How ChatGPT Is Shaping the Localization of E-commerce Projects
In recent years, the demand from the e-commerce industry for machine translation post-editing has been steadily rising. The popular DeepL platform is increasingly being replaced by ChatGPT, which brings not only higher-quality translations but also broader functionality. Machine translation is no longer just a compromise between quality and lower costs. In this article, we take a closer look at the situation in 2025 and the outlook for the coming years.
The Rise of Post-Editing Demand
One of the key trends in recent years has been the growing adoption of machine translation in localization projects. Globally, however, this trend is not yet dominant. The situation is different in Central and Eastern Europe (CEE), where companies are increasingly prioritizing machine translation over human translation, primarily for cost optimization.
The real turning point came only last year, with a significant surge in demand for machine translation post-editing (MTPE). While globally the share of MTPE projects hovers between 25 and 30 percent, in markets like Slovakia and the Czech Republic this figure is two to three times higher, particularly across the e-commerce industry.
E-commerce companies are understandably seeking to scale internationally faster and more cost-effectively. However, they often stretch the limits of what is achievable without compromising quality.
Several factors are driving the increased reliance on machine translation. On one hand, it’s a way to manage localization budgets more efficiently in a challenging economic climate. On the other hand, the performance of AI-powered translation engines continues to improve.
The Quality of Machine Translation Outputs
Machine translation outputs have been steadily improving over the past few years. However, the true game-changer was the introduction of ChatGPT. Prior to that, the pace of improvement was relatively slow, and linguists often had to rework or retranslate significant portions of content to achieve acceptable quality – even when translating into English.
More recently, improvements in output quality across additional languages have become evident. While the results are still far from perfect, the progress is notable, especially considering that ChatGPT has been on the market for just about two and a half years.
In practice, some language pairs still require more human intervention than others. Some more, some less. Nevertheless, without human-in-the-loop workflows, it remains impossible to reach the quality expected from a full localization process, including localization strategy, terminology management, translation memory (TM) leverage, and review through comprehensive quality assurance (QA).
Machine outputs alone still fall well short of enterprise-grade standards.
Language Services Provider’s Standpoint
The impact of AI on translation processes depends heavily on scale and context. Translating a 100-word marketing snippet via DeepL or ChatGPT may look seamless, but managing 1,500 segmented strings in a translation management system (TMS) for a major localization project is a completely different challenge.
Moreover, when ChatGPT is integrated into CAT tools, its capabilities are constrained by the integration parameters set by the TMS provider, limiting the possibility of fine-tuned prompting or dynamic adaptation. Translator intervention therefore remains crucial.
While direct translation via a chat interface is theoretically possible, in real-world e-commerce localization, ChatGPT cannot reliably handle embedded code typical of XML, JSON, or custom CMS exports.
Another key challenge is the proper handling of repetitions and leveraging of translation memory assets, which is only possible through CAT tools.
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Human Oversight Remains Essential
AI is a powerful accelerator in localization workflows, but it cannot fully replace expert human linguists. Knowing how to integrate AI effectively is critical to streamlining translation pipelines.
Many organizations still struggle with this balance, leading to inefficiencies and quality risks. AI alone cannot deliver 100% accuracy or cultural relevance. Expertise from language professionals remains indispensable.
After all, localization is not simply about translating words. It’s about crafting an international brand voice, defining communication strategies, ensuring cultural alignment, and maintaining linguistic consistency across the markets.
As of 2025, AI is a valuable tool, but it still cannot replicate the strategic, cultural, and quality-driven elements that human localization experts deliver.
And when it comes to translations into less widely supported languages, without the creativity and expertise of professional linguists, the output will still only be fragmented, error-prone machine translation.