AI search optimisation is marketing for machines

Five years ago, a buyer with a problem would probably start with Google.

They’d search a few keywords, open a handful of tabs, scan comparison pages, read reviews, check a few websites and slowly build a point of view.

The brand’s job was to be visible in that journey.

1. Rank well in an index.
2. Win the click.
3. Keep the visitor engaged.

Of course that journey still happens, even in the grand old year of 2026, but it’s no longer the only option.

Today, a buyer can ask ChatGPT, Claude, Gemini to do much of that work for them. The model can research the category, compare providers, summarise trade-offs, shortlist options and explain which product looks like the best fit.

The buyer still makes the decision. But more of the research, comparison and recommendation layer is now happening inside answer engines.

That changes what search optimisation is for.

AI search optimisation is about making sure your brand is understood by the systems doing that work.

You can call it AEO. You can call it GEO. You can call it SEO. The name doesn't really matter.

The debate seems obsessive about whether every AEO activity is different from SEO.

That misses the bigger point.

Traditional SEO has helped brands understand rankings, clicks, content performance and organic demand.

AEO/GEO helps brands understand how they’re being spoken about in answer engines, which prompts carry commercial intent, which AI systems are crawling their site, how those systems interpret their content and whether that activity turns into real traffic.

What matters is this: over the next few years, more of your marketing will be interpreted by machines before it’s seen by humans.

That means brands need to think differently about how they’re discovered, understood and recommended.

SEO fundamentals are non-negotiable

Good SEO principles are still the foundation.

If your site can’t be crawled, indexed or understood, AI search will have a difficult time finding and understanding your brand. Technical health, helpful content, authority, internal linking and clear product information all matter.

For Google’s AI search experiences, that link is especially direct. In its guide to optimising for generative AI features in Search, Google makes the point that AI visibility in Search is still grounded in Search fundamentals.

That’s useful advice.

Strong SEO helps your content become available as a source. It helps search engines and AI systems access, parse and understand what you offer.

But AI search adds another layer.

Once your content can be found, the next question is whether it’s useful enough to be selected, summarised and recommended.

That’s where AEO becomes useful as a practical way to describe the work.

Machines don’t consume content like people

Human readers care about story, tone, trust and flow.

That still matters. People still buy from people. Your website still needs to persuade, reassure and convert.

But machines don’t consume content in the same way.

A model doesn’t doesn’t care about your carefully paced narrative or story. It doesn’t need the argument to unfold like a blog post.

ChatGPT, Claude, Perplexity or Gemini takes the source material, forms the answer and creates the narrative for the user.

That means your content needs to do two jobs.

It needs to work for humans when they land on your site.

It also needs to give machines the raw material they need to understand, compare and recommend you accurately.

That raw material looks different from traditional marketing copy.

It includes:

• clear product facts
• specific use cases
• evidence and proof
• pricing context
• customer examples
• comparisons and alternatives
• limitations and caveats
• up-to-date entity information
• third-party corroboration
• concise explanations that make sense out of context

This is the heart of AI search optimisation.

You’re making your brand easier for machines to understand before they build the answer for the human.

The work sits across configuration, content and analysis

AI search optimisation becomes clearer when you break it into three areas.

This is where the work moves beyond traditional SEO reporting. You’re not only asking whether a page ranks. You’re asking whether AI systems understand the brand, whether they recommend it, whether they cite it, whether they crawl it and whether their behaviour creates real commercial outcomes.

1. Configuration

Configuration is about making sure the right systems can access the right content.

For traditional SEO, teams focus on things like Googlebot, Bingbot, robots.txt, canonicals, redirects, rendering and indexation.

For AI search, crawler policy gets more complex.

OpenAI, Anthropic and other AI companies use different crawlers for different purposes. Some relate to search visibility. Some relate to user-triggered retrieval. Some relate to training.

That means you may want to allow one bot and block another.

For example, you might want AI search tools to access your commercial pages so they can cite and recommend you. At the same time, you might have a different policy for training crawlers.

That’s a real operational difference.

Configuration also includes making sure important pages are accessible, render properly, avoid unnecessary friction and contain the information AI systems need to retrieve.

2. Content

Content needs to become more answer-ready.

A useful SEO page is structured, relevant and helpful. An answer-ready page goes further by making important claims easy to extract, verify and compare.

That means:

• direct answers
• clear summaries
• named use cases
• evidence and examples
• dates where freshness matters
• limitations and caveats
• pricing context
• alternatives and comparisons
• screenshots or visuals where useful
• strong About, author and product information

Generic content has less value in AI search.

If a model can produce the same answer without you, your page has limited value as a source.

The stronger opportunity is to create content that’s specific, useful and difficult to copy. Research, benchmarks, case studies, practical examples, first-hand experience and a clear point of view all become more important.

3. Analysis

Analysis is where AI search optimisation becomes especially different from standard SEO.

Rank tracking tells you where a page appears in search results.

Prompt tracking tells you whether your brand appears when users ask AI tools commercially important questions. We cover this in more detail in our guide to how we measure AI visibility.

You need to understand:

• whether AI systems mention your brand
• whether they cite your content
• which competitors appear instead
• whether the answer is accurate
• whether your positioning is understood
• which prompts influence demand
• which sources shape the recommendation
• which AI platforms send traffic
• how AI-referred visitors behave once they land on your site

This is a continuous feedback loop.

You test prompts, analyse answers, find visibility gaps, improve content, fix entity issues, update technical configuration and monitor what changes.

Agents make that loop more powerful. They can monitor answer engines, identify gaps in truth or content, surface inaccurate descriptions, find missing comparison points and help create the content needed to close those gaps.

That loop is the work.

Marketing is moving towards systems

AI search pushes marketing towards systems, not just campaigns.

A marketer who understands this world needs to do more than publish content and check rankings. They need to understand agents, workflows, data, retrieval, automation, measurement and feedback loops.

This is where the idea of the marketing engineer becomes useful.

The competitive advantage is orchestration.

You need systems that can monitor answer engines, collect prompt data, identify inaccurate descriptions, spot competitor recommendations, detect source patterns and feed those insights back into content, technical configuration and positioning.

Agents can help with that. They can monitor outputs, compare answers, flag changes, draft recommendations and identify which sources seem to influence AI visibility.

The value comes from connecting those signals to action.

That’s different from treating AI search as a content calendar problem. It’s a search, data and systems problem too.

Google is part of the picture, not the whole picture

Google still matters enormously.

For many brands, Google will remain the largest search surface. Google’s AI Overviews and AI Mode also sit close to traditional SEO because they’re built around Google’s search infrastructure.

So yes, strong SEO is still essential for Google AI visibility.

But AI discovery now happens across more surfaces than Google.

ChatGPT, Claude, Perplexity, Gemini, Copilot and other assistant-led experiences have their own retrieval systems, crawler rules, citation behaviours and user journeys.

That means AI search optimisation can’t be treated as a Google-only SEO tactic.

For Google, the work may look more like SEO plus answer visibility monitoring. Across the wider ecosystem, businesses also need to understand how different AI systems retrieve information, cite sources and form recommendations. That’s where specialist AI search optimisation work becomes more useful.

What matters most

Start with the work that has a clear connection to visibility.

Make important pages crawlable, indexable and technically sound. Allow the AI crawlers you want to be visible to. Create useful commercial pages that answer real buyer questions. Make your positioning and product facts clear. Add evidence that supports your claims. Build comparison content that helps people choose. Check how AI systems describe you. Track mentions, citations, competitors and referral traffic.

This is the practical work that matters.

It’s also where AI search starts to move beyond standard SEO reporting.

The moment you start noticing traffic from answer engines, checking whether AI systems mention you, analysing prompts, tracking citations, reviewing AI crawler behaviour or working out why competitors appear instead of you, you’re doing AI search optimisation.

What matters less right now

Some AI search tactics are overhyped.

llms.txt is a clear example.

The argument for it makes sense on paper. A clean, curated file of important site content could help AI systems understand what matters and avoid messy HTML, navigation, cookie banners and outdated pages.

The problem is adoption and proof. Major AI platforms haven’t publicly made llms.txt a meaningful visibility protocol. Google doesn’t require it for AI Overviews or AI Mode. OpenAI and Anthropic both document crawler controls through robots.txt, not llms.txt.

So our view is simple: add llms.txt if it’s easy and you’ve already handled the important work. Treat it as a nice-to-have, not a serious lever right now.

The same applies to fake Q&A stuffing, thin pages for every prompt variation and special markup that isn’t supported by the platforms you care about.

Most of that work creates noise rather than durable visibility.

The team structure is up to you

AI search optimisation doesn’t need to become a separate team in every business.

It can sit underneath SEO. It can sit alongside SEO. You can have one search expert who owns both. The right structure depends on the business.

The important thing is making the work visible.

If SEO means rankings, content, technical health, digital PR, AI mentions, prompt tracking, citation analysis, brand accuracy, crawler policy and AI referral performance, it gets harder to know what work is actually being discussed.

AEO gives teams a clearer label for a specific job: improving how a brand appears in AI-generated answers.

That makes planning easier. It makes reporting easier. It makes ownership clearer. It also helps businesses see that AI visibility needs active monitoring, rather than assuming it’s already covered by existing SEO activity.

This doesn’t mean ripping up your SEO strategy. It means creating a new space to understand AI search performance and improve it.

SEO can remain the parent discipline. AEO can be the practical subcategory. The point is to make sure AI search optimisation is visible, measured and owned.

The name matters less than the capability

Over the next few years, more buyer journeys will be shaped by AI systems before a user lands on a website.

McKinsey estimates that AI-powered search could influence $750 billion of US consumer spend by 2028.

That’s the scale of the shift.

The important question is not whether you call the work AEO, SEO or AI search optimisation.

The important question is whether your business understands how AI systems retrieve, interpret and recommend brands.

Search is becoming more answer-led. More of the research process is being handled by machines. More recommendations are being formed before the user clicks.

Brands still need excellent SEO.

They also need a clear space to understand AI search performance, close content gaps, fix inaccurate answers and learn from the systems shaping discovery.

That’s why brands preparing for the future of search should care about AEO and GEO now.

FAQs

What is AI search optimisation?

AI search optimisation is the work of improving how your brand appears in AI-generated answers. It looks at whether tools like ChatGPT, Claude, Gemini, Perplexity and Google AI experiences can find, understand, cite and recommend your business.

Is AEO different from SEO?

Yes, in practice. SEO helps your pages rank in search results. AEO focuses on how your brand is understood, cited and recommended inside AI answers. The two overlap, but AEO adds new work around prompt tracking, AI visibility, crawler access, answer accuracy and AI referral traffic.

Does SEO still matter for AI search?

Yes. Strong SEO is still the foundation. If your site can’t be crawled, indexed or understood, AI systems are less likely to use it. Technical SEO, helpful content, authority, internal linking and clear product information all still matter.

Is llms.txt important for AEO?

Right now, llms.txt is a nice-to-have, not a core visibility lever. The idea makes sense, but major AI platforms haven’t made it a meaningful protocol. Robots.txt, crawler access, content quality, entity clarity and AI visibility tracking matter more.

Who can help with AI search optimisation?

A specialist AI search partner can help brands understand how they appear across answer engines, where they’re missing from important prompts and what needs to change across configuration, content and analysis. Tilio, the UK’s leading AEO/GEO agency works with brands on AI visibility tracking, prompt analysis, answer-ready content and measuring whether AI search turns into real traffic and leads.