The Comprehensive Guide to AI Visibility for PR Teams

What Is AI Visibility?

AI visibility is the practice of tracking and improving how often, how accurately, and how favourably a brand is mentioned in responses generated by AI answer engines such as ChatGPT, Claude, Google Gemini, and Perplexity.

It is the answer engine equivalent of share of voice. Instead of measuring press clips or search rankings, AI visibility measures whether a brand makes it into the limited set of names an AI engine surfaces when someone asks a question for a recommendation, and whether the engine describes that brand the way its communications team intends.

Being named in an AI answer is only valuable if the surrounding context is accurate and favourable. A brand can be highly visible and still be mischaracterized or described in a way that doesn’t align with the brand messaging. Actual AI visibility comprises two factors:

• Presence (are we in the answer?), and
• Portrayal (what does the answer say about us?)

AI visibility is sometimes called answer engine optimization (AEO) or generative engine optimization (GEO). All three terms describe the same process: optimizing for visibility in a generated answer rather than a search engine result page (SERP).

SEO vs. AEO vs. GEO: What’s The Difference?

SEO has long been the north star for digital marketing and online brand presence. But by now you must have noticed an increase in the mentions of AEO and GEO. The acronyms are often used interchangeably and just as often confused. The clearest way to separate them is by what each one optimizes for:

• Search engine optimization (SEO) focuses on optimizing your website to rank higher in the list of results provided by a search engine (like Google) to ensure a higher probability of users clicking through.
• Answer engine optimization (AEO) optimizes for inclusion in a direct answer provided by an AI model, whether that is a featured snippet, a voice result, or an AI-generated response, where the user consumes the answer rather than clicking through.
• Generative engine optimization (GEO) is the newest of the three and focuses specifically on how large language models understand, structure, and represent a brand so it gets cited or recommended inside a synthesized answer.

In practice, AEO and GEO describe the same commercial goal: getting a brand named in an AI answer, and most teams and tools use the terms interchangeably. SEO is different in that it operates on a list-based model, whereas AI engines use an answer-based model. A brand can rank first on Google and still be absent from the AI answer to the same question, because the two systems select and surface content differently.

That is not to imply that they are intrinsically different. AI models have been found to rely on many of the same authority and relevance signals that traditional search algorithms use. In fact, industry research on 7500 brands reveals that brands in the top 25% of web mentions earn over 10X more AI citations than those in the next quartile, linking a healthy SEO profile to an optimized AI presence.

Why AI visibility is a core business objective in 2026

An evolving media landscape and changing consumer preferences are driving a drastic change in how brands are discovered. These measurable shifts combine to make AI visibility a critical PR priority in 2026.

Zero-click searches are increasing every day

Google SERP rankings were valuable because once consumers typed a query, most clicks went to the top-ranked pages. With the integration of AI overviews, that pattern has shifted dramatically. Industry research highlights roughly 68% of US Google searches ended without a click in the first months of 2026, up from about 60% in 2024.

This marks a structural shift in the consumer journey and brand discovery process. Previously, while top brands received the most attention, brands on the first page still received impressions and clicks. With zero-click search surging, brands that are not present in the AI-generated answer are virtually absent from the conversation.

AI has become a primary research channel for buyers

AI has gone from a buzzword to an everyday staple. ChatGPT has reached 900 million weekly users, while Google’s Gemini has surpassed that number in monthly active users. Major LLM platforms like Gemini, Perplexity, Claude, and Copilot attract a large number of visitors every month.

And that reflects on how buyers research. Forrester’s report finds that generative AI is the single most-used platform for purchase research, ahead of vendor websites, peer recommendations (!), and analyst reports.

With 94% of B2B buyers reporting they use LLMs during their buying process, LLMs have emerged as a key research channel influencing user behaviour.

Early movers have a distinct advantage

LLM models rely on cumulative, compounding data to extract answers from. Organizations that build a brand identity based on consistent messaging across multiple structured touchpoints (web pages, directories, earned media, etc.) are likely to establish a stronghold in their niche.

AI platforms are likely to consider established, optimized brand presences as authoritative and draw on them for relevant queries. As entire industries shift their focus to AI visibility, this channel is likely to become hypercompetitive. For brands looking to close the AI visibility gap, the right time is now.

AI visibility banks on sustained PR efforts

As earned media emerges as the most influential content type for AI visibility, it inevitably ties AI visibility to an organization’s overall PR strategy. Brands winning visibility are banking on strong, credible third-party content, underscoring PR’s critical role in securing and increasing such mentions.

These factors combine to make AI visibility an important part of an organization’s PR strategy.

How PR teams are uniquely positioned to impact AI visibility

The function of PR and communication teams has long involved exercising narrative control over conversations surrounding the brand and shaping its perception. As AI engines increasingly index and cite third-party earned content, PR is uniquely placed to ensure a brand positions itself as a trusted and reliable source for AI engines.

As the criteria shift from keyword density and SEO best practices to authority, trust, and consistency of portrayal, AI visibility becomes more of a PR function than a marketing one.

When buyers ask a direct question, the AI engine names a finite set of brands, and any brands not included in that answer are not in the consideration set at all. This is the core dynamic which Agility PR Solutions’ 2026 research project, Closing the AI Visibility Gap, set out to measure. The study analyzed 24,480 prompt-and-answer pairs across OpenAI/ChatGPT, Perplexity, Gemini, and Claude, capturing every brand mentioned, every source cited, and the reasoning behind each recommendation.

And the results were conclusive. AI engines name an average of 7.5 brands per answer. Every prompt functions as a closed competitive list, and a brand that does not make that list is absent from the conversation entirely.

The same research surveyed 112 PR and communications professionals and found a clear gap between priority and execution. Forty-six percent of respondents now rate AI visibility and reputation management as a critical or high priority. Yet those same professionals rate their brand’s current AI visibility at only 5.5 out of 10 and the accuracy of its AI portrayal at 5.6 out of 10. Most PR teams know this channel matters. Few have the tools or strategy to act on it.

How AI Engines Decide Which Brands to Recommend

Understanding what drives a brand recommendation is the foundation of any AI visibility strategy. Agility’s research identified several factors that drive AI visibility and brand mentions, including performance, reputation, price, trust, and popularity.

However, across the 183,000 brand mentions captured in the study, a consistent pattern was observed that performance is what gets brands cited, not awareness. Across all four engines, an average of 79% of brand recommendations were driven by performance-based reasoning, meaning the AI engine cited evidence of what a product or service does and how well it does it.

Reputation accounted for 14% of recommendations on average, with price, trust, and popularity playing smaller supporting roles. This holds true across nearly every industry the study covered, with performance the top recommendation factor in all 48 categories analyzed.

The practical implication for PR teams is direct. Brand narrative and values-based messaging still matter, but they need to be built on a foundation of demonstrable, citable results. Case studies, benchmark comparisons, third-party performance data, and measurable outcomes are the content types most likely to trigger inclusion in an AI-generated answer.

Where AI Engines Get Their Information

If performance-based content is what gets cited, citations are how that content reaches the AI engine in the first place. Our study found that citations appear in nearly every AI response, ranging from 94.5% of OpenAI answers to 99.9% of Perplexity answers.

For PR teams, this confirms that AEO is a citation-driven medium: if a brand’s content is not accessible, indexable, and citable online, it is effectively invisible to AI engines and to the audiences that rely on them.

The breakdown of where those citations come from reveals how much of AI visibility sits outside a brand’s direct control. Setting aside the corporate category, which blends owned content with third-party organizational mentions, every other major citation type in the study- trade publications, major media, reviews, forums, and social content- is a form of earned media.

Trade publications stand out as a structurally important channel. They rank as the second-most-cited category across all four engines, averaging 12.4% of citations and exceeding 20% in sectors such as Finance and Telecommunications. PR teams working in these industries need to identify outlets that move the needle for AI visibility.

Reputation and Sentiment: How AI Portrays Your Brand?

Showing up in an AI answer is only half the objective. It is critical that the portrayal reflects your intended brand messaging and communicates to the right audience. As these models synthesize data from multiple sources, there is a chance they might get it wrong or draw on a negative citation that affects how your brand is portrayed.

Agility’s survey results highlighted this gap. The 112 participants rated the accuracy of their brand’s AI portrayal at just 5.6 out of 10, indicating that most are not confident that the platforms are describing them accurately.

This has profound impacts on how your brand is perceived. Two of the most significant risks include:

• Unfavourable framing: This occurs when the generated answer doesn’t contain any incorrect information but positions your brand unfavourably relative to your competitors. This may include exaggerating any potential weaknesses or downplaying your strengths. Potential reasons can be outdated reviews, false information, or biased competitor comparisons.
• Inaccurate positioning: Unclear positioning causes mixed messaging, buyer confusion, and loss of trust. If your brand is positioned incorrectly, or the messaging lacks key aspects that you want to highlight, it hurts the brand reputation and affects the sales pipeline. Some reasons for inaccurate positioning include conflicting sources, inconsistent messaging, or AI hallucinations.

Left untracked, an inaccurate claim can get cited, repeated, and amplified, and it can surface in front of a buyer at the exact moment of decision. This is why it is important to integrate sentiment and reputation tracking into the core of your AI visibility strategy to proactively monitor each mention and assess the accuracy of brand portrayal.

The way to counter and mitigate the chances of such an event happening is to ensure that the information being presented to AI engines is consistent, there are no content gaps left unaddressed, and to continue to monitor content sources (such as earned media mentions, review sites, etc.) that AI engines trust to extract information from for brand mentions.

Managing how AI describes a brand is a new form of reputation management and a natural extension of what communications teams already do.

How to Get Your Brand Cited by AI Engines

Knowing that citations drive AI visibility raises the obvious next question: how does a brand actually earn them?

Getting cited is not about understanding how an algorithm works, but about positioning yourself as a reliable, trustworthy source in your industry that AI systems can find, index, and trust.

Here are five ways to increase your chances of getting cited:

Build earned media authority

AI engines cite brands whose claims have been independently verified by credible third parties. Every placement in a respected trade or major outlet becomes a signal the model can draw on when deciding whom to recommend. This is where PR has a structural advantage over other functions, as the earned coverage a communications team already pursues is the raw material that AI citations are sourced from.

Keep your brand entity consistent everywhere

AI models assemble a picture of a brand from fragmented sources across their training data and live retrieval. If a company describes itself one way on its website, another way on LinkedIn or Crunchbase, and is characterized differently again by journalists, those conflicting signals reduce the model’s confidence and affect how the brand is portrayed. Define the brand, its category, and its primary use case in the same precise language across every profile, from the corporate site to Wikipedia, Wikidata, and industry directories.

Structure content for extraction

AI engines do not read a page the way a person does. LLM platforms look for the passage that most directly answers a query, extract it, and attribute it to the source. Content wins citations when the answer is front-loaded, headings clearly signal which section answers which question, and definitions, comparisons, and steps are formatted for clean extraction. A direct answer in the first few sentences of a section is far more citable than the same point buried in paragraph eight.

Make the content technically accessible

A brand with strong authority and well-structured pages will still fail to earn citations if AI crawlers cannot reach its content. Confirm that the crawlers used by the major engines are permitted by updating the robots.txt file to allow AI engine crawlers to index your website. Additionally, ensure priority pages are indexable and not hidden behind JavaScript that is hard to parse, and implement structured data such as Organization, Article, and FAQ schema.

Publish evidence-based content

Because performance reasoning drives roughly four in five recommendations, the content most likely to be cited is quantified and verifiable. Content types such as original research, benchmark data, comparison content that naturally names competitors, and case studies with concrete outcomes are likely to perform well.

In contrast, promotional copy heavy on superlatives and light on evidence might not get cited. However, it is important to note that these signals don’t work on their own. It is a mix of an optimized website and content that signals authority to AI crawlers.

AI Visibility Varies by Engine: What PR Teams Need to Know

A strategy built for one AI engine will not necessarily perform on another, because each engine has a structurally different approach to sourcing and surfacing brands. Agility’s deep dive into each engine clearly quantifies these differences.

ChatGPT

ChatGPT (OpenAI) produced the highest rate of answers delivered without any citation in the study, at 5.5%, and sources most heavily from major media outlets relative to the other three engines. While the overwhelming majority of answers (94.5%) still carried at least one citation, this shows that OpenAI’s model can also answer from its internal knowledge without performing a live web search.

Perplexity

Perplexity had the highest citation inclusion rate of any engine at 99.9% and leaned more heavily on corporate content than any other engine tested, making owned and partner content more influential here than elsewhere. Perplexity runs a live web search for every query and values content freshness, so newly published pages can surface within days. It is also the engine where a citation most directly translates into measurable referral traffic.

Google Gemini

Google Gemini consistently surfaced more brands per answer than any other engine, with the highest brand density across all nine core industries the study examined. A wider competitive set means a different bar for what counts as meaningful visibility for PR teams.

Claude

Claude (Anthropic) produced the highest average citation count per answer at 12.8, drawing on a broader range of sources per response than any other engine. Claude cited roughly 3.5 times as many sources per answer as OpenAI, which means a wider and deeper earned media footprint has more opportunities to surface here.

A brand’s earned media footprint can produce a completely different AI presence depending on which engine an audience actually uses. PR teams need to know which engines matter most for their specific audience and tailor source strategy accordingly.

How AI Visibility Differs by Industry

Industry context changes the rules of AI visibility as much as engine choice does. Citation patterns, brand density, and recommendation factors all shift meaningfully by sector in the Agility research, and understanding an industry’s specific AEO profile is a prerequisite for building an effective strategy.

A few patterns stand out. Telecommunications has the highest trade press citation rate of any industry studied, at 23.5%, meaning nearly one in four citations comes from specialist trade media. Healthcare, Entertainment, and Education each show reputation accounting for 20% or more of recommendations, well above the 14% cross-sector average, signalling that institutional credibility and third-party validation carry more weight in these categories. Beauty and Hair Care shows performance driving 91% of recommendations, among the highest of any sector, meaning results-forward content is close to non-negotiable. Entertainment is the most competitive landscape in the study, with an average of 9.4 brands per answer across engines.

These differences mean a single industry playbook does not translate across sectors. A PR team in financial services needs a different source and content strategy than a PR team in travel, even though both are optimizing for the same underlying goal.

AI Visibility Metrics Every PR Team Should Track

Measuring AI visibility means adapting familiar PR measurement concepts to a channel that behaves differently from both traditional media and search. A proven measurement framework tracks a set of core metrics that together answer two questions: whether the brand is present and whether it is portrayed correctly.

Many platforms offer a consolidated ‘visibility’ score, but the component metrics are where the actionable insight lives.

Presence metrics

• Brand mention frequency. How often the brand appears across a defined, representative set of prompts. This is the AI-channel equivalent of reach and the clearest single indicator of whether a brand is in the consideration set at all.
• Platform coverage. Which engines mention the brand, and where the blind spots are. Because behaviour varies widely across ChatGPT, Perplexity, Gemini, and Claude, a strong aggregate score can mask a complete absence on a platform that an audience actually uses.
• Prompt-cluster and query coverage. What share of the question groups that matter to the business actually trigger a mention. A brand may be visible for “what is [category]” yet invisible for “best [category] tools,” and mapping that coverage reveals exactly where to focus.
• Position and placement. Where the brand appears within a generated answer, whether it leads the recommendation, sits mid-list, or appears only as a footnote citation. Not all mentions carry equal weight.

Portrayal metrics

These are the metrics most often overlooked, and the ones that separate real visibility from raw presence. They should carry as much weight in reporting as the presence metrics above.

• Sentiment. The tone and context in which an engine describes the brand. Sentiment analysis distinguishes a genuine recommendation from a neutral reference or a cautionary comparison, so a team can see not just that it was mentioned but whether the mention helps or hurts.
• Portrayal accuracy. Whether the engine’s description of the brand matches its actual products, positioning, and messaging. Tracking accuracy is how a team catches hallucinations and outdated claims before they spread, and it directly protects reputation in a channel the brand does not control.
• Consistency. Whether the brand is described the same way across engines and over time. If one engine frames the brand as an enterprise leader and another associates it with a different category entirely, that inconsistency signals conflicting source material and weakens overall visibility.

Competitive and trend metrics

• Share of voice. The brand’s proportion of mentions relative to competitors within the same prompt clusters. Since every answer is a closed list of names, relative position matters more than absolute mention count, and this metric turns a visibility score into a competitive benchmark.
• Citation and source analysis. Which outlets and source types does the engine reference alongside the brand, and which are driving competitor mentions. This is the metric most directly actionable for PR, because it names the specific earned-media channels to prioritize.
• Historical trajectory. Whether visibility and portrayal are improving, flat, or declining over time. A one-off audit shows where a team stands today; a trend line shows whether the strategy is working.

The value of these metrics comes from consistently tracking them against a stable set of prompts and linking them to the earned-media activity that drives them. Monthly tracking gives enough data to spot real trends without overreacting to normal model fluctuation, with a deeper competitive review each quarter and an ad hoc check after any major brand announcement or model update.

Building an AI Visibility Strategy: Five Core Steps

The Agility research identifies five specific actions PR and communications teams can take to close the gap between AI visibility priorities and current performance.

Measure AI visibility with purpose-built tools

Traditional media monitoring and share-of-voice metrics do not capture AI answer engines. PR teams need monitoring that tracks whether a brand appears in AI answers to relevant prompts, which engines surface it most often, which sources are cited alongside it, how competitors are positioned in the same answers, and whether the resulting portrayal matches intended messaging. The Agility survey found that roughly 62% of PR professionals already monitor their brand across AI engines at least monthly. Still, consistent measurement without the right framework leaves teams unable to act on what they find.

Build performance-forward content that AI engines can cite

Given that 79% of recommendations are performance-driven, the content most likely to earn citation is evidence-based: case studies, benchmark data, third-party performance validation, and measurable outcomes. Brand narrative still has a role, but it should sit atop demonstrable results, not replace them.

Identify and invest in the channels that actually influence AI citations

Trade publications, in particular, deserve a higher priority than many PR teams currently assign them. The outlets driving AI visibility are not always the outlets that have historically topped a PR team’s media list, which makes channel-level intelligence essential before reallocating outreach effort.

Develop a platform-specific approach

Because brand density and citation behaviour vary meaningfully among ChatGPT, Claude, Gemini, and Perplexity, PR teams should identify which engines their specific audience relies on and tailor their strategy accordingly, rather than assuming uniform performance across all four.

AI visibility is a continuous process

AI answer engines update their models and expand their capabilities on an ongoing basis, and the citation behaviours that hold today may shift. Tracking AI visibility needs to become a standing part of a PR team’s measurement cadence, just as share-of-voice and sentiment tracking already are.

How to Choose AI Media Monitoring and Visibility Tools

As AI visibility becomes a standing requirement rather than an emerging concern, PR teams are evaluating tools against a new set of criteria. The most useful evaluation framework covers:

• AI engine coverage. Does the tool track brand mentions specifically across ChatGPT, Claude, Gemini, and Perplexity, or only one or two of these engines?
• Citation-level detail. Does the platform show which sources and outlets are being cited alongside a brand, not just whether the brand was mentioned?
• Accuracy tracking. Can the tool flag when an AI engine’s portrayal of a brand diverges from intended messaging, values, or positioning?
• Competitive visibility. Does the platform show how competitors are positioned within the same AI-generated answers, enabling real share-of-voice comparison in this channel?
• Integration with existing workflows. Does AI visibility data connect to the media outreach, reporting, and content workflows a PR team already uses, or does it sit in an isolated dashboard?

This is the gap Agility’s Visibility Intelligence is built to close. It gives PR and communications teams a structured, real-time view of brand presence, reputation accuracy, and source influence across all major AI platforms. It turns that intelligence into targeted media outreach within the same platform teams already use for traditional monitoring and distribution. Learn more about Visibility Intelligence.