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By Kate LaVail, PhD
SEO and GEO in public relations (PR) now demand answers to the same practical question for communications teams: When stakeholders search, ask, compare, or investigate your brand, can they find an accurate, authoritative version of the story?
In a recent piece, I argued that the work PR professionals have done for decades (e.g., creating authoritative and discoverable information, is exactly the kind of work Generative Engine Optimization (GEO)—and by extension, SEO —now requires. Press releases, expert commentary, authoritative trade coverage, Wikipedia entries—these are the raw materials AI systems now draw from when answering questions about your brand.
The natural follow-up question is: okay, so what does AI actually say about your brand right now? And how would you even know if it was wrong?
For most communications teams, the honest answer is that they don't know. There is no baseline. No monitoring cadence. No equivalent of the morning media report for AI-generated brand representation. That gap is what this piece is about.
The good news is that the audit I'm going to walk through is not technically complex. It draws on instincts and workflows PR professionals already have. The discipline is familiar; the surface is new.
This article is a practical framework for running a generative AI (genAI) brand audit. In other words, I’m going to outline a way to see what ChatGPT, Perplexity, Google's AI Overviews, Microsoft Copilot, Claude, and similar systems say about your brand; where those answers come from; and what to do when the answer is incomplete, outdated, or wrong.
Because the terminology is still settling, it is worth defining the terms plainly.
In digital PR, search engine optimization (SEO) is the practice of making brand content, earned media, executive thought leadership, and owned resources easier for search engines and audiences to find, understand, and trust. It does not replace media relations. It extends the life and discoverability of the record PR teams already work to create.
Generative engine optimization (GEO) is the related practice of making that same record clear, authoritative, and accessible enough to be used in AI-generated answers. The research language around GEO is still young, but the practical question for communications teams is straightforward: when an AI system answers a question about our brand, category, or leadership, are we included, described accurately, and sourced well?
The two disciplines are not separate efforts. Strong SEO gives AI systems a clearer, more crawlable source base. Strong PR gives those systems the authority signals they need: credible coverage, expert commentary, third-party validation, and a consistent public narrative. That connection is also reflected in Google's guidance for AI features in Search, which says the same foundational SEO practices still matter for AI experiences.
A useful way to compare SEO and GEO, as it applies to the PR industry:
Discipline
Core question
PR application
Common measures
SEO in PR
Can people find and understand our brand through search?
Optimize press pages, executive bios, thought leadership, campaign content, and earned coverage strategy around audience search behavior.
Organic visibility, rankings, branded search demand, referral traffic, backlinks, and conversions.
GEO in PR
Can AI systems accurately represent and cite our brand?
Build a consistent, authoritative source record across owned content, earned media, Wikipedia, analyst coverage, and third-party sources.
Citation rate, accuracy, source quality, message alignment, and share of model.
Before getting tactical, it helps to put the audit in a simple strategic framework. Communications teams are already developing more sophisticated ways to navigate GenAI, including approaches that account for how different large language models select, weight, and cite sources. But most teams can begin with four practical questions: awareness, authority, alignment, and action.
Download the 2026 LexisNexis Future of AI in PR report
Awareness: Can stakeholders find and recognize you? Gated content still has a role in lead generation, but communications teams now have to balance that with the value of discoverable, structured content that AI systems can access, interpret, and cite.
Authority: Are you viewed as a trusted source? Trust has become even more central in a GenAI environment. Authority comes from expertise, evidence, experience, and third-party validation. Planning should account for the signals that help establish a brand, executive, or institution as credible in the sources AI systems are likely to draw from.
Alignment: Are your intended messages being understood? A clear, cohesive narrative is essential when AI systems are synthesizing information from many places at once. If the same core ideas are not showing up consistently in AI-generated responses, the narrative may not be distinct, structured, or repeated enough across authoritative sources.
Action: Are communications driving meaningful outcomes? Because LLMs, search systems, and source ecosystems continue to evolve, measurement has to be built in from the beginning. What works initially may need small adjustments or larger pivots over time. The only way to know is to monitor impact with discipline.
These four questions map directly to the audit that follows: listen for visibility, trace the sources of authority, identify narrative gaps, and measure whether your interventions are changing what AI systems say.
By following this five-step framework, you can integrate both GEO and SEO into your day-to-day life as a PR professional. The goal is increased. positive visibility for your client's brand.
The first step is the simplest and most neglected: Ask AI models the questions your audiences are actually asking, and see what comes back.
Open ChatGPT, Perplexity, Google's AI Overviews, and Microsoft Copilot. Query each with the kinds of questions a journalist, investor, potential employee, or prospective client might ask about your brand. Not your brand name alone—the contextual questions:
What you're looking for across those responses:
This last point matters more than most brands realize. AI systems don't just answer questions about your brand in isolation, rather they construct narratives about categories, and your brand's position within that narrative is shaped by everything the system has absorbed about your space. If a competitor has more structured, authoritative content in the ecosystem, that asymmetry shows up in AI responses.
A useful concept here is share of model — a rough measure of how frequently and favorably your brand appears in AI responses to relevant queries, relative to competitors. It won't be perfectly quantifiable at this stage, but directionally it tells you a great deal.
AI answers are only as good as the sources they're built from. The second layer of the audit is understanding which sources are currently shaping your brand's AI representation and which aren't.
The categories to investigate are: news archives and trade press, Wikipedia, Reddit and community forums, your own press releases and owned content, analyst and industry reports, and executive thought leadership.
Not all of these carry equal weight. AI systems increasingly favor licensed, publisher-approved content over scraped open-web sources — in part because licensed content tends to be more consistently accurate, structured, and verifiable. This has a direct implication for PR strategy: coverage in authoritative outlets doesn't just reach human readers. It feeds the source pool that shapes AI-generated answers, potentially for years.
This is where having access to a comprehensive, licensed news intelligence platform becomes genuinely useful. Rather than manually reconstructing what the authoritative record says about your brand, you can query across the full landscape of credible coverage — understanding not just what was written, but the pattern of how your brand has been characterized, cited, and contextualized over time. Nexis+ AI™'s conversational search, built on the industry's largest collection of publisher-approved news content, is one tool that supports exactly this kind of systematic source audit.
The question you're trying to answer in this step is not just what is being said, but where it's coming from and whether the sources carrying the most weight in AI outputs are the ones you'd want representing your brand.
With a picture of what AI currently says and where it sources from, the third step is gap and risk analysis.
Gaps are questions your brand should be able to answer clearly in AI-generated responses, but currently can't — either because the information doesn't exist in structured form anywhere in the ecosystem, or because what exists is outdated or thin. Common examples: precise descriptions of what a company actually does, clear articulation of its point(s) of difference, accurate leadership information, recent milestones or repositioning that hasn't yet made it into authoritative sources.
Risks are narratives that exist in the source material and are being amplified by AI systems in ways that could be damaging — critical coverage that AI is surfacing disproportionately, outdated information that creates a misleading picture, or competitor framing that is filling a vacuum your brand hasn't addressed.
The absence of a clear brand narrative, it's worth emphasizing, is not neutral ground. AI systems will synthesize an answer from whatever is available. If authoritative, structured content about your brand doesn't exist to answer a question, the answer will be assembled from whatever does — which may include critical coverage, outdated positioning, or adjacent content that wasn't written with your brand in mind.
The audit produces a prioritized list of gaps and risks. The response plan turns that list into content and coverage actions.
Not every gap matters equally. A missing or inaccurate answer matters most when the question is likely to be asked by an audience whose decision has real consequence. The better approach is to assess both the query’s standing importance and the conditions that should move it to the top of the response plan.
Query Focus
Standing Priority
Priority Accelerators
Example AI/Search Prompts
PR Response
Decision-stage brand questions
High
AI answers are inaccurate, outdated, thin, or inconsistent across platforms.
“What is [brand] known for?” “Is [brand] reputable?” “What has [brand] been in the news for recently?”
Correct factual gaps, strengthen About/newsroom content, update executive bios, and reinforce core positioning across owned and earned sources.
Category and competitor questions
Competitors appear more often, are described more clearly, or are framed as category leaders while your brand is absent or underdefined.
“Who are the leading companies in [category]?” “How does [brand] compare to [competitor]?”
Secure authoritative trade coverage, publish category POVs or research, and make differentiators consistent across credible third-party sources.
Reputation and risk questions
Medium, with escalation potential
Recent negative coverage, litigation, regulatory scrutiny, leadership change, crisis activity, or AI systems surfacing outdated or disproportionate criticism.
“Has [brand] faced criticism?” “What are the concerns about [brand]?”
Add current context through credible sources, address outdated information, correct inaccuracies, and ensure the public record reflects the most complete and verifiable picture.
Expertise and authority questions
Medium
The brand is entering a new category, trying to lead on an issue, launching research, or competing for visibility around a topic where authority matters.
“Who are the experts at [brand]?” “What does [brand] say about [issue]?”
Build executive thought leadership, expert commentary, bylines, analyst mentions, conference visibility, and well-sourced third-party validation.
General awareness and entity questions
Lower, but foundational
Basic facts are wrong, inconsistent, or missing across AI answers, search results, Wikipedia, LinkedIn, directories, or media profiles.
“What does [brand] do?” “Where is [brand] located?” “Who leads [brand]?”
Tighten entity consistency across owned content, press releases, executive bios, Wikipedia, LinkedIn, directories, and media-facing materials.
The practical test is not simply whether a query is “high” or “low” priority. It is whether the answer could shape a consequential decision, reinforce a competitor’s advantage, or amplify a misleading narrative. When any of those conditions are present, the query moves up the response plan.
This means different things for different gaps. A factual inaccuracy on Wikipedia needs a correction. A missing narrative around a company's point of difference might need a bylined article in a relevant trade outlet, a structured FAQ on the company website, or a press release that establishes the record clearly. The goal is not to "trick" AI systems, rather it's to ensure that accurate, authoritative content exists and is findable.
Prioritize outlets and formats that carry weight
Not all coverage feeds AI systems equally. Structured, factual content in authoritative outlets with clear attribution, specific claims, and proper sourcing tends to be more extractable and citable than discursive brand content. Knowing which outlets AI systems treat as authoritative in your category should inform pitch priority.
Search data can help communications teams understand how audiences actually describe a category, not just how the brand describes itself. Keyword research, search intent analysis, and organic performance data can inform pitch angles, executive bylines, newsroom content, FAQs, and campaign landing pages. The goal is not to turn PR into keyword placement. The goal is to make the public record more discoverable around the questions stakeholders are already asking. As Google's people-first content guidance makes clear, SEO is most useful when it helps search engines discover and understand content created for people.
Tighten entity consistency. AI systems construct meaning by connecting information across sources. If your brand name, leadership titles, product names, or company history are described inconsistently across Wikipedia, press releases, LinkedIn, and trade coverage, that inconsistency creates ambiguity in AI outputs. A basic entity audit—ensuring that core facts are described consistently across your most authoritative touchpoints—is low-effort, high-impact work.
AI systems do not evaluate a brand narrative only by whether it appears on the brand’s own website. They look for corroboration across the broader information ecosystem. That makes thought leadership, expert commentary, original research, analyst mentions, trade coverage, conference participation, and well-sourced bylines more than reputational assets. They are the evidence base that helps search engines and AI systems determine whether a brand is credible, relevant, and worth citing. For PR teams, the mandate is not simply to publish more. It is to build a durable public record in which the brand’s expertise, point of view, and proof points are repeated and validated by authoritative third parties.
The audit described above is not a one-time exercise. AI systems are continuously updated, the source landscape shifts, and new narratives emerge. The brands that treat this as a recurring monitoring practice, rather than a project to be completed and filed, will maintain an advantage over those that don't.
In practical terms, this means adding AI representation to the monitoring stack alongside traditional media measurement. Examples of AI visibility metrics include:
For SEO in PR, measurement should include both search and communications signals: organic impressions and clicks for campaign themes, rankings for branded and non-branded category queries, referral traffic from earned coverage, backlink quality, brand search demand, assisted conversions, and engagement with owned campaign content.
For GEO, the measurement layer should be more question-led. Track a fixed set of prompts across ChatGPT, Perplexity, Google AI Overviews, Microsoft Copilot, and other relevant systems. Record whether the brand appears, how it is described, which sources are cited, whether the answer is accurate, and how competitors are framed in the same response. Over time, this becomes a competitive benchmark, not just a visibility check.
These metrics won't slot neatly into existing dashboards on day one. But they don't require a full infrastructure rebuild either. Most teams can begin with a structured manual audit on a quarterly cadence and build from there as tooling matures.
Everything in this framework will feel recognizable to an experienced PR professional. Listen to what's being said about your brand. Understand where it's coming from. Identify gaps and risks. Build a content and coverage plan to address them. Measure, monitor, repeat.
The instinct is identical to what our profession has always done. The surface is new, the feedback loop is less visible, and because AI-generated answers are increasingly where brand perception is formed, the stakes are higher than most teams currently appreciate.
The firms that build this into standard practice now won't just be better positioned in AI-generated search results. They'll have developed a new form of brand intelligence that becomes more valuable as the information environment continues to shift.
If you're ready to start, the first query is the hardest and it takes about thirty seconds. Open any AI system and ask what it says about your brand. What you find will tell you everything about where to begin.
Interested in how Nexis+ AI's licensed news intelligence can support your brand's AI audit? Request a demo.
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GEO in PR is the practice of improving how a brand, executive, institution, or issue appears in AI-generated answers. It focuses on whether AI systems can find, understand, accurately summarize, and cite authoritative information about the brand.
SEO in PR is the use of search insight and optimization practices to make PR-created assets more discoverable. That includes newsroom content, press releases, executive bios, bylined articles, campaign pages, and earned media strategies.
SEO helps PR teams understand audience questions, shape content around real search behavior, extend the life of earned media, strengthen backlinks and referral traffic, and make brand narratives easier for search engines to interpret.
SEO is primarily concerned with visibility in search results. GEO is concerned with visibility, accuracy, and citation in AI-generated answers. SEO helps people find pages; GEO helps AI systems use those pages correctly.
Measure organic impressions, clicks, rankings, referral traffic, backlink quality, branded search demand, conversions, and engagement with campaign content. For AI visibility, also measure citation rate, source quality, answer accuracy, message alignment, and share of model against competitors.
Kate LaVail, PhD, has spent her career at the intersection of data and communications strategy. She has held senior roles, leading teams in intelligence and analytics at organizations including the Centers for Disease Control and Prevention, Hill+Knowlton Strategies, McDonalds, and Ketchum. Kate currently serves as Segment General Manager of PR, Communications, Media, and Government at LexisNexis Legal & Professional.