AI Localization: How to Use AI Tools for Global Growth

AI localization uses artificial intelligence to translate and adapt your store’s content for shoppers across different markets, cultures, and languages. Before AI, the localization process meant either hiring human translators or working with a localization agency. Today’s AI tools automate more of that work. 

If you sell globally, localization is a key part of turning international traffic into sales. Shopify’s data shows that localizing your store can boost conversion by up to 40%.

Here’s what AI-powered localization is, the workflows it automates, and how to use it to expand your brand into new markets.

What is AI localization?

AI localization is the use of artificial intelligence to adapt content for a specific market. AI does the heaviest lifting in two areas:

• Translation. Translation engines and large language models (LLMs) can translate the copy shoppers read on your site, including product descriptions, titles, blog posts, navigation, checkout flow, and support content, into multiple languages.

• Cultural messaging. AI localization adapts content and cultural references in your marketing messaging and store to fit local expectations.

In two other areas, AI works alongside your ecommerce platform and your team:

• UX and design. Layouts and components that hold up when translated text takes more space than the original or when reading direction shifts (e.g., from English to Arabic). AI models can flag potential website issues with translated content: for example, where text expansion will break a layout.

• Commerce details. Your ecommerce platform handles adapting currency and payment methods mechanically, while AI localizes the copy around it, including checkout instructions, payment labels, and form copy.

How AI localization works

AI-powered localization relies on AI translation tools to adapt content. Early machine translation tools, like the system that powered Google Translate when it launched in 2006, worked by matching phrases using patterns drawn from millions of translated documents. Neural machine translation, which Google rolled into Translate in 2016, analyzed full sentences but didn’t adapt the context or tone.

Today’s AI translation technology is built on LLMs and machine learning systems trained on extensive bodies of text. Unlike earlier systems, AI models apply context across every language you’re translating into. When asked to translate a product description to a local context, an LLM analyzes the surrounding sentences, product category, and any examples you provide in the prompt. 

For example, when given a few examples from in-market sites (such as competitor product pages or local reviews), it can usually pick the right tone for a target audience—for example, selecting the informal versus formal “you” in a language like Spanish.

Context-awareness also helps with words that carry more than one meaning. Consider the English word “light.” In a translation, it could mean “not heavy” or “not dark.” The right word in the target language depends on whether the product is a lightweight jacket or a light-colored one. An LLM gleans the meaning from the surrounding copy, whereas a word-for-word translation tool might pick the wrong one. 

While AI handles the content-heavy translation work, strategic calls about how to reposition for a market are still primarily done by humans. For example, Los Angeles–based helmet brand Thousand, for example, focuses on style and convenience in its US marketing, but, “In Germany, it’s a highly functional market,” founder Gloria Hwang says on Shopify Masters. In the German market, Gloria has observed that the brand’s German shoppers respond to messaging that prioritizes a product’s features and how well it performs for the price.

Once strategic positioning decisions are made, AI can help adjust website copy to align on the various focus areas per market.

How to implement AI localization

1. Configure the model for your brand’s voice and context
2. Choose AI tools that fit your scale
3. Combine AI workflows with a human review process

AI handles the heavy lifting of translation and adaptation, but the quality of the results depend on your inputs and review process. Here are the steps:

1. Configure the model for your brand’s voice and context

The same LLM might produce different translations depending on the context you provide, like what you tell it about your store, your customers, and how you want to sound. For example, you might tell the model that your brand tone is casual, second-person, and playful, and that you sell skate apparel to ages 16 to 25. That guidance shapes the output—including word choice, punctuation, and sentence length—for each market, culture, and language. 

According to Alex Pilon, a senior developer at Shopify, AI becomes useful for a specific task when you draw what he calls “a little boundary” around the model. This means you prompt the model about which knowledge to use, how to approach the task, what audience to target, and what tone to take.In practice, this is called configuration: feeding the model the information it needs, including a system prompt (for example, guidance on audience, tone, and rules, like “don’t translate product names”); resources the model can draw from (such as your style guide and brand documents); and tools it can use (including approved past translations and access to your product catalog). 

Once those pieces are set up, the model can translate within the bounds of how you’ve already decided to present your brand and products.

Alex’s advice is to test prompts on specific cases first, then run AI processes on small batches before scaling. For a translation prompt, the test cases might be a couple of product pages from different categories, plus a piece of marketing copy. The goal is to find content with enough variety to surface a range of potential issues.

Common issues to catch at the testing stage include consistency of brand voice, missed product terminology, and tone (e.g., formal vs. informal, enthusiastic vs. measured). If you don’t speak the language you’re translating into, or aren’t familiar with the nuances of local culture, you could engage a local in that market to review the test output. 

2. Choose AI tools that fit your scale

Choosing AI localization tools comes down to how much content you have to translate, how much human review you want to include, and how the tool integrates with the rest of your tech stack. Tools that connect directly to your store admin save you the work of moving content back and forth between systems.

Translate & Adapt is a free starting point for Shopify stores. It auto-translates up to two languages using Google’s machine translation, supports unlimited manual translations, and includes a side-by-side editor for source and translated copy. The app also adapts content between markets that share a language—like spelling differences between UK and US English, or copy adjustments for region-specific holidays and sales events. 

Her lip to, a Japanese apparel and lifestyle brand, expanded into eight countries including Taiwan, Hong Kong, and the US. It paired Translate & Adapt for content translation with Shopify’s Theme Contextualization, which tailors the storefront design to each market. Cross-border sales grew approximately 400% in the first six months of using these tools.

Third-party apps in the Shopify App Store, including Weglot, Transcy, and Langshop, have additional capabilities, with different apps emphasizing different combinations. When comparing them, look for choice of AI translation engine (DeepL, OpenAI, Gemini, or others), glossary management to keep brand names and key terms consistent across different languages, translation memory that reuses past translations on similar content, and review queues that route AI translations to a human editor for approval before they go live.

To manage multiple markets from a single store, Shopify Markets lets you sell in up to 20 languages, with automatic checkout translation in 33 languages and market-specific URLs that handle hreflang tags for SEO.

3. Combine AI workflows with a human review process

For Alex, working with AI is about building an instinct for what it does well and where it falls short. The first few rounds of review teach you which content categories the AI handles well on its own and which ones consistently need editing.

Derek Gleason, senior lead of content at Shopify, suggests applying human review to checkout copy, legal pages, and any text where a mistranslation would change the meaning. Ideally, your editor is a native speaker of the target language and also familiar with your brand. 

Antonio Santarsiero, a senior SEO specialist at Shopify, raises an example when human expertise is useful. In French, both “POS” and “PDV” are used for “Point of Sale.” Linguists or native French speakers can tell you which fits in different contexts, while AI may use them interchangeably. “A native speaker can help you determine which term fits your brand best,” Antonio says.

In practice, AI does the translation and a person reviews it before it goes live. The AI produces a first draft and a human reviewer checks high-impact pages before they publish.