I recently gave a presentation on media relations in the age of AI with a premise that still feels a little uncomfortable to me:
Our primary audience is no longer the human reader. It’s the machine.
Search has fundamentally changed, and we’re no longer optimizing for clicks; we’re optimizing for inclusion. The goal is to show up in a two-line AI-generated summary that someone skims and trusts without ever visiting your site.
That creates a real problem for media relations professionals, because the traditional model still makes sense on paper. You land a strong placement, ideally in a top-tier outlet, and you move on. That has always been the win.
Except now, it’s not enough on its own.
A single media hit is fleeting. News cycles are short and attention spans are shorter. And AI systems don’t remember individual moments; they recognize patterns across time and across sources.
So, the question changes from, “did we get coverage?” to, “what can we teach the machine?”
I’ve been approaching this by shifting from focusing on coverage alone to what I call authority engineering. Whether you call it AEO, GEO, or something else, the goal is to make it clear, to an algorithm, that our brand consistently shows up in credible conversations about a specific topic.
That requires balancing two things: volume (how often our brand appears in relevant contexts) and repute (the credibility of the outlets where those mentions appear).
One mention in a top-tier publication carries weight. Repeated mentions across credible, even if less prominent, outlets build pattern recognition. Both matter, and the right balance is something I’m still actively figuring out.
What do you do when a media hit is no longer the finish line?
Now, after every placement, we focus on what happens next. Each media hit becomes the raw material for an earned-to-owned strategy that makes us more discoverable by AI and search engines alike.
We publish structured summaries and through circular links connect new coverage back to owned content—so it can be crawled, understood, and referenced over time.
Through this new lens, I’ve added some channels I previously avoided back into the media mix. Podcasts are a good example.
In the early days of this AEO-focused media strategy, I booked our dean and faculty on shows with small audiences and minimal reach; often, I didn’t expect anyone to listen to the final product. The value of every podcast appearance is the transcript.
In a long-form conversation, our dean might naturally repeat core themes and messaging pillars (AI, sustainability) multiple times. That kind of natural keyword density is difficult to replicate elsewhere, and it aligns with how AI systems process information.
Measuring media success in an AI-driven content landscape
This isn’t a perfect strategy, but it’s working right now. And the reality for strategic communicators is that none of this is static. The way AI systems crawl, weight, and summarize information is changing quickly, and my approach is evolving with it.
The way we measure success is changing, too.
Success used to be “we got the hit.” Now, it’s when someone types a question into Google or an AI engine about “best business schools in DC with AI majors” — and Kogod is referenced, or our expertise is summarized, without us even being asked.
That’s not luck. That’s media relations strategy, Spring 2026.
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