Gretel Going, President at Channel V Media – Interview Series

Gretel Going, President at Channel V Media, is a seasoned communications leader and entrepreneur who has spent nearly two decades building and scaling a global public relations firm known for its strategic storytelling and media impact. As Founder and President since 2008, she has guided the agency’s growth while actively contributing to the broader industry through memberships in the International Public Relations Network and the Forbes Agency Council. Her career began in editorial and marketings, including positions at The Rosen Group, the Custom Publishing Council, and various publishing outlets, giving her a strong foundation in content, brand positioning, and media relations that continues to shape her leadership approach today.

Channel V Media is a global public relations and communications agency that focuses on helping high-growth companies and established brands build visibility through storytelling and strategic media engagement. The firm is known for blending traditional PR with modern digital strategies, offering services that span media relations, content development, thought leadership, and integrated communications campaigns. With a strong emphasis on measurable results and narrative clarity, Channel V Media works across industries such as technology, finance, and consumer brands, positioning clients to stand out in competitive markets while adapting to the evolving media landscape.

You founded Channel V Media in 2008 and built it into a firm working with fast-growing tech companies. What early signals made you realize that narrative control would become a competitive advantage in the AI era?

We’ve been controlling narratives for clients through PR long before AI made doing so existential. When a company wants to put out a news announcement, for instance, we think beyond the face-value media coverage we can generate and consider how we can use an otherwise basic business story as a vehicle to shape the company’s market perception. Everything a company does is an opportunity to shape how they’re seen and how their audiences understand theirect value. What has been missing is a way for all of these micro-signals to come together into a cohesive narrative that anyone can see at once, and now AI has stepped in to create that visibility layer.

One specific early signal that confirmed this type of narrative control would become a competitive advantage came from our own client work. In 2016, we were among the first AI companies to break into mainstream business media. At the time, the business press had barely touched on artificial intelligence and didn’t know how to talk about it. (That seems crazy now, but just 10 years ago, we were getting responses from media outlets saying, “AI? You mean like the Terminator?”) We realized then that we weren’t just publicizing what our client did–we were taking AI mainstream and educating the media along the way. The narrative we built through earned media became the default way people understood not only our client but also AI’s potential for businesses overall.

That experience crystallized something I’ve suspected since founding the firm. When companies are deliberate about defining their narratives and laser-focused on reinforcing them in everything they do, they can shift markets and influence human audiences. What changed with AI is that these narratives now have a compounding effect. The stories you place, the language you use, the data points you put out there through any type of marketing–it all gets ingested by AI engines and becomes the raw material for how millions of people learn about you going forward. Rather than disappearing or fading out as new efforts are deployed, these things become the permanent infrastructure of your brand’s identity in the eyes of both humans and machines.

If a company hasn’t built a clear, deliberate narrative for itself and gotten it validated through credible third-party sources, then AI is assembling one for them from whatever fragments it can find. And that’s a scenario no serious company should accept.

Your recent research suggests AI engines rely heavily on earned media when constructing brand narratives. What does that reveal about how these systems actually evaluate credibility? 

In our “Tapping Into the Attention Economy” report, we added a section at the end where, instead of asking marketers for their opinions on where AI engines get their information, we asked the AI enginesectly. We found that traditional media sourcesectly account for anywhere from 40 – 60% (which we conservatively averaged down to 43%) of what AI engines draw on when constructing brand narratives. Company-owned content contributes about 22%, and user-generated and community content about 13%.

We then went a step further and examined how PR indirectly supports these other sources, and found that PR could realistically be responsible for shaping as much as 75% of a company’s narratives. The good news is that this is within their control.

This tells us a lot about how AI evaluates credibility. These models have essentially built their own trust hierarchy, which mirrors what communicators have always known: third-party validation carries more weight than self-promotion. When a respected journalist at TechCrunch or the Wall Street Journal writes about a company, there is an implicit editorial vetting process behind it. The journalist chose to cover it. An editor approved it. That chain of credibility is exactly the kind of signal AI engines are weighing.

AI is essentially doing at scale what a diligent buyer, investor, or partner does naturally: it’s asking, “Who is saying this about the company, and how trustworthy is the source?” Companies that have a deep footprint in earned media benefit enormously from this because they’ve been building credibility with these systems—often without even realizing it. The companies that have relied primarily on their own content and paid channels are discovering that AI gives that content less narrative authority.

When an AI model generates a response about a company, what are the most important signals it is weighing behind the scenes? 

Across the AI engine audits we’ve conducted for our clients, there are a few signals that consistently determine how a company gets represented.

AI engines are disproportionately influenced by coverage in authoritative, established and editorially vetted publications. A single placement in Fortune or Forbes, for instance, carries more narrative weight than dozens of self-published blog posts. This is one reason earned media accounts for such a large share of AI-generated narratives.

AI engines also look for narrative consistency as they synthesize information from across the internet. If your messaging is fragmented—say, different positioning in your press releases than on your website, different language in your thought leadership than in your media interviews—AI will reflect that confusion. The companies that show up most clearly in AI are the ones with disciplined, consistent narratives across every touchpoint.

Vague positioning like “industry-leading” or “best-in-class” similarly gives AI nothing to work with. Instead, the AI engines latch onto concrete, quantified claims. If you’ve published a data report showing that your platform processes $1 billion a month, or that your technology reduced retail shrink by 30%, those data points become anchored in the AI’s understanding of your company. And finally, since AI engines are constantly updating, they value recency and frequency. A company that produces a steady cadence of earned media coverage and thought leadership will see its narrative evolve and strengthen. A company that had a great press cycle two years ago but has gone quiet will find that AI’s understanding of them is either stale or has been overtaken by competitors who are actively putting new signals into the market.

We are seeing AI become a discovery layer for both B2B and consumer decisions. How does this shift the of PR compared to traditional search and social media? 

It’s funny because a lot of people are just now starting to see PR’s impact on traditional SEO. Now we need to educate them on PR’s in AI visibility.

With traditional search, PR’s is to generate links. You get covered in a publication, and that article appears in Google (GOOGL ) results, which the reader still has to click and evaluate on their own. With AI, PR influences the narrative the reader consumes. Instead of a list of links to sort through, the reader gets a fully formed answer about who your company is, what it does and why it matters.

In the search era, a single great article was a win because it created a discoverable artifact. In the AI era, every piece of earned media is a data point that feeds an evolving, composite narrative about your company. PR’s in this is feeding the system that shapes how millions of people understand your company before they ever visit your website.

Fifty-two percent of marketers in our Attention Economy study are already aware of this shift, saying PRectly influences how AI engines present their companies. This new AI discovery layer is giving PR a compounding effect that search doesn’t and social influences to a far lesser degree. A story placed today doesn’t just reach the audience who reads it, it shapes the AI narrative that reaches every audience from that point forward.

Many companies still focus heavily on owned content. Why is that approach less effective when it comes to influencing AI-generated narratives? 

Owned content is still important and contributes to AI’s understanding of your company. But it functions more as supporting evidence than undisputed source material. The companies that are best positioned in AI engines are the ones where the third-party narrative and the owned narrative tell a consistent story. When a journalist’s article uses the same positioning language that appears on your website, for instance, it creates a reinforcing signal that AI engines pick up on and amplify.

This is because AI engines have a credibility filter, and owned content sits lower in the hierarchy than earned media (AI engines themselves say they get an average of 22% of content from owned media vs. 43% from earned media). That’s a significant gap.

The reason is pretty intuitive when you think about it from the AI’s perspective. An AI model’s job is to give the user the most credible, useful answer. If a company’s website says “We’re the market leader in cloud security,” that’s a claim. If TechCrunch writes “This company has emerged as a leading force in cloud security,” that’s a signal. AI engines are sophisticated enough to distinguish between the two.

The risk for companies that rely too heavily on owned content is that AI engines may represent them less authoritatively–or worse, let competitors with stronger earned media footprints define the narrative for the entire category.

From your work with global clients, what are the most common mistakes brands make when trying to influence what AI says about them? 

The most common mistake among both international and domestic companies is thinking about AI visibility as a separate, standalone initiative rather than something they need to embed into everything they’re already doing. Some companies hear about influencing AI Visibility and think they need to launch a dedicated “AI SEO” project. In reality, the most effective approach is to make AI influence a lens through which you run your entire communications and PR program.

Another mistake is putting inconsistent or contradictory messaging into the market. This isn’t to say you can’t have different messages for different audiences, but your core positioning should be consistent. For instance, you don’t want to be out there identifying as a “marketing technology with 100 enterprise users” in one place, and a “workplace technology with 1,000 SMB users” in another. In the past, this type of behavior didn’t matter as much because there wasn’t a single entity synthesizing all of it. Now AI engines are seeing every press release, every article, every executive quote, every piece of owned content—and assembling it into a single narrative. If the pieces don’t align, or worse, contradict each other, the result is a confused or diluted narrative that doesn’t serve anyone.

Going quiet will also have a magnified effect on AI engines. We see companies do this after a strong product launch or funding round. But AI narratives are not static, so companies can’t be either. If companies are not actively putting new content into the market, their narratives will either become stale or get overwritten by competitors. We’ve run AI audits where a company’s current positioning bears almost no resemblance to how AI engines actually describe them. Their work within their highest value industry sector is virtually invisible, and legacy incumbents with dated technology look more innovative than them. All of this because they haven’t generated new earned media in over a year.

One final mistake worth noting is ignoring niche and trade publications. Many companies fixate on securing coverage only in the biggest outlets. But AI engines prioritize topical authority and source specificity over raw audience size. We’ve seen cases where a placement in a niche trade publication with only 10,000 monthly visitors had a greater impact on a client’s AI narrative than a mention in a much larger general-interest outlet with 5 million monthly visitors. This is because the niche publication carried more signal weight in that specific category. A smaller outlet with a concentrated, authoritative readership in your space can actually move the AI needle more than a brief mention in the New York Times.

Channel V Media emphasizes a narrative-first approach. How does that translate into measurable impact in an environment where AI is synthesizing information rather than simply ranking it? 

AI has made the narrative-first approach that’s always been core to our PR philosophy not just strategically sound, but measurably provable. The narrative-first approach works in the AI era because these models are fundamentally narrative engines. They don’t rank pages; they synthesize stories.

Take, for instance, when we launch a PR campaign for a client. We always design it with specific narrative outcomes in mind. The goal is never just “get coverage.” It’s “shift how this company is understood.” We can now measure that shiftectly by auditing AI engines before and after a campaign. We query ChatGPT, Claude, Perplexity and Gemini (or others if a client requests it) with the same prompts, compare the responses, and track exactly how the narrative has shifted.

In one campaign, we were able to achieve 86% narrative alignment–between the media coverage we secured for them and the way the 4 major AI engines were talking about them–within a single quarter. Specific phrases we crafted were adopted verbatim by the AI platforms and are available to millions of users. In another case, we were able to get the AI engines to reflect our earned media narratives in their responses in under two weeks. These aren’t abstract brand metrics. They’re concrete, observable changes in how we’re able to influence how AI engines describe our clients to millions of people.

If you’ve been disciplined about putting a coherent, consistent narrative into the market through credible sources, AI will faithfully reflect that narrative. If you haven’t, AI will assemble something on its own, and it’s anybody’s guess what the result will be.

How should companies think about AI discoverability differently from SEO, especially as large language models become the primary interface for information retrieval?

SEO is about increasing the chances of being found, while influencing your AI visibility increases the chances you’ll be understood once they do.

With SEO, you’re optimizing your website to rank for specific keywords so people can find you and then decide what they think. With AI discoverability, you’re shaping the narrative that AI engines tell about you before anyone ever clicks. The user has already formed an impression by the time they reach your website, assuming they visit it at all. In many cases, the AI’s answer is the final stop. That’s a fundamentally different environment.

How search and AI engines each get “fed” is also different. SEO is primarily about optimizing a property you own (your website) and creating backlinks to it. AI discoverability, by contrast, is about optimizing your entire public footprint. It’s a far more outward-facing effort that includes everything from earned media coverage, executive thought leadership and data reports to press releases, analyst mentions, customer reviews and community discussions. Your website is one input among many, and often not the most influential one.

Companies that have strong SEO but weak earned media footprints will increasingly find themselves in an awkward position. They rank well on Google but get overlooked or poorly positioned in AI-generated answers. As more decision-makers, especially in B2B and technology, use AI engines as their primary research tool, that gap becomes a real business problem. The companies that will thrive are the ones investing in both, but recognizing that AI visibility requires a narrative strategy that extends far beyond what they do on their own website.

Do you see a future where companies actively optimize for AI outputs in the same way they optimized for Google rankings? What would that ecosystem look like?

This is already happening. We’re doing it at Channel V Media today, and the companies that recognize it early are gaining a significant first-mover advantage.

But the ecosystem looks very different from SEO. With Google, the optimization was largely technical (keywords, metadata, backlinks, site speed, etc.). With AI engines, the optimization is fundamentally shaping a narrative by influencing the sources that AI draws from to tell your story. That requires a very different skill set and navigating a far broader landscape. It requires strategic communications, credible third-party validation and an understanding of how earned media flows into AI training data and retrieval systems.

I do think we’ll see an emerging discipline that sits at the intersection of PR, content strategy and AI. We call that AI Visibility. Companies will regularly audit what AI engines say about them and their competitors. They’ll track how specific PR initiatives shift their AI narrative. They’ll design communications strategies with dual impact in mind: reaching human audiences now and shaping the AI-generated narrative that reaches every audience from that point forward.

The firms and in-house teams that are not already thinking about this will find themselves playing catch-up. And the longer they wait, the harder it becomes, because competitors that are actively shaping AI narratives right now are building a compounding advantage. Everything they do is training AI to tell their story instead of yours.

Looking ahead, how do you see the relationship between PR, data, and AI evolving over the next three to five years as models become more autonomous in shaping brand perception?

Three things are going to converge in a way that fundamentally changes how companies think about their market presence.

We’re already seeing PR and AI visibility become inseparable. Right now, many companies still think of AI as a tool they use or a topic they should be aligning with. Within three years, AI will be the primary environment through which their brand is perceived. Every earned media strategy will need to be designed with AI engines as a core audience and AI response influence as a core objective. The firms that treat PR and AI discoverability as separate workstreams will be outperformed by those that have already integrated them.

Content will continue to be a currency, but proprietary data will become the most valuable asset in communications. Right now, every company has access to the same tools and can produce endless amounts of content. But not every company can produce original data and research, which is one of the reasons they’re quickly becoming a primary source of differentiation. AI engines love specific, citable data because it gives them something concrete to reference. Companies that invest in publishing proprietary research (think: market reports, benchmark data, original surveys) will have a structural advantage in AI-driven narratives.

Measurement will finally become more tangible in PR and communications. We can already track how individual PR placements shift AI narratives—which articles get cited, which phrases get adopted and how quickly new coverage is reflected in AI responses. Over the next few years, this will become standard practice. Companies will measure PR not just in terms of impressions or clip counts they don’t know what to do with. Rather, they’ll measure AI narrative share—how much of what AI says about your category is shaped by your content versus your competitors’.

Traditional means of controlling brand perception will be replaced by AI that autonomously creates companies’ narratives. You can’t buy your way into an AI narrative the way you can buy a Google ad. You have to earn it through credible coverage, consistent messaging and genuine authority. The result is a world that rewards the fundamentals of good PR, and where companies that aren’t actively shaping their AI narrative are leaving what the masses think about them to chance.

Thank you for the great interview, readers who wish to learn more should visit Channel V Media.