This shift has given rise to answer engine optimization (AEO) — the practice of structuring content so AI systems can extract, cite, and recommend it in generative responses. But while many marketers are experimenting with lists, tables, and FAQs, few teams fully understand which strategies actually produce business results.

That’s where real-world examples matter. By analyzing recent AEO case studies across SaaS, agencies, and legal services, clear patterns begin to emerge about what drives AI citations, brand mentions, and revenue.

In this article, we’ll break down answer engine optimization case studies that demonstrate the real ROI of AEO in 2026 — including how companies increased AI-referred trials, boosted citation rates, and even generated millions in revenue from AI discovery.

Table of Contents

• What these answer engine optimization case studies reveal now.
• Answer engine optimization case studies that prove AEO’s ROI.
• Takeaways From These AEO Case Studies
• Frequently Asked Questions About Answer Engine Optimization Case Studies
• Answer engine optimization is your growth lever.

What these answer engine optimization case studies reveal now.

Across recent AEO case studies, one pattern shows up consistently — visibility shifts before traffic does. Brands see earlier gains in AI citations, brand mentions, and assisted conversions.

Another finding touches upon measurements and ROI.

Before AEO, teams measured rankings and clicks. Now, measurement shifts toward AI Overview visibility, citation frequency, and CRM influence. Marketers start attributing value to assisted deals, influenced revenue, and brand recall surfaced through generative answers rather than direct visits.

Similarly, the AEO case studies recognize a clear sales impact, albeit indirectly, in many of them. Agencies report higher baseline brand familiarity in early sales conversations, fewer “what do you do?” questions, and shorter evaluation cycles after AI citations increase. Likewise, more than half of marketers report AI-referred visitors convert at a higher rate than traditional organic traffic.

HubSpot’s AEO Grader evaluates websites based on how they show up across LLMs and offers suggestions for improvements.

Answer engine optimization case studies that prove AEO’s ROI.

Answer engine optimization delivers measurable ROI when brands increase their visibility inside AI-generated answers, leading to higher-quality traffic and stronger brand recall. The following case studies showing ROI from answer engine optimization campaigns demonstrate how companies across different industries implemented AEO strategies to improve how AI systems interpret and cite their content.

From B2B SaaS companies driving thousands of AI-referred trials to agencies generating sales-qualified leads directly from LLMs, these examples highlight the tactics that helped both established brands and emerging players compete for AI visibility and turn citations into real business outcomes.

Discovered: From 575 to 3,500+ trials per month in 7 weeks for a B2B SaaS

This is the story of how Discovered, an organic search agency, pulled off a miracle for their client and 6x AI-referred trials.

Source

The Before

The client’s company had a mature SEO program that was no longer delivering and had no deliberate AEO strategy, which translated into minimal business impact. Potential buyers simply couldn’t find the company because it was invisible inside AI answers.

What made the matter worse is that the existing strategy focused primarily on top-of-funnel informational content that wasn’t converting.

So the fix had to be immediate and tied to business outcomes.

Execution Teardown

The work began with a thorough technical SEO audit and AI visibility audit. The team found issues with broken schema (a major red flag for AI citations), duplicating content, and poor internal linking. Needless to say, there was no optimization for LLMs.

Once the technical issues were fixed, Discovered moved to publishing dozens of content pieces targeting buyer-intent queries that LLMs had already answered. Instead of the usual 8–10 monthly posts, they published 66 AEO-optimized articles in the first month.

Here’s the winning AEO content framework the teams used to structure articles:

• Clear, verifiable facts that LLMs could cite with confidence.
• Entity optimization and schema markup for better knowledge graph integration.
• Answer-focused structures targeting actual buyer questions.
• Intentional internal linking to high-intent conversion pages.

Although the result of publishing 66 decision-level intent articles brought in an influx of AI citations within 72 hours, that wasn’t enough.

To make the client’s tool top-of-mind for LLMs, the Discovered team had to increase trust signals. To do so, they extended the strategy beyond owned content and went on Reddit. Using aged accounts, they seeded helpful comments in relevant subreddits that ranked #1 for the target discussion.

The Results

The downstream impact didn’t take long to show up. Within just seven weeks, Discovered delivered astonishing AEO results:

• 6x increase in AI-referred trials from 575 to 3,500+ trials attributed to ChatGPT, Claude, and Perplexity recommendations.
• 600% citation uplift.
• 3x SERP performance on high-intent keywords, driving qualified traffic that converted.
• #1 Reddit rankings.

Curious if your business’s website is AEO-ready? Run it through HubSpot’s AEO Grader to get a detailed competitive analysis, brand sentiment scoring, and strategic recommendations to optimize your brand’s AI visibility.

How Apollo lifted its brand citation rate by 63% for AI awareness prompts.

Brianna Chapman leads Reddit and community strategy at Apollo.io, so she greatly influences how LLMs cite Apollo today. Without revamping its website content, Chapman increased the brand citation rate solely by using Reddit as the main source of information for AI search engines.

The Before

When Chapman started digging into whether Apollo was actually showing up in ChatGPT, Perplexity, or Gemini about sales tools, she found herself frustrated. “LLMs kept positioning us as ‘just a B2B data provider’ when we’re actually a full sales engagement platform. Competitors were getting cited for capabilities we had, and sometimes did better,” shares Chapman.

The major problem was that LLMs were pulling content from old Reddit threads with incomplete or outdated information about Apollo, but because those threads existed and were crawlable, the information kept being treated as truth.

Execution Teardown

Chapman stopped treating AI visibility as an SEO problem and began thinking of it as narrative control. The goal was to shape conversations in places LLMs already trust (mainly Reddit) without being sketchy about it.

Here’s what Chapman did precisely to flip the narrative and drive brand citations.

First, she figured out which prompts actually mattered (aka how people ask inside LLMs) and audited the brand’s visibility in AI search engines.

To do so, Chapman pulled first-party data from Enterpret (customer feedback), social listening, and prompts people were giving inside Apollo’s AI Assistant. She got about 200 prompts per topic, like:

• “ai that verifies emails before sending outreach”
• “what ai sales tools don’t feel spammy?”

From there, she tracked all of them in AirOps to see where Apollo was (or wasn’t) getting cited.

Then it was time to act.

She built r/UseApolloIO as a credible resource and grew this subreddit to 1,100+ members with 33,400+ content views in over five months. The major shift happened when Chapman posted a detailed comparison in r/UseApolloIO about when teams should choose Apollo versus a competitor.

Within a couple of days, AirOps showed the new thread getting picked up, and within a week, it had displaced the old one, gaining +3,000 citations across key prompts in LLMs.

The Results

The results speak for themselves: 63% brand citation rate for AI awareness prompts, 36% for category prompts. Reddit sentiment also got more positive, driving beta sign-ups and demo requests.

Featured resources:

• User Engagement Is the New SEO: How to Boost Search Rank by Engaging Users
• A Roundup of Case Study Examples Every Marketer Should See

How Broworks generates SQLs directly from LLMs after AEO.

One day, Broworks, an enterprise Webflow development agency, wondered what if they could build a pipeline from AI tools instead of just traditional search engines? So the team rolled up their sleeves and dug deep into AEO optimization of their entire website.

The Before

Broworks had their brand already cited in LLMs here and there, but those mentions didn’t translate into anything the business could measure. On top of that, there was no structured way to influence AI-generated answers and no attribution tying AI-driven sessions back to pipeline outcomes.

Execution Teardown

First, the Broworks team realized they had had a schema markup problem. So they implemented custom schema markup across key landing pages, case studies, and blog posts. They added FAQ Schema, Article Schema, and Local Business, and Organization Schema — essential schema attributes for LLM indexing.

They also placed comparison tables directly on the landing pages.

Source

Their second step was to align the website’s content with prompt-driven search. Meaning, optimize content not around traditional keywords but questions people ask ChatGPT, like: “Who is the best Webflow SEO agency for B2B SaaS?”

They also added FAQ sections to most pages and summarized key takeaways at the top of articles.

Even Broworks’ pricing page has an FAQ section.

Source

The Results

Within three months, AEO and GEO outcomes became visible in both analytics and sales data:

• 10% of organic traffic originated from LLMs, including ChatGPT, Claude, and Perplexity.
• 27% of AI-referred sessions converted into SQLs.
• 30% higher time on site compared to traditional organic traffic.

Sales teams reported stronger baseline awareness and fewer introductory conversations. Prospects arrived already aligned on the problem and solution, shortening qualification cycles.

Intercore Technologies achieved $2.34M in total revenue attributed to AI discovery over six months.

Intercore Technologies, a digital agency for law firms, helped an established Chicago personal injury firm rise from an invisibility crisis. The brand’s SEO was stellar; they ranked #1 for “Chicago personal injury lawyer” and had over 15,000+ monthly organic visitors — but their lead volume dropped.

The brand actually leaked its clients to competitors that were more visible in AI search engines, as search behavior drastically shifted in this niche.

The Before

In short, Intercore’s client was not recognized by AI search engines at all. The brand didn’t appear in LLM results for the query “personal injury lawyer Chicago,” despite strong domain expertise. Competitors, on the other hand, were mentioned 73% of the time.

Execution Teardown

Intercore Technologies approached AEO as a precision problem. They focused their work on making the firm’s expertise legible and quotable for AI search engines evaluating legal intent.

Execution centered on four pillars:

• Legal entity clarification. Practice areas, case types, and jurisdictional relevance were explicitly defined so LLMs could associate the firm with specific legal scenarios (e.g., personal injury claims, settlement processes, local statutes).
• Answer-first content restructuring:

• 50 core pages were rewritten to lead with direct answers to high-intent legal questions commonly surfaced in AI responses.
• Added 500+ word FAQ sections to each practice area.
• Created “Ultimate Guide to Personal Injury Claims in Illinois.”
• Implemented semantic HTML structure (H1–H4 hierarchy).
• Created comparison tables (Auto vs. Slip & Fall vs. Medical).

• Schema and the site’s speed. Structured data was applied to reinforce legal services, locations, and professional credibility, thereby improving extraction accuracy across AI platforms. They optimized page load speed to under two seconds.
• Established a multi-platform presence for maximum AI visibility. LinkedIn was used for a thought leadership campaign with over 5,000 engagement actions in the first month. They also launched a YouTube channel and published on Reddit, Quora, and Forbes Legal Council.

The Results

After this massive undertaking, AI visibility started translating into both reach and revenue. AI visibility increased to 68% across ChatGPT, Perplexity, and Claude.

The revenue impact followed quickly:

• 156 new clients attributed directly to AI recommendations.
• $47,500 average case value from AI-referred clients.
• $2.34M in total revenue attributed to AI discovery.
• 16.9% average AI conversion rate.

Takeaways From These AEO Case Studies

Let’s develop a playbook from these answer engine optimization ROI case studies so growth specialists can easily modify their AEO efforts and see similar results.

1. AI visibility compounds before traffic does.

Across all case studies, brands saw AI citations, mentions, and awareness lift weeks or months before any meaningful traffic changes. Marketers should treat AI visibility as a leading indicator of their answer engine optimization efforts.

Use HubSpot’s AEO Grader to learn and monitor how leading answer engines like ChatGPT, Perplexity, and Gemini interpret your brand. The AEO Grader audit reveals critical opportunities and content gaps that directly impact how millions of users discover and evaluate your brand using LLMs.

2. Answer-first content is your new textbook for content creation.

Answer-first content consistently outperforms keyword-first content. Pages that open with direct answers, summaries, or FAQs were cited more reliably by LLMs than traditional blog-style introductions. This pattern shows up across SaaS, agency, and legal services examples. Answer-first content flips the traditional SEO model by prioritizing immediate clarity over keyword stuffing or narrative build-up.

To put this into practice, start every page with a clear answer to the top-intent question, followed by context, examples, or supporting detail. Use headings that mirror natural queries, like “How can I optimize my SaaS website for AI search?” and provide a short, self-contained answer immediately below. By doing so, marketers increase the likelihood that AI systems extract their content confidently and cite it as a trustworthy source. Over time, this approach compounds visibility and can drive higher-quality AI-referred traffic.

3. Schema markup is no longer optional for AEO.

Schema markup is the backbone of machine-readable content, allowing AI systems to understand pages and determine how to cite them. Case studies repeatedly show that implementing structured data — including FAQ, HowTo, Product, Offer, Breadcrumb, and Dataset schema — directly improves AI extraction and citation rates. Without schema, even high-quality content risks being overlooked by LLMs because it’s harder for them to parse and verify information.

Actionably, audit all high-value pages for relevant schema types. Start with FAQ and HowTo for decision-stage content, Product and Offer for transactional pages, and Breadcrumb or Organization for site hierarchy and entity clarity. Test the schema using Google’s Rich Results Test or other structured data validators, and iterate based on AI citation performance. Proper schema not only increases the likelihood of being surfaced but also ensures that AI systems interpret the content accurately, improving trust signals and downstream conversions.

HubSpot Content Hub helps marketers publish schema-ready content across websites.

4. Narrative control matters as much as on-site optimization.

On-site AEO optimization alone isn’t enough. LLMs pull from trusted external sources, which means a brand’s AI visibility is influenced heavily by third-party content. Apollo’s case demonstrates that managing a brand’s narrative in platforms like Reddit or Quora can shift how AI systems describe and recommend it. If outdated or incomplete information dominates these sources, LLMs will continue to propagate misaligned messages, even if the website is fully optimized.

To take control, identify the key prompts or topics an audience is querying inside AI tools. Then, actively shape the conversation in trusted communities by providing accurate, detailed, and helpful content. For example, creating dedicated subreddits, participating in niche forums, or posting authoritative comparisons can guide AI systems toward citing a brand correctly. By pairing on-site optimization with external narrative control, marketers increase both the quantity and quality of AI citations, which can drive higher conversions and strengthen brand recognition.

HubSpot’s AI Content Writer helps marketers create high-quality content at scale across channels.

5. Internal linking to high-intent conversion pages is a must.

Internal linking signals context and relevance to AI systems as much as to human users. Case studies show that AI crawlers benefit when content across a site is connected intentionally, particularly linking answer-first pages to high-intent landing pages or product offers. Without a clear internal linking structure, LLMs may surface content that is informative but fails to guide users toward conversion opportunities.

To implement this, map out high-value pages and identify key answer-first articles that can serve as entry points. Link these strategically to product pages, service pages, or other high-intent conversion targets. Use descriptive anchor text that aligns with user queries, so AI systems understand the relationship between pages. This approach ensures that AI-referred traffic not only discovers the content but also moves through the conversion funnel efficiently, improving assisted conversions and pipeline influence.

6. Page speed counts for AEO.

AI systems rely on fast, reliable access to content. Pages that take too long to load may fail to be fetched or fully parsed by AI crawlers, limiting citations and AI visibility. Case studies show that even sites with excellent content and schema lose out when load times exceed two seconds. Slow pages increase fetch latency, raise the risk of incomplete parsing, and reduce the likelihood of the content being surfaced in AI answers.

Action steps include auditing page speed with tools like Google PageSpeed Insights or HubSpot’s Website Grader, optimizing images and scripts, enabling caching, and minimizing render-blocking resources. Additionally, prioritize mobile performance, as many AI systems evaluate content using mobile-first indexing. By improving load times, businesses not only enhance user experience but also ensure that AI systems can reliably extract and cite their content, translating into higher AI visibility and measurable ROI.

7. Question-based subheadings are AEO gold.

Question-based H2s and H3s work wonders because they directly match how users query answer engines. For example, add an H2 “How can marketers structure pages for answer engine optimization?” and then expand using informative H3s.

Answer the query immediately below the heading, so as not to leave room for misinterpretation for AI.

Marketers can simplify their lives with the HubSpot Content Hub that includes built-in AEO and SEO recommendations for headings and structure, as well as drag-and-drop modules for FAQ sections and lists.

Featured resources:

• Best practices for answer engine optimization (AEO) marketing teams can’t ignore
• On-Page SEO Tips to Optimize the Most Critical Parts of Your Website

Frequently Asked Questions About Answer Engine Optimization Case Studies

What is answer engine optimization, and how is it different from traditional SEO?

Answer engine optimization (AEO) focuses on making content easy for AI systems and LLMs to extract, understand, and reuse as direct answers. The goal is visibility inside AI Overviews, chat responses, and generative search results, where users often never click through to a website.

Traditional SEO prioritizes rankings, clicks, and traffic. AEO prioritizes answerability, entity clarity, and citation likelihood. In practice, AEO builds on SEO foundations but shifts success metrics toward AI mentions, assisted conversions, and CRM influence rather than sessions alone.

Which schema types should I start with for AEO?

Teams should start with schema that clarifies intent and relationships. FAQ, HowTo, Product, Organization, Breadcrumb, and Article schema consistently improve AI extraction and citation accuracy across AEO case studies.

The priority is not schema volume but relevance. Schema should reinforce what the page is clearly about and how concepts connect.

How do I adapt my content for AI Overviews and chat answers without hurting my UX?

The most effective approach is an answer-first structure. Sections should begin with a direct, self-contained answer, followed by context, examples, or depth for human readers. This pattern serves both audiences without duplicating content.

AEO case studies show that short paragraphs, clear headings, summaries, and FAQs improve AI reuse while keeping pages scannable and readable. AEO works best when it aligns with good UX principles rather than competing with them.

How do I prove ROI for AEO when traffic does not always increase?

AEO ROI rarely shows up first in traffic. Instead, teams track AI citations, brand mentions, assisted conversions, influenced deals, and sales feedback inside CRM systems. These indicators surface earlier and compound over time.

Many AEO case studies validate ROI by correlating AI visibility gains with higher lead quality, shorter sales cycles, and lower acquisition costs. The key is expanding measurement beyond last-click attribution.

When should I consider bringing in AEO services versus keeping it in‑house?

In-house teams perform well when they already own content, schema, and analytics workflows and can iterate quickly. This works best for companies with mature SEO foundations and access to CRM-level attribution data.

External AEO services make sense when teams lack entity modeling expertise, schema depth, or visibility into how AI systems reference their brand.

Answer engine optimization is your growth lever.

AEO delivers real business impact when teams stop treating AI visibility as a byproduct of SEO. And it delivers fast: From the first week of optimizing their website for AEO, digital marketers can see a forming pipeline directly attributed to AI recommendations.

If you want to speed up AEO implementation, tools matter.

Platforms like HubSpot Content Hub help teams publish schema-ready, answer-first content at scale, while visibility checks through tools like HubSpot’s AEO Grader or Xfunnel reduce guesswork and speed up iteration.

Gear up and make AEO your growth lever.

It might be challenging to get everyone in agreement on what’s what, but let’s try. AEO and GEO are not going away, and the faster the industry can align on what these acronyms mean, the better. From a strategic perspective, it doesn’t matter that much since all SEO specialists should already be laying the foundations for AEO, GEO, and, of course, SEO. But with a unified definition, it’ll be much easier to talk about it all.

If you’re not sure you’re laying down the work required for AEO or GEO or how to measure their impact, stay tuned because we’ll cover that after defining our terms.

Table of Contents

• AEO vs. GEO: What’s the difference?
• AEO vs. GEO: Do you need both?
• Shared Tactics Between AEO and GEO That Drive Results
• How to Measure the Impact of Both AEO and GEO
• What’s next for AEO & GEO?
• Frequently Asked Questions About AEO vs. GEO

AEO vs. GEO: What’s the difference?

AEO stands for Answer Engine Optimization. AEO focuses on direct answers in search results. It helps website content appear as direct answers in search results.

Think:

• Featured snippets.
• People Also Ask.
• Knowledge Panels.
• And other SERP features.

GEO stands for Generative Engine Optimization. GEO optimizes for brand citations in AI-generated summaries. It helps brands get cited inside AI-generated summaries on platforms like Google AI Overviews, Perplexity, and ChatGPT.

In simplest terms: AEO optimizes for answers while GEO optimizes for citations.

Here’s a comparison table:

AEO vs. GEO vs. SEO

Traditional SEO focuses on three core pillars:

• Content strategy.
• Technical SEO.
• Backlinks.

SEO is a broad marketing tactic that encompasses a lot, and many of the elements described under AEO and GEO also fall under its “umbrella.” However, these tactics are increasingly bearing a greater onus due to their impact on AEO and GEO in modern-day SEO.

AEO focuses on delivering answers that search engines can extract cleanly.

GEO focuses on earning citations inside AI-generated responses — often without requiring a click.

When combined, these three strategies ensure brands are:

1. Discoverable in search.
2. Present in the AI tools buyers now rely on for research, vendor comparison, and decision-making.
3. Appear in AI Overviews and other SERP features for maximum visibility.

AEO vs. GEO: Do you need both?

Both GEO and AEO are rapidly emerging as core marketing priorities as AI-powered search becomes a popular format for consumers to discover brands, compare solutions, and make decisions. According to the HubSpot Consumer Trends Report, 72% of consumers surveyed indicated they intend to rely more heavily on AI-powered search when shopping.

From experience, brands absolutely need both (and SEO, of course).

I’ve had leads come in from ChatGPT and other generative tools for my own agency and for clients, and those results only happened because my brand is visible across both answer engines and generative engines.

AEO and GEO require structured content and clear entities. AEO ensures a website’s content is extractable, structured, and eligible for direct answers in Google and other search engines. GEO ensures that when someone asks an AI model for recommendations, comparisons, or best-of lists, your brand is one of the citations the model pulls into its summary.

In today’s search landscape, where buyers increasingly start research in ChatGPT, Perplexity, or Google AI Overviews, relying on SEO alone is no longer enough.

Pro tip: Read HubSpot’s AEO guide here.

Shared Tactics Between AEO and GEO That Drive Results

AEO and GEO may show up differently across search and generative search platforms, but they’re powered by many of the same foundational practices. The brands that perform best in AI search are the ones that build structured, answer-first content and maintain strong entity clarity across every page. Below are five core tactics that strengthen both AEO and GEO performance: answer-first content structuring, entity management and consistency, quotable insights and data passages, schema and structured markup implementation, and reinforcement through repetition.

Answer-First Content Structuring

Answer-first content structuring means leading with the most straightforward answer to a user’s question before adding supporting detail, examples, or context. Instead of burying the key point halfway down the page, writers must surface the most important point immediately in a clean, skimmable format that answer engines and generative engines can extract with zero ambiguity. Writers and AEO or GEO specialists must design content to provide the answer, then elaborate later.

For example, in a piece of content, there is a heading, “What is Answer Engine Optimization?”

The response, designed to perform well in AI search, will define AEO immediately, like this:

“Answer Engine Optimization (AEO) is the practice of structuring content so search engines can extract direct, authoritative answers for featured snippets, AI summaries, and other answer-driven results.”

Writing content like this isn’t new to search. SEO specialists have been using this method of writing for years because it helps secure featured snippets or rankings in People Also Ask. But now, with generative engines pulling answers instead of links, content writers need to pay even closer attention to how cleanly and confidently the first 1–2 sentences answer the core question. That opening line is no longer just for users; it’s for the AI systems deciding whether your brand deserves to be cited.

Pro tip: Journalists have used a similar structure for decades with the inverted pyramid: Start with the headline and core facts, then layer in context, quotes, and background. Answer-first content is simply the search-optimized version of that same newsroom principle — and it’s now one of the most important practices for AEO and GEO success.

Entity Management and Consistency

Entity management is the practice of defining your key entities, be it people, products, or concepts. A brand, for example, is an entity. Once established, marketers control entities and ensure they remain consistent wherever they appear.

Consistently maintaining accurate, unified references across your website, blog, product pages, documentation, PR, and external mentions means generative citations are more likely to be accurate.

When your product names, features, claims, and categories are described consistently across multiple surfaces, AI tools can reliably connect those references back to you. The more precise and consistent your entities are, the more confidence generative engines have when deciding which brand to cite in overviews or summaries.

With AI models pulling from thousands of sources (your site, competitor sites, Reddit, forums, UGC, reviews), inconsistent entity signals become a real risk. If your materials list is described one way on your product page but differently in a press release or a reseller listing, AI systems may merge or misinterpret your data. Entity management fixes this by making your information stable, repeatable, and unambiguous across the entire web — which is now essential for earning citations in AI-powered search.

For example, if you sell running shoes, you will likely cover the shoes’ lifespan. Mentioning the sneakers’ lifespan on the product page might make sense since the entities are connected, but the manufacturer’s guarantee of the shoe’s lifespan might differ from experience. Users on Reddit might claim they last 200 miles, others say 1,000. There’s no universal truth, but if you clearly cite the accepted industry ranges (e.g., 300–500 miles) and explain why, you give AI models the best possible chance of repeating the correct information and citing you as the source.

Entity clarity is becoming a form of quality control in AI search.

Unfortunately, it won’t guarantee citation. Here’s an example I found when I tested AI search engines for Backlinko: A search for the lifespan of running shoes returned information stating 450–500 miles. But the actual range on the manufacturer’s website is 300–500 miles.

Source

Quotable Insights and Data Passages

Quotable insights are short, authoritative statements or data points that AI engines can lift directly into summaries. These might be stats, expert explanations, definitions, or clear recommendations.

Pro tip: Use quotable insights in a separate paragraph, and don’t forget to answer the heading directly first. This means quotes or additional insights should come after the short paragraph that defines the main point.

Generative engines prefer clean, self-contained passages that can be cited without restructuring. Give them a “ready-made” quote; it may increase the chances of appearing in AI Overviews or ChatGPT responses. It also improves AEO because those same passages often get pulled into answer boxes.

Clear definitions, strong statements, data, and expert opinions have long been part of SEO, helping demonstrate experience, expertise, authority, and trust (E-E-A-T). Still, AEO and GEO ask SEO specialists to remember and emphasize the importance of insights and data.

Schema and Structured Markup Implementation

Schema markup is structured data that helps search engines understand the meaning of content — from products, FAQs, authors, how-tos, ratings, and more. It turns plain text into clearly defined entities and relationships that machines can trust. Basically, schema markup is additional code that crawlers can read.

Schema is crucial for AEO and GEO because it tells answer engines exactly what content represents, increasing a website’s eligibility for snippets and rich results. It’s equally important for GEO because structured markup reinforces entity consistency, which generative engines use to verify information and decide which brands to cite.

As an SEO specialist, I’ve been adding schema for years. For me, it’s non-negotiable.

Some of my most used schema types for B2B include:

• Person schema helps understand who a subject-matter expert is, including their credentials, roles, specializations, and publications. This is especially powerful for E-E-A-T because it ties authoritative content directly to a real expert.
• Organization schema defines the company as an entity, including the legal name, brand name, industry category, contact details, social profiles, and subsidiaries. It creates the “source of truth” about a company.
• FAQ schema explicitly marks up questions and answers, giving search engines and AI models a clean, structured understanding of what each section of content represents.
• Service schema defines the specific services a business provides, including what the service is, who it’s for, what problems it solves, and any related offerings.
• Product schema provides structured data about products, including specs, features, benefits, variations, materials, ratings, and more.

Reinforcement Through Repetition

Reinforcement through repetition means getting key facts, claims, and definitions repeated consistently across multiple reputable sources so AI systems start treating your version as the authoritative one. AI models don’t take websites at face value; they triangulate. They look for patterns, overlaps, and repeated assertions across the web.

If only a brand’s website says a product reduces downtime by 30%, AI treats it as unverified. If 10 independent sources say the same thing, including press, partner pages, documentation, industry publications, and comparison sites, then AI models adopt it as truth, and citations become more representative of the message brands want to share.

Pro tip: I know how it is to worry about repetition, but marketers must remember that only a small percentage of their audience sees the content they publish. Lots of variables play into this, including what the algorithm shows, when people log into their devices, and what they’re looking for at the time. A social media post, for example, may only reach 8% of a large audience. It doesn’t hurt to post things twice, or again on another platform.

How to Measure the Impact of Both AEO and GEO

Measuring AEO and GEO requires a shift away from traditional SEO metrics like rankings and traffic. AI-driven search changes where users discover information, how they evaluate brands, and what signals influence their decisions.

Instead of tracking only clicks, marketers now need to measure visibility within AI-generated answers, citation accuracy, and the downstream impact on conversion quality and pipeline.

Below are the five metrics that give the clearest view of AEO/GEO performance and where to optimize next. They include AI visibility and citation coverage, content quality and answer readiness, conversions and revenue influenced by AEO/GEO, lead quality from AI-influenced discovery, and page performance and user behavior.

AI Visibility and Citation Coverage

AI visibility and citation coverage measures how often a brand appears in generative search experiences like Google AI Overviews, ChatGPT, Perplexity, and Gemini. Instead of tracking only clicks or rankings, this metric tells marketers whether AI systems are pulling content into their answers, summaries, and recommendations.

Plus, marketers can establish whether AI tools are mentioning a brand positively or negatively.

The easiest way to track this is with HubSpot’s AI Search Grader. AI Search Grader measures brand visibility and citations in AI search. It’s a free tool that analyzes any domain and shows how visible a brand is across AI engines. It highlights where the brand is earning citations, what’s missing, and which pages need improvement to gain traction in generative search.

Here’s what the dashboard looks like; it offers a full report, too.

To manage this metric, regularly audit the most important topics and pages.

Look for:

• AI Overview appearances.
• Mentions or citations in ChatGPT or Perplexity.
• Whether generative engines use your definitions, stats, or product data.
• Which competitors are being cited.
• Pages that show up without being clicked.
• Content gaps where your answers aren’t being surfaced.

Content Quality and Answer Readiness

Content quality and answer readiness measure how effectively content meets the structural, clarity, and formatting requirements that AEO and GEO depend on. Content must be cleanly extractable, well-researched, entity-consistent, and answer-first. This metric evaluates whether pages are written in a way that answer engines and generative engines can confidently understand, reuse, and cite.

This is where Breeze Content Assistant, HubSpot Marketing Hub, and HubSpot Content Hub work together to improve and monitor answer readiness across your entire content library.

• Breeze Content Assistant helps marketers and writers generate structured, answer-first content that’s optimized for AEO/GEO from the start. Breeze Intelligence supports entity monitoring and consistency. It understands HubSpot’s AEO best practices, so Breeze can generate definitions, FAQs, schema-ready structures, and entity-aware passages that AI engines are more likely to extract.

Best for: Quickly producing AEO-ready passages, FAQs, definitions, and structured updates.

• HubSpot Marketing Hub includes SEO tools that evaluate the SEO and AEO fundamentals that underpin answer readiness, such as page structure, metadata quality, internal linking, topic coverage, and readability. Marketing Hub orchestrates campaigns and reporting for AEO and GEO.
• HubSpot Content Hub includes an AI content writer that ensures content is built on a foundation that’s SEO- and AEO-friendly. Content Hub enables answer-first, structured content creation. It offers in-editor SEO suggestions, internal linking recommendations, and on-page analysis so your content remains aligned with AI ranking and extraction criteria.

To measure content quality, review the content for:

• Clear, answer-first introductions.
• Definitional statements and quotable insights.
• Consistent use of entities and terminology.
• Strong internal linking to reinforce meaning.
• Well-structured FAQs, headers, and schema.
• Frictionless readability and minimal fluff.

Conversions and Revenue Influenced by AEO/GEO

Conversions and revenue influenced by AEO/GEO measure how often AI-powered search surfaces contribute to the pipeline, whether through:

• Direct clicks.
• Assisted influence.
• Unclicked brand citations that steer buying decisions.
• Conversions and sales made in sessions started from AI sources like ChatGPT.

Visibility matters, but conversions and revenue will always be the ultimate benchmarks of performance. AEO and GEO are only doing their job if they help businesses grow.

The best way to measure conversions and revenue influenced by AEO/GEO is to measure behavior on site within sessions that started with a referral from an AI source like ChatGPT or Perplexity.

I do this on Looker Studio. Here’s a look at my report. I show how many referrals came from AI sources:

And how many conversions took place:

Reporting gives marketers the data they need to ask questions to sales. If marketing knows they secured a top lead, they can see whether or not it converted.

Pro tip: Qualify marketing leads by adding qualifiers on contact forms. For example, I add “budget.” From doing this, I know ChatGPT led to a 10k lead for my client. That’s the level of insight you need to quantify AEO/GEO impact.

But here’s the nuance: Not all influence is trackable.

Many users see brands inside an AI Overview or conversational answer, don’t click in the moment, but return later through another channel. Those unclicked citations still shape decision-making, which is why conversion analysis is one of the most important AEO metrics.

When reporting, look at:

• Assisted conversions influenced by AI exposure.
• Conversions on pages that appear in AI answers.
• Conversion-rate shifts after implementing AEO updates.
• Multi-touch attribution where AI surfaces are part of the journey.

Lead Quality From AI-Influenced Discovery

Lead quality from AI-influenced discovery measures how well the leads generated from AEO/GEO align with ideal customer profiles (ICPs) and whether those leads move through the funnel faster than traditional organic traffic. AEO doesn’t just expand visibility; it improves the type of visibility brands receive.

How?

Content appears in highly contextual AI answers, and the traffic that follows is often warmer, more targeted, and already primed with problem-awareness.

AI-generated recommendations act as an intent filter. If someone finds a website through a generative engine’s answer or vendor comparison, it usually means they’re actively researching a problem you solve. That’s why AI-sourced leads often show stronger fit scores, higher qualification rates, and faster progression into the pipeline.

What to measure:

• Fit score of leads generated from pages appearing in AI answers.
• Sales-qualified lead (SQL) rate from AI-originating sessions.
• Lead velocity and time-to-first-action (e.g., demo booked, asset downloaded).
• Topics and pages that consistently drive high-quality conversions from generative engines.

High-quality leads are one of the clearest indicators that answer-first content, structured entities, and topic clarity are working. When AI repeatedly recommends your brand to the right audience, your pipeline improves even before attribution fully captures the source.

Pro tip: For a sophisticated setup, use HubSpot lead scoring to compare leads influenced by AI surfaces with those from traditional organic search. HubSpot lead scoring allows sales and marketing teams to quickly see whether the AEO/GEO strategy is attracting the right buyers that the sales team wants and can convert.

Page Performance and User Behavior

Page performance can give marketers an idea of which pages are performing well. The more a page has sessions from AI sources, the more times it’s recommended.

Once marketing knows the top page cites, they can analyze user behavior to see how people interact with the page.

To track this, monitor sessions where the referrer is an AI tool.

Look at how visitors behave:

• Do they stay on the page or bounce quickly?
• Do they view multiple pages?
• Are they interacting with high-intent elements like CTAs, pricing pages, or demo forms?
• Are they triggering key events like downloads or form fills?

Combining AI-originating behavior data with AEO/GEO visibility provides a clear picture of which pages are doing the real heavy lifting and which ones deserve priority for schema enhancements, answer-first rewrites, quotable insights, entity reinforcement, or deeper optimization.

What’s next for AEO & GEO?

AI search is evolving fast. I’ve been writing about AEO and GEO for a while, and it moves so fast that sometimes, I have to make significant edits to my articles between the first draft and publication (which takes about two weeks!) because things have already changed significantly.

Here are the three trends I expect to define the next phase of AEO and GEO.

AI discovery will become the new “top of funnel.”

More buyers will start their research in ChatGPT, Perplexity, Gemini, and other conversational tools. We already know, thanks to HubSpot’s Consumer Trends Report, that 72% of consumers surveyed said they plan on using AI-powered search for shopping more frequently.

This means the first impression of brands may no longer be your website; it’s whatever the AI model says about you. AEO and GEO success depends on question coverage, schema, and distribution.

I think this is the biggest mindset shift marketers need to make. Your homepage isn’t the first touch anymore; AI presence is, and visibility is crucial.

Here’s an example of how visibility impacts consumers. In a search for “best free CRM for small business,” HubSpot was recommended in the AI Overviews, then again in “Sources across the web.” The citation in AI Overviews is not HubSpot but Zapier (third-party credibility).

All of this visibility and trust is built from sources across the web (not just HubSpot).

This goes to show the power of consistent brand messaging and third-party credibility, as well as having content on a brand’s website.

The search industry will settle down.

I firmly believe that the search industry will settle down about AEO, GEO, and SEO, and remember what’s important: The consumer and reaching them wherever they search or hang out online.

When I wrote The Future of SEO, I spoke to Mark Williams‑Cook, who had some SEO predictions. He believes we’re “near the peak of where we are going to be with LLMs” in terms of novelty and hype.

In other words, the explosive growth, the dizzying promises, the confusion from everyone’s stance on what’s what, and the rapid experimentation phase of AI search are beginning to plateau.

Supporting that view, data shows that conversational AI tools like ChatGPT still capture only a tiny slice of all search activity. Reports estimate the click-share to be around 1.3%. Here’s a graph from Datos’ State of Search Q3 2025. In Q3, visits to AI tools hit around 1.3% and steadied. Before, it was slowly growing, from 0.85%.

SEO teams will report on AEO and GEO as much as SEO.

Although the AI hype is plateauing (I believe), it doesn’t mean it’s not important. SEO specialists must adapt SEO reporting to include AEO and GEO. It’s becoming too important to ignore, and those who do risk falling behind.

AEO and GEO now need to be a standard component of every SEO audit and reporting workflow. The same way we evaluate rankings, backlinks, Core Web Vitals, and keyword visibility, we also need to measure AI visibility, citation frequency, entity consistency, and AI-originating sessions. If your brand isn’t appearing in generative results, that’s a performance gap, not an accident.

What this looks like in practice:

• Add AI sources (ChatGPT, Perplexity, Gemini, Claude) to your acquisition reporting.
• Track which pages AI engines are recommending — and whether those are your high-intent assets.
• Monitor AI-originating sessions as a standalone channel.
• Evaluate how often your definitions, stats, and product data appear in AI summaries.
  Identify missed citation opportunities where competitors are being selected instead of you.

I built this into my clients’ Looker Studio dashboards months ago.

Once you embed AEO metrics into your reporting cadence, patterns emerge quickly — which pages earn citations, which topics attract high-quality traffic, and where you need to tighten entities or restructure content.

Pro tip: Treat AI visibility exactly the way you treat keyword rankings. Add AEO metrics to your monthly reporting and review them with the same rigor — that’s how you stay ahead of competitors who are still only tracking organic traffic.

If you want to understand how visible your brand is across AI engines, start with the HubSpot AI Search Grader. It gives you an instant view of your AEO/GEO performance and actionable steps to improve. And when you’re ready to build AEO-ready content at scale, HubSpot’s Content Hub, Breeze Content Assistant, and Marketing Hub make it easier to create, manage, and measure search visibility across every modern surface.

Frequently Asked Questions About AEO vs. GEO

How do I measure AEO vs. GEO performance without relying on traffic?

Track citation frequency, AI Overview appearances, entity consistency, AI-generated mentions, and the fit score of leads influenced by AI-derived surfaces. Tools like the HubSpot AI Search Grader make this easier.

What schema helps with AEO and GEO?

Some of the best schema to help with AEO and GEO include FAQ, Product, Service, Person, Organization, and SameAs. They improve entity clarity, answer extraction, and citation reliability. Don’t rely on just these schemas, though; there are so many!

How do I get my brand cited in ChatGPT or Perplexity?

Use answer-first formatting, entity consistency, quotable passages, and schema. Then reinforce those facts across authoritative external surfaces so AI models trust your version of the information.

How often should we refresh AEO-ready content?

At least quarterly for key pages, or whenever product updates, regulations, or competitive shifts occur. AI engines reward freshness, accuracy, and clarity.

AEO and GEO are now essential layers of search visibility.

AEO and GEO aren’t add-ons; they’re the new foundation of brand visibility in an AI-first world. AEO wins the direct answers. GEO wins the citations. Together, they shape how buyers discover your brand, evaluate your solutions, and move toward a decision. It’s not AEO vs. GEO, but both working together.

The marketers who adopt answer-first content, structured entities, and strong distribution will dominate modern search. HubSpot’s AEO grader can help marketers optimize their sites for the new era of search.

I’ve seen firsthand how AEO and GEO drive warm, high-intent leads. When you focus on clarity, structure, and citation-worthiness, AI models start doing your distribution for you, and the results can be game-changing.

A modern audit goes beyond identifying issues that further traditional blue-check rankings in Google Search. With AI search reshaping how users discover brands, marketers now need to evaluate entity signals, brand visibility in AI answers, and how well their content performs in generative engines.

In this guide, you’ll learn how to run an SEO audit that’s helpful in today’s search landscape. I’ve included clear steps, examples, and an SEO audit checklist to help SEO professionals at any skill level drive measurable traffic growth.

Table of Contents

• What is an SEO audit and why does it matter?
• SEO Audit Checklist for Quick Wins
• How to Run an SEO Audit Step-by-Step
• How to Interpret Your SEO Report and Prioritize Fixes
• Tools to Run an SEO Audit
• Frequently Asked Questions About SEO Audits
• Modern SEO audits go beyond blue links.

What is an SEO audit and why does it matter?

An SEO audit is a structured review of your website. SEO specialists conduct audits at regular intervals, such as quarterly or yearly. Sometimes third-party consultants conduct site audits to bring a fresh set of eyes to the project.

The audit identifies the issues preventing your pages from ranking, being crawled, or converting. Then, the SEO strategist turns audit findings into a prioritized plan that directly supports traffic growth, lead generation, and pipeline.

Typically, an audit includes:

SEO Audit Checklist for Quick Wins

Conducting an SEO audit can feel overwhelming, even for experienced marketers. At a minimum, an SEO audit checklist includes crawlability, indexability, page speed, on-page SEO, content quality, technical SEO, and backlinks.

A good SEO audit will surface hundreds of insights, and in-house teams often find themselves swimming in data without knowing where to start. Quick wins help cut through the noise. Spotting these early gives teams momentum and makes the rest of the audit far easier to interpret.

Here are high-impact, low-effort opportunities SEO specialists should look out for as they move through the audit checklist (the step-by-step audit guide is coming next):

• Content audit. Identify thin or outdated pages. Nearly every site has blogs that share trends or content that is completely irrelevant now. For example, “Wedding Trends in 2002” or content about services the business no longer offers. These pages almost always have close to zero clicks and can nearly always be removed. Look at HubSpot’s article Why We Removed 3,000 Pieces of Outdated Content From the HubSpot Blog. It’s genuinely brilliant and provides the thought process and rationale for their decision.
• Technical audit. Look for critical blockers, such as noindex tags, 404 pages, broken links, redirect chains, and slow-loading pages. Use Screaming Frog or HubSpot to identify all of these (there’s a section about tools later). Alongside the heavy-hitting technical issues, complete any task that takes less than 30 minutes to clear a bunch of problems fast. Sometimes momentum inspires further action.
• Image compression and lazy loading. Reducing image weight is a fast way to improve page speed without developer support. Image compression and lazy loading are highly recommended to improve website performance.
• Broken UX or CTA elements. Fix friction points that hurt conversions, such as broken forms, unclear calls-to-action (CTAs), or mobile layout issues.
• Local SEO audit. Check that your Google Business Profile is up to date, ensure NAP (name, address, phone number) consistency, and look for duplicate listings or missing local citations. Resolve any of these issues. NAP consistency is especially important because AI tools summarize data. Inconsistencies may reduce the likelihood of a citation or lead to incorrect citations.
• Metadata improvements. Spot missing or weak title tags. These are fast fixes that often lift CTR immediately. I recently improved my client’s click-through rate just by adding a site favicon and optimizing the title tags. The title tag edits meant other, more relevant pages ranked higher (instead of their homepage), and therefore, people clicked more.
• Internal linking opportunities. Add contextual links pointing to your most important pages, especially those that drive conversions or support key topics. Identify orphan pages and work to reduce them to zero; many can be deleted, consolidated, or deindexed. Orphan pages are often a trove of audience and content insights; marketers create them with the best intentions (usually to close deals), then forget about them. An internal linking sweep helps resurface these pages, strengthen your site architecture, and direct authority where it actually matters. Or, orphan pages can inspire improved campaigns.
• Duplicate or cannibalizing pages. Identify pages competing for the same keyword and consolidate them for a cleaner, stronger ranking signal.
• Schema audit. Check for missing or incorrectly structured data on key templates (articles, products, FAQs). Proper schema helps search engines understand your content and can unlock rich results.
• Low-hanging content refreshes. Update pages with high impressions but low clicks — a few strategic improvements can unlock quick traffic wins.
• Backlink gap analysis. Compare your domain authority and backlink profile to competitors. Quick wins often include reclaiming unlinked brand mentions or refreshing link-worthy assets. This matters for both SEO and AEO/GEO. AI search engines lean on strong authority and brand mentions when choosing which sites to cite in generated answers. If competitors earn better links from trusted, authoritative sources, see if your business can earn the same.

Noticing a significant gap in your competitors’ backlinks compared to yours?

Watch this video and learn how to get more high-quality links:

How to Run an SEO Audit Step-by-Step

An effective SEO audit follows a straightforward process, from setting intent to translating data into strategic action.

Here’s a simple five-step framework I use with clients to stay focused.

Important: Although I’m calling this an “SEO audit,” it should always include AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) to reflect how people discover brands today.

Step 1: Outline what the business wants to achieve from the audit.

Defining the purpose of your audit means getting crystal clear on why you’re doing it and what problem(s) you’re trying to solve. Every audit should start with business goals, not just a list of technical checks, and every SEO audit improves website traffic and conversions. Still, SEO consultants or SEO team members creating the audit should ask what the underlying issue is and focus efforts on the pages, templates, and metrics that matter most.

How to do it:

• Talk to stakeholders and ask: What triggered the need for an audit?
• Identify whether the problem is traffic loss, declining conversions, falling rankings, poor AI visibility, or a push into a new market/topic.
• Map the issue to specific pages, funnels, or content clusters.
• Document what success looks like (e.g., “recover 20% of lost traffic,” “improve AI answer visibility for X topic,” “increase conversions on high-intent pages”).
• Set the audit’s boundaries so you don’t end up analyzing the entire site without direction.

Note: An audit is a good practice; sometimes its purpose is to enable SEO specialists to step back and view the site with a fresh set of eyes. But as a best practice, each scheduled audit should have a purpose or goal.

Before I pull any data, I clarify what the business wants to solve. If a client tells me they’ve lost traffic, conversions have dropped, or a new product isn’t ranking, I shape my audit around that problem. This gives me a clear roadmap. I know which pages matter most and which elements or metrics deserve the closest attention. That doesn’t mean I ignore everything else. My rule is simple: Anything I come across goes into the audit document. There’s no need to gatekeep findings because the person running the audit prioritizes them later.

Step 2: Research and gather data.

Research and data gathering is the phase in which SEO specialists collect all quantitative signals that show how your site is performing. It’s the foundational layer of any SEO audit.

You might pull:

• Rankings
• Traffic trends
• Technical errors
• Backlink data
• Content performance
• AI visibility metrics

Pull all the data into one place, like a Google Sheet.

Pro tip: Keep this information stored safely, as it also serves as a benchmark for your next audit. All being well, the next audit should demonstrate an increase in metrics such as rankings, traffic, and AI visibility.

How to do it:

• Pull data from core platforms, such as Google Search Console, Google Analytics, your CMS, crawl tools, backlink tools, and AI visibility tools.
• Export everything into Sheets or Excel and use conditional formatting to help analyze it.
• Set up conditional formatting to highlight anomalies (e.g., pages with high impressions but low click-through rates, URLs with 404 errors, slow Core Web Vitals, orphan pages, thin content, redirects).
• Collect data on competitors: ranking keywords, backlink gaps, content performance, and AI search visibility.
• Organize your tabs by theme — technical, content, on-page, backlinks, local, AI — so patterns start to emerge.

At this stage, I’m gathering everything — exports from crawlers, GSC, analytics, and backlink tools. This is what I call the “cookie-cutter SEO” phase: The tools do most of the heavy lifting, and anyone can technically do it. I move all the data into sheets, set up conditional formatting, and highlight anything unusual. I’m not trying to solve anything yet; I’m simply collecting and quietly analyzing the raw material.

Step 3: Analyze the research.

Human analysis is where the raw data becomes insight. This is the strategic layer of the audit; the part that tools can’t do for you. A sophisticated SEO reads between the lines, connects patterns, and understands why the issues exist and how they impact traffic, rankings, conversions, and AI visibility. It’s where the audit stops being a spreadsheet exercise and starts becoming a roadmap.

How to do it:

• Interpret the patterns in your data: drops, spikes, plateaus, and anomalies.
• Identify causes, not just symptoms — for example, whether a ranking drop is due to algorithm changes, content quality, technical regressions, or stronger competitors.
• Connect your findings to user behavior — where people land, where they bounce, what content they trust, and what pages they convert on.
• Evaluate how the site performs across traditional SEO and AEO/GEO — entity clarity, topical authority, and how well the brand is referenced in AI outputs.
• Start grouping findings by theme (technical, content, on-page, authority) and by impact.
• Determine which insights actually move the needle and which simply clutter the audit.

What’s critical: Align SEO insights with your business strategy — product priorities, revenue-driving pages, seasonal demand, campaigns, and sales goals. Refer back to the team’s notes and comments from step one.

During this stage, I start forming ideas about where we could take the site. For example, if a brand has told me they’re interested in reaching a specific audience, I quietly spot opportunities to do so and record all insights in a spreadsheet.

Then, even though I have a clear direction from the client in step one, I like to meet again in step four. By then, we can review the SEO data and determine whether priorities or goals need to change. Sometimes the data aligns fully with what the client said in phase one; if so, a quick confirmation is helpful before I dive into creating the plan.

Step 4: Huddle with stakeholders.

This phase is where your SEO insights meet the realities of the business. At this stage, SEO specialists can work with stakeholders to ensure the recommendations make sense in the broader context of strategy, priorities, capacity, and upcoming campaigns. This step validates your assumptions, fills in knowledge gaps, and ensures the audit isn’t happening in a vacuum.

Sometimes, reviewing the site through the lens of an SEO audit uncovers new insights that need discussion. For example, identify an untapped audience segment, a high-potential content cluster, or a topic area that wasn’t mentioned in step one but could significantly benefit the business. This is the moment to bring those findings to the table and realign on what truly matters moving forward.

How to do it:

• Share a summary of key findings rather than the full spreadsheet. Stakeholders don’t need to see all the workings out (well, unless they really want to!). Focus on themes and patterns.
• Ask stakeholders to validate context: upcoming product launches, resourcing limitations, sales feedback, seasonal trends, or known technical constraints.
• Confirm the importance of the high-impact pages you’ve identified. Some may no longer be strategic priorities. Others might benefit from other resources, like ads or social media, to make them aware of what’s coming their way.
• Discuss any surprises the audit surfaced — traffic drops, content gaps, missing schema, or AI visibility issues.
• Align on what success looks like — which goals matter most and what timelines are realistic.
  Identify owners early (SEO, content, developers, product, design) so there’s clarity on who will handle each recommendation.

Important: When you get to this stage, you’ve likely got a pretty solid idea of where you want to take your strategy. Get stakeholder buy-in before creating it.

This is one of my favorite phases of the SEO audit checklist. As a consultant, in step one, I’m a passive listener to how the website performs. In this stage, I know what’s going on. I’m excited about the project and have my own insights. This meeting has more energy, and more insights are unlocked. When data supports ideas, it’s encouraging, exciting, and motivating.

Step 5: Refine the audit and build an actionable plan.

This is the moment where your findings become a real strategy. After aligning with stakeholders, refine the audit into a clear, prioritized plan that the business can actually execute.

It’s not enough to list issues. The value of an audit lies in translating insights into structured actions, with owners, timelines, and expected outcomes.

This step turns the audit from a diagnostic into an actionable roadmap.

How to do it:

• Revisit all findings and filter out anything low-impact or non-actionable.
• Prioritize recommendations using a simple framework like impact vs. effort or “now / next / later.”
• Combine related issues into themes or projects (e.g., “content refresh sprint,” “template cleanup,” “AI visibility improvements”).
• Assign owners to each item: SEO, dev, content, design, product. Accountability is so important for completing actions.
• Add estimated effort and dependencies to help teams plan realistically.
• Tie each recommendation back to the business goals identified in steps one or four.
• Create a clear, digestible roadmap: what to fix first, what will drive revenue or visibility, and what can be parked for later.
• Provide optional “quick wins” lists to help teams build momentum early.

A well-structured plan makes the audit usable, something the business can act on week by week, rather than a document that gets filed away.

I want my audits to be so actionable that anyone could take the document and run with it, feeling confident to implement it. I assign owners, estimate effort, and rank recommendations by impact so the team knows exactly where to start. This is the step where the audit stops being a list of interesting insights and becomes a clear, focused execution plan that actually drives results. If I’m working with the business long term on implementation, I take the audit and manage the actions in a project management tool like Asana.

How to Interpret Your SEO Report and Prioritize Fixes

SEO specialists should prioritize audit findings by impact, effort, and owner. Interpreting the SEO audit is where the real impact happens. Once the SEO team has collected its findings, the next step is turning them into a clear, prioritized plan that the business can act on. Here’s how to evaluate what matters most and where to start.

Here are some ways to interpret the SEO report, in the order I’d prioritize:

Prioritize fixes that unblock crawling and indexing.

Anything preventing search engines from crawling or indexing key pages should rise to the top of the priority list. These issues, such as accidental noindex tags, broken internal links, or faulty robots.txt rules, can instantly suppress visibility.

Fixing them often delivers the fastest and most noticeable traffic lift.

These five categories (crawlability, indexability, accessibility, rankability, and clickability) and how they stack within the technical SEO hierarchy are best shown in this graphic, which echoes Maslow’s Hierarchy of Needs but reimagined for search engine optimization.

Source

Flag issues with true business risk.

Some findings need immediate attention, not for SEO reasons, but for revenue or reputation reasons. Security vulnerabilities, broken checkout flows, incorrect pricing pages, or inaccessible and broken forms should be treated as non-negotiable priorities. These directly affect conversions and trust.

Align tasks with business goals.

SEO specialists should prioritize the content clusters and pages that support the company’s specific goals, whether that is targeting a new audience, promoting a key product, or expanding into a new region.

An SEO audit should always reflect the business’s direction.

Pro tip: Ask stakeholders for SMART goals, so they’re specific, measurable, attainable, relevant, and time-bound. The graphic below shows what SMART goals look like.

Identify content updates that support multiple channels.

Prioritize content that does more than rank. Pages that support SEO, email nurturing, sales enablement, or product education create compounding value. One high-quality asset can close gaps across multiple touchpoints, especially when tied to a defined content cluster or campaign. HubSpot’s free AI content writer can help with this step.

Tackle high-impact, low-effort wins first.

Look for actions that take less than 30 minutes and deliver measurable improvements.

Updating a title tag or adding a favicon can make a big difference. Sometimes, it’s all that’s needed to move the needle, and if that’s the case, just get it done.

Adding a few internal links, compressing images, or deleting an irrelevant, outdated page can get actionable work moving and build momentum early in the process.

Pro tip: Celebrate the little wins, especially if other departments, like developers, are working on the project. A bit of positivity is motivating, and these small fiddly tasks are surprisingly impactful. The goal? Get the team motivated to complete the work.

Cluster recommendations into sprints.

Group related issues so teams can work efficiently. A “page speed sprint,” “schema sprint,” or “content refresh sprint” helps teams stay focused and reduces context switching.

This makes implementation smoother and helps deliver improvements faster.

Plus, you can report on that particular sprint as soon as it’s done and show everyone the fruits of their labor.

Focus developer time on sitewide, template-level issues.

Developer resources are usually limited, so use them wisely. Prioritize fixes that affect the entire site: template-level speed issues, schema improvements, navigation changes, or structural improvements. These updates can influence hundreds or thousands of URLs at once.

Pro tip: Want to upscale your SEO skills? HubSpot Academy’s SEO Course will help teams learn the skills needed to do SEO work that drives results.

Tools to Run an SEO Audit

The right tools make your audit faster, more accurate, and far easier to prioritize. Below are the tools I use most often.

HubSpot AI Grader

HubSpot’s AEO Grader is one of the best tools for an SEO audit with AI insights. AI search grader assesses brand and entity visibility in AI search results. It evaluates how well your brand appears in AI search results, including generative engines, answer boxes, and conversational interfaces. This aligns directly with the AEO/GEO components highlighted throughout this article: entity clarity, authority signals, and brand visibility are now essential parts of a complete audit.

What it is: HubSpot’s AEO Grader is a free tool that analyzes your site’s AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) readiness.

Why it matters: Traditional SEO audits don’t tell how AI systems interpret your brand. The AEO Grader does. It evaluates entity strength, content signals, structured data, and authority markers, all of which heavily influence whether your brand appears in AI-generated responses.

Best for: HubSpot’s AEO Grader is best for marketers ready to move beyond classic rankings and understand how AI search systems perceive their site.

Pricing: Free

Read more about AEO in this comprehensive guide.

HubSpot Website Grader

HubSpot’s Website Grader is one of the simplest ways to get a quick snapshot of your site’s SEO health. Marketers can use free tools like Website Grader to start an SEO audit; it aligns perfectly with the “quick wins” section of this article. The tool surfaces issues around speed, metadata, and basic technical hygiene that can be fixed early in the audit process.

What it is: HubSpot Website Grader is a free tool that evaluates your website’s SEO, performance, mobile usability, and security.

Why it matters: HubSpot Website Grader provides a quick, easy-to-understand entry point into your audit before you dive into deeper technical or competitive analysis. It’s beneficial for spotting fast fixes that take under 30 minutes.

Best for: HubSpot Website Grader is best for marketers who want a simple, high-level snapshot before pulling data from heavier tools.

Pricing: Free

Semrush

Semrush is one of the most comprehensive SEO platforms on the market. I’ve used it for over 10 years. As mentioned earlier in this article, it continues to excel at keyword research, content insights, competitive tracking, and now AI/LLM-driven recommendations.

What it does: A complete SEO toolkit covering keyword research, competitive analysis, backlink auditing, content insights, site audits, and AI search intelligence.

Best for: Marketers or consultants who want deep keyword data, competitor insights, and robust reporting.

Pricing: Plans start at $165/month billed annually.

Screaming Frog

Screaming Frog is a must-have for technical audits, especially when you’re working through issues like orphan pages, broken links, redirect chains, thin content, or missing metadata.

What it does: A fast, locally installed crawler that scans your website and reveals all major technical issues.

Best for: Technical SEOs or marketers who want precise, crawl-based insights. To use this tool, you must know how to derive insight from your data. Unlike the HubSpot tools listed here, it doesn’t provide insights.

Pricing: Free version, plus licensed version for $279/year.

HubSpot’s Marketing and Content Hub

Used together, HubSpot’s Marketing Hub and Content Hub make marketers unstoppable.

HubSpot Content Hub combines content management with built-in SEO intelligence, making it perfect for implementing many of the opportunities uncovered in an SEO audit, especially those related to metadata, content quality, or outdated content.

What it is: HubSpot’s Content Hub is a CMS with AI-powered SEO recommendations, content suggestions, and intelligent content tools that support editorial and technical improvements.

Why it matters: Content Hub helps optimize metadata, improve on-page signals, and manage content clusters.

Pro tip: SEO specialists and writers who use Breeze AI within Content Hub are automating content production.

HubSpot Marketing Hub includes SEO tools that are directly connected to your website data, content strategy, and reporting, making it easier to find actions and implement the roadmap created in Step 5 of your audit.

What it is: HubSpot’s Marketing Hub is a comprehensive marketing platform that includes SEO recommendations, content optimization tools, analytics, and reporting.

Here’s a screenshot from Marketing Hub’s SEO report.

Why it matters: Marketing Hub connects your SEO insights to real business outcomes. Track performance, analyze SEO growth, manage content clusters, assign tasks, and measure the impact of your audit on traffic and conversions.

Pricing: Free plan; Starter – $9 per seat/month; Professional – $800/month; Enterprise – $3,600/month

Useful resources:

• View SEO recommendations in HubSpot
• Analyze SEO performance in HubSpot
• Understanding SEO recommendations

Frequently Asked Questions About SEO Audits

How long does an SEO audit take?

Most SEO audits take between two and eight weeks, depending on the size and complexity of the site, and the depth of coverage. For example, smaller sites can be completed in a few days, while enterprise sites with thousands of URLs, multiple templates, and complex technical structures take longer.

The analysis and stakeholder alignment phases often require the most time and are the most important. While a large portion of the audit involves data gathering and is fairly subjective, there are areas (such as content ideation) that require creativity. In my experience, creativity needs time to develop. Rush your audit, and risk missing out on creative ideas.

Do I need a developer to complete an SEO audit?

SEO specialists shouldn’t rely on developers to run the audit, but developers are often needed to implement parts of it. SEOs can diagnose and document technical issues, but fixes such as template-level changes, Core Web Vitals improvements, structured data implementation, and JavaScript cleanup typically require development support. The audit itself identifies the work; the developer helps execute it.

How often should you run an SEO audit?

Most businesses benefit from a quarterly, biannual, or annual audit. Regular SEO audits help maintain and grow search performance over time.

Fast-moving companies, sites with frequent content updates, or businesses heavily impacted by AI search changes may benefit from more frequent checks. At a minimum, run a full audit once per year to benchmark performance and flag unexpected declines.

What tools do I need for a free SEO audit?

You can run a basic audit using free tools, including:

• HubSpot Website Grader – A quick health snapshot.
• HubSpot AEO Grader – Evaluates AI visibility and entity clarity.
• Google Search Console – Performance, indexing, and coverage reports.
• Google Analytics – Traffic and engagement insights.
• Screaming Frog (free up to 500 URLs) – Technical crawling.

These tools cover core areas: crawlability, indexability, content quality, and AI search visibility.

What’s the difference between an SEO audit and a website audit?

An SEO audit focuses on the elements that influence rankings, visibility, and conversions — technical health, content quality, backlinks, and AI visibility.

A website audit is broader. It may include UX design, accessibility, CRO, branding, navigation, and overall site performance.

You can think of an SEO audit as one part of a full website audit. Both can be combined, but the SEO audit is more specialized and directly tied to traffic growth and search performance.

Modern SEO audits go beyond blue links.

A well-executed SEO audit doesn’t just surface problems; it turns your website into a growth engine. By reviewing your technical health, content quality, authority signals, and AI search visibility, you can uncover quick wins, shape long-term strategy, and build a roadmap that directly supports traffic, conversions, and pipeline.

Remember: Modern audits go beyond blue links; they evaluate how well your brand shows up in generative search and whether your content is truly understood as an entity. If you want a fast, accurate snapshot of where you stand, tools like HubSpot Website Grader and HubSpot AEO Grader make it easy to assess both SEO fundamentals and AI visibility in minutes.

From my experience, the most impactful audits are the ones rooted in business goals and executed collaboratively. I love the point in the process when the data clearly aligns with what stakeholders feel intuitively — or reveals something completely unexpected. When an audit is done well, teams walk away feeling focused, confident, and energized because the path forward is so clear. That’s the sign of a great audit: One that doesn’t just diagnose, but inspires action and drives real results.

With both leaving their mark on consumer search behavior, marketing strategies like generative engine optimization (GEO) are not just becoming popular but essential.

But that doesn’t mean generative trauma ensues. Let’s unpack how your business and marketing team can navigate the changes, unknowns, and competition with generative AI SEO best practices.

Table of Contents

• What is Generative Engine Optimization?
• Why generative engine optimization matters now
• Generative engine optimization best practices you can implement today
• Common generative engine optimization pitfalls to avoid
• Frequently asked questions about generative engine optimization best practices
• Generating Generative Success

What is generative engine optimization?

Generative engine optimization (GEO) is about making your website and content easy for AI-powered search tools (like ChatGPT, Gemini, Perplexity, and even AI overviews) to find, understand, and cite.

When someone asks one of these tools a question, the AI systems scan content across the web to create an answer. It doesn’t give you a list of resources that could be helpful, like search engine optimization, but it aims to directly answer your question while citing websites it thinks are reliable. GEO helps your content get chosen as one of those lucky resources.

TLDR: SEO gets you on the party guest list (SERP). GEO gets you a VIP seat and a shoutout from the DJ (Citation).

GEO vs AEO

Ok, so SEO is clearly different from GEO, but what about AEO? Lines are often drawn between answer engine optimization (AEO) and GEO, and historically, there was a distinction.

AEO targets direct-answer features that have been around for a while; think featured snippets in Google, knowledge panels, and voice assistant responses. It’s about showing up in those quick-answer boxes.

Generative engine optimization, on the other hand, focuses specifically on newer AI tools that generate original responses by combining information from multiple sources. It helps you be one of those sources.

Overall, many tactics work for both goals (and even SEO), but GEO requires extra attention to how you structure information and establish credibility so AI systems feel confident citing your work.

Why generative engine optimization matters now

Let’s not get it twisted: GEO isn’t replacing SEO. Rather, it’s extending it for a world where AI plays a bigger role in how people discover information. The marketers who figure this out early will have a significant advantage.

(So, if you’re reading this, congrats! You’re in good company.)

BrightLocal research shows that Google still drives 61% of all general searches, but AI platforms are noticeably growing as destinations where people start their research.

In fact, according to GWI, 31% of Gen Zers already say they use AI platforms or chatbots most frequently to find information online, and Gartner even predicts that 40% of B2B queries will be handled by an answer engine by the end of the year.

Add the prevalence of voice assistants like Siri and Alexa on our smartphones and in our homes, and the need to evolve is even more apparent. A list of links isn’t always helpful to users; they want synthesized, actionable answers with clear sources they can trust. That’s where generative engines come in.

If you don’t invest in GEO now, you could be missing out on all of these possibilities — but this challenge isn’t a bad thing. GEO just demands we level up. AI tools ultimately prioritize quality, and the best way to compete is to just keep delivering more and better value in your content.

Tools like HubSpot’s Content Hub can help by making it easier to create structured, well-organized content that aligns with GEO best practices.

Generative Engine Optimization Best Practices You Can Implement Today

Regardless of the tools you use, here are some best practices for generative engine optimization you use to put your best foot forward.

1. Lead with clear, direct answers

AI systems love resources that get straight to the point. In other words, they favor content where the information they need isn’t buried. That said, start each section by directly answering the target question as concisely as possible (aim for fewer than 300 words), then expand with context and details.

Think of it like this: if someone pulled out just one paragraph from your article, would it make sense and answer their question on its own? That‘s what you’re aiming for.

Answer the question first, then explain the nuances. This is how you should approach writing for AI search in general — clarity first, depth second. Here at HubSpot, we’ve been experimenting with “summaries” at the beginning of our articles to accomplish this:

Source

Pro tip: Use the “inverted pyramid” approach to writing journalists lean into: Put the most important stuff at the top, supporting details below. This makes it easy for AI to find and extract your main points accurately.

Source

You can also use HubSpot’s Content Hub to create templates that enforce this answer-first structure across all your content, so it becomes automatic.

2. Be specific about who and what you’re talking about

Sometimes when you’re reading about complicated topics, it’s easy to lose the thread. Maybe you’re reading an explanation of X and Y and how they relate to Z, but suddenly you’re unsure whether the last sentence was about Z or X or something else entirely.

AI systems are similar in a way. They process and cite content by recognizing its subject matter, like specific people, places, companies, and concepts. Depending on the context, vague references in your content can confuse AI and reduce your chances of being cited.

For example, saying “The company launched it in 2024,” may leave AI systems asking, “What company?” Instead, you’d want to write “HubSpot launched Content Hub AI in 2024,” so AI gets the details right.

Keep these clarity best practices in mind when writing for generative engine optimization:

• Use full names first (then you can shorten them)
• Spell out acronyms before using them repeatedly
• Link to official pages for companies and concepts
• Stick with consistent terms throughout your content
• Avoid unclear pronouns when they could refer to multiple things

3. Optimize the technical elements of your website

GEO is just as much about what’s off the page as what’s on the page. That means keeping your website running smoothly and organized in a way that AI can understand, with strong technical SEO is critical to getting found and cited.

Here’s what you can do:

Add Schema Markup

Schema markup is backend code that explains what your content is about in a way that’s crystal clear to AI systems. According to Schema.org statistics, pages with properly implemented schema are processed more accurately by AI systems because there’s no ambiguity about their meaning.

There are countless different types of schema, but don’t get overwhelmed. Focus on these types first for the most common “query” impact:

• Article schema with author information and dates
• FAQ schema for question-based content
• HowTo schema for guides and tutorials
• Organization schema to establish who you are
• Breadcrumb schema to show how your content connects

Test your schema using Google’s Rich Results Test to catch any errors that might confuse AI systems.

Source

Keep your website fast and functional

Both AI systems and search engines consider site performance a trust signal. Slow, broken sites are deprioritized because they’re seen as lower quality and, frankly, deliver a worse user experience. That’s the last thing anyone wants.

That said, use tools like Google PageSpeed Insights and GTmetrix to find and fix website performance issues.

I popped Apple in as an example, and even giants like it have room for improvement.

Pay special attention to maintaining:

• Page speed (aim for under 2.5 seconds to load)
• Mobile experience (AI systems prioritize mobile-friendly content)
• Security (always use HTTPS)
• Clear navigation (helps AI understand how content relates)
• Clean, functioning code (reduces confusion for automated systems)

Pro Tip: Use HubSpot’s CMS to automatically handle many technical requirements for fast, AI-friendly websites (e.g., mobile responsiveness and security).

To learn more about optimizing your site speed, you can also check out our articles, “Here’s How I Measure Website Speed and Guarantee Performance (+Tips)” and “19 Website Speed Optimization Strategies [New Data].”

Optimize your metadata

While traditional metadata targets search result pages, GEO-optimized metadata helps generative search quickly understand and accurately summarize what your content covers. In today’s search landscape, you ideally want to appeal to both.

With that in mind:

• Ensure all your images have alt tags
• Make your title tags, headers, and linked text as specific and keyword optimized as possible
• Write descriptions that:

• Clearly state what the content is about
• Highlight your unique perspective or value
• Use natural, conversational language
• Stay within 155-160 characters
• Include specific claims or numbers when relevant

AI systems often use well-written meta descriptions and data as the foundation for understanding your content and retrieving information.

4. Establish credibility

As a user, I’ve definitely seen my share of AI hallucinations and odd citations, but to their credit, most AI systems make an active effort to check whether websites actually know what they’re talking about before citing them. So, how can yours make the cut?

Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) has been around for a while now, but it is still important in the AI age. It’s actually the core evaluation criterion that AI systems (and Google) use to assess the credibility of sources. In other words, strong E-E-A-T signals dramatically increase the likelihood of citations.

Strengthen yours by adding:

• Author bios showing relevant experience and credentials (like mine, seen below). Even better, implement Author Schema markup.
• About pages. Talking about your company’s founding, history, mission, and accomplishments, among other things, helps establish its expertise.
• Links to authoritative sources. Like you want to see AI cite credible resources, it wants to see the same from you. This means sources that are original, current, and list specific accountable parties or authors.
• Publication dates showing your content is current
• Clear editorial standards demonstrating your commitment to quality

Source

According to Google’s Quality Rater Guidelines (updated regularly through 2024), expertise and trustworthiness are the primary ways to evaluate content quality — and AI systems are trained using these same standards.

5. Showcase deep subject matter expertise

Another part of establishing credibility is showing a deep understanding of your industry, product, and desired area of expertise. Here’s how:

Make your content comprehensive

One way tools evaluate subject-matter expertise is by looking for comprehensive coverage of a specific topic across your website and content. For example, think of how HubSpot covers digital marketing and business growth, and Healthline covers wellness.

Research from Clearscope shows that thorough content (2,500+ words with comprehensive topic coverage) received 3.2x more AI citations than shorter, surface-level pieces. Semrush also found that comprehensive, well-sourced content earns 77.2% more backlinks than shallow content, which helps both your GEO and traditional SEO performance.

So, go deep. That means:

• Covering multiple aspects of a subject.
• Providing real, unique examples and step-by-step guidance.
• Including data and statistics to support your points
• Addressing common questions and edge cases
• Linking to related resources for people who want to learn more

Bottomline: AI systems prefer sources that thoroughly address a topic rather than providing quick, incomplete answers.

Create pillar pages

Credibility and comprehensive coverage typically happen naturally over time with consistency, but you can help it along. Consider creating pillar pages on your core topics, then supporting articles that go deep into specific aspects under that umbrella.

For example, if your pillar topic is “email marketing,” create supporting content on segmentation, automation, deliverability, metrics, and platform comparisons.

From there, strategically link the pieces together. Linking is fundamental to effective content optimization in the AI era, as it signals to AI and search engines that pages are related. Make sure each piece leads back to the pillar and the pillar down to each piece.

The goal is to be complete. Hobbyists scratch the surface. Experts go deep.

Pro tip: Topic clusters and pillars can get complicated. Use HubSpot’s topic cluster tool in Content Hub and Marketing Hub to map out your content and spot gaps where you might be missing important pieces.

6. Include images, videos, and other visual content

Research from Princeton and Georgia Tech found that content with relevant images, charts, and videos got 40% more AI citations than text-only content. That’s a big advantage, but why exactly?

Not only do visuals make content more engaging and memorable for your audience, but they also help AI systems understand context. They also signal that you’ve put real effort into making information accessible and clear from many different angles. It’s a sign of being thorough, and AI loves thorough.

In your content, include:

• Custom images with detailed alt text descriptions. This is not just for accessibility, but because AI systems read that text to understand what the image shows.
• Charts and graphs that make data and trends easier to grasp
• Videos that explain complex ideas
• Infographics that summarize key points
• Screenshots that show step-by-step processes or examples of what you’re discussing.

(This article is a good example of this tip in action.)

7. Write like a real person to a real person

Don’t you hate it when AI sounds like a robot? Ironically, it hates that too.

AI systems are trained on conversational questions and natural language. Content that’s overly formal, technical, or stuffed with keywords is harder for AI to interpret and cite accurately.

Write as if you’re explaining something to a smart colleague who may be new to the topic:

• Address readers directly using “you.”
• Include personal experience and insights with words like “I,” “My,” “Our.”
• Include questions that readers might ask or leads typically ask.
• Define technical terms when you need to use them
• Don’t go overboard with jargon

This conversational style isn‘t just better for GEO — it’s also more engaging for human readers, improving your content performance across the board.

Pro Tip: If you’re using AI to actually write your content, make sure to edit and humanize it before publishing.

Search engines and AI engines claim they don’t penalize AI-generated content, but they do penalize unoriginal content, which is an inherent risk with AI tools. More on that shortly.

8. Publish regularly and keep content fresh

Freshness matters enormously for GEO. AI systems prefer recent content as it’s more likely to be up to date. Content Marketing Institute’s 2024 research found that organizations publishing weekly or more often had AI citation rates 67% higher than those publishing monthly or less often.

Build a content refresh strategy:

• Review content every quarter to catch outdated information
• Update dates when you make significant changes
• Add new sections on emerging developments
• Replace old sources with recent research and data
• Track what you’ve updated so AI systems notice the changes

Content that hasn’t been touched in over 18 months is much less likely to be cited, no matter how good it originally was.

Common Generative Engine Optimization Pitfalls (and How to Avoid Them)

1. Being vague or inconsistent about who/what you’re discussing

The mistake: Switching between different names for the same thing (like “HubSpot,” “the company,” “the platform,” “it”) without enough context, or using pronouns when it’s unclear what they refer to.

Why it hurts: AI systems identify specific people, places, and things to understand and recommend content. Vague references create confusion, preventing potential citations.

Fix it fast:

• Search your content for words like “it,” “they,” and “this.”
• Replace unclear references with specific names
• Create a style guide for consistent terminology
• Use structured data to explicitly define key terms

2. Skipping schema markup or implementing it wrong

The mistake: Publishing content without schema markup, using outdated formats, or implementing it incorrectly so it doesn’t work properly.

Why it hurts: AI systems use schema as a reliable way to understand your content. Missing or broken schema can affect how AI interprets what you’re saying.

Fix it fast:

• Check your pages using Google’s Rich Results Test
• Add Article, FAQ, and HowTo schema to existing content
• Use a schema generator or CMS plugin to automate the process
• Schedule monthly checks to make sure the schema is working

3. Citing questionable or outdated sources

The mistake: Linking to unreliable sites, news aggregators, or research from before 2024 when current information is available.

Why it hurts: AI systems evaluate the credibility of sources. Weak or outdated citations signal low-quality content that shouldn’t be trusted.

Fix it fast:

• Replace citations older than 18 months
• Link to original sources or reputable sources instead of roundups
• Remove links or references to low-authority sites or voices
• Add publication dates to all citations
• Prioritize academic, government, and recognized industry sources

4. Publishing AI-written content without editing

The mistake: Using AI-generated content directly without adding a unique perspective, original research, your brand voice, or expert input.

Why it hurts: AI systems recognize and downrank generic, AI-generated content that lacks original value. Ironically, AI-written content often doesn’t perform well for GEO.

Fix it fast:

• Add real, unique examples from your experience
• Include your own data or case studies
• Add quotes and insights from experts on your team
• Include your personal perspective, commentary, and emotion

HubSpot’s brand voice tool can help with this.

Read: How to humanize AI content to rank, engage, and get shared

5. Never updating or revisiting content

The mistake: Creating content and never revisiting it, even as information becomes outdated or new developments happen.

Why it hurts: AI systems heavily favor recent content. Stale information is skipped in favor of fresher sources, even if your original content was of higher quality.

Fix it fast:

• Set up a quarterly content review calendar for high-quality and high-ranking pieces (aka Historic Optimization)
• Update statistics and examples to current year data
• Refresh publication dates after substantial updates
• Review and replace examples and screenshots
• Add new sections on recent developments

6. Leaving out author credentials and authority signals

The mistake: Publishing content without author information, credentials, or organizational background that helps AI systems evaluate trustworthiness.

Why it hurts: AI systems are trained to assess credibility based on an author‘s expertise and an organization’s authority. Anonymous or poorly attributed content is treated as less trustworthy.

Fix it fast:

• Add detailed author bios to all content
• Connect author bylines to credentials
• Create strong “about” and “team” pages
• Link authors to professional profiles (LinkedIn, company pages)
• Include your editorial standards and fact-checking process

Read: Professional Bio Examples: 29 Work Bios I Keep in My Back Pocket for Inspo [+ Templates]

7. Not tracking whether your GEO efforts are working

The mistake: Implementing GEO tactics without measuring whether they’re increasing AI citations, traffic from AI platforms, or brand mentions.

Why it hurts: You can‘t improve what you don’t measure. Without tracking, you might waste time on things that don’t actually help.

Fix it fast:

• Set up Google Search Console to track AI Overview appearances
• Monitor brand mentions in ChatGPT, Perplexity, and other AI tools
• Track traffic from AI platforms in Google Analytics
• Use tools like BrandWell’s AI Visibility Score to measure citations
• Create monthly GEO performance dashboards

8. Over-optimizing for specific AI platforms

The mistake: Tailoring content to a specific tool (i.e., ChatGPT or Perplexity) without considering how the landscape is changing.

Why it hurts: The AI search world is evolving fast. Platform-specific tricks might not work as new systems emerge, and existing ones change.

Fix it fast:

• Focus on fundamental content quality rather than platform hacks
• Build broad expertise that works across platforms
• Stay informed about new AI search tools
• Implement universal best practices (schema, credibility, sources)
• Avoid manipulative tactics in favor of genuinely useful content

FAQs About Generative Engine Optimization Best Practices

Is generative engine optimization replacing traditional SEO?

No, GEO isn‘t replacing traditional SEO; it’s complementing it. Search engines still drive the majority of website traffic, so SEO is critical. Plus, a lot of GEO, and AEO for that matter, is rooted in the same criteria as search engines.

All of them prioritize quality content, credible sources, technical excellence, and user value. The main difference is that SEO focuses on ranking in search results, while GEO focuses on getting cited by AI tools that create synthesized answers.

The smartest approach combines both strategies.

When you implement GEO best practices like thorough content, strong sources, clear language, and structured data, you’re also strengthening your traditional SEO. Think of GEO as SEO evolving for a world where AI plays a bigger role, not as a replacement.

How long does it take to see results from GEO?

You’ll typically start seeing GEO results within 4-12 weeks of implementation, though timing varies based on your existing content quality, site authority, and the extent of your optimization.

Quick wins (2-4 weeks):

• AI platforms start citing your newly optimized content
• Better structured data helps AI understand your content more accurately

Medium-term results (2-3 months):

• More frequent citations as AI systems recognize your expertise
• Higher visibility in AI responses for your target topics

Long-term gains (6+ months):

• Established authority drives consistent citations
• Comprehensive topic coverage makes you a go-to source

Unlike traditional SEO, where ranking changes can take months, GEO can show results faster because AI systems continuously update their source preferences. That said, building sustainable GEO performance requires the same long-term commitment to quality that SEO demands.

How can I get cited by AI tools more often?

TLDR: Getting more citations from AI tools requires a combination of content quality, technical setup, and strategic positioning.

Key citation drivers:

1. Show clear expertise: Include author credentials, organizational history, and evidence that you know the topic overall. Showcase social proof.
2. Cover topics thoroughly: Create in-depth content that really truly explores subjects rather than skimming the surface.
3. Use credible sources: Link to and work with trustworthy, verifiable references that AI systems can validate.
4. Add structured data: Use schema markup to clearly signal what your content is about.
5. Optimize your technical performance: Speed and functionality are signals of quality.
6. Keep content fresh: Regular updates with current data and information
7. Build connected content: Develop related articles showing comprehensive subject knowledge

According to research from Arizona State University published in 2024, the strongest predictors of AI citations are content depth, source authority, and technical quality — not keyword stuffing or link volume.

Tactical approach: Start with your highest-authority content (based on backlinks, traffic, and engagement), then optimize those pieces first with GEO best practices. This creates momentum that extends to newer content as AI systems recognize your site as reliable.

What schema should I start with for GEO?

If you’re just getting started with schema for GEO, focus on these four types that deliver the biggest impact:

1. Article schema: Tells AI systems about your content type, author, publication date, and headline. This is the foundation for all editorial content.

2. Organization schema: Establishes who you are and why you should be trusted as a source.

3. FAQ schema: Maps directly to how people ask AI tools questions, making your content highly relevant for conversational searches.

4. Breadcrumb schema: Helps AI understand how your content connects and relates, important for showing comprehensive coverage.

After getting these core types in place, expand to a more specialized schema:

• HowTo schema for guides and tutorials
• Product schema for reviews and comparisons
• Person schema for author credibility
• VideoObject schema for video content

Use Schema.org as your reference guide, and validate your implementation using Google‘s Rich Results Test. HubSpot’s Content Hub includes built-in schema tools that simplify implementation without needing technical expertise.

Do I need separate GEO workflows for enterprise and SMB?

The core GEO best practices work universally, but how you implement them should match your resources, scale, and organizational structure.

Enterprise GEO workflows should emphasize:

• Centralized standards: Consistent schema templates, content guidelines, and quality controls across teams
• Dedicated resources: Specialized roles for GEO implementation and monitoring
• Automated processes: Programmatic schema deployment and content auditing
• Cross-team coordination: Integration between SEO, content, and technical teams
• Advanced tracking: Sophisticated measurement and AI citation monitoring

SMB GEO workflows should focus on:

• High-impact priorities: Start with core schema types and your best content first
• Scalable processes: Template-based approaches that don’t require huge resources
• Integrated tools: Platforms like HubSpot’s Content Hub that bundle GEO capabilities
• Simple measurement: Track AI referral traffic and brand mentions rather than complex attribution
• Gradual expansion: Begin with top-performing content and grow from there

The goal is the same regardless of organization size: create trustworthy, well-structured content that AI systems cite. The path just needs to fit your resources and setup.

Generating Generative Success

I get it. While AI is technically not new, it feels like it is. With answer engines and generative engines, we’ve never seen artificial intelligence at this level or so easily accessible to the general public.

But don’t let the marketing tabloids scare you. Your old SEO playbook isn’t useless; in fact, much of generative engine optimization is rooted in the same principles.

Start with your most important content, get the technical foundations right (like schema and clear language), and commit to keeping your expertise fresh, current, and valuable. Organizations that treat GEO as a strategic priority rather than a checkbox will maintain their visibility as search continues to evolve.

Ready to implement these GEO best practices at scale? HubSpot’s Content Hub provides integrated tools for creating, optimizing, and measuring AI-ready content without needing a technical team.

HubSpot realized that our buyers were moving from search engines to answer engines like ChatGPT, Gemini, and Perplexity — but we had no reliable way to measure AI visibility and understand whether our AEO plays were working.

So, in June 2025, the HubSpot Marketing team started working with XFunnel, an AEO tool that allowed us to measure and optimize our AI visibility across ChatGPT, Gemini, Perplexity, and more. Here’s what we learned. 

Table of Contents

• Building Our AEO Measurement System 
• How We Built Our Three-Pillar AEO Strategy

Building Our AEO Measurement System

Defining the Buyer’s Journey Across Answer Engines for Prompt Tracking

The first question we needed to answer was: When a potential customer asks an answer engine about a problem our products solve, is HubSpot in the answer? To find out, we defined the buyer’s journey across answer engines:

Product-Led AEO

We set up XFunnel containers for each product line.

The AEO measurement architecture included:

• Top-level “HubSpot” brand container for overall brand monitoring
• Eight dedicated product containers: CRM, Marketing Hub, Sales Hub, Service Hub, Content Hub, Commerce Hub, Data Hub, and Breeze
• Feature-specific views within each product container (example: “Email Automation” within Marketing Hub)

This structure meant sub-teams could run experiments, track improvements, and optimize for their specific product’s AEO performance, while giving us a bird’s eye view across our entire AEO strategy.

AEO KPIs We Measure

Once we had defined the prompts, we could see and start to improve our four core AEO KPIs:

• Answer engine visibility (%): how often HubSpot appears for target queries.
• Answer engine share of voice (%): how often HubSpot appears for those same queries relative to competitors.
• Answer engine citations: how often HubSpot pages are cited as a source in AI answers.
• Answer engine citation share (%): how often HubSpot’s pages are cited for those queries relative to our competitors.

How We Built Our Three-Pillar AEO Strategy

After analyzing the data, we determined that a successful AEO strategy relies on:

1. AEO-friendly content on your own website with all the information you need answer engines to have about your business and its products.
2. A strong external presence across the key sources that answer engines are training and pulling information from.

We used this foundation to build a three-pillar strategy:

1. On-site content optimization.
2. Off-site amplification.
3. Community engagement and forum growth.

Pillar 1: On-site content optimization

Our AI visibility scores were strong from the jump, but Xfunnel showed our citation scores were weak. Answer engines weren’t often referencing the pages on HubSpot’s website. Brand awareness is the priority, but being cited increases the likelihood of influencing the answer (and driving direct traffic from AI).

After our Growth team analyzed the Xfunnel data, we realized we needed more ultra-specific content to match the hyper-personalized answers that AI generates for users.

When answer engines got buying questions or industry fit assessments, they struggled to surface HubSpot content worthy of citation. We needed to create content tailored to our key buyer personas.

“Will HubSpot work for my business?” Personalization comes down to being able to answer this question.

Industry-Specific Content

Many prospects want to understand if a solution is right for their industry. We created industry solutions pages at scale using an AI content system. We used AI to generate the content from HubSpot’s library of case studies and reviewed it with humans before it went live.

Because we know AI likes structured data, we used Breadcrumb and FAQ schema on these industry solutions pages.

92% ended up being cited by answer engines, generating a 49% lift in AI visibility.

We also published software comparison articles for target industries (e.g., “5 best CRMs for construction businesses“). We saw a 642% increase in citations for those posts and 58% increase in overall mentions.

FAQ Glossary for CRM, Marketing & Sales Terms

Our team also discovered HubSpot wasn’t appearing enough in the “Problem Exploration” stage of the buyer’s journey. We launched an FAQ glossary covering top-of-funnel terms like “what is marketing automation?” and “how does lead scoring work?” Each page features a concise definition, common related questions, and links to HubSpot features. Answer engines frequently pull from definition content, and owning these terms means being part of the first answer a prospect gets.

As a result, citation share for related prompts increased by +60%. Brand visibility for awareness-stage prompts increased +35 percentage points when the glossary was cited.

Optimizing Product Pages for AEO

We updated product feature pages to better match how answer engines actually understand and retrieve content: adding FAQs, rewriting headlines to address common buyer questions, and improving formatting with tables and lists. We also added structured data to help answer engines read and categorize the pages more easily.

The result: a 56% increase in citations from AI answer engines, and an improvement in average ranking position from 1.5 to 1.

Pillar 2: Off-site amplification

Our AEO benchmarks revealed third-party content shaped AI answers about HubSpot’s products. We needed to build HubSpot’s presence across the third-party ecosystem.

Using XFunnel data, our partnership team identified publishers already winning citations but not yet mentioning HubSpot. We gave partners AEO recommendations and templates so they could create answer-engine-friendly content and win even more citations.

We scaled the program rapidly. By the end of 2025, we’d partnered with hundreds of websites across the world, producing nearly a thousand new pages and earning hundreds of thousands of new AI citations — all with HubSpot mentioned.

Pillar 3: Forum growth

XFunnel benchmarks showed Reddit was one of the most cited sources for our tracked prompts.

Using XFunnel, we built always-on Reddit citation monitoring into our reporting, identifying high-impact subreddits and HubSpot mention gaps on a weekly basis. We then asked HubSpot community advocates to post content addressing some of the top buyer questions.

After XFunnel benchmarks revealed DE and FR markets had significant Reddit citation growth but zero HubSpot mentions, we launched localized campaigns. Within one month, HubSpot’s mention rate went from 0% to 33.5% in FR and 17.1% in DE.

Reddit-driven citations grew from 178 (May 2025) to 146K (December 2025).

Build Answer Engine Visibility With HubSpot AEO

Everything in this case study started with a single question: When buyers asked AI, was HubSpot an answer? Before we could optimize anything, we had to understand which prompts mattered, where we appeared, who was being cited, and where the gaps were.

The measurement came first. The strategy followed.

That capability is now available in HubSpot. With our AEO tools, you can get real-time visibility into how your brand appears across answer engines like ChatGPT, Gemini, and Perplexity.

• Track the prompts your buyers are likely using
• Benchmark your visibility against competitors
• Analyze which sources are driving answer engine citations

All of those insights power prioritized recommendations — built on the successful AEO tactics our team has piloted — so you know what to improve and can start acting on it right away.

And if you’re using AEO within Marketing Hub Pro or Enterprise, you get CRM-powered prompt suggestions so your tracking is informed by your business context from day one, not built from scratch.

Sifting through the noise is frustrating, though, because AEO (answer engine optimization) and AEO rank trackers are relatively new categories. In this guide, I’ll explain what you need to know to choose the best tool for you, including must-have features, a simple scoring framework, and examples of the best AEO tools available.

Table of Contents

• What AEO Rank Trackers Measure and How They Differ From SEO
• What to Look for in AEO Rank Trackers
• How to Turn AEO Rank Tracker Insights Into Content Wins
• How to Choose an AEO Rank Tracker for Your Team
• Examples of AEO Rank Trackers to Explore
• Frequently Asked Questions About AEO Rank Trackers

What AEO Rank Trackers Measure and How They Differ From SEO

AEO rank trackers measure whether your brand appears in an AI-generated answer, and if so, how prominently. By contrast, traditional SEO rank trackers measure where your website appears in a list of blue links on the search engine results page.

That distinction matters because AI answer engines don’t return ranked lists of webpages in the way traditional search engines do — they synthesize responses.

How AI Engines Build Answers

When someone asks ChatGPT, Perplexity, or Gemini a question like “what’s the best CRM for small businesses,” the engine doesn’t just pull a top result. It retrieves information from across the web, evaluates source authority and relevance, and composes a single narrative response, often referencing multiple brands, pages, and data points in the process.

Some of those references are citations: explicit links or attributions to a specific source the engine used to build its answer. Others are simple mentions: the engine names your brand without linking back to any of your content. Both signal visibility, but they mean different things. A citation tells you the engine treated your content as a credible source. A mention tells you your brand has enough presence in the broader information ecosystem to surface in the response, even without a direct link.

AEO rank trackers are designed to capture both citations and mentions, and to distinguish between them, something traditional SEO tools were never built to do.

AEO vs. SEO Tracking

Here’s where the metrics diverge most clearly.

SEO tracking centers on keyword rankings, clicks, and impressions — all tied to a specific page’s position in a search results list. AEO tracking measures a different set of signals entirely:

• Brand mentions and citations. How often your brand or content is referenced in AI-generated answers, and whether those references include a link back to your site.
• Answer position. Where your brand appears within the response itself — early in the answer or buried at the end.
• Share of voice. How your brand’s presence compares to competitors across AI-generated answers. If there are 100 brand mentions, and your brand shows up in 30 of them, your share of voice is 30%.

AI-generated answers are structurally different from ranked search results, and measuring them requires different instrumentation. If you’re still relying solely on keyword rankings to understand your search visibility, you’re measuring one channel (SEO) and missing the other (AEO).

What to Look for in AEO Rank Trackers

Not every AEO tracker measures the same things, and the feature gaps between platforms are wider than you might expect. The best answer engine optimization tools with LLM performance tracking combine multi-engine coverage with citation-level analytics. Before you evaluate specific AEO checking tools, it helps to know which capabilities matter most for your workflows.

Must-Have Features

• Multi-engine coverage. At minimum, your tracker should monitor ChatGPT, Gemini, and Perplexity. Some platforms also cover Copilot and Google AI Overviews. If you’re only tracking one engine, you’re getting a partial picture; answer engines don’t all pull from the same sources or weight them the same way.
• Prompt libraries and custom prompt tracking. You need the ability to define and organize the questions your buyers are actually asking. The best trackers let you build prompt groups by product line, use case, or buyer segment so you’re not analyzing everything as a single undifferentiated pile.
• Citation and mention analysis. The metrics I outlined above — citations, mentions, share of voice, answer position — are table stakes. But look for depth here: Can you see which URLs are being cited? Can you compare your citation rate against competitors over time? Surface-level mention counts won’t help you prioritize content next steps. The best tools for monitoring AEO citations in LLMs go beyond counts and show you exactly which URLs are earning those references.
• Brand sentiment tracking. Visibility alone doesn’t tell you the full story. If an answer engine is mentioning your brand but framing it negatively (citing poor reviews, outdated complaints, or unfavorable comparisons), that’s a problem you need to catch early. Look for trackers that score sentiment across responses.
• Dashboards, exports, and alerts. You’ll need to report on this data regularly, so look for clean dashboards you can share with stakeholders, export options for deeper analysis, and alerting that flags meaningful changes — like a competitor suddenly appearing in answers where they weren’t before.
• Integrations. The more connected your AEO data is to your existing stack (CMS, CRM, project management), the easier it is to act on what you find. Disconnected data leads to disconnected workflows.

Mapping Features to Real Use Cases

The features above aren’t abstract checkboxes. Here’s how they translate into practical workflows:

• Content prioritization. Citation analysis and prompt tracking together show you exactly where your content gaps are, such as which prompts mention competitors but not you, and which content types are getting cited most. From there, you can build an editorial calendar based on data instead of guesswork.
• PR triage. Sentiment tracking is where this gets actionable. If answer engines start describing your brand negatively, or a competitor’s earned media placement shifts the narrative against you, that’s your signal to step in with counter-messaging, outreach, or updated content.
• Monitoring workflows. Dashboards and exports turn AEO from a one-time audit into an ongoing practice. Weekly score tracking lets you measure whether content changes are actually moving the needle, which is critical for justifying continued investment. Pairing your AEO tracker with a broader content performance framework ensures you’re connecting AI visibility to real business outcomes.

Pro Tip: When evaluating trackers, don’t just compare feature lists. Run the same set of 5-10 prompts through each tool’s free trial and compare the depth and accuracy of what comes back. The differences will be obvious fast. The HubSpot AEO tool offers a free 28-day trial that lets you track 10 prompts on ChatGPT.

How to Turn AEO Rank Tracker Insights Into Content Wins

Once your tracker is collecting data, the most productive place to start is often by scoping out your competitors — specifically, the ones showing up in AI answers where you aren’t.

Reverse-Engineering Competitor Visibility

Most AEO trackers let you compare citation rates and mention frequency across brands for the same set of prompts. That comparison is where the real editorial strategy lives.

Start by identifying the prompts where a competitor is consistently cited and you’re not. Using competitive analysis tools alongside your AEO tracker can give you deeper context on where rivals are winning. Then look at what’s being cited: the specific URLs, content types, and source categories the engine is pulling from. You’re not just asking, “Are they showing up?” You’re asking, “What did they publish, and where, that earned them that citation?”

The revealing patterns tend to cluster around a few common factors:

• Content format. If 70% of citations for a prompt point to listicles or comparison pages, and your coverage of that topic is a single long-form guide, the format mismatch is likely costing you visibility. Match the format the engine is already rewarding.
• Third-party presence. Competitors often earn citations not from their own site, but from being mentioned on review platforms, industry publications, or community forums like Reddit. If your competitor’s brand appears in a cited Wirecutter roundup and yours doesn’t, that’s a PR and partnerships gap.
• Recency and specificity. Answer engines tend to favor content with current data and precise claims over broad, undated overviews. If a competitor’s 2026 benchmark report is being cited and your most recent version is from 2024, updating that asset should become a top priority.

Turning the Analysis Into an Action Plan

Once you’ve identified the patterns, map each gap to a specific action: Publish a new comparison page, pitch for inclusion in a third-party roundup, refresh an outdated report with current data, or create content in a format the answer engine is favoring for that prompt cluster.

The goal isn’t to copy what competitors are doing. It’s to understand what the answer engine values for each prompt category and create something even better with your own expertise and data.

How to Choose an AEO Rank Tracker for Your Team

Knowing what features matter is one thing. Deciding which tool fits your team depends more on how you work than on which platform has the longest feature list.

Before comparing tools, run through these questions to narrow the field:

• Coverage and scope. Which answer engines do your buyers actually use? If your audience skews toward Gemini but the AEO rank tracker lacks that data, then look elsewhere.
• Workflow alignment. Where does AEO data need to go after your team reviews it? A tracker that integrates with your CMS or CRM eliminates the manual step of exporting CSVs and rebuilding context elsewhere.
• Analytics depth vs. simplicity. Enterprise teams may want granular citation data they can slice across dozens of prompt groups. A lean team of two or three needs a cleaner dashboard with actionable recommendations.
• Governance. Larger organizations should ask about role-based access, prompt change approvals, and audit trails, especially if multiple teams are tracking prompts independently.
• Budget. Pricing models vary: per prompt, per engine, per seat. Map the structure to your actual usage, because a tool that looks cheaper on paper may cost more once you add the coverage you need.

A Simple Scorecard for Comparing Platforms

I’d recommend building a weighted scorecard with five to seven criteria based on the factors above. Rate each tool on a 1-5 scale, weight by priority, and let the math surface the best fit. It removes the bias that creeps in during polished product demos.

Examples of AEO Rank Trackers to Explore

The AEO rank trackers below represent different approaches to monitoring. This isn’t an exhaustive list, and the category is evolving fast. Use the must-have criteria and scorecard from the previous section to evaluate each option against your team’s needs.

1. HubSpot AEO

Best for: Teams that want visibility tracking and content execution in the same workflow.

HubSpot AEO tracks brand visibility, citations, share of voice, and sentiment across ChatGPT, Perplexity, and Gemini. You can track up to 25 prompts on the paid plan ($50/month) — the same prompt volume as Marketing Hub Pro. Marketing Hub Enterprise increases that to 50 prompts.

Where it pulls ahead is what happens after you see the data. Citation analysis identifies which domains and content types are influencing AI answers. For Marketing Hub Professional and Enterprise customers, CRM data informs prompt suggestions, so your tracking is tailored to your business rather than starting from generic queries. Get started with a free 28-day trial of HubSpot AEO with 10 prompts on ChatGPT.

2. Semrush AI Visibility Toolkit

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Best for: SEO teams adding AEO to an existing Semrush workflow

Semrush tracks brand mentions and sentiment across ChatGPT, Google AI Mode and AI Overviews, Gemini, and Perplexity. The AI Visibility Toolkit’s Prompt Tracking measures Average Position, showing you where your site usually appears in a list of citations in AI answers for prompts that you’ve defined. The standalone AI Visibility Toolkit is $99/month; Semrush One starts at $199/month. If you already use Semrush, consolidating reduces tool sprawl.

3. Profound

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Best for: Enterprise teams that want deep analytics

Profound covers up to 10 answer engines, including ChatGPT, Gemini, Claude, Perplexity, Copilot, Grok, and DeepSeek, with features like query fanout and prompt volumes. Like Semrush’s AI Visibility Toolkit, Profound measures your brand’s Average Position in AI responses. Profound pricing starts at $99/month for ChatGPT-only tracking (50 prompts); multi-engine coverage begins at the $399/month Growth tier.

4. Otterly

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Best for: Lean teams starting AEO tracking without enterprise complexity

Otterly covers six answer engines: ChatGPT, Perplexity, Google AI Overviews, and Copilot, with Google AI Mode and Gemini offered as paid add-ons. It has average brand position in AI answers, sentiment tracking, and an audit evaluating 25+ factors. Otterly pricing starts at $29/month for 15 prompts, scaling to $489/month for 400.

Frequently Asked Questions About AEO Rank Trackers

Do AEO rank trackers replace traditional SEO tools?

No. They measure different things. SEO tools track where your site’s blue link appears in the list on a search engine results page, as well as clicks, impressions, and the like. AEO trackers measure citations, mentions, and share of voice within AI-generated responses, though they can also measure where your brand appears in a list of citations in AI answers. Most teams will need both AEO and SEO tools for a complete picture of search visibility. For the SEO side, learning how to find SERP features opportunities can complement your AEO data.

How often should I refresh prompts and measurements?

I’d recommend reviewing prompt performance weekly and refreshing your prompt list monthly. Weekly check-ins help you spot sudden shifts, such as a competitor entering answers where they weren’t before, or a drop in your citation rate after a model update. Monthly prompt reviews ensure you’re still tracking the questions your buyers are actually asking, since those evolve as markets and products change.

Can I measure persona-level visibility with AEO trackers?

Some trackers support this through geo, language, and audience segmentation controls. The idea is that the same prompt can return different answers depending on the user’s location or profile context. If your tracker lets you run prompts with persona-level parameters, you can compare how visibility shifts across buyer segments or regional markets. This is especially useful for teams investing in local SEO, where AI answers may vary significantly by geography. Not every platform offers this, so it’s worth confirming during evaluation.

What’s the simplest way to start if I have limited budget?

Start with HubSpot’s free AEO Grader. It gives you a baseline AI visibility score without requiring a paid subscription, so you can see where your brand stands before committing to a full tracker.

Table of Contents

• What is AEO and how is it different from SEO?
• AEO Insights That Matter Most Right Now
• Unearthing AEO Insights for Your Brand
• AEO Insights for Top Answer Engine Optimization Strategies
• Practical Ways to Optimize Your Site for AI Answer Engines
• Frequently Asked Questions About AEO

Content with good SEO already has strong foundations for AEO, and the two practices certainly complement (not replace) one another. But AEO is different from SEO in a few ways:

• The goal of AEO is to show up in an AI-generated answer, while the goal of SEO is to rank at the top of a search engine results page.
• Answer engines in AEO seek a specific, direct answer; search engines in SEO seek a holistic, best resource.
• AEO is hyper-personalized with prompts that can be several paragraphs long; SEO is less personalized with “long-tail” keywords that are rarely more than nine words.
• AEO success metrics include brand mentions, citations, and share of voice; SEO success metrics include ranking positions, clicks, and traffic.
• In AEO, the results show up as a response in an answer engine or an AI-generated summary at the top of the Google SERPs — not always with clickable links. In SEO, the results are typically blue links that users must click through to read and fulfill their query.

AEO Insights That Matter Most Right Now

Before you start optimizing for answer engines, it helps to know what’s actually working — and what the data says about where this is heading. Staying current on answer engine optimization trends gives you a strategic advantage. These are the AEO insights I’d prioritize if I were building a strategy from scratch today.

AI referral traffic is growing fast — and it converts better.

Referral traffic from LLMs like ChatGPT and Gemini tripled in 2025, according to a Search Engine Land analysis. And it’s high-quality traffic. A Semrush study covering more than 500 high-value digital marketing topics found that LLM-referred visitors converted at 4.4 times the rate of those arriving through traditional organic search, per Growth Marshal’s analysis. The takeaway: Even a small share of AI referral traffic can meaningfully impact your pipeline.

Most searches don’t result in a click anymore (hello, “zero-click” era).

A 2024 study from SparkToro and Datos found that about 60% of Google searches don’t end in a click. Between AI Overviews, featured snippets, and direct answers, buyers are getting what they need without visiting your site. That means brand visibility inside the answer is becoming as important as ranking on the page.

Buyers are already using AI to evaluate vendors.

McKinsey 2025 research found that 40-55% of shoppers in popular sectors use AI search to help them decide what to buy. Your buyers aren’t just browsing; they’re making decisions based on what AI tells them.

Your competitors may already be showing up where you’re not.

One of the most common blind spots in AEO is simply not knowing what AI is saying about your category. A competitor might be consistently named in ChatGPT responses to prompts your buyers are asking, and you’d never know unless you’re tracking it. Tools like HubSpot AEO can give you a quick baseline of where your brand stands relative to competitors across answer engines.

AI cares a lot about what others say about you.

Answer engines don’t just pull from your blog. They synthesize information from review sites, social media, Reddit discussions, news coverage, and third-party mentions. That means your AEO visibility is shaped by your broader brand presence, not just your owned content strategy. If your social channels are quiet, your reviews are thin, or your brand rarely gets mentioned on third-party sites, that gap will show up in AI answers.

Pro tip: Not sure where you stand? Run your brand through HubSpot AEO to see how visible you are across major answer engines.

Unearthing AEO Insights for Your Brand

Understanding the AEO landscape is useful. But the insights that actually move the needle are the ones specific to your brand — where you’re showing up, where you’re invisible, and what you’d need to create or change to close those gaps.

That’s where an AEO tool comes in. Rather than manually prompting ChatGPT and Gemini to see if your brand gets mentioned (which is inconsistent, time-consuming, and hard to scale), a dedicated tool automates the process and gives you structured data you can act on.

I’ll walk through how this works using the HubSpot AEO tool as an example, since it covers the core workflow most marketers will need: tracking prompts, monitoring visibility, analyzing citations, and turning all of that into an action plan.

Step 1: Set up your brand, competitors, and prompts.

The first thing you’ll do in any AEO tool is define what you want to track. That means entering your brand, your top competitors, and the prompts (questions) your buyers are likely asking answer engines.

In HubSpot AEO, you can add prompts manually (e.g., “What are the best email marketing tools for ecommerce?”) or get suggestions informed by your CRM data if you’re signed up for Marketing Hub Professional or Enterprise. You can also organize prompts into groups by product line or customer segment so you can isolate how each segment of your business is performing in answer engines instead of viewing a single blended score.

Step 2: Check your Brand Visibility score.

Next, check your Brand Visibility score: how often your brand gets mentioned when answer engines respond to the prompts you’re monitoring. Track 25 prompts and appear in five of those responses? That’s a 20% Brand Visibility score — your baseline to improve from.

This is your top-level scorecard. You can see it broken out by answer engine (ChatGPT, Perplexity, Gemini) and tracked over time, so you can spot whether your visibility is trending up or down.

Step 3: See how you stack up against competitors.

AEO isn’t just about your own visibility — it’s about your visibility relative to the competition. The competitor analysis shows your Share of Voice: what portion of brand mentions in AI responses belong to you versus your competitors.

So if answer engines mention brands 100 times across a set of prompts and your brand accounts for 25 of those mentions, you hold a 25% Share of Voice. More importantly, you can see which specific prompts your competitors are showing up on and which you’re not. Those gaps are where the highest-leverage opportunities live.

Step 4: Analyze your citations.

When an answer engine responds to a prompt, it pulls from sources across the web to inform its response. The HubSpot AEO citations view shows you what answer engines are actually referencing when they respond to prompts in your category. You can slice this by format (are blog posts or comparison listicles earning more citations?), by channel (owned content versus Reddit threads versus earned media), and by individual domain — so you know exactly which sites are shaping the answers your buyers see.

Use this analysis to inform your AEO content strategy. For example, if you notice comparison-style content earning the majority of citations for your highest-intent prompts, you know that’s the format to invest in. Or if one review site keeps surfacing across multiple prompts, that’s a clear signal to try and get your product on that site.

Step 5: Turn data into an action plan with recommendations.

Data without a next step is just a report. The Recommendations feature synthesizes your prompt performance and citation data into a ranked list of content and outreach actions, ordered by potential impact on your visibility.

Each recommendation comes with context: a suggested content title, the target audience, primary and secondary keywords, and the reasoning behind it. You can see which prompts and citation patterns drove the suggestion, so you understand why the tool is recommending a specific blog post or page update.

Step 6: Filter and refine by engine, date, and prompt group.

As your AEO strategy matures, you’ll want to slice the data more precisely. Most AEO tools let you filter by answer engine, date range, and (in HubSpot’s case) prompt groups — so you can analyze performance for a specific product line, customer segment, or time period.

This matters because different answer engines can behave differently. Your brand might have strong visibility on ChatGPT but be nearly invisible on Gemini, or vice versa. Filtering by engine helps you prioritize where to focus your optimization efforts, and filtering by prompt group keeps your strategy targeted rather than trying to improve everything at once.

Pro tip: Set a recurring monthly cadence to review your AEO data. Check your Brand Visibility trend, review new recommendations, and track whether actions you’ve taken are moving the needle. Much like SEO, AEO is an ongoing practice.

AEO Insights for Top Answer Engine Optimization Strategies

The previous section focused on finding your AEO gaps. This one is about closing them — the formatting, technical, and strategic decisions that improve your chances of getting cited. For a deeper dive, see these answer engine optimization best practices. Let’s tackle the common AEO questions; they’ll touch on the top answer engine optimization strategies for AI visibility.

How should I format pages so AI engines cite my content?

Answer engines extract specific pieces of information to synthesize into responses, so structure matters, as well as substance.

• Lead with a direct answer. Put a clear, self-contained answer within the first 100-150 words. Think of it as a TL;DR that an engine can extract without parsing your entire article.
• Use subheadings that mirror natural questions. “How much does X cost?” gives an engine a clear signal. “Key Considerations” does not.
• Make claims specific and attributable. “Marketing Hub customers saw a 3x increase in inbound leads in six months, according to HubSpot’s ROI Report 2025” is more citable than “Marketing software can help you generate more leads.” The more precise your statements, the more useful they are to an engine assembling an answer.

What schema helps AI understand my content?

Schema markup gives AI engines machine-readable context about your page. It doesn’t guarantee a citation, but it reduces ambiguity — and that’s a meaningful advantage when engines are parsing millions of pages.

The most AEO-relevant types:

• FAQPage schema maps question-and-answer pairs directly to how engines process queries.
• Article schema provides author, publisher, and recency context that supports authority signals.
• Organization schema clarifies who you are as an entity, which is foundational when engines need to confidently associate your brand with specific topics.
• Product and Review schema help with commercial prompts that have high buyer intent and buyer-specific questions.

Schema can be helpful, but it only takes you so far. Implementing FAQPage schema on a poor-quality blog post, for instance, probably won’t earn citations. Think of it as a way of making good content more legible to machines.

What are the fastest technical wins for AEO?

• Verify AI crawler access. ChatGPT uses specific bots like OAI-SearchBot and GPTBot to crawl the web. Check your robots.txt to make sure you’re not blocking them. If you are, your content likely won’t be considered for citation regardless of quality. (More on the block-or-allow decision in the FAQ below.)
• Audit page speed and crawlability. Slow-loading pages buried behind excessive JavaScript are less likely to get reliably crawled and cited.
• Set up AI referral tracking. In its answers, ChatGPT often appends utm_source=chatgpt.com to outbound links. Make sure your analytics captures this so you can measure AI referral volume and conversion separately from organic search.

How AEO Tactics Compound with Inbound for Sustainable Growth

If you’ve been doing inbound marketing or content-led SEO, here’s the good news: AEO builds on the same foundations (helpful content, topical authority, brand trust) and extends them into a new channel.

Where AEO adds a new dimension is in the breadth of signals it rewards. Traditional SEO weighted heavily toward on-page optimization and backlinks. AEO, as I noted in the insights section above, also weighs your presence across review sites, social media, Reddit, and news coverage. A blog post targeting a buyer question can earn organic traffic and increase your chances of citation in AI answers. A G2 review can support domain authority and appear as a cited source when an engine recommends tools in your category.

The compounding effect works over time, too. The more consistently your brand appears across channels, the more “consensus” answer engines detect — and consensus is a strong signal driving AI recommendations. Brands that have invested in inbound for years may find their AEO starting position is already stronger than competitors who focused narrowly on paid acquisition alone.

That said, AEO does surface gaps that inbound might not reveal. You might rank well in Google but be invisible on ChatGPT for the same queries. The tool workflow above is designed to surface exactly those gaps.

Practical Ways to Optimize Your Site for AI Answer Engines

The strategies above cover the why behind each tactic. This section shows you how to optimize your site for AI answer engines with a single, prioritized checklist you can hand to your team, organized by what to do first, what to build over time, and how to keep it all current.

Start here (Week 1).

These are the actions with the highest leverage-to-effort ratio. Most take less than a day and remove blockers that prevent everything else from working.

• Unblock AI crawlers. Specifically, check your robots.txt for rules that may be blocking OAI-SearchBot. If it’s blocked, you reduce your chances of getting cited by ChatGPT in its answers.
• Set up AI referral tracking. Confirm your analytics platform captures utm_source=chatgpt.com and referral traffic from perplexity.ai and gemini.google.com. You need this baseline before you can measure anything.
• Run an AEO benchmark. Use HubSpot AEO to get your Brand Visibility score — the percentage of tracked prompts where your brand appears in AI-generated answers. Record your score and your competitors’ scores.
• Identify and start tracking your top prompts. Think about the questions your buyers are most likely typing into ChatGPT or Gemini when evaluating solutions in your category, things like “How does [your product] compare to [competitor]?” Start with 10-15 prompts that reflect real purchase-intent questions, not just broad category terms.

If you have a HubSpot Marketing Hub Professional or Enterprise subscription, prompt suggestions are generated based on your CRM data, which can surface relevant questions you might not think of on your own. Once your prompts are set, organize them into groups by product line or audience segment so you can track performance for specific parts of your business rather than treating everything as a single score.

Build your foundation (Weeks 2–4).

• Restructure your highest-traffic pages for citability. Prioritize pages that already rank well in organic search — they’re your best candidates for AI citation, too. Apply the formatting principles from the strategy section above: direct answer up top, question-based subheadings, specific and attributable claims.
• Add schema to key pages. Start with FAQPage schema on pages that answer common buyer questions, and Article schema on cornerstone content. Don’t try to mark up your entire site at once; focus on the 10-15 pages most relevant to the prompts you’re tracking.
• Audit your off-site presence. Use HubSpot AEO’s citation analysis to see which third-party domains and content types are being referenced for your category. If competitors are being cited from review sites, comparison listicles, or Reddit threads where you’re absent, those are your next content and outreach priorities.

Maintain and compound (Monthly cadence).

AEO isn’t a one-time project. Answer engines refresh their sources regularly, and your competitive landscape shifts as others begin optimizing too.

Each month, set aside time to:

• Review your Brand Visibility trend. Is it moving up, down, or flat? Flat isn’t neutral. If competitors are improving while you’re static, your relative position is declining. HubSpot AEO tracks this over time by engine, so you can spot whether a dip is happening across the board or on a specific platform.
• Act on new recommendations. As HubSpot AEO collects more data, it surfaces new gaps with full content briefs — suggested titles, target audiences, and the citation patterns driving the recommendation. Prioritize the ones tied to high-intent prompts where competitors are currently being cited and you’re not.
• Refresh aging content. Pages with outdated statistics, discontinued product details, or stale examples become less citable over time. Treat content freshness as an AEO hygiene task, not just an SEO one.

Frequently Asked Questions About AEO

Should I block or allow AI crawlers like GPTBot?

Allow or block AI crawlers based on your business goals. The two main OpenAI crawlers you need to know about for this purpose are GPTBot, which crawls content for model training, and OAI-SearchBot, which crawls to generate cited responses when ChatGPT conducts a web search. Blocking OAI-SearchBot generally prevents your site from appearing as a source in ChatGPT answers, regardless of quality. If you specifically do not want ChatGPT to train on your data, you can block GPTBot while keeping OAI-SearchBot allowed (they’re controlled separately in your robots.txt). Check your robots.txt today; some sites still carry blanket AI-bot blocks that may reduce eligibility for AI-generated citations or summaries.

Which schema is most impactful for AEO?

There isn’t one proven universally “most impactful” schema for AEO; the best choice depends on the page type. For question-led content, FAQPage can be especially useful because it makes question-and-answer pairs explicit. For editorial content, Article schema helps engines interpret author, publisher, and publication-date context. For commercial pages, Product and Review schema are often more relevant because they align with buyer-intent queries. But schema alone will not earn citations for weak or thin content. It is best understood as a way to make strong content more machine-readable, not as a way of fixing weak content.

How do I track traffic from Perplexity or ChatGPT browsing?

ChatGPT appends utm_source=chatgpt.com to outbound links automatically when it returns search results, so most analytics platforms will capture it if you’re already tracking UTM parameters. For Perplexity, look for perplexity.ai in your referral traffic reports. Set up dedicated segments or filters in your analytics for these sources so you can track volume, engagement, and conversion separately from organic search.

As a professional blogger, I’ve been snagging top positions in Google for well over a decade, but when answer engine optimization (AEO) started taking off last year, I dove in headfirst. Since then, I’ve gotten posts to show up in ChatGPT, and I’m proud to be part of a team that’s helped HubSpot become number one in AI visibility in its category, with a 1,850% increase in qualified leads in 2025 driven by our AEO strategy.

Below, I’ll break down the essentials of how to show up in AI answers (particularly ChatGPT), including how the answer engine sources information, tactics for increasing your AI visibility, and common mistakes to avoid.

Table of Contents

• How to show up in ChatGPT results starts with how answers are sourced.
• Tactics for Increasing Visibility in ChatGPT Searches
• Identifying Gaps in ChatGPT & AI Visibility
• How to Show Up in ChatGPT Results Without Common Missteps
• How to Measure What Matters When Showing Up in ChatGPT Results
• Frequently Asked Questions About Showing Up in ChatGPT

How to show up in ChatGPT results starts with how answers are sourced.

There’s more than one way to appear in ChatGPT results. Two main sources for answers are relevant here: ChatGPT training data and live web search. Let’s break down each of these sources below.

Training Data

OpenAI trains ChatGPT’s models on immense amounts of data (hence the term “large language model” or “LLM”) from publicly available sources from the internet, third-party partnerships, and user-provided data (depending on the user’s privacy settings).

From this training data, ChatGPT learns patterns, how words and concepts are related to each other. From these learned patterns, the model is able to predict the next word in a string of words (an oversimplification, I admit). ChatGPT is not like a library, where its model stores all of its training data in “books” and pulls them from the shelves based on user prompts. Instead, it’s more like a human brain that has done extensive studying and can form an answer based on what it has learned.

The “knowledge cut-off date” refers to the date at which the training data was last pulled. At the time of writing, ChatGPT’s latest model, GPT-5.4, has a knowledge cut-off date of August 2025. This fact is important to understand the next way ChatGPT figures out its answer for you: live web search.

Live Web Search

Let’s say new information that’s relevant to your question dropped in January 2026, but the current knowledge cut-off date is August 2025. In that case, ChatGPT can run a live web search to find the latest info online instead of relying only on its training data.

This is particularly useful for time-sensitive information, such as news and pricing. OpenAI states that
it uses third-party search engines like Bing, and for Enterprise and Edu customers,
it solely names Bing as its search provider. However, experiments from
external parties indicate that OpenAI sometimes uses Google Search. This is important because it means that SEO absolutely still matters in the era of AI because it can influence ChatGPT’s answers. For a deeper look at the intersection of SEO and AI, see our guide on
ChatGPT for SEO.

Source

Another interesting thing is that ChatGPT’s web search results are not usually the same as Google’s SERP. See below for my Google versus ChatGPT results for the phrase “ai search statistics 2025.” There is no overlap.

Here’s the Google AI Overview:

Here are the top five non-sponsored Google Search results:

And ChatGPT’s results from conducting a web search:

To me, this indicates a couple of things: One, Google Search and ChatGPT weigh things differently. And two, because of that, even if SEO has let you down because you can’t seem to get to the top of the search engine results page, you might see success with answer engine optimization (AEO) by showing up in ChatGPT answers.

What This Looks Like in Practice

To illustrate, I ran the same prompt (“What’s the best CRM for publishers in 2026?”) in two ChatGPT configurations to see if an AEO-optimized article of mine would show up.

First, I ran the prompt in a temporary chat with Auto selected (which means ChatGPT will decide which model to use). You can see that ChatGPT recommends HubSpot first in its list of best CRMs for publishers, and when I hover over the citation bubble, you’ll see it’s the HubSpot blog post that I wrote.

To better understand how ChatGPT’s live web search approaches queries, I find it helpful to run the prompt with Thinking mode on. You’ll see it answers a little differently, though HubSpot is still mentioned and my HubSpot blog post is still cited.

The really interesting part, though, is clicking to expand and view some of its thinking process. To me, it’s like a partial peek under the hood.

You’ll see that it broke out my single prompt into multiple queries. This is called query fan-out, and it has a practical implication for marketers: The prompt your customer types into ChatGPT is not necessarily the query that determines whether your site gets found. ChatGPT may break that prompt into sub-queries you wouldn’t have predicted from the original wording alone. That’s one reason why prompt research (which I’ll talk about below) is such a critical part of AEO strategy.

Tactics for Increasing Visibility in ChatGPT Searches

Unlike Google Search, OpenAI doesn’t publish any detailed guidelines on how to rank in ChatGPT search results, which makes leaning on internal and external experimentation necessary. That’s why I’ll try to back all my recommended tactics in this article with research and experiments from marketing pros.

To be fair, OpenAI has said this: “Any public website can appear in ChatGPT search.” It also said to make sure your site isn’t blocking its crawler (I’ll go into detail on how to do that below).

Ensure proper indexing and crawler access.

Think of this section as a checklist before you move on to the other three content and authority tactics below. Verify that:

1. Your key pages are indexed in Google and Bing.
2. OAI-SearchBot is allowed in your robots.txt
3. Your content loads in crawlable HTML rather than relying entirely on client-side JavaScript.

Proper indexing and crawler access form the foundational layer of showing up in ChatGPT results. Indexing and crawling are SEO terms, yes, but they affect AEO, too. Here are the three ways they affect ChatGPT answers:

1. ChatGPT’s Web Search

As I mentioned above, ChatGPT pulls live results through search engines like Bing and Google. That means traditional search engine indexing is still a prerequisite for AI visibility. If your pages aren’t indexed, they won’t appear in ChatGPT’s live search results.

2. OpenAI’s Own Crawlers

OpenAI also operates its own web crawlers, and each one serves a different purpose. Here’s what you need to know about them:

• OAI-SearchBot affects ChatGPT’s live web search. According to OpenAI’s crawler documentation, sites that opt out of OAI-SearchBot won’t appear in ChatGPT’s search answers (though they may still show up as navigational links). If you want to be cited in ChatGPT responses, this bot needs access to your site.

• GPTBot affects OpenAI’s training data. This is the bot that feeds ChatGPT’s training data — the knowledge it carries between conversations even without running a live search. Blocking GPTBot means your content likely won’t inform future model training.

Your robots.txt file controls access to these two OpenAI web crawlers. Each bot is configured independently, which means you can allow OAI-SearchBot (so your pages appear in search results) while blocking GPTBot (so your content isn’t used for model training), or vice versa. Here’s what that looks like in practice within your robots.txt file (note that the lines preceded by “#” are comments that are ignored by crawlers):

# Allow ChatGPT search to surface your pages

User-agent: OAI-SearchBot

Allow: /

# Allow training data collection (optional — your call)

User-agent: GPTBot

Allow: /

Pro Tip: After updating your robots.txt, it takes about 24 hours for OpenAI’s systems to reflect the changes, per OpenAI’s documentation. Don’t panic if results aren’t immediate.

3. OAI-SearchBot and GPTBot struggle to crawl JavaScript-heavy sites.

Simply put, they can’t “see” your content, and if they can’t see it, they can’t add it to ChatGPT’s answers.

The fix: If you want to make your website appear in ChatGPT (though there’s no guarantee), ensure your most important content is available in the initial HTML response. Server-side rendering (SSR) or pre-rendering are the most reliable approaches here. This isn’t just good practice for AI crawlers — it also helps with traditional SEO, since Googlebot can struggle with JS-heavy pages too.

Pro Tip: Unsure if ChatGPT can see your webpage? Use this free AI Crawlability Checker. Yes, it’s a pain to have to register, but once you do, you can use it for free. And it’s the best AI crawlability/JavaScript checker I tested, as it gives the most detail and focuses specifically on JS issues and fixes.

Lead with the answer, then expand.

Put the most important information at the top of your article, and begin each paragraph with the key point the paragraph seeks to answer. Don’t make readers (or ChatGPT) dig for it. After you give the direct answer, you can dive into the details.

At least two independent analyses have found that AI citations trend heavily toward the top of a page. Kevin Indig’s February 2026 analysis of 18,012 verified ChatGPT citations found that 44.2% came from the top 30% of a page’s content. Citation likelihood dropped sharply after that. A separate CXL analysis of Google AI Overviews found a similar distribution: 55% of citations came from the top 30% of a page.

An important caveat: Both studies are observational and establish correlation (a connection), not causation (the reason for the connection). This means they show that cited content tends to be near the top of a page but don’t prove that putting content higher causes it to be cited. It’s possible that ChatGPT favors direct definitions, entity-rich statements, and clear answers, and that those are the same qualities that good writing naturally puts up front.

My take: Put key information upfront because it’s a strong editorial and UX practice (it makes it easier for busy readers to skim), and it may improve the odds of being cited by ChatGPT.

Below is a before-and-after example of how you might change the way you write so that you can show up in ChatGPT results. The “before” is an actual excerpt from an article I wrote pre-AI about design thinking.

Before:

Heading: What are the 5 methods or stages of design thinking?

Body paragraph: The five methods of design thinking are more aptly called the five ‘stages’ or ‘phases.’ Let’s briefly touch on those five phases before I jump into the exact tactical methods you can use to apply design thinking. Here’s the most important thing, though: The design thinking stages are not linear.

The problem: Notice how it rambles and doesn’t immediately answer the question posed in the heading above it: “What are the 5 methods or stages of design thinking?”

After (using answer-first phrasing):

Heading: What are the 5 methods or stages of design thinking?

Body paragraph: The five stages of design thinking are empathize, define, ideate, prototype, and test. These stages are not linear — there’s no fixed order, and they often overlap or repeat. You don’t stop empathizing with users once you move to defining the problem; empathy carries through the entire process.

The fix: State the answer in the first sentence, then go on to explain the nuance. The subsequent paragraphs, where I break down each stage, remain exactly the same — they’re the supporting evidence. But I’ve given ChatGPT’s crawler the most important information right at the start.

Add schema markup to help AI parse your content.

Another way to help make content visible on ChatGPT is to implement schema markup. Schema markup is code that you add to your site’s source code that tells search engines and answer engines exactly what your content represents (who wrote it, what type of content it is, and what entities it references). Your readers won’t be able to see it, though. I like to think of it as speaking the AI model’s native language instead of forcing it to understand ours. It enhances what we’ve written in plain language.

For a deeper primer, check out HubSpot’s beginner’s guide to structured data and our walkthrough on how to use schema markup.

Why it matters for ChatGPT visibility: Adding schema markup doesn’t guarantee you’ll be cited, but it reduces the ambiguity answer engines face when deciding whether to trust and reference your content.

Some schema types that matter for AI visibility:

• Organization establishes your brand as a recognized entity. Include sameAs links to your Wikipedia page, Wikidata entry, LinkedIn, and social profiles so AI models can cross-reference who you are.

  Here’s a real-life example of organization schema in action on Ahrefs.com:

Source

• Article (or BlogPosting) tells AI what the content is, who wrote it, and when it was published. This helps AI evaluate source credibility.
• FAQPage maps questions directly to answers in a format AI models can extract verbatim. Even though Google deprecated FAQ rich results for most websites, the schema type itself still helps AI models identify Q&A content structure.
• HowTo structures step-by-step instructions so AI can surface them for procedural queries.

My take: Schema is added infrastructure, but it won’t save weak content. It simply removes friction for AI models trying to understand strong content.

Pro Tip: Start with Organization and Article schema on your most important pages, then add FAQPage to any content with genuine Q&A sections. Next, run the code through Google’s Rich Results Test and Schema Markup Validator to make sure it works before you add it to your webpages.

Check out our answer engine optimization guide to see how schema fits into a broader AEO strategy. And then read about entity-based SEO to understand how schema has long been a core part of search.

Build up a good reputation outside of your website.

ChatGPT considers external factors when evaluating whether to cite your site as a source in its answers. Similar to how Google established EEAT to identify helpful content, ChatGPT looks for signals that indicate your brand is trustworthy. It does that by looking for consensus (or recurring information) in sources across the web.

That’s why it’s crucial to think beyond your website. Here are some external sources to consider getting good brand mentions in:

• Social media
• Wikipedia
• News outlets
• Third-party blogs
• Review sites
• Forums

How much does this matter? A McKinsey analysis found that only 5-10% of Google AI Overview citations come from a brand’s own website. That means what other people say about you online matters more to AI than what you say about yourself. Here’s how to address that across two areas: brand mentions and reviews.

Strengthen your brand’s entity through third-party mentions.

Entity strength is how clearly and consistently AI models recognize your brand as a distinct, real-world thing — not just a name on a website, but a known entity with verified attributes and a track record across multiple independent sources.

Here’s what to prioritize:

1. Contribute expert commentary. Offer quotes to journalists, participate in industry roundups, and publish guest perspectives.
2. Ensure your Wikipedia and Wikidata entries are accurate. Research by Brandlight looked at data from 50 million+ user journeys across ChatGPT, Copilot, Google AI Overview, and Perplexity. Among ChatGPT’s top 10 most-cited domains, Wikipedia alone accounted for 40% of citations. If your brand meets Wikipedia’s notability requirements, having accurate entries there could increase your chances of being recognized as an entity.
3. Participate authentically in community platforms. Reddit and Quora threads are actively retrieved by answer engines when forming responses. The fact that OpenAI partnered with Reddit in 2024 is a signal that if you want to show up in ChatGPT results, it would be wise to be on Reddit.
4. Use consistent brand naming. Don’t confuse AI models with too many name variations. Stick to one canonical brand name that you use everywhere so that when a potential customer asks about your product, the answer engine can accurately name it.

Claim your review profiles and directory listings.

Reviews and business directories are a separate signal from brand mentions. They’re structured, platform-specific identity records that AI models can use to verify your business is legitimate and to assess how customers perceive you.

Domains with a presence on major review platforms earn triple the amount of ChatGPT citations of domains without such a presence, according to November 2025 research by SE Ranking.

Your action list:

1. Claim and complete profiles on major review platforms. At minimum: Google Business Profile, Yelp, and industry-specific platforms (G2 and Capterra for software, Trustpilot for consumer brands, etc.) Fill out every available field so AI models can extract data from these profiles.
2. Build review volume with recent feedback. Ask customers after positive experiences, and respond to reviews (both positive and negative) to show the profile is actively managed.
3. Monitor what AI is pulling from these platforms. Run your brand through ChatGPT, Perplexity, and Google AI Mode for commercial queries in your space. If the AI is citing outdated reviews or pulling from a directory with incorrect information, that’s your cue to update those listings. HubSpot AEO can help establish a baseline for how visible your brand currently is across AI platforms — a critical first step in making your business visible to ChatGPT.

Identifying Gaps in ChatGPT & AI Visibility

Prompt research is crucial to doing good AEO. If you were trying to rank in Google, you could conduct keyword research for free by manually entering keywords into Google Search and seeing what search results popped up. But to show up in ChatGPT, you need to do prompt research by manually entering prompts into ChatGPT and seeing how it answers. This means testing the questions your target audience is asking the LLM chatbot and evaluating whether your brand shows up in its responses.

Pro tip: To do this process manually, be sure to log out of ChatGPT or use a temporary chat. Why? ChatGPT’s memory remembers important details about you so it can tailor its answers specifically to you. You want a clean slate when you do prompt research in ChatGPT. This is similar to the guidance to use Google in Incognito Mode when you do keyword research so that it doesn’t personalize results based on your data.

Here’s the process I’d recommend:

1. Map the prompts that matter to your business.

Think about the questions a prospective customer would type into ChatGPT before buying. This is part of how you figure out how to appear in ChatGPT for your industry. For a pest control company, that might look like, “Why am I seeing more ants in my apartment in the summer?” or “What’s the best pest control company in Atlanta that uses eco-friendly methods?” These are the prompts you need to track.

In my experience, measuring AI visibility is wildly different from measuring Google rankings. After all, there isn’t a “position 1” to track, and unlike Google Search Console, which shares keyword data, OpenAI doesn’t share that kind of data with us.

Here’s the core tension:

• In SEO, if I want to know which keywords my blog post is ranking for, I can go to Ahrefs and enter its URL and see a detailed list.
• But in AEO, if I want to know which prompts my website is getting cited for, there is no tool where I can submit the URL and get the full list of prompts. Instead, I have to hypothesize which prompts I think I should be showing up for, and then an AEO tool can confirm if it’s true.

Frustrating? A little bit. But the right tool makes it less so. For instance, for Marketing Hub Pro and Enterprise customers, the AEO tool can tap into CRM data and suggest prompts based on your customer segments, industries, content library, and competitors.

2. Run those prompts in ChatGPT and study what gets cited.

Note whether your brand appears, and if it doesn’t, look at who does and what content ChatGPT is pulling from. Is it a competitor’s blog post? A review site? A Reddit thread? That tells you exactly which content types and authority signals are winning for that prompt.

3. Close the gaps with targeted content and authority work.

If ChatGPT is citing a competitor’s comparison page and you don’t have one, that’s your next content priority. If it’s pulling from a G2 category page where your profile is thin, that’s a review strategy gap.

For more info, be sure to check out our guide on how ChatGPT decides which products to recommend.

Of course, doing those three steps manually every day takes up a lot of time. It’s why SEOs use Ahrefs or Semrush instead of Googling keywords all day.

In a similar vein, marketers use HubSpot AEO to streamline their entire prompt research workflow. The tool tracks your brand’s visibility across ChatGPT, Perplexity, and Gemini from a single dashboard, shows you where competitors are being cited instead of you, and gives you prioritized recommendations for what to fix. If you want a free starting point, AEO Grader gives you a baseline snapshot of where your brand stands today.

How to Show Up in ChatGPT Results Without Common Missteps

The tactics above — proper indexing, answer-first content, schema, and off-site authority — won’t help much if you’re undermining them with avoidable mistakes. Here are the most common ChatGPT visibility mistakes and how to fix them.

Don’t keyword stuff or game the system.

SEO taught us this lesson early: Cramming keywords into your content doesn’t boost rankings — it gets you penalized. The same goes for AI. ChatGPT isn’t interested in seeing how many times you can mention a keyword; it’s looking for credible content that directly and clearly answers a user’s question.

This also means you should avoid unsupported claims. If you state that your product is “the best” or “the fastest” without evidence, you’re not giving ChatGPT anything useful to cite. Aim for content that’s specific, verifiable, and backed by data or concrete examples.

Frequently update your content.

SEOs know that Google rewards freshness for certain queries, but with ChatGPT, that signal is even stronger. An Ahrefs study found that, among the five AI platforms it tested, ChatGPT was the one that cared most about content recency. Ahrefs analyzed roughly 17 million cited URLs across ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews, and traditional organic Google results.

I recommend at least updating your top 10 pages, whether that’s by traffic or revenue, every three to six months. Try to add new, valuable details. Typically, the lowest-hanging fruit are your product’s pricing and any cited statistics — both of which go stale quickly.

Avoid JavaScript-only sites (or implement server-side rendering).

I covered this in the indexing and crawler access section above, but it bears repeating here because it’s one of the most common technical mistakes that hinders AI visibility. If your key content only loads via client-side JavaScript, OpenAI’s crawlers (OAI-SearchBot and GPTBot) can’t access or interpret it reliably, which could hurt your chances of showing up in ChatGPT’s answers.

A good fix is server-side rendering (SSR) or pre-rendering, which ensures your content is available in the initial HTML response.

Don’t put important information in images alone.

ChatGPT’s crawler cannot “see” images in your blog posts, and it can’t cite what it can’t see. So don’t put important information like pricing in an infographic. Instead, convert it to plain text, such as a bulleted list or a table.

There’s data to back this up. A March 2026 Writesonic study that tested 60+ webpage elements across six major AI platforms confirmed that ChatGPT, Claude, and Gemini only fetch raw HTML and extract text from it. They can’t interpret graphics.

Pro tip: When optimizing for showing up in ChatGPT specifically, do not rely on image alt text to convey the important information from an image. Unlike Claude and Gemini, ChatGPT doesn’t receive the alt text, according to Writesonic’s study. Therefore, make sure you write it out in visible text in your article rather than putting it in the metadata.

Lastly, Graph Digital’s analysis of 200+ B2B websites found that image-rendered specifications were among the most common structural failures blocking AI visibility. Its takeaway: A page can rank number one on Google while providing almost nothing for an AI model to extract if the critical content lives in images rather than parsable text.

How to Measure What Matters When Showing Up in ChatGPT Results

Measuring ChatGPT success requires a mental shift from SEO metrics to AEO metrics. Marketers used to care intensely about rankings and clicks, but now we need to add zero-click metrics like brand visibility, share of voice, and citations to the mix.

Here are the metrics that will help you measure what matters with showing up in ChatGPT:

• Brand mentions are when your brand gets named in AI answers.
• Citations are the sources on the web that the AI uses to inform its responses. It might include clickable links to the sources.
• Brand visibility measures how often your brand appears in AI answers to the prompts that matter to your business. HubSpot AEO calculates a brand visibility score as the percentage of your tracked prompts where your brand shows up in the response, broken out by engine so you can see whether you’re stronger on ChatGPT than Gemini, or vice versa.

• Share of voice tells you your percentage of mentions compared to those of your competitors across those same prompts. If your brand accounts for 25 out of 100 total mentions, your share of voice is 25%. You want to see this metric grow. It tells you whether you’re overtaking your competitors or not.
• Sentiment measures how answer engines “feel” about your brand. Appearing in AI answers doesn’t help if ChatGPT is associating your brand with outdated information or negative reviews. HubSpot AEO’s sentiment analysis scores how positively or negatively your brand is described in AI responses, so you can spot perception problems before they get worse.
• AI referral traffic tells you how much traffic AI engines like ChatGPT sent your way. Be sure to track sessions, engagement rate, and conversions from this channel over time.

Once you’re tracking those metrics, the next step is citation analysis, where you dig into which domains, content types, and source categories AI engines are pulling from when they answer prompts in your space. This is where measurement turns into strategy. So, for instance, if listicles dominate the citations for your key prompts but you don’t have any, it’s time to start creating some. HubSpot AEO surfaces this in its Citation Analysis view, broken out by top domains and content channels.

Pro tip: If you want a free baseline before committing to any tool, HubSpot AEO offers a free trial where you can track 10 prompts on ChatGPT for 28 days.

Frequently Asked Questions About Showing Up in ChatGPT

What’s the fastest way to increase visibility in ChatGPT searches?

The fastest way to get cited in ChatGPT is to show up as a result when ChatGPT performs a live web search (as opposed to waiting to be added to its training data). For that reason, start by confirming that your key pages are indexed in Google and Bing, allowing OAI-SearchBot in your robots.txt, and making sure your content loads in crawlable HTML rather than relying on client-side JavaScript. These steps remove the barriers that could prevent ChatGPT from ever seeing your content in the first place.

From there, the highest-impact content change is restructuring your existing pages to lead with direct answers. As I mentioned earlier, independent analyses have found that AI citations skew heavily toward the top of a page, so putting your key information upfront could improve the odds.

Off-site, the fastest lever is usually your review and directory profiles. Claiming and completing listings on platforms like Google Business Profile, G2, or Yelp gives answer engines structured identity data they can verify immediately — and it doesn’t require creating any new content. If you have a Bing Places listing, prioritize that too, since ChatGPT’s live search pulls from Bing’s index.

If you want a quick read on where you stand right now, AEO Grader gives you a free baseline snapshot of your brand’s current AI visibility.

Do I need separate content for ChatGPT search SEO?

No, you don’t need to create separate pages, markdown files, or “AI-friendly” versions of your content to show up in ChatGPT. Both Google and Bing have publicly advised against creating separate markdown for LLMs. For every piece of content, create just one SEO- and AEO-friendly version, and you’re good to go.

How long does it take to get noticed on the ChatGPT platform?

There’s no official statement from OpenAI, but small-scale studies by SEO practitioners confirm that ChatGPT can show new information in its results within hours if it uses its web search feature. That means you could publish a blog post and, within the same day, start seeing information from that blog post cited in ChatGPT’s answers that are pulled from the web. (Note: This is different from showing up for prompts that rely on ChatGPT’s training data alone. For that, how quickly you show up depends on future model updates, which might happen a couple of times per year.)

Gus Pelogia, Sr. SEO & AI Product Manager at Indeed, documented this in a test where he published a new blog post and queried ChatGPT about it at two different times. At 7 a.m., ChatGPT had no relevant information. By 1 p.m. the same day, it was citing the new post in its answer. Pelogia noted that both URLs were submitted via IndexNow, so Bing’s index knew about them within minutes.

This aligns with Conductor’s crawl frequency research, which found that ChatGPT crawled its pages about eight times more often than Google.

Having said that, don’t expect your brand to show up immediately in ChatGPT results. Give it time, especially if you’re new.

Should I build llms.txt and schema if I’m a small team?

Schema markup is worth it for a small team to implement: It’s simple to do, doesn’t cost anything, can help traditional search, and might have value for AI engines too. However, I do not want to overstate the importance of schema markup for ChatGPT search specifically. OpenAI hasn’t made any official statements on whether schema helps ChatGPT, but again, it’s such a low-lift task, and it can at least help your SEO.

I’ve added schema myself by using Claude to generate the schema markup, validating the code in both Google Rich Results Test and Schema.org’s validator, and then adding the code snippets to individual posts in the CMS.

For llms.txt, however, I personally wouldn’t bother — especially if you’re a small team with limited time. The llms.txt file is a proposed standard that acts as a kind of AI sitemap, listing your most important pages in a simple text file so AI models can find them more easily. It sounds promising in theory, but the evidence says otherwise.

In November 2025, SE Ranking analyzed nearly 300,000 domains and found no correlation between having an llms.txt file and being cited by answer engines. Only about 10% of the sites in the study had one, and when researchers removed llms.txt as a variable from their predictive model, the model’s accuracy actually got better.

More importantly, the major platforms haven’t confirmed they use llms.txt to influence their LLMs. Google’s John Mueller addressed this directly on Reddit and Bluesky in January 2026.

My take: If you’re a small team, your time is better spent on schema, answer-first content, and off-site authority — all of which have clearer evidence behind them. The llms.txt standard may evolve into something useful down the road, but right now, I haven’t seen any AI platform confirm that it influences citations or visibility. Don’t add it to your to-do list unless that changes.

How do I prioritize prompts for my industry?

Start with the questions a prospective customer would ask before they’re ready to buy, and work backward from the purchase decision. Comparison prompts (“How does BambooHR compare to Rippling?”) and solution-aware prompts (“What’s the best HR software for mid-size companies?”) should rank higher than broad problem-aware prompts because they’re closer to buying. From there, prioritize prompts where ChatGPT is already citing competitors but not you, since those are gaps you can close.

If you’re doing this manually, pick 5-10 prompts, run them in ChatGPT (logged out or in a temporary chat), and document who’s getting cited and what content types are winning. If you want to skip the guesswork, HubSpot’s Marketing Hub Professional and Enterprise plans get you access to AEO, a tool that can suggest relevant prompts based on your CRM data — your customer segments, industries, and competitors — so you’re tracking prompts that reflect your actual business rather than starting from a blank list.

For the last decade, conventional wisdom has been, “Do good SEO, and the rest takes care of itself.” That assumption was safe, and many brands benefited from a well-executed SEO strategy (hello, revenue!). But it doesn’t work anymore.

The mismatch isn’t because SEO is broken. SEO is doing exactly what it was designed to do. The problem is that search engines prioritize ranking the best resource, and answer engines prioritize providing the best answer..

Those are two very different machines, and they reward two very different things.

Table of Contents

• What is the FSA Framework?
• The FSA Framework Breakdown
• How to Apply the FSA Framework

What is the FSA Framework?

The FSA Framework stands for Freshness, Structure, and Authority — the three signals that answer engines actually evaluate when deciding which sources to cite inside a generated answer. It’s the diagnostic lens I use to figure out why a brand is or isn’t showing up in ChatGPT, Perplexity, Gemini, and Google’s AI Overviews, and what to fix first when they’re not.

Each pillar does a different job:

• Freshness determines whether your content gets reconsidered when new prompts come in.
• Structure determines whether a model can actually lift a clean answer out of your content.
• Authority determines whether the model comes back to your brand the next time a related prompt shows up.

Miss one, and the others can’t fully compensate. When all three are working together, your content stops being a candidate and starts being the obvious choice inside an AI-generated answer.

Where the FSA Framework Came From

In 2025, I started using my own website as a testing ground for answer engine optimization. I had a hunch about AEO, and no one was running the experiments I wanted to read. So, I ran them myself across ChatGPT, Perplexity, Gemini, and Google’s AI Overviews, tracking what surfaced for each prompt and — more importantly — what didn’t.

In one experiment, I updated a single page using the principles I’d been developing, and tracked AI Share of Voice across the entire window. The page was on a topic where Search Engine Journal — a legacy publisher with the kind of domain authority most marketers would kill for — had been the dominant cited source for months.

Within 96 hours, AI Share of Voice for Cassie Clark Marketing on that topic moved from around 27% to 72.7%. Search Engine Journal dropped to 0% visibility in the same window. There were no new backlinks and no promotional push. I just had a better-structured, fresher, more extractable version of the same idea.

Under traditional SEO logic, this should not have been possible. A solo strategist’s site shouldn’t displace a legacy publisher in four days. That does not happen — especially that quickly — in traditional rankings.

But under AEO logic, it made perfect sense. The legacy page had stopped being maintained, and its structure was built for crawlers, not for extraction.

When I went back through every test I’d run that year, I noticed engines were regularly skipping high-authority domains. Instead, they cited content that was recently updated, cleanly structured, referenced consistently across multiple sources, and easy to lift into an answer.

Freshness, structure, authority. The same three signals, every time, across every model.

Why We Need a New Framework in the First Place

Traditional SEO was built around a simple premise: A user types a query, the search engine identifies the most relevant pages, and those pages compete for position on a results page. Pages are the destination, and the whole job of SEO is getting your destination higher up the list than the next person’s.

That model assumed two things that answer engines no longer assume:

• The user wants a list of options.
• The user will evaluate those options themselves.

AI models don’t work that way. They retrieve information from multiple sources, synthesize it, and hand the user a single, confident answer. The user gets a summary, not a list. And inside that summary, sources are mentioned, not as a reward for ranking well but as evidence that the answer can be trusted.

So the question the engine is asking has changed completely. It’s no longer “which page should we show?” It’s “which sources help us explain this clearly and accurately?”

That sounds like a small distinction when you read it on a page, but in practice, it changes everything about what your content has to do in order to be useful to the system. Your content is no longer a destination, but an input.

And, once you internalize that shift, the FSA Framework stops feeling like a new set of tactics. It becomes the only logical response to how answer engines actually work.

Featured Resource: How AEO is changing the search landscape.

The FSA Framework Breakdown

Freshness

In AEO, freshness is a weight — one that influences how confidently a model reuses your content, how often it gets reconsidered when new prompts come in, and whether it stays eligible to appear in assembled answers at all. Stale content gets dropped from the candidate pool entirely.

The way I think about it is this: Freshness is recency, relevance, and reinforcement.

• Recency is the time-based piece. When was this last touched?
• Relevance is contextual. Does this still match how the topic is actually discussed today with the language people are actually using?
• Reinforcement is behavioral. Has this source continued to show up, get cited, and hold its place over time?

All three feed the same signal, and a page can fail on any one of them and lose ground.

What Freshness Really Means

Answer engines do not need a “last updated” badge to determine whether content is current. Instead, they notice when the language doesn’t match how a topic is being discussed now, when you reference a tool that doesn’t exist anymore, or when the surrounding topic space has evolved past what your page is describing.

In fast-moving verticals — SaaS, AI, fintech — content has roughly a 90-day shelf life before it starts losing relevance signals. For more evergreen topics, you have closer to six months. After that, you risk falling out of the answer pool entirely.

The practical takeaway is simple:

• Don’t just update the date.
• Add a current example.
• Pull in a recent stat.
• Reference something that’s actually changed in the space.

The volume of updates matters way less than their consistency and their substance. One real update every quarter beats five cosmetic changes a month.

Freshness gets your content reconsidered, but getting reconsidered isn’t enough on its own. The model still has to be able to use what it finds.

Structure

Structure for AI is different from structure for crawlers, and the two don’t always align.

AI models don’t read your page the way humans do. They parse it and scan for clean hierarchies, self-contained explanations, and clearly labeled sections they can lift into an answer without needing the rest of the page to make sense.

Content that performs well in AI answers shares a lot of the same structural traits:

• Clear H2s and H3s.
• Short paragraphs that resolve one idea at a time.
• Explicit definitions near the top of a section, before the explanation unfolds.
• Labeled steps.
• FAQ sections.
• Callouts.

If your best idea is buried three paragraphs into a section that requires the previous section to follow, the model is going to skip it. Not because it’s a bad idea, but because it can’t be extracted cleanly.

Why Structuring for Answer Engines is Different From Traditional SEO

If your content forces the model to do interpretive work, the model will find something structured in a way that is easier to break apart.

The mistake I see most often is teams optimizing structure for crawlers — meta tags, clean header hierarchy, internal links — and assuming that’s the same job. It’s not. Crawler structure focuses on navigability, while AI structure prioritizes extractability.

The right question to ask of any page is: Can ChatGPT lift a clean, accurate answer out of this without needing the rest of the page?

If the answer is no, you have a structure problem, no matter how well your headings are nested.

Authority

In SEO, authority meant domain authority. It took years to build and was almost impossible to displace once a brand had it. Entire agency business models were built around link acquisition.

In AEO, authority is now entity authority. The question isn’t “how strong is this domain?” It’s “is this brand the one that consistently explains this specific topic, across every channel I can find them on?”

Entity authority gets built one mention at a time, in a way that has almost nothing to do with backlinks. Every time your brand appears somewhere a model can learn from — a podcast, a Reddit thread, a guest post, a quote in a third-party article, a LinkedIn post, your own website — it adds to what the model knows about you.

One mention is a data point. But repeated mentions in similar contexts across multiple channels help build a pattern and create model confidence. Confidence is what gets you cited.

Why Smaller Brands Have Strong Entity Authority

Inside AI answers, smaller brands are suddenly winning fights they have no business winning on paper. Digging deeper, the reason why is obvious.

Smaller brands often create content only for their core audience and rely on social media or influencer marketing to build brand authority across surfaces, not just their own website. When a model encounters those brands repeatedly, it gains confidence in reusing the explanation.

The massive publisher, by contrast, has a hundred contributors writing about everything. None of them is building a recognizable entity around a specific, user-focused topic. Distribution is often nonexistent because traditional SEO wisdom says that domain authority is enough. When this happens, the model has nothing to anchor to.

Authority work is now closer to reputation management across channels than link building. None of this looks like an SEO campaign, but it’s exactly how you become the brand the model recognizes.

How to Apply the FSA Framework

So if this is how answer engines actually work under the hood, the next question is: What should teams be doing differently to put the FSA Framework to work?

Here’s the way I frame it for clients. SEO gets you into the room. AEO gets you chosen once you’re there. Here’s how to apply the FSA framework in practice.

1. Start with an audit — and find your money prompts

Before you touch a single page, you need to audit your visibility to know where you actually stand inside AI answers. That means running real prompts in ChatGPT, Perplexity, and Gemini for the topics tied to your pipeline — not the topics tied to your keyword list.

These are your money prompts. Think about the questions your buyers are actually typing when they’re evaluating a solution, comparing options, or trying to figure out if you’re the right fit. They usually sound like:

• “Best [category] tool for [specific use case]”
• “[Your brand] vs. [competitor] for [buyer context]”
• “How do I [solve the problem your product solves] as a [your ICP]”
• “What should I look for in a [category] tool if [specific constraint]”

Run your money prompts across multiple engines and pay close attention to whether your brand shows up at all, who’s showing up instead, and what the AI-generated answer actually says about your space. That single exercise will tell you more about your real AI visibility than any keyword report.

Pro Tip: You can measure mentions with HubSpot AEO — track prompts across ChatGPT, Perplexity, and Gemini, and see exactly where your brand stands.

Once you’ve done the initial scan, audit your top five pages through the FSA lens with an honest eye toward where each pillar is or isn’t holding up:

• Is the content current and reflecting how the topic is being discussed today, or is it quietly aging out of relevance?
• Is it structured in a way that a language model could lift a clean answer out of the first few hundred words?
• Is your brand consistently represented across the channels where buyers in your space are actually paying attention? Or are you essentially invisible everywhere except your own domain?

Diagnosis before tactics, every single time.

2. Replace volume targets with refresh targets

Maintaining and updating existing content on a consistent cadence does more for AI visibility than publishing net-new content every week. If your editorial calendar is built around how many posts you ship, rebuild it around how many of your top-performing pages get meaningfully refreshed each month.

3. Structure for extraction, not just indexing

Audit your top pages with one question in mind: Can a model lift a clean, complete answer out of the first few hundred words?

If not, restructure with:

• Definitions up top.
• Labeled sections.
• FAQ blocks.
• Comparison language for prompts where buyers evaluate you against alternatives.

4. Build entity authority across channels

Your website alone isn’t doing all the work anymore. Answer engines learn from content diversification, meaning:

• Podcast appearances.
• LinkedIn company and employee content.
• Reddit comments and threads.
• Guest articles.
• Expert quotes.
• Community participation.

The brands that build a consistent presence across multiple surfaces are the ones models start to trust.

5. Measure AI Share of Voice, not just rankings

AI Share of Voice tracks how often your brand appears inside AI-generated answers compared to competing sources. It’s a zero-sum metric — when one brand gains share, another brand loses it.

HubSpot’s AEO features now let you see how your brand is showing up across answer engines and where competitors are being cited instead — which is genuinely useful as a starting point, since most teams don’t know where their gaps are until they can see the data.

6. Pick one pillar to fix first

Once you know where you stand, pick one pillar to fix first rather than trying to address all three at once:

• If your content is stale, start with freshness. That’s the fastest signal to move.
• If your content is comprehensive but dense, restructure for extractability.
• If your brand is invisible despite having genuinely good content, the problem is almost certainly entity authority, and the fix lives outside your website.

Most AI visibility problems fall cleanly into one of those three buckets. A lot of what looks like a visibility problem is actually an authority problem in disguise.

Pro tip: Pair the FSA Framework with these AEO best practices for a more comprehensive approach.

What This Means for Your Content Strategy

The FSA Framework is a diagnostic lens for figuring out why visibility is or isn’t happening for your brand inside AI answers. You can stop guessing and start working on the right thing in the right order.

The specific signals answer engines weigh will change as the models evolve. The tactics built on top of the framework will need to be adjusted as the surfaces shift. But the underlying logic — favor freshness, reward clarity, trust consistency — has held steady across every model I’ve tested, and I expect it to continue to hold as the engines evolve.

The brands that win inside AI answers over the next few years aren’t going to be the ones chasing every new tactic. They’re going to be the ones who understand how AEO actually works, diagnose their visibility gaps honestly, and fix the right pillar first.

Build on those principles, and the FSA Framework adapts as the surface changes.

Their AI writes emails nobody responds to. It researches accounts and surfaces leads the sales team already closed six months ago. Finger-numbing sessions copying and pasting between tools generate content that sounds exactly like what every competitor is publishing. Leaders invest in tool after tool, run training session after training session, and still find themselves staring at the same question: why isn’t AI actually moving the needle?

Here’s what you’re not being told. The problem is not your model. The problem is not your data. The problem is context: the specific knowledge of your business, your customers and what they need right now, and how your team actually works. It is also the hardest problem to solve, and the one the industry has been slowest to address.

Context is the Infrastructure, Not the Feature

Here is the distinction that I think is getting lost. Data is what happened. Context provides meaning around real events, what they mean, why they matter, and what to do about it. Context is not a feature; it is necessary infrastructure.

Your CRM has a record that a deal closed eighteen months ago. That is data. Context is knowing the deal closed because your champion switched companies, the pricing had to be adjusted three times before it landed, and that customer now refers several new deals a year and hates being contacted by automation. A human who worked that account knows all of this. Almost no AI does, because almost no platform is built to capture it.

This is the gap. Not a model gap. Not a data gap. A context gap. And it is the problem HubSpot is solving with the Agentic Customer Platform. When Yamini introduced our Agentic Customer Platform earlier this year, she described the foundation underneath it: one place where all your customer data and business context lives, available to your team and your AI agents at the moment they need it.

The best infrastructure is invisible. It runs in the background, stays current as your business changes, and doesn’t make your team repeat themselves. That is the standard AI should be held to, and almost never meets.

The Hidden Cost of Context Gaps

There is a cost your team pays every single day that does not show up in your AI budget. We call it the briefing tax: the time and repetition required to give AI enough background to produce something useful.

You explain your brand voice before you ask it to write. You paste in the account history before you ask it to research. You describe your pricing structure, your competitor landscape, your customer profile, before every meaningful task. And the next day, you do it again. It does not learn your business. The real cost isn’t the hours your team loses to re-briefing AI, it’s the opportunity cost: the insights AI could have surfaced if it actually knew your business.

The briefing tax is just the daily friction. The harder problem is the one you don’t see: what happens to context over time. Your competitive positioning changes. Your ideal customer profile shifts. Your playbook gets updated. Your AI does not know any of that. It is not that it forgot. It has memory of the conversation. It just has no connection to the business behind it.

For GTM teams, this looks like AI that is confidently wrong. A project changes, your team adjusts, but AI keeps drawing on outdated context. Outputs start to sound off. Recommendations no longer fit your goals.

When your AI isn’t connected to the full picture, it can never develop the complete, dynamic knowledge it needs to create genuine value. It stays a tool. It never becomes a trusted teammate.

Growth Teams Need Their Own Context

Not all context is created equal. Personal AI tools like ChatGPT are building personal context: your preferences, your conversation history, your communication style. Enterprise tools like Glean are building organizational context: your documents, wikis, and institutional knowledge. At HubSpot, we are building Growth Context: The rich, high-quality, and precise understanding AI needs to drive outcomes across marketing, sales, and customer success.

This isn’t a concept. We’re building real infrastructure that will mean we’ll both capture and maintain this context for customers, while also giving them the ability to self-manage. We view Growth Context as having five dimensions:

• Business context is everything about what you do, how you compete, and what makes you worth buying. Your product positioning, your differentiation, your pricing rationale, your brand voice. This is the context that makes AI sound like your company instead of sounding like every other company. your category. Capturing it requires more than uploading a brand doc. It requires a system that structures that knowledge and applies it automatically across every interaction.
• Team context is how your people actually work. Your sales methodology, your qualifying criteria, your escalation paths. Not the version that lives in your onboarding documents, but the version your best reps actually use. This is what separates an AI that follows a script from one that exercises real judgment. This kind of context does not live in any CRM field. It lives in call recordings, deal notes, and the patterns only visible across thousands of interactions.
• Process context is what your workflows look like in practice. What triggers a handoff. What makes a deal high priority. How your campaigns are built and what success looks like for each one. This is what allows AI to take action, not just provide information. Building this into AI requires understanding your actual workflows, not just describing them, so the system can act on them rather than reference them.
• Customer context is the accumulated history of your relationships. What each account has bought, why they bought it, what their goals are, where friction has occurred, what the next logical conversation should be. This is what makes outreach feel like a conversation instead of a cold call. This is the hardest category to maintain because it changes constantly. Keeping this current automatically, across every touchpoint, is the infrastructure problem most platforms have not solved.
• Network context is the one dimension of Growth Context that no individual company can build alone. HubSpot works with more than 280,000 companies. That means we see broad trends in how teams go to market, how campaigns perform, and how customers buy, at a scale no individual company could replicate on its own. That collective intelligence becomes a layer of Growth Context available to every company on the platform, shaping what your AI recommends before you have ever run a single campaign.

What the Right Questions Look Like

If you are evaluating AI for your team, the questions that actually matter are not about the model. Models are increasingly commoditized. The right questions are about context.

• Can it capture and act on the full picture? Not just the structured and unstructured data in your CRM, but the reasoning, judgment, and institutional knowledge that typically lives in people’s heads.
• Is context maintained automatically? Or does your team have to keep it current manually, turning a platform investment into a maintenance burden?
• Is it built for growth specifically? Or is it a general-purpose knowledge layer that happens to include some customer data?
• Does it compound over time? Or does it require constant reinvestment to stay relevant?

Answer “no” to any of these, and your AI isn’t working with your business, it operates on a version of your business that no longer exists.

That is the real AI race. The companies that get Growth Context right do not just use AI better. They get further ahead every time they use it.