Artificial intelligence has made translating marketing content faster, easier, and more affordable. Businesses can now adapt websites, campaigns, and other materials for international audiences without the time and expense traditionally associated with translation. But producing content in another language does not automatically mean it will connect with local customers or generate meaningful results.
For global marketing teams, the real challenge goes beyond translating words accurately. Content must reflect local expectations, preserve the company’s brand identity, and encourage customers to take action. A campaign that performs well in one country may struggle in another, even when the translation appears correct.
Language continues to play an important role in the online customer experience. A European Commission study published in April 2025 found that 44% of web users across six European countries considered it especially important for e-commerce websites to be available in their preferred language. This highlights how language accessibility can influence the way customers interact with businesses.
However, language alone is not enough. Research from the Oxford Internet Institute in 2025 found that stronger multilingual capabilities do not necessarily mean AI models can accurately reflect local cultural values. Concerns about AI-generated information also remain significant. In April 2025, 66% of U.S. adults were very or extremely concerned about people receiving inaccurate information from AI. Pew Research later reported that 76% considered it very or extremely important to distinguish AI-generated content from human-created work, while 53% had little or no confidence in their ability to do so.
For businesses expanding internationally, these findings point to an important consideration: localized content must be evaluated by how well it performs in each market, not simply by how accurately it has been translated.
In a conversation hosted by Emerj Editor Yolandi de Weerdt, Andrew Thomas, Head of Marketing at Acclaro, shared practical ways companies can improve the performance of AI-translated content. Drawing on more than 25 years of experience in the language industry, Thomas explained how businesses can connect marketing performance with localization, test new markets, maintain brand consistency, and determine where human expertise remains essential.
Use Market Performance Data to Improve Localized Content
One of the most important steps in improving localization is understanding how content performs after it has been published.
According to Thomas, properly adapted content should generally perform similarly across markets, assuming the product or service meets the needs of each audience. If a landing page generates strong conversions in Germany but performs poorly in Brazil, businesses should investigate the reason rather than immediately assuming that Brazil is simply a weaker market.
The product-market fit must always be considered. If an offering does not meet local demand, changing the translation will not solve the problem. However, when the product is relevant to the audience, a persistent performance gap may indicate that the content needs better adaptation.
Thomas points out that many organizations struggle to determine whether localized content is achieving its intended goals. Marketing operations teams often have access to engagement and conversion data, while localization teams or external translation providers manage the language versions. Because these responsibilities are separated, performance information does not always reach the people who can improve the content.
The solution is to connect these teams through a continuous feedback process.
Marketing operations should share the results of each localized asset with the team responsible for translation. Localization specialists can then review the performance data alongside the translated content, identify possible issues, and make revisions where necessary. Once changes are published, the updated version should be measured against the same objectives.
Thomas compares this process to search engine optimization, where teams regularly analyze performance and make adjustments to improve results. Localization should follow the same principle. Instead of treating translation as a one-time production task, businesses should view it as an ongoing process that continues after publication.
This approach gives organizations a clearer understanding of which markets are performing well and where improvements are needed. When a localized campaign falls short, teams can investigate whether the offer itself is relevant or whether the content needs to better reflect the expectations of local buyers.
Test Market Interest Before Expanding Local Operations
AI translation also creates new opportunities for businesses considering expansion into unfamiliar markets.
Traditionally, companies interested in entering a new country may begin by hiring a local salesperson to explore demand. That employee often requests marketing materials from headquarters, and the company begins producing content based on what the salesperson believes the market needs.
After the materials are introduced, the business may discover that the content is not suitable or does not generate the expected response. This can lead to repeated requests, revisions, and delays before the company understands what local customers actually want.
Thomas recommends reversing this process by testing localized content before making larger investments in local operations.
With AI translation, companies can publish basic content in a target market and observe how audiences respond. Engagement provides an early indication of interest and can help determine whether further investment is worthwhile.
If the content attracts attention and demonstrates potential demand, the business can move forward with hiring local employees and involving human reviewers to refine the material for a more complete market launch. If engagement remains low, the company can reconsider its plans without having already committed significant resources to recruiting, salaries, and management.
This approach allows businesses to use relatively low-cost content experiments to inform larger expansion decisions.
However, Thomas also recognizes an important limitation. AI-translated content used for initial market testing may not be fully adapted to local cultural expectations. It should therefore be used carefully, particularly when it represents a company’s first impression in a new market. The approach is more appropriate for lower-stakes demand testing than for major flagship campaigns.
The broader lesson is that engagement with localized content can help businesses identify potential opportunities, while local professionals and culturally knowledgeable reviewers remain important when preparing to compete in that market.
Maintain Brand Voice Across AI Translation Tools
The growing availability of AI assistants such as ChatGPT, Claude, and Gemini has made translation accessible to employees throughout an organization. Anyone with access to these tools can quickly translate marketing materials, product information, or business communications into another language.
While this accessibility improves efficiency, it can also create a problem for brand consistency.
Marketing teams are familiar with the challenges that arise when employees create promotional content without following official brand guidelines. The same issue now appears in translation workflows. Employees may use general-purpose AI tools because the approved localization process feels too slow or complicated. Thomas refers to this practice as “shadow localization.”
The result can be inconsistent messaging, incorrect terminology, and content that does not sound like the company.
A survey of more than 1,000 U.S. employees, reported by the Journal of Accountancy in November 2025, found that 59% use unapproved AI tools to complete their work. This illustrates why businesses need to address how employees use AI rather than simply relying on restrictions.
Thomas explains that general-purpose AI tools tend to produce generic translations when they are not given company-specific terminology and brand instructions. A company may have a distinctive voice in English, but its translated content can sound similar to that of competitors in Japanese, German, or other languages.
Instead of banning these tools, Thomas recommends making approved AI workflows easier for employees to use. Businesses can create customized assistants within the tools employees already rely on and connect them to the localization team’s systems. This allows translation requests to draw from approved terminology, existing translations, and company-specific instructions.
An important part of this process is adapting the brand style guide for multilingual use. Thomas explains that Acclaro converts clients’ existing style guidelines into instructions that AI can apply during translation.
These instructions should also reflect the type of content being produced. Marketing materials generally require strict adherence to brand voice and terminology because they directly shape how customers perceive the business. Support content and product documentation may require more flexibility because they serve different purposes and may follow additional guidelines established by their respective teams.
When brand guidance is built directly into AI translation workflows, employees can work more efficiently without sacrificing consistency. It also reduces unnecessary revisions and helps ensure that customers receive messaging that reflects the company’s identity in every market.
Use Human Expertise Where It Matters Most
Despite improvements in AI translation, cultural understanding remains an important limitation.
Languages and cultures constantly evolve. Words change meaning, expressions gain new significance, and social expectations shift over time. AI models, however, learn from existing data and may not always recognize changes quickly enough.
Thomas uses the word “mask” as an example. Before 2020, Google Translate tended to associate the term with beauty products because that was its more common usage in the data the system had learned from. When the COVID-19 pandemic changed how people used the word, referring more frequently to protective medical equipment, translations could become inaccurate.
A human reviewer familiar with the language and cultural context would have been more likely to recognize the intended meaning. Thomas argues that involving language experts during the initial setup of AI translation systems can also help prevent these problems.
Human involvement should not necessarily mean reviewing every piece of translated content. Instead, businesses can focus professional attention on the areas where cultural nuance and accuracy have the greatest impact.
Thomas describes a process in which AI handles approximately 80% to 90% of the translation work before human reviewers refine the content that needs additional attention. Marketing campaigns are often among the materials that benefit most from this final review.
The challenge is determining which content requires human intervention. Reviewing everything can eliminate much of the efficiency AI provides, while reviewing nothing increases the risk of inappropriate or inaccurate content reaching customers.
Thomas recommends combining automated quality checks with market performance data. Separate AI-based checks can evaluate translations and estimate whether human review is necessary. Content flagged by these systems can then be sent to internal language specialists or external reviewers. Poor performance in a particular market can also trigger additional investigation.
This creates a more targeted review process. Rather than choosing between reviewing everything and reviewing nothing, businesses can identify content that presents a greater risk or fails to meet its intended objectives.
Thomas describes this approach through the concept of “quality of outcome.” Instead of judging a translation solely by linguistic accuracy, organizations should consider whether the content achieves the result it was created to deliver.
A marketing page should generate interest and conversions. Customer support content should help users understand and resolve their concerns. Product documentation should communicate instructions clearly and accurately. Each type of content requires its own expectations and quality standards.
By defining these outcomes, content owners can establish thresholds for what may be published through AI alone and what must be reviewed by an in-country expert.
Making AI Localization a Performance Strategy
AI has changed the economics of global content production. Businesses can now translate and publish materials in multiple languages faster and at a lower cost than before. But speed and affordability do not automatically produce meaningful customer engagement.
The companies that get the most value from AI localization will be those that connect translation with business performance. They will track how content performs in each market, use early engagement to guide expansion decisions, and make brand guidelines accessible within the AI tools employees already use.
They will also recognize that human expertise remains important, particularly when cultural context, brand reputation, and customer trust are involved.
Ultimately, successful localization is not simply about making a message understandable in another language. It is about making that message relevant, credible, and effective for the people receiving it.
As AI continues to reshape global marketing, businesses should measure localized content by the outcomes it creates. When technology, performance data, and human expertise work together, companies can build stronger connections with customers across markets while maintaining the consistency and identity that make their brands recognizable.