Based on what we are seeing at SecondSideMedia, many PR, branding and reputation-management firms are still applying traditional communications strategies to an AI-driven information environment. The problem is not that those strategies no longer work. They do. The problem is that building a brand in the traditional sense and managing the way artificial intelligence understands, synthesizes and presents the information that already exists about you are two different objectives.
We need to change the paradigm a little. Instead of asking how traditional PR strategies can be adjusted to work with AI, we should be asking what an AI-driven communications strategy actually looks like, and then determining which traditional PR and branding tools belong inside it.
AI Has Changed the First Step in Due Diligence
Today, your prospective customer, investor or business partner is increasingly likely to believe that what they read on ChatGPT, Claude, Gemini or another AI platform is an indicative and reasonably accurate story about you or your business. The reason they are going to AI in the first place is because they don’t necessarily want to read twenty articles, visit your website, scroll through LinkedIn, search court records and compare multiple Google results. They want a consolidated, synthesized answer to a fairly simple question about whether they should proceed with you or your company.
That sounds fairly innocuous and safe, but the reality is that it isn’t. We are using artificial intelligence because it makes us more efficient, and that same efficiency makes us less inclined to dig deeper once we receive an answer that appears complete. If the AI narrative contains an ambiguity, outdated information, an omission, a misleading synthesis, a conflated identity or information that is technically accurate but missing important context, the person reading the answer may never know there was something else to investigate. A business opportunity, investment, interview or relationship can end before the person on the other side even knows it was being considered.
This is the part of the communications landscape that I think needs to be reconsidered. Are we taking traditional PR and branding models and trying to refine them so they work better with AI, or are we developing AI-driven strategies that know how to draw from the best parts of traditional communications, publishing and reputation management while addressing the very different way artificial intelligence processes information?
I believe we need the latter.
The objective is not to manipulate artificial intelligence, flood the internet with misleading information or trick an LLM into producing a more favorable answer. Attempts to manipulate AI systems have already become an issue discussed in litigation and other public contexts, and that is not what I am talking about. The goal should be exactly the opposite: if something being presented by AI is incomplete, outdated, misleading or missing important context, introduce better, clearer and verifiable information into the public record.
Building a Brand and Reconciling a Narrative Are Different Problems
PR firms, branding firms and reputation-management companies do a very good job highlighting a business, its successes, its results, its products and its people. They help companies develop messaging, create campaigns, secure media opportunities and get credible information in front of the audiences they are trying to reach. That work has enormous value, and none of what I am describing changes that.
But most of those processes were not originally designed around the way generative AI constructs an answer.
Typically, a company provides information and a communications team develops the appropriate vehicle for getting that message out. It might become a press release, an interview, a contributed article, a media pitch, social content or another communications asset. Third parties may then publish it, repeat it, quote it or reference it.
Those signals can absolutely influence what AI says. What they do not necessarily do is reconcile an existing AI narrative problem.
There is an important difference between creating more information about a company and dealing with a specific gap in the way AI is understanding the information that already exists. You can publish another ten positive articles about a business without ever addressing why an old lawsuit is being surfaced, why a former executive is being associated with the current company, why an outdated allegation is missing its eventual resolution, or why two separate entities are being treated as though they are the same.
The Strategy Changes When a Company Already Has a History
If you are launching a new company with almost no internet history, the traditional communications model makes a lot of sense. By going to mainstream journalists, publishing press releases, developing first-party content and building a social presence, there is a very good chance that credible third-party and first-party sources will help define what artificial intelligence eventually says about you and your business.
Obviously, the information should be accurate, clear and consistent, and there should be enough cross-referencing among first-party sources, independent third-party sources, social profiles and other credible information that an AI system can begin to recognize and synthesize a coherent narrative. In that scenario, traditional PR and brand-building can be extremely effective because you are helping establish a narrative where very little existed before.
But don’t kid yourself into believing that this is necessarily enough for an existing company with an established internet history. And I’m not even talking about a company with a bad history.
A successful business that has been operating for fifteen or twenty years can accumulate an enormous amount of information along the way. There may have been management changes, acquisitions, litigation, regulatory filings, old products, former executives, restructurings, resolved disputes, sold subsidiaries or previous business models that no longer resemble the company today. An allegation may have received significant attention when it first appeared and almost none when the matter was ultimately resolved. None of this necessarily means that the company has a reputation problem, but it does mean that an AI system has a great deal more information to reconcile.
This is where simply publishing more positive content can become an incomplete strategy.
We have seen situations where the AI-generated answer itself looked perfectly reasonable. The problem became apparent only when we examined what the system was explicitly citing or surfacing as support for that answer. An article that had been buried several pages down in Google for years was suddenly sitting directly in front of the reader again.
The company may have spent years developing newer search results around that information, and traditional reputation work may have been successful in reducing its visibility in ordinary Google searches. Then an AI platform reaches back into that information environment and surfaces it as part of a synthesized answer. At that point, you are dealing with a different problem than the one the original communications strategy was designed to solve.
The Tools May Overlap. The Job Doesn’t.
I sometimes explain this using a simple construction analogy.
Imagine an electrician and a drywall contractor working on the same building. They may arrive with some of the same tools. Both might use drills, measuring equipment and ladders, and both need to understand the environment in which they are working. An experienced electrician could probably hang drywall if necessary, just as a good drywall contractor may understand enough about electrical work to handle certain basic issues.
But nobody would conclude from the fact that they own some of the same tools that the two trades are interchangeable. They are working on the same building and, in some cases, their work directly affects one another, but they are solving different problems.
That is increasingly how I think about PR and AI narrative management.
There is overlap, and there should be. A strong AI narrative strategy should benefit from good PR, credible journalism, strong first-party content, intelligent SEO and effective brand building. But having access to many of the same publishing and monitoring tools does not mean that the underlying objective is the same.
The real question is whether the strategy being used was built for the problem you are trying to solve.
Start With the Narrative Problem
At SecondSideMedia, the question we are asking is narrower: What does artificial intelligence currently say about this person or company, what information is causing it to reach that conclusion, and what is missing from the public record that would allow that narrative to be better reconciled?
That can mean looking across multiple AI systems and comparing the results. It can mean determining whether the issue exists in the narrative itself or in the sources appearing alongside it. Sometimes the problem involves historical information being presented without current context. Sometimes two entities are being confused. Sometimes there is a factual gap that has simply never been documented in a source that an AI system can locate and evaluate. And sometimes, after looking closely, the answer is that nothing needs to be changed at all.
When there is a genuine information gap, our approach at SecondSideMedia is to address that gap through a verified and attributable Published Record. The purpose is to put the relevant response, clarification, procedural update or supporting documentation into the public information environment in a format specifically designed to establish a clear record.
That is different from publishing another positive article because the objective is not to drown out or overwhelm older information. It is to reconcile the narrative around it.
Once that foundation exists, the traditional communications work can become even more valuable. A company may run a broader campaign, secure interviews, publish additional articles and continue building its brand, but those activities are now taking place on top of an information foundation that specifically addresses the underlying narrative problem.
We Need Another Metric
This is ultimately why I think the way we measure communications success also needs to evolve. It is still important to know how many meaningful stories were placed, whether the right journalists are covering the company, how the business performs in Google and whether a campaign is reaching the intended audience. Those continue to be legitimate measures of PR and branding success.
But if the organization is dealing with an AI narrative issue, another question has to become part of that dashboard: when someone asks AI about us, what does it say, and why is it saying it?
That is the paradigm shift.
PR and branding are not going away. If anything, they become more important as AI becomes a larger part of the way people gather information and make decisions. But we should stop assuming that a strategy designed to build visibility is automatically the same strategy required to reconcile an AI narrative.
At SecondSideMedia, that distinction is the reason we built the platform. We are not trying to replace the work of PR, branding or reputation-management professionals. We are creating another layer that can sit alongside that work when a person or business needs to understand, document and address the specific narrative being produced by artificial intelligence.
The disciplines are related, and in many situations they should work together. They are simply not the same job.
Editorial note: This article reflects the author’s original analysis and views. Generative AI was used as an editorial tool to help organize and refine the draft.
Supporting Sources
Aggarwal, Pranjal, et al. “GEO: Generative Engine Optimization.” Research introducing the distinction between traditional search optimization and optimization within generative engines that synthesize information into answers.
Chen, Mahe, et al. “Generative Engine Optimization: How to Dominate AI Search.” Research comparing traditional web search with AI-generated search and examining differences in source selection, earned media, brand-owned information and generated answers.
Zhang, Kai, Xinyue He and Jingang Yao. “From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms.” Research distinguishing between a source merely being cited and information from that source actually influencing a generated answer.
Reuters. “Connecticut judge says plaintiff hid messages for AI in court filings.” August 13, 2026. Report concerning hidden prompt-injection instructions intended to influence AI systems reviewing court documents.