Mentions in LLM outputs as a new visibility metric
If your brand isn’t showing up in large language model (LLM) responses, you’re not visible. It doesn’t matter how much you’ve invested in SEO or how well your content ranks on Google. LLMs, like ChatGPT, Claude, Gemini, and Perplexity, don’t give users lists of links. They generate direct answers. That means if you’re not included in those answers, your brand essentially doesn’t exist in that moment of decision-making.
We’re at a point where brand visibility is shifting. People ask AI systems for recommendations, definitions, and best options. When they get one synthesized answer, your name either shows up or it doesn’t. Those mentions are now your frontline exposure. This isn’t a vanity metric. Mentions reveal how well your messaging is embedded in the systems people now trust for answers.
You should be breaking these mentions down. Look at the different types of queries where your brand appears, or doesn’t. Are you in foundational searches like “what is analytics software?” Or only in head-to-head comparisons like “Google Analytics vs. Mixpanel”? If your business is invisible in early-stage queries, you’re not shaping how the category is defined. If you’re absent from solution-focused queries, you’re not influencing purchase decisions.
Executives who act on this now can lead the shift. Track where your brand appears. Create content that answers key questions in your space, and make sure AI can recognize and use that content. The sooner you begin, the more likely you are to shape how your industry is represented in this new way of searching.
Sentiment analysis as a real-time brand perception barometer
Being mentioned isn’t enough. What matters is how you’re described. When an AI system says your product is “trusted” or “complex,” it’s not improvising. It’s using the information it’s been trained on, what people say online, what reviewers publish, what’s in your own content. That sentiment, built into how AI summarizes your brand, becomes what the market hears.
You need to know those descriptors. Are people seeing you as innovative but also expensive? Powerful but hard to use? If AI systems pick up on those themes, they’ll keep repeating them. And since many users don’t check other sources after getting a single AI response, whatever is said first often becomes the final impression.
That’s why sentiment analysis matters. Look at the words AI uses when referencing your brand. Spot recurring positive and negative patterns. Then turn the negative ones into targets for improvement. If the system says your product is costly, show value, publish ROI studies, pricing comparisons, and examples that prove your long-term efficiency. If it frames your offering as complicated, tell better onboarding stories, simplify walk-throughs, and highlight user success.
Positive sentiment, when consistent, isn’t just validation. It’s fuel. Use it. Integrate it into your brand story, campaign messaging, and investor narratives. Executives who treat AI sentiment as a live feedback loop, not a retrospective survey, will be able to adjust fast, stay sharp, and lead positioning in real time.
Competitive share of voice as a benchmark for market positioning
You’re not competing for clicks anymore, you’re competing for narrative space inside AI engines. Mentions and sentiment only matter if you compare them to your competition. If your brand appears in 30% of the relevant AI-generated answers and your closest rival is in 70%, you’re not leading. You’re reacting. And if the AI describes their product in more positive terms than yours, they’re shaping perception while you’re trying to catch up.
This isn’t just about being present. It’s about where and how you’re present versus others. Track the types of prompts where competitors dominate. Are they getting named in product evaluations, while you’re barely visible? Are they seen as easier to use, more innovative, or a better value? That’s competitive intelligence, direct from the systems that your buyers are now using during research and decision-making.
What you learn from this gives you a clear market map. If your competitors are leading in certain types of conversations, double down on content that speaks directly to those scenarios. Create material that positions you clearly, explains your differentiators, and delivers credible proof. If you have an edge in specific areas and the AI is picking it up, reinforce that strength in your messaging.
This kind of competitive share tracking isn’t optional unless you’re okay with letting others define your category’s narrative. The more you monitor how AI views your rivals, compare descriptors, and adjust based on those inputs, the better you’ll position your brand for strategic advantage.
Authority through cited sources in AI responses
LLMs generate content based on what they trust. That “trust” comes from the sources they’ve been trained on and what appears consistently accurate, structured, and comprehensive. If those sources include your materials, whitepapers, case studies, FAQ pages, then your brand doesn’t just get mentioned. You become the input the model relies on to answer user prompts. That gives you control over part of the narrative.
When you see competitors’ blogs, research or documentation cited in responses rather than your own, it’s a clear signal: your content isn’t seen as authoritative. Executives need to be focused on this. You don’t publish content so people can click it now. You build content so AI includes and cites it long after. That visibility isn’t dependent on running ads or driving rankings. It comes from being recognized as reliable data.
Audit this carefully. Check what types of documents are being cited when your space is discussed in platforms like ChatGPT or Claude. If they’re pulling from analyst reports, competitor materials, or public forums more than your site, that’s a content gap. Fill it with something stronger, structured, data-driven, well-attributed material designed to inform and be reused by AI.
When your brand becomes the cited source, you’re not just reacting to how the market moves. You’re the one defining what information gets repeated, reused, and trusted. That level of influence is earned, but attainable, with deliberate content strategy. For leadership teams, this isn’t a marketing problem. It’s a credibility signal with real downstream impact on sales, partnerships, and category leadership.
Early adoption of AI KPIs as a competitive advantage
Right now, AI KPIs don’t have standardized tools or dashboards. You can’t just log in and get a complete view. That’s not a weakness. It’s an opportunity.
We’ve seen this before. The brands that took early steps in search optimization before frameworks matured ended up owning attention and discovery for years. We’re seeing the same shift now, only the arena has changed. Today’s shift isn’t about keywords; it’s about how AI systems understand and represent your business in conversations.
You don’t need a dashboard to start. Run structured prompts through LLMs relevant to your domain, decision-making queries, product comparisons, industry trends, and log what comes back. Record whether you’re mentioned, how you’re framed, who else shows up, and which sources are cited. Over time, that’s a signal set. It tells you whether buyers will see you or forget you when using AI tools to decide.
The earlier you build these insights into your marketing roadmap, the faster you learn. And while others wait for tooling to catch up or platforms to launch monitoring solutions, you’ll already be adapting. That’s how you gain momentum. Executives who invest in these early-stage efforts will move fast, shape their category’s visibility in AI environments, and make decisions backed by forward-looking signals, not just lagging indicators.
Translating AI KPI insights into strategic marketing adjustments
Mentions tell you if you’re present. Sentiment tells you how you’re seen. Competitive share tells you your relative strength. Sources reveal whether you’re trusted. Put together, they give your team a clear direction, not dashboards, but actions.
Use these KPIs to focus and prioritize. If you’re not visible in high-volume AI prompts, fill that content gap with materials designed to answer them. If sentiment tilts negative, fix the cause, whether it’s pricing clarity, onboarding experience, or product complexity. If competitors dominate certain query types, build responses and campaigns that match or exceed that positioning. And if you’re not the cited authority, publish the kind of content that large models can classify as definitive.
This isn’t about campaign cycles. It’s about building long-term visibility and influence in a space where information flow is changing. Most buyers won’t even reach your site until after AI has already framed who you are. When AI becomes the first touchpoint, these KPIs become your most important signals.
Executives should think of AI KPIs as levers to move faster and act smarter. Use them to test assumptions about your brand, validate your differentiation, and decide where to invest next. The results won’t play out over a week, but over quarters, you’ll see your market traction shift. Marketing that operates with AI signal awareness already has an edge. Marketing that ignores it risks becoming background noise.
Main highlights
- Focus on AI visibility metrics: Decision-makers should monitor how often their brand is mentioned in AI-generated responses, especially for high-intent and early-stage queries. If you’re not showing up in these results, you’re already behind in the new buyer discovery journey.
- Track and act on AI-driven sentiment: Leaders must understand how LLMs describe their brand, positive or negative, and respond accordingly. Address weak spots with targeted messaging and reinforce positive recognition to shape perception upstream.
- Benchmark against competitors in AI environments: Executives should evaluate how often and how positively competitors appear in AI outputs relative to their own brand. This insight helps prioritize content investments and sharpen positioning where needed.
- Establish authority via cited sources: Leadership teams must prioritize content that AI systems recognize as credible and referential. Publish structured, expert-led material that increases the chance of being cited in AI-generated answers.
- Move early while AI KPIs are still maturing: Early adopters gain strategic leverage while others wait for standardized tools. Even basic prompt analysis can reveal trends and gaps that support faster, smarter decision-making.
- Translate AI signals into content strategy: Use AI KPIs to identify actionable gaps in visibility, narrative, and authority. Leaders should guide teams to build content that aligns brand perception with business goals and search relevance.
