Leveraging localization to reach international audiences can drive exponential growth for your business. But there’s a catch - every detail matters, whether functional or linguistic.

It’s not just about keeping your brand voice consistent across multiple languages and cultures - although that’s no small feat. Every “t” must be crossed, every “i” must be dotted, and every umlaut must be correctly punctuated. Text formatting and function errors are even more important and require the same level of careful attention. If they aren’t corrected promptly, launch deadlines are missed, costs go up, and your customers may go in the other direction.

Localization testing can help, but traditionally, it has been a painstaking and time-consuming process. Courtesy of artificial intelligence (AI), it no longer has to be that way. From cost savings to enhanced efficiency, AI-powered solutions are transforming the testing process, making it easier to deliver high-quality localized products to every corner of the globe.

But first, what is localization testing?

Localization testing ensures that a product - whether a website, an app, or a marketing asset - is accurately adapted for a specific market, checking for linguistic, cultural, and functional accuracy. This includes examining user interface elements, functionality, and text formatting to catch and correct problems like:

• Untranslated text
• Truncated or overlapping text
• Incorrect formats for currency, time, or numbers
• Character encoding errors
• Translations that don’t make sense in context, and more.

This process guarantees that the product resonates with local audiences, is formatted correctly, and works as expected.

Six ways to get more thorough localization and linguistic testing with AI

Artificial intelligence is now the way businesses approach localization testing and quality assurance (QA). Here’s how AI is making these critical processes more efficient and effective:

Managing tests

AI automates the setup and execution of localization test cases across multiple languages, eliminating manual errors and speeding up the process.

Managing updates with AI-driven continuous localization

For localization testing in software, AI can be a game-changer. It enables continuous localization workflows, automatically translating, reviewing, and testing content updates without disrupting the development cycle. This keeps your product up-to-date and relevant in all target markets.

Regression testing

AI automates regression testing by simulating user scenarios in different languages, which helps quickly identify issues from recent updates. This ensures that the localized content remains accurate and consistent.

Automating testing processes with AI integrations

Automated, AI-powered localization testing tools can be integrated into the overall localization process to do quite a few things:

• Version control systems can test automatic translation updates before they go live.
• Content management systems that test newly translated content.
• AI-powered workflows that move QA’d content to the next stage of the process automatically.
• AI tools that take screenshots of builds to identify possible errors and route them to human testers for more thorough evaluation.
• AI that generates automated unit tests that are more thorough and comprehensive than template-based tests.
• AI that monitors code and updates existing functional tests accordingly.
• AI that generates test cases tailored to your needs
• AI that builds test automations
• AI and ML smart locators can keep your tests working even as your applications change.
• AI can diagnose failed tests, finding the root cause so you can release faster.

These automations allow you to start testing projects more quickly, be more thorough, test for more use cases, and keep the testing going over time.

Automating linguistic quality assessment

Automated translation quality assessment tools provide instant feedback on linguistic accuracy, reducing the need for extensive manual reviews.

Ensuring cross-team alignment

AI-driven project management tools provide a centralized platform for development, localization, and QA teams to access real-time updates and insights. These tools help teams track progress, flag issues, and suggest corrective actions, improving communication and collaboration.

With AI, businesses can manage localization testing and QA more efficiently, so high-quality, culturally relevant products reach global markets faster.

Seven benefits of AI in localization testing

Integrating AI into localization testing offers numerous advantages that streamline the process and enhance results. Here’s how your business can benefit:

• Faster processes: AI automates repetitive tasks, slashing turnaround times. This efficiency lets you meet tight deadlines without cutting corners.
• Fewer errors and bugs: AI-powered tools spot linguistic errors, formatting issues (like truncated or overlapping text), and software bugs that humans might miss. Automated QA systems provide instant feedback on accuracy, keeping your content polished and professional.
• A stronger brand voice: Maintaining a consistent brand voice throughout the localization process can be challenging, especially with multiple translators working on your content. AI keeps everyone on the same page by flagging translations that don’t align with your translation memories and style guides, preserving your brand’s unique tone across multiple languages.
• An improved customer experience: Accurate, culturally appropriate, and error-free content enhances customer experience and builds trust with international audiences. With UX being a key differentiator in a crowded market, this is more critical than ever.
• Reduced localization costs: Automating parts of the localization process reduces reliance on human translators for routine tasks, freeing up resources for complex, creative aspects.
• The power to scale: AI enables your localization efforts to grow effortlessly to meet your business goals, handling increased workloads without compromising quality.
• Data-driven insights: AI-driven analytics offer valuable insights into localization performance. They help you track error rates, identify common issues, and measure the overall quality of translations. With these insights, you can fine-tune your strategy for continuous improvement.

AI challenges and what to consider before adding it to your localization program

Using AI in localization testing offers tremendous benefits, but there are a few caveats to be aware of before you get started.

Integration can be complex

It can be tricky to integrate AI into your existing workflow. The secret is knowing when to use it and then picking the right AI tools for your needs. At Acclaro, we use AI for high-volume, repetitive manual tasks but rely on human experts for more complex projects.

Data privacy and security are crucial

AI needs lots of data to work well, which raises privacy and security concerns. To keep information safe, it’s important to ensure your AI tools comply with data protection regulations.

Training and maintenance will be necessary

AI isn’t a set-it-and-forget-it solution. Regular updates and training are essential to keep the system sharp.

Up-front costs and resource allocation can be challenging

While AI can save money over time, there is an upfront investment in implementing it. Weighing these costs against potential savings is key to determining if AI is the right move for your business.

A language services provider with a strong foundation in AI can help you maximize the effectiveness of AI integrations for your localization testing program.

How to automate localization testing: real-world examples

AI has been pivotal in enhancing localization testing for many of Acclaro’s customers. Here are some practical examples of how we use AI to get better results for our clients:

• We score translations with AI using the Multidimensional Quality Metrics (MQM) model to ensure they meet our high standards.
• We use AI to validate machine-translated content, reducing costs and efficiently allocating the budget.

What does this look like in the real world?

In a project for an e-commerce client, Acclaro used AI to determine content types and ensure each type entered the appropriate translation workflow. This ensured only suitable content was machine-translated using the most effective process, saving time and costs.

For a tech company utilizing development sprints, AI streamlined the localization of large volumes of content quickly and accurately. AI tools prioritized content needing human translation while routine updates were handled by MT, maintaining quality and reducing turnaround times. This method ensured that localization kept pace with rapid development cycles.

These examples illustrate how AI enhances localization testing and quality assurance, complementing human expertise to deliver high-quality results efficiently.

Embrace the future of localization

Adopting AI in localization testing is crucial for speeding up work, improving quality, and staying competitive in a global market. AI enhances efficiency, accuracy, and consistency, making delivering high-quality, culturally relevant products easier without errors or bugs.

Ready to transform your localization process? Partner with Acclaro to leverage strategy-driven AI solutions that help your business thrive across cultures.