Learning how to identify content gaps for AI visibility starts with a question that traditional content gap analysis doesn’t always answer: where could a brand contribute something useful to an AI-generated answer, but isn’t being mentioned or cited?
With AI and SEO trends changing how people discover brands, finding a content gap isn’t always as simple as identifying a missing article. A company might already have a relevant page that AI search engines aren’t citing, or an AI assistant might mention the brand while relying on another website for supporting information. Creating more content without understanding what’s missing may not solve the problem.
This guide walks through how to use prompt-tracking data and existing content to identify AI visibility gaps, review competing sources, and decide what to do next.
Table of Contents
- What is an AI visibility content gap and how is it different from SEO gaps?
- How to Analyze AI and Search Results to Understand What to Fix
- How to Create or Update Content to Close AI Visibility Gaps
- How to Measure Results and Iterate
- Common Pitfalls to Avoid When You Identify Content Gaps for AI Visibility
- Tools and Templates to Speed Up Content Gap Analysis
- Frequently Asked Questions About Identifying Content Gaps for AI Visibility
- Close the gaps that matter, not every gap that appears.
What is an AI visibility content gap, and how is it different from SEO gaps?
An AI visibility content gap is a relevant information need, represented by a prompt or group of prompts, for which a brand is absent from AI-generated answers, lacks supporting citations, or does not have content that adequately addresses the question.
Traditional content gap analysis typically compares a website’s keyword coverage and search performance with competing websites. It helps marketers identify topics and queries that competitors rank for, but their own websites do not. AI SEO uses artificial intelligence to support traditional SEO tasks, such as keyword research, content analysis, and optimization. Both approaches — traditional SEO and AI-assisted SEO — focus on improving a website’s performance in traditional search results.
AI visibility analysis examines a different set of outcomes. A page can rank well in Google and still be absent from a ChatGPT, Gemini, or Perplexity response. And, to add to the complexity, an AI assistant may mention a brand without directing readers to the brand’s website at all. That difference makes it important to understand three types of AI visibility gaps:
Coverage gap
The brand lacks a page that adequately answers a relevant question.
AI visibility consultancy has a services page but no resource explaining when a company should choose a consultant-led audit instead of a monitoring platform.
Create a page or expand an existing resource to address the unmet need.
Extractability gap
A relevant page exists, but its useful information is difficult to locate, interpret, or reuse.
A detailed feature page describes capacity planning in promotional language without clearly explaining the workflow.
Improve the explanation, headings, examples, and presentation of key facts.
Citation-support gap
The brand appears in an answer, or has relevant content, but another source provides the evidence used to support the response.
An AI assistant recommends the platform but cites a third-party comparison for its capabilities.
Review the cited evidence and strengthen the brand’s supporting documentation where appropriate.
For marketers, treat these categories as diagnostic rather than mutually exclusive. A page may cover the right topic but lack both a direct answer and sufficient evidence to support a citation. The objective is to identify what is missing from the answer ecosystem, not simply what is missing from a content calendar.
AI Mentions vs. AI Citations
An AI mention occurs when an answer names a brand, product, or company. An AI citation occurs when the answer provides a source reference or link supporting information in the response. For marketers, understanding these differences in outcomes makes it easier to identify content opportunities.
For example, an AI assistant answering “What are the best project management tools for agencies?” might recommend a particular platform but cite an independent review site for its pricing, integrations, or suitability. The brand has achieved mention visibility, but its own content is not necessarily being used as the supporting source.
A citation to the brand’s website provides a different signal. A citation to the brand’s website means the assistant has surfaced that website as a source for the answer. However, a citation alone does not establish that the assistant recommended the brand, that a reader clicked the link, or that the answer generated revenue.
Pro tip: Record mentions and citations separately. A brand can be mentioned in an AI answer without citing a single piece of its own content as a source. That is a completely different situation with a completely different fix, and combining them into a single visibility metric can conceal whether the opportunity involves brand inclusion, source selection, or both.
An AI visibility content gap analysis follows four steps: collect relevant prompt data, classify mentions and citations, map findings to existing content, and prioritize gaps by business relevance and the work required to address them. The content gap analysis process begins by reviewing the answers AI platforms currently produce, rather than assuming which articles a company should publish. Here’s what to do.
1. Pull prompt tracking data to see where the brand is missing.
Prompt tracking establishes where a brand appears in AI-generated answers and where relevant questions produce no brand mention or citation.
Start with a defined set of prompts representing the questions prospective customers ask during discovery, evaluation, and purchase. Group prompts by topic and intent so that individual wording variations do not become separate content projects.
I use HubSpot AEO to identify groups of prompts related to the services I offer through Cassie Clark Marketing. HubSpot’s AEO tool tracks prompts across ChatGPT, Gemini, and Perplexity, showing how a brand appears in answers alongside competitors and citations. The marketing team can then use those observations to identify which prompts or topic areas need a closer look.
Using Cassie Clark Marketing as an example, HubSpot AEO suggested several prompts related to answer-engine optimization consulting:
- “Which answer-engine optimization consulting services should enterprise brands compare?”
- “How should a CMO choose answer-engine optimization consulting services?”
- “Which answer-engine optimization consulting services support large content operations teams?”
These prompts reflect a related need (finding an answer-engine optimization partner), but they approach that need from different angles. One asks for services to compare, another focuses on selection criteria, and the third introduces a specific operational requirement.
Grouping related prompts helps identify the broader topic a brand needs to cover without treating every variation as a separate content opportunity. The next step is to examine the AI-generated answers and determine whether existing content addresses those needs, and where coverage may be missing.
2. Track brand mentions and citations in a spreadsheet.
The next step is to record what happens when an AI assistant answers each prompt. Run the prompts through an AI answer engine and note which brands appear in the response and which websites are cited as sources.
I like to use a spreadsheet to keep track of these observations. For example, when I tested the prompt “Which AI search visibility audit service suits a scaling B2B SaaS startup?” ChatGPT recommended Profound, Otterly, Scrunch, and Peec AI. Cassie Clark Marketing was absent from the answer, and the cited sources focused on AI visibility software.
I would record that observation in my spreadsheet, along with the prompt, platform, date, brands mentioned, and cited sources. From there, I can use the following outcomes to identify what needs a closer look:
Brand absent; competitors present
Whether the brand has relevant content and whether the answer favors capabilities or evidence the brand does not address.
Brand mentioned; brand website not cited
Which sources support the mention and whether the brand’s own pages provide comparable, verifiable information.
Brand cited; brand not recommended
Whether the cited page answers the informational question without establishing relevance to the recommendation criteria.
Brand mentioned and cited
Whether the answer accurately represents the brand and whether visibility remains consistent across related prompts.
Brand absent; no clear competing source
Whether the prompt is commercially relevant and whether the answer requires a brand recommendation at all.
The goal of this step is a shortlist of prompt groups with a clearly documented visibility pattern. The classification system prevents teams from treating every missing citation as a reason to write another article.
For instance, if a brand is regularly mentioned but a third-party directory supplies its feature information, the immediate task may be to review the brand’s product documentation. A new thought-leadership article might do little to address that particular gap.
3. Map prompts to existing content and verify true gaps.
Prompt-to-page mapping determines whether the company already has content that answers the questions surfaced by prompt tracking. For each priority prompt group, search the company’s website and content inventory for pages that address the underlying information need. Include product pages, documentation, comparison pages, case studies, research, and relevant blog posts. Assess each candidate page against four questions.
- Coverage: Does the page answer the actual question, including its important subquestions?
- Clarity: Can a reader quickly identify the answer and the facts supporting it?
- Evidence: Does the page provide verifiable details, examples, data, or firsthand experience where the topic calls for them?
- Alignment: Does the page address the same audience, use case, and decision criteria as the prompt?
Consider the prompt, “Which AI search visibility audit service suits a scaling B2B SaaS startup?”
When I tested this prompt, ChatGPT recommended several AI visibility software platforms, including Profound, Otterly, Scrunch, and Peec AI. Cassie Clark Marketing didn’t appear in the answer, even though I offer AI search visibility audits for startups and enterprise brands.
The response revealed a potential content opportunity beyond simply getting my brand mentioned. ChatGPT treated the question primarily as a request for software rather than a consultant-led service. The cited sources were also comparisons of AI visibility tools.
My existing audit services page clearly explains who the service is for and what it includes. However, the answer I captured focused on software rather than consultant-led audits, even though the prompt specifically asked about audit services.
With that in mind, I would consider creating content that explains the difference between an AI visibility monitoring platform and a consultant-led audit, including what each provides and when a scaling B2B SaaS company might need one, the other, or both.
The goal is to address an information need that my existing services page doesn’t fully cover, rather than creating another page simply because my brand was missing from the answer.
Pro tip: Before assigning a content project, confirm that the company actually offers the product or service the prompt asks about. A missing mention could point to a content gap, but it could also reveal a capability gap, a positioning issue, or a question that isn’t relevant to the business.
Record the most relevant existing URL for each prompt group, if one exists. If no page adequately addresses the question, consider whether new content could fill that gap before adding another article to the calendar.
4. Prioritize which gaps to close first.
Content gaps should be prioritized according to business relevance, the strength of the observed visibility pattern, the nature of the missing information, and the effort required to address it. To decide which content gaps to tackle first, look at two things:
- Business impact. How closely does the prompt align with the company’s target audience, actual capabilities, and important customer decisions? Is the topic relevant to a product, service, or problem the company wants to be known for?
- Effort. Can an existing page be improved with a focused update, or does the gap require new research, product documentation, expert interviews, technical changes, or a new page?
This creates a decision matrix:
Higher business impact
Prioritize a focused refresh or evidence update.
Scope a larger content or cross-functional project.
Lower business impact
Address opportunistically when it supports an existing initiative.
Defer unless the business case changes.
For Cassie Clark Marketing, the prompt about AI search visibility audit services for scaling B2B SaaS startups is closely aligned with my target audience and the services I offer. The answer also suggests an information gap worth exploring: it recommends monitoring software without clearly addressing consultant-led audits.
Creating a resource that explains the difference would take more work than updating a paragraph on my services page, but the topic is directly relevant to prospective clients evaluating their options. I would prioritize it as a potential new content project.
Pro tip: Be sure to also look at the third-party sources ChatGPT cited. Since those sources focus on software comparisons, there may be an opportunity to contribute a consultant’s perspective to relevant industry publications. That would be a separate effort from creating content for a brand website, but both initiatives could help address the information gap identified in the answer.
How to Analyze AI and Search Results to Understand What to Fix
Analyzing AI and search results helps marketers identify the information, evidence, and source characteristics present in answers where their brand is absent or not cited. For each prioritized prompt group:
- Review the AI-generated answer.
- Open the cited sources.
- Compare those findings with conventional search results for the underlying topic.
Don’t copy a competing article. The objective here is to understand what the answer requires and whether the company’s existing content satisfies that need. Look for recurring patterns:
- Do cited pages define a term more directly?
- Do they provide product-specific documentation, current data, worked examples, or independent evidence?
- Are they addressing a different audience or use case than the company’s page?
Then compare those findings with the page mapped to that prompt.
For example, with Cassie Clark Marketing, if an AI answer explains how an enterprise content team can build an AI visibility strategy, but my services page only says I offer AI search consulting, I may need to explain what that work looks like for a large team. That could mean adding details about the discovery process, how I work with internal stakeholders, and what the team can expect from the engagement.
Make your pages easy for AI to extract.
Extractability describes how clearly a page presents information that can be identified, understood, and reused in an answer. Improving extractability requires making the useful information accessible without forcing readers — or retrieval systems — to reconstruct it from vague or scattered statements. Here are a few ways to improve an existing page.
- Answer the main question directly. State what a product, process, or concept does before expanding into benefits and context.
- Use descriptive headings. Organize sections around distinct questions, capabilities, or steps rather than generic labels.
- Keep related information together. Place a claim near the explanation, qualification, or evidence needed to understand it.
- Use appropriate formats. Tables can clarify comparisons, while numbered steps can explain workflows. Prose remains useful for nuance and interpretation.
- Define unfamiliar terms. Avoid relying on internal product language when the intended audience uses broader industry terminology.
- Make important content accessible. Review whether essential information is available in the page content rather than only in images, inaccessible interfaces, or elements that may not be available to every crawler.
- Check factual consistency. Align product descriptions, specifications, and terminology across relevant pages.
The goal is a page that communicates clearly to its intended reader. Better structure supports that goal, but structure alone doesn’t establish that an AI platform will retrieve or cite the page.
Add originality signals that LLMs value.
Original information gives a page something substantive to contribute beyond a summary of material available elsewhere. Depending on the topic, that contribution may come from:
- Firsthand experience
- Original research
- Expert analysis
- A documented customer example
- Detailed product information that only the company can provide
Originality should help answer the reader’s question. A proprietary statistic with no explanation of how it was collected may be less useful than a real example that shows exactly how a user can apply the information.
For research-led content, include the source, date, sample size, and methodology. For client examples, confirm permission and remove confidential information. When incorporating expert commentary from an interview, preserve the guest’s intended meaning rather than stretching a quote to support a broader claim.
While adding original information can’t guarantee an AI citation, it makes the content more useful and gives readers a clearer basis for evaluating its claims.
How to Create or Update Content to Close AI Visibility Gaps
Once a gap has been identified, the next step is to determine whether an existing page can address it or new content is needed. A missing brand mention alone isn’t enough to make that decision.
A relevant page exists but contains outdated or incomplete information.
Refresh: Update the answer, examples, product details, and supporting facts.
A relevant page exists but does not clearly explain the topic.
Improve extractability: Rewrite unclear passages, add descriptive sections, and present the answer more directly.
The page makes relevant claims but lacks sufficient support.
Strengthen evidence: Add verifiable documentation, firsthand examples, research, or expert input.
No suitable page addresses a relevant customer question.
Create: Develop a new resource with a distinct purpose and audience.
The prompt is peripheral, the brand lacks the claimed capability, or the evidence is too weak.
Defer: Record the reason and revisit if business priorities or source evidence change.
Before creating a new page, check whether the topic belongs within an existing resource. Several closely related articles may fragment a useful explanation across URLs without addressing the underlying question more effectively.
- For a net-new page: Build the content brief around the prompt group and the information missing from current answers. Specify the intended audience, the question the page must answer, the evidence required, and how the page differs from existing resources.
- For an update: Preserve material that already serves the reader. A focused improvement may be more appropriate than rewriting an entire article around an AI-generated answer.
After publishing, keep a record of what changed, including the URL, the gap the update addressed, the publication date, and the prompt group being tracked. This makes it easier to measure the results over time.
Pro tip: Recommendations in HubSpot’s AEO tool may help teams identify potential actions, such as creating new content, updating existing pages, or fixing technical gaps. Take a second to review those recommendations against the existing content inventory and the underlying prompt evidence before assigning work to the content team.
How to Measure Results and Iterate
Track how often the brand is mentioned, how often its content is cited, and how well AI-generated answers address the prompts being monitored to measure whether content updates are making a difference.
Keep in mind that AI-generated answers can vary from one run to the next. Competitor activity and platform changes can also affect visibility, so an increase in mentions or citations doesn’t necessarily mean a content update caused it.
Start with a baseline measurement from before the update, then track the same prompt groups across the same platforms using consistent measurement methods. This makes it easier to spot changes over time without drawing conclusions from a single response. Here are a few AEO metrics to track.
Brand Mention Presence
Brand mention presence measures how often a brand appears in answers to the prompts being tracked. The calculation is straightforward: Mention presence = Answers mentioning the brand ÷ Total tracked answers × 100
For accurate comparisons, use the same method each time. For example, decide whether each observation represents a single prompt run or a unique prompt, and stick with that approach.
If mention presence increases after a content update, look at the actual answers to see how the brand is being discussed. A passing mention or an inaccurate description may not be the result the team was hoping for.
For prompts where the brand is still absent, review the relevant pages to see whether they clearly explain how the company relates to the topic or use case.
Citation Presence
Citation presence measures how often a brand’s own content is cited in the answers being tracked. The calculation is: Citation presence = Answers citing at least one brand-owned URL ÷ Total tracked answers × 100
Citation presence measures the share of answers that cite the brand, not the total number of citations. If one answer cites three pages from the same website, it still counts as one answer with a brand citation.
When citation presence changes, look at which pages are being cited and how the AI assistant is using them. More citations may mean the brand’s content is appearing as a source more often, but the context of those citations matters, too.
For prompts where the brand is mentioned but its content isn’t cited, compare the brand’s pages with the sources the assistant uses. Look for information that may be missing, explanations that could be clearer, or supporting evidence that could be added.
AI Share of Voice
AI share of voice measures how often a brand appears compared with other brands across the prompts being tracked. There are two ways to calculate it, depending on what’s being measured.
- Entity-based share of voice: The brand’s appearances ÷ Total appearances of all tracked brands × 100
- Citation-based share of voice: The brand’s citations ÷ Total citations across the defined source set × 100
Entity-based share of voice and citation-based share of voice measure different things, so it’s important to choose one and use it consistently.
When share of voice changes, look beyond the overall percentage. Check which brands are appearing, how they’re being discussed, and which prompt groups are driving the change. An increase in one topic doesn’t necessarily mean the brand is gaining visibility across every topic being tracked.
AI-Referred Traffic and Downstream Outcomes
AI-referred traffic can help show whether AI search visibility is translating into website visits. When referral data is available, look at which pages visitors land on, how they engage with the site, and whether they take actions such as submitting a form or becoming a customer.
