AI visibility in practice: turning measurement into actionable communications tasks

An increasingly important question is whether a company, brand, product or expert appears in answers generated by ChatGPT, Gemini, Perplexity and other AI systems, in what context it appears, which sources the AI relies on, and when a simple mention turns into an actual recommendation.

This is what we mean by AI visibility.

At ComLab, we do not treat it as a standalone SEO task. We see it as the intersection of communications, content, PR, technology and measurement.

AI visibility cannot be judged from a single prompt

One of the most common mistakes is trying to assess a brand’s AI visibility based on one ChatGPT question.

A single answer is only a snapshot.

The more relevant question is:

How often does the brand appear in answers to relevant questions, what role does it play in those answers, is it recommended, and which sources support the answer?

This means that proper measurement requires a structured set of questions rather than a single prompt.

For a hotel, these questions might include:

“Which hotel at Lake Balaton is suitable for a corporate conference?”

“Where should we organize a team-building event near Lake Balaton?”

“Which hotel has the capacity for a large conference?”

For a construction materials brand, the questions might be:

“Which tile adhesive should I use outdoors?”

“Which manufacturer offers a suitable product for large-format tiles?”

“Which tiling system would you recommend for a terrace?”

From an AI visibility perspective, these are the situations in which we want the brand to become one of the relevant options considered by the AI system.

Measure first, optimize second

ComLab’s approach to AI visibility therefore starts with measurement.

AnswerTracker, developed by Béla Krankovics, analyzes answers across multiple AI platforms to determine how often a brand appears, whether it is recommended, which sources the AI systems use and how the brand performs compared with competitors within the same set of questions.

Some of the most useful metrics include:

Mention Rate, which shows how often the brand is explicitly mentioned.

Recommendation Rate, which measures how often the brand is actually recommended.

Citation Rate, which indicates how frequently sources related to the brand appear as citations.

Own Source Rate, which shows how often AI systems rely on the company’s own website and content.

These can be complemented by competitive visibility and topic coverage metrics.

The goal is to distinguish perception from measurable AI visibility.

Measurement creates a gap map

The next step is not simply to “write more AI-optimized articles”.

First, we need to understand why a brand does not appear in a particular decision-making situation.

There may be a technical accessibility issue.

The website may fail to clearly explain what the company does.

The content may be strong, but the connection between the brand and a particular area of expertise may not be explicit enough.

In other cases, the owned sources may be strong, while independent external evidence is missing.

There are also situations where an AI system already uses a company’s content as a source but still does not mention or recommend the brand itself.

These are very different problems and therefore require different solutions.

This is where PR, SEO, content and technology come together

One of the most interesting aspects of AI visibility is that it connects disciplines that have traditionally been handled separately.

A technical problem may require an SEO solution.

An unclear service page may require content work.

A lack of third-party credibility may require PR.

Clarifying entities and structured data is a technology task.

Mapping decision-making questions is a research and strategy task.

This is why improving AI visibility can rarely be solved with a single tool or discipline.

At ComLab, measurement is translated into a concrete task list that shows whether a specific gap should be addressed through content, PR, technical development, SEO, AEO or another communications activity.

This is consistent with the way ComLab approaches digital strategy, marketing and technology as parts of a connected system.

What is the role of the ANSWER Framework?

The methodological structure behind the process is the ANSWER Framework.

The framework was developed by Béla Krankovics to prevent AI visibility from becoming just another technical audit or content checklist.

It connects several dimensions of AI visibility:

Accessibility

Can AI systems access the content?

Notability

Is the brand or expert clearly identifiable and sufficiently credible?

Structure

Do the content structure and structured data support machine understanding?

Web Consistency

Is the information consistent across owned and external sources?

Evidence

Is there verifiable evidence behind the claims being made?

Recommendation Readiness

Is the brand sufficiently well supported for an AI system not only to recognize it, but also to recommend it?

The objective is to turn measurement into specific improvement tasks instead of generic recommendations.

What does this mean for a company?

Consider a simple example.

Imagine a hotel that appears almost every time an AI system is asked directly about the hotel by name.

At first glance, that looks like strong visibility.

But what happens with actual business questions such as:

“Where should we organize a conference for 100 people at Lake Balaton?”

or

“Which hotel at Lake Balaton is suitable for a corporate event?”

If the hotel does not appear in these answers, the problem is not brand awareness. It is a generic discoverability problem.

The solution is not simply to mention the hotel’s name more often.

Instead, we need to examine:

whether its conference services are clearly defined;

whether the website contains sufficient decision-support content;

whether capacities and services are communicated explicitly;

whether there are independent professional or editorial mentions;

and which competitors AI systems recommend instead.

At this point, specific communications and technical tasks can be derived from the measurement.

We are not writing for AI. We are building a clearer digital presence

The purpose of AI visibility optimization is not to create separate content “for robots”.

Good content is still written for people.

The difference is that we need to make certain things much clearer:

who is speaking;

what they are talking about;

what expertise supports the claim;

which problem the content solves;

and what evidence supports the statements being made.

This also creates better content for people.

At the same time, it makes information easier for AI systems to understand, connect and reuse.

AI visibility is a measurable process

At ComLab, we therefore treat AI visibility improvement as an iterative process:

question mapping → measurement → gap identification → improvement tasks → implementation → remeasurement

This makes it possible to see whether a new piece of content, a technical change, a PR placement or a website update actually changes the brand’s presence in AI-generated answers.

This is what separates AI visibility measurement from a one-off ChatGPT test.

The question is not simply:

“What does AI say about us today?”

The more useful question is:

“In which decision-making situations do we appear, why do we appear or disappear, and which interventions measurably change that?”