Are AI and Machine Translations Sufficient For Japan Entry?

Introduction

Localization is a big first step in acquiring customers in a different country or region. AI is changing the overall GTM landscape and the quality of AI and machine translations has made significant progress over the past few years. AI and machine translation can help with translating large quantities of content. The question at hand is whether AI and machine translations are sufficient for Japan market entry. The short answer is no. AI and machine translations can reduce the burden and work, but having the output of AI and machine translations as the final draft can do more harm than good in the market entry efforts and can have costly consequences.

Limitations of AI and Machine Translation

The limitations of AI and machine translations lie in the specificity and nuances of the translations. Nuances and terminology are key to strong localization. These nuances as usually specific to products and industries. The industry may use specific terminology. Making sure that the proper terminology is the first step striking the right nuances. For example, if you are localizing a developer tool, you must use the local terminology. This goes for any other product or industry. The outputs from these AI and machine translation tools frequently use the wrong terminology.

If the terminology is wrong, it can give the impression that the product for service doesn’t fully have a good understanding of the Japanese market. It can also hint that the localization was done by AI or machine translation, giving the impression that the product or service is not serious about the Japanese market. Japanese buyers need the assurance that the product is serious about Japan and gaining their trust is a key aspect. Unvetted AI and machine translations can lead to distrust and can do more harm than good.

Consistency is another limitation of AI and machine learning. Even if you input the same word, it may output a different translation depending on the context of the overall sentence. For example, AI and machine translations use “client” and “customer” interchangeably. The nuance of each translation is different, but the consistency is as big of an issue. Lacking consistency can confuse and reduce usability. It also may lead to end-users questioning the quality of the localization.

Tone is also an issue with AI and machine translation. In Japan, there are many forms and tones that are used situationally. Depending on the context, a specific tone should be used. AI and machine translations cannot detect this.

Losing the trust of the market is the biggest negative impact of the bad translations through solely relying on AI and machine translations. More tangible effects include increased numbers of support tickets, misunderstanding of the product/features, and poor user experience. On top of the reputational damage, poor localization can have tangible impacts that delay the market entry efforts.

Hybrid Approach

AI and machine translation offer a great place to start for localization. Having a hybrid approach is an efficient way to localize the Japanese market.

The quality of AI and machine translations has improved drastically over the past years. The outputs of free tools like DeepL and Google Translate offer a great first draft of the localization. You can input a lot of content and instantaneously get an output. This process alone can reduce the amount of work needed to localize for the Japanese market. That being said, realistically, the outputs of these tools should not be treated more than a first draft.

Having a real person proofing and adjusting for the nuances is the next step in having a hybrid process. Having an industry expert would be ideal to make sure that the right terminology and nuances are struck.

The industry expert can research the right terminology. Having the industry expert put together a glossary of the terminology would provide the consistency necessary for robust localization. Beyond the terminology, having a local expert is necessary to match the local nuances. Making sure that cultural nuances and technical terms are correct is important for the localization to feel natural. Having a local and industry expert would help ensure that the end-user can easily consume the localized versions of the content and product.

Conclusion

There is undoubtedly a need for an industry expert/native-level speaker in the hybrid approach of localization. AI and machine translation can be a great first step and reduce a lot of the busy work. The value add of having an industry expert/native-level speaker involved in the process is to make sure the right terminology and tone are used and to properly navigate the cultural nuances. If you are considering entering the Japanese market and a looking for industry experts for localization, book a consultation here.