Automated translation systems exhibit a tendency toward cultural drift when processing non-literal language, often favoring standardized outputs that diverge from the original pragmatic intent. Although Large Language Models (LLMs) have introduced more sophisticated context-handling capabilities, the transition from literal decoding to effective cultural adaptation remains inconsistent.This study investigates these linguistic detours by evaluating ChatGPT-4o, Gemini 1.5 Pro, and Google Translate using a corpus of 100 Brazilian Portuguese expressions. To ensure contemporary relevance, the expressions were validated through the Corpus Carolina and categorized into four groups: classical idioms, regionalisms, metaphors, and intensifiers. Translation quality was assessed using the Multidimensional Quality Metrics (MQM) framework, focusing on adequacy, fluency, and cultural adaptation.The analysis reveals that, even when grammatical accuracy is achieved, automated systems frequently overlook the socio-cultural weight embedded in the source language. Such semantic shifts pose significant challenges in high-stakes professional communication, where nuanced mediation is essential. The findings underscore the limitations of current AI systems in cultural competence and reinforce the ongoing necessity of human intervention to bridge the gap between algorithmic processing and regional identity.