20/05/2025

Many companies are already relying on machine or AI-generated translation solutions to speed up processes and reduce costs. In practice, however, the savings are often not as large as expected. Just because AI is being used does not mean that optimal results or significant cost reductions are guaranteed. Realising the full potential requires a well thought-out strategy, targeted adjustments at various points in the translation process and human interventions in exactly the right places. With our five-step plan, we show you how you can sustainably and noticeably reduce your translation costs with AI support, without compromising on quality.

The number of companies in Germany actively using AI solutions rose to 27 percent in 2024, double the figure recorded for the previous year. But despite this impressive rate of change, the reality remains sobering for many: without the right framework conditions, the hoped-for cost savings are rarely achieved. In practice, machine translation (MT), for example, presents numerous challenges. Contextual processing presents one key difficulty. AI systems often fail to adequately grasp the technical and cultural context of a text, which means that industry-specific terminology, ambiguities and subtle nuances of meaning remain problematic, even for advanced systems. This results in translations that are grammatically correct but inaccurate, or inappropriate in context. This problem is particularly apparent when specialised terminology is involved. Different AI systems can deliver completely different translations for the same term.

The inconsistency of the results presents a further hurdle. As AI systems are constantly evolving, identical source texts may be translated in different ways at different times. This lack of consistency not only makes standardised processes more difficult, it also jeopardises reliable planning. This is because the effects are felt throughout the entire workflow: each translation run requires full post-editing and comprehensive checks.

The impact of the language combination should not be underestimated either. While the translation quality between German and English is usually impressive thanks to extensive training data, performance can drop significantly with other target languages, especially those that are less common. In addition, English is used as a relay language for many languages, meaning that a text is always translated into English first and only then into the target language. This goes unnoticed in the background, but harbours significant potential for error. This is because any “intermediate translation” can lead to translation errors due to ambiguities, misinterpretations or generalisations.

However, these challenges do not mean that AI-supported translations cannot deliver on their promise of optimisation. With the right approach, the pitfalls mentioned above can be avoided and the full potential of the technology can be exploited. A structured approach that takes into account both the technical and process aspects is crucial. Below, we present our five-step plan to help you optimise your AI translations and sustainably reduce your translation costs.

A sound feasibility analysis lays the foundation for successful AI translations. Not every text is equally suitable for machine translation, even if it is almost always technically possible to use it. How cost-effective it is depends on numerous factors: the MT system used, the language combination, the subject area and, last but not least, the quality of the source text. The analysis should be differentiated and there should be no blanket exclusions applied to text types. This is because suitability can also vary depending on the target language: a marketing text may deliver excellent results when machine-translated into English, for example, while the AI translation of the same text into Italian requires extensive post-editing. There are also considerable differences between text types: in the software sector, for example, using MT for short, context-free GUI elements often leads to errors, while significantly better results are achieved with longer error messages with greater context.

The feasibility analysis is particularly critical for highly specialised technical documentation. DIN EN IEC/IEEE 82079-1 requires the utmost precision in this area, as incorrect translations can have serious consequences. The standard recommends using qualified professional translators, for example for instructions for use. Machine translation should only be used for support. The same applies in highly regulated areas, such as medical technology, and generally when a text has a high risk level.

The quality of the MT output is largely determined by the initial quality of the text. Translation-oriented writing is an effective lever for achieving better results without direct intervention in the MT system. In concrete terms, this means that texts should contain clear, precise sentence structures and should not be unnecessarily complex. Ambiguities, convoluted sentences and culturally specific idioms pose problems, even for advanced AI systems. Potential sources of error in machine translation can be significantly reduced by deliberately simplifying and standardising texts.

Formatting is just as important. This is because standardised formats and uniform structures prevent incorrect segmentation and ensure that the MT system can process the text coherently and correctly. This is particularly important for technical documents with complex lists, tables or embedded graphics. For a comparatively low amount of effort when creating the text, the efficiency of machine translation can be increased and costly misunderstandings and post-editing minimised.

Combining machine translation and structured language data is a particularly effective approach to increasing quality and reducing costs. Two key tools take centre stage here: glossaries and translation memories (TMs).

A glossary of terminology acts as a binding guideline for the AI and ensures that terminology is used correctly and consistently. It contains a company’s most important technical terms together with the preferred foreign-language equivalents. By enhancing the process in this targeted way, AI systems translate technical terms according to the company’s specifications rather than by using statistical probability. This can make all the difference to the quality, especially in highly specialised industries or for companies with individual terminology.

While glossaries ensure the correct specialised terminology is used, integrating translation memories provides consistency with previous translations. This is because texts are stored in TMs at segment level so that they can be reused at any time. These are usually sentences, sometimes individual words, which are automatically compared for each new translation project. The system identifies exact matches that can be used unchanged, as well as similar segments that only need to be partially adapted. Reusing previously translated content leads to direct cost savings, as reduced rates apply to identical or similar passages of text.

Combining AI, glossaries and translation memories creates a synergistic effect: the AI provides a basic translation for all text sections that are not yet available in the TM and, thanks to the glossary, translates the specialised terminology as desired. Translations that already exist in the TM are pre-translated by the TM and merged with the AI output. This approach not only optimises the quality of the translation, but also reduces costs for the long term by efficiently reusing existing content.

Pure machine translation without human intervention is like an untested autopilot – a risk that is rarely acceptable in professional contexts and comes with considerable costs. The solution is found in a hybrid approach. Machine translation and post-editing (MTPE) combines the strengths of both worlds and creates an optimised workflow for high-quality translations.

With MTPE, the AI first creates a raw translation, which is then checked and optimised by specialist post-editors. These specialists not only correct obvious errors, but also refine nuances, adapt specialised terminology and ensure a consistent style. The process is significantly more efficient than a fully manual translation. Using a machine first considerably reduces the time required.

Contrary to the intuitive assumption that every additional step in the process increases the costs, integrating post-editing actually leads to savings. Machine translation provides a solid foundation, while human post-editing has a targeted focus on problematic aspects. This combination avoids both the high costs of fully manual translations and the quality risks of purely machine-based solutions. It has a particularly valuable learning effect, because translators can identify, categorise and document recurring errors in company-specific MT and LLM systems. This structured feedback flows into the continuous improvement of the system. Ideally, the results become more accurate and the amount of post-editing required is reduced with each run.

The performance of AI translation systems stands and falls with the quality of the training data. Even the most advanced algorithms cannot generate outstanding results from inferior databases. There is a high risk that the output will get worse and worse as the amount of junk data increases, because unclean language data has a lasting negative impact on machine translation systems. A system trained with contradictory translation variants reproduces these inconsistencies and may even fuel them. It is a similar story for terminology databases: excessive or contradictory entries confuse the system and lead to poorer translation output.

Cleaning up data systematically involves various aspects:

• Removing data with an untidy form
• Correcting incorrect data allocations
• Eliminating duplicates and contradictory entries
• Updating outdated terminology and content

Automated analysis tools such as oneCleanup efficiently analyse large volumes of data for their clean-up potential. Script-based analyses are combined with linguistic expertise and enable a quick assessment of the actual data clean-up required. The investment in clean databases pays off several times over: the quality of machine translations improves immediately, while the costs for post-editing and quality assurance are reduced. Cleaned-up data also forms a solid basis for further developing company-specific AI models, which become more accurate with each iteration.

Regularly maintaining language data should not be seen as a one-off project, but as a continuous process – a central component of any long-term AI strategy in the translation sector.

AI is not successfully integrated into translation processes by chance. It is the result of a well-thought-out strategy. The five-step plan presented here offers a structured approach to sustainably reducing translation costs while ensuring a high quality result: from strategically selecting suitable content and optimising source texts to systematically cleaning up data. Every step helps in exploiting the full potential of AI.

It is crucial to understand that artificial intelligence is not a substitute for human expertise, but a powerful tool that reveals its true value through targeted adaptations and appropriate integration into existing processes. Companies that adopt this approach benefit from significant cost savings while maintaining or even improving their translation quality.

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