What if your brand’s biggest competitor isn’t another company — but the AI answer that appears and quietly pushes you off the list?
Buyers are no longer typing queries into Google or scrolling through websites. They’re asking AI agents and answer engines for buying advice.
The surprising part? AI isn’t basing those recommendations on your campaigns or your content calendar. It doesn’t distinguish between a Forbes byline and a product page, between a G2 review and an analyst report. It just wants one thing: coherent evidence. The same story, told the same way, everywhere it looks.
This creates a perception challenge that most PR and SEO teams have yet to solve: PR is still optimized for human readers and media impressions, while SEO is still optimized for search rankings and traffic. But the systems now mediating buyer decisions, like ChatGPT, Perplexity, or Gemini, don’t rank pages or count impressions. They synthesize patterns. They decide which brands to trust and recommend based on whether your signals align or contradict each other.
Let’s break down why this shift is happening, what it means for brand visibility, and how PR and SEO teams can finally operate as one integrated function.
TL;DR
- PR and SEO teams can no longer operate as separate teams. PR shapes the story; SEO structures the proof. AI only trusts brands whose signals reinforce each other.
- Brand mentions in high-trust sources (like press, G2 reviews, social media, and communities) now matter more than keywords or backlinks.
- Every PR win should be turned into structured SEO assets: transcripts, Q&As, schema, and internal links.
- Visibility in the AI era must be continually maintained through consistent signals, not one-off campaigns.
What is changing for PR and SEO in the AI era?
One interesting thing about AI is that it does not “rank” brands. It interprets them. Instead of indexing pages and ranking them, answer engines synthesize signals into entity-level knowledge: who you are, what you sell, how others talk about you, and whether people trust you. Discovery is no longer about chasing SERP spots. It’s about shaping the evidence pool AI consumes.
In this scenario, two big changes matter:
First, AI rewards consistency across surfaces. If a CEO uses certain language in interviews, but product pages use different terminology, or reviews contradict the claims, AI’s confidence declines. Bozoma Saint John, Forbes’ number one most influential CMO, who recently spoke at our Reach 2025 event, explains:
Consistency is so key…you start to lose trust the moment the brand switches up its story.
Bozoma Saint John
Forbes’ #1 CMO
Second, the mix of sources AI cites is dynamic and sometimes volatile. One Semrush study shows that citation patterns change across engines and over time. For example, sites like Reddit, Wikipedia, LinkedIn, and Forbes all saw fluctuations in citations on ChatGPT. PR and SEO teams need to track which domains are influencing answers in their categories, because the model’s “trusted” sources can shift quickly.
PR teams need to make sure that brand perception is high, while SEO teams should aim to make that presence tangible in rankings. Since AI demands both to cite your content on answer engines, brand mentions now carry more weight than ever.
Why do brand mentions matter more in AI search?
Brand mentions are no longer a vanity metric. The more a brand appears in authoritative contexts — analyst reports, industry features, reviews, community threads, expert quotes — the more likely it is to be recommended in AI search results.
Here’s an example: A CFO asks, “What procurement software should we use for a distributed team?”
The answer engine doesn’t search your site in real-time. It recalls patterns from its training and retrieval. The brand with the strongest mention footprint wins the answer.
AI models don’t “trust” a brand because of a press release or a single viral post. They trust patterns of authority, checking for:
- Multiple credible sources are saying similar things
- Mentions in category-defining content (“top CRMs for…”)
- Association with real use cases, customer names, and outcomes
If your brand’s low visibility means that you’re outside your own domain, the AI has little reason to believe you’re a category leader.
There are three ways brand mentions impact AI search results:
- Signal reinforcement: Repetition across high-trust sources creates a pattern. AI treats that pattern as proof, so if your company is repeatedly cited in analyst reports and reputable articles, the model infers authority.
- Entity association: Mentions in topical contexts (e.g., “sales engagement,” “supply-chain visibility”) build semantic associations. Over time, AI links the brand to specific categories and problems.
- Sentiment and safety: Mentions include both sentiment and safety. Negative, contradictory, or noisy mentions lower confidence. Positive, detailed user reviews, such as those on G2, and clear documentation increase the likelihood that AI will include your brand among its recommendations.
AI search doesn’t reward siloed teams but coherence. PR might land the story, but if SEO doesn’t structure the brand’s digital ecosystem, AI may never ‘see’ that story. We’ve moved from optimizing for editors and algorithms to optimizing for how models interpret and verify expertise.
Nikki Festa O’Brien
CEO of Greenough Communications
If mentions form the raw material of AI visibility, the next question becomes: who is responsible for generating, shaping, and structuring those signals?
See how Spiky AI used G2 reviews to increase brand visibility and build category presence.
How PR & SEO teams can partner to drive AI visibility?
Most companies today are still running two separate playbooks: PR chases mentions, and SEO chases rankings. But the brands winning in AI search are the ones that stopped treating PR and SEO as separate functions and started building a unified evidence base. Here’s how they can partner to drive results:
1. Build from a shared narrative spine
AI visibility requires a single coherent strategy that aligns narrative, structure, and distribution. PR teams must lead with the story the brand wants to own — what problem it solves, who it serves, and the terminology it uses. And the SEO teams must translate that story into machine-friendly constructs.
For example, if PR is pitching “consumption-based pricing” as your differentiator, SEO should build a cluster around that exact phrase: a pillar page defining it, supporting content showing how it works, comparison pages contrasting it with seat-based models, and case studies proving the outcome.
PR measures coverage, SEO measures rankings. Neither captures AI citation rates. One way to fix this is to build a shared dashboard tracking how often your brand appears in answers generated by answer engines. When both teams own that number, alignment follows.
Evan Sherbert
AI Search and Discoverability Lead at Atlan
Without alignment, you get PR mentions that don’t map to searchable content — and SEO pages that tell a story no one in the press is repeating.
2. Turn earned media into structured assets
When the PR team lands an executive interview or byline, that’s not the end of the distribution chain — it’s the beginning. SEO teams’ job is to mirror that content in crawlable, structured formats that AI can connect back to your brand.
- Publish the transcript as an AEO-optimized blog post with proper H-tags.
- Pull key insights into standalone Q&As.
- Add structured data (FAQ page, person schema, etc.) so the claims are machine-readable.
- Link the interview back to product pages, case studies, or documentation that prove the assertions.
This creates a reinforcement loop: the earned placement boosts authority, and your owned assets give AI a clear path to verify and cite that authority.
3. Use data to close the loop
This is where your strategy should become measurable. Platforms like Profound and G2 now map how LLMs cite vendor pages across categories — showing which G2 reviews, blog posts, and integrations pages are actually being surfaced in AI responses.
That visibility lets you see:
- Which earned mentions are translating into AI citations (and which aren’t)
- Where your structured content is strong (and where it’s missing)
- Which competitors are out-narrating you in AI-recommended contexts
4. Own the category in AI surfaces
PR sets the narrative for the category; SEO reinforces the semantics of the category.
Together, they ensure the brand becomes a default association when AI explains or defines your space.
This means that:
- PR should consistently place executives in conversations that shape category language.
- SEO should lock that same language into your site structure, so AI finds the same pattern everywhere.
AI needs pattern alignment, not clever phrasing. This is reflected in the “three-headed search beast” framework, where social proof, traditional PR, and AEO must all reinforce the same terminology for AI to trust it.
What the future of PR–SEO collaboration looks like
PR and SEO are no longer separate disciplines — they’re two sides of the same capability: controlling how your brand is understood, trusted, and recommended by the systems that now mediate buyer decisions.
Today’s reality points toward a future where the boundary between PR and SEO fades into a single discipline dedicated to managing how humans and machines perceive a brand.
PR will bring cultural relevance and authoritative voices; SEO will bring semantic rigor, structured signals, and knowledge architecture. Together, they will build the brand’s “AI profile,” the version of the company that models store, reference, and recite back to buyers.
In this future, visibility is not something you chase — it’s something you maintain. A loop that listens to how AI describes your brand, corrects inconsistencies, reinforces your narrative, and ensures every signal points in the same direction.
The brands that excel will be those that invest in consistency: one story, one set of truths, one identity reflected across press, product, documentation, reviews, creators, and category conversations.
FAQs
- What matters more to AI: backlinks or brand mentions?
AI looks for consistent, repeated, trustworthy references across the web and not just links. Brand mentions help create the entity-level understanding models that rely on.
- What is a brand’s “AI profile”?
A brand’s AI profile is the version of their company that AI systems store and recall, which is built from their mentions, reviews, schema, documentation, press coverage, and public narrative.
- How can marketing teams increase brand visibility in AI answers?
Marketing teams can increase brand visibility by aligning brand messaging, strengthening third-party proof (such as reviews, press, and analyst references), structuring content clearly, and ensuring the brand appears consistently across authoritative sources.
Ready to go deeper? Our latest e-book on “Build Your Brand for the LLM Era” explores the strategies brands are using to earn trust, citations, and visibility in AI-driven discovery.
Edited by Supanna Das
