We all want to act locally while going global, and maintain an inclusive multilingual work environment for the international workforce. Every AI model has its linguistic, cultural, and geopolitical biases. Besides providing better linguistic quality for specific languages and domains, a particular Machine Translation system may not be fully compliant with local dialect, tone of voice, gender, and data locality rules. In this talk, we consider practical cases when those biases create obstacles in building a global presence and an inclusive multilingual work environment for an international company. We discuss how to dodge those biases by using multi-vendor international AI, and in some cases go further, by leveraging those biases to create more diverse and inclusive translations.