AI can save a lot of money in text production, customer service and translation. To make these efficiency promises a reality, the AI has to know the company and also have a precise understanding of the subject area and correct language use. Otherwise, errors and misunderstandings will quickly cancel out the time savings you hoped to make. Therefore, the data used to train AI systems and chatbots is crucial: it must be clean, structured and correct in order to deliver successful results. If you want to make long-term productivity gains using AI, having a solid, cleaned-up database is essential.

For companies with a high volume of translations, language data is worth its weight in gold. For years, translation memories were filled and terminology databases built up – always with the aim of reducing translation costs, ensuring consistency and saving time. However, in many companies what began as a strategic investment has developed into an unmanageable mountain of data. The hoarding of language data is increasingly leading to the opposite of what was originally intended: rising costs, inefficient processes and even a loss of translation quality. So it's high time for a digital spring clean.