Study finds Polish outperforms English for AI communication accuracy

A surprising new study reveals that Polish, not English, ranks as the most effective language for communicating with artificial intelligence systems. This counterintuitive finding challenges widespread assumptions about AI language capabilities and could reshape how global businesses approach AI implementation.

Researchers from the University of Maryland and Microsoft tested how well major AI language models—including systems from OpenAI, Google, Meta, and others—responded to identical prompts across 26 different languages. The results defied expectations: Polish achieved the highest accuracy rate at 88%, while English ranked sixth at 83.9%.

This matters because “prompting”—the process of giving instructions or questions to AI systems like ChatGPT or Google’s Gemini—has become a critical business skill. The language you choose for these interactions can apparently influence how accurately the AI understands and responds to your requests.

Why Polish outperforms English

The study’s most puzzling finding centers on Polish’s unexpected dominance. Despite having significantly less training data available online compared to English or Chinese, AI systems demonstrated remarkably strong comprehension of Polish instructions.

This phenomenon suggests that certain linguistic characteristics—rather than data volume alone—may influence AI performance. Polish’s complex grammatical structure, including its intricate case system and verb conjugations, might actually provide clearer semantic signals to AI models during processing.

The finding contrasts sharply with Chinese, which ranked fourth from the bottom despite representing one of the world’s most digitally documented languages. This suggests that factors beyond mere data availability drive AI language comprehension.

The complete ranking breakdown

The study evaluated AI accuracy across multiple tasks, revealing a clear hierarchy of language effectiveness:

1. Polish – 88%
2. French – 87%
3. Italian – 86%
4. Spanish – 85%
5. Russian – 84%
6. English – 83.9%
7. Ukrainian – 83.5%
8. Portuguese – 82%
9. German – 81%
10. Dutch – 80%

European languages dominated the top performers, with Romance and Slavic language families showing particularly strong results. This pattern suggests certain linguistic features common to these language groups may align well with how current AI models process information.

Testing methodology and scope

The researchers conducted their analysis using several major AI language models, including OpenAI’s systems, Google’s Gemini, Qwen, Meta’s Llama, and DeepSeek. Each model received identical prompts translated into the 26 test languages, with performance measured by accuracy in completing specified tasks.

The study focused on “long text” assessments, meaning the AI systems were evaluated on their ability to process and respond to substantial written instructions rather than simple, short commands. This methodology better reflects real-world business applications where users typically provide detailed context and complex requests to AI systems.

However, the researchers haven’t yet published detailed information about the specific tasks used in testing or the exact criteria for measuring accuracy, which limits the ability to fully interpret these results.

Business implications

For multinational companies, these findings raise intriguing questions about AI deployment strategies. Organizations with Polish-speaking teams might consider leveraging this linguistic advantage when working with AI systems for critical tasks requiring high accuracy.

The results also suggest that businesses shouldn’t automatically default to English when implementing AI tools globally. Companies operating in markets where top-performing languages are spoken—such as France, Italy, or Spain—might achieve better AI outcomes by conducting AI interactions in local languages rather than translating everything to English.

Customer service operations represent one immediate application area. Companies using AI chatbots or automated support systems might see improved performance by deploying these tools in Polish, French, or Italian rather than relying solely on English-language implementations.

Limitations and considerations

Several important caveats accompany these findings. The study tested specific AI models at a particular point in time, and language performance can vary significantly between different AI systems and versions. As these models continue evolving through updates and retraining, the language hierarchy could shift.

Additionally, the practical advantages of using Polish or other top-performing languages must be weighed against other factors. English remains the most widely understood language among global business teams, and switching to less common languages could create communication barriers even if it improves AI accuracy.

The research also doesn’t address whether these language advantages persist across all types of AI tasks. Performance in text completion might not translate to similar advantages in creative writing, technical analysis, or other specialized applications.

Looking ahead

This research opens new avenues for understanding how AI systems process different languages and could influence future model development. As businesses increasingly integrate AI into operations, language choice emerges as another variable to optimize alongside prompt engineering and model selection.

The findings also highlight gaps in current understanding of AI language processing. If Polish’s complex grammar enhances AI comprehension, researchers might explore whether other linguistically complex languages show similar advantages, potentially reshaping assumptions about AI training and deployment.

For now, businesses should consider these results as one factor among many when designing AI workflows, while researchers work to understand the underlying mechanisms driving these unexpected language preferences.