When AI Speaks Different Languages: An Intercultural Perspective

Reflections Through the TCAP™ Model

During an international Train-the-Trainer workshop in Belgium, I suggested a solution to a group exercise but chose not to insist because I did not want to create what I called “an argument.” Later, one of the English facilitators smiled and said, “Why would you call it an argument? It would have been just a discussion.”

That conversation stayed with me because it reminded me that even when people share the same language, they do not always attach the same meaning to the same words. We may understand the vocabulary perfectly, yet interpret the conversation very differently because we bring different cultural experiences, expectations and communication styles with us.

Last week, while reading Anthropic’s recent study Claude Values Across Models and Languages, I found myself thinking about that conversation again.

Artificial Intelligence has become part of our daily work. Many of us use it to write emails, prepare presentations, summarize information and communicate with colleagues around the world. As AI becomes more integrated into global business, it is also becoming increasingly relevant to the field of intercultural communication.

The Anthropic researchers explored a fascinating question: Does the same AI model communicate differently when responding in different languages?

They found small but consistent differences in Claude’s responses. For example, responses in English tended to be more rigorous and cautious, while responses in Hindi showed more warmth. Rather than viewing these findings as descriptions of cultures, I see them as an invitation to ask new questions about the relationship between language, communication and culture.

The study also reminded me of one of the central ideas I discuss throughout my book, Global Cultural Interplay. Language is much more than a collection of words. Every language carries cultural assumptions, communication habits and different ways of expressing respect, building trust and interacting with others. People may speak the same language fluently and still misunderstand one another because they interpret the same words through different cultural perspectives.

As someone who has spent many years helping managers and global teams navigate cultural differences, I also introduce in my book the TCAP™ model, which I developed to help managers, global teams and intercultural professionals better understand the dynamics behind international business interactions.

The model focuses on four practical dimensions: Trust, Communication, Authority and Process. Together, they help us explore four practical questions:

• How do we build trust?
• How do we communicate with one another?
• How does authority influence behavior?
• How are decisions made, and how does this affect the execution of projects?

Reading Anthropic’s study made me wonder whether this same framework could also help us better understand how AI communicates across languages. Perhaps a framework originally developed to better understand people can also provide a useful lens for exploring how AI communicates in global business.

Rather than asking whether one communication style is better than another, I believe the more interesting questions are different. How might these different communication styles influence trust between business partners? How might they affect expectations regarding authority, collaboration or decision making? Could the same AI response be interpreted differently by people from different cultural backgrounds?

These are familiar questions for anyone working in intercultural communication. Similar communication behaviors may have very different meanings depending on the context, and understanding those meanings is often more valuable than simply measuring the behavior itself.

One of the strengths of AI is its ability to identify patterns across enormous amounts of data. Intercultural professionals, together with the managers and teams they support, work with another important layer: context. Cultural models help us recognize general tendencies, but every individual is influenced by many additional factors, including personality, professional background, international experience and generation. Generalizations are an excellent starting point, but they are never the final answer. As I emphasize throughout Global Cultural Interplay, curiosity remains one of our most important tools for understanding people.

Reading this study also gave me an idea for future research. It would be interesting to compare several AI models using identical international business scenarios in multiple languages. Instead of asking only whether the responses are different, we could begin asking why these differences appear and whether existing intercultural frameworks can help explain them.

As AI continues to evolve, its role in global business will almost certainly grow. What should also grow is our ability to understand the cultural context in which communication takes place. AI may help us recognize patterns across languages, but people are still needed to interpret those patterns, understand the business context and give them meaning.

Looking back, I still remember that conversation in Belgium. It reminds me that successful communication has never depended only on speaking the same language. It depends on understanding the meaning people attach to their words. Perhaps that is why research such as Anthropic’s is so interesting. It encourages us to look beyond language itself and continue asking not only what people or AI say, but also what they mean.

If this topic interests you, you may enjoy reading Global Cultural Interplay, where I introduce the TCAP™ model together with more than 50 real stories shared by intercultural professionals from around the world.

References

Anthropic. (2025). Claude Values Across Models and Languages.

https://www.anthropic.com/research/claude-values-models-languages

Lautman, O. (2025). Global Cultural Interplay: Elevating the Art of Business Success Across Cultures.