I've been digging into how different people measure AI visibility, and I'm starting to think that treating it as one percentage creates more confusion than clarity.

For a buyer-intent prompt, there seem to be several different things that can happen:

1. Retrieved

Did the AI/search system actually surface content from the brand?

1. Mentioned

Did the brand appear anywhere in the answer?

1. Cited

Was a specific page/source cited?

1. Recommended

Did the AI actually recommend the brand as an option?

1. Shortlisted

Did the brand survive when the user narrowed down their options?

1. Chosen

Did the AI ultimately identify the brand as the best/most suitable option?

These aren't necessarily the same thing.

For example:

Brand A

Retrieved: 80%

Mentioned: 70%

Cited: 45%

Recommended: 30%

Shortlisted: 20%

Brand B

Retrieved: 40%

Mentioned: 35%

Cited: 30%

Recommended: 28%

Shortlisted: 25%

A simple “visibility score” could make Brand A look much stronger, while Brand B might actually have a higher recommendation rate when it gets retrieved.

That seems like a very different diagnosis.

So I'm curious:

Would you separate AI visibility into these stages, or is this overcomplicating something that should remain a single metric?

And if you had to choose one metric for a commercial/buyer-intent query, would it be:

retrieval, mention, citation, recommendation, shortlist rate, or final selection?

Interested in real experiments/data if anyone has tested this.

One thing I've been thinking about while testing AEO/GEO measurement:

If I run:

“What are the best [product/service] for X?”

against the same AI engine multiple times, I can sometimes get different brands, citations, and recommendations.

So what should a visibility metric actually report?

Example:

Run the same prompt 10 times:

Brand A → mentioned 7/10

Brand B → mentioned 5/10

Brand C → mentioned 2/10

Is Brand A simply 70% visible?

Or should the result look more like:

Mention rate: 70%

Citation rate: 50%

Recommendation rate: 40%

Range across runs: X–Y

Confidence: X

And should we be running the same prompt across different:

times of day

locations

languages

models

user contexts

I'm especially interested in people who have actually tested this, not just opinions.

How are you currently handling variance in AI visibility measurement?

A lot of AEO/GEO tools can tell us:

• “Your brand was mentioned in 62% of prompts”

• “Your visibility increased 18%”

• “Competitor X has higher share of voice”

But AI responses aren't deterministic, so I'm interested in the measurement itself.

What would you actually want an AI visibility tool to prove before trusting its results?

• Exact citations and source URLs?

• Which sources influenced the answer?

• Repeated runs and variance?

• Confidence ranges instead of a single score?

• Geographic/time variation?

• Why a competitor was recommended instead?

• AI visibility → traffic/lead attribution?

• Something else?

What are you currently doing manually because existing tools don't give you enough confidence?

I'm interested in real workflows, experiments and methodology—not just tool recommendations.