AI localization: Definition, benefits, and best practices

Consider how many customer touchpoints your brand launches into the wild. Or, consider a phrase like “into the wild.” Some customers might colloquially understand this to mean: in public, in the market. Others might feel some confusion: Are you talking about the wilderness? 

It’s important to think about the words you choose when marketing materials need to be translated into different languages. Sharing assets across regions helps streamline the workload, and a lot can go right—hopefully, you’ll gain brand awareness and new customers. But a lot can also go wrong, and you may accidentally alienate the very customers you’re trying to reach. According to PWC, 17% of customers in the U.S. will walk away after a single bad experience with your brand. Globally, that number increases to 32%. In Latin America, it rises even higher to 49%.

Today, it’s easier than ever to ensure a damaging translation error doesn’t occur by using AI localization. Even better, you can automate the costly and time-consuming work of localization by putting artificial intelligence to work at scale across campaign content.  

What is AI localization?

AI localization reduces localization efforts by using artificial intelligence to rapidly translate marketing copy and creative assets for use across every region or new market you want to support, going beyond translation to adjust and adapt content for cultural nuances. This might include the imagery that accompanies text, design and color choices, tone of voice, and more.

Brief history of AI localization

It was an early machine translation gaffe that led to the creation of Google Translate (not the first translation tool, but an iconic frontrunner). In its early days, Google Translate was a statistical machine translation tool that used predictive algorithms to break down sentences and translate text, often without grammatical accuracy. With the advent of machine learning and large language models (LLMs), it shifted in 2016 to become a neural machine translation (NMT) service. NMT systems use Natural Language Processing (NLP) and train on large datasets, with a focus on accurate and fluent translation. AI localization is similar, but goes a step beyond to consider content through the lens of people in a specific place, and to make adjustments based on everything it knows about the region and any linguistic nuances. 

How AI localization differs from traditional localization

Traditional localization goes beyond a simple translation project to ensure that the content reaching global audiences conveys a consistent and appropriate message. 

Historically, this process relied on human translators or early translation tools and human reviews. This meant that regional marketers often needed to wait to receive localized assets (of varying quality) from headquarters or might even need to localize the approved assets themselves. 

AI localization works to achieve your brand’s same overarching localization strategy and goals, but much faster and at scale. With human oversight, AI localization tools produce high-quality translations, providing considerable time and cost savings.

How AI is changing localization as we know it

As AI tools learn and improve, the localization process continues to shift in a variety of ways—from streamlining localization workflows to rapidly localizing content (or creating multilingual content) at scale across mediums. 

Project managers no longer need to meticulously walk deadlines far enough backward to ensure there’s enough time for localization. Instead, they can build it into the project plan and, with the right tools, localize all campaign assets in a matter of minutes—almost in real time—even adjusting copy on images without needing edits from a designer.

This is meaningful for teams burdened by manually adjusting dozens or hundreds of assets, or without the resources to support other regions. Now brands can launch campaigns across global markets simultaneously, with confidence that the messaging and look-and-feel are consistent. AI localization also unlocks the ability to rapidly adapt and reuse assets for rapid, shorter-term campaigns for things like seasonal promotions, and to create truly global content libraries.

Going forward, generative AI will reshape how content is created, allowing teams to create assets across content mediums and languages almost instantaneously.

The benefits of AI localization

As critical as it is, localization itself can be a barrier to effective global marketing efforts, delaying time to market and placing additional strain on already overtaxed teams, timelines, and budgets. Here’s what AI localization helps with:

Scale

AI localization enables businesses to scale content across multiple languages simultaneously while maintaining consistency in brand voice and messaging. This allows teams to bring campaigns to market quickly, without duplicating efforts, expanding the reach of campaigns and the number of campaigns you can launch to wider markets.

Cost

AI localization is cost-effective because it can handle high-volume translations at a fraction of traditional human translation costs, making global expansion more accessible to businesses of all sizes. It also means that you can spend more time fine-tuning campaigns and quality checking so that they’ll have the maximum impact for the investment you’ve made.

Efficiency

AI localization reduces bottlenecks caused by time zone differences or translator availability. It dramatically accelerates translation workflows, reducing project timelines from weeks to days or even hours. This doesn’t mean that campaigns should be rushed, but it does mean that you can react quickly to changing market environments and prioritize regions equally. 

Common misconceptions about AI localization

There are a few myths and misconceptions around AI localization, whether you’re already running an AI-driven organization or are hesitant to adopt:

No human oversight is needed

AI translation still requires human oversight and careful planning or model training. From our view, it’s helpful to think about AI as a collaborator in localization. The most effective AI localization benefits from human expertise for more nuanced cultural context, consideration of current world events, brand alignment, and quality assurance. This collaborative approach is similar to how teams successfully implement AI assistants in their daily workflows—the technology enhances human capabilities rather than replacing them.

AI localization isn’t high quality 

The idea that AI localization isn’t high quality is outdated. Today’s AI tools can produce localized content that’s not only accurate but increasingly nuanced—especially when they’re trained on your brand guidelines, voice, and past campaign examples. While human review is still important for cultural and tonal precision, AI is becoming a reliable first draft partner, accelerating time-to-market without compromising brand consistency. The more context AI has, the more it can reflect your intent across markets.

AI only handles simple text translations 

Like humans, AI is not perfect, but it can handle complex technical documentation, marketing materials, and creative visual content with effective training and refinement. There are many tools on the market that can help translate text, imagery, video, and audio content.

Common challenges for localizing content with AI (and how to solve for them)

Know what to look out for so that you can set yourself up for success and avoid these common challenges. Then, test and learn so that you can optimize your workflows and improve the results.

Missing context

AI technologies need help learning messaging or phrasing that relies on specific contexts or industry-specific terminology. Without preparing for effective AI use up front, you’ll be faced with a lot of post-editing.

Solution: Take the time to build comprehensive glossaries and style guides. The technology you use matters, too. It’s helpful to use tools that support context injection (adding additional content into an LLM), and domain-specific training.

Lacking cultural sensitivity

AI is here to help, but may miss cultural references or produce culturally inappropriate content. Again, this is because it needs to be trained on correct information so that you don’t risk a poor user experience with your brand.

Solution: Implement processes for humans and native speakers and/or cultural experts to provide final approval.

Brand voice consistency

This one is tough because maintaining a consistent tone across languages is truly an art and a skill. 

Solution: Identify existing translated content that does a good job of this and use it to train AI models. It’s also important to establish clear brand voice guidelines for each target audience. 

Integrations

Not all AI localization tools integrate seamlessly with existing content marketing tools, campaign management software, and project management systems, making it harder to manage localization at scale—especially when multiple teams and stakeholders are involved.

Solution: Choose AI localization tools that are built to work within your existing systems—integrating with content repositories, translation workflows, and project management platforms. This ensures smoother collaboration, better version control, and faster turnarounds across global teams. 

3 best practices for AI-powered localization

AI-powered localization can be transformative when done well. Here are a few recommendations to help ensure the best outcome.

1. Do the prep work

Put in the work up front to ensure the best outcome. Start with high-quality source content—clean, well-structured original text produces better translations. Before launching a major campaign, establish comprehensive translation memories and glossaries to ensure consistency across projects and markets. Project management tools can help you organize and track all the preparation work needed for successful AI localization.

2. Pair AI with human expertise

Implement a hybrid approach by combining the efficiency of AI with human expertise during preparation and AI training, and for final reviews. Create feedback loops where human editors can correct AI outputs, allowing the system to learn and improve over time. This approach aligns with broader AI implementation strategies that emphasize human-AI collaboration.

3. Segment content by complexity

Consider how much you want to rely on AI for localization. It may make sense to use it for the most straightforward content or smaller assets, while reserving the most complex, creative, or culturally sensitive materials for human translators. Always maintain version control and track changes to ensure quality standards are consistently met.

How to choose the best AI translation tool

There are many AI translation tools to choose from, but they’re not all designed for the same purposes. Some providers offer point solutions while others are custom-built to integrate into your tech stack and improve workflows within the context of where you’re already working. Consider your use of AI and business needs, and look for:

Localize images and copy for every region with Airtable

Ready to localize copy and images? With Airtable, teams can localize assets in seconds—right in the same platform they already use to plan and launch campaigns. It serves as a central hub for AI-powered localization, helping you manage content across languages and regions while staying on-brand. Easily track and organize all assets—text, images, and multimedia—in one place, so your team can move faster and use AI strategically, together.