When you enter a query into Google today — especially with the SGE (Search Generative Experience), ChatGPT, or Perplexity — you’re no longer just getting traditional search results. You’re shown an AI-generated answer: a summary of relevant information built by an AI model based on its understanding of the topic — not on exact keyword matches. And that completely changes the rules of the game.

💡 In the world of AI search, we’re not just optimizing for keywords anymore. We’re designing content that can be used across hundreds of possible queries. This approach is called Relevance Engineering.

1. Traditional SEO vs. AI Search

In the past, search engines focused on keywords. Algorithms matched search queries with website texts and ranked the best matches. Today, thanks to large language models (LLMs), it’s all about meaning. AI interprets a query as a user’s intent — not as a literal string of words. It looks for context, not exact matches.

Take these three queries:

• “Best coffee machine for an office”

• “Compare DeLonghi vs. Tchibo”

• “Affordable coffee machine for company under €200”

AI interprets all of them as different ways of expressing the same need: to find a suitable coffee machine for the workplace. If your content meaningfully addresses that need — even if it doesn’t include any of those phrases word-for-word — AI can still use it to generate an answer.

2. Embeddings: How AI “Understands” Text

At the core of this understanding are embeddings — vector-based (numerical) representations of meaning. Instead of literal words like “office” or “coffee machine,” AI uses these vectors to measure how semantically close two texts are.

That means AI can recognize that:

• “Affordable espresso maker” is close in meaning to “coffee machine under €200”

• “Business supplies” relate to “office equipment”

• “Brand comparison” is part of a purchase decision

So AI selects snippets not based on keyword repetition, but on context and relevance.

3. Query Fan-Out: How AI Searches for Content

Some systems — like Google SGE — use a method called query fan-out. That means the AI generates dozens of alternative versions of the original question to better capture what the user might be looking for.

For example, from the query “cheap coffee machine for the office,” AI might generate:

• “Office espresso machine under €200”

• “What’s best for coffee in an open office?”

• “Capsule vs. manual machines — which one for business use?”

4. Selecting Relevant Content

Next, the AI scans available content (websites, articles, reviews, product descriptions) and picks out the most relevant snippets to build its answer. The clearer and more precise your content is, the more likely it is to be selected as a source.

5. How AI Understands Concepts: The Knowledge Graph

Google and some AI systems also use a knowledge graph — a network of connected concepts, brands, locations, people, and topics.

For every word or phrase, AI builds context:

• “Apple” might refer to a tech company or a fruit

• “Jaguar” could be an animal or a car brand

To make sense of this, AI relies on the knowledge graph. For example, it “knows” that Apple is a brand that sells iPhones, is headquartered in the U.S., and has a CEO named Tim Cook — so it understands which Apple you mean.

If your website uses structured data (more on that below), clear terminology, and well-defined context, you’re helping AI place your brand correctly into this graph.

AI doesn’t look for the biggest brand, the most famous domain, or the article with the most backlinks. It looks for the bestcontent — the one that answers the question most effectively. In AI search, you can succeed even without a big budget — if you meet the following criteria:

• You explain the topic clearly and practically

• You use plain language and a clear structure

• You cover the topic in depth and from multiple angles

This opens the door for smaller players with high-quality content. You don’t need to rank first in traditional search results. What matters is being first for the AI.

1. Be Specific and Use Clear Language

Language models prefer content that directly answers the question. As we’ve mentioned, they work with meaning. If you use vague terms like “new arrival” or “this product,” AI won’t understand what you’re referring to.

Instead of ❌ “a new product for the home”, write ✅ “Dyson V12 cordless stick vacuum for apartments up to 80 m².”

This ensures what's called monosemanticity — every term has one clear meaning. That helps AI better understand and use your content.

2. Show That You’re an Expert in Your Field

AI agents trust websites that cover a topic thoroughly. They value what's known as topical authority.

Here’s what that means in practice:

✅ Create content series and clusters of related pages
✅ Explain the origin of the product, its health benefits, and how it’s made
✅ Interlink your content clearly and meaningfully

Even in the age of AI, the E-E-A-T principles (Experience, Expertise, Authoritativeness, Trustworthiness) matter more than ever. Content that reflects real-world expertise and credibility — either from an author or a brand — is more likely to perform well not only in search results, but with users too.

Google uses E-E-A-T to assess content quality. And in the context of AI-generated results, it's especially important to demonstrate human expertise and trusted sources — through author commentary, citations, or context.

✅ Add a short author note at the end of the article that states who wrote the piece or who stands behind it professionally. Mention relevant experience, areas of specialization, or hands-on practice.

✅ Link to high-quality external sources (such as research papers, official guidelines, or expert blogs) or your own internal materials. This boosts credibility and signals that the information is grounded.

✅ Include real-life examples, personal insights, expert warnings, or specific recommendations in your content.

3. Use Clear and Logical Structure

AI prefers content that’s easy to follow and well-organized:

✅ Clear headings (H2, H3)
✅ Bullet points and lists
✅ Questions and answers
✅ A summary at the beginning
✅ FAQ-style sections

This kind of structure makes it easier for AI agents to understand and work with your content.

4. Think About the Customer Journey

As mentioned earlier, AI doesn’t just respond to the exact wording of a question — it generates multiple variations of it. Your content should reflect different angles and ways of thinking that users might have.

Recommendation:

✅ Track query journeys — how people search and explore a topic step by step. Create content that flows logically, builds on previous questions, and covers different perspectives on the topic.

5. Incorporate Multimedia Content

AI doesn’t just process text — it also works with images, videos, and graphics. ✅ If you enrich your content with the right attributes (like alt text, captions, and structured data), you increase the chances that your website will be referenced by AI models.

6. Make Your Content Easy to Crawl

AI models (for now) operate on simpler crawling principles than Google — they can’t navigate websites as intelligently as Google bots do.

That’s why technical SEO best practices are more important than ever in the age of AI. Your site should:

• ✅ Load quickly

• ✅ Render content without complications

• ✅ Use structured data

If an AI crawler can’t access your content, all your efforts — great copy, structure — will go to waste.

Optimize for the “least capable” bot. Expect a wide range of agents to be crawling the web, and not all of them will be as sophisticated as Googlebot.

So when optimizing your website, make sure that even less advanced crawlers (like AI models) can extract all the key information.

✅ Ensure that important content loads in HTML, not only dynamically via JavaScript
✅ Optimize your Core Web Vitals

7. Implement Structured Data

Structured data helps both AI tools and search engines understand what different parts of your website represent — whether it’s a product, a review, an organization, or an article. In blog posts, for example, structured data can clearly identify your FAQ section.

Using the schema.org standard turns your website into a more readable and reliable data source — one that AI models can draw on when generating answers. ✅

8. Build a Strong Brand Reputation

AI doesn’t just look at how many times your brand is mentioned, it cares where, how, and in what context you’re mentioned. Natural mentions on trusted platforms — like reviews, industry blogs, discussion forums, or community websites — matter most.

These signals help AI understand that your brand is real, reputable, and relevant to the topic.
It’s not about quantity — it’s about quality and context.

✅ A strong reputation within your niche or community carries far more weight than hundreds of low-quality, artificially created mentions.

Brand Strength in the Age of AI Search

The less stable and predictable the answers from Google or LLMs become, the more important your brand becomes. If users remember you (or if your site ends up in their search history) you’re already halfway there. You’re that much closer to being recommended by AI.

That’s why you can’t afford to be interchangeable. You need a distinct tone of voice and genuine USPs. It could be outstanding, helpful content, top-tier customer service, or an exceptional product or experience that makes you stand out from the competition.

Ranking position no longer tells the whole story. AI-generated search results are:

• Dynamic – answers can change from minute to minute

• Personalized – depending on who’s searching and what they’ve searched before

• Unpredictable – often shown as direct answers rather than clickable links

That’s why, instead of traditional metrics like “keyword ranking,” it makes more sense to track:

• Appearances of your content in AI answers (e.g. in Google’s AI Overviews)

• Clicks from those answers (soon to be available in Google Search Console under the “AI Mode” section)

• Mentions of your brand in tools like ChatGPT, Perplexity, or Claude (as product cards or even without a direct link)

Search is also becoming increasingly fragmented. There are now multiple parallel channels where people search — Google SGE, Bing Chat, ChatGPT, Perplexity, and others — so brands must track their visibility across platforms, not just in the traditional SERP.

It’s important to start shifting focus now, because classic SEO metrics are slowly being replaced by a new kind of visibility: How much space your brand occupies in AI-powered search results. Being in first position no longer guarantees traffic — that’s the reality of zero-click search.

Take the Data with a Grain of Salt

When it comes to measuring performance in AI search, we’re still largely limited by what the platforms themselves — Google, OpenAI, Microsoft, and others — allow us to track. Visibility tracking in the AI landscape is still in its early days, and available data will gradually increase over time.

For now, you can monitor tools like Ahrefs, which are beginning to incorporate AI visibility signals into their tracking.

Note: Data from ChatGPT or AI Overviews in Ahrefs Are Only Estimates

Don’t take visibility metrics in ChatGPT or AI Overviews from tools like Ahrefs at face value, they’re just approximations. However, they can serve as a litmus test and provide valuable benchmarking when comparing your own performance with competitors.

If Ahrefs shows that a competing brand is performing three times better in AI Overviews, it’s likely they’re three times more advanced in AI optimization, too.

In AI search, the goal isn’t to rank first — it’s to be among the sources AI cites.
That requires a new mindset:

• Think in terms of context, not just keywords

• Write in a clear, simple, and structured way

• Build topical authority and strong internal linking across your content

SEO Isn’t Dying — It’s Evolving

And finally: The most important principles of good old SEO still apply (as even Google confirms).

• Focus on the quality of your product

• Build a strong brand reputation

• Create content that’s genuinely helpful for your audience

That hasn’t changed — not even in the AI era.

💡 Want to make sure AI tools can find and use your content? Get in touch with us. We’ll assess how your brand performs in the AI search landscape, help you with a technical audit, and define a content strategy built for the age of AI.

Sources

From It Depends to Action: Rethinking SEO in the Age of AI

Ahrefs – Zero-Click Search

Wikipedia – Knowledge Graph

iPullRank – How AI Mode Works

iPullRank – Relevance Engineering

Search Engine Land – What to Do When AI Overviews Take the Spotlight

Aleyda Solis – AI Search Optimization Checklist

iLoveSEO – A Guide to Semantics

Search Engine Journal – AI Mode & Google Search Console

Search Engine Journal – SEO & AI Search Journeys

Search Engine Journal – Structured Data for AI Search