BY EVAN PONDEL, IRC
The digital landscape is shifting rapidly. Search engine optimization has been replaced by AI-driven algorithms sourced from large language models.
Recent headlines in a key business publication had cast a negative shadow on the reputation of a prominent executive who wanted to tell his side of the story. The problem was that the same publication had written a steady drumbeat of these stories for nearly 18 months. At this point, it was virtually impossible to Google the executive without dredging up a page of unflattering stories. Even more troubling, prompting ChatGPT to write a profile on him generated a vitriolic biography based on the same stories.
A Shifting Digital Landscape
The landscape for what makes or breaks digital reputations has changed. The flow of information that was once prioritized online through search engine optimization (SEO) is now finding its way to large language models such as ChatGPT.
These models digest information differently, reframing SEO as generative engine optimization (GEO) and answer engine optimization (AEO).
While the terms are often used interchangeably, there are nuances between the two. GEO focuses on influencing what AI engines include in response to prompts. AEO tries to position content as the authority on a topic, according to media intelligence platform Meltwater.
The good news is that many of the same principles that drive SEO apply to GEO and AEO, said Mark Healey, founder of Heals LLC, which specializes in these strategies for brands and high-profile individuals.
The bad news is that investor relations and public relations professionals now have more outlets to manage (i.e., artificial intelligence (AI) engines) when it comes to maintaining the reputation of their companies and C-suite.
“The level of the content game has been lifted,” Healey says. “And now that everyone can write an article with the help of AI, you need to be at the top of your game with the type of information you use to seed the web.”
AI as First Reader
The stakes are particularly high for public companies whose investor base increasingly relies on AI-generated summaries to understand business fundamentals.
Katie Perry, a marketing and communications strategist who works at the intersection of investor relations and emerging technology, puts it bluntly: AI is often the “first reader” of your information.
“Your investors and prospective investors are encountering your company through summaries, snippets, and answers generated by models, and not always through full filings or long-form disclosures,” Perry says. “GEO and AEO are about making sure those systems understand your business correctly before they compress it for everyone else.”
This isn’t just about visibility. It’s about accuracy.
When AI systems lack clear, structured information, they fill in the gaps through what’s known as “AI hallucination,” generating realistic-sounding but incorrect information. Once an inaccurate summary begins circulating through ChatGPT, Perplexity, or other AI platforms, it can shape investor perception long after the original disclosure.
“Optimizing for generative AI reflects the science side of your storytelling,” Perry says. “It’s part proactive storytelling and part risk management.”
The Wikipedia Factor
One of the most important shifts in the GEO/AEO landscape involves the sources that large language models prioritize. Wikipedia, Reddit, Quora, and Forbes rank among the highest-weighted sources for AI systems.
“These sites act as upstream inputs for generative models,” Perry said. “Third-party coverage is paramount here.”
The challenge is that companies cannot simply create their own Wikipedia pages from scratch. Wikipedia requires legitimate coverage from trusted publications before a page can be established. This creates a strategic imperative for communications and public relations teams to pursue what Perry calls “easy wins” from mid-tier publications in order to boost their volume of third-party cited content.
Healey encountered this dynamic directly when working with a C-level executive of a public company. After publishing a comprehensive biography page on the company website, that page became the source of truth for the executive when people searched on Perplexity or ChatGPT.
“Now all other platforms are linking to their company bio,” Healey says. “The engagement was the first time someone came to me to be proactive rather than reactive. Now you’ve built a moat that helps protect your reputation.”
For executives facing negative coverage, the strategy shifts to content production. Healey said the only way to counter negative content is to produce more accurate content. His approach includes building out company and executive biography pages, and linking to assets such as LinkedIn and Crunchbase. He also recommends claiming your personal domain name and publishing a bio there.
“It becomes almost a real estate grab,” Healey said. “If you can get 80% control of the first page of search results, that will usually push down negative content.”
Schema: Teaching Machines to Read
While content production remains essential, the technical infrastructure supporting that content has become equally important. This is where “schema” markup enters the picture.
Schema is standardized coding vocabulary that helps search engines and AI systems understand the meaning and relationships within web content. Think of it as providing subtitles for large language models.
When a human reads a webpage, they intuitively understand that a name at the top of a bio is the person’s name, or that a number preceded by a dollar sign represents financial data. Machines need explicit instruction.
“Schema feeds information into machines better than raw text,” Healey said.
He recommends that IR professionals examine each page on their website to ensure the right schemas are applied to the right content.
For articles, this means implementing “article schema,” which includes adding structured data to your article pages to help Google show better title text, images, and date information to enhance search results on Google Search, Google News, Google Assistant and other engines.
For executive bios, it means using person schema with “sameAs” properties that link to the executive’s LinkedIn profile, X account, or other verified profiles.
According to a Google AI Overview, “An effective executive bio is a 280–340-word, third-person narrative focusing on leadership, career achievements, and unique value proposition. Structure it with a strong professional hook, current role/impact, key accomplishments with measurable results, industry expertise, and board/leadership experience.”
For financial disclosures, financial properties within schema can help AI systems correctly interpret and categorize the data.
The standardized schemas are maintained at www.schema.org, which serves as the repository for different properties that can be implemented.
Healey suggests asking ChatGPT for guidance on building schema for specific pages, though he cautions that AI systems sometimes “hallucinate properties that aren’t real,” so verification is important.
Perry frames this work as treating investor communications as data structures rather than just documents.
“This means using clear headers, consistent terminology, logical sequencing, and explicit definitions, especially around strategy, metrics, and risks,” she says.
The best approach: Companies and executives should act proactively to establish their presence and narrative before negative content takes root.
Models don’t understand intent the way humans do.
“If something is vague or described inconsistently, there’s a higher likelihood it will be misinterpreted or omitted entirely,” Perry says.
Monitoring and Measurement
Several technology platforms now exist to help companies understand how AI systems are interpreting their information. These tools work by querying large language models with company-specific prompts and analyzing the responses for gaps, inaccuracies, or inconsistencies.
“That feedback loop makes it possible to spot gaps before misinformation hardens into a default summary,” Perry said. “It gives you a practical roadmap for where additional third-party coverage, clearer explanations, or better-structured content will have the most impact.”
For Healey, the motivation is clear: Once a company or executive secures a top position in search results or AI-generated content, “that signal is telling systems that this content is super relevant. It’s hard to displace.”
The Compression Test
Perhaps the most useful framework for thinking about GEO and AEO is that it’s “less about gaming algorithms and more about offering enough clarity to survive compression,” Perry says.
Every public company now communicates through what Perry calls “an interpretive layer they don’t control.”
Just as marketers learned to navigate Google’s search algorithms through SEO, companies must now balance optimizing for people and optimizing for machines.
“There will be tradeoffs,” Perry says. “But at the end of the day, it’s about ensuring your core story holds together no matter how it’s summarized, shortened, or reframed.”
For investor relations professionals, this means several practical steps:
- Maintaining comprehensive, schema-enhanced bio pages for executives.
- Producing regular content that seeds the web with accurate information.
- Pursuing third-party coverage from credible publications.
- Monitoring how AI systems describe the company.
- Treating financial disclosures and investor communications as structured data that machines can accurately interpret.
The executive mentioned at the beginning of this article learned these lessons the hard way.
The best approach: Companies and executives should act proactively to establish their presence and narrative before negative content takes root.
“In this day and age, you almost never see a negative article just go away,” Healey says. “You need to be proactive about building that moat.”
Evan Pondel, IRC, is CEO of Triunfo Partners, LLC; epondel@triunfopartners.com.