Off-Page SEO and Brand Authority in the AI Era

The rise of generative AI in search means that off-page SEO – the signals and content about your brand beyond your own website – has never been more critical. In traditional SEO, tactics like link-building and PR established authority and boosted rankings. In the AI era, those same off-page efforts take on new dimensions. Large language models (LLMs) and AI-driven search tools draw on vast swaths of web content to formulate answers. They tend to favor information from well-known, authoritative sources, and they often repeat the narratives and opinions prevalent across the web.

As a result, building your brand’s authority off-site doesn’t just help with Google rankings – it can directly influence whether and how your brand is mentioned in AI-generated answers. In this article, we explore how to bolster off-page signals and brand authority in ways that resonate with AI models and generative search experiences. From digital PR and community engagement to leveraging reviews and open data, we’ll examine strategies (with real examples from 2024–2025) to ensure your brand is both trusted by AI and prominent in AI-driven results.

We’ll also discuss tools for monitoring your brand’s presence in ChatGPT, Google’s Search Generative Experience, and other AI platforms, so you can continually refine your off-page strategy. The future of search visibility will be about more than just blue links – it will be about being part of the conversations and content that intelligent systems use to answer user questions.

Digital PR for Authority in AI Search

Off-page SEO has long been about establishing authority : earning backlinks, media mentions, and references from credible third-party sites. In the era of AI search, this digital PR aspect is even more crucial. LLMs like ChatGPT or Google’s generative search AI don’t literally calculate PageRank, but they are heavily influenced by what content they ingest and deem trustworthy. Generally, LLMs favor content from authoritative domains – those that are widely cited, well-known, or associated with expertise ( ^[1] ). This means securing mentions in trusted publications, getting experts to cite or quote your work, and having a presence on high-authority sites can increase the likelihood that AI models regard your brand as worthy of inclusion in answers. 

The “Mentions” Currency: Unlike Google’s ranking algorithm which is built on links, AI models prioritize mentions and context in their training data ( ^[2] ). SEO expert Rand Fishkin describes it succinctly: “The currency of Google search was links… The currency of large language models is mentions (specifically, words that appear frequently near other words) across the training data.” ( ^[2] ) In practice, if your brand or product is frequently mentioned alongside important keywords or topics (especially on respected sites), an LLM is more likely to recall or include it when generating an answer about that topic.

For example, if many articles and lists about “top project management tools” mention a particular software brand, an AI answer to “What’s a good project management tool?” might very well include that brand by default, due to the patterns learned from those sources. 

Earning Credible Mentions: The key, then, is to get your brand talked about in the right places. Traditional PR techniques – press releases, thought leadership articles, expert interviews – can lead to coverage in news sites, industry blogs, or research papers. These are precisely the kind of high-authority, factual sources that AI models are trained on. A mention or quote in a New York Times article, a citation in a university study, or inclusion in a “Top 10” list on a reputable blog not only boosts your human credibility but also means that when an AI combs through text to answer a question, your brand has a foothold in that knowledge base.

For instance, when OpenAI’s GPT-4 was asked about the “best brands for small business marketing,” it produced a list of well-known software brands with citations from Wikipedia and a NerdWallet review article ( ^[3] ). The brands that appeared – Constant Contact, Mailchimp, HubSpot, ActiveCampaign, etc. – were all those with strong digital PR: they are frequently reviewed by third parties and discussed in authoritative contexts.

In the ChatGPT-generated excerpt below, we can see that these brands are surfaced with sources like Wikipedia and NerdWallet highlighted: 

ChatGPT’s answer (April 2025) to “What are the best brands for small business marketing?” cites a NerdWallet affiliate review and Wikipedia as sources. The response lists only well-known brands in the marketing software space, reflecting a bias toward companies with significant off-page presence and coverage. Smaller or less-cited brands are absent, underscoring how AI answers gravitate to what authoritative sources have discussed ( ^[3] ). 

Inclusion in such lists doesn’t happen by accident – it’s often the result of successful outreach and PR. A tactical example: if you run a boutique CRM software company, you’d want to be mentioned in “Best CRM” roundups on sites like PC Magazine, TechRadar, or relevant industry blogs. Even if those mentions start as part of an earned media effort (e.g., pitching your product for review or contributing an article), the long-term benefit is that when an LLM later “reads” those articles during training or via real-time retrieval, it learns that your brand is associated with that category and carries credibility.

As Rand Fishkin advises, to get your brand into AI answers, you might need to “make sure that our brand is mentioned [on] the places on the web” that discuss your topic, even if “that’s a PR process and a pitch process” – it is absolutely worthwhile ( ^[4] ) ( ^[5] ). 

Authoritative Domains and LLM Trust: Search engines have long used domain authority or E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals as a proxy for credibility. LLMs don’t have an explicit E-E-A-T score, but they implicitly reflect these signals by relying on authoritative text. For example, if your company’s research report is cited by Wikipedia or appears on a .edu site, an AI model will likely treat the information as more reliable. Indeed, marketers have observed that ChatGPT’s newer browsing or integrated versions heavily cite Wikipedia for company or product information ( ^[3] ).

Being on Wikipedia (with a well-sourced page) thus becomes an off-page priority – it’s a seal of notability that not only helps with Google’s knowledge panels but also virtually guarantees an LLM like GPT knows about your brand. In April 2025, one test found that ChatGPT cited Wikipedia five different times when listing top marketing brands ( ^[3] ). While not every brand can have a Wikipedia page, you can aim to be referenced within Wikipedia articles (for instance, a study or infographic your company published might be cited as a source on a relevant Wikipedia page). Such references mean your brand’s name or URL is literally in the training data that many models ingest. 

Digital PR Case Study – HubSpot: A real-world example of digital PR’s impact involves HubSpot, a prominent marketing software company. HubSpot invests heavily in content and PR, resulting in numerous off-page mentions. When SEO experts queried a generative AI (ChatGPT-4) for “Tell me about HubSpot,” the answer came back with a well-rounded summary – and multiple citations to HubSpot’s own site and external sources ( ^[6] ). Interestingly, the AI cited HubSpot’s legal page and knowledge base, but not the official About Us page ( ^[7] ). This indicated that those were the pages where the model found the most concrete info about the company’s background (possibly because HubSpot’s about page was sparse on details).

The takeaway for HubSpot’s team was to enrich their About page with more substantive content ( ^[8] ) – yet the bigger picture is that HubSpot’s widespread presence (press coverage, a large Wikipedia entry, many third-party articles) ensured the AI had plenty of material to draw from. Smaller brands with little third-party recognition often get no mention at all in similar AI queries. In other words, if the world at large isn’t talking about your brand, AI likely won’t either. Building digital PR and off-site authority is how you change that. 

Impact on AI Citations and Summaries: Strong off-page authority not only influences whether an AI mentions you, but how. Google’s Search Generative Experience (SGE) and Bing Chat both provide AI-generated summaries with citations. These citations tend to favor sites Google/Bing already trust (news outlets, popular blogs, high-authority domains). If you’ve executed a good PR strategy, your brand might be the source being cited in those AI overviews. For instance, a well-placed thought leadership piece by your CEO on TechCrunch could lead to SGE citing TechCrunch (and by extension mentioning your CEO or brand) in an overview on that topic. Even if your site isn’t the one cited, getting an influential publication to include and credit your insights means the AI can pick up on that content. 

In short, off-page authority creates a ripple effect in AI: it increases the odds of your brand being part of the AI’s knowledge, and it elevates the credibility of your own content in the eyes of AI. It’s also worth noting that LLMs have a popularity bias : they are more likely to mention well-known entities simply because those appear frequently in the training data ( ^[9] ) ( ^[10] ).

A 2023 study on brand bias in LLMs found that models “favor established global brands while marginalizing local ones,” often associating big brands with more positive attributes ( ^[11] ) ( ^[10] ). This is a double-edged sword. On one hand, it means incumbent brands with lots of press and mentions enjoy an advantage – AI might default to recommending them. On the other hand, it’s a challenge for up-and-coming players. The only way to counteract that bias is to increase your share of voice in reputable sources. If you can generate a steady drumbeat of coverage – say, a mention in a Forbes article here, a quote in a Gartner report there – over time the AI’s “memory” of your brand grows.

In fact, marketing strategists in 2025 often talk about “feeding the model”: ensuring that at least some of the billions of words being used to train GPT-4, GPT-5, Gemini, etc. include your brand in a positive, relevant light. Peter Buckley of Meta argued that “we now need to market to two brains: the human and the model”. Human audiences respond to emotional storytelling, but “LLMs rank brands based on facts, structure, and relevance”, meaning brands need factual, cite-worthy content in the public domain ( ^[12] ).

Multiple studies, he notes, show that “the volume of brand mentions across the web directly affects whether a brand is surfaced by LLMs”, giving earned media a whole new level of strategic value ( ^[1] ). In other words, every PR win not only boosts brand awareness for people, but also plants a seed in the AI ecosystem that can yield visibility later. 

Tactical Tips for Digital PR in the AI Era: 

1. Aim for High-Authority Coverage: Prioritize getting features or mentions in sites that AI models are likely to “trust” (news sites, popular Q&A sites, .edu and .gov resources, etc.). A single reference in Wikipedia or a respected journal can do more for AI visibility than dozens of minor blog mentions. 
2. Create Original Research/Data: One effective PR tactic is publishing studies or reports that others will cite. If your data point gets picked up widely (e.g., “Company X’s survey finds 60% of consumers do Y”), it might end up referenced across the web and even in knowledge repositories like Wikipedia. Generative AI often regurgitates such statistics if they were prominent in its training set. Original, citable content thus serves both as link bait and as “AI bait.” (For example, the Bain & Company stat that “80% of consumers rely on AI content for at least 40% of their searches” is now echoed in many articles ( ^[13] ) – any AI trained on 2024 web data has likely absorbed that tidbit.) 
3. Leverage Thought Leadership: Get your experts (or brand) quoted on authoritative platforms. A quote in a New York Times article or a mention on a popular podcast that later gets transcribed can become part of the AI narrative about your industry. LLMs like to name-drop known experts and companies when answering questions like “what do experts say about X?” – so being among those quoted voices enhances your AI visibility as a recognized authority. 
4. Monitor and Amplify Earned Media: Whenever your brand does get a high-profile mention, make sure to amplify it (through social channels, press sections on your site, etc.) and possibly get it linked or referenced elsewhere. The more it spreads, the more entrenched it becomes in the training data supply. Consider also connecting with the author or site to correct any inaccuracies – you want the information that propagates to be accurate, since AI might later present it as fact. (This is part of reputation management in the AI context – an extension of PR where you not only get mentioned but ensure the mentions are correct.)

In summary, digital PR and off-page authority tactics lay the groundwork for brand inclusion in AI-driven results. They establish your brand as one of the names that comes up in credible discussions – and thus one of the names an AI might confidently present to a user. As generative search grows, those brands that have invested in off-site authority will often find themselves one step ahead in the new zero-click, AI-answered world.

Social and Community Presence: Feeding the Conversational Web

In the modern search ecosystem, content isn’t confined to websites and news articles. A huge amount of knowledge and discussion lives on social networks, forums, and community-driven platforms. Users ask questions on Reddit and Quora, share reviews on YouTube and TikTok, and offer advice in specialist communities. Importantly, LLMs sometimes draw from these less-formal sources – either through their training data or via real-time search integration.

Therefore, participating in online communities and cultivating a social media presence can indirectly influence what AI systems “know” and say about your brand or product. 

Forums and Q&A Sites: Platforms like Reddit, Stack Exchange, Quora, and niche forums are goldmines of crowd-sourced information. For years, Google’s search results have often surfaced Reddit threads or Quora answers for long-tail queries. Now, AI-driven engines are following suit. Tools like Perplexity.ai explicitly incorporate forum content in their cited answers. Google’s SGE has a “Discuss” or “Perspectives” feature that can pull in forum posts or social commentary for certain queries.

Moreover, many LLMs were trained (up to their knowledge cutoff) on data from these platforms. Reddit, in particular, has been a significant part of AI training sets – so much so that in 2023 Reddit announced plans to charge for API access and data licensing to AI companies ( ^[14] ). The message is clear: popular subreddit discussions are valuable training data. If a topic or brand is frequently discussed on Reddit, an AI model might have “seen” those discussions. From a marketer’s perspective, this means engaging authentically in relevant online communities can pay dividends.

Consider an example: A developer asks on Stack Overflow about the best cloud hosting for a certain use case. If an employee or advocate of a hosting company provides a helpful, non-promotional answer that gets upvoted, that Q&A might become part of the data that an AI like Claude or Llama2 was trained on (many open-source LLMs have indeed ingested Stack Exchange data).

Later, if someone asks the AI, “What are some reliable cloud hosts for X use case?”, the model might recall the solutions mentioned frequently – including that company from Stack Overflow. Similarly, a highly upvoted Reddit comment describing the pros and cons of a new smartphone might influence an AI’s answer to “Is [Smartphone XYZ] any good?”. 

Authentic Participation: The key word here is authentic. Online communities can spot overt marketing a mile away, and heavy-handed self-promotion can backfire (and may even violate platform rules). The goal is to contribute value : answer user questions, provide insights, share knowledge freely. It’s acceptable to disclose your affiliation (in many forums it’s required), as long as you’re genuinely helping and not just plugging your brand. In fact, being transparent about your company role while offering useful info can build goodwill.

Other users may start to mention your brand positively even when you’re not in the conversation. For instance, representatives from tech companies often participate in subreddit AMAs (Ask Me Anything) or help troubleshoot issues on forums – this humanizes the brand and generates content that could later serve as training data. As one SEO expert noted, “Engaging authentically on behalf of a brand (with affiliation revealed) is often welcomed to clarify user questions, and it will likely benefit your generative AI optimization journey too.” ( ^[15] ).

In short, if people are talking about your brand (and in the right way) on forums, then AI will eventually talk about your brand as well. 

Social Media Signals and AI Models: Social networks like Twitter (now X), LinkedIn, YouTube, and TikTok represent another facet of off-page presence. Traditionally, Google has maintained that social signals (likes, shares) aren’t direct ranking factors. But for LLMs, the relationship is different: the content of social media can become part of the AI’s knowledge. For example, OpenAI’s GPT models up to GPT-4 were trained on a portion of the public web which likely included some Twitter content (especially before certain dates). Meanwhile, Elon Musk’s new AI venture xAI explicitly uses public Twitter/X data to train its chatbot Grok ( ^[16] ) ( ^[17] ). Grok is designed for real-time, up-to-date responses and “integrates real-time learning from X (Twitter) to provide context-aware answers.” ( ^[18] ).

This means that what’s trending or frequently discussed on X can influence Grok’s output. If your brand is a frequent subject of tweets – say tech influencers often mention your product – Grok might “know” a lot about it. Conversely, a brand with no Twitter presence or discussion might be invisible to Grok except for whatever it scrapes from the web. It’s not just Grok. Other models and search AIs are likely to incorporate social content in various ways: 

• Google has hinted at incorporating more “voices” from forums and social in its results (hence the SGE “perspectives” section). Future Google AI could use public social content to add nuance or opinions to answers. 
• Bing integrates some Live Search and could theoretically pull in social posts if relevant (especially for real-time queries, e.g., Bing’s chatbot might show a live tweet for a breaking news question). 
• Localized AI models (like Baidu’s ERNIE in China) might ingest data from their local social platforms (Weibo, Zhihu, etc.). We’ll discuss international specifics later, but the concept holds: in any market, the prevalent social/community platforms influence that locale’s AI knowledge. 

Being Present and Valuable: So how can a brand leverage social and community presence for AI benefits? 

1. Establish expertise on Q&A platforms: If you are in B2B, for instance, answer questions on Quora with depth and objectivity. If Quora content is used by an AI (some models likely trained on Quora answers), your explanations might teach the AI about concepts and connect them to your brand. It’s not about promoting, but about being part of the informational canon. 
2. Foster discussion in communities: Engage with communities like Reddit in your niche. Some companies create official accounts or even brand-specific subreddits to host discussions. Even broader participation helps. Imagine a popular Reddit thread “What’s the best mattress for back pain?” If a lot of users happen to mention a particular D2C mattress brand (because the brand seeded a few trial units to Redditors or simply has a good product that people naturally recommend), that thread’s content might later shape an AI’s answer to “Recommend a mattress for back pain.” 
3. Leverage YouTube and visual platforms: YouTube comments and transcripts are often indexed by search and possibly consumed by AI training. If your brand has a strong presence on YouTube (through educational videos, for example) and those are discussed or referenced widely, it adds to the off-page signal. Additionally, AI models are evolving to be multimodal – they might parse video transcripts or audio in the future. Already, voice assistants and AI can parse YouTube transcripts for info. A tutorial video where an influencer reviews your product favorably could indirectly influence AI answers to related questions (“How to do X” – AI might recall the method described in the video). 
4. Be mindful of sentiment: Community and social content often include subjective opinions – which AI might pick up. If many users complain about a specific flaw of your product on forums, an AI could surface that as a “common con” when asked about your product (because it has effectively done a tally of what people say). Inversely, if a particular feature is consistently praised in user discussions, the AI is likely to mention that pro. This connects to the next section (reviews), but the principle is: widespread social sentiment can become AI “knowledge.” Brands should therefore monitor and engage in these spaces not just to push positive messages, but to address issues. Demonstrating customer support publicly (responding to a complaint on Twitter, for example) can even turn a negative into a positive knowledge point (“Brand X is known for responsive support”). 
5. Reddit and others as cited sources: Interestingly, some AI search engines cite Reddit or Stack Exchange directly in answers. Perplexity.ai, an AI search assistant, often provides answers with footnotes from sources, and it’s not uncommon to see a Reddit URL in those footnotes for certain queries (especially technical or niche questions). Google SGE, in its early 2023 demo, showed an example of an AI snapshot answer that included info likely drawn from forums (Google also launched a “Perspectives” filter to highlight forum/social media answers alongside the AI summary). The implication for marketers is: the content you or your users create on community platforms can get directly quoted or surfaced by AI. Imagine hosting a highly informative thread on your own community forum – Google’s AI might actually pull a sentence from a user’s post there into the answer box (with a citation). If that happens, it’s as good as an organic ranking, even if it’s not on your main site. Thus, investing in community building (like forums, user groups, Discord servers) not only helps traditional community engagement but could become part of the AI answer ecosystem. 
6. International Note – Community Presence Beyond English: Different markets have their own dominant platforms. For example, in China, people turn to Zhihu (a Quora-like Q&A site) or Weibo for discussions, and Baidu’s search AI will likely tap into Baidu Zhidao (Q&A) and Baidu Tieba (forums). In Russia, VK and local forums matter. Ensure that your international marketing teams are engaging where local users ask questions. If your brand has a global footprint, being present in those native-language discussions is crucial – not only will it reach human audiences, it will be reflected in any region-specific AI models or search engines. Many non-English LLMs (and multi-lingual ones) have training data drawn from local Wikipedia editions and forums, so raising your brand profile in those languages (through PR and community talk) feeds the model similar to English content.

To summarize, social and community presence is the new “word-of-mouth” in the AI training corpus. What humans say in these spaces becomes what the machine repeats. By actively participating and fostering positive, informative conversations about your brand on these platforms, you increase the chances that when an AI is answering a related question, your brand’s perspective or name will surface. It’s a long-game, largely indirect, but it aligns with an authentic marketing approach – helping people in the channels they already frequent, which in turn helps the AI pick up your brand’s trail.

Reviews and User-Generated Content: The New Influencers of AI Recommendations

When consumers are looking for products or services, reviews and user-generated content (UGC) play a pivotal role. In the AI era, this dynamic remains – but now the LLMs themselves act as intermediaries, synthesizing and conveying the collective voice of users. If you want your brand to be recommended or favored by AI in contexts like “What’s the best [product] for [need]?”, earning positive reviews and fostering UGC is critical. Models trained on e-commerce data, forums, and Q&A sites will pick up on these signals (like “best rated”, common pros and cons, etc.) when formulating answers. 

UGC as Training Data: Think about what an LLM knows regarding a product category. A large portion of that knowledge might come from scraping sites like Amazon (product descriptions and possibly some reviews), Trustpilot, TripAdvisor, CNET, niche review blogs, and more. Even if not intentionally included, models might ingest review content that’s prevalent on the open web. For example, a generative AI might have learned that “Restaurant ABC has a 4.7/5 rating and people often praise the ambiance but mention slow service” simply because those points appeared across dozens of Google Reviews or Yelp snippets that made their way into some web crawl. Now, when a user asks “Is Restaurant ABC good for a date night?”, an AI could respond, “It’s highly rated (4.7) and known for great ambiance, though some reviews mention the service can be slow.” In doing so, it has effectively echoed user-generated sentiment. We already see glimpses of this in how AI chatbots answer product queries:

ChatGPT with browsing or plugins : OpenAI has experimented with a browsing mode and plugins that can pull real-time info. If you ask, for instance, “What are the pros and cons of the XYZ camera?”, a browsing-enabled AI might fetch a page like a Best Buy customer reviews summary or an expert review, then tell you “Pros: excellent image quality, durable build; Cons: expensive, heavy – as noted by many reviewers on BestBuy.com.” Even without browsing, base GPT-4 may have seen enough commentary to enumerate common pros/cons (with some risk of hallucination if not retrieved live). 

Bard (Google’s AI) : It can draw on Google’s Knowledge Graph which includes aggregated review info. Bard or SGE might actually say “This vacuum is rated 4.5 stars, with users liking its suction power but disliking the noise.” That kind of response directly reflects UGC. Similarly, when asked about “best laptops under $1000”, it might cite sources like LaptopMag or Reddit threads, often incorporating consensus opinions (“most users find the battery life of Model X to be excellent”). One telling example came from the earlier scenario with HubSpot. When ChatGPT-4 was prompted to give pros and cons of HubSpot for small business marketing, it did so and the cons it listed correlated with points from a NerdWallet review page ( ^[19] ). The AI’s citations even pointed to NerdWallet (an affiliate site known for product comparisons) as a source.

This shows that AI will mine third-party reviews and comparisons to answer specific brand questions, effectively acting like an ultra-fast review aggregator. If your product has a recurring negative mentioned across many reviews, don’t be surprised if an AI brings it up. Conversely, if you have genuinely rave reviews highlighting a strength, the AI will likely emphasize that point. 

Encourage Positive Reviews (Genuinely): No, this doesn’t mean spam the system with fake 5-stars – AI may eventually catch patterns of inauthenticity and it certainly violates ethics. Instead: 

Provide great products and service so that positive reviews happen organically. (It always starts here!) 

Invite feedback from satisfied customers on platforms that matter (Google, Amazon, industry-specific sites). Often, a steady stream of recent positive reviews not only boosts SEO and conversion but also seeds the AI’s “memory” with favorable content. 

Address negative feedback openly and promptly. If you can turn a 2-star customer into a 4-star by solving their issue, that follow-up might become part of the narrative that an AI learns: “Customers initially had issues with X, but the company was quick to resolve them.” This could mitigate an otherwise damaging generalization. 

Structured Data for Reviews: There’s a technical side to this as well. By using schema markup (Review and AggregateRating schema) on your site’s testimonials or product pages, you feed structured information to search engines and potentially to AI. Google’s SGE has shown product summary boxes that include star ratings and price ranges. These are pulled from structured data and Google’s own shopping data. If AI has access to that, it will use it. Also, companies like OpenAI and others might use schema-structured data as part of training or retrieval for factual consistency.

The bottom line: make sure your high-level review info (average rating, number of reviews) is easily machine-readable. It helps traditional SEO (rich snippets) and gives AI factual fodder. 

Models Trained on E-commerce Data: One emerging area is AI-powered shopping assistants. OpenAI launched a ChatGPT Shopping feature in 2025 that integrates with Shopify and uses real product data to make recommendations ( ^[20] ) ( ^[21] ). Early indications show it leverages reviews data as a primary source of truth when deciding what to recommend. Adobe’s e-commerce reports found that traffic to retail sites from generative AI sources rose 1200% from mid-2024 to early 2025 as these tools rolled out ( ^[21] ).

That’s an astronomical jump (albeit from a small base) and signals that AI shopping helpers are becoming a real referral channel. To “win” in those interactions, a brand needs to look attractive in the data the AI sees – which includes review sentiment, ratings, and product specs. A 2025 guide from Reviews.io (a review platform) emphasizes how high-quality review content can boost AI-driven product visibility : “Reviews offer the kind of high‑quality structured data that generative AI engines love.” ( ^[22] ).

The guide suggests that it’s not just having lots of reviews, but having the right kind of detail in them. For instance: Verified reviews add trust (AI can distinguish verified purchases, which carry more weight). Reviews that mention specific attributes or use-cases (“this stroller is great for traveling because it folds compactly”) provide context-rich content that AI can latch onto ( ^[23] ). Questions and answers (Q&A content) on product pages – often UGC where customers ask something and the brand or community answers – give clear, concise info that AI can directly use.

One interesting tactic is excerpting and summarizing reviews in a way that machines can easily parse. Some brands use widgets that compile common pros and cons from multiple reviews (like a summary: “ Pros: long battery life, lightweight; Cons: limited color options”). This effectively does the AI’s job of aggregation, and if it’s visible on your site (marked up properly), an AI might pick that up. In fact, Reviews.io offers an “Expert Answers” Q&A widget and review tagging for themes ( ^[24] ) ( ^[25] ) – all aimed at structuring UGC so AI can digest it.

The mantra is: treat your reviews as data for AI. High volume of authentic, detailed reviews = robust data. Structure and label that data = easier for AI to learn from it or retrieve it. 

Influencer Content as UGC: Don’t overlook user content beyond text reviews. YouTube reviews, Instagram testimonials, TikTok unboxings – these are UGC too. As AI becomes multimodal, it might interpret spoken/written words in videos or images. Already, Bing’s AI can summarize YouTube videos upon request; tomorrow’s AI might integrate that knowledge proactively. So encouraging customers or influencers to create content around your product can indirectly feed the AI narrative. If many tech influencers say “Battery life of this laptop lasts ~8 hours” in their videos, an AI that’s connected to a transcription service might state that fact when asked. This is speculative but within the realm of possibility given how fast AI capabilities are evolving with multimedia. 

UGC on Your Own Properties: A quick note on hosting user-generated content on your site (like comments, forums, Q&A). This can be double-edged. On one hand, it creates on-page content that could rank (traditional SEO) and could be picked by AI as part of your page content if it’s retrieving from your site. On the other, if not moderated, it can include incorrect info or spam which you wouldn’t want propagated by an AI. So, moderate carefully. Foster a positive community whose content can serve as a knowledge source. 

Case in Point – The “Best Product” Queries: Everyone wants to be the answer when someone asks an AI “What’s the best [your category product]?”. AI tends to answer such questions with a list of a few options (often 3-5) with brief rationales. What determines those picks? Based on observed behavior in 2024-2025: Brands frequently recommended on affiliate “best” lists (the AI might have seen 10 different “Top 10 X” articles and noticed certain names keep appearing at the top). Products with very high average ratings (if the AI has access to aggregate ratings, it may favor those above a threshold). Products with distinguishing features that stand out in reviews (the AI loves to justify its choice with something – “X is best for budget-conscious buyers, Y is best overall for quality,” etc. It learns those associations from the way people talk about the products).

So, if you are not at least in the conversation on those fronts, you’ll be absent from the AI’s answer. This might require not just one tactic but a synergy: get into the review roundups (PR), ensure your product truly satisfies customers (product dev), encourage them to leave reviews highlighting why they chose you (marketing/CRM), and even subtly highlight those strengths in your own content so that they get quoted.

To illustrate, imagine Company A and Company B both make a smart home device. Company A has a 4.8-star rating with hundreds of reviews mentioning “easy to set up” and “great customer support.” Company B has a 4.2-star rating with some reviews praising advanced features but many mentioning “glitchy software.” If a user asks the AI, “Which smart home hub should I buy?”, the AI might answer: “You have a few options. The XYZ Hub (Company A) is highly rated for its ease of setup and reliable support, which makes it a great choice for most people ( ^[24] ). Alternatively, ABC Hub (Company B) offers more advanced features, but some users report software issues.” Here, clearly, Company A wins the recommendation. Why? Because the user consensus in UGC favored it, and the AI mirrored that consensus. 

Managing and Monitoring Reviews for AI Impact: It’s now important to monitor your reviews not just for human reputation but for AI portrayal. Are there misconceptions propagating in reviews? (e.g., multiple people wrongly assuming your product lacks a feature and complaining – the AI won’t know they’re wrong; it will assume your product indeed lacks it because “many users say so.”) If so, you might need to address it in an FAQ or response so that corrected information exists for the AI to find. Also, keep an eye on how AI currently describes your product. You can literally ask ChatGPT or Bing, “What do people like about [Product] and what do they dislike?” See if the answer aligns with your understanding. If an AI says something like “Many people say it’s overpriced,” you know that’s out there in the collective feedback and should be a concern to tackle (either via product pricing strategy or via communicating value better).

Lastly, UGC extends to broader discussions about your brand outside of dedicated review channels. This overlaps with social/community presence: a Reddit thread “Anyone try [YourApp]? What’s your experience?” is effectively a user review discussion. These informal discussions can influence AI too. Encouraging happy customers to share their experiences in forums or communities (when appropriate) can create positive “grassroots” UGC that shapes perception.

In summary, user-generated content – especially reviews – are the lifeblood of an AI’s recommendations. They represent the voice of the customer at scale, and AI listens to that voice attentively. By ensuring your off-page presence includes a strong constellation of positive, authentic reviews and by structuring that content for machine consumption, you greatly enhance your chance of being the brand that AI suggests when it’s playing the role of salesperson or advisor.

Collaboration, Knowledge Sharing, and Building a Web of Influence

Another powerful way to boost off-page authority in the AI era is through collaboration and knowledge sharing. This means contributing value to the broader industry ecosystem – via research, data, open-source projects, or educational content – such that your brand becomes ingrained in the collective knowledge that AI draws upon.

When your insights or resources get referenced by other websites, analysts, or even Wikipedia, you effectively feed the AI new information with your brand’s name attached. Over time, these contributions make your brand an “entity” that AI models recognize and respect.

Contribute to Industry Research & Whitepapers: Many companies have started to publish original research or partner with academic institutions to conduct studies. If your organization produces a well-regarded annual report (for example, “State of Remote Work 2025” or “Cybersecurity Benchmarks Report”), this can get widely cited. Not only does it earn backlinks (traditional SEO win), but it also means any AI trained on 2025 web content will very likely ingest parts of that report and the fact that “according to [Your Company’s] research, XYZ.” If that stat is useful, AI might even quote it in answers. In fact, LLMs often preface facts with “according to [Source]…” if their training included that phrasing. Imagine a user asks, “What percentage of companies plan to increase AI spending next year?”

If your company’s whitepaper found “45% plan to increase AI spend,” an AI might reply, “According to a 2025 report by [Your Company], 45% of companies plan to increase AI spending next year.” – This directly inserts your brand into the AI-generated answer (and establishes you as an authority on that topic). For this to happen, your research needs to be openly accessible (at least key findings) and preferably covered by secondary sources (news sites writing about your report).

A mention in high-authority contexts – e.g., your stat gets into a Wikipedia article or a Forbes piece – further solidifies it. There’s a virtuous cycle: you share knowledge → others cite it → AI sees multiple citations and trusts/learns it. “If your data or insights get referenced by other websites or Wikipedia, they are more likely to become part of the corpus AI learns from,” as our outline noted.

A single Wikipedia mention could mean an AI will “know” your brand or findings and might cite them when relevant in conversations.

Open Data and Open-Source Projects: In tech fields especially, contributing to open-source or open data initiatives can amplify your off-page presence. For example, if your company open-sources a useful software library on GitHub, developers worldwide might use and discuss it. GitHub discussions and stars are one indicator (some LLMs have knowledge of popular GitHub repos up to a point). Also, documentation might be indexed. There’s evidence that Meta’s Llama model, for instance, internalized a lot of knowledge from coding sites and GitHub. If your brand name is tied to a popular open-source tool (think of “Facebook’s React library” or “Google’s TensorFlow” – those brands get huge authority by association in AI’s mind), that’s a massive boost in AI-era clout.

Likewise, releasing open datasets or participating in community data challenges (Kaggle competitions, for example) can get your brand name circulating among practitioners and in published solutions. If researchers use your dataset and credit your company in papers or blogs, that’s creating intellectual backlinks. LLMs that read arXiv papers or blogs may pick up those references. Even something like being listed as a data provider in a prominent dataset index could raise your profile.

Knowledge Sharing via Wikis and Forums: Many companies also share knowledge via channels like Medium posts, developer forums, or knowledge hubs. If done in a way that others link to it as a reference, it’s beneficial. One notable angle is Wikipedia editing. While directly writing your own Wikipedia page is discouraged (conflict of interest), you can legitimately contribute to Wikipedia on topics you know, citing reliable sources (not your own marketing material, but perhaps your research). If your work is truly notable, someone else might add it. Having your brand or product mentioned in relevant Wikipedia articles (not just the page about your brand, but in the context of some technology or concept) can be huge.

For instance, the “ChatGPT” Wikipedia page might list companies that have partnered with OpenAI – being on that list means every clone of Wikipedia (and there are many in datasets) includes your name.

Entity Recognition and Knowledge Graphs: Collaboration and being cited feeds not just the raw text AI, but also the structured knowledge systems like Google’s Knowledge Graph or Microsoft’s LinkedIn-based graph. If you contribute to standards bodies, have executives on association boards, or partake in notable collaborations, you become part of those networks of information. Google’s Knowledge Graph, for example, might give your brand an “entity” with various connections (CEO name, founded date, industry, key product).

Bard and SGE likely consult that for factual consistency. Ensuring that public knowledge about those facts is accurate (via Wikipedia/Wikidata or authoritative sites) is important. One can add or edit entries in Wikidata (which is a structured database many AIs use for grounding). Ensure your company’s Wikidata entry is up to date with proper references. It’s a behind-the-scenes task that could pay off when AI needs to spit out your headquarter location or tagline correctly.

Collaborate with Influencers and Thought Leaders: Another form of collaboration is co-creating content with respected voices. For example, co-author a paper with a university lab, or have a well-known expert contribute a chapter to your eBook. When those experts mention the work, it adds credibility and visibility. AI models might not know every niche influencer, but they certainly know some (especially if those people have Wikipedia pages or are cited frequently). If, say, a famous AI professor tweets about your research and blogs it – that might indirectly propagate into the AI’s sphere. The AI might not know your brand initially, but it knows the professor is trustworthy (from their published work), and now that professor’s blog discussing your research becomes a source the AI trusts.

Community Knowledge Sharing: On a smaller scale, sponsoring or actively participating in community knowledge bases (like answering on Stack Exchange, writing in Medium Publications, speaking in webinars or podcasts that get transcribed) spreads your brand’s expertise. Many of these content forms (Q&A answers, blogs, transcripts) become web text that LLMs train on or search engines index. For instance, if your CTO answers many questions on Stack Overflow about a certain programming language, an AI might one day answer a user query with, “According to [Your CTO’s Name] on Stack Overflow, the best way to optimize in that scenario is to do X.” It sounds far-fetched, but we’ve already seen ChatGPT cite individual’s answers from forums in some plugin-enabled modes.

Educational Resources and MOOCs: If relevant, create free educational content – like a mini-course, certification, or tutorial series. If widely used, this can become part of the canon. E.g., HubSpot’s free marketing academy got their terminology and frameworks into widespread use, some of which even AI might reference (e.g., “According to HubSpot’s Flywheel model…” etc.). Offering value without paywall makes it more likely external sites will reference it (and thus AI too).

In short, sharing knowledge freely and collaborating beyond your own walls builds an external web of references to your brand. It positions your company as a thought leader or at least a contributor to the advancements in your field. AI models, which are essentially giant prediction machines, lean on these bread crumbs of facts and references to form their answers.

By increasing the density and quality of those bread crumbs with your name on them, you create pathways for the AI to mention or credit you. One more angle: public data contributions. Some organizations have started putting datasets or insights on public data portals or even contributing to government or NGO research.

For example, a fintech startup might provide anonymized trend data to a central bank’s annual report. If cited, that could later be reflected in any AI summarizing the report. Or a healthtech company might share statistics with the World Health Organization for an advisory – later an AI might say “WHO data indicates X” (and the fine print source was originally that company’s contribution).

Lastly, monitoring this aspect is tricky – how do you know if your collaborations are noticed by AI? One way is to see if your brand or key personnel have made it into knowledge base entries (like Google’s Knowledge Panel or Wikipedia). Also, simply ask AI models: “What do you know about [Company Name]’s research on [Topic]?” or “Does [Company] have any open-source projects?” If the AI can answer with specifics that you’ve indeed put out there, it’s working.

If it says “I’m not aware” or answers incorrectly, that shows a gap in the AI’s training recognition of your contributions – maybe your stuff didn’t circulate widely enough or is too recent to have been ingested. That’s feedback to either promote it more or ensure it’s indexed.

Global and Non-English Collaboration: Ensure that your knowledge sharing isn’t confined to English if you operate in other languages. For instance, publishing a Spanish version of your whitepaper can lead to Spanish language sites citing it, thus Spanish LLMs or multilingual models getting it. Some countries have local encyclopedias or knowledge bases (e.g., Baidu Baike, the Chinese Wikipedia equivalent). If your brand is global, consider working with local partners or agencies to establish a presence in those. Being part of an international research effort or standard (like W3C, ISO, etc.) can also put you on the map globally in AI’s eyes, as those are highly respected sources.

In essence, think of collaboration and knowledge sharing as seeding the information landscape. It’s planting seeds of data and insight that others water by referencing, and AI harvests when generating answers. Unlike SEO where sometimes content stays siloed on your site, here you want your ideas to spread and be rehosted or mentioned elsewhere (with credit ideally). It’s a slightly altruistic approach – give more to get more presence – but it can yield high authority returns that no amount of on-page keyword tweaking could achieve.

Monitoring Brand Mentions in AI Outputs

After investing in off-page SEO and brand authority efforts, it’s crucial to measure their impact in the AI landscape. Traditional SEO has an arsenal of tools for tracking rankings, backlinks, and mentions on the web. Now, analogous tools (and methods) are emerging to track brand mentions in AI-generated content – essentially keeping an eye on how and when your brand appears in responses from chatbots and generative search results. Monitoring this helps you understand your current visibility and reputation in AI outputs, and it provides feedback for further optimization. 

Manual Testing with AI Chatbots: A simple starting point is to ask the AI directly about your brand. As recommended by SEO experts, use generative AI tools themselves to learn what they know (or don’t know) about your brand ( ^[26] ).

For example: 

• “Tell me about [Your Company].” – Does the AI give a correct summary? Does it cite sources? Are those sources your site or Wikipedia or something else? 
• “What are the best companies/products in [your industry]?” – Does your name appear? If not, who is dominating and why? 
• “What are the pros and cons of [Your Product]?” – What negatives does it list, and what sources is it drawing from (reviews, competitor comparisons, etc.)? 
• “Is [Your Brand] reputable for [service]?” – This can surface any trust or quality issues the AI might have picked up (maybe from reviews or news).

Alli Berry, in the Search Engine Journal article we examined, did this for HubSpot ( ^[6] ) ( ^[27] ). She found that ChatGPT’s answer cited HubSpot’s own legal pages and KB articles, and in a “best brands” query it cited Wikipedia and NerdWallet for context ( ^[3] ). By drilling down with follow-up questions (pros and cons of HubSpot), she could identify exactly which review sites or sources were feeding the AI information ( ^[19] ). This is invaluable. It’s like reverse-engineering the AI’s brain to see which content pieces influenced its knowledge. 

If you find the AI referencing certain articles or sites frequently in answers about your space, those are sources you either want to be present on or improve your presence on. Perhaps a specific review blog holds a lot of sway (as NerdWallet did in that case); you might consider engaging that site for an updated review or partnership so that future AI answers have better info.

Alli’s approach and advice: If the AI cites an obscure or less credible site for info on you, that’s an opportunity to provide better, more authoritative content on the topic, or to get coverage from a more authoritative site ( ^[28] ). She noted that some citations were not well-known, so PR could aim to get more authoritative coverage, which hopefully the AI would incorporate next time ( ^[29] ). If the AI lists cons that have validity, feed that back into product improvement, and build relationships with the sources that mention those cons so they can update their content when you address the issues ( ^[30] ).

Essentially, treat AI as another channel where your brand reputation is playing out, and manage it accordingly. 

Tools for AI Brand Monitoring: As the importance of AI visibility grows, several SEO and analytics companies have rolled out features to track this. For example: 

• Ahrefs’ Brand Radar – A feature launched in 2025, which lets you track brand mentions in ChatGPT and Perplexity, with Google’s Gemini promised to be included as well ( ^[31] ). It provides an index of questions where your brand appears in AI answers and how you show up. This helps quantify, for instance, “Our brand was cited in 25 different ChatGPT answers last month” – something that was impossible to know before. It’s updated regularly, showing trends over time. 
• Authoritas / SGE Monitor – Some tools focus on Google’s Search Generative Experience. Authoritas claims to have an AI Overview rank tracker that not only detects when an AI summary appears for a keyword, but whether your site was cited in it and at which position ( ^[32] ). This is akin to rank tracking but for AI inclusion. If you optimize a page and suddenly it starts getting cited in SGE, these tools would catch that. 
• Keyword.com’s AI Rank Tracker – A platform that touts tracking across multiple generative AI platforms (ChatGPT, Google SGE, Perplexity, Claude, even DeepSeek and Mistral – basically any notable LLM chat/search) ( ^[33] ) ( ^[34] ). It logs which queries produced an answer that mentions your brand and in what context. They often include features like prompt tracking (seeing what questions trigger your brand) and citation analysis (which of your pages or content are getting cited) ( ^[35] ) ( ^[36] ). There’s also an aspect of sentiment analysis – some tools try to gauge if those mentions are positive, neutral, or negative. 
• Specialized Monitoring Tools – Besides the big SEO suites, some startups and SaaS tools have emerged purely for AI monitoring. For example, PromptMonitor.io or the ones mentioned in search results (like Irene Chan’s list of 9 tools ( ^[37] )) and others on Reddit. These might allow you to input your brand name and then periodically query various AI models via API to see how you’re mentioned. It’s like doing the manual Q&A method but automated and at scale. 

Tracking AI Referral Traffic: Another angle is to look at your web analytics for traffic coming from AI tools. For instance, Bing Chat and ChatGPT (with browsing) may actually send traffic if users click the citations. In GA4 or server logs, you might see referrals from domains like bing.com with unusual parameters, or from chat.openai.com (if someone using the browsing tool clicked your link).

One SEO shared that they saw referral traffic from perplexity.ai and chat.openai.com after allowing those bots to crawl ( ^[38] ). Setting up segments or alerts for such referrals can indicate, indirectly, that you were mentioned and someone clicked through. Granted, if AI answers get so good that users don’t click, you might not get traffic even if you were mentioned – but tracking a bump or drop in these new referral sources is useful to gauge AI impact.

GA4 can be configured with custom events to track, say, “Visits from AI summaries” if you tag those referral patterns ( ^[39] ) ( ^[40] ). This is bleeding-edge analytics; not all companies are doing it yet. 

Monitoring Brand Sentiment in AI: It’s not just whether you’re mentioned, but how. Some tools and studies look at sentiment analysis of AI mentions ( ^[41] ) ( ^[42] ). For example, are you being recommended enthusiastically (“highly regarded”) or cautiously (“some customers report issues with…”)? If an AI consistently frames your brand negatively, that’s a red flag that the source information it has is skewed negatively (or possibly the AI has hallucinated something harmful).

Either way, you’d want to investigate and counteract: maybe bolster positive content, address the root cause of negatives, or provide clarifications. There is even talk of a concept called “narrative anchoring”, where the first exposure an AI got about your brand (like an old news article or a big controversy) can anchor its narrative unless new information supplants it ( ^[43] ).

Monitoring tools that highlight the descriptors or common context around your brand in AI answers can help catch that. For instance, if the AI always mentions a 2023 data breach whenever your brand comes up, you need to flood the ecosystem with more current info and positive developments to dilute that association. 

Competitive Insights: While monitoring your brand, don’t forget to also monitor key competitors in the same way. Many AI tracking tools let you watch multiple brands. If you notice competitor X is constantly mentioned by ChatGPT in answers where you are not, analyze why. It could be their stronger off-page presence or a successful PR campaign.

This can inform your strategy: you might need to mirror their tactics (e.g., get into the same review articles or get a Wikipedia page if they have one, etc.). Conversely, you might discover gaps – maybe the AI gives outdated info about a competitor (like a product feature they no longer have).

That’s an opportunity: if even the AI is behind, maybe that competitor hasn’t been active in sharing updates, and you can take advantage by being more present with updated content (making you the fresher source). 

Staying Updated with AI Platform Changes: Keep an eye on updates from OpenAI, Google, Microsoft, etc., about how their AI systems incorporate content. For example, OpenAI’s policies around its web crawler GPTBot evolved – initially many sites blocked it (concerned about their content being used without credit), but if you choose to allow it, you’re essentially agreeing to let ChatGPT train on your site’s content.

As of 2024, about one-third of top websites blocked GPTBot ( ^[44] ) ( ^[38] ), including some big brands. If your competitors block it and you don’t, your content might feature more prominently in future GPT models (because their content wasn’t included). It’s a strategic decision: visibility vs. intellectual property concerns. Either way, being aware of these developments is part of monitoring the landscape. 

Direct Feedback from AI Companies: While not common yet, we might see tools from the AI providers themselves. OpenAI has experimented with a “Browse with Bing” that cites sources. Google’s SGE is citing and might eventually show site owners data in Search Console about AI appearances (nothing official as of early 2025, but logically they might). Bing Webmaster Tools could provide something on how often your site was used in Bing’s chat answers. So, keep an ear out for such features – they would greatly ease monitoring if/when they arrive.

In practice, a monitoring routine could be:

1. Every month, use ChatGPT, Bard, and Bing to run through a list of key brand queries (the ones we listed earlier). Document the responses and sources. This is a manual “audit” of your AI presence.
2. Use an automated tool (if budget allows) like Ahrefs or Keyword.com to continuously watch a broader set of queries and send alerts when something changes (e.g., your brand appears for a new query or disappears from one).
3. Track referral traffic and set up alerts for spikes or drops from domains associated with AI (OpenAI, Bing, Perplexity, etc.).
4. Regularly prompt the AI in a neutral way: “What is [Brand] known for?” and see if any misinformation creeps in. If yes, that’s a crisis prevention flag – you might need to do damage control in the source content or via PR.
5. Using AI to Audit Content: Ironically, you can also use AI itself to help with monitoring. For instance, you could use a tool like GPT-4 to analyze a large set of AI responses about multiple brands and have it summarize comparative mentions. Or use Python with AI APIs to simulate hundreds of user questions and parse the answers for mentions of your brand. This is more technical, but some advanced SEO teams are doing such programmatic analysis.
6. Finally, treat the insights from monitoring as actionable data. If you find, for example, that: You’re not being mentioned where you should be – revisit Sections 11.1–11.4 to amplify off-page efforts in those weak spots. You are being mentioned but incorrectly – consider an outreach to correct the sources (e.g., a wiki edit if factual, or contacting a blogger who had outdated info). A competitor gets a lot of love from AI – analyze their off-page footprint and emulate or outdo it. Users via AI are asking something related to your domain that no one (neither you nor competition) has covered well – perhaps create that content and propagate it so you become the go-to reference (first-mover advantage in AI answers).

The era of “AI SEO” or GEO (Generative Engine Optimization) is still new and evolving. Monitoring is how you keep your finger on the pulse and adapt quickly. As one expert said, “We will have to be more reliant on ourselves to reverse-engineer what we’re seeing in the data and run our own experiments” ( ^[45] ). By actively checking how AI portrays your brand, you essentially close the loop on your off-page strategy – ensuring all the brand authority you’re building externally is indeed translating into the AI-driven search results of the future.