The thesis explores the possibility of collaboration between Artificial Intelligence and corporate localization, focusing on the design and implementation of an automated pipeline for digital marketing content. The proposed system integrates large language models, API-based workflows, and database-driven validation to manage translation, legal compliance, and stylistic consistency across markets. Developed in Python, the pipeline automates key localization tasks while maintaining human-in-the-loop supervision to ensure brand accuracy and contextual relevance. The case study on FIAT (Stellantis Group, Italy) demonstrates how localization with AI can enhance efficiency and governance without compromising on linguistic quality. Future work aims to expand scalability across brands, reduce human supervision, and optimize processing speed through semantic recognition and parallel execution.