27 Aug The Social Media Playbook for AI Search Visibility

As a social media agency, we are often brought in to help develop social media strategies for brands based on their overarching goals and objectives.

Over the past 6 months, we’ve noticed an increase in clients asking us to develop social media strategies to drive AI search visibility. Many brands are noticing the impact LLMs (Large Language Models) like Google AI Overviews, ChatGPT, Google Gemini, and Claude are having on brand discovery and purchase consideration, and we’re hearing even more anecdotes and questions like:

• Our website traffic is down; we want to make sure we’re visible in LLMs.
• LLMs are our newest target audience because our customers are using these tools to help evaluate or shortlist brands.
• As zero-click searches are on the rise, how can social media be used to influence them?
• How should our content and channel strategy change based on the impact of social platforms on LLMs?
• What should our Reddit and YouTube strategy be?

Before we dive into how your brand should navigate this new area of AI search visibility, let’s start with unpacking how LLMs are changing the customer journey and a little understanding of what type of content LLMs like to trust and cite.

How are LLMS Changing the Customer Journey?

The way customers discover and evaluate brands is fundamentally changing as more consumers are using AI tools and social search in discovering solutions or brands. For instance:

• AI-generated answers can answer a customer’s questions and influence their opinions before they ever click to a website
• Roughly 2/3 of US consumers say they use at least one social network for search and nearly half use several of them
• According to a March 2026 forecast from eMarketer, 34% of internet users will use AI for search, rising to nearly half (45%) of internet users in 2030.

Understand what LLMs Trust and Cite

According to eMarketer’s FAQ on GEO and AEO, “Traditional SEO aims to rank a page among a list of search results. GEO aims to get a brand mentioned in an AI-generated answer. The distinction matters because AI responses are highly variable”.

This variability is in part because AI engines pull from slightly different source types than traditional search – including places like Reddit, LinkedIn, YouTube, and other social platforms. SEM Rush has conducted a 3 month study on the most cited domains, analyzing over 100M citations, and released the following chart ranking the most cited domains:

What Tactics Help Contribute to AI Visibility?

Given how new this space is, be wary of experts who promise predictive gains in AI visibility. Search Engine Land reports that when it tracked 2,500 prompts across Google AI Mode and ChatGPT, they noticed that between 40-60% of cited sources changed from month to month.

According to eMarketer:

No single GEO playbook is likely to remain reliable for long. Visibility on one AI platform does not transfer to another, and each model surfaces brands differently. Organizations learn more from testing and measurement than from committing to a fixed optimization framework. Choosing which platforms matter most for a given audience remains part of the work. – CMO Guide GEO, eMarketer

How to Structure a Content Strategy Built for AI, Not Just Entertainment or Engagement

In the social space, particularly organic social, it’s easy to focus social media content on trends and content designed to be highly engaging and accidentally miss the opportunity to develop content that can aid with brand discovery and consideration within LLMs.

Many brands will realize their content strategy needs to be updated, as content strategies have shifted to trend-inspired or more entertaining and/or engaging content types in recent years. While many brands will still choose to develop this content type to achieve their goals, we anticipate content strategies to also include purpose-built AI-optimized content series so that content built for social media can help a brand get discovered.

The following are tactical tips for how to structure a content strategy built for AI, not just engagement.

1. Build a Target Audience Question Library First:
   • • LLMs are asked more personal and specific questions, which means a good part of your content strategy is to answer the kinds of questions real people may ask. As you brainstorm this, identify the real questions your customers ask at each stage of their customer journey. Gain inspiration from Reddit threads, TikTok, and YouTube auto complete. If you are a B2B client with a sales team, seek inspiration from real questions they hear from your customers. Aim for a working list of at least 15-20 questions to inspire future content series.
     • Tip: Consider sourcing and featuring employees or subject matter experts in answering these questions. Experts within your company can lend the kind of experience and expertise that LLMs crave.
2. Brainstorm Comparison + Decision-Making Content Series
   • LLMs are often helping customers decide what product is best for them, so comparison-based content should be an intentional content type to be produced. In brainstorming this content it’s helpful to think about the following questions:
     • • What types of comparisons are your customers naturally making when deciding to purchase your type of product over your competition?
       • How will your customers know that your product is right for them and their particular lifestyle, life stage, preferences, or personality?
       • What credible voices may help them overcome their skepticism and develop trust for your brand?
       • What natural curiosities do you often see your audience have that your brand can help address?
3. Consider Channel Mix
   • • It’s important to note that multiple source types feed into the answers that AI systems surface and the priority of these channels is continually changing.
     • Take note of the SEM Rush most cited domain study – showing dips and rises in the frequency of Reddit and LinkedIn citations over the course of the study. For instance, ChatGPT cited Reddit in close to 60% of prompt responses in early August 2025 before reducing to around 10% by mid-September of 2025. Meanwhile, LinkedIn was cited in nearly 15% of Google AI Mode responses and citations surpassed Reddit and YouTube in the same timeframe in September (likely coinciding with the decrease in Reddit citations).
     • Tip: This data suggests that it’s wise to develop a channel mix and look at a variety of channels rather than just putting all your eggs in just one basket. It’s also important to keep an eye on these fluctuations as they may shift channel mix more frequently than a traditional content strategy.
4. Consider Credible Content Sources:
   • SEM Rush has developed the E-E-A-T model for thinking of content sources from creators. This prioritizes experience, expertise, trustworthiness, and authoritativeness when LLMs evaluate credibility. In this case, authors with relevant credentials or experience are valued.
   • As you consider credible content sources, consider whether you have internal experts like leaders, employees or subject matter experts at your company that would be natural for content to be sourced from. Also – consider activating these experts on their own LinkedIn channels/profiles, as these can be sourced from LLMs at a higher rate than even owned company profile pages. A recent Meltwater study found that LinkedIn was the #2 most-cited source after YouTube in AI-generated answers, with 77% of citations coming from individual experts – not company pages
   • Consider what kind of original data, insights, or expert commentary these internal experts can add to their perspective.
   • Rather than one-off influencer campaigns, consider an always on creator program. LLMs build trust through repeated, independent mentions over time. Nano and niche creators or even brand advocates who may talk about a brand or related subject consistently carry more weight with AI models than an unrelated influencer with more reach. Expertise signals get cited more than follower counts.

How Can You Measure AEO/GEO Performance?

Like social algorithms, LLMs are continually being updated and the way they prioritize answers is also going to be a work in progress. Most brands are going to need to be comfortable with a test and learn approach to begin understanding the impacts of content on brand visibility and also understanding there will be natural fluctuations as the LLMs themselves are continually changing and adapting.

Within the past 6 months, a rush of tools are rapidly being developed to begin monitoring brand visibility and AEO/GEO optimization – from sources like SEM Rush, Hubspot and Ahrefs offering AI analysis and AI optimization tools as an add-on – and other sites like Mangools AI search grader providing freemium tools. These websites typically can help monitor the following:

• • Brand Visibility: How often your brand and competitors appear in AI-generated answers
  • Citations by Content Type: The types of content cited most in AI answers for tracked prompts
  • Social Amplification Insights: How each channel drives AI citations and visibility vs. competitors

• Need Help Developing a Social Strategy for AI Search Visibility?

  We know that change is constant in social media marketing, but the impact that AI search is under and the implications to your social media strategy are happening even faster. If you find yourself needing help developing a social strategy, we’d love to help. Just contact us to get the conversation started.

  Frequently Asked Questions

  What is the difference between SEO and GEO?

  Traditional SEO aims to rank a page among a list of search results, while GEO (Generative Engine Optimization) aims to get a brand mentioned in an AI-generated answer. The distinction matters because AI responses are highly variable and pull from different source types than traditional search.

  Which platforms do AI engines cite most often?

  AI engines pull from source types that include Reddit, LinkedIn, YouTube, and other social platforms, and the priority of these channels changes continually. For example, ChatGPT cited Reddit in close to 60% of prompt responses in early August 2025 before dropping to around 10% by mid-September, while LinkedIn citations rose and surpassed Reddit and YouTube in Google AI Mode during the same period.

  How should brands structure content for AI search visibility?

  Build a target audience question library of at least 15 to 20 real customer questions, create comparison and decision-making content series, develop a diverse channel mix rather than relying on one platform, and source content from credible internal experts and always-on creator programs that AI models trust over time.

  What is the E-E-A-T model and why does it matter for AI search?

  E-E-A-T stands for experience, expertise, authoritativeness, and trustworthiness. It is a model SEMrush uses for thinking about content sources, and it matters because LLMs value authors with relevant credentials or experience when evaluating credibility. A Meltwater study found LinkedIn was the #2 most-cited source after YouTube in AI-generated answers, with 77% of citations coming from individual experts rather than company pages.

  How can you measure AEO or GEO performance?

  A rush of tools are being developed to monitor brand visibility in AI answers, including SEMrush, HubSpot, and Ahrefs offering AI optimization add-ons, and freemium tools like the Mangools AI search grader. These typically track brand visibility, citations by content type, and social amplification insights across channels versus competitors.

  Why is a test-and-learn approach recommended for AI search visibility?

  No single GEO playbook is likely to remain reliable for long. Visibility on one AI platform does not transfer to another, each model surfaces brands differently, and cited sources change frequently. Search Engine Land found that 40 to 60% of cited sources changed from month to month, so brands learn more from ongoing testing and measurement than from a fixed optimization framework.