For example, “BMW”. Same brand. Same three letters. Completely different pronunciations depending on the language.

Imagine launching a global brand campaign without considering how your brand name is actually spoken by different audiences...

A pronunciation that feels completely natural in one market can sound very different (or even awkward) somewhere else. It’s a small detail, but it’s a good reminder that global brands need to think beyond translation.

How do you pronounce BMW where you’re from?

#CulturalIntelligence

We’ll go first:

😂 Giving an honest answer to “How are you?” instead of the classic “Good, and you?”. In some places, it’s essentially a greeting that calls for a quick response. In others, it’s a genuine invitation to share how you’re actually doing. Mix the two, and it gets messy.

🤯 Another favourite: “1st floor”. In the US, the first floor is generally at street level. In the UK and some other countries, the “first floor” can mean the level above the ground floor. Easy to get lost!

The words are the same. The meaning? Not always.

Have you ever encountered a translation that was technically correct but completely wrong in context? Share your favourite examples! 👇

Since it was first proposed in 2021, the EU AI Act has evolved significantly. And with the latest milestone on 2 August 2026, there's been plenty of discussion about what's changing.

One thing that hasn't changed is what responsible AI is supposed to achieve.

It comes down to four things:

• Protecting people's data
• Being transparent about when and how AI is used
• Keeping humans accountable for important decisions
• Building systems people can actually trust

Whether you're developing AI products, deploying AI internally, or simply using tools like ChatGPT at work, these principles affect all of us.

Curious to hear from this community: Do you think regulation like the EU AI Act ultimately helps innovation by increasing trust, or does it risk slowing things down?

Why recalibration of the EU AI Act is a win for responsible innovation

In the wrong place at the wrong time: the cultural gap in AI translations

Ask a state-of-the-art large language model (LLM) to translate into Swahili: "The official start of the school day is 7:00 am", and it will produce a translation with flawless syntax and correct vocabulary. Except, the Swahili children reading it will turn up at school an hour after lunchtime.

 

Prompt it then to translate into German, French or Czech "Our clinic is located at 350 Madison Avenue, 1st floor, New York, NY" and it will provide a fluent sentence with impeccable grammar. However, visitors will look for the clinic one floor too high.

 

Combine the two, and you will have people coming to the wrong place at the wrong time despite a perfectly fluent translation. Where is the issue?

 

In the cultural pragmatics.

 

Swahili time tracking, rooted in the geography of equatorial East Africa, starts the clock at sunrise. What we call 7:00 am is 1:00 am in Swahili: saa moja meaning the first hour of the day. Large language models preserve the time as 7:00 am, following the Western convention of resetting the clock at midnight.

 

In American English, "first floor" refers to the ground level. In most European languages this translates to "ground floor", and the first floor is the one above the ground floor. Again, LLMs tend to literally (and wrongly) translate American first floor as floor #1 in European languages.

 

Solving the gap in cultural grounding defines the next era of localization.

AI is multilingual but monocultural

AI’s monoculture is WEIRD: Western, Educated, Industrialized, Rich and Democratic.

 

Trained on 70–90% English data, AI models inadvertently absorb Western cultural values. So, when we prompt them to produce non-English text, they handle the grammar and syntax well but fail to adapt the content to the cultural reality of the people who speak the language.

 

This is a well-known problem, and plenty of research groups and AI labs have taken a swing at it.

 

But cultural benchmarks are asking the wrong questions – predominantly measuring cultural competence as trivia. They test whether AI understands culture by probing the model’s factual knowledge. For instance, they ask:

• Does the model know whether you can touch food with your left hand in India?
• What do Singaporeans use tissue packets for?
• What food do people eat on their birthday in Ethiopia?

These are superficial culture questions. An AI model that produces the correct answers demonstrates encyclopedic knowledge but leaves it open as to whether it can generate text that actually resonates with the culture of the target audience.

Culture is multilayered

To tackle the problem of cultural deafness, we first need a solid grasp of what culture actually is. This turns out to be surprisingly hard to pin down. When most people picture culture, they think of food, clothing, music, and traditions. And yes, those are part of it. But culture runs much deeper than that.

 

A practical way to understand culture is offered by the Hofstede Cultural Onion – a model developed by the Dutch social psychologist Geert Hofstede that has become a cornerstone of cross-cultural communication research.

 

The metaphor of an onion is deliberate. Culture has layers, and the deeper we go, the more fundamental and the more invisible the culture content becomes.

 

The outer layer of the onion holds the cultural symbols – fashion, food, colors, gestures – everyday objects that carry cultural meaning. They make up the surface culture, which is immediately visible. We can catch it within hours after landing at the airport of a foreign country.

 

One layer in, we have the heroes and role models: real or fictional people who embody what the culture admires. They can be historical figures, celebrities, or even cartoon characters. Then come the rituals and customs: the collective practices performed not for practical reasons, but because they carry social meaning. All of these make up the intermediate culture. This layer isn't obvious to a passing tourist, but one starts to notice it if one stays longer and knows where to look.

 

And then we reach the very core of the onion, holding the cultural values. These are the deeply ingrained, often unconscious, beliefs about what’s right and wrong, attitudes toward authority, time, or success. These core culture values are the most difficult to access and the hardest to change.

 

Figure 1: Adapted from Cultures and organizations: Software of the mind (p. 8), by G. Hofstede, G. J. Hofstede, and M. Minkov, 2010, McGraw-Hill.

Culture is an onion, and AI keeps eating the skin

The different culture layers require different AI capabilities.

 

For the surface and intermediate layers, cultural competence means accurate knowledge of cultural objects and practices, everyday life norms, factual conventions, and the right cultural terminology. State-of-the-art LLMs hold some of this knowledge latently, to a certain extent at least. They acquire it during the pretraining stage where they ingest vast amounts of text. The more text a culture has generated online, the more the model knows about it. Which is why LLMs generally excel at Western high-resource cultures and fall short on under-represented languages and cultures, as they leave a smaller footprint in the training data.

 

But when it comes to the core invisible layer, LLMs start to falter even within Western cultures – performing best, understandably, on the US-centric end of the spectrum. At this level, cultural competence means understanding implicit cultural values, adjusting communicative style, and framing reality in the way the target culture does. For example, does this culture prefer agent-heavy language ("I decided to...") or agent-light language ("It has been decided that...")? Does it draw fine-grained, grammaticalized distinctions across many levels of formality, as Japanese and Korean, or settle for a simple two-way T-V split, as Spanish and German?

The six primary cultural dimensions

Those core values are encoded in the deep, subconscious layer of culture. That raises the next question: how do cultures actually differ? Here Hofstede comes to the rescue again, this time with a model that maps culture along six dimensions:

1. Power distance
2. Individualism
3. Motivation toward achievement
4. Uncertainty avoidance
5. Long-term orientation
6. Indulgence

Thanks to this work, we have data for more than a hundred countries plotted against the dimensions of cultural variance. If we overlay the United States and Russia on the same chart, it is evident that they significantly differ on all six axes. So, US and Russia are two drastically different cultures, where communication is also different.

 

Deep cultural values surface as linguistic choices

Cultural differences surface in language itself. For instance, high power distance shows in language through honorifics and agent-suppressing passive voice. High individualism corresponds to prominent "I" usage and clear self-reference. High achievement orientation drives superlatives, intensity of expression, and directness. It is the difference between the American "This is awesome!" and the British "Not too bad", both referring to an above-average achievement.

 

Culture, in other words, leaves its mark all over the language of its speakers: how thoughts are expressed, what gets said and, just as importantly, what goes unsaid.

Making culture something a machine can measure

But there is a problem. Hofstede’s six-dimensional model measures cultural values, not linguistic features. Large language models, on the other hand, are language generators. So, we need to somehow bridge the two: teach a model to express cultural values through the right linguistic choices.

 

The way forward is to treat core culture as a set of observable linguistic features. Luckily, we already know what those features are: decades of sociolinguistic research have mapped exactly how social and cultural reality leaves its fingerprints on language.

 

Halliday’s Systemic Functional Linguistics shows that every sentence does three jobs at once: it signals a relationship, organizes a message, and describes the world. 

 

Martin and White’s Appraisal Theory gives us a precise measure for evaluative language: how warmth, judgement, and emphasis get dialed up or down.

 

Brown and Levinson’s Politeness Theory explains how speakers manage “face" (the public self-image of a person) and why some cultures lean on deference and hedging while others prize directness.

 

And Edward Hall’s distinction between high- and low-context cultures captures how much meaning is stated outright versus left for the reader to infer.

 

Put together, these works describe a compact set of about 10 features that recur across languages and capture much of what makes communication feel culturally right. To name a few:

• Emotional expressiveness: what kind of emotion are expressed and how much
• Openness to other views: how strongly a claim is asserted or hedged
• Expression of politeness: how politeness is encoded
• Action attribution: whether an agent is named or suppressed
• Context-first vs point-first: whether the main takeaway comes first or is implied at the end

Crucially, each of these sociolinguistic dimensions is something a human rater can judge, and a model can be trained to reproduce.

Teaching AI to speak culture, not just language

So how can a model be taught to generate content with the right sociolinguistic profile, so that it feels natural to the target culture? There are two main routes.

 

The first one uses prompting: describe to the model where a culture sits on each sociolinguistic dimension and explain in plain language how this is reflected in the language. For example: “Use warm and informal phrasing." This approach is relatively cheap, fast, and needs no specialized data. The drawback is that it relies entirely on whatever knowledge the model has already picked up in training. And that knowledge is often thin, skewed toward English-speaking norms, and inconsistent from one run to the next. Even if we feed the model with explicit and detailed descriptions of the target style, we're still hoping it already knows how to comply.

 

The second route is fine-tuning: train the model on text annotated for the key sociolinguistic features, so it learns to deliberately shift its output along each dimension and land inside the range a given culture expects. This is the harder, slower path. It needs data that mostly doesn't exist yet, and skilled annotators to build it. But this approach can turn cultural resonance from a vibe into something a model can reproduce and be measured against.

The next era of language AI

For years, progress in language AI has been measured by whether machines can produce text that is grammatically correct, fluent, and accurate. But this is no longer enough. A model that translates words without translating culture can still send people to the wrong floor, at the wrong time, using the wrong tone – and offending the users in the process.

 

In global communication, these are failures of cultural intelligence.

 

The next generation of language AI won't be judged by how well it translates sentences. It will be judged by how well it captures culture.

 

Fluency is fast becoming the baseline. The real challenge is knowing when meaning changes with context – when conventions, expectations and cultural norms shape what people understand, not just the words they read.

 

The difference is measured in outcomes. People arrive where they're expected, at the right time, with the right understanding. Messages build trust instead of confusion because they reflect local ways of thinking, not just local vocabulary.

 

The result is AI that serves more people, in more places – by reflecting the richness and diversity of how humans actually communicate.

Ready to build AI that speaks to every market, not just every language? Let's talk.

The world's leading global brands sound recognizably themselves in every market they enter – not by accident, but by design. They combine documented brand frameworks, AI translation built specifically for marketing content, and human expertise where it matters most. But how do they do it? This article draws out the patterns they share – and what CMOs scaling into new markets can take from them. 

Brand consistency across markets is one of the most visible measures of marketing maturity. When a brand sounds like itself in English, German, Japanese and Central American Spanish – when the personality and creative intent survive the move from one market to the next – it signals something deeper than good translation. There’s a system at play that’s built to hold a brand intact at scale. 

Understanding what that system looks like is central to any serious multilingual marketing strategy. 

The 40% of online shoppers who never buy from websites in other languages are not being lost to bad products or poor pricing. They're being lost because the brand experience in their language doesn't carry enough trust or resonance to convert. That's not a translation problem. It's a brand consistency problem – and it starts long before the content reaches a linguist.

Read more: How global brands maintain brand consistency

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