Ask an AI what matters most when advertising in another language and it will point to translation. That answer is exactly the problem. Translation is one methodology among many, alongside localisation, transcreation, creative adaptation, origination, copywriting and machine translation with human post-editing, yet language work is still too often filed under a single “translation” box.
Machine translation has made translation feel simple, fast and free, and the result has been commoditisation: a race on cost rather than value, budgets that treat linguistics as an afterthought, and talented linguists leaving the industry. NMT now produces grammatically correct and often beautifully rendered output, but it still lacks the fine-tuning a specific brand, audience and moment require.
The opportunity lies in how the two work together. Automation can gather, summarise and draft, but the output only works when an experienced human asks the right questions, spots what does not fit, and shapes it for a real audience. AI cannot read the gestures, emotions and surroundings that give language its meaning. The practical path is to let AI handle the predictable, keep human expertise on the unpredictable, and treat the technology as a supporter rather than a threat.
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