Machine Relations: How AI Newsrooms Are Filtering Your Pitches (And What to Do About It)

Your pitch didn't reach a human. It was filtered out by an algorithm that decided—in milliseconds—that your message wasn't relevant, your source wasn't credible, or your angle didn't match the patterns journalists are searching for.

Welcome to machine relations.

Gab Ferree, founder of Off the Record, coined this term during an Axios HQ webinar in early February 2026, and it instantly crystallized what PR professionals have been experiencing for the past 18 months: the relationship between brands and media coverage has fundamentally changed because journalists now rely on AI systems to decide what deserves their attention.

The data validates this shift:

• 96% of publishers now prioritize AI for back-end automation, including pitch filtering, transcription, and source discovery (Ring Publishing analysis of Reuters Institute data)
• 59% of PR practitioners say AI and automation will grow in importance over the next five years—ahead of media relations itself at 35% (Muck Rack's State of PR 2025)
• 45% of PR professionals have already seen AI create something that puts their brand at risk (Axios HQ and Off the Record research)

This isn't a future trend. It's the current operating environment.

If your PR strategy still assumes that well-crafted pitches reach journalists who read them with human judgment, you're operating with a playbook designed for a media ecosystem that no longer exists. The question isn't whether to adapt to machine relations—it's how quickly you can implement the tactics that work when algorithms, not editors, control your first point of contact.

Ready to see where you stand in AI-driven discovery? Get your free visibility audit and find out which AI platforms are citing your brand—and which ones aren't.

What Machine Relations Actually Means: The Three-Way Relationship That Replaced Traditional Media Outreach

Traditional media relations operated as a bilateral relationship: your PR team pitched journalists, who used their editorial judgment to decide what stories to pursue. The dynamics were human—relationships mattered, timing mattered, and understanding a reporter's beat could give you significant advantage.

Machine relations introduces a third party: AI systems that filter, prioritize, and recommend sources before a journalist even sees your pitch.

Here's how the new workflow actually operates in newsrooms:

1. Pitch ingestion: Your email arrives alongside hundreds of others. AI tools scan subject lines, body text, and sender reputation to assign initial relevance scores.
2. Pattern matching: Algorithms compare your pitch content against the journalist's recent coverage, search queries, and editorial focus areas. If patterns don't align, your pitch gets deprioritized or filtered entirely.
3. Source validation: AI systems cross-reference your brand's digital footprint—owned content quality, earned media history, social signals—to determine whether you're a credible source worth surfacing to the reporter.
4. Human review: Only the pitches that clear these algorithmic hurdles reach journalists, who then apply editorial judgment to what's already been pre-filtered by machine logic.

The Associated Press has publicly documented using AI to sort news tips and coverage pitches, automatically populating them into coverage planning systems. The Reuters Institute reports that 2026 will see news organizations increasingly use agentic AI for end-to-end automation of complex workflows—not just individual tasks but entire editorial processes.

This means your pitch isn't just competing with other PR professionals for a journalist's attention. It's competing with algorithmic criteria designed to filter out noise—and if your pitch doesn't meet machine-readable signals of relevance and credibility, it never reaches the human decision-maker.

Why 96% of Newsrooms Are Automating (And What That Means for Your Pitch Success Rate)

The shift to AI-filtered pitches isn't a preference—it's a necessity driven by brutal newsroom economics and information overload.

Consider what journalists are dealing with:

• 72% of PR practitioners blame low response rates from reporters as their biggest challenge (Muck Rack State of PR 2025)
• Nearly half of PR pros pitch more than 20 journalists per campaign, meaning reporters receive exponentially more outreach than they can possibly review manually
• Newsroom headcount continues shrinking while content demands increase, forcing journalists to produce more with less time for source discovery

AI automation solves a real problem for journalists: it gives them a fighting chance to identify genuinely newsworthy sources amid the avalanche of generic pitches.

According to Ring Publishing's analysis of Reuters Institute data across 2023-2025, newsroom AI priorities have evolved rapidly:

• 2024: 56% of publishers considered back-end automation the most important AI application
• 2025: 96% now prioritize automation, with expanded focus on newsgathering functions (73%) and content creation with human oversight (77%)
• 2026 forecast: End-to-end workflow automation becomes standard, with AI handling everything from tip sorting to initial draft generation

The implication for PR teams is stark: if your pitch doesn't contain machine-readable signals that match what AI systems are trained to recognize as newsworthy, you're invisible—not just to that journalist, but to the algorithmic gatekeeper that controls access to that journalist.

The Owned Content Test: Why Journalists Now Use AI to Evaluate Your Brand Before Responding

Here's where machine relations gets even more complex: AI doesn't just filter your pitch content. It evaluates your brand's entire digital footprint as part of the source validation process.

Amanda Coffee, a communications leader named to PRWeek's 40 under 40 in 2024, explained the journalist perspective in Stacker's Earned Media Edge report: if reporters don't see rich, credible content like original articles or substantive LinkedIn posts when they research a source, "they move on."

But here's what's changed: journalists aren't manually checking your company blog anymore. AI tools are scanning it automatically as part of pitch evaluation.

This creates a new quality bar for owned content. Steve Kearns, Senior Director of Customer Evangelism and Community Marketing at Jasper AI, offered this benchmark in the same report: "Every piece of owned content should probably be interesting enough to turn into earned media, because that would mean it is newsworthy and industry-relevant."

That's not aspirational advice. It's the functional requirement for owned content to pass AI validation when journalists use automation to vet sources.

If your company blog is a bulletin board for press releases and product announcements, AI systems flag you as a promotional source rather than a credible expert. If your executives don't publish original analysis or thought leadership, AI tools can't identify subject matter expertise when matching sources to story angles.

The brands winning in machine relations treat owned media like actual newsrooms. Zillow operates an in-house editorial team producing market analysis. Salesforce publishes research that journalists cite as source material. These aren't vanity projects—they're strategic investments in the trust signals AI systems use to validate source credibility.

Our analysis of AI citation patterns found that 95% of AI citations come from earned media, with the highest-performing brands maintaining both strong earned media presence and high-quality owned content that demonstrates expertise.

The 45% Problem: When AI Creates Brand Risk Instead of Visibility

Machine relations doesn't just change how you earn coverage—it introduces entirely new risk vectors that traditional PR training didn't prepare teams to handle.

The statistic that should alarm every communications leader: 45% of PR professionals have seen AI create something that puts their brand at risk (Axios HQ and Off the Record research).

That's not hypothetical future concern. That's nearly half of PR practitioners dealing with active consequences of AI systems that:

• Cite brands incorrectly or attribute statements executives never made
• Associate brands with topics or controversies that damage rather than build authority
• Generate "coverage" summaries that misrepresent product capabilities or market positioning
• Compound errors by propagating misinformation across multiple AI platforms once indexed

The compounding effect is what makes AI-generated brand risk fundamentally different from traditional media corrections. When a journalist publishes an error, you can request a correction and the original article gets updated. When an AI model indexes incorrect information from a low-quality source, that misinformation can get:

1. Cited across multiple generative AI platforms (ChatGPT, Perplexity, Gemini, Claude)
2. Incorporated into training data that influences future responses
3. Amplified by other AI systems that treat the initial error as validated source material
4. Spread to AI-assisted research tools that journalists themselves use to validate sources

This creates a cascade where fixing one error requires correcting the source, monitoring for AI citation updates, and potentially engaging with multiple platform providers—a vastly more complex remediation process than traditional media relations ever required.

Gab Ferree's framing becomes critical here: "It's on the comms professionals to learn the patterns [of AI] and then take action on them."

You can't relationship-manage your way out of algorithmic misattribution. You need to understand how AI systems gather information, which sources they trust, and how to flood the information environment with high-quality signals that give algorithms correct inputs when evaluating your brand.

The Multi-Layered Playbook: Different AI Models, Different Source Preferences, Different Strategies

Here's where machine relations strategy gets even more complex: different AI platforms have different source preferences and citation patterns.

Stacker's research documented what many PR teams are discovering through trial and error:

• Claude shows strong preference for authoritative sources like the CDC for health queries and government data for policy topics
• ChatGPT heavily cites wire services (AP News, Reuters) and major publications with structured data
• Perplexity builds citation chains from academic journals, industry reports, and trade publications
• Gemini (Google's AI) integrates signals from traditional search ranking factors plus Knowledge Graph entities

This means your earned media strategy now requires answers to questions PR teams never had to consider:

• Which AI platforms are most relevant to our target buyers' research behavior?
• Which publications have partnerships or citation patterns with those specific platforms?
• How do we create owned content that meets both journalist standards and AI trust signals?
• What's the feedback loop between earned placements and AI visibility across different engines?

Research indicates that 89% of links cited by AI originate from earned media sources—but not all earned media performs equally across different AI platforms.

Local and trade publications matter more in machine relations than traditional media value hierarchies would suggest. Rob Powell, Director of News Product and Head of Local at Stacker, pointed out in their report that local and trade press often serve as the bridge between owned content and mainstream coverage—and they're frequently cited by AI models looking for specialized expertise on specific topics.

If your earned media strategy ignores local and trade publications because you're focused exclusively on tier-one outlets, you're missing the publications that provide trust signals AI models use to validate broader expertise.

How 57% of Communicators Are Using AI to Craft Press Releases (And Why That Creates New Quality Challenges)

The machine relations equation has another variable: PR teams themselves are now using AI to create the content journalists' AI tools are filtering. (See also: Pr retainer trap brands overpay underperformance)

According to PR Newswire's 2025 Global State of the Press Release Report, 57% of communications professionals now use AI to craft some press release components. (See also: Ai search traffic worth 10x google traffic)

This creates an interesting dynamic: AI-generated pitches are being filtered by AI-powered newsroom systems, with human journalists reviewing only what survives the machine-to-machine interaction.

The quality implications matter. PR Newswire's report noted that despite predictions of decline, the press release is proving as essential as ever in 2025 as a credible source specifically for AI engines. But the bar for what constitutes "credible" has shifted: (See also: Reddit perplexity geo strategy)

• 91% of communicators reuse press release content on other channels, meaning releases need to function as multi-platform source material
• 9 in 10 companies now always or sometimes include multimedia, recognizing that AI systems evaluate content richness beyond just text
• 57% of comms professionals report that press releases have increased visibility for their brand or products—but only when distribution reaches the right publications that AI engines cite

The risk is that AI-generated pitches default to patterns that other AI systems recognize as promotional rather than newsworthy. Generic language, lack of specific data points, missing expert quotes—these are red flags that both journalists and AI filtering systems have learned to identify.

The solution isn't to avoid AI assistance in content creation. It's to understand that machine relations requires higher quality thresholds precisely because both creation and evaluation are partially automated.

The New Media Ecosystem Loop: How Earned Media, Owned Content, and AI Citations Reinforce Each Other

Machine relations isn't just about getting past AI filters to reach journalists. It's about understanding how earned media, owned content, and AI citations create compounding loops.

Stacker identified what they call the "New Media Ecosystem Loop":

1. Journalists use AI to filter pitches and discover sources, meaning your owned content needs to signal expertise that AI systems can validate
2. Earned media placements provide third-party validation that AI platforms cite when answering user queries about your market category
3. AI citations drive discovery, leading new journalists to consider you as a source for future stories
4. The cycle compounds as fresh earned media creates new citation opportunities while owned content demonstrates ongoing expertise

This loop explains why recent research found that distributing content across trusted news outlets increases AI citation rates by 325%—from 8% baseline visibility on brand domains to 34% when syndicated versions appear on third-party publisher sites.

The compounding effect means machine relations strategy can't be purely tactical. You need:

• Owned content that demonstrates expertise AI systems can validate when vetting sources
• Earned media distribution that gets your insights onto publication sites AI platforms trust and cite
• Measurement systems that track how both owned and earned content influence AI visibility across different platforms
• Feedback loops that show which topics, publications, and content formats drive the highest citation rates

Kevin Fowler, Head of SEO at Stacker, emphasized that brands can no longer separate PR and SEO strategies—they're now the same strategy, just optimized for different types of information retrieval systems.

What to Do Now: The Machine Relations Playbook for 2026

Adapting to machine relations doesn't require abandoning traditional PR skills—it requires expanding them to account for algorithmic gatekeepers and AI-powered discovery.

1. Audit Your Owned Content for Machine-Readable Expertise Signals

AI systems evaluating your brand need to identify subject matter expertise quickly. That means:

• Original research and data: First-party data, surveys, market analysis—content AI can't generate and journalists want to cite
• Executive thought leadership: Published perspectives on industry trends, not promotional product content
• Structured data and schema markup: Help AI systems understand what expertise you're claiming and in what categories
• Regular publishing cadence: Freshness signals matter for both journalists' AI tools and AI platform citation algorithms

Steve Kearns' test is useful: if your owned content isn't interesting enough to become earned media on its own merit, it won't pass AI validation when journalists' systems evaluate your source credibility.

2. Map Your Earned Media Strategy to AI Platform Source Preferences

Different AI engines cite different publication types. Research which platforms your buyers use, then prioritize publications those engines trust:

• Wire services and major publications for ChatGPT and Google AI Overviews
• Trade and industry publications for Perplexity and specialized AI search tools
• Authoritative sources (CDC, academic journals, government data) for Claude and research-focused queries
• Local and regional news for location-specific expertise and category authority

The goal isn't just coverage—it's coverage in publications that feed the AI platforms your buyers query.

3. Build Citation Monitoring Into Your PR Measurement

Traditional PR metrics (impressions, AVE, referral traffic) don't capture AI citation impact. New measurement should include:

• AI platform visibility tracking: Which engines cite your brand for category-relevant queries
• Earned vs. owned citation analysis: How much visibility comes from third-party publications vs. your domain
• Source credibility signals: What types of content (research, executive quotes, data) drive the highest citation rates
• Recency impact: How quickly fresh earned media translates to AI citations (highest impact within 7 days)

AI visibility audits can baseline where you currently appear across different platforms and identify gaps where competitors are cited but you're not.

4. Train Your Team on Algorithmic Source Validation

PR professionals need to understand not just editorial judgment but algorithmic pattern matching. That includes:

• How AI systems evaluate source credibility: Third-party validation, content freshness, expertise signals, citation networks
• What machine-readable patterns look like: Specific data points, quotable soundbites, clear category positioning
• How to create content that passes both journalist and AI filters: Newsworthy angles with structured data AI can extract
• Risk mitigation strategies: Monitoring for misattribution, correcting errors before they compound across platforms

Gab Ferree's point bears repeating: "It's on the comms professionals to learn the patterns [of AI] and then take action on them." This is new literacy for PR, similar to how SEO became essential knowledge for digital marketers.

5. Invest in Distribution That Reaches AI-Cited Publications

Getting your content syndicated to publications AI engines trust matters more than volume of pickups. Focus on:

• Earned distribution partnerships: Platforms like Stacker that place content on sites AI systems cite
• Trade publication relationships: Category-specific outlets that validate expertise for niche queries
• Data journalism collaborations: Research partnerships that turn your data into cited source material
• Local news presence: Regional coverage that establishes geographic and sector credibility

The 325% citation lift from earned distribution isn't magic—it's algorithmic preference for third-party validation over owned content claims.

Timing matters more in machine relations than traditional PR. Research shows the highest citation rate occurs within 7 days of publication, with more than 50% of AI citations coming from content published in the last 12 months. This means your distribution strategy needs velocity—getting placements live quickly while topics are fresh matters more than perfecting every angle.

The brands winning machine relations aren't necessarily producing more content. They're producing better-distributed content that reaches publications AI systems already trust, creating the citation signals that compound across platforms faster than competitors can match.

Frequently Asked Questions

What does 'machine relations' mean for PR?

Machine relations signifies that AI systems now filter and prioritize pitches before journalists see them, creating a three-way relationship between PR teams, AI tools, and journalists. This shift, identified by Gab Ferree of Off the Record, demands new tactics focused on algorithmic visibility.

How do AI newsrooms filter PR pitches?

AI tools scan subject lines and body text for relevance, compare pitch content against a journalist's coverage patterns, and validate source credibility based on digital footprint. Pitches that don't align with these criteria are often deprioritized or filtered out entirely.

Why is AI becoming more important in PR?

AI and automation are growing in importance because they help publishers manage the overwhelming volume of information and automate back-end processes. According to Muck Rack's 2025 State of PR report, 59% of PR practitioners believe AI's importance will increase over the next five years.

What are the risks of AI in PR?

AI can create brand risks by misinterpreting information, amplifying negative content, or excluding credible sources based on flawed algorithms. Research from Axios HQ and Off the Record indicates that 45% of PR professionals have already witnessed AI-related risks to their brand.

How can I improve my AI visibility?

Improving AI visibility requires optimizing your brand's digital footprint, crafting pitches that align with journalist's AI-driven search patterns, and monitoring which AI platforms are citing your brand. Conducting a visibility audit can reveal which AI systems recognize your brand and which ones don't.

The Brands That Win Machine Relations Understand: This Isn't About Technology, It's About Trust at Scale

Machine relations might sound like a technical challenge—optimize for algorithms, understand AI source preferences, track citation patterns—but fundamentally it's about the same thing traditional PR always prioritized: building credible third-party validation that influences how audiences perceive your brand.

What changed is that "audiences" now includes AI systems making split-second decisions about which sources to surface for billions of queries. And "third-party validation" now needs to exist in machine-readable formats across publications those systems trust.

The data makes the stakes clear:

• 96% of publishers are using AI to automate newsroom workflows—your pitch either passes algorithmic filters or never reaches journalists
• 89% of AI citations come from earned media—visibility depends on coverage in the right publications
• 59% of PR professionals already recognize AI as their top priority over the next five years—late adopters will find themselves shut out of both journalist attention and AI discovery

Stacker framed their research as achieving "word of mouth at scale," and that's precisely the right goal. Traditional word of mouth built reputation through individual conversations. Machine relations builds reputation through patterns AI systems recognize as credible—at the scale of billions of queries across every major discovery platform.

The brands that adapt fastest aren't the ones with the biggest PR budgets. They're the ones that understand machine relations isn't replacing human relationships—it's adding an algorithmic layer that determines whether those human relationships ever get the chance to form.

Your competitors are learning these patterns. The question is whether you'll adapt your strategy before they own the citations AI engines return when buyers research your category.

Stop guessing where you rank in AI discovery. Get your visibility audit and see which platforms cite your brand, which competitors are winning machine relations, and what gaps you need to close.