Search has a tendency of equating database size with quality. Where keyword coverage forms the foundation for Google organic, prompt databases have now filled the same role in AI visibility.
There’s logic to this, as the more customer questions you can monitor, the more opportunities you have to understand where your brand might appear. More coverage then means more extensive intelligence.
For businesses who are accustomed to evaluating SEO platforms by the breadth of their keyword databases it’s a natural association.
But simply applying this same logic to AI visibility data in a vacuum and calling it a new channel creates serious gaps. Because AI search is changing rapidly, acting as an expansion of search touchpoints in customer journeys.
It’s why Gartner made similar statements advocating for data and strategies that are connected across organic, AI, and paid search.
To be informative and trustworthy, AI visibility data requires wider context. It needs demand to be established through search and customer journey insight, topical organization that enables brands to really see where they stand against their competitors, and validation of actual AI responses.
It’s time to reconsider these assumptions about data quality in AI visibility.
Why prompt counts alone don’t tell the full story
As investment in AI visibility grows, prompt counts have become a common way for vendors to demonstrate the scale of their datasets. And extensive coverage is useful, particularly for enterprises operating across different categories and international markets.
But a prompt is fundamentally a question asked to an AI system. It doesn't tell us what was returned, or whether the question is meaningfully representing needs most valuable to customers—and therefore for brands to be recommended as solutions for.
These are all different prompts but there’s considerable overlap in the underlying need. And it's easy to generate hundreds of these variations through repeated rephrasing.
Tracking all those variations doesn't necessarily tell us more about actual demand or how strong a brand's visibility is across strategically-vital topics where it aims to position itself as a leader.
Therefore tracking data can grow considerably without necessarily improving your grasp of the market.
Why understanding must be demand-led, not from hypothetical questions
If the goal is to understand AI search as a place where brands compete for customer attention, the starting point should be the customers themselves.
This is where established search and behavioral data become particularly valuable:
- Search data provides an established and extensive record of the questions people ask, the problems they're trying to solve, and the products or services they're researching.
- Audience intelligence can then add further context, helping us understand how people navigate the web, which platforms they use, and how their interests change over time.
These signals provide a foundation for identifying demand before deciding which AI prompts to investigate.
For example, a brand selling project management software might generate and track hundreds of prompts about both project management methods and software integrations. But without understanding the demand behind these topics, they have no idea of which of these prompts are actually being used by customers.
They’re then left with an inexact understanding of their AI visibility and guessing the prompts that have commercial relevance. This potentially limits the impact of any subsequent work on content and optimization.
Search data, such as keywords, help establish where demand is concentrated, while behavioral data provides additional evidence of how customers are researching these subjects.
This demand-led approach establishes which customer needs matter, where opportunities are emerging, and which questions should form the basis of AI visibility measurement.
It forms a much stronger foundation than generating prompts in isolation and assuming they represent the market.
How priorities become clearer following a topic structure
Topics are a well established concept. Clusters, pillar pages, and semantic SEO are no doubt all extremely familiar.
But topics are no longer only for organizing content. They’re a vital measurement of your AI visibility, where 85% of topics remain open for brands to win if they can build consistent coverage.
They help to establish a more actionable approach by clarifying where content gaps are across groups of related prompts that reflect different customer needs
For the project management category, this could mean separating remote collaboration from enterprise security requirements. Each potentially reflects different people searching, competitors, and purchase considerations.
At this level, topics can be considered similarly to market share. It can be measured, benchmarked against named competitors, and set a target. It’s also much easier to see if any visibility gaps are growing or closing over time.
This gives understanding where their brands have meaningful coverage, rather than spend their time interpreting endless variations of individual questions. Insights that are especially important for enterprises managing multiple product categories, domains, or geographic markets.
Why AI response validation provides vital context to prompts
Once which customer needs matter most is established, the next step is understanding how AI responds to them.
Generative AI naturally has a huge degree of variability to each response. The same question will produce different recommendations, brand mentions, and cited sources across repeated answers.
Research from Rand Fishkin and Patrick O'Donnell found the probability of receiving exactly the same list of recommended brands across two responses was less than 1%.
Therefore a brand appearing first in hundreds of individual prompts doesn't necessarily hold a consistent competitive advantage. Wider responses to the same questions might recommend entirely different businesses.
But collecting and analyzing these responses over time establishes a much more grounded understanding of AI visibility in your market. With clear trends for which brands are recommended, how frequently competitors appear, and which domains and pages are cited as supporting sources.
The same research demonstrated that meaningful patterns could be observed by examining how frequently brands appeared across repeated answers.
Consider how this comes together for that same brand selling project management software.
Scenario 1 offers a misleading picture, whereas the weighted AI response in Scenario 2 gives a much more balanced view. Especially when combined with the lens of topics which points to prioritization of the Automation topic.
This brand has repeated evidence to know where to focus their content efforts. This then shapes which competitive opportunities deserve investigation, its own content pipeline, and which third party publishers might be most valuable.
A prompt defines what’s being investigated. An observed AI response provides evidence of what the system actually returned. Together, they offer a stronger foundation for competitive benchmarking and identifying where the largest opportunities lie.
What makes AI visibility data trustworthy
Consistent coverage across brands, markets, and time makes for trustworthy, reliable data.
The way AI visibility data is evaluated needs to reflect the realities of the systems being measured. Businesses need a clearer understanding of what that data represents, how it was collected, and whether it supports meaningful decisions.
Semrush believes this standard should be built around three principles:
Demand-led: AI visibility intelligence should begin with real-world behavior and search demand. This helps establish which prompts and topics matter, as well as where market opportunities exist.
Topic-structured: Related customer needs should be meaningfully organized and supported by representative prompt coverage across relevant markets. This keeps measurement focused on genuine demand rather than unnecessary variations of questions.
Response-validated: AI visibility should be grounded in observed answers, with repeated measurement to understand competitive positioning and how recommendations change over time.
Together, these principles give brands a more complete picture of their performance wherever their customers search, and a clearer basis for deciding where to focus their efforts.
How to evaluate AI visibility data
For brands evaluating AI visibility data, database size is an understandable starting point. However, it’s vital to understand what sits behind the numbers.
Here are eight key questions to ask when assessing AI visibility databases:
These criteria give buyers a more practical way to assess data quality and also help explain why a larger database isn't automatically more useful.
A provider might offer more prompts but offer limited evidence of what AI platforms actually returned. Another might collect extensive responses without representing actual customer needs or markets relevant to a particular business.
No one number defines AI visibility data. It comes from the system behind it.
The strongest datasets connect representative market demand with observable AI responses, preserving enough context for businesses to investigate and trust the findings.
How Semrush puts these principles into practice
Semrush’s connected data brings these principles together, integrating established search and competitive intelligence with real-world behavioral data, AI prompts and observed responses.
With more than 18 years of search and competitive intelligence and direct access to behavioral signals from millions of panelists, Semrush is uniquely positioned. It enables a system to identify where meaningful demand exists, enable competitive benchmarking with topical analysis, and steer confident action through prompts that are validated by AI responses.
At the core of these capabilities is a connected data foundation:
- 200M+ all-time panelists as Audience Intelligence
- 28.9B+ keywords across the search database
- 2.18B+ AI prompt responses observed and analyzed
- 61.1M+ distinct brands and domains identified
- 117+ markets represented in AI response data
But again, size and breadth of data is only useful in respect of what it can enable.
Being able to connect AI visibility with broader search intelligence and real-world browsing behavior provides vital context around the opportunities being identified. It also supports analysis of how AI visibility may contribute to downstream traffic, moving beyond visibility measurement toward an understanding of potential business impact.
The result is a holistic view of discovery. Crucial because visibility doesn’t operate within a single channel.
Customers move freely between touch points in their decision making. This means different teams need a shared understanding of the competitive landscape while accounting for the customer needs and local conditions relevant to their responsibilities.
It’s time to raise the bar for AI visibility data
AI search results vary much more than by just model or market. They change from interaction to interaction. Businesses need reliable AI visibility to properly understand this environment.
Extensive prompt coverage is valuable, but its usefulness depends on how well those prompts represent distinct needs and actual demand. This forms one part of the required system.
Response collection adds evidence of what AI systems actually return. Its value increases when the observations cover relevant topics, brands, and markets, and can be compared consistently over time.
Then connecting this combination to established search and behavioral intelligence provides the broader context needed to interpret the findings and prioritize action.
As AI search continues to mature, buyers should expect and challenge vendors to explain what their database numbers represent, where the demand originates, and how the underlying evidence supports the metrics being reported.
At Semrush, we believe that's the standard that must be built: AI visibility data that’s demand led, topic structured, and response validated.
