Traditional localization vs. AI localization

Localization has a surprisingly long history, dating back to the 1980s. While the goal of adapting content for different cultures remains the same, the methods and tools used have undergone a dramatic transformation. It’s fun to see how things changed over the years, so let’s take a look at this traditional localization vs AI localization comparison.

The 80s

Back in the day, in the absence of sophisticated software, translations were meticulously conducted by human linguists, often using typewriters or handwritten drafts. Reference materials like dictionaries and glossaries were primarily physical, making research a time-consuming and resource-intensive task. As for software and applications, they were primarily static, with limited need for dynamic updates or real-time translation.

Present-day

Today, AI engines handle the bulk of the translation workload, significantly increasing speed and efficiency. Extensive online databases and translation memory tools provide immediate access to relevant information, streamlining the research process.

Content is also more dynamic; localization now encompasses not just text, but also audio, video, and user interface elements, requiring constant adaptation and updates. And software interfaces are increasingly designed with multilingual support, ensuring a seamless user experience across different languages.

Now let’s see this side-by-side traditional localization vs AI localization comparison to observe the advantages and disadvantages of each method.

Traditional localization	AI localization
Relies heavily on human translators, editors, and linguistic experts. They manually translate and adapt content, ensuring cultural nuances and context are accurately conveyed.	Utilizes machine learning algorithms and artificial intelligence to automate the translation process. AI can analyze patterns, learn from vast datasets, and generate translations without direct human involvement.
May take longer to complete, especially for large projects, as it depends on human translators who have limited processing speed.	Can be faster and more efficient for certain tasks due to its ability to process large amounts of data quickly. However, it may still require human oversight for quality assurance.
Human translators excel at understanding the context, idioms, and cultural nuances, ensuring accurate and contextually appropriate translations.	While AI can provide translations, it may struggle with nuanced context, idiomatic expressions, or cultural subtleties. Human review is often necessary to refine and improve the output.
Easily adapts to specific requirements and unique challenges, as human translators can make subjective decisions based on their expertise.	Adapts well to repetitive and standardized content but may struggle with highly creative or context-specific materials. Continuous training and improvement are required to enhance adaptability.
Can be costly due to the labor-intensive nature of manual translation and the need for skilled linguists.	May offer cost savings for large-scale, repetitive tasks, but the initial investment in AI technology and ongoing maintenance costs should be considered.
Allows for meticulous quality control through manual reviews, ensuring high-quality and culturally sensitive translations.	Requires human oversight for quality assurance, as AI-generated translations may contain errors or lack the finesse of human understanding.
Offers high customization based on specific project requirements, with translators tailoring content to match the target audience precisely.	Can be less flexible in handling unique requirements or specialized content without additional customization or human intervention.

The future evolution of localization

Localization is likely heading towards a future dominated by a collaborative approach, where AI and human expertise work in tandem to offer the best possible experience.

Machine translation engines are expected to continue to improve in accuracy and fluency, particularly with the advancement of natural language processing (NLP) and deep learning techniques. But despite advancements in AI, human linguists will remain essential for ensuring cultural relevance, nuance, and brand voice.

Localize efficiently with POEditor

With POEditor, you can harness the power of both AI and human expertise. Leverage the speed and cost-effectiveness of machine translation from Google Translate, Azure AI Translator, or DeepL Translate. We also offer integrations with popular LLMs like Claude, Gemini, and Open AI, if you wish to try out AI-generated translations.

In addition, you could go for more fine-tuned translations by ordering human translation services from Gengo or TextMaster. There’s also the option to add contributors (translators) to join your project, or even make your localization project public so that anyone can volunteer to translate.

The platform allows for seamless collaboration with your team, translators, and stakeholders to help you get things done. Utilize features like glossaries, tags, and screenshots to provide context and ensure consistency across projects. Potential errors and inconsistencies can be identified with our QA checks, ensuring high-quality translations.

Choose the plan that best suits your budget and project requirements, with options for free and paid subscriptions.