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Last week, I had to dig deep into a new client’s background \u2014 fast. They were in the middle of a substantial PR crisis<\/a>, and time was not on our side.<\/p>\n In the past, I would’ve turned to Google and manually sifted through page after page of results. I’d look at their website, news mentions, social media activity, reviews and even obscure forum posts. The goal was always the same: get a full picture of who they are, how they operate and what’s already public that could help \u2014 or hurt \u2014 their reputation<\/a>.<\/p>\n Doing that kind of research the old way can take hours.<\/p>\n Now, it’s far more efficient thanks to AI<\/a>. Tools like ChatGPT, Claude and Grok can quickly summarize public information, giving me a snapshot in seconds instead of hours. But this shortcut comes with a big caveat: we also have to consider what these<\/i> systems are saying about people and companies, and how they’ve come to those conclusions.<\/p>\n Large language models (LLMs), the tech powering these AI tools, are trained on massive datasets pulled from across the open web. That means your brand’s online presence isn’t just being seen by people anymore \u2014 it’s being interpreted and summarized by machines, too.<\/p>\n This changes the game for public relations<\/a>.<\/p>\n Because while LLMs can be incredibly powerful, they’re still prone to hallucinations \u2014 a polite term for making things up<\/i>. And if you’ve spent even five minutes with Google’s new AI Overviews (AIOs), you’ve seen it firsthand.<\/p>\n Some examples I’ve personally encountered in AIOs:<\/p>\n Related: Why AI-Forward Communication is the Future of Public Relations<\/a><\/b><\/p>\n Ridiculous? Absolutely. But it underscores a bigger issue: these systems can spread false or misleading information quickly and at scale.<\/p>\n Even with less extreme topics, hallucinations happen. I once asked Grok to summarize my background. It confidently told me I’d served in the Army Airborne. In reality? I was a Marine.<\/p>\n As more people rely on AI to answer questions they once typed into search engines, the accuracy and relevance of your brand’s presence in these models is becoming critical. Not only do you want to make sure the information is correct, but you also want your brand<\/a> to show up at all. Ideally, you want to appear in answers about your industry, not just yourself.<\/p>\n So, how do you influence what these systems say? Unfortunately, it’s not as easy as feeding them your preferred narrative. If it were, AI tools<\/a> would already be flooded by spam from low-quality marketers.<\/p>\n Instead, LLMs prioritize information from trusted sources across the web, and not all sources are weighted equally. Your company’s official website helps, but third-party credibility matters far more.<\/p>\n That’s why editorial media coverage remains the most powerful tool in modern PR \u2014 and it matters now more than ever. There are two core elements here: high-quality editorial features and press releases<\/a>.<\/p>\n Editorial features \u2014 stories published by reputable media outlets that quote you or spotlight your work \u2014 carry the most weight. Why? Because they’re difficult to manipulate. Getting published requires a compelling topic, a unique perspective and often, relationships with journalists. You have to earn<\/i> it. That’s exactly why LLMs treat this kind of coverage as a strong trust signal.<\/p>\n The more insight you share in those features, the better. If you’re quoted briefly, it suggests your voice is just one of many. But if your expertise shapes the bulk of the story, that sends a much stronger signal \u2014 both to readers and to the algorithms parsing it.<\/p>\n That’s also why it’s smart to pursue interviews and contributor content in addition to being quoted. These allow you to go deeper, share your thinking more fully and increase the likelihood that your perspective makes it into an AI summary.<\/p>\n Press releases still matter, too \u2014 but in a more limited way. They’re a paid channel, so anyone can publish them, but there’s still some editorial oversight. Editors at distribution services do basic fact-checking and screen for hyperbole before syndicating them to media outlets. The key is to make sure your press release is actually newsworthy<\/i>. A strong release can also prompt journalists to cover your story further.<\/p>\n While LLMs pull data from various formats \u2014 text, audio, video \u2014 text-based articles still produce the fastest and most reliable impact when it comes to influencing AI responses.<\/p>\n Related: Yes, AI Might Take Your PR Job. Here’s What You Can Do About It.<\/a><\/b><\/p>\n In many ways, AI has transformed PR. But the fundamentals haven’t changed. You still need to earn high-quality media coverage. The difference is that now, those features are no longer just about reaching human audiences \u2014 they’re about training the machines that shape perception at scale.<\/p>\n The companies and individuals who recognize this shift and act on it now will gain a long-term advantage. Those who don’t? They’ll get left out of the conversation \u2014 by people and by AI alike.<\/p>\n<\/div>\n Last week, I had to dig deep into a new client’s background \u2014 fast. They were in the middle of a substantial PR crisis<\/a>, and time was not on our side.<\/p>\n In the past, I would’ve turned to Google and manually sifted through page after page of results. I’d look at their website, news mentions, social media activity, reviews and even obscure forum posts. The goal was always the same: get a full picture of who they are, how they operate and what’s already public that could help \u2014 or hurt \u2014 their reputation<\/a>.<\/p>\n Doing that kind of research the old way can take hours.<\/p>\n<\/div>\n\n