Named expert quotes are one of the few structural signals that reliably lifts AI citation share across every major engine, and the lift is measurable. Digital Bloom's 2025 analysis of 680 million citations put the numbers on it: quotations lifted AI visibility 37% on Perplexity, 22% on ChatGPT. Muck Rack's Generative Pulse now actively tracks which named journalists appear most often in LLM outputs, which tells you the systems are treating attribution as a first-class signal:
🔎 Meet the Top AI-Cited Journalists of 2025 🔎 As AI becomes a bigger part of how people discover brands, certain journalists are shaping the way large language models interpret today’s media landscape. Using insights from Generative Pulse by Muck Rack, we identified the
Muck Rack@muckrackDec 15, 2025If LLMs are attributing content to named humans, and named humans lift citation share by 22-37%, then the mechanic is obvious: put more named humans on your pages. The rest of this guide is how to actually do that in 2026: where to find them, how to interview them so the quotes are citable, and how to attribute them so AI engines can parse the attribution.
Key Takeaways
- Quotations lift AI citations 37% on Perplexity and 22% cross-engine when paired with statistics, per Digital Bloom's 2025 AI Citation LLM Visibility Report (680M+ citations analyzed).
- HARO is back, kind of. Cision's Connectively shut down December 9, 2024; Featured.com acquired HARO from Cision on April 15, 2025 and relaunched HARO 2.0 April 22, 2025. Qwoted, Source of Sources, and direct LinkedIn outreach are the practical alternatives.
- LinkedIn is where experts live in the citation index. Semrush's 89K-URL study found LinkedIn is the #2 most-cited source across AI engines; 51% of citations come from members with under 10K followers.
quotedPersonisn't a real schema.org property. UseArticle.contributoror theQuotationtype withcreatorinstead.- Freshness matters more than most guides admit. Interviews aged over 12 months drop to 9% of top citation share per Salespeak's freshness data; interviewing 6 months before publishing is a trap.
Why Named Experts Move the Citation Needle
The mechanism runs through EEAT. Google added Experience to the Quality Rater Guidelines in December 2022, and every subsequent update has weighted lived experience and credentialed expertise more heavily. AI engines built retrieval layers on the same principle: attributed content, especially attribution to a named person with verifiable credentials, gets weighted higher than anonymous content.
Digital Bloom's decomposition of the signal weights (extrapolated from 680M citation events): expert quotes contribute roughly 41% of the credibility signal, statistics contribute 30%, inline citations 30%. The specific 37% Perplexity lift on quotations is measured directly. The cross-engine 22% is when statistics and quotes appear together, which is the pattern that wins across every study.
Lennart Nacke, a full professor at Waterloo's HCI Games Institute, captured why the shift is durable rather than temporary:
POV: AI-native expertise in 2026. Every few weeks there’s a new prompt pack, a LinkedIn tactic that takes the edge off your judgement, or the inevitable “Experts are obsolete” take. But let's be real for a second: 1. Serious buyers will always search for visible expertise 2. https://t.co/KYJOCbzcFJ
Prof Lennart Nacke, PhD@acagamicMay 16, 2026
Serious buyers still search for visible expertise. A LinkedIn tactic might take the edge off judgment for a quarter, but the human byline and the named voice compound over years. AI engines aren't different from serious buyers on this. They're looking for the same signal.
What changes is the delivery surface. What doesn't change is that a quote from someone who has actually done the work weighs and cites differently than a paraphrased statement from a company blog.
Where to Find Experts in 2026
The 2025 HARO reset changed the ecosystem more than most people realize. The current stack:
HARO 2.0 (via Featured.com). Free for both journalists and sources. Three-times-daily email digests. Relaunched April 22, 2025 after Featured acquired the HARO brand from Cision the week prior. Success rates run 3-15% for quality pitches, versus 1-2% during legacy HARO's spam-flooded final years.
Qwoted. 130,000+ vetted experts, 250,000+ reporters. Filled the gap during the Connectively shutdown and is now a top-tier alternative to HARO 2.0. Free tier available.
Source of Sources (SOS). Peter Shankman, HARO's original founder, launched this after selling the original HARO to Cision. Newsletter-style, focused on quality over volume.
Help a B2B Writer. B2B-focused source discovery, smaller than HARO but higher signal-to-noise for enterprise topics.
LinkedIn direct outreach. Meltwater's 9.5M-citation study found LinkedIn ranks #2 most-cited source across AI engines; 51% of citations come from members with under 10K followers. Experts on LinkedIn are already in the citation index. A DM with "I'm writing about X, would love a 15-minute call to include your perspective" converts at 20-40% for cold outreach.
Academic experts via ORCID. ORCID is the free persistent researcher identifier that serves as the strongest external anchor in academic Person schema. Finding an ORCID means AI engines can verify the entity graph.
The mix that works for most SaaS content: HARO 2.0 for spam-filtered breadth, LinkedIn DM for targeted depth, Qwoted as backup.
The Interview Format That Actually Works
Fifteen to thirty minutes is the sweet spot. Longer and the expert regrets it; shorter and you don't get enough quotable material. The mechanics:
Pre-share 8-12 questions 24-48 hours before the call. Experts arrive with actual answers rather than improvised takes. Higher quote density per minute.
Use extractable-answer question shapes. "What do teams get wrong about X?" beats "Tell me about X." "What's the specific metric that separates good from great?" beats "How do you measure success?" Directional prompts produce quotable answers; open-ended prompts produce discussion.
Record video, transcribe with a human review. Rev's human-reviewed transcription hits about 99% accuracy. Otter.ai in meeting conditions runs 85-90%. That difference is the difference between citation-grade quotes and quotes AI engines skip because the transcription mangled the numbers.
Target 4-6 quotable claims per interview. Each needs a specific number, outcome, or framework paired with the expert's name and role. AirOps recommends placing 2-4 quotes per article (opening for authority, mid-section for evidence, closing for framework).
End with a follow-up. "Is there anything I didn't ask that would matter?" The best quote often comes here.
A r/copywriting thread from July 2026 captured the working reality of this better than most content guides:
Getting Input Isn't Easy or Simple
If you're an experienced writer, you already know that clients very rarely give you anything substantial to include in their content. They might give you a title or a keyword phrase or maybe a content brief. But what you really need is quot...
The consensus in the thread: clients almost never volunteer the quotable specifics you need. You have to interview them (audio, video, or a structured written form) and turn them into the SME whose name and expertise the content is built around. "Quote the dentist" was one commenter's summary. Same principle for SaaS founders, engineers, product leads, academics. Get on the call and mine for the specifics.
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Question Design That Produces Citable Quotes
Sidebar for a second: the difference between an interview that yields quotes and one that yields general commentary is entirely in the question design.
Framework questions. "What's the framework you use to decide X?" produces a numbered framework that structures beautifully in an article.
Number-anchor questions. "What percentage of Y actually [outcome]?" Elicits quantitative claims.
Contrarian questions. "What common advice about X do you think is wrong?" Elicits opinion plus reasoning.
War-story questions. "When did you learn X the hard way?" Elicits a specific anecdote with named entities and numbers.
Named-comparison questions. "How does A compare to B in practice?" Elicits comparison quotes that lift comparison-page citations.
Avoid open-ended questions ("Tell me about your approach to X"), which produce paragraph-length answers that don't extract cleanly. Avoid yes/no questions, which produce single-word answers with no quotable material. Every question should be designed to produce a 1-3 sentence answer with a specific number, outcome, or framework.
Attribution and Schema
The schema layer is where most content programs mess up the attribution. Two options that actually work:
Option 1: Article.contributor. Schema.org's Article type supports contributor as "A secondary contributor to the CreativeWork or Event." Point it at a Person node with name, jobTitle, worksFor, sameAs, and knowsAbout. This is the cleanest form for a single primary quoted expert.
Option 2: Quotation type. Schema.org's Quotation type has creator (the Person quoted) and can nest inside Article.mentions. Use this when you have multiple quoted experts and want each attribution structured independently.
Do not use quotedPerson. It shows up in some AEO guides but it's not a real schema.org property. Google's Rich Results Test will not throw an error (schema.org permits unknown properties), but AI engines can't verify it against the schema vocabulary. Stick to the two options above.
The Person node itself needs the full treatment: name, url (to their landing page or LinkedIn), image, jobTitle, worksFor, sameAs (LinkedIn, Wikidata QID if available, employer bio, personal site), knowsAbout (the specific topics they're expert on). Every one of these is a disambiguation signal AI engines cross-reference.
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Distribution Amplification
Fair point though: the expert interview is only half the equation. The expert sharing it to their network is the other half.
Ahrefs' analysis of a billion data points found that distributing content through third-party outlets produces a 239% median lift in AI search visibility, with some cases reaching 325%. Brand web mentions correlate 0.664 with AI Overview visibility versus 0.218 for backlinks. When your quoted expert shares the piece to their LinkedIn network, X account, or newsletter, every mention is a brand-web-mention signal that compounds the citation lift the quote itself produced.
Practical amplification checklist:
- Send the expert a plain-text version of their quote 48 hours before publication for approval.
- Send a LinkedIn share-ready caption at publish time.
- Send a personal thank-you post-publish with a specific number ("This piece got X views/citations, and Y people quoted your framework").
- Add the expert to your author schema
sameAsif they're contributing enough content to warrant a landing page.
The Foundation Marketing case study of Atlan is a strong example of the mechanic in action. Atlan won 35% of AI citations in the observability guide vertical and their guide became the second most-cited page in the study, with 26 citations across four engines. The pattern: named experts, structured attribution, multi-channel distribution.
The Traps
Five failure modes to avoid.
Cherry-picked experts with no independent authority. If your only quoted "expert" is your own CEO, the piece reads as marketing and AI engines discount it. Every serious piece needs at least one external voice.
Self-promotional guest quotes. If the expert's contribution reads as a promo for their company, it's not a citable quote. Push back in the interview for the specific claim, not the sales pitch.
Uncredited quotes. "According to industry experts..." isn't attributed. Every quote needs a name, role, and (ideally) a link to their profile.
Poorly transcribed audio-only interviews. Otter.ai at 85-90% accuracy garbles specific numbers, which is the exact material AI engines cite. Human review or Rev's paid tier for anything you're publishing.
Interviewing 6 months before publishing. Content freshness data is unforgiving. Salespeak's analysis found content aged 6-12 months earns 22% of top citations; past one year drops to 9%. If you interviewed someone six months ago, refresh the quote or re-interview. AI engines don't reward stale expertise.
What to Ship This Week
If you're starting an expert-interview program, the sequence:
- Pick one piece from your Q3 calendar where an expert quote would materially lift citation share. Comparison, buyer's guide, and use-case content benefit most.
- Identify 3 potential experts. One from your ICP network, one from HARO 2.0 or Qwoted, one via LinkedIn direct outreach.
- Send the outreach. Warm intro > cold DM > HARO pitch. 15-30 minutes, 8-12 pre-shared questions.
- Interview, transcribe with human review, extract 4-6 citable quotes.
- Publish with
Article.contributorschema and a Person node includingsameAsandknowsAbout. - Send the expert the plain-text quote for approval, then the LinkedIn share caption at publish.
- Track citation share on the piece over the next 60 days.
Our content engine handles the expert-attribution schema and interview-scheduling workflow, and our AI visibility tracking samples the buyer prompts where your quoted experts should be surfacing. If you want the underlying schema pattern for the Person node, our author schema deep dive covers the entity graph in detail.
Expert interviews are one of the highest-payoff patterns in AEO content that most SaaS teams underuse. The quote data lifts citation share directly. The distribution multiplies it. The schema makes it durable. Do all three and every piece with a named quote compounds harder than the same piece without one.
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