A UAE software company checks ChatGPT after a sales call. It asks about its own product by name and receives a favourable answer. The team takes a screenshot, circulates it internally and concludes that its AI visibility work is paying off.
Then someone asks a less comfortable question: which providers does the same tool surface when a buyer asks for software in the category, compares options or looks for a supplier in the UAE? The company rarely appears. Its named competitors do.
That is the difference between an anecdote and AI share of voice. For founders and marketing teams, the useful question is not whether a brand can appear in one answer. It is how often it appears against relevant competitors across the buying questions that matter, on a consistent set of AI platforms, over time.
The checklist for a defensible AI share-of-voice measure
Use the following checks before reporting a number. They are deliberately unglamorous. Measurement becomes unreliable when the prompts, competitors or platforms change between checks and the resulting figures are presented as if they describe the same market test.
1. Define the decision you are trying to measure
Start with a commercial use case, not a generic request to track ChatGPT visibility. A B2B software firm may care about shortlist inclusion, category recognition, comparison prompts and use-case fit. A Dubai consultancy may care about which firms appear when a buyer asks for a specialist rather than a household-name agency.
Write down the market, audience, category, service and geography. This prevents a prompt set from drifting into questions that are interesting but commercially peripheral.
2. Build a fixed prompt set around real buyer questions
Include branded prompts, but do not let them dominate. Branded prompts usually test whether the platform can identify and describe your organisation. Unbranded prompts test whether it places you in the competitive conversation at all.
- Category prompts: requests for providers, products or services in your category.
- Use-case prompts: questions tied to the job a buyer needs done.
- Comparison prompts: requests to compare named providers or alternatives.
- Buying prompts: questions about selection criteria, implementation, pricing approach or local suitability.
- Branded prompts: questions about your company, offer, location and differentiators.
Keep the wording stable. Changing best CRM software for UAE logistics teams to leading enterprise automation providers is not a harmless rewrite. It changes intent, likely competitors and the type of answer being tested.
3. Name a competitive set before collecting results
Choose direct competitors that buyers genuinely encounter, plus any large category player that repeatedly shapes the answer space. Document why each is included. Do not swap competitors in and out after every run because one happened to appear more often.
For a local business, the set may include UAE competitors and international providers selling into the same market. The correct list is commercial, not flattering.
4. Decide which platforms belong in the study
ChatGPT is useful, but it is not the whole AI market. A repeatable programme may include ChatGPT, Gemini, Perplexity, Copilot and relevant Google AI experiences where access and collection methods allow. The right mix depends on the audience, market and how prospects actually research.
Record the platform version, collection date, geography where relevant and whether you are using a logged-in environment. AI outputs can vary with product changes, personalisation, retrieval behaviour and query interpretation. That is a reason to standardise collection, not a reason to give up measuring.
5. Separate mentions from citations
A brand mention and a citation are different observations. A platform may name a business without linking to or citing a source. It may cite a source that mentions the business without recommending it. Both can be useful, but they should not be collapsed into one vague visibility score.
Ahrefs’ explanation of AI Share of Voice similarly frames the measure as a comparison of how often a brand appears against competitors across relevant AI conversations, with visibility tracked over time. That comparison is the useful part. A raw count without a stable competitive context is mostly decoration.
| Signal to record | What it tells you | Collection note |
|---|---|---|
| Brand mention | Whether the brand entered the answer | Record prominence and answer context |
| Competitor mention | Who else owns the same prompt space | Use the pre-defined competitor list |
| First-position mention | Whether a brand leads a shortlist | Do not treat it as a recommendation guarantee |
| Citation or source reference | What information the platform appears to rely on | Capture the cited domain and claim supported |
| Answer accuracy | Whether the brand is described correctly | Flag vague, outdated or incorrect claims separately |
6. Use a calculation that can be explained in one sentence
AI share of voice is usually calculated as your brand’s recorded visibility signals divided by the total recorded visibility signals for all brands in the defined competitive set, across a fixed prompt set and platform set. The precise weighting can vary. You may weight a first-position mention differently from a passing mention, for example, but any weighting must stay consistent and be visible in the methodology.
Do not imply that a share-of-voice percentage represents market share, lead volume or a platform endorsement. It measures observed visibility within the test design.
7. Preserve the raw evidence behind the metric
Store the prompt, platform, date, full answer, named brands, citations and analyst notes. Screenshots alone are awkward to compare and easy to cherry-pick. A structured collection sheet lets a team review whether a mention was meaningful, whether a citation actually corroborated a claim and whether the model confused two similarly named businesses.
This is where website detail often reappears. A company may be absent from category answers while its own site uses three inconsistent names for the same service, or its Organisation structured data has no clear relationship to its product or service pages. Prompt tracking can reveal the pattern. It does not replace fixing the underlying entity clarity and answer-ready content problem.
8. Repeat the same experiment at a sensible interval
Monthly collection is often a practical starting point for an active category. Quarterly may be enough for a stable, low-volume B2B market. More frequent checks can be justified around a major site migration, product launch, authority campaign or substantial content change, provided the prompt set remains intact.
Report trend lines with context. If visibility changes, check whether the platform changed, the competitor set changed, the prompt wording drifted or collection conditions differed before claiming that the brand moved.
A minimum viable scorecard for the software company
For the UAE software company, a defensible first scorecard might include ten to twenty fixed prompts across category, buying, comparison and branded intent. It would track the company and its named competitors across selected platforms, then record mentions, first-position appearances, citations and accuracy flags at each collection point.
The initial finding may be uncomfortable but useful: strong branded recognition, weak unbranded inclusion and few citations supporting the company’s category claims. That points to a different work plan from simply publishing more branded content. The team may need clearer service definitions, better product and use-case pages, machine-readable organisation relationships and credible external corroboration.
FlareFalcon’s GEO programmes and AI visibility work use tracked prompts and competitor benchmarking in this way: as a repeatable diagnostic, alongside technical AI-readiness, entity clarity and authority work. The objective is not to manufacture a flattering dashboard. It is to establish what is actually being observed and what may be worth improving.
Questions teams ask before setting up tracking
Which prompts should count in AI share of voice?
Count prompts that reflect meaningful discovery, evaluation or comparison behaviour for your offer. Include a mix of unbranded category questions, use-case questions, buying questions, competitor comparisons and branded checks. Exclude prompts that are overly niche, artificially favourable or unlikely to be used by a real prospect unless they serve a defined research purpose.
How often should AI share of voice be measured?
Measure often enough to identify a trend without mistaking normal answer variation for a major change. Monthly tracking suits many active markets, while quarterly tracking can work for slower B2B sales cycles. Keep the collection conditions, prompt wording, competitor set and scoring rules stable so one period remains comparable with the next.
Does a citation matter more than a mention?
Neither is universally more valuable. A prominent mention may indicate category presence, while a citation can show which sources support an answer. Record both separately, then assess their context. A citation from an irrelevant directory may add little, while a well-placed mention in a comparison answer may be commercially significant.