How Reddit, LinkedIn, and AI Search Shape B2B Brand Perception
Less than a third of what AI says about your brand comes from your own website.
Jun 02, 2026
Less than a third of what AI says about your brand comes from your own website
Most B2B marketing teams spend the majority of their brand efforts on assets they control, such as the homepage.
Our audit data suggests that's the wrong distribution of effort in 2026. Across the brands we audited, no more than 30% of the sources used to define an AI response come from the company's own website. The other 70% is Reddit threads, LinkedIn posts, comparison sites, and industry 3rd-party media. Conversations and sources many brands aren't part of, and in many cases, aren't even monitoring.
In this post, we are going to share an in-depth analysis of the audit we conducted on how social media and earned media are shaping your brand narratives, and how to monitor and influence that perception.
The Audit
We ran a live teardown of three B2B brands to show what this looks like in practice.
Lily is the founder of Saltanat Labs, and a former Head of SEO at Verto Digital, where she has done work for SaaS unicorns like Payhawk, Gong, and Contentful.
She ran full AEO audits on three popular brands: n8n, Ahrefs, and Notion. 10 branded and non-branded prompts each across ChatGPT, Gemini, and Google AI Overviews. And walked through the citation data live.
What she found:
The AI's description of each brand leaned heavily on community discussions and external pages, and much less on the brand's own published content.
The brands were picked specifically because everyone would know them and could judge the accuracy of the AI's framing themselves.
AI Doesn't Invent - It compresses
"The AI systems do not invent brand perception. What they do is they compress the existing talk about your brand in different communities and in your whole ecosystem."Lily Grozeva
Instead of asking yourself, "does the AI understand our positioning?" or “do we show in ChatGPT?”, you should ask yourself:
What community discussions, listicles, and reviews is the AI weighing most heavily when it answers a buyer's question about us?
Those are very different problems with very different solutions.
For instance, when Lily ran a prompt about the best workspace software for organizing product and marketing teams. The AI's answer cited CodeGen.com, a Reddit thread, and an Airtable comparison table. Third-party sources that described Notion through the lens of how users talked about it in context. Not Notion's homepage or their case studies.
This process compounds over time. When the same language appears across multiple community sources - the same phrases, descriptors, comparison framing - the AI learns to treat that language as authoritative. LLMs read what users are saying about you and look for consistency across large volumes of data, so your homepage is just a single data point.
Below, we are going to dive deeper into each brand and compare its homepage narrative vs. its real brand perception across LLMs.
The n8n Positioning Gap
n8n is the clearest example in the dataset, and it's worth spending time on because the difference is quite specific.
The brand's current positioning mentions:
- Enterprise orchestration platform
- AI native
- Capable of building AI agents.
- Their homepage talks about workflow automation at scale. They have a Vodafone case study and are pitching to CTOs.
What LLMs says: open source, self-hosted, developer-first, Zapier alternative.
"The AI narrowed the perception of that brand to pretty much a Zapier alternative, and that could be problematic for a brand like that."Lily Grozeva
Every one of those descriptors is accurate. n8n is open source, self-hostable, and appealing to developers. But they're describing n8n the way developers talked about it when it was a scrappy Zapier alternative for technical teams who wanted to self-host. That community of early adopters created a consistent, repeated description in Reddit threads about self-hosted automation setups, and the AI absorbed it.
Because the community writing the large volume of consistently phrased content about n8n is still the developer community, the enterprise narrative has not yet been established. The enterprise customers, if they're talking at all, are doing it in private Slack groups (or wherever enterprise people talk)
The implication for n8n isn't that their website is wrong. It's that the community narrative that's feeding the AI is three years behind their current positioning.
How a Phrase Compounds: the Ahrefs Case
For n8n, the AI is using the wrong frame entirely. For Ahrefs, it's using a frame that's accurate but deliberately narrow.
The AI describes Ahrefs as:
- Gold standard for SEO tools
- Best backlink index
- Content gap analysis
- Backlink intelligence.
All these explanations are accurate, but Ahrefs has been pushing a broader positioning in their own content and SERP presence. They call themselves an AI visibility platform, a marketing intelligence platform, and an enterprise intelligence platform. That broader framing isn't what the AI says.
"Gold standard for link building and competitive analysis. These are repeatable wording, repeatable phrases across the sources that shape the story about Ahrefs."Lily Grozeva
The same phrase in different contexts, across different sites, over time. When that phrase appears consistently enough, the AI treats it as the authoritative description. Ahrefs has been saying "enterprise intelligence platform" on their homepage for months, but the community built the "gold standard SEO tool" into the training data years before.
This is why the difference between AI perception and brand self-positioning isn't a messaging problem you can solve by updating your homepage. The community discussions that shaped the AI's framing were written before your latest repositioning. They'll take time to shift, and the only way to shift them is to be present in the conversations where they're forming.
Notion: The community-first Brand
The AI describes Notion as:
- Flexible workspace
- Startup-friendly,
- Collaborative,
- Customizable
- Knowledge base
- Company brain.
Accurate and positive, but Notion's current push is toward AI agents, building custom workflows, and enterprise use cases. They want buyers to think "AI productivity platform," not "glorified Wiki."
Where is the community-sourced framing coming from?
- Reddit threads about how teams structure their Notion setups.
- Community posts comparing Notion to ClickUp and Craft Docs.
- LinkedIn posts describing exactly how people use Notion to run their operations.
- People writing about their experience with the product.
"People describe Notion in big detail. They tell exactly what they assemble it with. And this is then represented in the AI answers."Lily
Notion built a user community that talks about the product in specifics. The problem is that those specifics cement a picture that's two or three product generations behind where Notion is trying to go.
One thing the teardown also surfaced: negative descriptors show up in AI answers too. For Notion, "messy without governance" appeared in the framing alongside the positive descriptors.
"The question is not if you're included or not included. The question is, you should monitor how you are included, because the negative sentiment is there as well."Lily
The Buyer's New Discovery Path
The implication that threads through all three teardowns is about what happens when AI is the buyer's first stop.
The traditional B2B marketing model assumes a discovery path:
- Buyer searches Google
- Lands on your website or a comparison site
- Reads your messaging
- And forms an opinion.
The homepage carries a lot of weight in that model. Positioning pages, case studies, competitive comparison pages. All of that is built around a model where the buyer reads what you wrote.
If the buyer asks an AI, "What's the best workflow automation tool for enterprise teams?", they're getting an answer assembled from community discussions and external sources.
"The control over brand positioning is moving. That's the main thing."Lily
This isn't a theoretical risk for future buyers, but the current reality for any buyer who starts their research with an AI prompt instead of a Google search.
What to Actually Monitor
Start with your own citation profile.
What sources is the AI actually drawing from when it describes your brand? Tools like RankScale can tell you this.
But also map your competitors' citation profiles. The communities your competitors appear in are the communities that matter for your category. If Ahrefs is being cited from Capterra and G2, those are the venues where the category narrative is being written.
"It's better to...also run a citation profile of your competitors, so that you map the whole citation ecosystem, and better understand where you need to actually get more active."Lily
Then watch for repeated wording. The phrases that appear consistently across multiple sources are the ones the AI will treat as authoritative. Both the positive framing ("gold standard") and the negative ("messy without governance"). Monitoring those patterns tells you what narrative is being reinforced.
Finally: recurring comparisons. Google Suggest still works for finding what buyers are actually asking. The comparison threads your brand is showing up in reveal both the buyers asking the questions and the language being used to answer them.
The Power User as a Power Driver In AI Citations
All three teardowns showed the same structure:
A community of users (mostly technical early adopters, power users, or people who had a specific use case) wrote detailed, consistent, repeated content about the brand at a particular stage of its development.
The AI absorbed that content. The brand's positioning then shifted - toward enterprise, toward AI, toward a broader category, but the community narrative lagged behind.
The brands best positioned for AI search are those whose community narrative aligns with their current positioning.
This is a new kind of brand debt. It's not reputational damage, the legacy community content isn't bad, it's just old. It takes time to write over because new community content needs to appear consistently, across the right venues, at sufficient volume, before the AI starts weighting the updated narrative differently.
The practical move is to stop treating community presence as a distribution channel for marketing content and start treating it as the place where AI training data is being written in real time. Engage and educate that community.
Where Sensorhub Fits
Most AEO tracking tools work backward from the AI's output. They show you which sources the AI cited for a given prompt. That's useful, but it only shows citations that already made it into AI answers. It doesn't show you the broader community conversations that will become future citations.
Sensorhub monitors Reddit, LinkedIn, and X directly for conversations about your brand, competitors, and finds high-intent leads - people in the market for such tools, looking for alternatives, etc.
For Notion, Sensorhub found over 1,000 signals in the initial run. Comparison threads, category questions, competitor mentions. All scored by relevance and buying intent. These are the signals that you need to engage with to shape the perception of your brand, before it’s solidified in the AI index.
You should engage with these discussions by providing value and sharing how your solution solves the problems. Be upfront about your involvement with your business, and avoid using fake profiles and motives.
This is the strategy Fluent Frame used to get 3,000+ views and 700+ users by simply commenting on relevant posts for 15-20 minutes a day. Watch the full case study below:
The 20-minute daily process that Fluent Frame use:
- Open Sensorhub, scan new high-intent signals
- Pick 5-6 threads worth engaging
- Write a useful comment on each, plain text, no link
- Close laptop, back to building
Pro tip: Engage with comments more than you write new posts. A recent study found that Reddit comments have a slightly greater impact on AI visibility than the posts themselves. Most brands that engage on Reddit focus on the post, but they are missing on a lot of potential AI visibility impact. Reddit Comments correlate higher than Reddit Posts. Comments at ρ=0.111, Posts at ρ=0.096.
Closing the gap between your brand's positioning and what AI says about you is a community problem, not a messaging problem. The fix starts with knowing which conversations are happening and being present in the right ones.
Share article