2025-11-28

The Real Metrics of AI Translation: Data-Driven Clarity on Automation

How can you be sure that using AI for translations is reliable, and not just introducing more risks? We take a look at the key metrics and use-cases that show the most effective ways to implement AI translation.
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Is AI Translation Reliable Enough for Enterprise Content?

The short answer is: Yes, but only if you govern it.

When it comes to multilingual content, AI can ease up the task. But relying on AI alone is a strategic risk; you must use it as a co-pilot, not an autopilot. More than yes or no to fluency and reliability, the question is: how to separate scalable automation from unacceptable critical risk?

Let's be honest, risk is everywhere, and even humans make mistakes.  Like when HSBC’s "Assume nothing" was mistranslated, causing a $10 million problem. That's why we have to quantify AI risk before scaling any translation workflow. This article uses real performance data from thousands of evaluated segments to show:

  • where AI truly excels,
  • where it still struggles,
  • and how to make confident decisions using quality metrics rather than guesswork.

In this deep dive, you’ll learn:

  • Why terminology and accuracy, not style, are the danger zones in AI Translation.
  • How AI translation performance shifts based on the content domain.
  • How to build a data-driven governance model for AI translation.

How We Measure AI Translation Performance?

To remove the guesswork from AI adoption, we focused on gathering real-world data and measuring performance. That was exactly our goal 12 months ago when we launched a pilot with Lia: analyze AI translation quality at scale and quantify the impact of automation.

Throughout this pilot, we processed over 4.5 million words across 125 language pairs in real-world enterprise environments spanning finance, life sciences, technology, and luxury. This data gives us hard evidence to show where AI delivers ROI and where governance must step in.

AI Translation Performance: Effort and Quality

The data confirms it: AI quality is highly variable by language pair. Efficiency gains are greatest in high-resource languages, while structurally different or low-resource languages still require significant human support.

 

Language Pair Quality Score Post-Editing Effort AI Retraining
English > Portuguese ⭐⭐⭐⭐⭐ Low Not required
English > Swedish ⭐⭐⭐⭐ Low Not required
English > Chinese ⭐⭐⭐ High Recommended
English > Korean ⭐⭐ Very High Recommended

What the data tells us:

  • Most European languages require minimal human editing (strong AI output).
  • Asian languages require high to very high human intervention, confirming that AI struggles with structurally different and low-resource language pairs.  

Where AI Translation Introduces the Highest Risk: Terminology & Accuracy

Our analysis also confirms a common misconception: AI is usually fluent, but often unreliable. The most frequent “light” issues include:

  • Style - 31% of all errors
  • Fluency - 28% of all errors

These errors may be noticeable, but they rarely cause compliance or brand damage.

The real risk lies in terminology and accuracy, which represent the majority of major and critical errors. This is where AI poses the greatest risk:

  • Terminology (44%). This includes mistranslated product names, technical terms, or legal wording.
  • Accuracy (38%). This involves distorted meaning, omitting important details, or adding unintended information compared to the source. 
Error category
Major and critical
Minor and neutral
Terminology
44%
23%
Accuracy
38%
23%
Style
8%
31%
Fluency
12%
28%
Conclusion: Raw AI output is not safe for mission-critical or regulated content without human validation.

“The most serious risks in AI translation are not style or fluency, but terminology and accuracy. These major issues impact meaning, compliance and brand trust”

Petra Angeli portrait


Petra Angeli
Head of Global Solutions, Acolad

Content Domain Matters: Technical vs. Creative

AI's performance is not uniform. Beyond terminology and accuracy risks, it heavily depends on the domain of the source content. We pinpointed exactly which content is ready for high automation and which demands human expertise. The following table shows which translation method is best suited for each content domain by mapping AI effectiveness, expected post-editing effort, and the primary risks to watch for.

  AI Translation with automated optimization Hybrid AI Translation + Human Review Hybrid or Human Transcreation for brand tone AI + Domain Expert Linguist
Content Domain Technical IT & Software Creative (Luxury) Legal & Finance
AI Suitability Score ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐ ⭐⭐⭐⭐
Post-Editing Effort Minimal Low High Low
High-Risk Areas No significant Accuracy Terminology, Accuracy, Style Accuracy (Compliance)

What this means for your content:

  • Technical & Structured Content – Ideal for high automation.
  • IT & Software – Automate the bulk, human-check the details.
  • Creative and Brand-Heavy Content – Human expertise is essential, if not fully mandatory. AI struggles with tone, nuance, and emotional intent.
  • Regulated Content (Legal, Finance) – Zero tolerance for errors; requires domain-specific human review. 

 

AI Is Your Co-Pilot, Not Autopilot

The most successful enterprises treat AI as a co-pilot, not an autopilot. AI accelerates production, but humans safeguard meaning, compliance, and trust. Our pilot validates the efficiency of this hybrid model:

  • 10-20% effort reduction vs. Neural Machine Translation (NMT)
  • 40-50% effort reduction vs. Human Translation.

Efficiency is real, but only when paired with the right guardrails. Your AI translation platform should offer:

  • Full visibility and control at every stage.
  • Data and terminology alignment for accuracy.
  • Enterprise-grade security (private AI models, ISO-certified, and GDPR-compliant).

By embracing this co-pilot approach, you eliminate the chaos of uncontrolled AI while maximizing speed and scale.

Key Takeaways for Your AI Translation Strategy

The path to scaling multilingual content isn't full automation, but intelligent, data-driven governance. Our analysis proves that:

  • Data Leads: Only measured performance can show AI quality and how to allocate resources effectively.
  • Human-in-the-loop True Value: The primary value of human expertise is protecting multilingual content against high-risk errors (terminology and accuracy), not fixing basic style or grammar.
  • Content Purpose Is Key: AI excels at technical content but requires significant human intervention for creative and regulated domains.
  • AI + Human Expertise: Leverage an AI platform that shifts your linguists' focus to high-value validation of nuance and accuracy. 
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Ready to Adopt AI Translation with Proven Quality Performance?

Get in Touch
Frequently Asked Questions

New to AI translation? We have answers

How can you ensure quality and consistency in AI translation?

Quality and consistency in AI translation are achieved by moving beyond generic machine translation models and actively injecting corporate linguistic assets as context. This process effectively customizes the AI's output, ensuring it aligns with your specific brand, domain, terminology, and translation memories (TM). 

What content should not be translated purely by AI?

Content that requires a high degree of subjective nuance, persuasive tone, or legal/regulatory precision should not be translated purely by AI. Our data shows that luxury, creative, legal, and finance content carries the highest risk due to the potential for major errors in accuracy and terminology. 

What is the difference between NMT and LLM translation?

Neural Machine Translation (NMT) uses trained models that are fast and tailored for translation, often resulting in predictable quality. Large Language Models (LLMs) are generative, context-aware, and fluent, but can sometimes produce inconsistent or unpredictable results, requiring more human control.

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