20 Insights on the AI Evolution in the Translation Industry

AI is reshaping translation, but not replacing human expertise. This article highlights the top takeaways from one of Interpro’s most experienced localization professionals, Maria Fagrelius, after completing an AI and translation education boot camp. Having worked in the translation industry for over 20 years, she’s watched from the sidelines as AI has evolved from a clunky concept to something clients are actively testing and deploying in real projects. This is her perspective, grounded in two decades of hands-on experience of human translation.

Human in the Loop: Today’s AI Translation Process

As someone who’s worked in everything from manual glossary creation to large-scale localization rollouts, I’m watching the shift toward AI evolve in real time.

More companies are exploring and shifting to AI translation workflows. Machine Translation Post-Editing (MTPE) is part of daily conversations. From trying out new tools to rethinking quality assurance, the role of a translation vendor has shifted to be a strategic global growth partner.

The goal isn’t to “automate translation,” but to apply AI where it helps and route human effort where it matters most.

Definition: “AI Evolution in the Translation Industry” or “Human-in-the-Loop Translation” references the shift from traditional human-based workflows to hybrid systems where AI does the initial translation, Quality Estimation predicts where humans should edit, and expert linguists post-edit within privacy, security, and compliance guardrails.

Key Takeaway on AI Translation

From what I’ve seen, there is really only one thing we know about using AI for translation:

Not all content is fit for AI. And even when it is, humans still play a critical role.

One of the most valuable lessons I’ve learned is that not every piece of content is right for machine translation. When the tone is nuanced, the stakes are high, or the subject matter is complex (like creative educational courses or compliance training), human translation is often the better choice.

But when AI is a fit for translating your content, we still have to decide: Where should human effort go? What should a linguist review, and what can we safely trust AI to handle?

The Modern Translation Hybrid Workflow is key to understanding how and where human’s are involved in AI translation. A human will determine if the content is a fit for AI translation, and will also review the translations provided by the AI.

Some tools are great for certain languages. That same tool will tank in another language. (Do you know how hard it is to find good AI translation tools for French Canada?)

There is one solution that bridges the gap between AI translation tools called Machine Translation Post-Editing. This is where a professional human translator with subject matter expertise reviews and edits the translation.

How To Determine What Can be Translated by AI

So how do you determine what to review, and what AI translation can handle?

That’s where Quality Estimation (QE) becomes essential. QE helps us predict where human post-editing is needed and where the MT output is already good enough. That means faster turnaround without sacrificing accuracy.

Every material needs to be evaluated, but in general, we recommend:

• Use AI for high-volume, low-risk, repetitive content and first drafts.
• Use expert humans for high-risk public-facing, legal, safety-critical, brand voice, or culturally sensitive content.
• Language pair & domain drive quality more than hype; pilots beat assumptions.

Translation Quality Estimation is key to understanding if your content is the right fit for AI translations. This ensures high quality translations and reduces turnaround time if your content is a fit.

20 Insights on AI Translation from an Expert

I recently took a professional course on AI translation from The Localization Institute. Here are 20 insights that stuck with me from the course:

1. Quality Estimation (QE) can flag which MT segments don’t need post-editing, saving time and budget.
2. Hybrid post-editing workflows help segment content by risk or complexity.
3. QE can also assess human translations, identifying areas that may need additional review.
4. Translation Memory cleaning helps eliminate “bad” legacy translations from future reuse.
5. Effort estimation models help predict how much human editing an MT output will require.
6. Machine Translation quality isn’t universal; different engines perform better depending on content type.
7. AI can’t replace linguistic nuance in highly regulated industries like healthcare and law.
8. Synthetic data is becoming a safe training method to simulate real-world content without violating privacy.
9. Anonymization must happen before content hits any public or third-party AI engines (especially in healthcare).
10. Be aware of the regulatory bodies that require AI explainability, such as the emerging regulations under Canada’s AI & Data Act.
11. “Limited risk” AI tools (like MT) still require user transparency under the EU AI Act.
12. Real-time translation tools raise new compliance challenges, especially for live interpreting, support, and chat.
13. You should never feed confidential content into public AI engines without safeguards.
14. Domain-specific MT tuning significantly improves output quality.
15. Clean data reduces the need for extensive human post-editing later on.
16. AI output needs linguistic validation, especially when tone, intent, or policy interpretation is at stake.
17. Partnering with certified vendors (HIPAA, ISO, etc.) matters more than ever.
18. The industry is moving toward AI+Human hybrids as a default, not an exception.
19. Ethics must be part of the procurement conversation when adopting new tools.
20. AI governance isn’t just an IT issue; it’s a localization issue, too.

Final Thoughts: Localization is a Hybrid of AI + MTPE

The future of localization isn’t about choosing between humans and machines. It’s about designing systems where they work together. If you’re in the thick of managing multilingual content, I highly recommend reaching out to our team to create a practical translation workflow.

If you want to talk about what I learned or how I’m applying it, let’s connect!

—Maria

FAQs: AI Evolution in the Translation Industry

Is AI ready to replace human translators?

No. AI accelerates drafts and assists consistency, but humans are essential for nuance, risk, and brand voice.

What is MT post-editing (MTPE)?

Human linguists edit MT output to reach publishable quality; see ISO 18587 for process and competence requirements.

How does Quality Estimation (QE) help?

QE predicts segment quality, so editors focus on high-impact fixes, reducing time without sacrificing accuracy.

What compliance issues should we consider?

Confirm data handling and transparency duties (e.g., EU AI Act), regional rules (e.g., AIDA), and sectoral privacy (e.g., HIPAA).

Talk to Interpro about practical AI + translation workflows.

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