What is AI translation? A localization manager's guide to reliability, security, and evaluation

You're running a localization program with machine translation (MT), some flavor of AI translation, and professional linguists for anything brand-critical. That mix has probably been in place for a while, but the technology behind LLM-based translation is continually evolving.

Localization teams that keep routing content the way they always have are either over-investing in human review for work that no longer needs it or leaning on AI translation for content it was never built to handle well.

This guide breaks down what AI translation actually is, how it's different from machine translation, and where AI Human Translation (AIHT) and professional translation still fit today. By the end, you'll have a clear way to decide which tool handles which content, instead of running on assumptions that were true when you first built your workflow.

What is AI translation, and how is it different from machine translation?

MT and AI translation are related but separate: machine translation is a dedicated engine that converts source text into a target language, using rule-based, statistical, or neural methods depending on the engine.

AI translation is a distinct workflow that uses large language models to translate content directly, and it's typically paired with AI-driven quality estimation, a tool that scores each translated segment automatically to flag which ones need a closer look, rather than relying on a manual sample.

The 2017 introduction of the Transformer Architecture, and the attention mechanisms it's built on, gave translation models a way to weigh context across an entire sentence instead of translating word-by-word, the breakthrough behind the multilingual machine translation engines still in wide use today. AI translation has moved past that foundation since.

General-purpose LLMs, the same models used for chat or coding, can now translate directly. But a foundation model with no translation-specific tuning will still mishandle brand terminology and tone across a long document.

AI translation increasingly routes work through models adapted for the job: fine-tuned on a company's translation memory and glossary, or paired with retrieval-augmented prompts that pull in brand context at translation time.

Smartling's AI Hub works this way, giving teams access to more than 20 LLMs and MT engines, along with retrieval-augmented generation (RAG) prompts that reference a customer's own glossary and translation memory (TM) rather than translating cold.

Quality estimation runs underneath this, scoring every segment automatically so review time goes to the strings that actually need a linguist. That combination, model fit plus automated scoring, is why AI translation covers more ground today than machine translation did a few years ago.

Is AI translation reliable enough to trust?

Reliability depends on the technique and the content's risk level, not on AI translation as a category.

How AI translation differs from human translation

• Speed: AI translation delivers results in seconds to minutes, not the multi-day timelines of human or professional translation. Smartling Translate applies fit-for-purpose LLMs to produce secure, on-brand translations in seconds, a safer alternative to free consumer machine translation (MT) tools like Google Translate or DeepL.
  

AIHT, which pairs AI output with human review, cuts that timeline down substantially. Gemini's localization team saw AIHT complete in one day what traditional human translation took two days to finish. Fully human translation takes the longest, since every word passes through a translator with no first-pass draft to work from.

• Cost: When comparing AI translation to human and machine translation, MT is generally cheapest, professional translation is priciest, and AI translation and AIHT sit in between. The right tier is the one that matches the content's purpose, not the lowest price tag.
• Scalability: Human translation scales linearly: doubling your content roughly doubles translator hours and timeline.

A model's cost to translate one more sentence is close to zero, so raw AI translation scales far more efficiently than adding translators. But scaling a program depends on TM and glossary keeping that output on-brand as volume grows. Without them, more volume means more errors, and errors means more human review, which erodes the efficiency gain.

• Quality: A structured framework like Multidimensional Quality Metrics (MQM) measures quality by scoring translations by error type and severity, rather than a single pass/fail judgment. What differs is how quality gets built in: through pre-editing, where source content, glossaries, and style guides are prepared before translation happens, and post-editing, where a linguist reviews AI output afterward. Automated instant quality scoring, the kind LQA Agent performs, adds a layer that predicts which segments need that human review, so review time goes where it's actually needed rather than across every string equally.

None of that replaces human judgment about where review belongs. As Andrew Batwash, Smartling's Associate Director of Language Services, put it:

"You can design AI systems to have effective and meaningful human intervention. What we want to do as a service provider in the language services space is really think about where the human comes into the human-in-the-loop system. How do they add value? And is it in the right place at the right time to generate the output that the customer is looking for?"

The goal isn't a fully automatic workflow. It's putting people where they add the most value, which tends to make the whole process more productive rather than less.

For the full decision framework on when AI translation alone is sufficient versus when human review is required, see AI vs. human translation.

How do you evaluate an AI translation solution?

Supported languages: Look past the total language count to quality per language pair. A platform that claims 100+ languages but treats them unevenly isn't more capable than one that supports fewer languages consistently well.

Customization: The system should be trainable on your brand terminology and translation memory, not just generically fluent. This is the kind of customization that sits underneath the Smartling LanguageAI Platform, Smartling's umbrella platform for applying brand-specific language data across AI translation, quality tooling, and workflow automation.

Integrations: Translation only helps if it connects to where your content already lives: your CMS, your code repositories, your design tools. This is the job of Translation Workflow Management, routing content automatically between the systems your teams already use and the translation step itself.

Security and governance: Security and governance are critical elements of a high-functioning AI translation solution. Without them, your organization could face issues like data breach or legal action.

What should you look for when choosing an AI translation tool?

Look for four elements together, not in isolation:

1. Consistent quality per language pair (not just a language count)
2. The ability to train the system on your own terminology and translation memory
3. Native integrations with your existing content systems
4. Formally audited security and AI governance certifications

Is AI translation secure enough for business use?

Yes, when the platform provides LLM provider controls, defined human-review checkpoints, and documented, audited certifications. That security isn't a given by default, and it isn't true of every vendor, so it's worth knowing what to actually check for.

Start with the model layer. Enterprise teams need the ability to choose and control which of 20-plus LLMs and machine translation engines process their content, rather than being locked into whichever single model a vendor has decided for them. The Smartling AI Hub provides that control. Different LLMs and engines carry different data-handling and hosting profiles, and a content-sensitive team needs to be able to route around the ones that don't fit.

In addition to capabilities, pay attention to security certifications. Smartling holds ISO/IEC 42001:2023, described as the world's first international standard for AI management systems, alongside ISO 27001, SOC 2, HIPAA, HITRUST e1, and PCI Level 1. The AI Localization Hub goes further — Smartling’s ISO 42001 certification was awarded with zero nonconformities and covers the full platform with no exclusions.

Marriott International's scale-up shows the outcome: a 5x increase in supported languages and a 40% reduction in translation costs, achieved without the security concerns that keep many enterprise teams on the sidelines.

Where a platform fits

Everything above applies regardless of which tools you use: matching each type of content to the right translation mode, evaluating vendors, and setting security requirements that hold up under scrutiny.

In practice, though, the hardest part of running a localization program is often the handoffs, not any single decision. If your TMS, your machine translation engine, your quality checks, and your language service provider (LSP) are all separate, every piece of content passes through multiple systems, contracts, and owners before it ships.

Each handoff adds cost, delays, and another place where context or terminology can get lost. That’s why it pays to look for a solution that covers the technology and the people in one place, so you can route each piece of content to the right mix of AI and human translation without rebuilding the workflow every time.

Smartling was built for that. It combines a translation management system, AI Hub, workflow automation, quality tooling, and Smartling’s own professional linguists in a single platform, so you don’t have to manage a separate LSP alongside your software. Teams get one workflow, one source of truth for terminology and quality, and one partner accountable for the result.

Treat AI translation as a spectrum, not a single yes-or-no

AI translation covers a wide range of techniques, from raw machine output to AI-assisted human review to fully human translation for high-stakes content. Most bad decisions happen when teams pick one approach and apply it to everything.

The better approach is to sort your content by what’s at stake if a translation goes wrong. Legal, HR, and compliance content carries regulatory and contractual risk, so it needs expert human translation.

Brand-critical content like campaigns and product launches depends on tone and nuance, so it benefits from AI translation refined by linguists who know your voice.

Technical documentation needs precise terminology more than polished prose, so AI translation backed by a strong glossary and quality checks usually does the job. High-volume, low-risk content like support articles or user reviews can often go straight through machine translation.

Once you’ve mapped each content type to a tier, you’re no longer debating AI translation in the abstract. You’re applying the right level of quality, speed, and cost to each piece.

Watch our five-minute demo to see how the Smartling LanguageAI Platform helps your team scale translation into new markets without losing quality or control.