Why press releases are no longer enough
For decades, the press release has been the central tool of corporate communication. Concise, controlled, distributed to the right journalists at the right time—it was the way companies spoke up about what mattered.
Today, the press release has not lost its usefulness. But it has lost its centrality.
Not because journalists no longer read it—they do. But because a new intermediary has inserted itself between the company and its stakeholders, one that the press release, in its current form, is not equipped to handle: the generative artificial intelligence system.
How do AI systems process a press release?
A well-crafted press release contains everything a journalist looks for: the fact, the context, the quote, the supporting data. It is designed to be read by a human exercising editorial judgment.
An AI system processes it differently.
It aggregates it with dozens of other sources on the same topic. It evaluates it for consistency with previous content from the same sender. It compares it with what other sources say about the same subject. It weighs it based on the authoritativeness of its distribution—how many publications pick it up, with what prominence, with what amplification.
A press release published on a distribution platform, not picked up by any media outlet, and not linked to any external evidence, produces a weak signal in the algorithmic synthesis. It often does not enter into the representation the system builds of the company.
The problem is not the press release itself. It's that the press release alone—without an ecosystem—is no longer sufficient.
What produces strong signals for AI systems?
To understand what works, it is useful to understand what AI systems look for when building a representation of an organization.
They look for coherence: information that is mutually confirmed across different sources. They look for verifiability: data, numbers, and concrete evidence that can be compared. They look for distribution: signals that appear across multiple channels over time, not just a single publication. They look for authoritativeness: third-party and independent sources carry more weight than proprietary content.
In practical terms: an article in an industry publication that cites verifiable data, is echoed by an independent analysis, and is consistent with what appears on the corporate website and the company's LinkedIn channels, produces a strong signal. A press release distributed on a wire service, not picked up by any media outlet, and not linked to any external evidence, produces a weak signal.
What tools now complement the press release?
It's not about abandoning the press release. It's about building an ecosystem of signals around it that amplify it and make it algorithmically readable.
The tools that complement the press release in an AI Reputation strategy are:
Structured earned media: articles, interviews, and in-depth pieces in authoritative publications that cite verifiable data and are picked up by other sources. Not a one-off news story—but news that generates editorial conversation.
Long-form content structured for AEO: articles, white papers, and guides that directly answer the questions stakeholders ask AI systems. With interrogative headings, explicit FAQ sections, and highlighted data.
Wikipedia presence: for many organizations, the Wikipedia page is the primary source from which AI systems draw basic information. Keeping it updated, accurate, and well-structured is an action with a direct impact on AI Reputation.
Original data and research: publishing original data—studies, surveys, analyses—that other sources cite is one of the most effective ways to build algorithmic authoritativeness. AI systems prioritize sources cited by other sources.
Distribution over time: temporal consistency matters as much as thematic consistency. Signals distributed over time produce a more stable representation than those concentrated in a single moment.
How does editorial planning change in the era of AI systems?
Traditional editorial planning followed an event-based logic: the product launch, the annual report, a crisis to manage. Communication was episodic—intense at key moments, silent in between.
In the era of AI systems, the logic changes.
AI systems read information ecosystems distributed over time. A company that communicates only during key events produces a discontinuous ecosystem—with strong signals at some moments and silence at others. This translates into an unstable algorithmic representation.
Editorial planning must become continuous and systemic: not just during newsworthy moments, but as a constant effort to reinforce the dimensions one wants to emerge in the algorithmic representation. Not every piece of content needs to be a press release. But every piece of content must contribute to the ecosystem's coherence.
What does this mean for media relations professionals?
The role of media relations is not disappearing—it is being enriched with a new dimension.
Beyond the traditional goal—obtaining quality media coverage—media relations in the era of AI systems has a second objective: to build an ecosystem of authoritative citations that feed the organization's algorithmic representation.
This means that the quality of the coverage—the structure of the article, the data cited, the presence of verifiable links—becomes as important as the quantity. A well-structured, data-rich article in an authoritative publication produces a stronger algorithmic signal than ten superficial articles in minor outlets.
In the Aurelian Lab AI Reputation Framework, media relations are part of what the company communicates: they are high-trust reputational levers that amplify the corporate narrative for both human stakeholders and artificial intelligence systems.
Learn more about how corporate communication is changing → Corporate communication in the age of LLMs See how CSRD connects to AI Reputation → ESG, CSRD, and AI: how sustainability communication is changing Understand what AI systems look for in content → How LLMs describe your company Return to the complete model → Governing Corporate Reputation in the AI era: the Aurelian Lab pillar Want an assessment of your information ecosystem? → Contact me