AI can’t understand cultural nuance. This is how leaders can use the right prompts to mitigate risk

AI is here to stay. For businesses, the pressing question isn’t just whether to use it, but how.

It’s well known that AI has its limitations—even more so with the nuances and complexities of human culture. But how deep do these limitations go? Can businesses confidently rely on AI to navigate their global strategies—such as understanding what people in different countries value, local contexts, and cultural nuances? Should AI play a role in shaping go-to-market strategies for international markets?

Challenges to cultural nuance with AI

Cultural nuance and understanding matter when operating across diverse groups. AI often struggles here. Take, for instance, Google’s AI image generator, which misrepresented the ethnicities and genders of historical figures. Such errors underscore AI’s struggles with cultural sensitivity. With text-based AI, such as Large Language Models (LLMs) like ChatGPT, these challenges extend to language and cultural biases.

A study from the University of Sydney found that English-speaking LLMs, such as ChatGPT-3, often reflect U.S. cultural values when prompted on issues like gun control and immigration. This bias arises from the training data, predominantly sourced from English-language content, and skews the AI’s understanding of global cultures.

For example, an LLM trained primarily in English data might suggest marketing strategies without cultural nuance that inadvertently offend cultural sensibilities in another culture. English makes up 48% of training data, and when combined with other European languages, this figure climbs to 86%. 

These biases in LLMs aren’t just linguistic. If the trainers or developers behind these models share similar educational and cultural backgrounds, lacking diversity, the AI they create can inadvertently reflect those biases. This leads to outputs that may favor Western perspectives or overlook cultural subtleties, posing risks for businesses operating in diverse markets.

Testing cultural competence and nuance in the real world

Despite these cultural limitations, avoiding AI entirely in global strategy could mean missed opportunities. The best approach is for you and your teams to understand its cultural awareness capabilities so you can leverage AI effectively while recognizing its limitations.

To test LLMs’ cultural competence, as experts in cross-cultural strategy and global localization, we conducted experiments simulating real-world scenarios for a digital company planning to launch products in Saudi Arabia and China, then smaller markets like Morocco to determine if cultural awareness diminished in less prominent regions. We compared the performance of English LLMs, like ChatGPT-4o, with local models like Jais (Arabic) and Ernie 4.0 (Chinese).

Our goal was simple: assess how well these LLMs could guide cultural insights for go-to-market strategies and evaluate the usefulness and accuracy of the guidance provided.

We tested multiple prompts, refining them over 20 iterations to improve the quality of the output. As we all know, the AI’s output is only as good as your prompt.

Initially, our prompts were broad. We started with an open-ended prompt to assess LLM’s ability to comment on various cross-cultural factors. Our very first initial prompt was:

“What are the cross-cultural considerations for a Western Game Company seeking to acquire Chinese users aged 19-34 in regional China versus a Western User? Identify a non-exhaustive, detailed list of all the specific cross-cultural factors that should be considered when forming a partnership with a local distributor in China to expand sales. Cite specific statistics, case studies, text, prominent studies, law, or regulation. Provide at least two explicit examples for each factor.”

Not surprisingly, the results varied significantly between English and local LLMs in quality and quantity. The outputs were vague, lacked detail, substantiation, and relevance, and had no cultural nuance. The prompt was too broad and lacked the necessary context and structure.

We refined the prompts to provide more context: Clearly defined objectives such as a playbook for entering x market, cultural factors, steps, and a structured output format. Here’s what the final prompt looked like for the China market:

“The Context is: You’re an expert on business in China. You understand Chinese culture, customs, and regulations very well. You are writing a playbook for a non-Chinese video game company planning to enter the China market. The company targets users who enjoy watching or listening via their PC, tablet, or phone, aged between 12 and 27.

Your objective is that this playbook should be written so that:

1. The book is detailed, specific, and thorough in all aspects.
2. The gaming company is aware of all Chinese localization and culturalization matters.
3. The gaming company can build a solid rapport with the local partner, local government, and users.
4. The gaming company respects cultural sensitivities, local laws, and the customs of the Chinese people.
5. The gaming company avoids any cultural missteps that need to be handled sensitively.

The cultural themes you need to address are the eight below:

1. China’s religions, values, beliefs, and history
2. China’s political environment and government relations
3. China’s events, customs, and social norms
4. China’s market entry and expansion strategy
5. China’s regulations, standards, and compliance
6. China’s cultural appropriateness of branding, marketing, and campaigns
7. China’s infrastructure, operations, and payment technology
8. China’s user experience, interface, and patterns

Follow these steps:

1. The playbook should be detailed, specific, and thorough in all aspects.
2. The playbook should advise on establishing a local partnership in China.
3. Each of the above eight themes must be addressed in this particular order.
4. Each of the above eight themes must include a description relevant to forming a local partnership.
5. For each localization theme, add real-life examples of companies that have done well and companies that have not done so well/badly.
6. Expand each example to give details.
7. Ensure the content for each theme does not overlap with each other.

The structure of the output will be: 

Title of the cultural theme 1:

• Description related to forming a local partnership
• Good example: [Company name . . .] 
• Not-so-good example: [Another company name . . .]
• A list of actionable strategies
• A list of names of relevant laws, government bodies, experts, or advisors who could be contacted

Title of the cultural theme 2: 

• Description related to forming a local partnership
• Good Example: [A company name . . .]
• Not-So-Good Example: [Another company name . . .]
• A list of actionable strategies
• A list of names of relevant laws, government bodies, experts, or advisors who could be contacted

By testing outputs across these eight cultural themes, we aimed to exhaust all culturally relevant insights. Each theme focuses on a distinct domain, allowing an in-depth exploration of specific areas critical for market success.

These themes do not exist in isolation. The interconnections and overlaps stem from the complex nature of operating in a new market, for example, cultural, business, and political elements influence one another.

By providing this list of key culturalization themes, we intend to foster an integrated approach, where all aspects of market entry and operation are mutually reinforcing, leading to a more culturally sensitive business strategy.

After settling on this final prompt with a reasonably good output, we used a sequence of follow-up prompts to extract further detail, data, and facts to substantiate the output. Examples include:

• Add more detail to the above output.
• Be more specific, details, names, statistics, numbers, and facts for the above text.
• Provide a more detailed list to the above text of the relevant names of relevant laws, government bodies, experts, or advisors who could be contacted for each theme.

LLM’s competence for cultural nuance

Here’s what we discovered: Local LLMs were up to 60% more culturally relevant than ChatGPT-4o and also performed better in small markets. However, when we narrowed the prompts to a single cultural theme instead of eight, for example, focusing only on theme three, social norms, ChatGPT-4o produced better-quality output.

Despite these findings, all LLMs provided a sound but generic baseline. For experienced global leaders, the cultural relevance in output was mostly rudimentary and basic, though not without insights. Notably, all models regularly recommend seeking external advice and support from industry or cultural experts. In addition, while the output of all LLMs we tested exhibited some tailoring to the target industry (e.g. video games), they didn’t address specific demographics (e.g. Gen Z).

So, what does all this mean? How can global leaders effectively use LLMs while mitigating cultural risks?

Refine your prompting strategy

Rubbish in, rubbish out. Always provide detailed cultural context for the target market in your prompts. The more context you give, the better the LLM can meet your needs

Continuously refine prompts based on initial outputs, focusing on key cultural themes to enhance accuracy and cultural relevance.

Establish common prompting principles 

Develop and follow a set of principles for content, structure, and process in AI prompts to ensure consistency and quality across your team

As your team’s skills in prompt writing improve, continuously revise these guidelines to reflect new learnings and best practices

Designate an “administrator” to gather feedback, refine prompts, and transcribe the prompt to ensure that these principles are consistently applied across all projects.

If possible, assign a diversity officer or cross-functional team to vet these principles regularly, ensuring they incorporate diverse perspectives and avoid cultural biases.

Leverage both global and local LLMs with caution

Use both English and local LLMs, where possible, to maximize cultural insights. Remember, local LLMs can often operate in English, reducing the need for third-party translators.

If a local LLM isn’t accessible, English LLMs can still be a good source of insights as a starting point, but keep it simple and divide and conquer cultural themes.

Educate your teams

Train your teams to recognize the cultural limitations of LLMs. Encourage a critical approach, prioritizing factual information like real-world examples cited by the LLM that substantiate its findings. Always request more detail, names, and contacts from both English and local LLMs as it can provide more confidence in outputs.

Ask for case studies

In your prompts, ask for successful and unsuccessful case studies in similar markets. We found that the most meaningful cultural insights came from LLMs providing valuable context through these examples. Case studies showcase practical applications and pitfalls, offering actionable lessons that go beyond theoretical knowledge.

Use LLMs as a baseline, not the final word

LLMs should serve as a starting point—great for brainstorming and identifying trends—but shouldn’t be the definitive source for decision-making. As all models state, critical data and insights should always be cross-checked with external culturalization experts, particularly for culturally sensitive decisions.

Prioritize human oversight and insights

Engage cultural experts to review AI output, especially when planning market entries. Complement AI insights with in-depth cross-cultural research insights to understand local nuances better.

AI can be a valuable tool for global business strategy, but it comes with significant limitations. By refining prompts, cross-checking data, and using both global and local LLMs judiciously, businesses can harness AI’s potential while minimizing cultural risks. However, AI should never replace human expertise and cultural knowledge.

The saying “AI is for content, human is for context” doesn’t fully apply with cultural intelligence. LLMs alone cannot (yet) provide the accurate and complete content (or more precisely insights) needed to shape go-to-market and global growth strategies, even when given full context. 

By using LLMs to establish a baseline and complementing this with in-depth cross-cultural research and expert guidance, you can create more culturally nuanced and effective global strategies. 

As Albert Einstien quipped, “Artificial intelligence is no match for natural stupidity.” The future of global business will be shaped not by those who replace human judgment with AI, but by those who skillfully blend AI’s power with the irreplaceable wisdom of cultural understanding.