly being interpreted and re-presented by AI systems.

The rise of AI search platforms

Usage data highlights why this matters. ChatGPT has grown into one of the most widely used AI platforms, reaching hundreds of millions of weekly active users in 2025, according to Digital Informational World. Meanwhile, The Verge stated that Google has indicated that its AI search features, such as AI Overviews, are being accessed by over two billion users per month as part of the core search experience. Perplexity has also positioned itself as a popular answer-oriented AI search tool, widely used for concise responses across research and decision-making contexts.

How users are searching with AI today

Each platform plays a slightly different role in how users search:

• ChatGPT is widely used for research, planning and decision support.
• Perplexity focuses on answer-led search with explicit citations.
• Google AI Overviews and Gemini are embedded directly into traditional Google search journeys.
• Claude is often used for long-form analysis, documentation and complex reasoning.

Together, these tools signal a move away from search results pages and towards answer ecosystems. Optimising for AI search means ensuring your content is discoverable, trusted and reusable inside those ecosystems.

How do AI search results work?

AI search platforms do not simply rank pages and return links. They retrieve information, assess trust and relevance and then generate a response designed to resolve the user’s question in a single interaction.

While implementations vary by platform, most modern AI search systems follow a similar underlying model. They combine traditional information retrieval with large language models, a setup often referred to as retrieval-augmented generation. In practical terms, this means AI systems pull in external information and then synthesise it, rather than relying purely on what they were trained on.

How AI systems find information

The first stage is retrieval. When a user submits a prompt, the AI system breaks it down into one or more sub‑queries. This allows it to search across multiple angles of the same question, a process Google has described as query fan‑out.

At this stage, AI platforms may draw from:

• Traditional search indexes
• Licensed or curated data sources
• Fresh web content retrieved in real time

Content that performs well here shares many traits with strong SEO content: it’s crawlable, clearly structured and topically relevant. If content cannot be reliably accessed or understood, AI models are unlikely to progress beyond this stage.

Natural language processing, entities and context

Once information has been retrieved, AI systems analyse it using natural language processing. This is where meaning, rather than keywords, becomes critical.

AI models identify entities such as brands, products, locations and concepts, then map how they relate to one another. They also assess context, intent and sentiment to determine whether a source genuinely answers the question being asked.

This is why entity-based SEO – structuring content around clearly defined people, places, organisations and concepts rather than just keywords – and consistent terminology are important. Content that clearly defines concepts, avoids ambiguity and reinforces relationships between ideas is easier for AI systems to interpret and trust.

Synthesising an answer 

The final stage is generation. Here, the AI model combines information from multiple sources into a single response. Rather than quoting entire pages, it summarises, paraphrases and extracts key points.

Only a small number of sources are typically cited or referenced. These are selected based on perceived authority, clarity and alignment with the query intent. If a piece of content is vague, overly promotional or poorly structured, it is less likely to be included at this point.

How does this differ from traditional search?

In traditional search, visibility is largely determined by ranking position. A page that ranks first receives the majority of clicks, but those in second and third will still secure traffic.

Where in AI search, visibility is binary. Content is either included in the generated answer or it is not. There is no page two and often no opportunity for discovery beyond the response itself. This makes inclusion more important than position.

Another key difference is presentation. AI systems rarely display content as discovered. They prioritise sources that are easy to summarise accurately, which places a premium on clarity and structure.

What this means for brands

For brands, optimisation is no longer about chasing individual rankings. It is about becoming a reliable source that AI systems feel confident reusing, supported by consistent authority signals across the wider web.

Factors that matter when optimising for AI search

SEO’s foundations remain, but expectations have moved on. To perform well in AI-driven results, content now needs clearer structure, greater depth and a stronger focus on answering real questions, not just matching keywords.

Content structure and answer first writing

How content is written and structured have become two of the most important factors for AI search visibility. While strong structure has always helped SEO, AI systems are far less forgiving of content that buries answers or lacks a clear hierarchy.

At a basic level, AI models need to quickly identify the question and answer of a section. If that relationship is unclear, the content is unlikely to be reused.

Why answer-first writing matters

Answer-first writing means leading with the conclusion before expanding with explanation or context. This mirrors how AI systems generate responses, by prioritising a clear, direct answer and then adding supporting detail.

For example:

• Traditional SEO approach: Introduce the topic, provide background and answer the question after several paragraphs.
• AI-optimised approach: State the answer in the opening sentence, then explain why it is true and how it applies.

This does not reduce depth or nuance. It simply ensures the core response is immediately extractable.

How AI systems extract content

AI models often rely on structural cues to identify answers. These include:

• Headings that clearly state the question or topic
• The first one or two sentences under a heading
• Lists, tables and step-based formats

If an answer is spread across multiple paragraphs without a clear lead, the model may struggle to summarise it accurately. Clear structure reduces the risk of misinterpretation.

Modular content beats long-form blocks

Large, uninterrupted paragraphs are difficult for AI systems to parse. Modular content, broken into self-contained sections, performs better.

Each section should answer a single question or sub-question. This allows AI platforms to use specific parts of a page without losing context or accuracy.

Using lists and tables effectively

Lists and tables reduce ambiguity. They make relationships between ideas explicit and are easier for AI systems to summarise.

For example, a table comparing AI search platforms communicates differences more clearly than a narrative description. Bullet lists are effective for steps, criteria and definitions.

Common structure mistakes to avoid

• Burying the answer halfway through a section.
• Using vague headings that do not reflect the question being answered.
• Overusing narrative introductions at the expense of clarity.
• Combining multiple questions into a single, unfocused section.

Strong structure and answer-first writing do not replace research or insight. They ensure that it’s visible and usable. As AI search continues to mature, clarity will increasingly outperform creativity that obscures meaning.

Content and keyword strategy

Keywords still matter, but not as isolated targets. AI search relies on semantic understanding rather than exact matches. Covering a topic comprehensively, including related questions and concepts, is more effective than optimising for a single phrase.

Technical foundations

Technical SEO still matters. Clean HTML, fast load times and accessible content help AI systems retrieve and interpret information reliably. Structured data reinforces context and intent.

Brand mentions

Authority is no longer defined purely by backlinks. Consistent brand mentions across reputable sites, media, social platforms and video transcripts contribute to how AI systems assess credibility.

AI search strategy to increase visibility on AI platforms

AI search optimisation is a relatively new discipline and will continue to evolve. As with early SEO, best practices are emerging through platform guidance, experimentation and observed behaviour. A successful strategy needs to be flexible by design.

Query fan out: a practical framework for AI search

Query fan out is an evolution of traditional keyword research. Instead of optimising for a single query, it focuses on mapping the full set of related questions, refinements and follow-ups a user is likely to ask when interacting with an AI system.

This approach is particularly important for AI search because user searches are rarely just one prompt. A single question often represents the start of a conversation, not the end of it.

Google has explicitly described a query fan out technique in its AI Mode, where the system decomposes a user’s prompt into multiple subqueries and executes them behind the scenes to generate a unified answer.  

Why query fan-out matters

In traditional SEO, ranking for one high-volume keyword could be enough to drive visibility. In AI search, that is rarely sufficient.

AI systems aim to answer questions completely. Query fan out helps ensure your content aligns with how AI systems evaluate completeness and relevance.

It also reduces the need for multiple thin articles. Instead of publishing separate pages for closely related queries, brands can create one authoritative resource that answers the full range of related questions.

The query fan-out framework

A practical query fan-out process can be broken down into four steps.

1. Identify the seed question

Start with the core question you want to be known for. This should reflect a real user need rather than an abstract keyword. For example: “How does AI search affect SEO?”

2. Expand into related questions

Use a combination of tools and prompt testing to collect variations and follow-ups. These might include:

• Clarifying questions (Does SEO still matter for AI search?)
• Process questions (How do AI search engines rank content?)
• Comparative questions (AI search vs traditional search)
• Action-oriented questions (How can brands optimise for AI answers?)

Prompting AI tools directly is useful here, as it mirrors how users phrase questions in natural language.

3. Validate with search data

Cross-check these questions against People Also Ask results, internal site search data and SEO tools such as Semrush or Ahrefs. This helps prioritise questions that reflect genuine demand rather than theoretical interest.

4. Map questions to content modules

Instead of treating each question as a separate page, group them into logical sections within a single piece of content. Each section should be self-contained, with a clear heading and an answer-first structure.

Worked example: query fan-out in practice

Seed question: “Does SEO still matter for AI search?”

Fan-out questions might include:

• Why do AI search tools still rely on search indexes?
• What SEO signals do AI systems use?
• How is AI SEO different from traditional SEO?
• What types of content get cited by AI tools?

Rather than writing four separate articles, these questions can be answered within one comprehensive guide. Each becomes a potential extraction point for AI systems, increasing the likelihood of inclusion.

How query fan-out improves AI visibility

Query fan out aligns content with how AI systems assess usefulness. It signals depth, reduces fragmentation and improves the chances that a single page can satisfy multiple sub-queries.

For brands, this means fewer pages, stronger authority and better alignment with conversational search behaviour.

How to structure your content

Concise answers should appear immediately after headings. Lists and tables reduce ambiguity. Modular sections allow AI systems to use specific answers without misinterpreting context.

Include FAQs

FAQs are particularly valuable because they mirror conversational search behaviour. Microsoft reports that AI-driven queries are significantly longer and more question-based than traditional searches. 

Effective FAQs are built from real data. People Also Ask results, internal site search queries and AI prompt research all help identify the questions users are actually asking. Adding FAQ schema markup reinforces clarity and increases the likelihood of inclusion.

Use ‘How to’ content

‘How to’ sections remain highly effective for both SEO and AI visibility. They provide step-by-step logic, clear outcomes and defined actions. This structure aligns well with how AI models generate instructional responses.

The importance of brand mentions

Backlinks still matter, but they’re no longer the only authority signal. AI systems also evaluate unlinked brand mentions across blogs, forums, podcasts, video transcripts and social platforms. A broad, consistent brand footprint helps reinforce legitimacy.

User intent shift

User intent has shifted from exploration to resolution. AI users expect direct answers, comparisons and recommendations, often within a single interaction. They are less tolerant of vague explanations or delayed answers.

This changes how content should be planned and written. Instead of publishing multiple surface-level articles, brands should prioritise depth and completeness, ensuring a single piece can resolve the core question and its likely follow-ups. This approach aligns more closely with how AI systems select, evaluate and trust sources.

Mistakes that could be hurting your AI search efforts

Many brands are limiting their AI visibility by applying outdated content practices. Here are some examples: 

Writing long, dense paragraphs with no clear answers

Good: “Yes, SEO still matters for AI search because AI models rely on trusted web content to generate answers.”
Not good: Several paragraphs of context before answering the question.

Unstructured content

Good: Clear headings, bullet points and FAQs.
Not good: Large blocks of text with no hierarchy.

Treating AI SEO as purely keyword-based

Good: Topic clusters and semantic coverage.
Not good: Repeating exact-match keywords.

Ignoring user intent behind AI queries

Good: Content designed to resolve a specific decision or problem.
Not good: Generic explanations with no outcome.

Skipping FAQs or treating them as an afterthought

Good: Research-led FAQ sections integrated into content.
Not good: Token FAQs added for compliance.

Relying only on backlinks for authority

Good: Diverse brand mentions across trusted sources.
Not good: Link-building without broader visibility.

Why is a dedicated AI search approach important?

AI search adoption continues to grow with traditional search. Google has stated that AI Overviews increase engagement and help users resolve queries more quickly, according to its May 2025 Search Central update.

While AI platforms may not always drive high volumes of referral traffic, their influence on decision-making is significant. AI search visitors tend to be more highly qualified than traditional organic search visitors because users often arrive with clearer intent, increasing the likelihood of conversion, according to Semrush. 

This positions AI search as a high-intent visibility channel rather than a pure traffic driver. Brands that ignore it risk losing influence at critical decision points.

How to measure success in AI search

Measuring success in AI search requires a shift in mindset. Traditional SEO metrics such as rankings and organic traffic still matter, but on their own, they no longer tell the full story.

AI platforms often resolve queries without sending users to a website. As a result, visibility and influence become more important indicators than clicks alone.

Visibility within AI answers

One of the clearest indicators of success is whether your brand or content is being referenced inside AI-generated responses.

This can be measured through:

• Manual prompt testing across platforms such as ChatGPT, Gemini and Perplexity.
• Tracking brand mentions and citations in AI answers over time.
• Monitoring how often your content is paraphrased or summarised rather than linked.

While this process is still partly manual, it provides direct insight into how AI systems perceive your authority.

Prompt testing as a research method

Prompt testing is both a diagnostic and an optimisation tool. By running consistent prompts across AI platforms, brands can observe which sources are surfaced, what language is used and where gaps exist.

Changes in responses over time often signal shifts in trust or relevance, making prompt testing a valuable early-warning system.

Assisted conversions and influence

AI search frequently influences decisions earlier in the journey. Users may research options using AI tools and convert later through branded or direct channels.

This means attribution models should account for assisted conversions. According to Semrush, AI-assisted journeys can convert at five to six times the rate of traditional organic search because users arrive with clearer intent.

Why traffic alone is misleading

Low referral traffic from AI platforms does not mean low impact. AI search often acts as a recommendation layer rather than a traffic driver.

Brands that focus solely on sessions risk undervaluing channels that shape consideration and preference. A broader measurement approach that includes visibility, influence and conversion quality provides a more accurate picture.

Building a measurement baseline

Because AI search is still evolving, consistency matters more than precision. Establishing a baseline for brand mentions, citation frequency and prompt coverage allows teams to spot trends and adjust strategy as platforms change.

What is the future of search?

SEO is not dead. It’s expanding. Search is becoming more conversational, more contextual and more outcome-focused. Deeper integration of AI into paid media, including conversational ad formats and AI-generated commercial placements can be expected. Google has already signalled ongoing experimentation in this area.

User behaviour will continue to evolve toward fewer queries, longer prompts and higher expectations of accuracy. Brands that focus on serving answers rather than chasing rankings will be better positioned for the next phase of search.

Different AI platforms for search

While AI search platforms often appear similar on the surface, they behave differently in practice. Each tool has distinct strengths, usage patterns and signals it appears to prioritise when selecting sources. Understanding these differences helps brands tailor content without fragmenting strategy.

ChatGPT

ChatGPT is primarily used for exploratory research, planning and decision support. Users often begin with broad questions and refine them through follow-up prompts rather than conducting one-off searches.

Content that performs well here supports multi-step reasoning and follow-on questions. Sources that define concepts clearly and maintain consistency across explanations are more likely to be reused as conversations evolve.

Perplexity

Perplexity positions itself as an answer engine with explicit citations, which makes source selection more transparent than most AI platforms.

Because citations are surfaced directly, Perplexity tends to prioritise sources that make factual claims that are easy to verify and attribute. This makes it particularly sensitive to accuracy, clarity and alignment with the specific question being asked.

Google AI Overviews

Google AI Overviews are embedded directly into the traditional search experience and build on Google’s existing ranking systems. As a result, established SEO performance strongly influences which sources are eligible for inclusion.

What changes is how content is surfaced. Pages that rank well but lack clearly extractable answers may be bypassed in favour of sources that allow Google to summarise information with greater confidence.

Gemini

Gemini powers conversational experiences across Google products and closely reflects Google’s entity-based understanding of the web.

Content that performs well reinforces clear entities and relationships between concepts. Gemini also appears more responsive to freshness for time-sensitive or rapidly evolving topics, making update cadence more important than on static informational content.

Claude

Claude is commonly used for long-form analysis, documentation and complex reasoning. Users typically arrive with more detailed prompts and expect nuanced, well-structured responses.

In this context, longer-form resources that explore a topic in depth and present a clear line of reasoning are more likely to be referenced than short, surface-level explanations.

What this means for content strategy

Despite their differences, these platforms share common preferences around clarity, trust and relevance.

Rather than creating platform-specific content, brands should focus on producing authoritative resources that answer questions directly, use consistent terminology and demonstrate depth without unnecessary complexity. This approach scales across platforms and reduces the risk of over-optimising for any single tool.

AI search rewards clarity, not shortcuts

AI search has not replaced SEO, it has raised the bar for it. The same fundamentals still apply, but they now need to work harder. Content must be clear enough to be extracted, structured enough to be trusted and comprehensive enough to answer real questions end-to-end.

There is no single factor that separates brands that appear in AI answers from those that don’t. It is a combination of consistency in entity signals, depth, intent alignment and visibility beyond owned channels.

AI platforms will continue to evolve, which makes flexibility essential. Strategies built around understanding users, covering topics properly and earning trust will adapt far more easily than those chasing short-term optimisation tricks.

If there is one mindset shift to take forward, it is to stop optimising content just to be found and start optimising it to be used. AI search systems select sources they can rely on. The brands that win will be the ones that make that decision easy.