Mapping visibility- The role of AI Visibility Audits in future business strategy.
Introduction
This paper defines the concept of an AI visibility audit, defines the core objectives of this framework, proposes how this methodological framework can be measured, the key distinctions between this technique and regular SEO and reputation management and how it can help businesses begin to understand the new search paradigm of generative engines.
What is an AI visibility audit?
As generative artificial intelligence (AI) systems increasingly mediate access to information, they have become powerful holders of reputation, knowledge, and visibility. Businesses and organisations are now discovered not just through search engines and social media, but also through AI-driven conversational and creative models, such as ChatGPT, Perplexity and Gemini. This shift introduces a new analytical and evaluative need: the AI Visibility Audit- a systematic evaluation of how, where, and to what extent an organisation or brand appears within generative AI responses.
Generative AI systems- such as OpenAI’s ChatGPT, Microsoft's CoPilot, and Google’s Gemini- are shifting how users seek and receive information. Instead of browsing search results, users increasingly rely on conversational AI to synthesise, interpret, and recommend content or answer transactional, informational and navigational queries. This paradigm shift diminishes the centrality of search engine optimisation (SEO) and creates a large alternative to traditional search engines. Based of these ideas, an AI Visibility Audit is a strategic and analytical process that evaluates how a business, brand, or organisation is perceived, referenced, and ranked within generative AI responses.
The core objectives of an AI Visibility Audit
An AI Visibility Audit seeks to:
Quantify visibility- Determine whether and how often a business appears in AI-generated responses to domain-relevant prompts.
Measure prominence- Analyse ranking or order of mention in AI outputs, indicating how “high up” the entity appears.
Evaluate sentiment- Assess the qualitative tone- positive, neutral, or negative- of the AI’s representation of the entity.
Identify gaps- Highlight areas or topics where the business lacks visibility or is misrepresented.
Quantitative Analysis and Metrics
To complement qualitative interpretation, AI visibility audits use devised quantitative measurement frameworks to systematically assess visibility strength. Core metrics typically include:
Mention Frequency (MF)- The proportion of sampled prompts in which a brand or organisation appears. This is measured using the calculation, "MF= M/N" where "M = number of prompts mentioning the brand", "N" = total prompts sampled. Range: 0-1. (Multiply by 100 for percent.) This calculation can be performed per platform or aggregated, however we recommend per platform.
Mention Frequency
Positional Prominence Index (PPI)- Analyse ranking or order of mention in AI outputs, indicating how “high up” the entity appears. First the weight given to a particular rank is figured out w(r)= 1/r where w(r)= weight of a ranking and r= the ranking. This part is very simple, for example if the ranking in the generative engine is 5th the rank weighting is 1/5 or 0.2. However this is only a basic representation it begins to get more difficult with the true PPI index, the calculation is:
Positional Prominence Index.
Step 1 – Define N: Set N as the total number of prompts tested. The sub-formula 1/N represents the averaging step applied at the end.
Step 2 – Apply the Indicator Function I(mentionedj): This acts as an on/off switch. If your brand appears in prompt j, I=1; if not, I=0. When I=0, the entire term for that prompt becomes zero.
The Indicator Function.
Step 3 – Calculate Rank Weighting 1/rj: Here, j represents the prompt number (e.g., first, second, third), and rj is the rank position of your brand in that prompt’s response. The value 1/rj assigns a weight based on position (e.g., rank 1 = 1.0, rank 5 = 0.2).
Step 4 – Multiply Components: For each prompt, multiply 1/rj×I(mentionedj). If the brand isn’t mentioned, the result is 0. If it is, you get a score between 0 and 1 depending on rank.
Multiply these components for each prompt.
Step 5 – Sum All Prompt Scores: Use the summation symbol ∑ to add together all the prompt scores (1rj×I(mentionedj).
Sum all prompt scores.
Step 6 – Average the Results: Finally, multiply the total by 1/N (or divide by N) to calculate the average visibility score between 0 and 1.
Cross-Platform Consistency (CPC)- This allows us to measure the consistency of visibility across Gemini, Bing Copilot and Chat-GPT. Mathematically, we quantify this using standard deviation, then normalise it to make it comparable across scales. The data our company compares if the PPI index (as discussed above) but this can be done using any variables.
Cross Platform Consistency formula.
Suppose you measure your Positional Prominence Index (PPI) on multiple platforms: Gemini, Bing copilot and Chat-GPT. Your PPI for Chat-GPT is 0.45, for Gemini it is 0.5 and for Copilot it is 0.6.
Step 1- xi = PPI on platform i, n = total number of platforms. Calculate the PPI on all 3 platforms (xi1, xi2 and xi2) ∑i1 to i3= sum of all PPIs. Multiply the sum of the PPIs on all 3 platforms by the 1/N or 1/3 in this case (This is the same as dividing by N.) In this scenario this would be 0.45+0.5+0.6 x 1/3= 0.5.
Step 2- Compute the deviation of the mean from each platform, di = how far platform i deviates from the mean. In our scenario, d1= -0.05 (Chat-GPT), d2= 0 (Perplexity), d3= 0.1 (Copilot.)
Step 3- Square the deviation from the mean. In our scenario d1(squared)= -0.0025, d2 (squared)= 0, d3 (squared)= 0.01.
Square the deviation from the mean.
Step 4- Compute the variance (Average Squared Deviation.) ∑d1(squared) to d3(squared)= the sum of the squared deviations. (0.01+0+0.0025) x 1/3= 0.00417.
Computing the variance.
Step 5- Compute standard deviation- take the square root of the variance to get standard deviation. The square root of our example is 0.065.
Computing the standard deviation.
Step 6- Normalise this standard deviation- this can be done by taking the previous mean or (x) and dividing the standard deviation by it. In our example, this would be 0.065/ 0.5= 0.13.
Normalising the standard deviation.
Step 7- Since higher variation = lower consistency, invert it: this can be done by taking away the normalised standard deviation from 1, which in our example would be 1-0.13=0.87. This value is our CPC. Converting this to a percentage our example would have an extremely high cross-platform consistency score of 87%.
The Method
Prompt Sampling- A structured list of controlled prompts is developed to simulate realistic user queries (e.g., “What are the best VPN providers?” or “Recommend eco-friendly hotels in London”). These prompts are used to generate AI-generated responses across platforms. Following these prompts we note down the data that the generative engine gives us. Such as: how many mentions your company has in total, rankings in each mention and the sentiment that your company holds. Firstly we rank the sentiment factor out of 10. We then use our collected data to calculate the mention frequency, the positional prominence index and the cross-platform consistency score.
How can these three metrics can be analysed to begin to create AI visibility solutions?
Each metric reflects a different dimension of AI visibility: MF measures how often the company appears, PPI measures how prominently it appears, CPC measures how consistently that prominence is maintained across platforms.
Mention Frequency- Mention Frequency captures how often a brand or company appears in AI-generated responses across a diverse set of prompts. It reflects recall strength- the likelihood that the AI model considers the company relevant when synthesising answers. A high MF score (approaching 1.0) indicates that the brand has strong associative weight within the model’s training context and retrieval layers. Conversely, a low MF suggests that the model rarely recalls the brand in response to thematically relevant prompts. A low MF score may indicate that the company lacks sufficient semantic visibility in the data sources or information that large language models (LLMs) draw from. This will allow a company or GEO expert to create AI visibility solutions by attempting to understand where these issues stem from.
The Positional Prominence Index- PPI evaluates how prominently a company is positioned within a generative model’s ranked or sequential outputs. It quantifies hierarchical prominence- whether the brand is mentioned early, frequently, or in high-priority contexts. A company that consistently appears in the top-ranked results demonstrates strong authority weighting within the model’s internal representation of expertise or relevance. A low PPI value, even with a moderate MF, indicates that the company is being recalled but not prioritised- suggesting weaker perceived relevance, authority, or topical dominance compared to competitors. Low PPI points to problems not of recognition but of ranking. The AI may “know” of the brand but favour other entities as more credible, representative, or contextually dominant. This can allow for the identification of problems that may cause lower rankings in generative engine and allow for AI visibility solutions to be devised.
Cross Platform Consistency- CPC measures the variance of visibility across different AI systems. It provides a normalised indicator of representation stability- how uniformly the company is perceived and ranked across distinct generative models. A high CPC value (close to 1.0) indicates consistent representation across systems, while a low CPC suggests fragmentation or bias, where certain platforms recognise the brand much more strongly than others. Low CPC scores imply ecosystem inconsistency- that visibility is strong in one model but weak in others. This can result from uneven coverage in training data, proprietary corpus differences, or differences in reinforcement learning alignment. Inconsistency reduces reliability and increases reputational volatility, as brand perception depends on which AI system users consult. This can help identify for which platform visibility may be low in and allow for solutions to be created.
How do AI Visibility Audits differ from SEO and reputation management?
The emergence of generative AI represents a fundamental shift in how visibility and reputation are produced and measured. While SEO and reputation management remain critical for managing web-based and social perception, they are insufficient for the synthetic representational logic of generative AI. AI Visibility Audits extend digital analytics into this new domain, measuring representation, prominence, and consistency within AI-generated outputs. They allow organisations to understand not only whether they are seen but how they are understood by the systems, who are becoming primary sources of information. AI Visibility Audits redefine digital visibility- a shift marking the next frontier in digital strategy and corporate intelligence.
How do AI Visibility Audits represent a huge development in corporate intelligence and strategic benefits?
The new kind of corporate intelligence- A company’s performance within this ecosystem determines whether AI-generated responses identify it as a leader, follower, or non-entity within its sector. As such, auditing AI visibility is becoming as critical as monitoring brand sentiment or SEO rankings. AI has created a third domain of intelligence- the AI reputation layer- within this layer there is corporate reputation, expertise, and influence. A company’s performance within this ecosystem determines whether AI-generated responses identify it as a leader, follower, or non-entity within its sector. As such, auditing AI visibility is becoming as critical as monitoring brand sentiment or SEO rankings.
Traditional corporate intelligence tracks external signals: market share, sentiment trends, and search traffic. AI Visibility Audits move beyond these to representation analysis- evaluating how a company’s identity is constructed and recalled by AI systems. This provides insight into how algorithms conceptualise the brand, a new and powerful domain of intelligence.
Detecting Blind Spots and Knowledge Gaps- A low Mention Frequency or Positional Prominence Index reveals representational blind spots where generative models fail to associate a company with its own industry domain. These gaps highlight missing data, under-optimised public documentation, low local digital footprint or inadequate digital knowledge signals- valuable insights for communication and data strategy teams.
Enabling for the creation of an AI Visibility Strategy (a key part of GEO)- The insights from visibility audits feed directly into AI Visibility Strategy- the process of optimising corporate data ecosystems to improve how AI models interpret and reproduce company-related information. (Check out our other articles and informational pages on our website, which describe some of these strategies.)
First Mover Advantage/ AI Influence as future market power- As generative AI systems become prominently used in search, enterprise software, and decision-support tools, companies with strong AI visibility gain huge informational influence. This creates a profound first-mover advantage. Early adopters that audit and optimise their representation within AI models secure a disproportionate share of algorithmic attention. Once a brand becomes embedded in the knowledge structures of large language models (LLMs), it tends to persist due to the self-reinforcing dynamics of training and retrieval: the more a brand appears in AI responses, the more it is subsequently learned and cited in future iterations. In this way, AI Visibility Audits.
How this applies to local businesses- For local businesses, the implications are particularly significant. Traditional local SEO once relied on proximity, directory listings, and keyword optimisation. In contrast, AI visibility now determines which local entities appear when users ask generative agents for context-rich queries such as “What’s the best coffee shop for remote work near me?” or “Which local accounting firm is most experienced with small businesses?” If a local business fails to appear- or is mentioned less favourably- its digital invisibility can directly translate into reduced real-world engagement. Conversely, local enterprises that embrace AI visibility audits early can capture disproportionate algorithmic exposure in their regions. By ensuring that generative systems recognise their brand, expertise, and relevance, they not only attract more AI-mediated traffic but also secure durable visibility advantages that competitors may struggle to overcome later. The first-mover advantage in AI visibility therefore represents both an economic and informational lead- one rooted in early recognition by the algorithms that increasingly shape market perception, trust, and consumer choice.
Conclusion
AI Visibility Audits are more than analytical tools- they are strategic infrastructures for the age of generative intelligence. By revealing how companies are represented inside AI systems, AI Visibility Audits redefine what it means to be visible, relevant, and trusted in digital environments. Businesses that integrate AI Visibility Audits into their intelligence frameworks gain the ability to: monitor and optimise their representation within AI models, detect algorithmic bias or misrepresentation early, strengthen competitive positioning, and create data-driven strategies for long-term AI viability.
References
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OpenAI. Representation and Recall in Large Language Models: A Technical Overview- 2024