The most useful question about AI visibility isn't "do I appear in responses". It's "from which source does the engine cite me — and why that source rather than another".
Understanding the source selection logic means understanding where to focus your effort. We analysed 500 B2B queries in ChatGPT and Perplexity, systematically identifying the primary source of each citation. Here's what the data shows — and what an observation published by Profound on 1.4 million citations confirms at larger scale.
The structure of the citation decision
Before the data, the mechanics. When an LLM generates a response to a B2B query, it doesn't do a search in the traditional sense. It goes through a multi-step pipeline.
LLMs build what researchers call "evidence graphs": they weight sources according to the consistency of the cited entity, the frequency of confirmation between independent sources, and the authority of the domain. It's not a ranking — it's a triangulation. A source that confirms what another source already says has more weight than an isolated source, even if the latter is technically more accurate.
The "information gain" mechanism structurally penalises content that repeats what other sources already say. Content that offers original research, unique data or new analysis receives a higher score — creating a competitive advantage that aggregator content cannot reproduce.
Two concrete implications: being cited by multiple independent sources on the same subject increases your citation probability. And publishing original data — even modest — is more effective than republishing existing content.
What our 500 queries reveal
Source distribution is a long tail, not an oligopoly
First counter-intuitive result: even the most cited domain on any platform rarely exceeds 5% of total citations. Wikipedia, Reddit, LinkedIn and YouTube combined don't exceed 5% of combined citations. The remaining 95% is distributed across thousands of domains.
This is a fundamental structural difference from traditional SEO, where the top 10 results capture about two-thirds of clicks. In LLMs, distribution is much more fragmented. This means specialised sources — a well-structured sector directory, a niche professional media outlet, a review platform in your category — can generate regular citations even with modest domain authority.
On our 500 B2B queries, the top 15 sources represented 41% of total citations on Perplexity and 38% on ChatGPT. The rest was distributed across 312 distinct domains. This is the most encouraging data from our analysis: AI visibility is not monopolised by a few giants.
LinkedIn has become the #1 source for professional queries
This is the most significant fact of 2026 in B2B AI visibility, confirmed by several independent studies.
Between November 2025 and February 2026, LinkedIn rose from 11th to 5th position in ChatGPT citations — the largest progression observed by Profound across 1.4 million analysed citations. For professional queries specifically, LinkedIn is now the most cited domain across the six main AI platforms.
Semrush confirms: across 325,000 analysed queries, LinkedIn appears in 14.3% of ChatGPT responses and 13.5% of Google AI Mode responses — ahead of Wikipedia, YouTube and all major press publishers.
In our own tests on 500 B2B queries, LinkedIn was the primary source in 17% of citations on ChatGPT and 9% on Perplexity — confirming the global trend with a slight overweighting on ChatGPT for our French-speaking panel.
The difference between individual profiles and company pages
A detail revealed by Semrush data that changes concrete strategy.
On ChatGPT and Google AI Mode, 59% of cited LinkedIn content comes from individual profiles rather than company pages. Perplexity inverts this ratio: 59% of LinkedIn citations come from company pages.
For a B2B SME, this means two practical things: optimise the LinkedIn company page for Perplexity (precise description, well-defined sector, regular structured publications) and encourage company leaders or experts to publish substantive content on their personal profiles for ChatGPT.
LinkedIn confirms that members with more than 3,000 followers have a significantly higher citation probability, and that 95% of all citations come from original content — not reshares.
Reddit as context source, not recommendation source
Reddit is the most cited domain by raw volume on most platforms. But its role differs from LinkedIn in B2B responses.
Reddit represents 46.7% of citations on Perplexity in responses where a community perspective is relevant. For B2B recommendation queries, Reddit appears mainly when users have asked the same question on professional subreddits (/r/entrepreneur, /r/b2b, specific sector subreddits) and answers have gained strong traction.
In our tests, companies cited via Reddit were almost always mentioned because a third-party user had positively mentioned them in a thread — not because they had an active Reddit presence. This is a signal difficult to build deliberately, but useful to monitor.
Review platforms: strong sector concentration
G2, Capterra, TripAdvisor, Avocats.fr, Les Échos rankings: in our tests, each sector had its dominant review platform, and this platform represented a disproportionate share of citations for that specific sector.
Brands present on 4 or more platforms are 2.8 times more likely to appear in ChatGPT responses than brands on a single platform. This isn't an argument to be everywhere — it's an argument to be complete on the platforms that matter in your sector.
The five selection mechanics our tests confirm
1. Freshness trumps volume
40 to 60% of cited sources change from one month to the next in longitudinal analyses. LLMs don't store a fixed list of trusted sources: they continuously reweight according to the freshness of available signals.
On Perplexity particularly, a G2 profile with 5 reviews from the last 30 days outranks a profile with 50 reviews from 18 months ago. Maintaining third-party signals is as strategic as their initial creation.
2. Content structure determines extractability
Sources with clearly autonomous sections of 50 to 150 words receive 2.3 times more citations than long unstructured content. LLMs extract chunks — coherent, self-sufficient text blocks — not entire documents.
Concretely: a paragraph that starts with a direct answer, contains a data point, and closes with an actionable conclusion is extracted more easily than an 800-word development without internal structure. This is the inverse logic of traditional SEO, which favoured long, dense content.
3. Named entities increase citation precision
Content with precise named entities (companies, people, places, products) and definitive language reduces the risk of misattribution and increases the probability of being correctly cited.
Content that says "large recruitment agencies" will be less often correctly attributed than content that names "Hays, Michael Page and Robert Half". LLMs anchor their citations on recognisable entities. The more your content contains, the more extractable it is with precision.
4. Cross-source confirmation multiplies visibility
The evidence graph LLMs build favours entities mentioned consistently by independent sources. A company cited in a press article, a G2 profile, a LinkedIn recommendation and a bar directory has a composite trust signal far superior to a company present on only one of these sources, even with more content.
This is why the "single dominant source" strategy — betting everything on your site — doesn't work in LLMs. Diversification of third-party signals isn't optional.
5. Position in the response depends on position in the sources
First and second mentions in a recommendation list receive disproportionately more exposure than end-of-list mentions. And this position in the response is correlated with the entity's position in the consulted sources — companies cited at the top in reference rankings and directories appear at the top of AI responses.
This is a concrete argument for targeting the top 5 of relevant sector rankings, not just a presence.
What our observations add to global data
On the 500 queries from our analysis, three patterns specific to the French-speaking market stand out.
The English-speaking training bias. ChatGPT cites English-speaking sources disproportionately even on French-language queries. A French SME well-listed on Clutch (English-speaking platform) appeared more often than an equivalent SME only listed on French-speaking directories. This bias is real and applies to Perplexity as well, to a lesser extent.
The weight of press rankings on ChatGPT. Rankings published by Les Échos, Le Monde, Challenges function as durable training signals on ChatGPT — far more than their digital equivalents. A mention in a major French economic press print/web ranking has a persistence in ChatGPT responses that dozens of blog articles don't reach.
Under-exploited French-speaking LinkedIn. On professional queries in French, LinkedIn is cited significantly less often than on equivalent English queries — not because the platform is less indexed, but because substantive French-language content on LinkedIn is far less dense. This is a direct opportunity: players who regularly publish structured analyses in French on LinkedIn have a disproportionate advantage on this channel for the next 12 to 18 months.
What this changes for your strategy
The conclusion of this analysis isn't "be everywhere". It's "be consistent, recent and confirmed by multiple independent sources in your sector".
The most profitable actions according to our observations, ranked by effort/impact ratio:
Low effort, quick impact: update your profiles on the dominant third-party platforms in your sector with structured descriptions and fresh data. Measurable result on Perplexity in 3 to 6 weeks.
Medium effort, lasting impact: publish structured substantive content on LinkedIn — personal profile for executives, company page for service descriptions. Original position, quantified data, named entities. Measurable result on ChatGPT in 6 to 12 weeks.
Sustained effort, long-term impact: obtain mentions in recognised press rankings in your sector. This is the most durable authority signal in LLMs — and the one that takes the longest to build.
Our scoring tool analyses your visibility on these three levels in 5 minutes. For a complete analysis with identification of sources citing your competitors but not you, our AI Diagnostic delivers an actionable report within 5 business days.
To go further on field data: We tested 50 French SMEs in ChatGPT and Perplexity and AI Visibility Benchmark: who really appears in 2026 responses.