Should we be afraid that neural machine translation (MT) will disrupt — or destroy — our industry? Progress recently seen with neural MT opens new perspectives. Until now, MT has been perceived either as a replacement for human translation, when volumes were too big (such as user generated content or support), or as a cost-killing constraint imposed on translators. Neural technologies bring a set of high-level features — generalization, understanding and global overview — that allow new ways to augment translators and more generally users in need of cross-lingual capabilities. We will share a vision and roadmap for the future for language-aware applications, initial feedback from actual experience and a case study on a qualitative comparison of translation output across different languages and content that will reveal insightful research results, weighing up the relative pros and cons of the different approaches to MT.