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| Katelyn Holbrook |
For decades, public relations operated on a widely understood hierarchy of influence. Top-tier national outlets sat at the apex, trade publications provided depth and sector credibility and everything else was largely considered supplementary. Communications teams built their media strategies accordingly, treating placement in a short list of prestigious outlets as the primary measure of success. For a long time, that approach was largely correct.
That model still has value, but it no longer tells the full story. An important trend is emerging from AI visibility audits that should prompt every communications team to reconsider these long-held assumptions.
Many of the sources carrying outsized influence in AI-generated responses aren’t the traditional top-tier outlets PR teams have long prioritized. AI models are frequently weighing niche blogs, community forums, regional publications, brand-owned properties and specialized digital properties more heavily than expected—sources that would rarely make it onto a conventional media list. For brands racing to optimize for Generative Engine Optimization, this is a critical insight: Relying exclusively on traditional media hierarchy leaves significant AI visibility on the table and leaves narrative control to sources that weren’t originally a part of the strategy.
How AI engines actually evaluate authority
Understanding why this is happening starts with recognizing that AI systems don’t assess authority the way a communications pro, journalist or editor does. They’re not impressed by a masthead or a publication’s legacy reputation. What large language models look for are patterns:
- Information that’s topically relevant.
- Direct answers to common questions, written like a human.
- Consistent messaging across multiple platforms.
- Content that is frequently updated.
For example, a focused article on an industry-specific blog that clearly and comprehensively addresses a well-defined question can exert more influence on how an AI answers that question than a passing reference in a major business publication.
This doesn’t mean top-tier coverage has become irrelevant—it hasn’t, and communicators shouldn’t interpret this shift as a reason to abandon media relations that target traditionally influential publications their core audiences know and trust. There’re still humans doing reading, subscribing and researching beyond and even before AI. Instead, investments in top-tier coverage and niche digital presence become complementary priorities.
However, the critical insight is that prestigious outlets shouldn’t stand on their own. Authority in the AI era is distributed across the full information ecosystem, not concentrated in a handful of influential publications. A brand’s AI visibility is only as strong as the breadth and consistency of its presence across that entire landscape.
What communications teams should do differently
There are three concrete areas where strategy must evolve, and none of them require abandoning what has always worked. Rather, they require building on top of it.
The first is expanding the definition of a valuable media placement. Trade publications, expert-driven digital outlets, industry community platforms and niche publications where practitioners exchange ideas are not consolation prizes for when top-tier pitches fall short. They’re deliberate strategic targets.
Equally important, and increasingly so, are owned channels. Brand websites, blogs and proprietary content aren’t only marketing assets, but primary source material for AI systems. LLMs appear to reward content that resembles journalism: clear headlines, FAQ-style formatting, named authors and regularly updated pages, giving communications teams direct, controllable levers to ensure their brand channels are properly optimized for AI engines.
A brand that dominates major outlets but remains absent from the forums, newsletters and specialized publications its audience frequents and fails to leverage the owned channels entirely within its control is leaving its AI visibility to chance.
The second is treating message consistency as a technical priority, not just a communications best practice. LLMs synthesize information across many sources when generating responses. When a brand’s core narrative and differentiation is articulated clearly and coherently across multiple channels, that consistency signals to AI systems that the story is reliable and worth surfacing. When messaging is fragmented or limited to a handful of places, AI-generated responses tend to reflect that inconsistency, either omitting your brand entirely or reducing it to generic, surface-level output. Only consistent, differentiated messaging ensures your brand is both visible and stands apart. A useful starting point is a systematic audit of how a brand’s key messages appear across the full range of channels where information about it exists, identifying the gaps and building a deliberate plan to address them.
The third is developing content with machine comprehension in mind, alongside human readability. Thought leadership, executive commentary and owned content have always been written for human audiences, and that remains essential. But communicators must now also consider how AI systems interpret and prioritize information. Content that’s clearly structured, written in direct, accessible language and regularly updated is far more likely to be surfaced in AI-generated responses. This effect is amplified when that content mirrors the language and phrasing of the questions your target audiences are actually asking, because AI engines are more likely to surface sources whose language closely matches the prompt being entered. If your target is searching for “best cybersecurity solutions for mid-size financial firms,” content that speaks directly to that framing will outperform content that only addresses cybersecurity in broad, generic terms.
This doesn’t mean stripping out voice or perspective; it means ensuring the substance is unambiguous and easy for a machine to extract and cite.
The bigger picture
Communications has always been about reaching the right audiences with the right messages at the right time. What has changed is the nature of the intermediary. AI systems are increasingly positioned between information and the people seeking it, determining what surfaces, how it’s framed and what context shapes its interpretation. That’s a significant power shift and an equally significant opportunity for brands that move thoughtfully and quickly.
The communications teams best positioned for the next era will be those that stop asking “did we land the big placement?” and begin asking: “is our story told clearly, consistently and comprehensively across the full information ecosystem?” The strongest strategies pursue both questions simultaneously. But right now, the second question is the one too few organizations are asking with the priority it deserves. The brands that get there first will not just improve their AI visibility but will define how AI surfaces their brands.
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Katelyn Holbrook is Chief Client Officer at V2 Communications, an integrated communications and PR firm headquartered in Boston.
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