How Different Cultures Use AI and Why Brands Need an LLM Share-of-Voice Strategy

Traditional search is a list. LLM search is a conversation.

In SEO, the user often types a short phrase, scans the search results and decides which link to click. In LLM search, the user gives more context, asks a richer question and expects the answer engine to do the filtering for them.

A 2026 Stella Rising study of 524 active LLM users found that 80% of prompts were six words or longer, while 60% were phrased as questions. The same research found that around a quarter of prompts used “best”, showing that people are asking AI systems to judge, rank and recommend rather than simply retrieve.

That is the death of the keyword as the centre of strategy.

Not the death of search. Not the death of SEO. But the death of pretending that a two-word keyword captures the full intent of a human being.

When people use LLMs, they reveal more of themselves. They include the use case. The budget. The location. The fear. The trade-off. The cultural nuance.

A keyword might be: “branding agency Dubai”

An LLM prompt might be:

“Which branding agency in Dubai would be best for a premium hospitality brand expanding into Saudi Arabia, and which one understands both Western and Arabic audiences?”

That is a completely different challenge.

It is not just about ranking for “branding agency”. It is about whether the model understands your agency as relevant, trusted, regionally credible and culturally fluent.

There is still a lazy stereotype in some boardrooms that AI tools are mainly used by English-speaking tech workers, developers and Silicon Valley early adopters.

The data says otherwise.

OpenAI’s 2026 Signals data shows that users who predominantly use a language other than English now represent over half of active ChatGPT users. The leading non-English languages are Spanish, Portuguese and Arabic, while Uzbek, Kazakh and Burmese saw the largest percentage increases in share of active users among languages with at least one million active users in June 2026.

That is a major strategic shift.

It means LLM visibility is no longer an English-first problem. It is a multilingual, multicultural discovery problem.

And that creates a very real risk for brands.

Many businesses still approach international marketing as a translation exercise. They build a strong English website, translate it into a few priority languages and assume the job is done.

But AI discovery does not work like that.

LLMs do not simply match words. They interpret meaning. They connect entities. They compare claims across sources. They decide which brands are credible enough to mention. They synthesise reputation, relevance and context into a single answer.

So the challenge is not “Can our page be translated?”

The challenge is:

“Would an AI system understand why we are the right answer for this person, in this market, asking this question, in this language?”

That requires more than translation. It requires cultural intelligence.

A British user may ask a direct comparison question. A Japanese user may provide more context and expect a more considered answer. A UAE-based user may look for regional proof, Arabic-language relevance, government or enterprise experience, and an understanding of local trust signals. An Indian user may care heavily about cost, scalability, productivity and practical use cases. A Brazilian user may use AI more heavily for translation and language learning than the global average, according to Anthropic’s research.

These differences matter because people do not prompt AI as generic “users”. They prompt as themselves.

They bring their culture, market, language, expectations and decision-making style into the conversation.

Anthropic’s Economic Index shows how sharply AI usage can differ by country. In its September 2025 report, Anthropic found that the United States accounted for the largest share of Claude usage overall, but that per-capita usage was led by smaller, highly digital economies such as Israel and Singapore.

It also found that coding represented over half of usage in India, compared with roughly a third globally, while higher-adoption countries showed more diverse uses across education, science and business.

In Anthropic’s India country brief, India ranked second globally by share of Claude.ai use, but only 101st out of 116 countries on a working-age per-capita basis. The same brief found that India had the highest share of AI use devoted to software-related tasks, at 45.2% of O*NET-mapped tasks, and that Indian users saw an estimated 15x productivity speed-up on tasks completed with AI.

The lesson is simple: AI adoption is global, but it is not uniform.

Different markets use LLMs for different reasons. Different cultures ask different questions. Different languages reveal different intent.

That is why a brand’s LLM strategy cannot be built from a single English prompt set.

A customer may use ChatGPT for advice, Perplexity for cited research, Gemini because it is built into their Google ecosystem, Claude for work tasks, or Google AI Overviews without even realising they are interacting with an AI-generated answer.

RAND’s 2026 analysis of global LLM use found that site visits to major LLM platforms increased from an estimated 2.4 billion monthly visits in April 2024 to nearly 8.2 billion in August 2025. It also found that US models captured around 93% of global 

LLM site visits in August 2025, while Chinese LLM market share surged from 3% to 13% in two months, largely driven by DeepSeek.

This matters because brands are no longer optimising for one results page.

They are optimising for a moving ecosystem of models, retrieval systems, answer formats, citations, local sources and user behaviours.

A brand might appear in ChatGPT but not Perplexity. It might be cited in English but invisible in Arabic. It might be recommended in the UK but absent in the UAE. It might be mentioned for a broad category prompt but not for the high-intent “best for my use case” prompt that actually drives enquiries.

That is why LLM share of voice needs to be measured across four dimensions:

Prompts. Engines. Countries. Languages.

Anything less is guesswork.

A customer may use ChatGPT for advice, Perplexity for cited research, Gemini because it is built into their Google ecosystem, Claude for work tasks, or Google AI Overviews without even realising they are interacting with an AI-generated answer.

LLM share of voice is the percentage of AI-generated answers in which your brand is mentioned, cited or recommended across a defined set of prompts.

A simple formula looks like this: LLM Share of Voice = your brand mentions ÷ total brand mentions across target prompts

But the useful version is more detailed.

• Whether your brand is mentioned
• Whether your brand is recommended
• Where your brand appears in the answer
• Which competitors are mentioned
• Whether your website is cited
• Whether third-party sources are cited
• Whether the answer is factually accurate
• Whether the sentiment is positive, neutral or negative
• Whether results differ by model, country and language

This is not traditional rank tracking. It is answer tracking.

And it is especially important because LLM answers can shape perception without sending a click.

Someone may ask an AI assistant for the best solution, read the answer, remember your brand and visit your site later through direct traffic or branded search. That influence may never appear neatly in your referral analytics.

This is the dark funnel of AI discovery.

Search engines rank pages. Generative engines synthesise answers.

The original Generative Engine Optimisation paper, accepted to KDD 2024, describes generative engines as systems that satisfy queries by synthesising information from multiple sources. The paper found that GEO methods could improve source visibility by up to 40% in generative engine responses.

LLMs need evidence.

That finding is important because it suggests brands are not powerless.

You can improve your chances of being included in AI-generated answers. But the tactics are different.

Keyword stuffing will not help. Vague brand copy will not help. Generic thought leadership will not help.

They need clear facts, strong structure, consistent entity signals, credible third-party validation and content that directly answers the kinds of questions users ask.

That means brands need to build answer-ready assets, not just SEO pages.

The best-performing content for LLM visibility is often:

• Clear
• Specific
• Structured
• Recently updated
• Statistically rich
• Easy to cite
• Written around real questions
• Supported by third-party authority
• Locally relevant
• Culturally aware

In other words, your content has to be useful enough for a human and legible enough for a machine.

Here is the problem with a lot of global content strategy.

It starts in English. It reflects the assumptions of the head office. It gets signed off by people who are often not from the market it is meant to influence. Then it is translated.

That may be enough for a brochure.

It is not enough for LLM discovery.

A translated page may use the right words but miss the way people actually ask questions. It may miss local competitors. It may miss local proof points. It may miss the social cues that build trust. It may miss the difference between a user who wants a direct answer and one who expects context before a recommendation.

This is where Crowd’s biggest asset becomes strategically important.

Crowd is not just a global agency because it works in different markets. It is global because its thinking is shaped by the people inside the business: 25 nationalities on staff and 17 languages spoken.

That matters in the age of LLMs.

Because the future of search is not just multilingual. It is multicultural.

A strong LLM visibility strategy needs people who can ask:

• “Would someone in this market actually phrase the question this way?”
• “Does this answer sound natural in Arabic, or does it sound translated?”
• “Are these trust signals meaningful in the UAE?”
• “Would this comparison matter to a buyer in India?”
• “Which local sources would an AI system trust?”
• “Are we visible in the language of the audience, or only in the language of the head office?”

This is the difference between localisation and cultural search intelligence.

And it is exactly where SightGeo can sit in the mix.

At a practical level, a SightGeo-style approach should help brands answer questions such as:

• Are we being mentioned when customers ask AI about our category?
• Which competitors are being recommended ahead of us?
• Which AI engines understand our brand correctly?
• Which markets are we visible in?
• Which languages are we invisible in?
• What claims are LLMs making about us?
• Which sources are influencing those answers?
• What content do we need to create to improve our share of voice?
• Where do we need third-party validation?
• How does our visibility change month by month?

The product value is measurement. The agency value is interpretation.

SightGeo can show where a brand is visible or invisible. Crowd’s multicultural team can explain why.

“Crowd is a global marketing agency specializing in multicultural search intelligence and LLM visibility.”

That combination is powerful.

A dashboard can tell you that you are not appearing in Arabic-language recommendation prompts.

A culturally diverse strategy team can tell you that your Arabic content reads like translated English, your regional proof points are weak, your local entity signals are inconsistent, and your competitors are being cited by sources the model trusts more.

That is the difference between data and direction.

Gartner predicted that traditional search engine volume would drop by 25% by 2026 as AI chatbots and virtual agents take share from conventional search. Whether the exact figure lands perfectly or not, the direction is already visible: people are increasingly comfortable asking AI systems to filter the world for them.

For marketers, this creates both a risk and an opportunity.

The risk is invisibility.

Your brand may still rank on Google, still have a beautiful website, still produce content every month — and still be absent when an AI assistant recommends solutions in your category.

The opportunity is that most brands have not yet built a serious LLM visibility strategy.

The field is still open.

The brands that move first will build stronger entity signals, better answer-ready content, richer third-party authority and more culturally relevant visibility across markets.

But they need to move with nuance.

Because the future of search is not just AI-powered.

It is multilingual. It is conversational. It is cultural.

And the brands that win will be the ones that understand how real people ask real questions in the real world.

That is why Crowd’s mix of technology, strategic thinking and lived cultural intelligence matters.

SightGeo can measure where brands show up.

Crowd’s 25 nationalities and 17 languages can help explain what those answers mean — and what to do next.

In the age of AI search, visibility will belong to the brands that are not only found, but understood.