At an AI visibility conference, ask vendors to show what they measure, how they produce the numbers, and the underlying answers and citations behind each finding. The goal is to determine whether the data reflects your audience, can be repeated and inspected, and supports a decision—not just whether a dashboard looks persuasive.
What do you mean by “AI visibility”?
Ask the vendor to define its metrics and map them to four distinct dimensions: whether you appear, how prominently you appear, how accurately you are portrayed, and whether that visibility prompts action. The Interactive Advertising Bureau’s August 2026 framework describes these as presence, prominence, portrayal, and persuasion. It offers examples including mention and citation rate, placement, sentiment, factual inaccuracy, recommendation strength, and post-citation click-through rate. Read the IAB framework.
Do not assume similarly named scores from different vendors measure the same thing. Ask what goes into each score, what is excluded, and whether the vendor can show a sample calculation from underlying records.
How do I know if the numbers are reliable?
Ask whether the report is directional or decision-grade, and what evidence supports that label. In the IAB framework, directional measurement can reveal patterns and trends; decision-grade measurement faces a higher rigor bar and is needed before using results for budget or strategy decisions. Ask the vendor to explain how the exact report in its demonstration meets that bar.
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Request specifics rather than a general assurance of accuracy:
- How many queries and responses are included, and over what period?
- Which prompt types are covered, and how were they chosen?
- How frequently is each prompt tested, and how often are measurements collected?
- Can another run reproduce the result under the same conditions?
- Which engines, locales, languages, and search surfaces are included?
The IAB says more than 20 companies sell AI visibility measurement tools and notes that differing methodologies can produce different answers for the same brand or publisher. The framework establishes shared measurement vocabulary; it does not endorse or rank vendors.
Can I inspect the prompts, answers, dates, and citations?
Ask to see the exact prompts tested, complete answers, run dates, and cited URLs—not just a chart or summary score. Citation URLs can complement mention and competitor metrics by showing which sources appeared in an answer. Yext’s evaluation guide discusses this kind of evidence and recommends examining whether conclusions follow from the underlying records. Read Yext’s guide.
Request examples for your category and market. Check whether the prompt resembles a real question your customers might ask, whether the full answer supports the vendor’s interpretation, and whether the cited page is actually yours and relevant.
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How are comparisons kept reproducible?
Ask how the vendor holds the test conditions steady between runs. The IAB identifies stability and reproducibility as quality considerations. For a meaningful comparison over time, the vendor should be able to explain whether it uses the same prompt set, engine configuration, locale, and time window.
- How many times is each prompt repeated, and how are run-to-run differences reported?
- What happens when an engine is unavailable or changes its interface or response behavior?
- How are changed, added, or removed prompts identified in a trend line?
- Can you compare results using a fixed prompt set as well as the current one?
If the prompt mix or test conditions change, ask the vendor to make that change visible rather than presenting the new result as an uninterrupted trend.
Which engines, markets, languages, and search surfaces are covered?
Compare coverage with where your audience actually searches and what your team needs to understand. Ask separately which AI engines, countries or locales, languages, and traditional search surfaces are included; coverage of one engine or market does not establish coverage of another.
Yext reports that it studied 491,972 local questions across Gemini, Claude, Perplexity, and ChatGPT from February to August 2026. In that study, all four engines selected the same top location 4% of the time, while they selected four different top locations 48% of the time. Those are Yext’s results for that study’s local-question set, not a universal estimate of disagreement among answer engines.
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Does the platform measure accuracy and context?
A mention does not tell you whether the answer described your brand correctly. Ask the vendor to show examples of mischaracterizations, outdated details, or factual inaccuracies, and explain how those are scored. The IAB’s portrayal dimension includes sentiment, framing, hallucination rate, and factual inaccuracy rate.
Ask whether your team can inspect the source behind an error and what correction path exists. A vendor should distinguish what it observed in an answer from what it infers about why that answer appeared.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can the team do after a finding?
Ask for a walkthrough from a detected issue to a recommended action. Have the vendor show the observation behind the recommendation, what evidence supports it, and how your team can measure the result afterward. Yext’s guide treats actionability and the ability to update sources as evaluation considerations distinct from measurement.
Clarify what the product can change directly—such as content or listings, if offered—and what requires your team’s own workflow. Treat optimization suggestions as hypotheses until you can connect them to a documented observation and a measurable follow-up.
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What business outcome can the vendor substantiate?
Ask the vendor to separate visibility metrics from evidence of downstream actions or revenue. The IAB’s persuasion dimension includes recommendation strength and post-citation click-through rate; its August 2026 release notes that an attribution framework is forthcoming. A higher mention rate alone does not prove sales impact. Ask what attribution evidence is available now, what it can establish, and what remains a proxy.
How should I compare vendors after the conversations?
Use the same questions for each vendor, and record whether each answer is demonstrated, documented, or merely promised. A concise comparison should cover:
- Measurement scope and fit with your audience
- Method transparency, sample design, and repeat-run approach
- Reproducibility and visibility into changes to prompts or test conditions
- Engine, geography, language, and search-surface coverage
- Access to complete answers, prompts, run dates, and citation evidence
- Portrayal, error identification, and correction paths
- Actions the platform supports and the evidence behind its recommendations
- Strength of business-outcome attribution
For each feature, ask whether it is available now or planned. That distinction helps keep a product demonstration separate from capabilities your team can use today.
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