AI Visibility Wars: Why Brands Must Control the Media Supply Chain

The way brands get discovered and trusted is undergoing a seismic shift. Over the past year, consumers have migrated en masse from traditional search engines to generative AI platforms (ChatGPT, Gemini, Perplexity, etc.), with one survey showing 58% of people now using AI tools for product and service recommendations (up from 25% in 2023)hbr.org. This “post-search” era means readers increasingly receive answers directly from AI rather than clicking through to websites. As Bellecomm notes, digital discovery is “changing rapidly, with website traffic on a steady decline” as more decisions are influenced by content encountered off-sitebellecommunication.com. In practice, AI-generated answers serve as the new front door to informationbellecommunication.com. When users get in-line summaries instead of links, visibility shifts from your website to where your brand is cited in the sources that AI systems consultbellecommunication.com.

In short, brands can no longer rely solely on SEO rankings or pageviews. AI makes “being mentioned” the critical currency: if a company isn’t present in the trusted sources LLMs draw from, it essentially disappears from the conversationbellecommunication.compassion.digital. Gone are the days when PR meant chasing the front page; today, influence is decided by data pipelines, licensing deals and retrieval algorithms. As Search Engine Journal observes, we’re witnessing “AI-native brand discovery” where brand visibility is determined not by search rankings but by AI recommendation algorithmssearchenginejournal.com. In this environment, earned media coverage – quotes, interviews, thought-leadership – is a more powerful lever than everprsay.prsa.orgpassion.digital. This section explores the new battlefield of AI-driven visibility and how it rewrites the rules of PR.

From Headlines to Pipelines: How AI Rewrites PR’s Rules

Traditional PR has focused on story placement and website traffic. But now platforms like ChatGPT and Google’s AI Mode supply instant answers drawn from “trusted sources across the web”bellecommunication.com. This means that brand visibility no longer depends primarily on SEO ranking, but on where and how the brand is mentioned in authoritative contentbellecommunication.com. As one PR analysis puts it, “we’re entering a new era of online discovery – one where your brand’s visibility depends not on how well your site ranks, but on where and how you’re mentioned across the internet”bellecommunication.com. In practice, AI tools seldom return link lists; they aggregate knowledge. In 2025 Bellecomm found that ChatGPT still drives referral traffic, but to an extremely concentrated set of sources: 64% of all ChatGPT referrals go to just 120 domainsbellecommunication.com. In other words, AI is funneling attention to a few trusted outlets.

This shift has a second-order effect: while raw clicks may drop, the value of AI-driven traffic can be much higher. Semrush data shows that an AI search visitor converts at more than four times the rate of an ordinary Google visitorbellecommunication.com. That makes sense: by the time someone clicks from ChatGPT to a site, they often have most of their questions answered and a clear purchase intent. Still, brands must adapt. Marketers can no longer rely on chasing clicks to owned channels. Instead, they must build authority in the broader AI ecosystem. PR must evolve from earning a quick story mention to earning a permanent spot in AI’s reference universe.

Key implications include:

• No-Click Discovery: A growing proportion of searches yield answers without clicksbellecommunication.com. AI-generated answers serve as the “front door” to content; brand visibility depends on being cited in those answers.

• Citation over Ranking: AI “rewards citations” over keywordsbellecommunication.com. Rather than stuffing web copy with SEO phrases, brands need to be present in credible, contextual content. AI answers favor sources that give clear attributionsbellecommunication.comprsay.prsa.org.

• New Metrics: Traditional metrics (pageviews, clicks) are waning. Brands should track share of voice in AI outputs – how often they’re cited in LLM answers – and metrics like brand mentions and sentimentbellecommunication.comprdaily.com.

• PR at the Helm: As PRSA notes, “search is shifting again, and this time it’s PR’s game to lose”prsay.prsa.org. Earned media and expert commentary are now the core currency of discovery.

In practice, any digital content strategy must now consider AI. Brands should invest in high-quality bylines, thought leadership and media relationships (the “high-signal sources for AI models”bellecommunication.com), as well as structuring owned content for machine readability (using schema markup, etc.bellecommunication.com). Above all, visibility is now about presence within the right context – not optimizing for clicks but for citationbellecommunication.com. As one SEO expert summarizes, “brand visibility is determined not by search rankings but by AI recommendation algorithms with distinct personalities”searchenginejournal.com. The brands best positioned are not those spending big on SEO, but those strategically embedded in the information AI considers authoritativesearchenginejournal.com.

Why LLMs Prefer Licensed, Authoritative Outlets

One of the most striking changes is that large language models increasingly rely on licensed, premium content. AI developers now pay top dollar for access to up‑to‑date, high‑quality news and data. As Contenseo reports, LLM creators “are finding it prudent to negotiate licenses” for news content rather than risk lawsuitscontenseo.comcontenseo.com. In 2023–2024 alone, OpenAI struck deals with the Financial Times, News Corp (WSJ, NY Post), Le Monde, and Prisa Media (Spain’s El País) to feed ChatGPT with current articles and archivescontenseo.com. Google similarly paid roughly $60 million/year to license Reddit content, and even licensed wire feeds from Reuters and APcontenseo.comdigiday.com.

These “deal or no deal” arrangements reflect a broader industry trend. Lawsuits by publishers like The New York Times and groups like the News/Media Alliance have made clear that unlicensed scraping is riskycontenseo.comcontenseo.com. Meanwhile, AI firms have responded by hiring copyright experts and cutting deals. As Contenseo notes, the climate now “favors formalized content access” – those who want the best data are “negotiating licenses” or else facing litigationcontenseo.comcontenseo.com. For example, in early 2024 OpenAI’s News Corp deal reportedly ran to $250 million over 5 yearsdigiday.com, dwarfing anything we saw in past online news partnerships.

Why do AI companies pay so much? Because LLM performance depends critically on the quality of training data. Researchers have observed that models trained on curated datasets include a far higher share of content from premium news sites than models built on raw web scrapescontenseo.com. In one analysis, high-authority publishers (news, scholarly sources, etc.) comprised well under 1% of indiscriminate web crawls, but over 12% of the content in a selective datasetcontenseo.com. In other words, LLM developers deliberately seek out “the highest-quality, independent fact-based content” to make their models bettercontenseo.com. This trend gives established media outlets a built‑in advantage: generative search now “rewards trust” and authoritative reportingadweek.com. As Adweek observes, LLMs are trained on “authoritative sources, named bylines, structured reporting… [giving] premium publishers a built-in advantage”adweek.com.

For brands, this means coverage in licensed publications has outsized impact. OpenAI has explicitly built features to cite and link the articles of partner publishers, redirecting traffic back to thembellecommunication.comdigiday.com. For instance, The Atlantic reports that its OpenAI deal makes its content discoverable in ChatGPT answers, with attribution and links for readers who want the full storydigiday.com. In practice, when an AI answer is generated, it often points back to these paid sources – not to the brand’s own website. As one PR analysis warns, “visibility doesn’t come from the link anymore. It comes from being the source”prsay.prsa.org. In short, brands that lack presence in the paid/partnered media ecosystem risk being invisible to AI.

The Collapse of Traditional Influence Metrics

The AI era also upends classic PR metrics. Gone are the days when site traffic, impressions or simple media mentions were the gold standard. As Bellecomm advises, companies must “adjust performance metrics” for AI search: track brand awareness, share of voice and bespoke “AI visibility scoring” instead of relying on pageviewsbellecommunication.com. Because AI search drives fewer clicks (Google’s new Overviews have caused a 70–80% drop in click-through rates)prsay.prsa.org, website visits will no longer reliably measure success. Instead, the focus shifts to influence: how often does an AI assistant cite your brand when answering a question?

This shift means traditional KPIs like pageviews and backlinks lose value. LLMs reward content richness and authority, not quantity of links. One SEO expert notes that ChatGPT now “rewards citations” rather than keyword-stuffed contentbellecommunication.com. Likewise, PR Daily urges teams to redefine success beyond impressions: “It’s no longer just about impressions or clicks. PR must deliver value through deeper metrics: audience engagement, brand sentiment and long-term trust,” including brand mentions and share of voiceprdaily.com. In practice, agencies are already moving reporting away from vanity numbers. As Bellecomm puts it, “brand building and authority are now essential” to enter the buyer’s consideration set, and so tracking share-of-voice and AI-citation rate will “continue to rise in importance”bellecommunication.com.

New tools are emerging to meet this need. Agencies now recommend monitoring “AI visibility scoring” – a metric quantifying brand appearance in LLM outputsbellecommunication.com. They may sample LLM answers and record how often the client’s name appears (i.e. AI share-of-voice)passion.digital. Specialist platforms (e.g. Kaifootprint) even promise automated AI citation tracking. The goal is to capture the influence LLMs have: every time an LLM parrots a client’s CEO quote or surfaces a story a PR team landed, that is “measurable influence in the wild”prsay.prsa.org. These new metrics help demonstrate value in an AI-driven ecosystem – effectively treating LLMs themselves as distribution channels.

Share of Voice in the Age of AI: Citations, Retrieval, and Attribution

In AI search, “share of voice” means something new: it’s the fraction of AI-generated answers that mention your brand, relative to competitors. Passion Digital explains that unlike traditional share-of-voice (based on media volume), AI share-of-voice is measured by probing LLMs and logging brand names in the answerspassion.digital. This metric matters because AI assistants are reshaping the buyer journey. If customers get their answers from an LLM, the brands included in those answers are the ones being considered.

The analytics world is catching on: marketers urge scanning AI chatbot results as one would check PR coverage. Tools like Brandwatch or Meltwater already crawl text for mentions; now some track mentions in AI outputs. Some PR teams manually query ChatGPT or Google AI Overviews for key industry questions and note which brands are citedpassion.digital. Over time, this indicates a brand’s “presence” in the AI knowledge graph. In practice, we see that LLMs favor content from trade publications, news stories, analyst reports, expert commentary, and even forumsbellecommunication.com. These become the new “anchors” of discoverability: appear there often, and you are more likely to be cited.

Finally, attribution is key. LLMs generally provide citations or links back to their sources. When OpenAI publishes a partner summary, it includes a URL to the original articlebellecommunication.com. That means brands benefit most when their stories appear in the referenced outlets. In short, the old media clippings report must be replaced with an “AI Visibility Dashboard”: tracking how often your content is used by ChatGPT, your citations per query, and your comparative rank in the emerging AI-driven SERPs.

Part II: The Media Supply Chain

Media as Data: How LLMs Consume Journalism

In the AI ecosystem, media itself is being treated as raw data. News articles, white papers, and reports are ingested into LLM training sets or used at query time, and become the foundation of answers. This raises a fundamental question: where does the AI get its facts? The answers are gravitating toward established media. LLMs learn from “news, forums, academic sources, and other publicly available content” in bulkpassion.digital. Chatbots rarely provide random content; instead, they construct responses by sampling from their training and, in many cases, citing known reliable sourcesprsay.prsa.org. As the PRSA’s Gregory Galant notes, generative engines are “not ranking links” but deciding which sources to cite based on “verified, well-structured and authoritative” contentprsay.prsa.org. In practice, that means CNN, The New York Times, Wired, and trade outlets figure prominently in the responses users see. If your brand appears in an NPR story or in a McKinsey report, the LLM may well surface it when relevant.

This treats journalism almost like a raw commodity: brands that own or control data-rich media assets gain direct pipeline access to the AI layer. Some tech companies are effectively assembling knowledge warehouses. OpenAI, for example, reportedly tapped Factiva (Dow Jones archives) for its ChatGPT Enterprise trainingcontenseo.com. Google’s upcoming Generative AI Overviews will draw on its licensed news feed from Reuters and AP. Even Reddit’s user content became valuable enough that Google paid $60M/year for API accesscontenseo.com. Conversely, content that is not licensed or easily scrapeable may be underutilized by these models.

The upshot: Publishers are becoming the gatekeepers of AI content. As AI researchers conclude, “the most authoritative content… is disproportionately valued in making better AI models”contenseo.com. Much of that content is owned by a handful of legacy media and scholarly publishers. In effect, a brand’s story must be told within those trusted channels or it risks falling off the map. If your articles are behind a paywall and not licensed, the AI may only see excerpts or none at all. On the other hand, when your brand is featured in a top outlet (even via press release), those facts become part of the AI’s data fabric.

Licensing Deals and the Rise of Preferential Sources

A direct result of this data-as-asset mindset is the explosion of content licensing agreements between media companies and AI firms. Where LLMs once scraped the web indiscriminately, now they often rely on paid feeds. Contenseo reports that over 2023–2025, “multiple big deals” were struck: OpenAI with FT and News Corp; OpenAI with Le Monde and Prisa; Google with Reuters and Reddit; and morecontenseo.comcontenseo.comprsay.prsa.org. These agreements give the AI systems a legal, high-fidelity content pipeline. In return, publishers get much-needed revenue and the promise of transparency (ChatGPT, for instance, will cite and link to partner articles)bellecommunication.comdigiday.com.

Media executives are eager to assert control. TIME’s COO Mark Howard bluntly says sitting out is “just not something we would consider,” leaving only litigation or negotiationdigitalcontentnext.org. Publishers recognize they have the data AI needs. As one trade group executive puts it, AI companies are “trying to secure more original content” and must turn one-off scrapes into “well-structured… partnerships with strict IP protection and meaningful ongoing revenue”digitalcontentnext.org. Indeed, INMA and DCN research underscores that publishers now need to carefully value their content and negotiate complex attribution and compensation modelsdigitalcontentnext.org. The deals so far hint at various models: upfront licensing fees (OpenAI’s multi-million-dollar dealsdigiday.com), revenue share on AI referrals (Perplexity’s program pays sites based on how many pages are citeddigitalcontentnext.org), and “data-for-analytics” swaps.

These developments signal a new industry structure: A media licensing economy for AI. The era of freely scraped news is ending; the era of “paid news feeds to fuel AI” is beginning. Reuters’ CEO has explicitly said, “these models need to be fed… by the highest-quality, independent fact-based content”contenseo.com. For brands, this means that being published by licensed outlets is no longer just nice to have, it’s a requirement to be seen by AI. A glowing review in a mom-and-pop blog will likely be ignored by the model; a mention in an AP wire or New York Times article will be indexed and served.

Mapping the New Gatekeepers: Who Owns Visibility Today

In this media supply chain, who owns visibility? The primary “gatekeepers” are now AI platform owners (OpenAI/Microsoft, Google, Meta, etc.) plus the major publishers they’ve partnered with. OpenAI, for example, is striking partnerships with a long list of publishers – “dozens,” according to reportscontenseo.com – effectively making them the exclusive sources for ChatGPT. Google’s Gemini will rely on its partner news wire (AP) and crawling of the public web. (Even Wikipedia remains a de facto content source; inaccurate wiki info often propagates into LLM outputsbellecommunication.com.) These relationships create privileged pipelines: users asking Gemini about current events will overwhelmingly see answers drawn from AP and Reuters, not necessarily the open web. Similarly, Perplexity’s answer feed will favor articles from its publishing partners.

Brands thus find themselves at the mercy of these new multiplexed gatekeepers. If a competitor’s news ally has an exclusive AI license, that competitor’s narrative could dominate LLM answers. Moreover, the AI platforms themselves act as gatekeepers: they decide which licensed sources get prominent placement. It’s not enough to have a media strategy in old terms; companies must monitor which media outlets are “in the club” with ChatGPT, Gemini, etc. If your product review only appears in publications that have no AI agreement, the likelihood it will ever surface in an AI search is slim.

Tracking the Shift: Why Monitoring LLM–Media Deals Matters

Given this landscape, savvy brands will track the licensing map. Keeping an eye on deal announcements and lawsuit news is now part of competitive intelligence. Every news of a publisher partnering with an LLM is effectively a shift in the battlefield. For example, the wave of OpenAI–publisher deals in spring 2024 fundamentally changed who gets visibility. PR teams might follow sources like Press Gazette or trade press to see when tech companies announce new content partners.

Why does it matter? Because these deals can confer exclusivity. Contenseo warns that the first AI to license a valuable archive “has little incentive… to renew” and could leave others behinddigiday.com. Google, for instance, licensed subreddits in Feb 2024, so reddit content in ChatGPT may not be covered in models that didn’t get that dealcontenseo.com. Likewise, if OpenAI has paid for News Corp’s journalism, ChatGPT answers will use WSJ stories – potentially overshadowing another brand’s news coverage. In practical terms, brands should watch not just their own media placements, but also who their competitors’ outlets are partnering with. If a rival’s trade publication inks a partnership with Gemini, that trade’s coverage of the rival will be amplified in AI search.

In short, the old PR playbook (know the editors and reporters) is being supplemented with a new one: know the data licensors and who holds the AI keys. The media supply chain is now visible at both the top (which AI assistants have which publishers in their data) and the bottom (which newsrooms a brand is tapping). Failing to track either end means ceding visibility.

Part III: Owning the Pipes

If the media supply chain is the new battlefield, then brands must consider taking control of pieces of it. In many industries, controlling distribution or data sources is a classic strategic move. One recent commentary notes that “vertical integration… has unlocked insane wealth for companies” (Walmart, Amazon, Apple etc.)esteininger.medium.com. In the world of AI and information, a similar logic applies: the more you control the content pipeline, the more you control how the story is told to LLMs. This means moving beyond passive media coverage to owning or co-owning channels of trusted content.

Beyond Earned Media: The Case for Media Ownership

One radical implication is that brands might become media companies themselves. Rather than just courting existing outlets, firms could launch or acquire content platforms that appeal to their audience. The goal would be to feed AI directly: if a brand-owned magazine or trade journal is recognized by the big LLMs, then brand narratives flow unfiltered. For example, a tech hardware company acquiring a specialized industry publication could ensure its product line is regularly featured in articles that LLMs will index.

This idea may seem extreme, but echoes are already emerging. PR Daily suggests brands treat their owned channels “like the media”: develop an editorial voice, publish original research or newsletters, and build a loyal audienceprdaily.com. These owned channels, if made credible and content-rich, can become proxies that feed into the AI “media machine.” For instance, a B2B company might invest heavily in white papers and host them in a machine-readable knowledge base; an LLM scraping that data would then naturally surface the company as an authority.

The strategy parallels what powerhouse tech firms do. Consider how Amazon publishes product data that Google indexes, or how Apple controls hardware, software and retail ecosystemsesteininger.medium.com. In the AI context, content is the new hardware. Controlling media channels – whether by acquiring publishers or building high-authority content networks – amounts to vertical integration of a brand’s communications. It gives structural power: the brand controls the environment in which AI models operate. In effect, it flips the paradigm from “hoping to be quoted” to “ensuring we are the quotation source.”

Buying Influence: How M&A Creates AI Visibility Advantages

Following this logic, companies may even turn to mergers and acquisitions for visibility. Imagine a corporation purchasing a niche media outlet or a series of trade newsletters. It could then seed that platform with content highlighting its own products or values. Those articles would count as earned media (since they’re on a third-party site) but with guaranteed alignment to the brand message. In AI terms, this could translate to being the source whenever an LLM answers industry questions.

There are real-world hints of this strategy. For example, in recent years some brands have acquired content platforms (e.g. tech firms buying trade sites, or consumer brands launching their own magazines). With the AI visibility angle, such moves take on extra significance. By integrating content creation and distribution, a brand effectively ensures it occupies pipeline “real estate” that others must compete for.

Of course, any brand doing this must balance credibility. The media must still be seen as trustworthy; a sponsored platform that feels like propaganda might not earn LLM trust. But there is room for blended models: joint ventures with respected publishers, or exclusive data partnerships (e.g. a carmaker providing its telemetry data to a tech platform). The core idea is clear: becoming or co-owning a content gatekeeper is the new battleground asset.

Structural Power: Shaping Narratives Through Data Pipelines

Control of the pipeline yields influence over narrative. If a brand can ensure its data flows into AI systems first, it can shape the answers. For instance, feeding an LLM with proprietary research or archives can bias the model’s view of the market. Control over metadata and schema also matters: properly structuring content (with schema markup, open data standards, etc.) makes it more easily consumed by AIbellecommunication.com. Brands that excel at this essentially embed their key messages into the AI fabric.

One vivid example: the OpenAI–Factiva partnership will allow ChatGPT to cite decades of Dow Jones news. A brand featured frequently in that archive will effectively have built a lasting “knowledge base” inside the AI. Likewise, if Google’s Gemini uses only vetted data from a select news feed, any story the brand tells elsewhere might be invisible. Hence, by owning or influencing which sources feed which AI, companies can lock in competitive advantage.

Defensive strategies also emerge. Brands will want to prevent competitors from cornering the market on valuable content. That means not only expanding one’s own content, but possibly blocking or diluting others’ pipelines. This could involve exclusive data licensing (ensuring an AI cannot also license the same facts from a neutral party), or influencing platform policies (for example, demanding LLMs cite a variety of sources rather than just partners). In essence, the media supply chain is now as much about who you let into your pipes as who you’re in.

Defensive Strategies: Preventing Competitors from Controlling the Flow

As the saying goes, “get paid or get scraped”contenseo.com. Content owners are increasingly choosing “get paid” – and brands must ask: do we own any of that content or only leave crumbs on others’ plates? A competitor whose narratives end up being the default AI answer has effectively secured the high ground. Therefore, companies may need defensive plays: forging their own media deals, diversifying the sources their brand appears in, and even lobbying for regulatory standards on AI training (so that no single entity can hoard all the voice).

The environment is still evolving, but one thing is clear: the flow of data is power. In the words of one industry analyst, every AI answer represents a citation network, and earning mentions on one platform “often creates the validation needed for another”searchenginejournal.com. Brands that treat the media supply chain as a set of assets – some for offense (exclusive partnerships), some for defense (blocking exclusives of rivals) – will dominate share-of-voice in the LLM era.

Part IV: The PR Agency of the Future

All these changes spell a transformation for PR agencies and comms teams. Gone are the days when PR simply meant “cover this in the papers.” The new PR consultancy must be part newsroom strategist, part data analyst, part dealmaker.

From Media Relations to Media Brokers

First, agencies must expand their remit. Instead of just pitching editors, they will broker media partnerships. This could mean negotiating with publishers on content and attribution: for instance, securing a branded column in a partner media site, or arranging inclusion in an outlet’s AI licensing deal. The ethos shifts from “placement” to “partnership.” PR Daily advises teams to think strategically about how a brand can align with the evolving business models of media outlets (subscriptions, events, content hubs) and even facilitate co-branded initiativesprdaily.com.

In other words, agencies become media brokers. They might connect brands with publishers exploring new revenue streams (e.g. sponsoring a newsletter or contributing expert content). Or they might help shape a brand’s owned media to better interface with AI (using schema or API access so LLMs can easily ingest the data). The core skill remains building relationships and trust – but now those relationships include technologists and licensing officers, not just journalists.

Advisory Models: Guiding Clients on M&A and Licensing

Part of this brokerage role will involve M&A advisory. PR firms may find themselves counseling clients on the value of acquisitions or investments in content companies as visibility tools. They might help price the “data value” of a brand’s content, or steer negotiation teams during licensing talks. Already, research shows many publishers want standardized AI agreements with clear attributiondigitalcontentnext.org; PR advisors could act as intermediaries to craft those terms. If a brand’s owned data (say, user reviews or research reports) could enhance an LLM, agencies might propose licensing arrangements or joint ventures with AI firms.

This is speculative but emerging: an AP story notes that licensed deals can include analytics in exchange for contentdigitalcontentnext.org. PR agencies, with their media savvy and client knowledge, are well-placed to negotiate such arrangements (ensuring fair compensation for high-value journalism, or revenue-sharing models akin to Perplexity’s AI Publishing programdigitalcontentnext.org). In essence, the old PR retainer may expand to include “content licensing strategy” and even recommendations on whether to invest in M&A to secure data pipelines.

Building AI Visibility Dashboards and Metrics

As mentioned, metrics are crucial. Forward-looking agencies are already building “AI visibility dashboards” for clients. These track brand mentions in major LLMs, search snippets from AI overviews, and performance in AI-specific SEO. For instance, an agency might report: “In the past quarter, Client X was cited in 15% of relevant ChatGPT answers, ranking second among competitors.” Sophisticated tools can scrape the outputs of ChatGPT, Google AI, and others to quantify this.

To do this, agencies combine traditional media monitoring with AI-specific tools. As Passion Digital explains, LLMs “analyze text patterns and frequency of mentions to determine relevance”passion.digital. So agencies feed AI answers into text-mining software to log brand mentions and sentiment. They also calculate “AI share-of-voice” as described earlierpassion.digital. All this data then feeds into executive dashboards that replace old impressions charts. The goal is to show clients that PR is moving in lockstep with the technology that shapes public awareness.

Finally, PR measurement must incorporate the ultimate KPI: How does AI portrayal affect business results? If AI-assisted discovery grows, agencies may correlate LLM visibility with lead generation or sales. For example, if ChatGPT increasingly recommends a company’s solution, that should drive inquiries. Any PR activity that raises the likelihood of AI recommendation – whether a byline, a data contribution, or a strategic link – can be evaluated for ROI. This is the new frontier of PR analytics, marrying earned media instincts with data science.

The New Differentiator: Selling Structural Visibility to Clients

Ultimately, the major differentiator for agencies will be the ability to promise and deliver structural visibility. In the AI era, it’s no longer enough to say “we got you 10 press hits.” The pitch has to be “we ensure your brand is part of the AI conversation.” Agencies will sell expertise in navigating licensing deals, building content pipelines, and optimizing the entire media supply chain for AI. As Gregory Galant puts it, this is “PR’s moment”prsay.prsa.org – a chance to prove impact in ways old SEO never could.

Brands, not agencies, are ultimately the clients, so those who can convince CEOs and CMOs of this structural value will win out. The sales pitch might sound like: “We don’t just write a story; we ensure that story lives where LLMs will find it. We blend PR, partnerships and data strategy to lock in AI share-of-voice.” If an agency can measure a boost in AI visibility and tie it to outcomes (new customers who found the brand via an AI chatbot), that becomes their unique value proposition.

In a landscape where “AI visibility” is the new battleground, the agencies that evolve into brokers of media and data deals – that think beyond the page to the pipeline – will thrive. The next-generation PR firm is, in essence, a strategist of algorithmic influence.

Throughout this transformation, one theme is constant: content is king. Only now, “kingdoms” are defined by who controls the newsfeeds and data streams of AI. Brands must adapt from being mere players in the media game to becoming media supply chain architects themselves. By understanding how LLMs rank trust, by striking the right content deals, and by even owning pieces of the distribution network, companies can secure a commanding share of voice. In the emerging AI-driven marketplace of ideas, the winners will be those who control the pipes through which information flows.