Harnessing AI for Marketing Translations

Many marketers are exploring opportunities and considering risks associated with using AI tools for translations. The honeymoon phase of accessing new AI technology is over, and it's time to decide on a quality-assured process that guarantees quality across all communication channels and markets.

Since November 2022, marketers have faced new conditions in content creation and translations. What have we learned? Has our approach to content, language, and translations changed?

This article will clarify AI concepts and examine what time and cost savings are currently achievable by utilizing available AI tools in translation and localization work.

Unmissable Opportunities

AI technology opens up exciting possibilities for generating new content and fostering creativity. However, it's crucial to be responsible for AI usage and avoid spreading inaccurate information.

AI does not comprehend irony, humour, or cultural nuances as humans do. AI always requires an information source, and if a relevant source is absent, AI fills in the gaps. This is what we mean when we occasionally say that ChatGPT "hallucinates."

Value and Risks of AI Translations

Some marketers and communicators are tempted to input their company's texts and materials into tools like ChatGPT and request professional translations into other languages. 

However, if you do not possess proficiency in the target language, we advise against this approach.

Since AI often translates word for word, it can lose the nuances and meanings specific to the original language. AI may struggle to interpret grammar rules, resulting in inaccurate phrasing in the target language. 

Word order varies across different languages, and AI may have difficulty producing correct syntax in the target language. Additionally, words with multiple meanings can pose challenges for AI in accurately translating and placing them in an accurate context.

Remember that AI translation is still a relatively new technology and comes with challenges. Over time, AI translation will become increasingly reliable and helpful in bridging language barriers. However, language is highly complex and contains many subtleties that can be difficult to capture.

If, as the person in charge of your company's marketing and communication, you lack proficiency in the language you need to translate into, collaborating with a language partner who can guarantee accurate and culturally appropriate translations for your target market is highly recommended.

A Quality-Assured Translation Process with AI Technology

AI technology should be utilized where it provides maximum benefit, and undoubtedly, AI will continue to be part of the translation process.

A reputable language partner takes responsibility for implementing AI as a natural part of the workflow for translators, proofreaders, and localization specialists.

The interplay between technology and human expertise will enhance the efficiency and quality of translations and localization work in the future. Human roles will primarily involve monitoring, coordinating, correcting, and ensuring quality.

Find a Language Partner that offers a global network of language experts operating in the markets where your company sells. 

Tailor a process based on your company's quality and security requirements. As the translation process continually becomes more streamlined and language technology can ensure consistent language use and terminology, your team will save time.

Some facts about AI:

Artificial Intelligence (AI) refers to the ability of software to perform tasks traditionally requiring human intelligence.

Machine Learning (ML) is a branch of AI where a model acquires skills by training on example data and identifying patterns to make predictions and recommendations.

Generative AI is a type of AI that can independently create content such as text, images, and music that appears as if a human created it.

Prompt Engineering is designing and optimizing input instructions to guide generative AI models towards desired outcomes.

Large Language Models (LLM) are foundational models capable of processing vast amounts of unstructured text and generating natural language text.

Deep Learning is a branch of machine learning that utilizes deep neural networks to learn from unstructured data such as images, text, and audio.