In the communications industry, the press release has always been a fundamental tactic in the asset tool chest. You wrote it once, distributed it once, and hoped it did three things at the same time: inform journalists, signal credibility, and, at least in the last decade+ help with search. This no longer holds. Today, announcements are being read by two entirely different audiences, and only one of them emails you back. The Zen Media team is now the leader in this space and the only agency in the world with unlimited word count GenAI Wire Releases available at scale for brands. two entirely different audiences Zen Media The other audience is artificial intelligence. The other audience is artificial intelligence. Large language models now explain, summarize, compare, and recommend companies long before a human reaches out. And they do not read press releases the way journalists do. This is why the press release has quietly split into two distinct assets, built for two completely different jobs. two distinct assets two completely different jobs That shift was the focus of a recent episode of The Visibility Equation, a LinkedIn Live series examining how AI is reshaping communications and visibility. That shift was the focus of a recent episode of The Visibility Equation, a LinkedIn Live series examining how AI is reshaping communications and visibility. The Visibility Equation The Visibility Equation As Kropelin explained, the wire itself has not lost relevance. What has changed is how its content is used. Releases that are clear, factual, well-structured, and consistent are no longer just indexed for search. They are actively parsed, summarized, and reused by AI systems that now control discovery. In practice, this has created two parallel pathways: One release written for journalists and investors
Another written explicitly to inform answer engines One release written for journalists and investors Another written explicitly to inform answer engines Calling both of them “press releases” may feel convenient, but it obscures a structural difference that now shapes how companies are explained before a human ever engages. before The Questions AI Systems Are Already Being Asked About You AI systems are not passively reading content. They are actively answering questions — millions of them — every day. Here are the actual questions we see AI systems answering about brands right now: actual questions What does this company actually do?
Is this product legitimate or risky?
How is this different from competitors?
Is this suitable for enterprise use?
What industries does this serve?
Is this company credible?
What are the known limitations or risks? What does this company actually do? Is this product legitimate or risky? How is this different from competitors? Is this suitable for enterprise use? What industries does this serve? Is this company credible? What are the known limitations or risks? If you have not intentionally answered these questions in a machine-readable way, AI systems will answer them anyway. Why Traditional Press Releases Fail These Questions Traditional press releases were never designed to answer questions like these. They are: Time-bound
Announcement-driven
Optimized for journalists, not interpretation
Written in inverted-pyramid format Time-bound Announcement-driven Optimized for journalists, not interpretation Written in inverted-pyramid format AI systems, by contrast, look for: Complete explanations
Clear scope boundaries
Structured answers they can safely reuse
Consistency across sources Complete explanations Clear scope boundaries Structured answers they can safely reuse Consistency across sources This mismatch is why brands often see vague, incomplete, or incorrect AI summaries — even when they have “great press.” What AI-Focused Press Releases Actually Look Like (With Real Examples) AI-focused releases — often referred to as GenAI Wire Releases or AI Notices — are not announcements. They are answers. Below are the most common use cases, with the exact questions, example headlines, and why they work. GenAI Wire Releases AI Notices answers exact questions example headlines why they work 1. “What Does This Company Actually Do?” When this is needed: When AI answers are vague, misclassified, or overly generic. Question AI is answering Question AI is answering What does [Company Name] do? What does [Company Name] do? Example headline: How Does [Company Name] Help [Specific Customer Type] Solve [Specific Problem]? What the release includes What the release includes Plain-language explanation of the business
Clear definition of products and services
Explicit “what we do” and “what we do not do” boundaries
Links to authoritative owned content Plain-language explanation of the business Clear definition of products and services Explicit “what we do” and “what we do not do” boundaries Links to authoritative owned content Why this works: It gives AI systems a safe, canonical explanation instead of forcing them to assemble one from fragments. safe, canonical explanation 2. “Is This Platform Secure / Legitimate / Safe?” When this is needed: When trust, risk, or compliance is part of the buying decision. Question AI is answering Question AI is answering Is [Product] secure for enterprise use? Is [Product] secure for enterprise use? Example headline: Is [Product or Platform] Secure for Enterprise Use? What the release includes What the release includes Direct, factual answers
Verifiable proof points (certifications, controls, audits)
Defined scope and limitations
Links to security or compliance documentation Direct, factual answers Verifiable proof points (certifications, controls, audits) Defined scope and limitations Links to security or compliance documentation Why this works:  AI systems strongly favor specific, defensible claims over marketing language. specific, defensible claims 3. “How Is This Different From Competitors?” When this is needed” When AI summaries flatten differentiation or lump brands together. Question AI is answering Question AI is answering How does [Company] compare to [Category or Competitors]? How does [Company] compare to [Category or Competitors]? Example headline: How [Company Name] Differs From Traditional [Category] Solutions What the release includes What the release includes Clear positioning
Differentiation explained in plain language
What the company is designed for — and what it is not Clear positioning Differentiation explained in plain language What the company is designed for — and what it is not Why this works” It narrows how AI safely compares your brand instead of guessing. 4. “What Proof Exists That This Actually Works?” When this is needed: When credibility or validation is required. Question AI is answering Question AI is answering Does [Company] actually work? Does [Company] actually work? Example headline”  What Did [Company] Learn From [X] Customers Using [Solution]? What the release includes What the release includes Key findings from case studies or reports
Context around outcomes
FAQ-style insights tied to buyer questions Key findings from case studies or reports Context around outcomes FAQ-style insights tied to buyer questions Why this works:  It turns expensive reports and case studies into reusable validation assets inside AI answers. reusable validation assets 5. “What Is This Program / Partnership / Initiative?” When this is needed: When something matters long-term but is not “newsworthy.” Question AI is answering Question AI is answering What is [Program or Partnership], and why does it matter? What is [Program or Partnership], and why does it matter? Example headline” What Is [Program Name], and Why Does It Matter? What the release includes What the release includes Context-first explanation
Who it is for
Why it exists
How it fits into the broader strategy Context-first explanation Who it is for Why it exists How it fits into the broader strategy Why this works:  AI systems still need context — even when journalists do not. Why These Releases Perform Differently: The SOAR Framework On The Visibility Equation, Kropelin outlined four signals AI systems consistently favor — often summarized as SOAR: The Visibility Equation SOAR Structure: Clear headings, summaries, and FAQ-style blocks
Originality: Specific facts over generic filler
Authority: First-party data, named experts, linked proof points
Recency: Freshness and consistency over one-off bursts Structure: Clear headings, summaries, and FAQ-style blocks Structure Originality: Specific facts over generic filler Originality Authority: First-party data, named experts, linked proof points Authority Recency: Freshness and consistency over one-off bursts Recency As he noted during the discussion: “AI systems favor complete, structured answers over short announcements. If you don’t give them something safe and authoritative to reuse, they will assemble answers from whatever already exists.” “AI systems favor complete, structured answers over short announcements. If you don’t give them something safe and authoritative to reuse, they will assemble answers from whatever already exists.” A Critical Advantage Many Teams Miss: Language AI prompts are not English-only. Adding translation to AI-focused releases allows brands to surface as authoritative answers when the same questions are asked in other languages. For global organizations, this dramatically expands AI visibility and reduces dependence on a single linguistic narrative. This Is Not a Standalone Tactic AI-focused releases work best when paired with: Owned, canonical content
Narrative consistency
Ongoing PR and thought leadership
A broader AI visibility strategy Owned, canonical content Narrative consistency Ongoing PR and thought leadership A broader AI visibility strategy On their own, they help. As part of a system, they compound. The Question Brands Now Have to Answer AI systems are already speaking on your behalf. They are explaining what your company does, who it is for, how credible it is, and whether it should even be considered, often before a human ever reaches your website, your press coverage, or your sales team. At this point, the question is no longer whether AI is part of your communications ecosystem. whether It is whether you are comfortable letting those explanations be assembled from fragments, assumptions, and outdated signals—or whether you will deliberately publish the answers you want reused. One path is familiar: announcements written for journalists, measured in coverage, and evaluated after the fact. The other path is quieter but far more influential: structured, machine-readable explanations that shape how your company is understood when no one is watching. Brands that recognize this split early will not just earn attention. They will earn accuracy. And in an era where machines increasingly mediate trust, accuracy is the advantage that compounds.

This story contains new, firsthand information uncovered by the writer.

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