The translation industry is witnessing a rapid proliferation of smart technologies, especially neural machine translation (NMT) systems such as DeepL and large language models (LLMs) such as ChatGPT. This digital wave has strongly agitated the structure of the industry. These transformations have raised serious questions about the new definition of the translator's competence within the digitalized job market. This study aims to propose a revised model for translator competence by reviewing current theoretical frameworks and incorporating empirical evidence from recent studies. This model seizes the opportunities offered and counters the threats posed by AI-driven systems. In line with a body of literature on translator competence, including The PACTE group's 2003 translation competence model and the European Master’s in Translation (EMT) Competence Framework (2022), this study argues that the sub-competences targeted in translator training must be considerably restructured in Moroccan higher education. Specifically, this study endeavors to demonstrate that translator training curricula should incorporate expertise in AI, technology, and post-editing. It also stresses the focus on human intelligence, which involves cultural mediation, transcreation, and pragmatic interpretation. This underlines the fundamental human value, particularly the skills that current AI systems cannot replicate. The study concludes by outlining a three-dimensional curricular framework for adjusting translator training to the requirements of an increasingly digitalized translation industry.