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Most teams building a brand awareness strategy for AI search are spending effort on the smallest lever. An eight-month study of 30 brands across six categories found that roughly 63% of LLM brand visibility comes from long-term brand equity already embedded in training data. Another 22% comes from wider marketing activity. Citations (the thing most AEO guides tell you to chase) account for just 11%. If your current AI visibility plan leads with citation-building, you are optimising the smallest slice of the pie.
This is the reframe the field needs. The instinct makes sense: you see your brand absent from an AI-generated answer, you want to get mentioned, so you chase links and structured placements. But the data show that broader marketing activity is roughly twice as impactful for LLM visibility as citation-building. The allocation of effort in most organisations is almost perfectly inverted relative to the actual levers.
For B2B brands in the UAE, the implications are sharper. Most are challengers, not incumbents. Incumbents inherit their history in AI outputs (decades of press coverage, indexed content, and category associations are baked into the model's understanding of who matters). Challengers do not have that luxury, but they do have a real and underestimated window: the 22% that is genuinely controllable. The question is whether your strategy is actually aimed at it.
How Much of AI Visibility Comes From Existing Brand Equity?
The 63% you cannot manufacture. The majority of your AI brand visibility is a lagging indicator of historical brand strength. LLMs were trained on the web as it existed, and the brands that dominated category conversations, earned media, and authoritative coverage over years are structurally more visible in AI outputs as a result. INSEAD's analysis puts it plainly: incumbents like Tesla or BMW maintain strong AI visibility partly because years of specific, feature-led content is exactly what language models value. There is no shortcut into that layer. You cannot retroactively build the training data.
What this means in practice is that, for challenger brands, AI visibility is partly a debt problem. The gap you see in AI outputs today reflects underinvestment in brand-building over the past several years, not a technical failure you can fix with schema markup next quarter. That is a commercially important distinction, because it changes the timeline and the brief. The 63% is the long game. The 22% is where you should be focused right now.
What Should Your AI Visibility Strategy Actually Focus On?
The controllable third lies in broader marketing activity: the sources LLMs disproportionately draw on when constructing answers. Based on how current models are trained and what they over-index on, this means Reddit and high-signal UGC forums, review platforms, YouTube, earned PR and editorial mentions in respected publications. These channels need sustained presence in the places where genuine category conversation happens, not a one-off push.
Structured, authoritative owned content also belongs here. E-E-A-T principles (Experience, Expertise, Authoritativeness, Trustworthiness) and schema markup help models extract and attribute your content correctly. The "good SEO is good GEO" observation holds: the fundamentals of making content clear, credible, well-structured, and properly attributed did not fundamentally change when the acronym shifted from SEO to AEO to GEO or LLMO. What changed is that the tolerance for thin or ambiguous content dropped to near zero. AI models are not forgiving of content that hedges its claims or buries its expertise behind marketing language.
Why Does Talkability Matter More Than Reach for AI Visibility?
Upper-funnel and paid media deserve a specific reframe here. Passive reach does not generate the kind of signal LLMs draw from. What matters is whether your brand generates genuine conversation in the places models read. A YouTube video with high engagement and active comment threads is more useful for AI visibility than a display campaign with high impressions but no downstream discussion. A Reddit thread where your product gets compared, debated, and recommended carries more weight than a sponsored content placement that generates no reaction.
That means designing campaigns with talkability as an explicit objective alongside reach, not abandoning paid media. Creator partnerships that drive discussion, community participation rather than broadcast-only social, PR that generates editorial commentary rather than just syndicated pickups: these are the formats that compound into AI visibility over time.
How Does Semantic Fan-Out Affect Your AI Visibility?
When an AI model receives a query, it expands it semantically before answering, incorporating related subtopics, adjacent questions, and category context. If your brand is only present at the head term level, you are invisible across most of the territory the model actually searches. The practical implication: map the sub-queries around your core category by analysing "People Also Ask" results, related searches, and forum thread structures. Then align your PR, influencer content, and editorial calendar so the brand appears across those adjacent conversations, not just the obvious keywords.
For a UAE B2B challenger in, say, healthcare services or manufacturing technology, this might mean building visible presence in conversations about regulatory compliance, procurement criteria, regional certification standards, or implementation challenges: the surrounding territory where buyers are actually thinking before they reach a buying decision. That surrounding presence is what shifts a brand from absent to referenced in AI-generated answers.
Why Do Regional and Non-English Brands Have an AI Visibility Disadvantage?
LLM training data is overwhelmingly English-language and US-centric. Consumer-facing LLM traffic is dominated by English-speaking markets, which means brands operating primarily in Arabic, Mandarin, or other regional languages carry thinner equity in the underlying models by default. A challenger brand in Dubai or Shenzhen starts with a structural visibility deficit that goes beyond competitive positioning: it is a data representation problem.
This makes the controllable 22% disproportionately important for regional brands. English-language PR in globally indexed publications, participation in international industry forums, reviews on globally accessible platforms, and structured content that explicitly connects the brand to category terms in English, these are the primary mechanism for closing the representation gap for APAC and GCC challengers, not a nice-to-have. The brands in this region that are already investing in this will compound their advantage. Those waiting for a purely regional playbook may find the window has narrowed by the time they act.
What Is Share of Model and How Is AI Visibility Measured?
The framing that has gained traction is Share of Model, the AI-era successor to Share of Voice and Share of Search. The same INSEAD research defines it as a way to measure how different LLMs perceive and recommend a brand relative to its category. YouGov research has found that two-thirds of 18–24-year-olds already ask AI models for brand recommendations. For B2B buyers researching vendors, the pattern is developing at similar speed.
The measurement point matters as much as the strategic one. LLM visibility is relatively stable month-to-month, which means it rewards sustained investment rather than tactical bursts. More importantly, that same study found that in three of four cases, LLM-referred sessions carried higher revenue per session than organic search sessions. That reflects real buyer intent: users arriving via AI-generated answers are further along in their decision process and closer to a commercial action than typical search traffic. Building a measurement framework that captures this, tracking how the brand actually appears across real queries on real platforms, not just checking a citation list, is where the commercial case for investment gets made.
This is the work Oxygen does directly with clients: LLM visibility auditing and optimisation tied to measurable business outcomes, not just presence tracking. The audit comes first because most organisations do not have an accurate picture of how their brand appears in AI outputs today, which means the strategy is built on assumption rather than data.
How Do You Improve AI Visibility in the Next 90 Days?
- Audit before you build. Run structured queries across the AI platforms your buyers actually use. Record what the model says about your brand, your competitors, and your category. Most teams find significant gaps between what they assume and what the model actually surfaces.
- Redirect citation effort toward earned presence. Reviews, Reddit participation, YouTube, and editorially independent PR in indexed publications move a bigger lever than structured citation placement.
- Map semantic territory. Identify 10–15 subtopics related to your core category and assess your brand's presence across each. Gaps in sub-topic coverage are gaps in AI visibility.
- Reframe upper-funnel KPIs. Replace passive reach metrics with engagement-depth and talkability indicators: comment volume, discussion threads, and shares that generate responses.
- Invest in English-language authority if you are a regional brand. Globally indexed coverage compounds into training data in a way regional-only content does not.
The brands that lead in AI visibility over the next three years will be the ones that build genuine brand strength in the sources that models trust, sustain a presence across the full semantic territory of their category, and measure outcomes in revenue terms rather than mention counts, not the ones chasing the citation algorithm. The levers that matter (brand equity, trusted-source presence, structured content authority) outlast any individual model update or acronym cycle. Build for those, and the AI visibility follows.
