LinkedIn published findings from its internal testing on what drives visibility in AI-generated search results.
The company, reportedly among the most-cited sources in AI responses, shared what worked for improving its presence in LLMs and AI Overviews. For practitioners adjusting to AI search, this is a rare look at what a heavily-cited source tested and measured.
In a blog post, Inna Meklin, Director of Digital Marketing at LinkedIn, and Cassie Dell, Group Manager, Organic Growth at LinkedIn, detailed the tactics that got results.
Content Structure And Markup
LinkedIn found that how you organize content affects whether LLMs can extract and surface it. The authors wrote that headings and information hierarchy matter because “the more structured and logical your content is, the easier it is for LLMs to understand and surface.”
Semantic HTML markup also played a role, with clear structure helping LLMs interpret what each section is for. The authors called this “AI readability.”
The takeaway is that content structure isn’t just a UX consideration anymore. Proper heading hierarchy and clean markup may affect whether your content gets cited.
Expert Authorship And Timestamps
LinkedIn’s testing also pointed to credibility signals. The authors wrote:
“LLMs favor content that signals credibility and relevance, authored by real experts, clearly time-stamped, and written in a conversational, insight-driven style.”
Named authors with visible credentials and clear publication dates appeared to perform better in LinkedIn’s testing than anonymous or undated content.
The Measurement Change
LinkedIn added new KPIs alongside traffic for awareness-stage content, tracking citation share, visibility rate, and LLM mentions using AI visibility software. The company also said it’s creating a new traffic source in its internal analytics specifically for LLM-driven visits, and monitoring LLM bot behavior in CMS logs.
The authors acknowledged the measurement challenge:
“We simply couldn’t quantify how visibility within LLM responses impacts the bottom line.”
For teams still reporting traffic as the primary SEO metric, there’s a gap here. If non-brand informational content is increasingly consumed inside AI answers rather than on your site, traffic may undercount your actual reach.
Why This Matters
What caught my attention is how much this overlaps with what AI platforms themselves are saying.
SEJ’s Roger Montti recently interviewed Jesse Dwyer from Perplexity about what drives AI search visibility. Dwyer explained that Perplexity retrieves content at the sub-document level, pulling granular fragments rather than reasoning over full pages. That means how you structure content affects whether it gets extracted at all.
LinkedIn’s findings point in the same direction from the publisher side. Structure and markup matter because LLMs parse content in fragments. The credibility signals LinkedIn identified, like expert authorship and timestamps, appear to affect which fragments get surfaced.
When a heavily-cited source and an AI search platform land on the same conclusions independently, you have something to work with beyond speculation.
Looking Ahead
The authors are adopting a different mindset that practitioners can learn from:
“We are moving away from ‘search, click, website’ thinking toward a new model: Be seen, be mentioned, be considered, be chosen.”
LinkedIn indicated Part 3 of the series will include a guide on optimizing owned content for AI search, covering answer blocks and explicit definitions.
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