Managing AI translation in-house: 6 challenges organisations often face

If you’ve ever pasted a paragraph into an AI tool and received an instant, natural-sounding translation, it’s easy to see why AI has changed the way organisations think about translation.

The output can appear in seconds. The cost looks dramatically lower. And for teams managing growing volumes of multilingual content, the idea of doing more internally is understandably attractive.

What becomes less obvious during those first experiments is everything that sits around the technology once it starts being used across more content, more languages and more teams.

Over the past couple of years, we’ve had some interesting conversations with clients and organisations that have experimented with managing more translation internally using AI.

Some have made it work for particular types of content. Others have come back to us for support after discovering that the translation itself was only one part of the job.

The problem wasn’t necessarily the AI itself. It was everything needed to make the output consistently reliable: managing terminology, protecting brand voice, checking accuracy, coordinating internal reviews, handling files and formats, and deciding which content could safely be automated and which still needed human expertise. 

Those conversations have revealed a surprisingly consistent pattern. In this article, we’ll look at six challenges that organisations often face when using AI translation in-house, and what can make the process work better.

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The early experiments with AI translation are often deceptively convincing. On the surface, the prose can feel like it reads smoothly, which leads teams to assume the translation is complete and accurate.

One of the risks with current AI output is that your reviewers scan a piece of content, sign it off because it passes the “sniff test” of looking like it’s fluent and flawless, but to your target audience, something is missing. The AI translation has misinterpreted tone, used the wrong technical term, or got the context wrong, so it used the wrong word altogether. 

Unless an internal reviewer is fluent in the target language and knows the specific subject matter, these errors can go completely unnoticed. Over time, this means that the overall quality is diluted.

Generic AI tools don’t always have the wider context of what has been translated previously. Without a centralised language system working behind the scenes, the technology has no memory of what was translated yesterday.

Companies quickly find that key product names, feature descriptions, and industry phrasing vary wildly from one piece of content to the next. Because the tool isn’t tied to an approved terminology glossary or shared language guide, internal teams end up repeatedly correcting the same terminology mistakes across different departments.

The problem is multiplied if you have multiple AI systems in place. This is something we see a lot with our clients: Marketing might be using an AI plugin in Canva, Sales might be using built-in translation in CRM, and product teams might be relying on ChatGPT. Because each embedded tool operates on a different engine with no shared memory, the exact same feature or term gets translated differently across every platform.

This is easily fixed by setting up terminology style guides, approved glossaries, and firm brand guidance rules, but training an AI to use those correctly across all your languages is typically something you need an expert for.

When you bring things in-house, the external translation invoice might shrink, but the work doesn’t disappear. It simply moves inside the business.

Managing the AI tool itself is only a fraction of the job. Before a single sentence is translated, internal teams often find themselves doing the heavy prep work: making locked files editable, extracting text from embedded images, and rewriting complex source text to strip out ambiguities so the AI can process it without tripping up.

Add to that the hours spent prompting tools, cross-checking outputs, resolving conflicting regional feedback, and correcting repeated errors… The commercial reality is that if senior employees spend hours acting as informal pre-editors and translation coordinators, a low-cost tool creates a very expensive internal workflow.

Generic AI engines excel at standard, predictable language, but they lack your company’s specific context. They don’t know your brand personality, the emotional drivers of your target audience, or how you position your products against competitors.

The result is translation that is technically legible, but utterly generic, and quite possibly, forgettable. For marketing copy, blog posts, and customer-facing channels, that loss of distinct brand voice can flatten your appeal in international markets. 

Worse, poorly translated copy can set you off on a wild goose chase, hunting the missing “problem” – is the product fit not right, is your marketing mix not working, are your campaigns ineffective? All the while, the issue might be as simple as poor localisation.

An internal team memo doesn’t carry the same commercial risk as a global ad campaign, a legal contract, or technical product documentation.

When translation is managed informally in-house, organisations rarely have a structured system for matching content risk to the right level of review. High-stakes materials often get published with minimal oversight, exposing the brand to unnecessary reputational and commercial risk.

The first thing we do with clients we support is look at the overall mix of content to be localised, and assess it against a framework (you can try it yourself for free here). That tells us where we can safely use AI (always trained on your brand, industry terminology and tone of voice), and where high-risk content needs more human input.

Translation is rarely just about moving plain text from one document to another. The AI tool itself is only one part of the process; your team still has to handle the heavy lifting of preparing original content and managing file engineering.

Before a single word is translated, teams often spend hours making locked files editable, extracting text embedded in graphics, or simplifying source language so the AI engine can process it accurately.

We see this constantly with formats like PDFs, where dropping a file straight into an AI tool causes immediate issues. Generic engines struggle to parse visual layouts, meaning they can easily skip text in tables, scramble reading orders, or fail entirely to recreate the formatting on the other side.

There is also a major workflow trap around timing: reviewing AI translations in the wrong order. We recently saw this on an eLearning project where raw AI translations were used directly in a module build. Once the modules were complete, regional teams wanted to proofread the copy. Because the text was already locked into the final format, making minor corrections became a manual, complex exercise that added significant time and cost.

If your team is going to review AI translations (which we always recommend for customer-facing or high-stakes content), that review must happen before copy goes into final formats like videos, PDFs, or software modules.

Managing production and file formatting in-house breaks your team’s momentum and introduces unnecessary risk. Specialist partners handle file engineering, source prep, and workflow sequencing, leaving your team free to focus on higher-impact work.

Using a language specialist doesn’t mean stepping away from AI. In many cases, it means getting more value from the technology by putting the right context, controls and expertise around it.

The most effective approach is rarely to use one translation method for everything. Different types of content carry different levels of risk, require different levels of nuance and justify different levels of human involvement.

In practice, that might mean: 

• Managed self-service (e.g. Pronto): Giving internal teams fast, direct AI translation access via our award-winning tool, Pronto, but with pre-configured brand glossaries, context rules, and security controls operating automatically in the background.
• Managed AI workflows (MTAP): Machine translation with automated post-editing to process high-volume, lower-risk content consistently without human intervention on every phrase.
• Hybrid AI with human review (MTPE): Combining AI speed with professional human post-editing to give marketing, blog, and customer-facing materials natural flow and contextual accuracy.
• Full human translation and transcreation: Reserving expert human linguists for high-visibility brand campaigns, creative taglines, and sensitive communications where nuance is everything.

The truth is, the value of working with a language partner has changed as technology has evolved. It used to be about gaining access to translators, simply matching the right specialist to the right project.

Today, it’s much more technical and structural. The real value lies in helping organisations decide where AI fits, how it should be configured and controlled, and where human expertise still adds value. High-calibre linguists remain vital to that equation; they are the experts configuring the tools, training the models, and applying human judgement where context matters most.

If you’re already using AI translation in-house, it can be useful to step back and look at how the wider process is working – from terminology and review to technology, governance and internal workload. In some cases, relatively small changes to the setup can make a significant difference. 

Start with the AI Translation Health Check

Our free AI Translation Health Check is a simple way to take stock of your current approach. It looks at six areas: content suitability, terminology and brand, translation quality, internal review, technology and security, and governance and improvement.

It can help you identify where your current setup is working well, and where there may be opportunities to improve consistency, reduce internal effort or introduce stronger controls.

Through Comtec Advisory, we can work alongside your team to look at how AI translation is currently being managed and where it could work better.

That might include helping you decide which content is suitable for AI, testing different tools against your own content, improving terminology and quality processes, or reviewing your workflows, technology and level of human input.

The aim is to help you find the model that works best for your organisation — whether that means improving what you already manage in-house, bringing in specialist support for certain areas, or combining the two.

And if internal teams are spending more time than expected managing translation, we can also support some or all of the delivery, from controlled self-service through Pronto to managed AI workflows, professional linguistic review, human translation and transcreation.

Prefer to email? Drop us a note at info@comtectranslations.com with a little information about your current setup and we’ll point you towards the most relevant options.

Not sure where to start? Send us a sample

If you’d like a practical second opinion, send us one source document and its AI-generated translation.

We’ll provide some initial feedback on accuracy, terminology consistency, brand alignment, the suitability of the current approach and any obvious opportunities to improve the process.

There’s no charge and no obligation.