Key takeaways:
- Test which social formats are actually surfacing inside AI search. Members described moving from general curiosity into more structured experimentation around YouTube, YouTube Shorts, LinkedIn Articles, executive content, Reddit, and review platforms. Several members discussed whether informational, highly specific, and answer-oriented content may be more likely to surface than traditional brand storytelling.
- Expand visibility monitoring beyond owned social channels. Members said they are seeing AI search answers pull frequently from third-party commentary alongside, and sometimes instead of, brand-owned content. Reddit threads, reviews, forums, retailer pages, creator content, and third-party YouTube videos came up repeatedly as influential sources across Google AI Overviews, ChatGPT, Gemini, and Perplexity.
- Reassess LinkedIn strategy through an AI discoverability lens. Members described LinkedIn as one of the least predictable platforms in AI visibility testing. Several participants said LinkedIn Articles appear more consistently than standard posts, while executive shares and public-facing content sometimes surface more often than official brand-page content.
- Build more repeatable testing and benchmarking processes before scaling strategy decisions. Most teams still characterized themselves as operating in an early testing phase, but several practical patterns are beginning to emerge. Members discussed recurring prompt benchmarking across ChatGPT, Gemini, Copilot, and Perplexity; AI citation monitoring through tools like SEOClarity and Profound; and lightweight query tracking frameworks tied to social listening and sentiment analysis.
- Treat AI visibility as a cross-functional operating challenge, not a channel problem. Leaders repeatedly connected social media, SEO/AEO, executive communications, employee advocacy, reputation management, customer support, and market research throughout the discussion. The strongest operational examples came from organizations connecting publishing decisions with search behavior, sentiment monitoring, visibility tooling, and executive communications strategy.
The Story in Brief
“In this discussion, our SocialMedia.org and AEO Board members explored deep operational questions around content format, structure, visibility, and discoverability. Members got into the weeds comparing how LinkedIn Articles behave differently than posts, whether YouTube Shorts surface differently than long-form video, and how transcripts, overlays, captions, timestamps, and executive content may influence AI-generated answers. As AI systems pull from a wider mix of social and community sources, brands will need to treat AI visibility less like a search tactic and more like a coordinated reputation, content, and discovery discipline.” — Will White, Senior Membership Director for SocialMedia.org and AEO Board
Social Visibility Is Becoming Harder to Separate From AI Search
AI-generated answers are reshaping how teams think about social media visibility, but not in the ways many expected. Members described AI systems pulling from a broader mix of sources than traditional brand channels, including YouTube videos/shorts, Reddit threads, reviews, creator commentary, retailer pages, and executive content distributed across the web.
The operational challenge is that most teams still do not fully understand why certain content surfaces while other content disappears. Members described organizations operating in a mix of spot-checking, lightweight testing, and early-stage monitoring, often without consistent benchmarking processes in place.
At the same time, the discussion reinforced that third-party conversations — Reddit threads, forums, reviews, creator content, and other commentary — are becoming a more important part of the AI search picture alongside brands’ own content. As a result, teams are needing to rethink where visibility and reputation are actually being formed.
“Content that answers a question seems to matter. Some content that would feel thin from a traditional SEO perspective may still surface if it gets at the heart of an answer.”
Focus of the Discussion
Teams Are Testing How Social Content Can Influence AI Search, Even Without Clear Rules
One of the clearest patterns from the discussion was the growing influence of video, especially YouTube and YouTube Shorts. Members described seeing informational, answer-oriented video content surface more reliably in AI-generated responses than broader brand storytelling.
One member said their team has begun intentionally aligning “text overlay, captions, voiceover, post copy” and keyword targeting across video assets after observing content appearing in Google results within an hour of publishing. Another member described YouTube Shorts outperforming long-form content in multiple AI systems.
At the same time, members emphasized how little confidence they have in causation. LinkedIn became one of the most debated examples. Some members said LinkedIn Articles surfaced more consistently than standard posts.
Others described executive reshares surfacing more often than official brand-page content. One participant summarized the experience bluntly: “LinkedIn stuff feels very random.” Members compared conflicting guidance from LinkedIn reps, agencies, and testing observations around boosted posts, dark posts, and Thought Leader Ads, with little agreement about whether paid amplification influences AI visibility at all.
The discussion also reinforced how much AI search may be shaped by distributed third-party conversation alongside owned channels. Members pointed to Reddit, reviews, forums, retailer pages, and creator content as major drivers of how brands are characterized in AI-generated answers.
Several participants noted that years-old Reddit discussions can still influence AI responses long after brands lose the ability to participate or correct them.
