AI is great in localization…until it’s not: 6 risks you should not ignore

Artificial Intelligence is changing the way businesses go global. With machine translation and AI-assisted workflows, companies can reach international markets faster than ever. The promise is clear: speed, scalability, and cost savings. But overreliance on AI can introduce several unacceptable risks.

We’ve seen it happen. Machine translations miss the mark. AI-generated content strips out brand personality. Workflow integrations can create more problems than they solve. AI is a powerful tool, but only when used strategically.

So where does AI fall short? And how can companies avoid the most common pitfalls? Here are six of the biggest risks you may encounter by depending too much on AI in localization. And, more importantly, here’s what you can do to avoid them.

AI in localization: the good, the bad, and the overhyped

AI’s role in localization is growing fast. It speeds up translation, refines source content, and helps manage quality at scale. Businesses use AI to improve efficiency, reduce costs, and expand their global reach.

Here are a few ways that AI fits into localization today:

• AI Translation – From machine translation (MT) to large language models (LLMs), AI helps process large volumes of text quickly. AI can be fine-tuned for industries like life sciences, legal, and finance, improving terminology consistency.
• Automated post-editing – AI can not only translate content, but it can edit content that has been translated to make it clear and more fluent.
• AI-Assisted Source Text Cleanup – AI improves source content by detecting inconsistencies and clarifying phrasing.
• AI Multimedia Localization – AI subtitling and voice cloning speed up video localization significantly.
• AI-Generated Multilingual Content – AI produces rapid first-draft marketing copy, product descriptions, and other content to speed up market reach.
• AI-Powered Quality Estimation (AQE)– AI tools evaluate translation accuracy and flag potential errors before human review.

Some of these are well established in the space, and others, like AQE, are more experimental.

You can see what Acclaro is up to by reading this recent blog post.

AI is a meaningful way to help brands go global quickly, but it has its limits and risks. Leaning on it too much can introduce several pitfalls that affect your content, your brand, and your customers. Treating AI as a replacement for strategy rather than a tool to support it often leads to avoidable challenges.

The top six risks of overeliance on AI in localization

AI speeds up localization and can handle more volume than humans, but when businesses rely on it without guardrails, quality and accuracy suffer. Overuse of AI in localization leads to problems, including bland messaging, translation errors, workflow bottlenecks, and security risks. For example, Machine Translation processes vast amounts of text, but it doesn’t understand context, brand voice, or cultural nuance the way humans do, and the resulting translation can be culturally inaccurate or misaligned.

Matt Rodano, our VP of Account Management comments, “At Acclaro, we recognize the transformative potential of AI in localization, but we keenly understand its limitations. Our approach is to help our clients implement AI where it truly adds value and maintain human expertise to control accuracy, maintain brand voice, and deliver culturally nuanced content that their customers expect. We’re committed to helping businesses take advantage of AI’s benefits for global expansion without compromising on quality or introducing any unnecessary risks.”

But what are those risks? Let’s take a look.

1. Accuracy issues create multiple business risks

AI-generated content almost always sounds fluent and confident, even when there are subtle or critical errors. This is a dangerous issue for legal, medical, and technical industries, where even minor inaccuracies can lead to compliance failures or customer confusion.

Cultural and linguistic accuracy is another concern. AI models (even when prompted well) can struggle with understanding the cultural nuance required to render a precise translation, especially with content is meant to influence at an emotional level. Japanese keigo (a type of honorific language) and Arabic politeness require careful handling that AI doesn’t always manage well. AI also struggles with underrepresented languages, where limited training data leads to lower accuracy and reliability.

2. Security risks

Not all AI translation tools are designed with security in mind. Some store, process, or train on data externally, creating potential privacy concerns. For example, many businesses routinely translate confidential legal, financial, and medical documents, which can introduce serious risks if that data becomes public.

For companies in regulated industries, data protection is essential. If AI translation tools lack built-in safeguards, businesses may unintentionally expose proprietary information, customer records, or legally sensitive content. This can result in compliance violations, reputational damage, and loss of trust. To avoid these risks, AI workflows should be evaluated carefully to make sure they meet the strict privacy requirements of GDPR, HIPAA, and other industry regulations.

Businesses should always confirm whether AI tools train on proprietary or sensitive data without consent, which could introduce security and compliance concerns.

3. Failure to represent brand voice and unique messaging

AI-generated translations follow patterns, making them inherently repetitive. What AI translations often don’t consider is intent, creativity, or emotion…unless a skilled prompt engineer or custom models are involved. When used for marketing and brand communications, this can result in messaging that feels generic or out of sync with a company’s identity.

Industries that rely on strong end-user connections, such as ecommerce and retail, gaming, travel and hospitality, entertainment and media, and SaaS, need messaging that reflects their tone and personality. Machine translation can assist, but human oversight is needed to capture each brand’s distinct voice.

4. Harmful bias and stereotyping

AI learns from existing data, which means it also absorbs biases. Bias and gendered language are persistent issues in AI, especially in languages that require gender agreement in translation. A model that defaults to male pronouns in English will reinforce that bias in Spanish, French, or German, where every noun or adjective must align with a gender.

Unchecked bias in multilingual AI outputs can compound over time. If AI outputs are post-edited without proper oversight, biases may go unnoticed and be reused in future translations. Without active monitoring, businesses risk publishing content that conflicts with their inclusivity goals or offends users.

5. Process risks: solo AI tasks can undermine an effective localization strategy

Many companies turn to AI to cut costs or speed up workflows, but without a clear plan, automating tasks out of the context of a strategic plan can easily undermine your goals. For example, AI alone cannot solve the challenges of adapting content for global customers. It requires continuous training, human oversight, and thoughtful integration to be effective.

Businesses that focus only on short-term efficiency often spend more time fixing AI-generated translations. Without a strong strategy, AI becomes another layer of work rather than a solution.

6. Disruption of current localization workflows

AI does not always integrate smoothly into localization processes. When businesses implement AI without checking compatibility, it can create more challenges than efficiencies.

Some AI models don’t integrate well with content management systems, leading to workarounds that actually slow down production. Poorly trained AI models output content that can increase post-editing workloads, canceling out expected cost and time savings. Instead of improving efficiency, AI can leave linguists and their teams spending more time fixing errors than refining content for global customers.

Implementing AI will necessarily cause you to take a pause and look at your entire tool set. Recognize that this will be a big effort, requiring you to possibly change more than one tool.

Acclaro’s Approach to Smart AI: Smart Integration, Real Impact

Businesses are looking to AI as a valuable tool for market expansion. But only when paired with the right strategy does an implementation translate into impact. Acclaro integrates AI only where it truly adds value, combining it with human expertise to translate more, create better AI models, validate multilingual models, and create high-impact global multimedia content.

We’re also researching and experimenting with other ways AI can be used, including translation memory harmonization, automated post-editing, localization testing, and multilingual data services.

Unlike other LSPs, we do not and will not offer an all-in-one AI tool. Instead, Acclaro’s AI MicroServices are modular, specialized solutions that handle specific tasks within a larger tech ecosystem. These MicroServices can either be deployed individually or layered for greater impact and they integrate with other technologies to enhance specific processes without unnecessary complexity.

AI should solve problems, not create new ones. That’s why Acclaro only uses AI where it makes sense and human expertise where it matters. We don’t implement AI for AI’s sake. Instead, our experts can help you understand your options and avoid unnecessary risks.

AI will work to grow your business across markets only if the right strategy is in place. With thoughtful integration, you can grow your reach, streamline workflows, and scale with confidence, without losing what makes your brand unique.

Ready to learn how AI can support your global expansion? Contact us today!