AI in translation: what it can (and can’t) do

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AI is everywhere. But what does that really mean for translation?

Artificial intelligence has quickly moved from buzzword to business tool. From content creation to customer support, companies are actively exploring how AI can help them work faster and scale more efficiently.

According to Gartner, 40% of organisations already use generative AI across multiple business areas, highlighting how quickly AI is becoming part of everyday operations.

Translation is no exception. With tools capable of translating content in seconds, it is easy to assume that AI might replace traditional translation processes altogether. But the reality is more nuanced.

AI is transforming the translation industry. Just not in the way many expect.

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What AI can already do well

AI has brought undeniable advantages to translation workflows, especially when it comes to speed and scalability.

Machine translation allows companies to process large volumes of content in a fraction of the time it would take a human translator. For businesses operating across multiple markets, this can significantly accelerate international growth.

For example, an e-commerce company expanding into new regions may need to translate thousands of product descriptions, category pages, and support articles. Doing this manually would take weeks. With AI, the first draft can be generated almost instantly.

AI performs particularly well with:

• Technical documentation
• Product descriptions
• User manuals
• Structured or repetitive content.

These types of texts tend to follow consistent patterns and use standardised terminology, making them easier for machine learning models to process accurately.

According to recent industry reports, many organisations are already integrating AI into multiple business functions, including customer service, marketing, and content operations. Translation is naturally part of that shift.

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Where AI still struggles

Despite its progress, AI is not able to fully understand language in the way humans do.

Language is shaped by context, culture, and intent. And this is where AI continues to face limitations.

Content that requires nuance, creativity, or cultural sensitivity remains challenging, including:

• Marketing and brand messaging
• Legal and regulatory content
• Transcreation and campaign localisation
• Content with idiomatic expressions or cultural references.

AI can translate the words, but not always the meaning behind them.

In marketing, this can result in content that feels flat, unnatural, or disconnected from the target audience. In legal contexts, even small inaccuracies can create compliance risks or misunderstandings. In other words, the issue is not just correctness. It is relevance.

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Why human expertise is still essential

This is where human translators continue to play a critical role.

Humans bring a level of understanding that goes beyond language itself:

• Cultural awareness
• Emotional intelligence
• Sensitivity to tone and intent
• The ability to adapt content for specific audiences

A human linguist does not simply translate text. They ensure that the message resonates in the target market, aligns with the brand voice, and feels natural to the reader.

For companies investing in international growth, this is not a detail. It directly impacts how the brand is perceived.

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The real solution: combining AI with human expertise

Rather than replacing human translators, AI works best as part of a hybrid approach.

This is where machine translation post-editing (MTPE) plays a central role.

In a typical MTPE workflow:

• AI generates an initial translation using neural machine translation (NMT)
• A professional linguist reviews and refines the content
• Terminology, tone, and cultural nuances are adjusted
• Errors and inconsistencies are corrected

This approach allows companies to benefit from the speed of AI while maintaining the quality and accuracy of human translation.

At t’works, this hybrid model is already embedded in translation workflows. By combining advanced AI technologies with experienced linguists and client-specific glossaries, it is possible to deliver content that is both efficient and fully aligned with brand and market expectations.

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When should you use AI in translation?

AI-supported translation is a powerful tool, but it is not suitable for every situation.

It works best when:

• Speed is a priority
• Content volume is high
• A general understanding of the content is sufficient
• Texts are repetitive or highly structured.

Typical use cases include:

• Internal communications
• Knowledge base articles
• Product catalogues
• Large-scale content updates.

On the other hand, human involvement is essential when:

• Content is customer-facing
• Brand voice and tone matter
• Accuracy is critical (e.g. legal, medical, financial content)
• Cultural adaptation is required.

The key is not choosing one over the other, but knowing when to use each.

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Security and data protection: what to consider

Using AI in translation is not only a question of performance. It also raises important concerns around data security and compliance.

Companies often need to translate sensitive content, such as:

• Personal data
• Confidential business information
• Legal or financial documentation.

When using AI tools, it is essential to ensure that:

• Data is not exposed to unsecured third-party systems
• Content is processed in secure environments
• Solutions comply with regulations such as GDPR.

This is one of the reasons why many organisations choose to work with language service providers rather than relying on open AI tools.

A trusted partner like t’works ensures that both quality and data protection standards are consistently met.

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What this means for your business

AI is not a shortcut. It is a tool that requires the right strategy. For most companies, the real opportunity lies in combining efficiency with quality.

This means:

• Using AI to handle volume and speed
• Relying on human expertise for nuance and accuracy
• Building structured workflows that balance cost, quality, and turnaround time.

A common mistake is to rely entirely on AI to reduce costs, only to face issues later with inconsistent messaging, poor user experience, or even reputational risks.

A more effective approach is to define clear use cases and integrate AI where it adds value.

If you are scaling your multilingual content, working with a partner like t’works can help you design a translation strategy that supports both growth and quality.

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So, is the AI hype real?

AI is not just hype. It is already reshaping how translation is done.

But it is not a complete solution. While AI continues to improve, it still lacks the depth of understanding needed for complex, high-impact content. Language is not just functional. It is human.

The most effective approach is not about replacing translators, but about enhancing their work.

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AI has become an essential part of modern translation workflows. It brings speed, scalability, and efficiency. But quality, nuance, and trust still depend on human expertise.

Finding the right balance between the two is what makes translation truly effective.

If you want to explore how to combine AI and human expertise in your translation projects, get in touch with t’works. The team can help you build a solution tailored to your content, your audience, and your business goals.