Adding a quote from a named expert is one of the rare page changes that raises AI citation share on every major engine, by a measurable amount. Digital Bloom's 2025 AI Citation LLM Visibility Report went through 680 million citations and found quotations lifted AI visibility 37% on Perplexity and 22% on ChatGPT. Muck Rack's Generative Pulse now tracks which named journalists turn up most often in LLM outputs, which only makes sense if the models treat 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, 2025Follow the logic through: LLMs attribute content to named people, named people lift citation share by 22-37%, so get more named people onto your pages. This guide covers how to do that in 2026, from finding the experts to the markup that lets AI engines parse who said what.
Why named experts move the citation needle
EEAT is where this starts. Google added Experience to the Quality Rater Guidelines in December 2022, and each update since has leaned harder on lived experience and credentialed expertise. The AI engines built their retrieval layers on the same idea, so content attributed to someone, particularly a named person whose credentials can be checked, gets more weight than anonymous copy.
Digital Bloom also broke the signal down, extrapolating weights from those 680M citation events. Expert quotes make up roughly 41% of the credibility signal, with statistics and inline citations at 30% each. The 37% Perplexity lift for quotations was measured directly, while the 22% cross-engine figure applies when statistics and quotes appear together, the pairing that keeps winning across studies.
Lennart Nacke, a full professor at Waterloo's HCI Games Institute, made the case that this shift is here to stay:
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
I think he's right, because serious buyers still go looking for visible expertise. A LinkedIn tactic might get around their judgment for a quarter, but a human byline and a named voice compound for years, and AI engines want the same signal those buyers do. The surface people read you on will keep changing, while a quote from someone who has done the work will still weigh more, and get cited differently, than a company blog's paraphrase.
Where to find experts in 2026
The 2025 HARO shake-up changed this market more than most noticed. Cision had rebranded HARO as Connectively, and Connectively shut down on December 9, 2024. Then Featured.com acquired HARO from Cision on April 15, 2025 and relaunched it as HARO 2.0 a week later, on April 22. Quality pitches there land 3-15% of the time now, compared with 1-2% in legacy HARO's last years, when spam flooded everything.
| Source | What you get | Worth knowing |
|---|---|---|
| HARO 2.0 (Featured.com) | Free for journalists and sources | Email digests three times a day |
| Qwoted | 130,000+ vetted experts, 250,000+ reporters | Filled the Connectively gap, now a top-tier HARO 2.0 alternative; free tier |
| Source of Sources (SOS) | Newsletter-style, quality over volume | Launched by Peter Shankman, HARO's original founder, after he sold HARO to Cision |
| Help a B2B Writer | B2B-focused source discovery | Smaller than HARO, better signal-to-noise on enterprise topics |
LinkedIn is the one I'd push hardest. Semrush's 89K-URL study and Meltwater's 9.5M-citation study both rank it the #2 most-cited source across AI engines, and 51% of those citations come from members with under 10K followers, so the experts you'd approach there are already in the citation index. A short DM like "I'm writing about X, would love a 15-minute call to include your perspective" gets a yes 20-40% of the time, even cold.
With academics, look up their ORCID, the free persistent researcher ID that's the strongest external anchor in academic Person schema, because it lets AI engines verify their entity graph. For most SaaS content I'd run HARO 2.0 for breadth now that the spam is filtered out, LinkedIn DMs for depth, and Qwoted as the backup.
The interview format that actually works
Keep the call between fifteen and thirty minutes. Any longer and the expert starts regretting the yes, and any shorter leaves you without enough to quote.
Send 8-12 questions 24-48 hours ahead so they arrive with real answers, not improvised ones, which means more quotable lines per minute. Shape each question so the answer can be lifted out whole. "What do teams get wrong about X?" beats "Tell me about X," and "What's the specific metric that separates good from great?" beats "How do you measure success?" A directional prompt gets you a quote, where an open-ended one gets you a discussion.
Record on video and have a person check the transcript. Rev's human-reviewed transcription runs at about 99% accuracy, while Otter.ai manages 85-90% in meeting conditions, and that gap decides whether a quote is citation-grade or one AI engines pass over because the numbers came out mangled.
Aim for 4-6 quotable claims per interview, each carrying a specific number, outcome or framework next to the expert's name and role. AirOps suggests placing 2-4 quotes per article: one early for authority, some mid-section as evidence, and one at the close for the framework. Before you hang up, ask "Is there anything I didn't ask that would matter?" The best quote often comes out of that one.
A July 2026 r/copywriting thread describes the reality 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 is that clients almost never volunteer the quotable specifics you need. You have to interview them (audio, video or a structured written form) and make them the SME whose name and expertise the piece is built around. One commenter boiled it down to "Quote the dentist." The same goes for SaaS founders and engineers, product leads and academics: get them on a call and dig for the specifics.
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Question design that produces citable quotes
Whether an interview yields quotes or general commentary comes down almost entirely to question design. These five shapes work:
| Question type | Example | What it gets you |
|---|---|---|
| Framework | "What's the framework you use to decide X?" | A numbered framework that structures well |
| Number-anchor | "What percentage of Y actually [outcome]?" | Quantitative claims |
| Contrarian | "What common advice about X do you think is wrong?" | Opinion plus reasoning |
| War story | "When did you learn X the hard way?" | A specific anecdote with named entities and numbers |
| Named comparison | "How does A compare to B in practice?" | Comparison quotes that lift comparison-page citations |
Leave out open-ended prompts like "Tell me about your approach to X," which get paragraph-length answers that won't extract cleanly, and yes/no questions, which get one word with nothing to quote. Every question should aim at a 1-3 sentence answer holding a specific number, outcome or framework.
Attribution and schema
Schema is where most programs get attribution wrong, and only two approaches hold up.
The first is Article.contributor. Schema.org's Article type defines contributor as "A secondary contributor to the CreativeWork or Event," so you point it at a Person node for the expert, and it's the cleanest choice when one quoted expert carries the piece. The second is the Quotation type, whose creator property holds the person being quoted and which can nest inside Article.mentions. I'd reach for it when several experts are quoted and each attribution needs its own structure.
You'll also see quotedPerson in some AEO guides. It isn't a real schema.org property. Google's Rich Results Test won't flag it, since schema.org tolerates unknown properties, but AI engines can't check it against the vocabulary, so leave it out.
Give the Person node a name, an image and a url for their landing page or LinkedIn, and use jobTitle and worksFor for what they do. Then sameAs should link LinkedIn, a Wikidata QID if one exists, the employer bio and any personal site, with knowsAbout naming the exact topics they're expert in. AI engines cross-reference every one of them to disambiguate the person.
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Distribution amplification
An interview pays off twice, first when the quote lands on your page and again when the expert shares the piece with their own network.
Ahrefs analyzed a billion data points and found that distributing content through third-party outlets brings a 239% median lift in AI search visibility, with some cases reaching 325%. Brand web mentions correlate 0.664 with AI Overview visibility, while backlinks manage 0.218. Each time your expert posts the piece on LinkedIn, on X or in their newsletter, that's another brand web mention stacked on top of the lift the quote already earned.
Send a plain-text copy of their quote 48 hours before it goes live for approval, and a LinkedIn caption they can post as-is on publish day. Afterwards, thank them personally with a real number ("This piece got X views/citations, and Y people quoted your framework"). If they contribute often enough to deserve a landing page, add them to your author schema sameAs.
Foundation Marketing's case study of Atlan shows the whole mechanic at work. Atlan won 35% of AI citations in the observability guide vertical, and its guide became the second most-cited page in the study, with 26 citations across four engines. Named experts and structured attribution did the groundwork, and distribution across several channels did the rest.
The traps
Most expert programs that fail go wrong in one of five places.
The first is quoting only insiders. If the one "expert" in a piece is your own CEO, it reads as marketing and AI engines discount it, so every serious piece needs at least one outside voice. Its close cousin is the guest quote that reads like an ad for the expert's company, which won't get cited, so push back in the interview until you get the specific claim, not the pitch.
Uncredited quotes come third. "According to industry experts..." attributes nothing, and every quote needs a name and role, ideally with a profile link. Then there's the badly transcribed audio-only interview: at 85-90% accuracy Otter.ai garbles specific numbers, which are exactly what AI engines cite, so anything you publish deserves human review or Rev's paid tier.
The last trap is interviewing six months before you publish. Salespeak's analysis gives content aged 6-12 months 22% of top citations and content older than a year just 9%. If your interview is six months old, refresh the quote or talk to the expert again, because AI engines don't reward stale expertise.
What to ship this week
Starting an expert-interview program? Here's the order I'd work in:
- Pick one piece from your Q3 calendar where an expert quote would materially lift citation share (comparisons, buyer's guides and use-case content benefit most).
- Find 3 potential experts: one from your ICP network, one through HARO 2.0 or Qwoted, one by LinkedIn direct outreach.
- Send the outreach (a warm intro beats a cold DM, which beats a HARO pitch), asking for 15-30 minutes with 8-12 questions shared in advance.
- Interview, get a human-reviewed transcript and pull out 4-6 citable quotes.
- Publish with
Article.contributorschema and a Person node that includessameAsandknowsAbout. - Send the expert the plain-text quote to approve, then the LinkedIn caption at publish.
- Track citation share on the piece for 60 days.
Few AEO patterns pay back as well, yet most SaaS teams underuse expert interviews: the quote lifts citation share directly, distribution multiplies it and the schema makes it durable, so a piece with a named quote compounds harder than one without.
Our content engine works sourced expert quotes, with the speaker named, into the articles it writes, and AI visibility tracking samples the buyer prompts where your quoted experts should be showing up. For the full Person node pattern, the author schema deep dive covers the entity graph.
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