How LocServe helps clients introduce automation without losing control of quality

AI may feel like a sudden disruption in localization, but the industry has been here before. Machine Translation introduced a similar shift over the last few decades — promising speed, raising quality concerns, but only actually being effective when combined with terminology control and QA. Many of those same concerns still remain as we move on to AI solutions, but unlike MT, AI will not just have an impact on translation workflows and the way translators work, but it will have an impact on all aspects of the localization project, not least engineering and solutions specialists.

Just like for MT, organizations are now asking the same questions for AI:

• Can we translate faster?

• Can we reduce cost at scale?

• Can AI improve turnaround without introducing quality risk?

• How do we adopt MT and AI in a way that holds up over years of releases?

What we’ve learnt from integrating MT, is that AI can deliver real productivity gains — but only when integrated with the right workflow controls.

At LocServe, the focus is not on “adding AI,” but on building manageable solutions inside enterprise localization systems.

Below are ten proven ways MT and AI can assist — using real tools and platforms clients rely on today.

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1. Apply MT Selectively at Content Level

Not all content is equally suitable for MT and the decision to integrate MT into the workflow is based on the risk level associated with the type of MT, for example:

• UI strings → MT + review

• Support articles → MT + post-edit

• Medical/regulatory → human-only

• Marketing → transcreation

By understanding the risks and decision making when it comes to MT, we can apply much of the same logic to the implementation of AI. In other words, if MT is not suitable for a domain or hasn’t been as successful as originally predicted for a project, then the implementation of AI should be heavily scrutinised. The speed at which AI can translate a whole document does not negate the fact that the same algorithm has been used for years my MT engines, but with more freedom and creativity – not necessarily as positive!

LocServe helps clients define MT and AI eligibility upfront so automation is controlled, not blanket-applied.

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2. Use Enterprise-Controlled MT Instead of Ad-Hoc Translation

Successful MT adoption requires consistency, auditability and doman specific access.

Rather than translators using uncontrolled public tools, clients integrate approved engines via:

• Trados MT Provider Framework

• memoQ MT plugins

• Phrase Language AI

• Smartling Neural MT Hub

• XTM MT connectors

Common engines include DeepL Pro, Microsoft Custom Translator, Amazon Translate, and Google AutoML. These engines should not be used freely and without protocols. The checks and balances provided by a TMS and integrated local TMs and Term Bases are the differentiators when looking for quality content and optimized workflows.

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3. Enforce Terminology Automatically

Terminology drift is one of the biggest long-term risks of AI output. Even when providing precise guidelines and specific terminology databases, AI can start to form new terms and introduce them without following traditional approval processes. It is a common fault with AI and results will always be produced, even if those results would not pass a terminology check.

Real-world enforcement relies on integration with TMS for automated checks against Term Bases:

• Trados MultiTerm + QA Checker

• memoQ Term Bases + QA

• XTM terminology validation

• Phrase term restrictions

• Verifika / Xbench for external QA

LocServe workflows ensure AI output conforms to approved client and product terminology, not generic MT or AI phrasing.

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4. Protect Technical Integrity With Structural QA

Experience with MT and subsequently AL demonstrates that the more technical a file, the less likely the technical integrity of the file is maintained during automated translation. These technical attributes include:

• placeholders

• tags

• XML structure

• JSON validity

• UI length limits

Safeguards include:

• Trados tag verification

• XTM file-type QA profiles

• Custom automated validation scripts (Python/CI)

• Okapi Framework checks

LocServe frequently builds automated structural QA so AI cannot break production files.

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5. Define Post-Editing Levels With Clear Thresholds

AI works best when paired with clear human responsibility. Negative experiences with MT editing or the vague “MT Review” roles tell us that clear, accountable and quantifiable requirements are essential. How much time to spend editing and how to pay for and charge for this time?

Enterprise workflows can help define:

• Light post-editing (low-risk content)

• Full post-editing (client-facing documentation)

• Human-only translation (regulated material)

These controls are managed through pre-defined workflows in most TMS. For example, in XTM, multi-step linguistic QA means MT output is processed through structured post-editing, independent review, automated checks, and final approval — ensuring AI accelerates delivery without bypassing quality control.

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6. Separate MT-Assisted vs MT-Restricted Workflow Streams

One of the most effective ways to introduce AI safely is not by improving the MT engine, but by improving the workflow architecture.

In enterprise environments there is a risk that automation spreads into content where it should never have been applied.

A well defined localization project will have clear processes for different types of content:

MT-Assisted Content

Used where speed and scale matter most, and where post-editing is sufficient:

• support knowledge bases

• internal documentation

• high-volume update content

• lower regulatory exposure material

MT-Restricted Content

Reserved for content where accuracy, compliance, and liability are the priorities:

• medical or regulated documentation

• legal disclaimers

• safety-critical instructions

• high-visibility customer deliverables

This point becomes more important with AI than it was with traditional MT, because:

• AI tools are easier to apply everywhere

• Users can bypass controls unintentionally

• Output may sound fluent even when incorrect

• Risk is higher in regulated environments

In systems such as XTM, Smartling, Phrase, and Trados Enterprise, the distinctive workflows are implemented through templates, content profiles, and mandatory human-only stages.

The result is that AI can deliver productivity where appropriate, while sensitive content remains protected.

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7. Future-Proof Workflows Against Changing AI Providers

We’ve learnt from the implementation of MT that technology can shift, and we can expect AI vendors and engines to evolve even more rapidly.

LocServe designs workflows where the stable foundation remains:

• TM as the long-term asset

• Terminology as the control layer

• QA as the safeguard

• MT engines as interchangeable components

This is achieved through connector-based MT orchestration inside TMS such as:

• Trados

• Phrase

• Smartling

• XTM

This allows clients to swap MT or AI technologies without redesigning the entire process. Solutions can be implemented if they are not explicitly supported by your current TMS.

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8. Use Integrated Multimedia Platforms Instead of Toolchains

For subtitling and voice-over, integrated platforms outperform bolt-on toolchains.

Cascaded setups often introduce:

• sync issues

• file conversion overhead

• poor subtitle formatting

Dedicated platforms (e.g. OOONA, Matesub, Speechify Studio) provide better end-to-end control.

LocServe supports multimedia automation where it delivers speed without degrading output quality.

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9. Add Continuous QA and Regression Controls Across Releases

AI introduces risk of long-term drift:

• inconsistent terminology over time

• stylistic variation

• release-to-release instability

Best practice includes:

• QA dashboards

• regression comparison checks

• terminology drift detection

LocServe builds repeatable QA reporting into release pipelines, not just per-project checks.

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10. Treat AI as an Accelerator, Not a Replacement

The strongest results come when AI accelerates throughput but does not bypass governance.

Human expertise remains essential for:

• domain accuracy

• final QA

• regulated compliance

• accountability

AI becomes part of the workflow — not the owner of it.

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Conclusion

MT and AI can dramatically improve localization productivity, but only when implemented with:

• eligibility controls

• terminology enforcement

• technical QA

• workflow separation

• long-term release safeguards

LocServe helps clients operationalise AI safely — delivering faster turnaround without sacrificing quality or compliance.