AI mentions impact brand visibility and consumer decision-making
There’s a major shift happening in how people discover and evaluate brands, and it’s driven by large language models (LLMs) like ChatGPT and Google’s AI Overviews. These systems aren’t just answering simple questions anymore, they’re becoming digital gatekeepers. When someone asks about the best CRM, energy solution, or logistics provider, that answer gets filtered through what the AI system knows, and chooses to mention.
In this environment, an AI mention, your brand name appearing in an AI-generated response, is more than visibility; it’s influence. A neutral or favorable mention positions your company in front of decision-ready users. An unfavorable one does the opposite. If your brand doesn’t show up at all, it might as well not exist for that user.
These mentions are fundamentally different from traditional search rankings or ads. They don’t just put your logo in front of a user, they integrate your brand into the decision-making process. Users trust AI because the tone is informed and human-like, even conversational. So when your brand name shows up in that context, it implies authority and relevance. And unlike ads, it feels unprompted. That’s why it works.
We’re seeing hard data to back this. Semrush reported Google AI Overviews appeared in 13.14% of search result pages in March 2025. That number is growing. ChatGPT had nearly 600 million unique visitors in May 2025. And according to a Semrush analysis of one million non-branded queries, AI-generated content includes brand mentions in 26% to 39% of cases, depending on the platform. That’s reach you can’t afford to ignore.
Liz Reid, Google’s Head of Search, made it clear, AI Mode is “the future of Google Search.” She’s not exaggerating. These systems are shaping what people see, believe, and click. So the question isn’t whether your brand should care about AI mentions. It’s whether you’re doing enough to get in front of buyers who now trust AI more than search engines.
Understanding the difference between AI mentions and AI citations
To compete in this new landscape, you need to understand what you’re optimizing for. A lot of companies confuse AI mentions with citations. They’re not the same, and if you treat them like they are, your strategy will miss.
An AI mention is when your brand is referenced in a response, like “Semrush is a top SEO tool.” It can be linked or unlinked. What matters is that the brand enters the conversation. An AI citation, on the other hand, is a footnote, a source that supports what the AI is saying. It might be a source hyperlink or a quote. Citations are great, but mentions are broader. They shape perception.
Both can show up in the same response, but only mentions influence narrative tone and visibility directly. Citations lend credibility to what’s said. Mentions determine who gets talked about in the first place. You want both, but you need to recognize what drives each.
Ignore this distinction, and you might spend energy chasing citations on obscure academic blogs when you should be getting your brand mentioned in high-quality, contextual content that the AI systems actually pull from during real-world user queries.
Understanding how LLMs differ from traditional algorithms matters here. They’re not just indexing the web, they’re synthesizing knowledge based on training data, live data extractions, and prompt understanding. That means your brand’s presence in relevant, high-authority web content matters just as much as the citations themselves.
So, if you’re running growth, marketing, or overall strategy, here’s the takeaway: prioritize both visibility and influence. Mentions make sure you’re part of the conversation. Citations let you validate what’s being said. Know what you’re aiming for, and align your content efforts accordingly.
Increasing AI mentions through context-rich content is a powerful method to elevate brand prominence
Let’s be direct, if you want to be mentioned in AI outputs, you need to be mentioned across the internet first. These large language models don’t pull brand names from thin air. They pull from data across the web. The more your brand shows up in relevant, authoritative contexts, the more signal you send to LLMs that your company matters.
Context-rich content creates those signals. This isn’t just about frequency, it’s about relevance and depth. Blog posts, expert columns, product roundups, high-quality Reddit threads, and active Quora discussions, this is the kind of digital content that LLMs absorb. These mentions become part of a brand memory that systems like ChatGPT and Perplexity call upon during user prompts.
To make this real, focus on content that aligns closely with your industry and customer use cases. Write guest posts. Respond to media interviews. Comment in forums where your expertise is needed. Share case wins and product updates that people want to talk about. This is what creates a data trail for language models to follow.
This matters more than a traditional SEO strategy alone. AI models don’t weigh site structure the way search engines do. They look at topical depth, co-occurring language, and conversational relevance. When people talk about your brand naturally, without being forced, it signals authenticity, which LLMs are designed to prioritize.
For C-suite leaders, this isn’t something you leave to marketing alone. Think of it as brand infrastructure. When a model like Google Gemini scans the internet for signals on who’s credible in your space, every clear, context-aware mention gives you an edge in competing for attention, in user queries, buyer research, and beyond.
Targeting high-authority and frequently cited websites
Not all websites carry equal influence in the eyes of AI. Some domains get referenced repeatedly by LLMs, especially when they’re trusted sources in specific industries. If your brand is mentioned on these domains, your odds of being included in an AI-generated response go up.
These are the same websites that frequently appear as citations inside the responses from ChatGPT, Google AI Overviews, or Perplexity. Industry listicles, research-backed blog posts, technology roundups, these are digital assets that models often reference, and they have lasting influence on who gets visibility and who doesn’t.
Getting your brand featured on one of these authoritative sources has multiplier effects. It increases your chance of being cited, but more importantly, it triggers brand mentions in natural contexts, and AI systems are better at picking up on those than shallow backlinks or keyword stuffing.
Look at industry-specific review sites, ranking lists, and content hubs that already get pulled into AI tools. Use tools like Semrush Enterprise AIO to identify which links and domains get referenced the most. Then work your way into the content, whether through partnerships, PR, or direct contribution.
For executives, this isn’t a content play, it’s a relevance strategy. By situating your brand on the domains that modern AI respects, you bypass traditional search competition and insert your company directly into the source material influencing buyers at every stage of discovery. That’s leverage in a noisy digital market, and it’s measurable.
Publishing comprehensive, user-focused content
If you want your brand to appear in AI-generated responses, give the models something to work with. LLMs rely on depth, clarity, and specificity when making brand associations. When you publish detailed content that answers real buyer questions, clearly defines your offerings, and emphasizes your unique value, you raise your odds of being included in responses across a range of user prompts.
This is not just about volume. It’s about strategic depth. LLMs process nuance. Content that includes in-depth product specs, side-by-side comparisons, use-case breakdowns, and audience-specific benefits trains these systems to associate your brand with the problems users are trying to solve.
If someone asks ChatGPT for tools that connect with a specific tech stack or serve a narrow business vertical, the model is going to select from sources that demonstrate clear relevance. That means the more specific and varied your content is, across product categories, audience needs, geographic markets, the more likely it is your brand will align with those signals.
Take it further. Create dedicated landing pages for each use case. Go beyond your homepage and write about how your service solves customer problems in real-world terms. Emphasize the value expected by each audience, finance leaders, operations managers, or CTOs. That allows the AI to “see” your relevance in more paths through its internal reasoning system.
For C-suite executives, this approach compounds. Every clear answer you publish gives an LLM another reason to pull in your brand name when it tries to answer a question. It’s a long-term asset that pays off in incremental visibility and user recognition across AI platforms.
Technical optimization is key
Creating great content is important, but it won’t matter if AI systems can’t access it. Technical infrastructure plays a key role in allowing language models, and the search engines they sometimes rely on, to read, index, and retrieve your material.
Most of the modern LLMs still use external search tools like Google to complement their knowledge in real time. If your content is blocked by a robots.txt file, hidden behind a login, or dependent on JavaScript without fallback rendering, it likely won’t be seen or used. In practical terms, this means your brand gets passed over, even if your resources are valuable.
Server-side rendering should be prioritized. It ensures content is visible to crawlers without needing interaction or client-side processing. Pages should be included in your XML sitemap, marked for indexing, and avoid unnecessary redirects. If you’re using gated content, ensure there’s a crawlable version available, a preview or summary that language models can process.
Use tools like Semrush’s Site Audit to regularly assess whether your content is crawlable and index-friendly. These audits surface hidden blockers that might limit your visibility.
For executives, this isn’t a low-level technical issue, it’s a direct contributor to your growth strategy. If you’re producing resources to shape perception and visibility in AI systems, your investment is wasted if the infrastructure doesn’t enable access. Ensuring what you publish can be found, parsed, and reused by generative models is as necessary as publishing it in the first place.
Positive brand sentiment in AI responses directly reinforces consumer trust and purchase intent
AI mentions don’t just cover visibility, they shape tone and trust. When a brand is portrayed positively in a large language model’s response, that sentiment impacts how users perceive credibility and quality. The sentiment embedded in AI-generated answers is drawn from what machines learn online, reviews, feedback, thought leadership, customer experiences, and news coverage. If that data tells a positive story about your brand, LLMs tend to repeat it.
This makes sentiment management a strategic function for brand health. It includes reinforcing clarity in messaging, ensuring your UVPs (unique value propositions) are clearly stated, and maintaining control over how your brand is discussed in public channels. Satisfied customers become a signal. Encouraging online reviews, providing data-driven case studies, and addressing public criticism promptly are no longer optional, they feed the models shaping consumer conversations.
Keep in mind that AI systems don’t contextualize sentiment like a human brand manager would. If negative articles or forum comments dominate the digital footprint of your company, that perception can show up in the way your brand is mentioned to users, even if it’s factually incorrect or outdated. The models prioritize patterns, not nuance.
To protect against that, implement a clear review and feedback loop. Monitor changes not just in traffic or search rankings, but in AI sentiment trends. The Semrush AI Toolkit, for example, can track how LLMs describe your brand in real time and show whether the tone is improving or declining. That gives you the data to act.
For executives leading brand, marketing, or communications, this needs to be part of regular reporting. Positive sentiment isn’t about feelings, it’s about conversion, reputation, and market positioning. In an AI-first discovery journey, sentiment equals influence.
Utilizing specialized tracking tools is key for monitoring and enhancing AI mentions
Tracking how your brand appears in AI-generated responses isn’t straightforward. LLMs personalize their outputs based on user intent, query structure, and context. This makes manual tracking nearly impossible. Enterprise visibility requires purpose-built tools that run accurate tests across platforms, aggregate the results, and provide decision-ready data.
This is where tools like the Semrush AI Toolkit become important. It scans relevant user prompts across leading LLMs such as ChatGPT, Gemini, and Perplexity, then tracks how often your brand is mentioned, and in what context. These outputs are summarized with metrics like “Market Share” (your percentage of total mentions) and are broken down by platform so you can see where visibility is strong and where it’s lacking.
You also get sentiment insights, how often you’re being described positively, neutrally, or negatively, across different prompts. This allows teams to influence both quantity and tone over time. The ability to benchmark against competitors further adds value. You’ll know exactly where you stand, not just in terms of visibility but also in narrative position.
This kind of structured insight is critical at the executive level. It’s no longer enough to measure paid traffic or SEO performance alone. If half your prospective customers now start their journey with AI, including product research, vendor evaluation, and shortlisting, then you need visibility into that upstream path.
This kind of real-time monitoring does more than inform. It enables fast course corrections. If your share of voice is slipping, you know exactly where to dig in. If sentiment is declining, you have a feedback loop to respond before it becomes a reputational risk.
Leaders who understand how their brand is portrayed inside the AI ecosystem will have a strategic advantage. The ones who don’t will be left wondering why buyer attention is going somewhere else. Data answers that. You just need the right tools.
Concluding thoughts
AI is no longer downstream from the customer journey, it’s upstream. It frames the conversation, sets initial brand impressions, and often drives the shortlist before anyone hits your website. If your company isn’t being accurately and positively mentioned in LLMs, you’re not just losing visibility, you’re losing market relevance.
This isn’t a one-off initiative. It’s a shift in how brands earn trust and attention. You don’t buy your way in with ads. You shape how systems interpret your value, based on what exists across the open web, the content you control, and the signals others create when they talk about you.
As a leader, your responsibility isn’t to master the tech. It’s to make sure your teams understand how the landscape is changing. Where you show up in AI responses matters. How you’re portrayed in those responses matters more. And without the data to measure it, you’re flying blind.
Visibility and sentiment in AI aren’t future-facing, they’re live signals shaping decisions now. Prioritize them. Track them. Influence them. Or get comfortable being left out.
