AI in localization has a significant impact on brands worldwide by enabling the translation of just ten languages to reach 90% of the target market.
This is particularly noteworthy, given that there are more than 7,000 languages globally.
However, even localizing into five languages is a substantial undertaking that requires the adaptation of extensive content.
The traditional manual process is time-consuming and costly.
AI and machine learning now offer a more efficient approach that is revolutionizing the localization process.
As a result, the field of language localization is currently undergoing a significant transformation, driven by advancements in artificial intelligence (AI) and machine translation (MT), which eliminate misunderstandings and enhance efficiency.
What Exactly Does Localization in AI Mean?
AI in localization refers to the integration of artificial intelligence into the localization process to optimize translations and cultural adaptations of content, apps, software, and websites.
The technology enhances the efficiency, accuracy, and impact of the entire localization workflow through machine learning and natural language processing (NLP)
AI systems utilize translation memories, glossaries, and feedback loops not only to refine translation quality but also to accelerate review and project management tasks.
Through the data-driven approach of AI in localization, AI-based systems can make context-sensitive content adaptations on an unprecedented scale.
The comprehensive localization process includes the adaptation of language, brand voices, visual elements, user interfaces, and date, time, measurement, and number formats to ensure cultural relevance and appropriateness.
This also considers currencies and payment methods to guarantee a complete transformation of meaning that fosters understanding and acceptance in new regions or target audiences.
The evolution of AI in localization
To understand the current landscape of translation technology, it is important to trace its evolution.
From the first rule-based systems to the groundbreaking introduction of statistical machine translation (SMT) and the more recent neural machine translation (NMT), the field has made remarkable progress.
All developments have enabled not only more efficient but also more accurate translation methods, laying the foundation for AI in localization.
ChatGPT for Localization?
Although ChatGPT has attracted a lot of attention for its ability to generate conversational-like text, its role as AI in localization is overestimated.
ChatGPT is primarily focused on generating human-like text and is therefore particularly suitable for creative content and customer interactions.
However, the limitations of this technology become apparent when precision, accuracy and domain-specific expertise are required, as is typical for AI in localization.
➡️ Create a new user account for free and start with AI localization immediately
What role does AI play in localization?
AI-driven tools are revolutionizing the management of localization projects by automating repetitive tasks, streamlining workflows and providing predictive analytics to improve decision-making.
AI in Localization increases the quality of deliverables, accelerates time-to-market and increases consistency across languages and platforms, making the process faster, more accurate and more cost-effective.
Within AI in Localization, there are three specific sub-fields that are of particular importance in localization:
Machine learning (ML)
The algorithms, trained on extensive data sets, improve the accuracy of translations and automate quality assessments by translation management systems.
Deep Learning (DL)
An advanced form of machine learning, deep learning algorithms mimic the way the human brain works, enabling more accurate translations through neural machine translation (NMT).
Natural language processing (NLP)
NLP enables computers to understand and use human language, improving localization processes and the automated insertion of glossary terms.
Advanced AI in localization brings significant benefits to the localization industry by not only simplifying workflows but also significantly improving the quality of localization results.
In particular, machine learning and deep learning make it possible to analyze complex data and derive more precise linguistic patterns, which are essential for more effective translations.
AI in Localization changes numerous localization projects:
- Increased efficiency: automation of routine tasks speeds up the process.
- Improved decision-making: Using predictive analytics to optimize project management.
- Increased accuracy: ensuring consistency and quality across languages and platforms.
In addition, natural language processing (NLP) enables deeper interaction with text by capturing nuanced aspects of language that are essential to human communication.
What makes AI so interesting for companies besides AI in localization?
The importance and impact of AI on companies is remarkable given the rapid pace of technological development.
Numerous brands from a wide range of industries have the opportunity to use artificial intelligence to improve their operations, increase efficiency and promote growth.
But what impact can the use of AI really have?
Sales growth
Companies using AI report high average revenue growth compared to companies without AI. This shows the positive correlation between AI usage and increased revenue.
Profitability
Companies implementing AI experience a greater average increase in profitability compared to those without AI, emphasizing the efficiency of AI in operations and decision making.
Productivity
AI users report a significant increase in productivity, compared to a much smaller increase for non-AI users, highlighting the effectiveness of AI in optimizing business processes.
All points illustrate how AI not only improves operational efficiency, but also creates financial and strategic benefits for organizations.
AI localization vs. traditional localization: What are the differences?
AI in Localization is currently overtaking traditional translation processes through the use of automated systems that perform routine tasks and conduct extensive data analysis to ensure consistency and recommend content.
Unlike traditional methods, which rely heavily on the skills of individual translators, AI in Localization uses historical data and continuous learning to improve the quality and speed of translations.
AI adapts to feedback and evolving language usage, complementing human skills and making the translation process more efficient.
AI in Localization can also process large volumes of content quickly. This results in significant time and cost savings, as machine translation (MT) is significantly cheaper than human translation services.
In addition, translation memory promotes consistency and further reduces costs by storing and reusing previously translated content.
Scalability is another benefit of AI in localization, as AI-driven systems can quickly adapt to new markets without the extensive manual work traditionally required.
Localization in AI also enables greater personalization through technologies such as conversational AI chatbots that handle customer queries with personalized, human-like responses.
The human touch in localization
Although AI is superior in localization in many aspects, quality remains an important consideration. Native translators and linguists are essential to capture linguistic and cultural nuances that AI can sometimes miss.
Nevertheless, the quality and accuracy of AI-based translations has improved significantly with advances in neural machine translation and is expected to continue to do so.
A balanced approach to the use of AI that takes into account both human expertise and automated efficiency is therefore essential.
AI in localization therefore requires a deep understanding of cultural nuances, linguistic subtleties and industry-specific terminology.
Human translators are essential for accurate and culturally appropriate translations as they can capture context and bring creativity.
Direct communication with human translators minimizes the risk of missed project targets and reduces revisions.
There is a common misconception that AI can completely replace human translators. In reality, AI complements the translation process by automating repetitive and time-consuming tasks.
This allows companies to manage their resources more efficiently, while human translators can focus on more complex and challenging tasks.
The advantages of AI in localization
Neural machine translation (NMT) in particular has played a significant role in enabling us to use AI in localization today.
Unlike ChatGPT, which is designed for general text generation, NMT systems offer specialized, scalable and cost-effective translation solutions.
The integration of NMT into translation management system significantly improves the efficiency and quality of the localization process.
Best practices for AI in localization:
Define goals
Set clear objectives for localization projects to maximize the relevance and engagement of content and ensure it fits the business strategy.
Select suitable tools
Select the most suitable AI solutions based on accuracy, scalability and integration capabilities.
Combine automation and human expertise
Combine technological efficiency with human insight to capture cultural nuances and achieve the highest quality localized content.
Continuous monitoring and optimization
Regular evaluation and adjustment of AI in Localization to keep pace with linguistic and cultural developments and ensure the effectiveness of localization.
Good to know: Translation and localization platforms seamlessly integrate NMT and support multiple languages, enabling real-time collaboration and efficient project management. This integration ensures organized workflows, scalability and meets various translation requirements.
A look into the future of AI in localization
A look into the future already clearly shows that the competition between the AI giants Google and OpenAI will continue to intensify.
While OpenAI's ChatGPT excels in language generation, Google and DeepL are characterized by continuous optimization of their translation models.
The strengths of Google Translate and DeepL lie in their high translation quality and domain-specific expertise.
AI in Localization has created a completely new basis for the expansion of companies in international markets. Read more under market expansion strategy. Google, Microsoft and Amazon have already successfully integrated AI in Localization to improve user experience and expand global reach.
For example, Google Translate uses AI algorithms for accurate translations in multiple languages, while Amazon Alexa uses AI to understand and respond to user commands in different languages.
Translation management systems with machine translation are fit for the future
It is clear that ChatGPT's strengths, despite its impressive capabilities, do not lie in its accuracy and scalability for translation tasks.
If AI in localization is desired, the focus should be on machine translation (MT), especially when integrated with translation management systems (TMS).
➡️ Create a new user account for free and start with AI localization immediately
The combination of human expertise and AI-driven MT is the foundation for realizing the full potential of AI in localization.
AI in Localization represents a significant advance by offering scalability, efficiency and improved global reach.
Implementing AI in localization processes enables companies to remain competitive and relevant in the global marketplace.
By utilizing advanced AI tools and practices, businesses can successfully overcome the challenges of international expansion and achieve new levels of global engagement and customer satisfaction.