The AI Search Optimization Playbook: Remaining Visible Beyond Rankings

Here’s a fact that’s slowly dawning on brands everywhere:

Even if you have high organic rankings on Google, you still need to invest in AI search optimization.

Why is that?

It’s because AI-powered search has grown to the point where you simply can’t afford to ignore it.

For instance, if you have #1 organic rankings for keywords that generate AI Overviews on Google, you’ll likely see declines in:

• Click-through rates

• Organic traffic

Research has discovered that the presence of an AI Overview can reduce clicks by 34.5%, even for top-ranked content.

However, appearing as a cited source in an AI Overview has the potential to boost your click-through rate by a significant margin (Seer Interactive).

60% of consumers use other AI-powered search tools like ChatGPT to handle online shopping, and they take its recommendations very seriously. This type of buying influence isn’t something marketers should let slip by.

Combine all these reasons, and it becomes clear why AI search optimization is now crucial for maintaining online visibility.

In this guide, we’ll share everything we’ve learned about AI SEO through our own experiences and experimentations.

Here’s what we’ll cover:

1. The rise of AI search and how it compares to traditional search engines

2. Core optimization tactics for AI search

3. Technical setup: schema, formatting, and internal links

4. How to build backlinks for AI SEO

5. Real-life examples of strong AI search visibility

How Did AI Search Become So Prominent?

AI search has completely changed the search landscape in just a few years, and its popularity is continuing to rise.

While the generative AI craze officially began with the release of ChatGPT in November 2022, it wasn’t able to access the internet in real-time until September 2023. Prior to that, it relied on its training data to generate answers, which had a cutoff of late 2021 (meaning it wasn’t able to provide the most up-to-date information).

Technically speaking, Perplexity was the first true AI search tool since it was able to perform live internet searches from the get-go when it launched in December 2022. Other tools, like ChatGPT and Claude, didn’t add live internet search capabilities until much later.

However, AI search didn’t start to become a digital marketing necessity until the launch of Google’s AI Overviews (AIOs) in May 2024.

Suddenly, AI-generated summaries started appearing at the top of Google’s search results pages, pushing the organic search results farther down the page.

At first, AIOs were incredibly prominent, but Google rolled them back after receiving backlash over the quality (there were many hallucinations and false answers).

Once they were scaled back, they were only appearing for a handful of searches (6.49% in January 2025).

Everything changed in March 2025 when Google announced that AIOs were:

1. Receiving a Gemini 2.0 upgrade

2. Going to become more prevalent in the search results

Ever since this announcement, AIOs have been popping up left and right.

According to Semrush, AIOs saw a 100% increase from January to March 2025, and the numbers kept shooting up.

The latest research suggests that AIOs now appear for 50% of all Google searches, even for keywords that aren’t informational in nature.

They even appear for certain commercial keywords, although they do generate below the sponsored ads now:

Also, AIOs tend to show up for longer, question-based keywords. Shorter keywords are less likely to generate AIOs, but it still happens.

How does AI search differ from traditional search?

While AI search tools and traditional search engines like Google both perform the same function (enabling users to browse the web and find answers to questions), they’re two different beasts.

First, search engines like Google and Bing have crawlers that are constantly discovering new web pages and updating existing ones. They store everything they find in the search engine’s index, which is like a massive library of online content (and how it all relates to certain search keywords).

Google’s search algorithm is highly sophisticated since it’s been around so long.

At its core, it relies on:

• Keyword matching

• Link graph signals (the PageRank system assigns authority signals to backlinks)

However, there’s also:

• NLP (natural language processing)

• Machine learning (through BERT and MUM)

These enhancements give Google’s algorithm some semantic and contextual understanding, but it pales in comparison to that of large language models (LLMs).

The LLMs that power tools like ChatGPT and Perplexity boast advanced NLP that lets them fully understand human language.

Because of this, they don’t rely on the link graph the same way algorithms do. That’s why AI tools are able to infer authority from brand mentions that don’t contain backlinks, while search algorithms need backlinks to establish authority.

Yet, and this is important to understand, backlinks still impact AI search visibility because AI search tools rely on traditional search engine indexes.

Since LLMs don’t crawl or index the web, they indirectly access Google and Bing’s indexes through APIs, web scrapers, and plugins. Thus, they tend to select content that already ranks well on standard search engines to cite in their generated summaries.

As a result, the organic search results act as a quality filter for LLMs, so it’s still worth investing in regular SEO alongside AI SEO.

What are the Core Optimization Tactics for AI Search?

Since AI search tools differ from regular search engines, it only makes sense that you’ll have to use different optimization tactics.

Having said that, LLMs and search algorithms do value a lot of the same signals.

Both want to provide high-quality search results for users, so they value:

• Top-quality content

• Authoritative domains with strong reputations

• Trustworthy sites that protect user information

The trick is that these signals take different forms.

For search engines, authority is established through backlinks. On LLMs, they determine authority based on brand mentions (whether they contain links or not), contextual relevance, and content quality.

Accordingly, here’s a look at the most effective AI search optimizations.

Brand and entity signals

LLMs use named entity recognition (NER) to properly identify objects, organizations, places, and things, just to name a few.

Basically, NER is how LLMs are able to distinguish similar terms like jaguar the animal from Jaguar the luxury car manufacturer.

NER is important in AI search optimization because you need to get LLMs to recognize your brand as an ‘authoritative entity’ in your field.

For example, if you run a financial website, you want LLMs to recognize you as a leading authority figure in the financial space. That way, whenever they need to generate an answer based on a financial prompt, your site will be one of the places they check first.

How can you get LLMs to view your brand as authoritative?

According to research from Ahrefs, the most powerful way is to build brand mentions on trusted, relevant websites:

Brand mentions fuel entity recognition and establish authority to LLMs, so they’re extremely valuable AI search assets.

The catch is that for LLMs, your brand mentions don’t need to contain backlinks to contribute to your authority. They’ll pick up on the meaning and context, and will tie it to your brand’s ‘entity.’

Backlinks are still valuable, though, because they generate traffic and contribute secondhand authority signals to AI search tools.

Ideally, you want respected websites in your niche to frequently mention your brand:

• In a positive light (“brand X is renowned for their exceptional service)

• As an authoritative source (“check out brand X’s ultimate guide on the topic”)

Digital PR techniques are perfect for earning brand mentions, such as publishing original research, networking with journalists, and getting featured in roundups.

Building topical authority

LLMs look for websites that have built topical authority on a subject, like a finance blog that has:

• A learning hub containing common definitions and FAQs

• Extensive blog posts covering financial topics in great detail (content clusters)

• Lots of supporting citations from related websites (other financial sites linking to your ultimate guides and tutorials, which signal trust and quality)

At the same time, topical authority reinforces entity recognition. Whenever LLMs see that you’re consistently associated with an industry or niche, they’ll link you to it. The more you do this, the more likely it is that LLMs will cite your content for prompts related to the industry you’re tied to.

Freshness and content quality

Here’s where the principles of Google’s E-E-A-T (experience, expertise, authoritativeness, and trustworthiness) apply.

In the same way that Google only wants to reward helpful content with high rankings, LLMs only want to cite high-quality content.

Technical Setup for AI Search: Schema, Semantic HTML, and Internal Links

Next, let’s focus on the technical elements you need to optimize for AI search. In particular, you need to add structured data to your content.

Structured data is information that follows a standardized format so that machines (LLMs and search algorithms) can properly interpret and display it.

For SEO, the most important types of structured data are semantic HTML and schema markup.

Semantic HTML refers to HTML elements that contain descriptors. In other words, they describe what they contain, like <head> (which lets machines know the element is a header).

Non-semantic HTML elements have generic names, like <div> and <span>. These don’t describe the elements they contain, which makes it harder for LLMs and algorithms to parse your content.

However, semantic HTML is most effective when paired with schema markup.

Schema markup is a structured data code that labels and categorizes aspects of your content. It uses an agreed-upon vocabulary that you can find at Schema.org.

Every post you create should contain semantic HTML and schema markup. Some common schema types include:

• Author

• sameAs

• Article

• Review

• aggregateRating

Internal links also play a crucial role in AI search optimization. However, instead of passing 'link juice’ from one page to another (to build up domain authority), internal links help LLMs understand the relationships between your content pieces.

For example, if you include internal links in your content clusters (as you should), LLMs can quickly identify the relationship between the pillar page and cluster pages.

Link-Building for AI Search

Link-building, while still a crucial practice for AI search, has also taken on a distinct form from its search engine counterpart.

Instead of targeting domains with high authority scores (which were always third-party metrics), you should focus on contextual relevance.

In other words, LLMs want to see backlinks that:

• Make sense in context and don’t appear randomly (like a fitness blog linking to a fitness product)

• Add valuable resources to users (like linking to ultimate guides, free tools, and informative infographics)

• Appear on trusted websites and not spammy ones (like a respected industry blog instead of one from a PBN)

Thus, the types of backlinks that yield the best results are:

1. Editorial backlinks on respected news sites and media outlets

2. Topically-aligned backlinks to relevant blogs and community forums

3. Resource backlinks and supporting citations for your content

Real-World Examples of Visibility in AI Overviews

Before we wrap up, let’s take a look at some brands that are thriving with AI search.

These brands consistently show up for prompts related to their respective industries.

This isn’t because they’re industry giants per se, but it’s because of how they’ve established themselves as credible sources of information in their niches.

Harvard Health (healthcare/medical)

Thanks to their plethora of online resources and blog content, Harvard Health has become a leading authority in the healthcare space, despite not being as large as other competitors like Mayo Clinic.

Their website has excellent resources for subscribers:

They also have a detailed blog that covers healthcare topics in great detail:

Here are some examples of them getting cited by Perplexity for common healthcare prompts:

Stripe (fintech and finances)

The fintech company Stripe has done a fantastic job establishing itself as an authority figure in the fintech space.

Its website also boasts a diverse array of resources:

They also have a top-tier blog:

Here are some examples of Stripe getting recommended and cited by AI tools:

Following the steps in this guide will help you achieve similar results for your brand!

Final Takeaways: Our AI Search Optimization Playbook

To summarize, AI search has now become a permanent mainstay in the search landscape. Ignoring it could negatively impact your visibility, even if you’re already ranking well.

However, earning brand mentions, producing content clusters, and adding structured data will help you stand out to LLMs, heightening the chances that your brand will get cited and recommended.

Do you want to improve your visibility on AI search tools?

Ask about our AI SEO services to make it happen!