An AI translation can be produced at speed. However, the question of whether it’s appropriate for a specific text or not must be assessed as part of a feasibility analysis. After all, just because everything can technically be fed into AI systems, that doesn’t necessarily mean that it makes sense to use them. The feasibility analysis therefore provides clarity in advance as to whether the content, language and file format are suitable for machine translation and post-editing (MTPE).

More and more companies are using AI to translate their content quickly and cost-effectively. If an in-house AI solution is used, translations are often merely a ‘by-product‘. This is because the technology is usually designed for other usage scenarios and considered in isolation from existing language processes – which can have serious consequences for quality, consistency and effort. So it’s important that AI is integrated into translation processes right from the start, whilst ensuring that language experts and translators remain at the heart of the process. You can find out exactly how this process works in this blog post.

Many companies regularly use AI tools to translate content quickly and cost-effectively. However, this perceived increase in productivity comes at a price as without quality checks, flawed or substandard content can find its way into circulation and cause lasting damage to a brand’s reputation. In this article, you will find out where AI currently provides productive support in translation and where human expertise remains indispensable.

Whether text generation, translation or smart chatbots, Large Language Models (LLMs) are currently one of the most exciting AI innovations and are changing the face of corporate communications. They understand, process and generate language and already perform a wide range of tasks and processes. However, in order to deliver not just generic, but company-specific results, the systems must be instructed and trained. We explain the ways in which LLM output can be customised to your company's own style and the important role your language data from translation memories and terminology databases plays in this.