How to get generative AI to tell your company’s story correctly

Executives now ask: “What does ChatGPT say about us?”

Generative AI platforms and “answer engines” such as ChatGPT, Gemini, Claude, Copilot, and Perplexity increasingly act as the front door to discovery. People use them to understand companies, compare options, and check reputations.

The data backs this up. Muck Rack’s "Generative Pulse" report, which analyzes more than one million links cited by major AI tools, finds that 95% of AI citations come from non-paid content and 89% from earned media, with 96% sitting squarely in the communications and corporate-affairs realm. 

In other words, PR content, not ads, quietly fuels what AI says about brands. (We named this practice AI relations, when you’re looking to create AI visibility.)

This raises a practical question for any organization: How do you influence what generative AI says, and how do you make sure it cites your content correctly?

Below, we outline what current evidence shows about how these systems work and what your company can do about it.

How generative AI decides what to cite

Facts

1. AI answer engines synthesize, not simply “search.”
   
   Tools like ChatGPT and Perplexity retrieve information from a mix of pre-training data and real-time web sources, then generate a single synthesized answer with citations, instead of a list of links.
   
   
2. They favor trusted, third-party sources.
   
   Independent analysis shows that AI tools overwhelmingly cite earned media, news outlets, analyst reports, corporate blogs, and reputable reference sites rather than ads or sales pages.
   
   
3. Earned media leads the pack.
   
   Generative Pulse reports that around 89% of citations come from earned media, and multiple summaries of that research note that 96% of AI-generated citations fall into classic PR territory.
   
   
4. Authoritative consistency matters.
   
   Research and practitioner guidance on generative engine optimization emphasize that models reward clarity, consistency, and coverage — unambiguous definition pages, aligned boilerplates, and repeated confirmation of key facts across multiple sources.

No vendor fully discloses its ranking algorithm. Exact weightings for each source type remain proprietary, and we cannot confirm the precise formula any single platform uses. However, the public data above shows a consistent pattern: AI tools behave more like analysts than traditional search engines. They compare multiple sources, reconcile differences, and then present a consensus view.

The core objective: AI relations and generative engine optimization

AI relations refers to generating AI visibility. Generative engine optimization refers to the practice of aligning your brand’s visibility and credibility with what generative AI platforms consider trustworthy, so these tools cite you accurately in their answers. 

Think of it this way:

• SEO helps your site appear in search results pages.
  
  
• GEO helps your brand appear in AI-powered answers.

For most organizations, that means treating AI visibility as a PR and content-strategy problem, not a technical tweak.

Eight ways to earn the citations you want

Below are eight practical levers your company controls today. Each one aligns with what current evidence shows AI systems already reward.

1. Publish a definitive, well-structured explainer on your own site.

Create a single, canonical page on your domain that:

• Defines the concept, product, or issue in clear language
  
  
• States key facts up front (definition-first structure)
  
  
• Uses headings and short paragraphs that map cleanly to common questions

Analysis of how generative engines synthesize answers indicates that definition-first, unambiguous content is easier for models to quote and summarize. 

Treat this as the “source of truth” that all other assets reference and link back to.

2. Win high-authority earned media coverage.

Since most AI citations originate from earned media and journalistic content, you need strong coverage in outlets those systems already trust. 

That means:

• Targeting credible industry, business, and national outlets
  
  
• Supplying reporters with clean, quotable explanations of what you do
  
  
• Prioritizing depth and clarity over slogan-driven soundbites

These articles become training data, real-time retrieval sources, or both.

3. Strengthen your owned thought leadership.

While earned media leads, owned content such as fact sheets, leadership blogs, FAQ pages, and white papers still appear among AI citations, especially when they read like neutral, educational resources rather than sales copy. 

Focus your owned content on:

• Clear explanations
  
  
• Evidence-backed claims with sources
  
  
• Direct answers to the queries you want to own (“What does [Company] do?” “Is [Company] reliable?”)

4. Standardize your digital footprint.

AI tools look for consistency across sources. Conflicting information about your founding date, employee count, leadership titles, or product descriptions can weaken trust signals. 

Review and align:

• Website boilerplates and "about" pages
  
  
• Press releases and newsroom content
  
  
• LinkedIn company profile and executive bios
  
  
• Investor fact sheets, conference bios, and directory listings

When every credible source tells the same story, AI systems find it easier to present that story as fact.

5. Participate in reference ecosystems (carefully).

Many AI answers rely on reference-style properties such as Wikipedia, government databases, and structured directories. 

Where appropriate and ethically allowed:

• Ensure your organization has a neutral, well-sourced Wikipedia entry, if you meet notability standards.
  
  
• Keep public filings, regulatory disclosures, and industry listings accurate and up to date.
  
  
• Maintain accurate entries in data aggregators and business databases.

These reference points become anchor nodes for AI when cross-checking claims.

6. Engineer your press releases for AI as well as journalists.

Press releases now feed both newsrooms and generative engines. Axia’s own analysis and other industry commentary show that well-structured releases can support GEO and AEO when they present facts clearly and link back to canonical explanations. 

Optimize releases to:

• Lead with newsworthy, factual headlines and subheads.
  
  
• Include concise, accurate boilerplates.
  
  
• Provide context that explains why the news matters in your market.
  
  
• Link to your definitional content and supporting resources.

Wire distribution alone does not guarantee coverage or AI visibility, but it increases the pool of structured, machine-readable information about your brand.

7. Earn backlinks and mentions from authoritative domains.

Traditional SEO still matters because it influences what content AI tools discover and trust. GEO frameworks highlight that high-authority backlinks and mentions help your content surface as a candidate source for AI answers. 

Tactically, that includes:

• Contributed bylines in respected outlets that link back to your canonical pages
  
  
• Citations from industry associations, think tanks, and analyst firms
  
  
• Inclusion in reputable rankings, reports, and award lists

The goal is not volume for its own sake, but signal strength from credible domains.

8. Speak where your industry listens.

Conference talks, panels, and association presentations often lead to:

• Event recaps on association sites
  
  
• Slide uploads on platforms like SlideShare or conference hubs
  
  
• Coverage in trade media

Those assets create additional, high-context touchpoints that AI systems can read and reconcile when summarizing your expertise. 

When possible, ensure those materials point back to your canonical explanation and maintain consistent language.

How to correct generative AI when it gets you wrong

Even with strong GEO, AI tools sometimes misstate facts or surface outdated information. You cannot directly “edit the model,” but you can influence future behavior in two main ways.

Step 1: Fix the underlying content ecosystem.

First, correct the sources AI actually reads:

1. Update your own site with a clear correction or clarification.
   
   
2. Issue a press statement or blog post that explains the correction in factual, non-defensive language.
   
   
3. Secure updated coverage in trusted media or analyst outlets when the issue merits that level of visibility.
   
   
4. Align all profiles and reference entries with the corrected information.

Because AI systems draw heavily from third-party and owned content, cleaning up the ecosystem gives them better material to work with. 

Step 2: Use platform feedback channels (with realistic expectations).

Most AI tools offer basic feedback mechanisms:

• Perplexity, for example, lets users flag incorrect or inaccurate answers directly under the response.
  
  
• OpenAI indicates that user content and feedback can help improve future models and evaluations when organizations opt in to share that data.
  
  

These signals do not guarantee an immediate correction, and we cannot confirm exactly how each report influences model behavior. However, combined with a cleaner content ecosystem, consistent feedback increases the likelihood that future versions of the model adopt the corrected story.

What not to do

The temptation to “hack” AI visibility is strong. Some tactics create risk with little upside:

• Content farms and low-quality backlinks
  Evidence from SEO and GEO practice shows that low-trust networks tend to be discounted or ignored, and they can damage reputation with both humans and machines.
  
  
• Astroturfed reviews or fake third-party sites
  These may violate platform policies and, if discovered, create long-term trust problems that are difficult to unwind.
  
  
• Opaque or deceptive edits to reference properties
  Communities such as Wikipedia enforce neutrality and reliable sourcing. Manipulative contributions can be removed and may trigger scrutiny of your brand.

In an environment where AI systems try to avoid overtly promotional content, anything that looks like manipulation tends to backfire.

Putting this into an AI relations strategy

From a communications perspective, the playbook looks familiar:

1. Clarify your story.
   Own a single, canonical explanation of who you are and what you do.
   
   
2. Earn credible coverage.
   Use media relations to place accurate, substantive stories in outlets AI already trusts.
   
   
3. Reinforce with owned thought leadership.
   Publish factual, well-structured content that answers real questions.
   
   
4. Standardize your footprint.
   Make sure every serious source tells the same story.
   
   
5. Monitor and adjust.
   Regularly sample AI tools to see how they describe you, then close any gaps with targeted content and outreach.

This is GEO in practice, and it sits squarely in PR’s wheelhouse.

How Axia Public Relations can help

Axia’s AIVisibility℠ and GEO programs focus on the same levers outlined above: earned media, authoritative content, and consistent narratives that generative AI trusts enough to cite. 

If your organization wants to understand how AI currently summarizes your brand, correct misinformation or outdated narratives, and build a GEO-ready earned media and content strategy, explore our AI relations service and AI-focused resources at:

• AI relations service overview
  
  
• AI and GEO blog resources

To enhance your brand's online presence with AI relations and be part of AI-generated answers, explore Axia Public Relations' comprehensive approach with AIVisibility.

Photo by Airam Dato-on