For the past decade, B2B marketing leaders have been conditioned to think about visibility in one way: rankings. Where do we show up on Google? What keywords do we own? How are we trending quarter over quarter?
So it’s not surprising that as buyers shift toward tools like ChatGPT, Perplexity, and Claude, CMOs are asking the same questions in a new context: “How do we rank in AI?”
But that question is fundamentally flawed. And if left unchallenged, it’s going to lead to wasted spend, misleading analysis, and decisions that don’t move the business.
AI Visibility is Not Search Visibility
The assumption that AI visibility behaves like traditional search is understandable, but it’s not correct. Search engines index and rank. Large language models retrieve, synthesize, and generate.
That distinction is crucial. When a buyer searches Google for “best CRM software,” they may still see a ranked list of links, but they are increasingly seeing AI-generated summaries that collapse multiple sources into a single answer. In tools like ChatGPT or Perplexity, the buyer gets a synthesized response that blends sources, compresses text, and often removes any notion of rank entirely.
That means visibility is no longer about being number one. It’s about being included—and equally importantly, how you are being represented.
The Misinterpretation of AI Visibility Metrics
In response to this shift, a new category of AI visibility platforms has emerged, promising to help brands track their presence inside LLM-generated outputs. These tools are surfacing important signals, but the issue is how those signals are being understood.
Many teams are treating early-stage visibility metrics as if they are fully formed KPIs. In reality, they are more like directional indicators. Part of the challenge is that LLMs are probabilistic systems, not deterministic ones like traditional search. The same query can yield different results across runs, models, and contexts.
This also makes it tricky to use metrics like the number of brand mentions in generated responses, the relative position or order of appearance, or the frequency of inclusion across prompts. These signals can be useful, but they must be interpreted correctly.
Because of the probabilistic nature of LLMs, point-in-time snapshots can be unreliable. What matters is not whether your brand appeared in a single output, but how consistently it appears across many variations of prompts over time.
This is where prompt strategy becomes critical, because manual prompting can quickly lead to misleading conclusions. Using a prompts analytics tool can better approximate real-world query behavior and establish directional baselines.
Presence is Only Half the Story
Even when brands do show up, presence alone is not enough. After honing your prompt generation strategy to make sure the input is correct, the next critical layer is tracking how the brand is being portrayed. Was the brand mentioned positively or negatively? Was it positioned as a leader, a niche player, or an afterthought? Did the response align with the brand’s core narrative? Was the mention relevant to the buyer’s stage or intent?
Sentiment and framing matter significantly, and they must be tracked both in real time and over time. Appearing in an answer is only half the battle. Brands can still lose the deal if that appearance is not optimized to position them in the most advantageous light.
Most tools today treat visibility as a binary concept: you’re either there, or you’re not. However, AI visibility exists on a spectrum, ranging from an irrelevant mention to passive inclusion, to a credible option, to a category leader. Without understanding where your brand is falling on that spectrum, optimization is just guesswork.
Understanding the Different Tools
It’s important to recognize that not all AI visibility tools are designed to do the same thing. Some focus on measuring how brands appear within generated answers, surfacing the sources influencing those answers, or optimizing owned content for AI retrieval. Others emphasize external influence signals, sentiment and narrative framing, or competitive benchmarking across prompts.
No single tool provides a complete picture. Much like any complex GTM initiative, AI visibility requires a multi-tool approach, combining measurement, diagnostics, and optimization across both owned and external environments. And critically, tools should follow strategy, not define it.
The Coverage Problem
Another issue quietly distorting how CMOs interpret AI visibility is incomplete model coverage. Many platforms focus heavily on one or two models (often ChatGPT or Perplexity) but ignore others like Google Gemini, Anthropic Claude, or regional and vertical-specific AI systems.
The reality is that buyer behavior is already fragmenting across these environments. Each model uses different training data and weighting mechanisms, leading to different outputs for the same prompt.
Essentially, a brand that appears highly visible in one model may be nearly invisible in another. Without multi-model visibility, leaders are only measuring a slice of their presence and making decisions based on incomplete data.
What CMOs Should Be Measuring Instead
Traditional visibility metrics are insufficient. A more useful framework focuses on influence, not inclusion. That means tracking:
- Narrative alignment: Does the AI-generated description of your brand match how you position yourself?
- Contextual relevance: Are you showing up in the right conversations (not just more conversations)?
- Authority signals: How often are you referenced alongside credible sources, analysts, or category leaders?
- Sentiment and framing: Are you described as differentiated, trusted, and relevant or generic and interchangeable?
- Cross-model consistency: Does your presence hold across ChatGPT, Claude, Gemini, and others?
While these may be harder to measure, they are far more indicative of real influence.
A New Mindset for an AI-Driven Buyer Journey
The shift to AI-driven discovery doesn’t eliminate the need for branding. Rather, it enhances it. In a world where answers are synthesized instead of searched, the brands that show up best are determined by clear positioning, consistent narratives, credible sources, and strong reputations.
Technical optimization as the primary or sole driver of visibility is officially a thing of the past. It’s no longer about where we rank. The more important question is understanding how we are showing up.
AI visibility is real, but most of the ways we’re measuring it today are not. CMOs who focus on tracking the right signals and rely less on dashboards and more on strategic judgment will be the ones to come out ahead.
Lora Kratchounova is a seasoned tech marketing executive and the founder and CEO of Scratch Marketing + Media. With over two decades of experience in the field, she has a track record of building and leading high-performing, cross-functional teams that drive growth, impact and innovation. Under her leadership, Scratch has become known for ‘getting’ complex enterprise tech – from distributed systems, networking, and cloud to AI/ML to cybersecurity to health tech and life sciences. This expertise, coupled with Scratch’s strategic, full-service approach, have positioned challenger brands to punch above their weight in competitive markets. Lora mentors, teaches and supports numerous tech accelerators and communities across the globe, including the TechStars/ Boston, CIC/ Japan, Ignyte/ United Arab Emirates, and the Bulgarian Innovation Hub/ San Francisco.
