In our last article, we explored why earned editorial is becoming increasingly important in the AI era and how AI systems are learning from the broader scientific ecosystem, not just from company websites.
Since then, one question has come up repeatedly: How discoverable are we in AI?
As researchers increasingly use AI-powered tools to explore technologies, compare workflows, and evaluate potential solutions, companies naturally want to know whether they are appearing in those conversations.
That curiosity has given rise to a growing number of AI discoverability reports. While these reports can provide valuable insight, they often focus on metrics such as mentions, citations, and share of recommendations.
Those numbers matter, but they rarely tell the whole story.
The real value lies in understanding what the results reveal about your brand, your competitors, and the authority signals influencing the conversation.
The goal is not simply to understand whether AI knows you exist. The goal is to understand what AI appears to know you for.
AI Discoverability Is Contextual
One of the most important things to understand about AI-assisted discovery is that it is highly contextual. Unlike traditional search rankings, there is rarely a single definitive answer. Two researchers can ask similar questions and receive different responses because their objectives, workflows, assumptions, and circumstances are different. In some systems, previous interactions may also influence future responses. A researcher who has already discussed a particular technology, supplier, or application area may receive recommendations that differ from someone approaching the same question for the first time.
That means the most useful question is not simply, “Did AI recommend our company?”
It is, “Under what circumstances did AI recommend our company, and why?”
AI discoverability reports are best viewed as directional assessments, not precise measurements. They can reveal patterns, gaps, and opportunities, but only if you read them with the surrounding context in mind.
💡 Pro Tip: When reviewing a report, don’t focus only on the output. Ask what scientific problem was being explored, what assumptions were built into the prompt, what technologies were already under consideration, and what level of purchase intent the question reflected. Context often explains as much as the recommendation itself.
💬 Suggested Prompt
“What contextual factors may have influenced this AI recommendation, and what does that suggest about the company’s positioning?”
Not All Prompts Matter Equally
Most AI discoverability reports evaluate visibility across a collection of prompts. That’s a good start. The better question is whether those prompts actually reflect the way researchers make decisions.
Not all prompts represent the same stage of the buyer’s journey. Researchers rarely move from a scientific question to a purchasing decision in a single interaction. They explore approaches, compare technologies, evaluate suppliers, and gradually build confidence in a potential solution.
Viewed individually, a prompt is just a data point. Viewed collectively, those prompts can reveal something more useful: how your company appears throughout the evaluation journey.
The questions worth asking are simple, but they matter:
- At what stage of the journey do we appear?
- Are we visible during early-stage discovery, supplier evaluation, or both?
- Which competitors appear alongside us most often?
- Are we consistently associated with the scientific problems we help solve?
- How does our visibility change as purchase intent increases?
These insights often matter more than any individual prompt.
💡 Pro Tip: Look for patterns across the evaluation journey, not just isolated wins. A company that appears early may be helping shape the conversation. A company that appears later may simply be competing within it.
💬 Suggested Prompt
“In a report like this, which buyer-intent prompts most often surface this company, what sources seem to influence those answers, and what does that suggest about its positioning versus competitors?”
What You Should Actually Measure
The first question most marketers ask when reviewing a discoverability report is: Did we show up? It’s a natural place to start, but it is rarely the most interesting finding. Visibility is only the beginning. The more revealing questions focus on positioning.
Here are some questions you should be asking:
- What scientific categories is your company associated with?
- Which workflows consistently surface your brand?
- Which competitors appear alongside your brand?
- How does AI describe your company?
- Where are you absent, and are those gaps meaningful?
By answering the questions above, you are starting to understand how AI systems interpret your role within the scientific ecosystem.
It is also important to remember that visibility and preference are not the same thing. A company may appear frequently in AI-generated responses without being the most trusted, preferred, or ultimately selected supplier.
Discoverability creates opportunity. Preference influences choice.
💡 Pro Tip: Don’t just ask whether the company appears. Ask how it appears. If three suppliers show up consistently, the positioning may matter more than the presence.
💬 Suggested Prompt
“How does this company appear in the response, and what does that suggest about its positioning?”
Mentions, Citations, and Trust Signals
As teams review AI discoverability reports, it is tempting to focus on the numbers: how many mentions, how many citations, and what share of recommendations each company received. Those figures can be useful, but they do not all mean the same thing.
- A recommendation is an AI-generated suggestion of a company, product, technology, or supplier in response to a user’s question. It reflects relevance and preference within the context of that response.
- A citation is a source the AI system references to support its response. It provides supporting evidence.
- A mention is the inclusion of a company in the conversation because it is considered relevant, whether or not it is explicitly recommended or supported by a citation. It creates presence within the conversation.
Consider a prompt like: Which antibody suppliers are known for strong validation practices in immunofluorescence?
In a response, one company may be positioned as a clear recommendation, while others are included as relevant alternatives. The supporting articles, validation resources, or technical references behind that answer are the citations.
That distinction matters because each signal tells a different story. Recommendations reflect relevance. Citations provide evidence. Mentions create presence. Together, they contribute to trust signals.
The pattern is often more revealing than any single number. A company may be mentioned frequently but rarely recommended. Another may be recommended often but supported by relatively little visible evidence. Each pattern suggests something different about how the brand is positioned.
💡 Pro Tip: Don’t stop at the counts. Look at the relationship between recommendations, citations, and mentions. High visibility with weak supporting evidence may suggest awareness without strong authority. Strong citations with limited visibility may suggest expertise that has not yet been broadly recognized.
💬 Suggested Prompt
“What do the recommendation, citation, and mention patterns suggest about each company’s visibility and authority?”
Why Recommendations Happen
Most discoverability reports tell you who appeared. Far fewer explain why. The answer is often rooted in the broader scientific ecosystem: peer-reviewed publications, editorial coverage, conference visibility, product reviews, and third-party validation. In practice, the source pattern behind a recommendation can be just as important as the recommendation itself.
Questions like these can be revealing:
- What authority signals appear to support the recommendation?
- How visible is the company within scientific publications?
- How frequently is the company referenced in editorial content?
- What educational resources appear to contribute to its visibility?
- How consistently is the company associated with specific workflows or application areas?
Those questions often reveal opportunities that simple visibility metrics cannot.
💡 Pro Tip: Investigate the recommendation itself, but spend more time understanding the signals behind it. The “why” is usually more valuable than the “what.”
💬 Suggested Prompt
“What seems to be influencing why this company is being recommended?”
The Real Opportunity
When approached thoughtfully, AI discoverability reports can help organizations understand where they are visible, where they are absent, how they are being positioned, which competitors dominate specific conversations, and what authority signals may be influencing outcomes.
More importantly, they provide insight into how researchers may encounter your brand in the earliest stages of evaluation.
But discoverability is only the first step. As AI makes discovery easier, the competitive advantage may increasingly belong to the organizations that build trust, create familiarity, and deliver strong brand experiences.
Discoverability may determine who enters the conversation. Brand may determine who gets explored further, and ultimately selected.
In our next article, we’ll explore why brand may become the catalyst between discovery and selection in the age of AI.
