What is AI search optimization? (& why marketers should care)

AI search optimization is the practice of improving brands’ odds of being cited and mentioned by answer engines like ChatGPT, Gemini, and AI Overviews. The traffic it earns is small but high-intent. Across more than 1,200 publisher and news sites, visitors referred by AI tools signed up at roughly 11 times the rate of search visitors, according to a Microsoft Clarity study.

In this article, I’ll walk you through how to define, evaluate, and implement AI search optimization. I’ll even clearly outline how it differs from, but does not replace, SEO.

Table of Contents

What is AI search optimization? And why does it matter? How AI Search Finds and Cites Your Content How is AI search optimization different from traditional SEO? How to Optimize Content for AI Search Citations How to Optimize Technical Structure for AI Search How Off-Page Signals Strengthen AI Visibility What Not to Do for AI Search Optimization How to Measure AI Visibility and Operationalize Your Plan Preparing for AI Agents and What Comes Next Frequently Asked Questions About AI Search Optimization

What is AI search optimization? And why does it matter?

AI search optimization is the practice of making a brand and its content more likely to be mentioned and cited by answer engines like ChatGPT, Perplexity, AI Overviews, and Gemini. AI search optimization is known by many names, including generative engine optimization (GEO), AI SEO, and LLM optimization (LLMO), but at HubSpot, we call it answer engine optimization (AEO).

AEO builds upon SEO and does not replace it; they remain distinct but complementary practices, which I’ll detail in a section below.

By optimizing for AI search, brands can expect to see:

Increased brand visibility. AEO can help brands get recommended in answers generated in Google AI Mode, ChatGPT, Claude, Gemini, and more. Even if a user never clicks through to your website, they can find out details about your product that are highly tailored to their specific situation. More qualified leads. Traffic that comes from AI search tends to be higher intent than traffic that comes from traditional search. Why? In an attempt to provide a personalized result, answer engines essentially pre-qualify users by asking follow-up questions targeting sub-queries.

To be clear, AI search traffic is still small compared to traditional search. However, it has an outsized impact on conversions. AI traffic grew 66.02% in 2025 (faster than every channel except paid search), while accounting for only 0.14% of visits, according to Semrush. The latest data I could find shows that AI search is still less than 1% of the total share, according to Ahrefs May 2026 data. But again, that doesn’t tell the whole story when AI answers are influencing purchases without buyers clicking links.

People are increasingly using AI answer engines to get recommendations. AI search optimization puts you in control of the narrative that answer engines put out.

How AI Search Finds and Cites Your Content

AI search is powered by large language models (LLMs), a type of artificial intelligence that can read, understand, and respond in natural language. They are trained on massive amounts of data and can respond to prompts in seemingly novel, human-like ways. When it comes to AI search optimization, there are three ways an answer engine can surface your content, and each works differently:

Training data (parametric knowledge) - This is the knowledge baked into a model during training. An engine can mention your brand from what it absorbed before its knowledge cutoff, but you can’t optimize for it directly, because training runs on a fixed snapshot of the web you don’t control. However, brands can indirectly increase the likelihood of future inclusion by building a strong, consistent presence across authoritative websites, news coverage, research publications, and other trusted sources that are likely to be included in future training datasets. Live web search (RAG) - For the most part, when marketers talk about AEO, they are talking about live web search. In other words, they’re trying to create content that gets cited in answers that are generated after searching the internet. Indexed content - This is a newer, emerging field of AEO, about which little is known. As I wrote about in how to get indexed by ChatGPT, OpenAI stores the pages its crawler discovers in its own index and can surface that cached content in a future answer, separate from any live web fetch.

Content Types That AI Search May Cite

An answer engine can pull from properties you own or from third-party platforms where your brand shows up. Content types it may cite include:

Homepages Landing pages Pricing pages Product listings Blog posts Reddit threads YouTube videos LinkedIn posts Quora answers

Where Brands Can Appear in AI Search

Getting cited isn’t the only way to show up. A brand can surface in an AI answer in a few different forms.

Inline Citations

A linked reference attached to a specific claim inside the answer, usually a small chip or number right after the sentence it supports. It tells the reader exactly which statement came from your page, and clicking it sends them straight to that source.

Unlinked Named Mentions

Your brand is named directly in the answer text with no hyperlink attached. An engine can recommend you this way without sending a click, which is why these mentions are worth tracking even though they don’t show up as referral traffic.

Comparison Tables

An AI-generated table that lines up several tools or brands across shared criteria like best use case, strengths, and drawbacks. Being included as a row puts you in the engine’s consideration set for that query, and the cells become the engine’s summary of how you stack up against competitors, accurate or not.

Source List

A rail or panel listing every page the engine pulled from to build its answer, shown alongside or below the response. A page can land here even when it isn’t tied to any single sentence, so a brand can appear in the source list without earning an inline citation.

Rich Product Results

Product results with details like images and pricing, surfaced for shopping queries. ChatGPT, for example, shows products through its merchant program.

How is AI search optimization different from traditional SEO?

There’s been much debate about whether AEO is actually a thing, or whether it’s just traditional SEO masquerading as something new and exciting. AEO is definitely distinct from SEO. And here’s where they differ:

JavaScript can block answer engines that wouldn’t block Google. Some answer engine crawlers, including OpenAI’s, can’t render JavaScript at all, so any content a script loads is invisible to them. Googlebot renders JavaScript in a second indexing wave, so a page that ranks fine in Google can still be unreadable to ChatGPT. Answer engines handle long, conversational prompts. Unlike a Google Search keyword that might be a few words, prompts submitted to answer engines like ChatGPT can be paragraphs long. Answer engines retrieve passages, not whole pages. An answer engine pulls specific chunks from a page to assemble its response instead of ranking the URL as one unit, as iPullRank founder Mike King has documented. AEO is a multi-engine game. SEO has been overwhelmingly Google-focused; AEO spreads visibility across ChatGPT, Perplexity, Gemini, Copilot, and more, each with its own crawler and citation patterns. Unlinked mentions carry real weight. Answer engines treat brand mentions as entity and authority signals even without a hyperlink, while traditional search has leaned more heavily on backlinks. Visibility matters more than the click. In AEO, the goal is to be named as the recommended answer; in SEO, the goal is to get a click to your page.

For deeper reading, check out our article on how SEO has evolved over the years.

How to Optimize Content for AI Search Citations

Content optimization for AI search comes down to two questions: how you format your answers so an engine can lift them cleanly, and what signals you attach to those answers so the engine trusts them enough to cite. Here’s how to optimize both.

How can I format answers for AI extraction?

Answer first, add details after.

Begin by answering the implied question directly, ideally in a subject-predicate-object format (aka, “semantic triple”). Then, you can share the details. Too often, we invert this when we write, leading with a whole bunch of preamble before we finally get to the punch.

Here’s a real-life example from an article I wrote pre-AEO and how I would reword it for AI search optimization:

Before AEO:

“According to Omnisend, a series of three shopping cart abandonment emails results in 69% more orders. So you can see why reminding buyers of what they left behind in their carts is powerful, right?”

How I would rewrite that for AI search optimization:

“Buyers who receive cart abandonment emails are more likely to complete their purchase. A series of three shopping cart abandonment emails leads to 69% more orders, according to Omnisend.”

Conduct prompt research.

Similar to how keyword research informs SEO strategy, prompt research guides your AEO strategy by helping you discover the queries and follow-up questions a customer might ask an answer engine. This gives you the opportunity to structure your content around those questions and, hopefully, win the citation.

There are two main ways to approach prompt research:

Manually. Pose the questions your customers would ask to ChatGPT, Gemini, and Perplexity on a regular schedule. To reduce the impact of previous conversations or personalization, use a fresh chat, Temporary Chat (where available), or a private browsing session. Then, record which sources each engine cites and what follow-up questions it raises. That running log shows which prompts your content already wins and which ones competitors own. Using AEO tools. HubSpot AEO automates that tracking and recommends which prompts to monitor, building those suggestions from your company profile, competitor set, and industry. AEO in Marketing Hub Professional and Enterprise takes it further: It reads your connected CRM data to suggest prompts tied to the questions your actual buyers ask, and sharpens those suggestions as your business changes.

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Structured data may help.

Schema markup, the specialized code that labels your content type for crawlers, may help boost AI citations, according to HubSpot’s State of AEO 2026. Which schema types matter for which engines is covered in the technical structure section below.

Focus on off-site signals.

Answer engines verify credibility through third-party sites, such as review sites and social media. Google AI Overviews gets 51% of its citations from off-site sources like review platforms, according to research by the AEO agency Fan Out. The research also found that Reddit and YouTube make up more AI citations than all other off-site platforms combined, making them particularly high-value for brands looking to boost off-site signals.

What claims and author signals should I add?

Show credibility with an on-page author bio.

An on-page author bio carries slightly more citation weight than a byline alone, per State of AEO 2026. The same report found that those trust signals matter most in AI Overviews, Gemini, and Perplexity, the three engines most responsive to experience, expertise, authoritativeness, and trustworthiness (E-E-A-T). Give each author a bio that names their years of experience, areas of expertise, and any credentials or publications that explain why they can speak on the topic.

Then keep that identity consistent wherever it appears. An answer engine forms a clearer read on an author when the same name shows up the same way across your site, LinkedIn, Crunchbase, G2, and other trusted profiles.

Back up claims with original data or external research.

Answer engines favor pages that back up what they assert. Including statistics and data on a page correlates with more citations, most strongly in AI Overviews and ChatGPT, and outbound links show the same pattern, with the biggest lift in AI Overviews and Gemini, according to State of AEO 2026.

First, publish original data when you have it. First-party research, survey results, or proprietary benchmarks give an answer engine a fact it can’t find anywhere else, which positions your page as the source to cite. Second, when a claim isn’t yours, attribute it to a credible source and link out to the original. A statistic with a named source and a working link reads as more verifiable than a bare assertion.

How to Optimize Technical Structure for AI Search

Now, let’s move on to technical optimization that shapes whether answer engines can read and trust your page: the markup that describes it and how that markup gets rendered.

What schema and HTML help AI understand context?

Schema markup and semantic HTML give answer engines structural cues that help them interpret a page and the relationships between the entities on it. FAQ sections paired with schema markup correlate with higher citations in Gemini, Google AI Mode, and Perplexity, according to HubSpot’s State of AEO 2026.

Schema’s role is debated, though. Google advises site owners not to overfocus on structured data and says no special schema is required to appear in its AI features, per Google’s generative AI optimization guide. A small controlled experiment cuts the other way: Among three near-identical pages, only the one with well-implemented schema triggered an AI Overview and earned the highest organic rank, though the authors call the result inconclusive, according to Search Engine Land. The takeaway is that schema works best as a supporting signal when it accurately maps entity relationships, not as a guaranteed boost.

For HTML, Google says it’s generally a good idea to use semantic markup when possible because it helps screen readers parse and navigate your structure.

Pro tip: Run any markup through the Schema.org validator and Google’s Rich Results Test before publishing.

When should you use server-side rendering?

Use server-side rendering (SSR) or static site generation whenever you need answer engines beyond Google to read your content. As covered earlier, many AI crawlers can’t execute JavaScript, so anything a script loads after the initial response stays invisible to them. SSR and static generation fix this by delivering fully populated HTML in the first response, before any client-side script runs.

How Off-Page Signals Strengthen AI Visibility

Off-page signals are references to your brand on sites you don’t own. Earlier, I covered why Reddit and YouTube carry so much citation weight. Two more off-page levers deserve attention: earned media and the local or ecommerce details that feed Google’s specialized results.

How can PR and bylines boost authority?

ChatGPT leans heavily on publishers, drawing 78% of its citations from vendor- or publisher-controlled sources, which makes earned media one of the most direct routes to a ChatGPT citation, according to Fan Out’s analysis of 33,000+ AI citations. News and media sites make up 9.5% of all ChatGPT citations, according to Semrush.

The practical play is digital PR, getting your experts quoted and published on high-authority outlets. A byline on a trusted publication ties an author’s name to an authoritative domain, reinforcing the entity recognition I described in the author-signals section. Mentions in respected publications build that authority whether or not they link back.

How should local and ecommerce details be optimized?

For shopping and local queries, Google AI Overviews are the wrong place to concentrate. AI Overviews show up for just 3.2% of shopping searches and 7.9% of local searches, according to an Ahrefs study. The shopping opportunity sits in conversational engines instead, where product listings and landing pages were cited in 86% of ChatGPT queries and 84% of Perplexity queries tested, per HubSpot’s State of AEO 2026.

For ecommerce, here are three ways to optimize for AEO:

List products on marketplaces. AI citations in shopping categories cluster around a few retailers. Amazon earned 17.99% of AI citations in consumer staples and Walmart 6.25%, per Conductor’s 2026 AEO & GEO Benchmarks Report. Optimize category pages, not just product pages. Category pages drew 15.96% of AI citations in ecommerce, according to Wix Studio’s AI Search Lab (share of total citations). Surface detailed reviews. User reviews were cited in 90% of ChatGPT queries tested, per State of AEO 2026.

The payoff is conversion. ChatGPT-referred ecommerce visits convert at 11.4% against 5.3% for organic search, according to Similarweb’s 3rd Annual Global Ecommerce Report. If you generate product data with AI, label it per Google Merchant Center policy.

Local AI visibility is harder to earn than a map-pack spot. Multi-location brands surfaced in ChatGPT recommendations only 1.2% of the time versus 35.9% in Google’s local 3-pack, and just 45% of retail brands leading traditional local search carried into AI recommendations, according to SOCi’s 2026 Local Visibility Index (vendor data). To close that gap, complete your Google Business Profile and keep your name, address, and phone number identical across every directory an engine reads. Add LocalBusiness schema to each location page so engines can parse hours, service area, and category without guessing.

What Not to Do for AI Search Optimization

The flip side of optimizing for AI search is knowing which tactics waste your time. Most AI search “hacks” fall apart under scrutiny, and a few can actively hurt you. Here’s what to skip.

Don’t create special files just for AI.

You don’t need llms.txt files, separate Markdown versions of your pages, or any other machine-readable format to show up in generative AI results. Google states plainly that its search features, including AI Overviews and AI Mode, don’t use these files. Maintaining llms.txt won’t help or hurt your visibility, according to Google’s AI search optimization guide. Serving a bot-only version of a page carries a real downside, too: Publishing separate content for crawlers and users can read as cloaking, which violates Google’s spam policies.

Don’t over-chunk your content as a gimmick.

Logical structure helps, as the earlier sections on passage retrieval covered, but artificially fragmenting a page into one-sentence paragraphs and FAQ-style snippets because you think models prefer bite-sized text is a different move. Google’s Danny Sullivan has told creators not to do it, according to Search Engine Land. A well-structured page already creates natural retrieval boundaries through clear headings, logical sections, and focused paragraphs. It’s good practice to develop one idea per paragraph, but manufacturing extra fragmentation prioritizes perceived ranking signals over readability.

Don’t publish commodity or mass-produced content.

Recycling what’s already been said gives an answer engine no reason to cite you over the original source. Using AI to spin up high volumes of unoriginal pages designed to game rankings is classified as scaled content abuse and violates Google’s spam policies. The work that earns citations is the opposite: people-first content with a first-hand perspective, original data, or expert insight that can’t be found anywhere else.

Pro tip: If a tactic asks you to create something only a bot will ever see, treat that as a red flag. The lasting plays for AI search are the same ones that serve readers.

How to Measure AI Visibility and Operationalize Your Plan

Answer engines changed what you measure. Clicks still matter, but they no longer capture the full picture because a buyer can read an AI answer about your brand and form an opinion without ever landing on your site. Measuring AI search means tracking how often answer engines mention you, whether those mentions are accurate, and how that visibility shows up in the pipeline.

How can I assess AI visibility with a grader?

Start with a baseline. Before you can improve how answer engines represent your brand, you need to know how they represent it today.

HubSpot’s AEO Grader runs a free, one-time diagnostic that scores how ChatGPT, Perplexity, and Gemini currently describe your brand, returning a composite score out of 100 across sentiment, presence quality, brand recognition, share of voice, and market competition.

Because AEO Grader accepts any brand name, you can run the same check on a competitor and compare where they show up and you don’t. A grader is a single moment in time, though, not a monitoring system, so it tells you where you stand today but not how that’s trending.

Best for: Teams that want a quick read on AI brand perception before committing to ongoing measurement

How do I connect visibility to pipeline?

Visibility only matters if it leads somewhere, and the early data suggests AI-referred visitors convert at a higher rate than other channels.

Looking across all channels, AI-referred visitors in that same Microsoft Clarity dataset converted at about three times the rate of other traffic sources overall. The pattern holds because people use answer engines to research and compare before they click, so the ones who reach your site arrive further along in their decision.

HubSpot’s own results point in the same direction. After focusing on AEO, HubSpot grew qualified leads from AI by 1,850%, with those leads converting at three times the rate of leads from other sources.

To connect that thread, your AI visibility data has to sit next to your demand data. AEO in Marketing Hub tracks brand visibility alongside campaign metrics, so you can see whether a lift in citations corresponds to a lift in form fills.

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Preparing for AI Agents and What Comes Next

AI agents are moving from answering questions to completing tasks. Browser agents like OpenAI’s ChatGPT agent and Perplexity’s Comet can now navigate sites, fill forms, and act on a user’s behalf inside a logged-in session. Commerce agents go a step further: ChatGPT can surface products and, through Agentic Commerce Protocol, hand a purchase to the merchant’s own systems.

The readiness work is mostly an extension of what already earns citations. Agents read the rendered page and rely on structured, machine-readable signals, so the pages an agent can parse and act on are the same clean, well-structured pages I described in the technical and off-page sections. Where agents add a wrinkle is action: Agents can only reliably buy, book, or submit when the relevant controls are exposed in an accessible, machine-interpretable way.

You don’t need a stack overhaul to get ready. Try these steps first:

Serve content in the initial HTML response so agents that can’t run JavaScript still see it. ChatGPT’s commerce experience relies on structured product feeds, so keeping pricing and inventory synchronized is important. Label key actions like buy, book, and contact with semantic markup rather than script-only buttons. Keep your entity details consistent across the profiles that agents check.

Most organizations won’t need a new CMS. In many cases, improving rendering, structured data, accessibility, and product feeds is enough. Agents act on the pages they can already read, which is the same foundation AEO has asked for throughout this guide.

Frequently Asked Questions About AI Search Optimization

How long does it take to see results from AI search optimization?

There’s no fixed timeline, and it depends on which lever you pull. Technical fixes like server-side rendering can make a page citable as soon as engines recrawl it, often within days or weeks. Authority signals move more slowly: Earned media, consistent entity details, and training-data inclusion compound over months. Set expectations accordingly, and track movement with ongoing monitoring rather than waiting for a single before-and-after read.

Who should own AI search optimization across marketing and SEO?

AEO works best as a shared responsibility rather than a single owner. Your SEO or content team is the natural lead, since the on-page and structural work overlaps heavily with what they already do. But because citations also depend on earned media, consistent brand profiles, and product data, AEO pulls in PR, brand, and web teams too. Assign one person to coordinate, then make the supporting functions accountable for their piece.

Do I need to rebuild my site or change CMS to optimize for AI search?

No. You don’t need to overhaul your tech stack, switch CMS platforms, or add AI-only files to compete. Google states its AI features require no special structured data, chunking, or llms.txt files, and that maintaining them won’t help your visibility, per Google Search Central. The fixes that matter most are crawlability and rendering, which I covered in the technical structure section above.

How does AI search optimization impact paid search and social?

Differently for each. On paid: Bidding on a keyword doesn’t earn your page a spot in an AI Overview, and only 5% of AIO SERPs also showed PPC ads, according to Semrush. On social: Answer engines lean heavily on community and video platforms, with Reddit and YouTube driving more AI citations than all other off-site sources combined, per Fan Out.