Brands that want their content to appear in AI-generated answers need a structured approach to credibility. Lisa Peyton, senior AI marketing strategist and AI for strategic communications professor at the University of Oregon's School of Journalism and Communication, has developed a six-part citation framework designed to make owned content easier for AI systems to extract and cite.
"PR is having a moment right now when it comes to AI," Peyton said. "We need to be at the table and in the room when we're talking about AI visibility because more than ever before, credibility is what moves the needle when it comes to AI visibility."
Peyton outlined six components that make brand content more citable for AI tools.
- A distinctive point of view: Generic commentary won't get cited. A health system, for example, should explain where AI belongs in patient communication rather than restating that "AI is changing healthcare."
- An extractable claim upfront: The clearest finding or thesis should sit near the top of an article, in a direct, plain-language sentence.
- Expertise signals: AI systems look for credible bylines-chief communications officers, subject matter experts, or researchers who carry authority on the topic.
- Topical consistency: One article isn't enough. A cybersecurity firm needs a steady body of work on AI risk, not a single post, to build a reputation worth citing.
- Citation-friendly structure: Plain language, clear headers, FAQs, definitions, and scannable sections make content easier for AI to pull from. Peyton called many of these "table stakes at this point."
- AI accessibility: The content must be reachable. Sites that block AI crawlers prevent tools from ingesting information directly, forcing them to rely on outside references instead.
Beyond the checklist, Peyton said trust depends on external validation. "The way that AI is deciding if it's credible and trustworthy is how many other folks are talking about it," she said.
Earned media's growing role
AI systems don't just read what a brand says about itself. They scan journalists, trade outlets, researchers, forums, social platforms, and other third-party sources. PR teams need to map the "source pools" AI pulls from for the prompts that matter in their category.
For one industry, AI citations may come heavily from trade press. For another, Reddit, Wikipedia, or research aggregators may drive the answers. "We have to understand what sources that platform is pulling from," Peyton said.
If a quarter of AI citations for a key industry question trace back to earned media, communicators can push for stronger media strategy. If research reports win the citations, the brand may need better data. If community forums shape perception, improved listening and response plans become the priority.
PR leads AI visibility
"Having the answer and being able to say that lets us lead instead of sit behind and wait for marketing or digital marketing or whatever analytics team is currently running the SEO to give us direction," Peyton said. "I think we really need to lead here."
She urged communicators to claim the conversation. "PR owns AI visibility right now. We need to come forward. We need to have a point of view, which means that we have to understand it."
Why this matters for PR and Communications professionals
Peyton's framework turns AI visibility into a concrete PR function. Instead of reacting to search trends, teams can audit which sources feed AI-generated answers for their industry's top questions. That audit points directly to the next move: more earned media coverage, original research, or a community engagement overhaul. No guesswork, just a plan built on how AI decides what to cite.
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