Media Relations Are Becoming "Machine Relations" - And Most Brands Aren't Ready
AI now decides what gets seen and cited. Earned media is the signal it trusts for freshness and credibility, but many PR teams are still running pre-AI playbooks.
If you want visibility inside AI answers, treat earned media like a distribution engine, not a side project. That means fewer generic press releases and more real coverage, citations, and signals machines can verify.
Why earned media now drives AI visibility
Large models weigh authority, recency, and consensus across known sources. Some systems favor specific outlets and institutions. For example, ChatGPT frequently cites outlets such as AP News, while other models lean on government or health agencies like the CDC.
Your job: map those preferences, publish content that is easy to cite, and earn mentions from the outlets those models already trust.
Word of mouth at scale, not press release blasts
"Media relations are becoming machine relations," said Gab Ferree, Founder of Off the Record, in a webinar with Axios HQ. "It's on the comms professionals to learn the patterns [of AI] and then take action on them."
Translation: your program should produce consistent, verifiable coverage that surfaces in both human reporting and machine answers.
The New Media Ecosystem Loop
Reporters are stretched. Many use AI to shortlist sources. If your owned channels don't show depth - original articles, data, or expert posts - you get skipped. "They move on," said Amanda Coffee (PRWeek 40 Under 40 2024) in Stacker's report.
Owned media is no longer a bulletin board. It's your primary proof of expertise. As Steve Kearns of Jasper AI puts it: "Every piece of owned content should probably be interesting enough to turn into earned media."
The risk of getting it wrong
According to research by Axios HQ and Off the Record, 45% of PR pros have seen AI create something that puts brands at risk. Weak sources and fuzzy claims now scale faster than ever.
Tighten sourcing, make approvals faster, and put guardrails in writing. Assume anything unclear will be summarized by a model, then quoted back to your audience.
The 2026 earned media playbook
- Map model-to-media ties: List the outlets each model tends to cite (e.g., AP for general news, CDC for health). Prioritize those in pitching and content planning.
- Publish newsworthy owned content: Original data, expert analysis, and clear POVs. Host it in a newsroom and mirror key takes on LinkedIn for reporter discovery.
- Pitch for citations, not mentions: Local outlets validate relevance. Trade outlets validate depth. Build packages that make citing your numbers or quotes the obvious choice.
- Make content machine-readable: Clear headlines, publish dates, bylines with credentials, contact info, sources cited, and a concise summary up top.
- Create reporter- and AI-ready assets: One-paragraph abstract, a TL;DR with 3-5 bullets, a FAQ, and a downloadable dataset or methodology.
- Prove expertise: Author bios with background, links to past coverage, and third-party validations. Keep everything up to date.
- Instrument measurement: Track media pickups, citation quality, and presence inside AI answers for priority queries. Add "AI share of answer" to your dashboard.
- Set guardrails: Fact-checking rules, embargo handling, crisis red lines, and rapid corrections. Publish corrections visibly.
What to measure weekly
- AI answer presence: Are you referenced in top answers for your core topics?
- Citation quality: Do answers quote your owned content, a local outlet, or a top-tier source?
- Outlet mix: Balance of local, trade, and national. Fill gaps based on model preferences.
- Freshness: Percent of content updated in the last 90 days.
- Engagement on owned: Time on page, scroll depth, and inbound journalist referrals.
- Link authority: New referring domains and anchor text that reflects your key claims.
Team and workflow updates
- Machine relations lead: Owns model-source mapping and AI answer monitoring.
- Owned newsroom editor: Turns data and POVs into publishable, citable content.
- Data journalist: Builds repeatable studies and methodologies reporters can trust.
- Distribution ops: Coordinates pitches, syndication, and local/trade sequencing.
Q1-Q2 2026 checklist
- Document the top 25 questions you want AI to answer with your brand in the mix.
- Audit which outlets models cite for those topics; prioritize pitch lists accordingly.
- Stand up a lightweight newsroom page with bios, media contacts, and a style guide.
- Ship one original data study and one expert commentary piece per month.
- Create TL;DRs, FAQs, and method notes for every major post.
- Pitch local and trade first to build credible consensus, then go national.
- Track AI answer citations weekly; log shifts after each win.
- Set a correction protocol and publish it.
- Refresh top-performing owned pieces every 60-90 days.
- Review messaging against risk scenarios with legal and execs.
Get the report and go deeper
Stacker's whitepaper breaks down the research worth following, the roles of local and trade press in AI, and includes insights from leaders across PR, comms, and AI - plus an in-depth 2026 Earned Media Checklist.
Download the report today.
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