Thinking of using AI for translation? Here’s what you actually need to know

Who this is for:

• Localization managers exploring AI translation tools
• Marketing and product teams scaling global content
• Enterprises looking to reduce translation costs and increase speed

What this answers:

• Does AI translation replace human translators?
• How do I use AI in translation without losing quality?
• What are the best use cases for AI translation?
• How can AI speed up go-to-market timelines?
• What is MQM and how do I measure translation quality?

AI and machine learning continue to generate a lot of buzz — and for good reason. Global companies are eager to apply these technologies to long-standing challenges like translation, aiming for faster turnaround times, lower costs, and greater efficiency.

At first glance, it’s easy to assume AI will instantly solve all your translation problems. But the reality is a bit more nuanced. While AI is absolutely transforming translation and delivering major ROI, getting real results takes strategic implementation.

The companies seeing the greatest success with AI in translation aren’t cutting humans out of the process — they’re building the right translation mix. They blend AI, machine translation (MT), and human expertise, tailoring the mix to fit their content, context, and business goals.

Here’s what you need to know to start using AI in translation effectively.

Myth: AI translation means cutting humans out of the process

One of the biggest misconceptions about AI translation is that it fully replaces human translators.

Reality: The most effective strategies leverage AI to free up humans for more strategic and creative work — not to eliminate their role altogether.

Ejemplo: Secret Escapes, the luxury travel platform’s content demands grew, relying solely on human translators became a bottleneck. Working with Smartling, they trained models in their brand voice, built a human-in-the-loop workflow, and automated routine translation tasks. That gave their in-house linguists more time to focus on the kinds of work that really move the needle — like crafting hyper-localized, culturally resonant content and expanding marketing campaigns.

The result: Faster turnaround times, consistently high-quality output, and more bandwidth for strategic initiatives. With AI doing the heavy lifting, their team was able to support 20% more campaigns without increasing freelance costs. 

It’s a powerful example of how, when applied thoughtfully, AI enables human creativity instead of replacing it.

Mistake: Applying a one-size-fits-all translation approach

Not all content requires the same level of quality. Yet many teams fall into the trap of treating every translation task the same, relying on expensive, human-only workflows even for content that’s low stakes or high volume.

Think technical documentation, help center articles, or internal support materials. Clarity matters, but perfection isn’t the goal. This kind of long-tail content is ideal for AI-powered translation, which offers speed and cost savings while still delivering good accuracy.

Ejemplo: Therabody, a wellness-focused technology company, used Smartling to shift the translation of technical content like user manuals and packaging to an AI-powered workflow that paired AI translation with human review to ensure quality. They then used that reviewed output to train custom MT engines tailored to their brand tone and terminology which they could use to translate all of their longtail content.

The result: A 60% drop in translation costs, improved consistency, and a 99.7% on-time delivery rate.

Optimizing your translation mix means matching the right method to the right content. High-visibility marketing copy may demand human nuance. But product specs or FAQs? That’s where AI can shine.

Here’s a simple breakdown to help you match your content type to the ideal translation method:

Missed opportunity: Faster go-to-market

Why it matters: In global markets, launch delays = lost revenue.

Speed therefore isn’t just a convenience — it’s a competitive edge.

That’s why more companies are turning to AI-powered workflows to accelerate their time to market. Tasks that used to take weeks — preparing files for translation, the translation itself, review cycles, localization QA — can now be completed in days or sometimes even hours.

Example: Take Gemini, a crypto exchange in a fast-paced industry with complex terminology. As they expanded, their traditional translation process couldn’t keep pace. After integrating Smartling’s AIHT and content platform integrations, they cut turnaround times in half while still handling crypto-specific terminology with care.

Concern: Can AI translation match human quality?

Short answer: Yes, with the right setup.

Let’s be honest: AI translation hasn’t always had the best reputation when it comes to quality. From awkward phrasing to outright hallucinations, early models left plenty to be desired. But that’s changing quickly, especially for common languages, where AI is now approaching human parity. Advances in model fine-tuning, prompt optimization, and semantic quality checks have dramatically improved accuracy and consistency.

Example: After training a model on its brand tone and content style, Secret Escapes reached the point where many translations needed little to no editing. Their Italian model, in particular, reached a quality “very close to human parity,” showcasing how tailored AI can deliver on both quality and brand voice.

Example: a Fortune 500 technology company managing over 50 million words of annual translation through purely human workflows found this approach increasingly impractical. By adopting AIHT, they saved $3.4 million in the first year, delivered translations 50% faster, and maintained an average Multidimensional Quality Metrics (MQM) score above 99.

Today, AI translation is no longer a gamble when it comes to quality. With the right models, human oversight, and training, it’s a dependable and powerful asset in any localization strategy.

What is MQM?

Multidimensional Quality Metrics (MQM) is a framework for evaluating translation quality. MQM helps identify errors, assess their severity, and ensure that both human and machine translations meet your standards.

It scores for:

• Exactitud
• Fluidez
• Consistencia
• Terminología
• Style

Why it matters: A high MQM score (close to 100) signals quality you can trust, from both humans and machines.

A score close to 100% means your translation is high quality. In other words, you don’t have to guess if your AI is working — you can measure it.

Discover the right mix of AI and human expertise with Smartling

AI won’t solve every translation challenge. And it doesn’t replace human judgment or nuance. But when used strategically, as part of a flexible and well-optimized translation mix, it can feel pretty close to magic — and unlock results that feel transformative.

The companies succeeding with AI aren’t trying to automate everything. They’re applying it with purpose: aligning the right tools to the right content, freeing up human translators for high-impact work, and building workflows that scale with their business. 

They know AI isn’t a shortcut — it’s a smarter path to more content, more languages, higher quality, and lower cost.

Wondering where to start? Our latest ebook, Navigating the Shift: Why, when, and how to adopt AI, gives you all the details about how to approach this change.