AI-assisted translation systems offer impressive opportunities to speed up multilingual processes – but their effectiveness depends crucially on carefully considered terminology management. Only by systematically managing specialist terminology can a company fully harness the potential of AI whilst preserving its brand’s linguistic identity.

What is terminology management?

Definition:

Terminology management is the systematic compiling, maintenance and use of technical terms to ensure consistent communication across all languages.

Why it’s important:

• It defines how a company talks about its products, services and brand values
• It prevents inconsistencies and a loss of quality in translations
   

Example:

The German term “Leitung” can be translated in various ways in technical documentation, for example as “line” or “cable”. But it can also be rendered “head” in a business sense to refer to the person in charge of a department.
This person is responsible for the overall management, organisation and daily operations of the department under their control, acting as the primary bridge between their own team and upper management.

Centralised terminology management for AI translations ensures that the same, brand-compliant translation is always used.
 

Why terminology is crucial in the translation process

A lack of systematically managed terminology can lead to the following problems:

• Inconsistent translations of the same term
• Increased effort required for corrections
• Increasing costs for multilingual content
• Inconsistent brand communication
• Misunderstandings among customers or users
   

Key message:

Consistent terminology management is crucial for ensuring quality, efficiency and brand identity in translations.
 

Why terminology is becoming even more important in the age of AI

LLMs and generative AI programs enable rapid translations, but they are not familiar with company-specific terminology.

• Without clearly defined terms, inconsistencies arise
• Quality control and branding may suffer as a result
• Human expertise remains indispensable
   

Terminology management is essential to ensuring that AI-assisted translations are consistent and brand-compliant.
 

How AI makes terminology work more efficient

AI can support terminology work, but it can’t replace it. Typical applications:

• AI terminology extraction: Automatic identification of relevant technical terms from texts
• Establishing terminology databases: Suggestions for synonyms, variants and metadata
• Terminology checks: Assistance with revision, taking the overall context into account
   

Please note:

Final validation by human experts is always required.
 

The limits of AI in terminology work

LLMs can:

• “Hallucinate” terms (they can generate plausible but incorrect terms)
• Overlook customer-specific requirements
• Put confidential data at risk if it is processed on public systems
   

In summary:

AI supports translators, but it isn’t a replacement for human expertise.
 

Best practices for terminology management in the age of AI

1. Centralisation: Manage all terms in a central database
2. Integration: Direct access for translators via CAT tools
3. AI as an assistant: Assistance with research, data extraction and verification; final validation by humans
4. Security-conscious: Process sensitive data only in systems that comply with data protection regulations
5. Regular updates: Continuously adapt terminology to new products, markets or guidelines
    

Pro tip:

This approach ensures that translations are consistent, efficient and brand-compliant – regardless of the technology used.
 

Terminology as strategic corporate knowledge

Terminology is a company’s linguistic memory. It ensures that both man and machine speak the same language, builds trust, reduces errors and safeguards the quality of multilingual content.

Key message:

Companies that systematically maintain terminology and use it with the assistance of AI increase efficiency, consistency and brand value.
 

Frequently asked questions about terminology management and AI

What is the difference between a glossary and a terminology database?
→ A glossary is static. A terminology database is dynamic, centrally managed and directly integrated into the translation workflow.

Can AI generate terminology automatically?
→ AI can provide suggestions, generate synonyms and supply metadata, but it does not replace human validation.

Why is terminology crucial in LLM translations?
→ LLMs operate on a statistical basis, not in compliance with brand guidelines. Without standardised terminology, inconsistencies arise and quality suffers.

Conclusion

Terminology management is more important than ever in the age of AI. Used correctly, it combines human expertise with AI assistance, ensures consistency, achieves a coherent brand identity, and makes translation processes more efficient.

What next? Contact us to have your terminology professionally established, consistently maintained, and optimised with the assistance of AI.

Terminology work has always been the key to consistent, high-quality translations. The advent of AI and large language models (LLMs) is fundamentally changing translation processes. But this is precisely what makes terminology even more important.

“AI now translates everything perfectly – so why do we still need terminology management?”
As a language service provider, we are increasingly hearing this question being posed. But anyone who has ever seen how a single mistranslated technical term can distort a product description, manual or marketing message knows that terminology is not a side issue – it is the foundation of high-quality translation.

In the age of LLMs and generative AI, the how of handling terminology is changing. The why, however, remains unchanged.

Why terminology is the backbone of every translation

Terminology is much more than just a dictionary. It defines how a company talks about its products, services and values.

Whether we’re talking about “controllers”, “control modules” or “control units”, the right term ensures recognition, trust and legal certainty.

Without consistent terminology, inconsistencies are bound to arise. In practice, this leads to:

• different translations for the same term,
• increased correction effort,
• unnecessary additional costs, multiplied by the number of target languages,
• inconsistent brand communication,
• misunderstandings among customers or users.

Consistent terminology management is crucial for ensuring consistency and precision in the translation process, especially when dealing with high-volume, multilingual content from the fields of technical documentation or marketing.

From glossaries to integrated solutions: Successful translation processes thanks to terminology

In the past, terminology work was often handled outside of the actual translation process – in the form of Excel spreadsheets or static lists. Today, it can be seamlessly integrated into translation management systems (TMS).
This enables:

• automatic terminology suggestions directly in the CAT tool,
• terminology checks during translation,
• centralised maintenance and approval processes.

This makes terminology a living part of the workflow, not just an afterthought during quality control.

How AI and LLMs are changing terminology work

AI systems and LLMs open up new possibilities for maintaining terminology in a more dynamic and intelligent way. Some specific applications may include:

• AI terminology extraction:
  AI can quickly analyse multilingual texts to automatically recognise relevant technical terms and suggest them as term candidates. This saves time during the creation phase and helps to identify terminology that has not been taken into account previously. However, final validation remains the task of human experts.
  
• Building a terminology database:
  If translations or a defined structure have yet to be established, generative AI can support the creation of a terminology database. This allows variants and synonyms to be clustered efficiently, while metadata such as context, grammatical information or suggested definitions are generated automatically. However, the final review and validation stages are still handled by humans.
  
• Terminology checks by AI:
  Terminology errors identified by a rule-based check are sent to the AI, where they are evaluated and corrected in their overall context, taking into account additional terminological information.

These new approaches make terminology work faster, more scalable and more data-driven. At the same time, it remains dependent on human validation – because AI does not automatically understand corporate language or brand values.

Limitations and risks: When AI ‘invents’ terms

As powerful as LLMs are, they also pose a real risk. This is because an AI model can:

• ’hallucinate’ terms – i.e. it can create plausible but incorrect terms,
• overlook customer-specific requirements if these are not clearly specified in the prompt or system,
• confidential terminology data is at risk if it is fed into publicly accessible systems.

The conclusion? AI can support, but not decide. Human expertise remains indispensable when deciding whether a term is terminologically correct, brand-compliant and contextually appropriate.

Best practices: How we combine human expertise with the power of AI

As a language service provider, we see the added value in using technology sensibly – not automating everything blindly. Successful terminology work in the age of AI is based on five principles:

• Centralisation:
  All terminology data belongs in a central database – not in miscellaneous lists scattered far and wide.
• Integration:
  Terminology must be directly linked to CAT tools so that translators can access it in real time.
• AI as a support, not a substitute:
  AI tools can assist with research, extraction and checks – but final validation remains in human hands.
• Security-conscious:
  Sensitive terminology data should only be processed in data protection-compliant, controlled systems.

Terminology remains strategic corporate knowledge

Artificial intelligence and large language models are fundamentally changing how we work with language – but they are no substitute for terminology management. When used correctly, they actually make it more efficient and intelligent. Terminology is a company’s linguistic memory.
Particularly in the age of generative AI, clear and well-defined terms are crucial to ensure that man and machine truly speak the same language.

Contact us if you want to build your terminology efficiently, maintain it consistently and optimise it with AI support – we will support you every step of the way.

Learn more about our services in combination with AI for efficient terminology management