AI search visibility is a brand awareness metric and should be judged that way, with traffic treated as a weak supporting signal. The approach is the same for Arabic and English: one framework, one set of measures, applied to both languages.
The reasoning is straightforward. An AI answer does its job when a buyer reads a brand's name in it, whether or not they click. A traffic report captures a small fraction of that exposure and records the rest as nothing. For a B2B brand in the Gulf, that error is large enough to distort budget decisions.
Traffic misses most of the exposure because people rarely click out of an AI answer. The answer is the destination. A buyer asks a question, reads a summary that names three or four providers, and moves on with a shortlist already formed in their head.
The evidence on click behaviour is consistent. Pew Research Center found that Google users clicked a traditional result in 8% of visits when an AI summary appeared, against 15% without one. Sessions also ended outright on 26% of pages with a summary, compared with 16% without. Users rarely clicked the sources cited inside the summary.
That study covered 900 American adults, so it is not a Gulf dataset. The direction still matters. Being named in an answer produces far fewer visits than ranking in a list of links ever did, and a dashboard built on sessions will undercount influence every month.
Wider referral data points the same way. Similarweb figures reported by Digiday show search referrals to 1,000 domains fell from 12 billion visits to 11.2 billion in a year, and referrals from ChatGPT and Perplexity did not offset the drop. AI platforms are growing as a place where decisions start, without growing as a source of visits.
AI answers behave like a brand awareness channel, so they should be measured with awareness signals. The useful question is whether the brand appears when a buyer asks about the category, how it is described, and who appears alongside it. Those signals exist whether or not anyone clicks.
This also reframes what success looks like. Imagine a specialist manufacturer that is named in every answer about industrial suppliers in the UAE. Its analytics might show three referral visits a month. On a traffic report, that reads as almost nothing. Yet every buyer who asked the question saw its name, often next to only two or three rivals, and that is exactly why it is worth being there.
Framing deserves more weight than most teams give it. A mention that describes the brand wrongly can do more harm than no mention, which is the case set out in our breakdown of why visibility scores mislead without sentiment.
Referral visits from AI platforms are still worth tracking in analytics. They show which pages answer engines trust enough to cite, and a sudden change can flag a problem. They simply cannot carry the verdict on whether the channel is working.
Downstream signals are more telling. Rising branded search, direct visits and prospects who mention an AI tool on a discovery call all suggest awareness is turning into demand. They are corroborating evidence of that shift, not the measure of AI visibility itself. Industry commentators now argue for treating AI search as a branding channel for exactly this reason. Much of what models say also reflects brand equity that already exists across the web, which is another sign that this is a brand measure.
No. Arabic and English use the same approach: the same awareness signals, the same prompt logic, the same scoring and the same reporting format. The underlying question does not change with the language. A buyer asks, the engine answers, and the brand either appears in a useful way or it does not.
Answer engines do not simply translate one answer into another language. A cross-platform audit of ChatGPT Search, Gemini, Perplexity and Copilot found that Arabic queries returned a substantially higher share of local sources than matched English queries, drawing on visibly different source ecosystems. One platform cited sources less often in Arabic.
That study covered Lebanon, not the Gulf, so it is an indicator rather than proof for the UAE or Saudi Arabia. The implication still applies. A brand with strong English coverage can be absent from Arabic answers, and one blended score averages that gap away. That difference is a reason to report each language separately inside one framework, not a reason to build a second method.
Running two separate frameworks creates problems of its own. Definitions drift, scoring rules diverge, and within a few months nobody can say whether Arabic visibility is genuinely weaker or just measured differently. One framework keeps the comparison honest and lets a leadership team read both languages on a single page.
What does change is the execution inside the framework. Arabic prompts should be written the way an Arabic-speaking buyer would phrase them, including Gulf usage, rather than translated word for word from English. A Saudi buyer, for example, may write in Gulf Arabic or use local sector terms, such as references to Vision 2030 programmes or local-content requirements, that a direct translation would miss. The results are then reported side by side, one column per language, using identical measures.
In our experience, many Saudi and wider Gulf decision-makers research in both languages. Picture a chief executive in Riyadh who asks in Arabic, while the procurement team compares suppliers in English. If the brand only appears in one of those answers, half the buying group never sees it.
The shared framework handles this without a second methodology. Reporting each language separately, under identical measures, shows exactly where the brand is invisible.
In the GCC, traffic from AI answers is often too sparse to judge performance at all. In our experience with B2B clients in the region, search volumes in specialist categories tend to be small to begin with. We have seen healthcare groups, private equity firms and industrial suppliers record only a handful of AI referral visits a month, where one busy week can double or halve the number.
Numbers that thin cannot support a decision. A marketing director who reports AI performance on referral visits will see noise, draw the wrong conclusion and may cut investment in a channel that could be shaping shortlists. Awareness signals are less sensitive to click volume, because each prompt tested records whether the brand appears regardless of how many people click. They still carry noise of their own, which is why step 4 below judges movement over a quarter rather than a week.
| Question | Traffic view | Brand awareness view |
|---|---|---|
| Is the brand reaching buyers? | Few visits, unclear | Named in most category answers |
| Is Arabic weaker than English? | Too few visits to tell | Visible gap per prompt set |
| Who is winning the shortlist? | Not measurable | Competitor presence per answer |
| Is it improving? | Noisy month to month | Trend against a fixed baseline |
Five steps turn AI answers into a repeatable brand awareness measure, and they run the same way in Arabic and English. None of it needs special software.
Write a set of prompts that real buyers would ask at the start of a purchase, such as which providers to consider, how to compare options, or who specialises in a niche. In our work, around 20 to 40 prompts is a workable starting point, though that is a small sample, which is one more reason to read trends rather than single runs. Then mirror the set in Arabic, phrased natively. Each English prompt should have an Arabic counterpart with the same intent.
Pick the platforms your buyers actually use, typically ChatGPT, Gemini, Perplexity, Copilot and Google's AI summaries. Track the same platforms in both languages. Adding or dropping a platform mid-year breaks the trend, so settle the list before the baseline.
For each prompt on each platform, record whether the brand is mentioned, whether it is cited, where it sits in the answer, how it is framed and which competitors appear. A simple spreadsheet works. Consistency of logging matters far more than sophistication.
The first full run is the baseline. Repeat it monthly with the same prompts, platforms and scoring rules. AI answers vary between runs, so treat single results with caution and judge movement over a quarter rather than a week.
Report share of answers, citation rate, framing and competitor presence as brand awareness metrics, with Arabic and English shown side by side under one framework. Place the report alongside other brand measures, away from the traffic dashboard, so it is judged against the right expectations.
The practical decision is where this channel sits in reporting. Put visibility in AI answers next to brand awareness, share of voice and branded search, and stop asking it to justify itself in sessions. That one change means the channel gets budget on the right terms.
Arabic and English go through the same framework, reported side by side. The first action is simple: run the baseline this month, with the mirrored prompt set, so there is something to measure against. Timing the wider investment is a separate question, covered in our guide to when B2B companies should invest in AI answer visibility.
Oxygen helps UAE and GCC brands set up this kind of bilingual measurement as part of its AI search optimisation and SEO and GEO services. If you want to test how your brand currently appears in Arabic and English answers, a free 15-minute consultation is a straightforward place to talk it through.