AI has shown remarkable capabilities in language understanding and generation, but it also has notable weaknesses. Understanding where AI performs well, and where it doesn’t, is crucial for responsible and effective use.

Where AI Excels

AI systems, particularly large language models (LLMs), shine in:

• High-resource languages such as English, French, and Spanish, where training data is rich and diverse
• Common NLP tasks like translation, summarization, and customer support
• Generating well-structured text, drafting emails, or explaining concepts clearly

These successes stem from large amounts of training data and extensive fine-tuning on widely used languages.

Where AI Struggles

AI models can falter in several challenging areas:

• Low-resource or rare languages, where training data is sparse
• Code-switching or mixed-language prompts, particularly in informal dialects
• Complex reasoning, idiomatic expression, or cultural context, which aren’t easily replicated from English-centric training
• Biases such as gendered language or cultural assumptions embedded in model outputs—especially when cultural nuance is required

Why It Matters

• Equity: Overreliance on high-resource languages can widen digital language gaps.
• Accuracy: Misunderstood prompts can lead to poor or biased outputs.
• Sustainability: Misuse of AI due to misunderstanding can waste computational and environmental resources.

Knowing these limits empowers users to interact more effectively—and demands solutions through adaptation, fine-tuning, or human oversight.

Understanding both the strengths and weaknesses of AI is not just informative—it’s essential. This knowledge helps us choose when to rely on AI, and when to step in with human or linguistic expertise to ensure accurate, fair, and responsible language AI use.

Now that we know where AI shines and where it falls short, one of its biggest hurdles is clear: low-resource languages.
Our next article explores why AI struggles with these languages, what that means for users, and how we can bridge the gap.

👉 Read next: Challenges with Low-Resource Languages