What 120 Days of AI Visibility Data Revealed About How B2B Buyers Find Brands
AI is a new layer of B2B buyer discovery, shaping which companies appear, how brands are described, and which sources influence purchasing research. To understand what that shift means in practice, Gabriel Marketing Group (GMG) analyzed 55,671 AI-generated answers over 120 days (November 1, 2025 to February 28, 2026) across ChatGPT, Microsoft Copilot, Google Gemini, Grok, Google AI Mode, Google AI Overviews, and Perplexity. The research tracked visibility, share of voice, mention position, and citation activity across the buyer journey. The findings show that AI visibility depends not only on brand awareness, but also on the clarity, credibility, and accessibility of a company’s public information footprint.
Key Takeaways
- AI is now part of the B2B buyer journey. Buyers can use AI platforms to understand categories, compare options, evaluate providers, and gather decision-making information before visiting a company website.
- AI visibility is broader than brand awareness. Marketers should look beyond whether a brand appears in an answer and also measure prominence, context, citation activity, and consistency across platforms and over time.
- Public evidence matters more than promotional claims. Clear, useful, and well-supported content gives AI systems stronger information to draw from when explaining a company, its expertise, and its relevance.
- Mentions and citations signal different kinds of value. A mention shows that a brand is recognized as relevant, while a citation indicates that information associated with the brand is helping support the answer.
- Brand size alone does not determine AI visibility. Smaller and specialized B2B companies can earn meaningful visibility when their public information clearly demonstrates expertise and provides relevant, credible evidence.
How Gabriel Marketing Group Measured AI Visibility Across the B2B Buyer Journey
The way B2B buyers research companies is changing.
Search engines, industry publications, analyst reports, review sites, and peer recommendations still play an important role in discovery. But AI platforms are increasingly pulling information from across that broader digital environment and synthesizing it into direct answers to buyer questions.
That changes the starting point for research. A buyer no longer needs to begin with a company name—or even know exactly what kind of solution they are looking for. They can describe a business problem, ask which approaches might solve it, compare options, investigate potential providers, and continue refining their questions as they move closer to a decision.
For B2B marketers, that creates a new visibility challenge: When buyers ask AI about the problems your company solves, does your brand appear? If it does, how prominently is it presented, what does the AI say about it, and what information supports the answer?
Gabriel Marketing Group set out to examine those questions through its own AI visibility campaign. Over four months, GMG monitored questions spanning the B2B buyer journey, from early discovery and category education through evaluation and decision-making. The research looked beyond whether GMG appeared in AI-generated answers to measure its relative visibility, prominence, and citation activity over time.
GMG began from a position of very limited AI visibility. The agency was largely absent from relevant AI-generated answers at the start of the campaign, and its domain citation rate was 0%. That starting point created an opportunity to observe whether a coordinated effort to strengthen the clarity, credibility, and accessibility of GMG’s public information could produce measurable changes in how the brand surfaced in AI-generated answers.
Across the four-month study, GMG measured four dimensions of AI visibility:
- GEO Awareness: how frequently GMG appeared in relevant AI-generated answers.
- GEO Share of Voice: GMG’s share of visibility relative to other brands monitored in the category.
- Average Mention Position: where GMG appeared, on average, when included in an AI answer.
- Domain Citation Rate: how frequently gabrielmarketing.com was cited as a source in the monitored answers.
| Metric | Result After 120 Days | Change From Campaign Baseline |
| GEO Awareness | 5.4% | +844% |
| GEO Share of Voice | 2.6% | +1,225% |
| Average Mention Position | 2.4 | +65% |
| Domain Citation Rate | 25% | +650% |
The percentage changes compare GMG’s performance at the end of the 120-day period with its starting campaign baseline. They are not industry benchmarks or estimates of AI usage across the broader B2B market. Instead, the dataset provides a longitudinal view of how one B2B brand’s presence, prominence, competitive visibility, and citation behavior changed across a defined set of buyer-relevant questions and seven major AI platforms.
5 Findings From GMG’s 120-Day AI Visibility Study
The numbers showed substantial movement in GMG’s AI visibility. But the more important story was what the underlying 55,671 answers revealed about how brands surface as B2B buyers move from initial research to evaluation and decision-making. Those patterns produced five broader lessons about what AI visibility means—and what B2B marketers can do about it.
Understanding what those changes revealed about AI-assisted B2B discovery. Across 55,671 AI-generated answers, several consistent patterns emerged around when brands appear, what influences their visibility, how mentions differ from citations, and why public evidence matters. Together, those patterns point to five lessons for B2B marketers trying to understand—and improve—how their brands are represented in AI-generated answers.
1. B2B Buyers Use AI Across Multiple Stages of the Buying Journey
Traditional search marketing has conditioned companies to think in terms of individual keywords.
A buyer searches for a phrase. A company tries to rank for it. The buyer clicks a result.
AI-assisted discovery can be more iterative. Buyers can ask an initial question, use the answer to refine what they need to know, and continue asking increasingly specific questions as they research a purchase.
Early questions may focus on understanding the category or deciding whether a particular approach makes sense. Later questions become more specific as buyers begin evaluating options, expectations, and potential outcomes.
Eventually, the conversation shifts toward practical decision-making: What should the buyer expect? What risks should be considered? What evidence supports the investment?
GMG’s research was deliberately structured around this progression. Rather than treating AI visibility as a single measure, the campaign examined questions across awareness, evaluation, and decision stages.
A brand, therefore, can be visible during one stage of AI-assisted research and absent during another.
A company might appear when someone asks broadly about a category but disappear once the buyer starts asking more detailed questions about capabilities, outcomes, or implementation. Another company might not dominate early discovery but become highly visible when buyers seek deeper explanations.
For B2B marketers, the implication is that measuring one high-value prompt provides an incomplete picture of AI visibility. Brands need to understand whether they remain relevant as buyer questions progress from category education to evaluation and purchase considerations.
2. Brand Awareness and AI Visibility Measure Different Things
Marketers have traditionally treated awareness as a major advantage in discovery.
That advantage still matters. But the research suggests that conventional brand recognition and AI visibility are not identical.
A well-established company is not automatically prominent in every relevant AI answer. Likewise, a smaller company can earn visibility in contexts where publicly available information makes it relevant to the question being answered.
That makes it useful to think about AI visibility as several distinct layers.
First, does the AI system recognize that the company belongs in the category?
Second, does the company actually appear when relevant questions are asked?
Third, how prominently and consistently does it appear?
And finally, does information associated with the company help support the answer itself?
GMG’s research measured these dimensions separately rather than treating AI visibility as a single yes-or-no indicator. The gains summarized above show why that distinction matters: brand presence, competitive visibility, prominence, and citations can move at different rates.
A one-off check in ChatGPT, Gemini, or another AI platform, therefore, cannot establish whether a brand has strong AI visibility.
A single answer is a snapshot.
A more meaningful assessment looks for patterns: how consistently the brand appears, which topics and attributes are associated with it, how prominently it is presented, what sources support the answers, and whether those patterns change over time.
3. AI Systems Need Public Evidence, Not Just Brand Claims
Perhaps the most important lesson from the 120-day analysis is that brands need more than marketing claims.
They need evidence.
Traditional brand messaging often begins with what a company wants its audience to believe: that the company is innovative, experienced, trusted, differentiated, or uniquely qualified to solve a particular problem.
AI-generated answers are assembled from information that AI systems can access, interpret, and synthesize. As a result, a brand’s broader public information footprint can affect how clearly its expertise, capabilities, and relevance can be represented.
That information can include company webpages, educational resources, case studies, and other public materials that explain what an organization does and provide support for its claims.
The GMG initiative, therefore, emphasized content that answered recognizable buyer questions, used plain-language explanations, provided decision context, and avoided unsupported promotional claims. Content was deliberately structured to be easy to understand, summarize, and reference.
That points to a broader shift in how B2B brands should think about communications.
The traditional question is:
What do we want the market to hear?
AI discovery introduces another:
What evidence exists publicly that allows an AI system to understand and substantiate what we want the market to know?
Those are not the same thing.
A positioning statement can communicate an aspiration. Evidence gives that positioning substance.
For B2B companies, this makes clarity and substantiation increasingly important. Claims about experience, capabilities, or outcomes are more useful when the surrounding public information explains and supports them rather than simply repeating them.
4. AI Mentions and AI Citations Signal Different Types of Visibility
Another important lesson from the research was the difference between being mentioned and being cited.
An AI mention indicates that a brand was included in an answer. A domain citation indicates that the brand’s website was referenced as a source supporting the response.
That distinction matters because visibility and authority are related, but they are not interchangeable.
A brand might appear frequently because it is widely recognized. Another might be used repeatedly as a source because its information helps AI systems explain a topic.
Both matter, but they answer different questions.
For B2B marketers, measuring AI visibility, therefore, requires more than counting appearances. A broader framework can examine:
- Presence: How frequently does the brand appear in relevant answers?
- Prominence: Where does the brand appear within those answers?
- Context: What topics, capabilities, or attributes are associated with it?
- Citation: Which brand-owned or third-party sources are referenced?
- Consistency: Do similar patterns appear across AI platforms and over time?
Together, these signals help distinguish simple brand recognition from a deeper role in the information AI systems use to answer buyer questions.
Being present in AI-generated answers is useful. Becoming part of the information AI systems rely on to construct those answers signals something different: the brand’s public information is contributing to the explanation itself.
5. Brand Size Alone Does Not Determine AI Visibility
One of the most encouraging findings from the research is that AI visibility does not appear to be reserved exclusively for companies with the largest marketing budgets or biggest market footprints.
GMG began the campaign with limited visibility in AI-generated answers despite 15 years of experience and work with more than 300 clients. The firm was competing for visibility with larger organizations that already had substantial market presence.
Over the research period, that position changed significantly.
The broader implication is not that size has stopped mattering.
Large brands still benefit from extensive media coverage, established reputations, strong web footprints, and years of accumulated market recognition.
But brand scale is only one factor in an AI-generated response. AI systems also need information relevant to the specific question being answered.
That creates room for specialized companies with deep expertise and strong supporting information to participate in conversations where conventional brand size alone might not determine visibility.
In other words, a large information footprint and a useful information footprint are not necessarily the same thing.
For smaller and specialized B2B brands, that distinction matters. Clear expertise supported by accessible public evidence can give AI systems substantive information to use when answering questions about a market, capability, or business problem.
What Should B2B Marketers Measure to Understand AI Visibility?
The research also highlighted a measurement problem.
Traditional PR and SEO tools were designed for different environments. They can tell marketers about media coverage, search rankings, traffic, backlinks, and other important signals.
Those metrics do not show the complete picture of how a brand is represented inside AI-generated answers.
GMG, therefore, measured AI answer behavior, citation activity, and competitive share of voice rather than relying exclusively on conventional PR and search metrics.
Four dimensions provide a useful foundation for evaluating AI visibility:
- Presence: How frequently does the company appear in relevant AI-generated answers?
- Position: When the brand appears, how prominently is it presented?
- Context: What products, capabilities, attributes, or ideas are associated with it?
- Authority: Which sources are AI systems relying upon when constructing answers?
Just as important is measuring those signals over time.
Individual AI responses can vary. A larger longitudinal dataset makes it easier to distinguish an isolated appearance from a sustained visibility pattern.
Tracking those patterns also makes it possible to identify a change that conventional analytics may miss. A company’s website traffic may remain relatively stable while its presence in AI-generated answers is increasing—or declining. Without measuring AI directly, marketers may never see that change happening.
Why AI Visibility Is Now a Brand Reputation Issue
It would be easy to interpret AI visibility as another technical marketing discipline.
The data points toward something broader.
AI platforms can synthesize information from across a brand’s public digital footprint into answers that help buyers understand companies, categories, and potential solutions.
That puts AI visibility at the intersection of brand, content, search, PR, and reputation.
The underlying question is no longer simply:
Can buyers find us?
It is also:
When AI explains our market to a potential buyer, what information does it have about us?
That changes the role of brand communications.
Companies need more than visibility. They need a clear, credible, and consistent body of public information that helps AI systems understand who they are, what they do, and why they are relevant.
When that public information is incomplete, unclear, or inconsistent, AI systems have less reliable evidence from which to represent the brand accurately.
That makes AI visibility more than a traffic strategy. It is increasingly connected to whether a company’s existing reputation, expertise, and market presence are legible in the AI environments buyers use for research.
Frequently Asked Questions About AI Visibility for B2B Brands
How often should B2B companies measure their visibility in AI-generated answers?
B2B companies should measure AI visibility consistently over time rather than rely on occasional spot checks. Individual AI answers can vary by platform, prompt, and timing, so longitudinal measurement is more useful for identifying sustained changes in brand presence, prominence, citations, and competitive visibility. At Gabriel Marketing Group (GMG), we view AI visibility as an ongoing brand measurement discipline rather than a one-time audit. GMG uses Brandi AI to analyze patterns across AI-generated answers over time, helping distinguish meaningful visibility trends from isolated appearances.
What should a B2B company do when AI platforms describe its brand inaccurately or incompletely?
A B2B company should first determine what information AI platforms are presenting inaccurately or omitting, then examine the public sources that may be contributing to those answers. The goal should be to strengthen the underlying body of accurate, credible information available about the company rather than trying to correct individual AI responses one at a time. Gabriel Marketing Group approaches inaccurate or incomplete AI representation as a brand and reputation issue. We look at how a company’s positioning, expertise, proof points, owned content, and broader public information footprint are represented, then identify opportunities to make the evidence surrounding the brand clearer and more consistent.
How can B2B marketers connect AI visibility metrics to marketing, sales, and business outcomes?
B2B marketers can connect AI visibility to business outcomes by evaluating it alongside signals such as branded search activity, direct traffic, content engagement, inbound inquiries, pipeline influence, and sales conversations. AI visibility should not be treated as a standalone vanity metric; its business value becomes clearer when changes in AI discovery are examined alongside the rest of the buyer journey. Gabriel Marketing Group (GMG) takes this broader measurement approach because our goal is not simply to increase how often a client appears in AI answers. GMG looks at AI visibility as part of an integrated PR, brand, content, and digital strategy designed to strengthen how companies are discovered, understood, and considered by prospective buyers.
Who should own AI visibility strategy and measurement inside a B2B organization?
AI visibility should generally be managed as a cross-functional discipline involving the teams responsible for brand, PR, content, SEO, digital marketing, and product marketing. One team may own measurement and coordination, but the information that shapes AI-generated answers is often created across multiple functions. At Gabriel Marketing Group, we approach AI visibility through the communications and brand lens while connecting it to the broader digital presence of the company. Our perspective is that improving AI visibility requires coordination between the people shaping the brand narrative, creating authoritative content, generating third-party credibility, and measuring how that information surfaces across AI platforms.
AI Visibility Is Now Part of B2B Brand Visibility
The 120-day analysis shows that AI is now another layer of B2B discovery. Buyers can use AI platforms to understand categories, evaluate options, compare companies, and gather information before ever visiting a website or speaking with a salesperson.
For marketers, the implication is broader than simply appearing in AI-generated answers. Brands need to understand whether they are visible across the buyer journey, how they are represented, what sources support those answers, and whether their public information provides enough credible evidence for AI systems to accurately explain their expertise and relevance.
The strongest lesson from the research is that AI visibility should not be treated as a collection of isolated prompts or rankings. It is increasingly connected to the strength, clarity, and credibility of a brand’s overall public information footprint.
Strengthen Your Brand’s AI Visibility With Gabriel Marketing Group
If you want to understand how your brand currently appears in AI-generated answers, where visibility gaps may exist, and how your PR, content, brand positioning, and digital presence can work together to strengthen that visibility, schedule a consultation with Gabriel Marketing Group.
We can help assess your current AI presence, identify opportunities for improvement, and develop an integrated strategy for strengthening how your company is discovered, understood, and represented across AI-driven and traditional buyer journeys.