5 Questions to Ask Before Implementing AI in Game Localisation

Most conversations around AI in games ask questions around capability: What can this tool do? Can it make our process faster and cheaper? 

While it can deliver on both, faster and cheaper aren't the same as better.  

Because AI doesn't just scale your output. It scales everything around it – every gap, every assumption, every weak point in how work moves through your pipeline.  

So before you start relying on AI in game localisation, it's worth being clear about where it fits, where it doesn’t, and how to adapt your production process around it.  

Here are 5 questions to help you and your team determine whether AI-augmented localisation is right for your game pipeline – including common pitfalls and how to avoid them.  

1. What Production Problem Are We Trying to Solve?  

Before implementing AI, game studios should define the production problem they need it to solve. AI can offer real advantages to teams facing constraints like tight timelines and even tighter budgets: 

1. It brings speed. Across Side, we’ve seen AI-augmented translation reduce translation time by up to 40% and costs by up to 30%, depending on the content, language pair, and level of human review required. 
2. It brings scale. Because a live game doesn't just translate once. Between patch notes, event copy, seasonal content, and store updates – the stream never stops, and it multiplies with every language you add. 
3. It brings reach. Markets that couldn’t previously justify dedicated headcount become viable, so a game can ship where it previously couldn't and stay current there as new content lands. 

But the tool is only half the picture. Whether you see the benefit depends on how well you integrate it – and whether it’s actually suitable for your game’s needs.

2. Is AI Suitable for Our Game’s Localisation Pipeline?  

AI is suitable when the content type, risk level, review process, and downstream dependencies can support it. It is not suitable by default just because the tool can produce output. 

Innovative tech is disruptive by nature, and while AI introduces new possibilities, it also creates new gaps, exposes old ones, and amplifies their impact. That’s because the systems these tools drop into weren't built with them in mind. 

This is why capability isn't enough. Most AI decisions get made based on capability assumptions: Can it translate this? Can it generate this? The answer is usually yes, so the tool goes in. But capability is about the tool in isolation. The more important question to ask is about the tool in context: How will this tool affect the rest of our localisation production?  

Because suitability isn't a property of the tool – it's a property of the system the tool runs in. While the tools might behave as expected, common failure points sit in application, authority, and governance: how the output gets used, who has authority to sign off, and whether anyone downstream even knows that a machine made the call. 

A capable tool dropped into a pipeline that isn't built to account for it doesn't reduce risk. It scales whatever's already there. 

3. What Could Happen When AI Gets It Wrong? 

When AI gets localisation wrong, errors can spread quickly because the output often looks fluent, consistent, and ready for implementation in-game. Without the proper guardrails in place, an AI-generated error can have a cascading effect on your production.  

Here are two examples of AI scaling risk in game localisation that we’ve seen in practice: 

Case Study 1: A Fluent Error 

A game needed UI text localisation. Standard stuff like menu strings, short labels – the kind of low-risk content that's the easy sell for AI translation. One of the strings used the word "set." 

In English, "set" carries a dozen meanings – but each one lands differently in German: 

The AI picked only one option to translate all instances of “set”. It picked wrong, rendering a term tied to a core mechanic as the wrong sense entirely. 

Nothing about the output flagged the problem. It was fluent, grammatically clean, and internally consistent with everything around it. By every measure the pipeline was set up to check, it passed. The error only surfaced once players were looking at it in-game. 

And because it was consistent, it was everywhere. One interpretation choice, multiplied across every instance of the term, carrying exactly as much confidence when it was wrong as when it was right. 

Case Study 2: Pivot Language Canon Failure 

The second case is structural. 

A Japanese title needed localisation into French, Italian, German, and Spanish. Rather than translate from Japanese into each language directly, the pipeline used English as a pivot – Japanese to English first, then English out to the four European languages. 

The English pivot was AI-generated. The four human translation teams downstream worked from it faithfully and accurately, exactly as they should. 

Once the AI-generated English text became the source, it stopped being a draft and became canon. Every interpretation choice baked into that English – every ambiguity the AI resolved one way instead of another – got inherited by four languages at once, then rendered cleanly into each. The teams did everything correctly. They just translated a flawed source. 

When AI output becomes the thing everyone else trusts, its errors stop being local. They become systemic.

4. Who Owns the Source of Truth?

The source of truth should be owned by a defined stakeholder, not created by default through the structure of the pipeline. 

For instance, in the pivot language case study, the AI-generated English became the source of truth for four teams. Here's the uncomfortable part: nobody decided it should have been. Nobody owned that call. It happened by default, because of how the pipeline was built. 

Start with looking at how your pipeline is built now. Typically, work moves in handoffs – each team owns its piece and passes it on. It’s clean, it’s compartmentalised, and for a long time, that was good enough. 

But AI makes that structure fragile. Handoffs assume the thing being passed is finished and trustworthy. AI decisions made early in the pipeline are often invisible by the time the work reaches the team that would catch them. The choice that caused the problem already happened, and nobody downstream knows to look for it. Where teams don't overlap, assumptions fill the gap. 

Two things make it worse: 

1) Autonomy vs. Accountability 

AI decisions get made early, often by whoever's closest to the tool. The delivery risk from those decisions gets absorbed much later, by whoever owns the final output. Pipeline ownership and quality accountability can sit with entirely different stakeholders. When the people making the calls and the people answering for them aren't the same – and aren't talking – risk collects in the space between them.  

2) Invisible Rules 

A lot of pipelines quietly depend on knowledge that was never documented: a workaround only one person knows, a review step that happens informally, or a production rule that lives in someone’s head instead of the brief. 

That kind of knowledge can hold a pipeline together at a small scale. But once AI enters the workflow, those hidden dependencies become harder to see and easier to repeat. The system keeps moving, and AI scales it faithfully, weakness and all.  

To identify these kinds of gaps, try the evaporation test: remove a person, a decision point, a system, or an assumption, and ask: “What would break?” If the answer is "A lot," that's not a strong pipeline. Strong pipelines are designed around their failure points. Fragile ones just haven't been tested yet. 

Rethinking the System: From Handoffs to Overlap 

Most game pipelines are still built around handoffs: one team finishes, then the next team picks it up. 

That works when the context is clear. But in AI-enabled workflows, decisions happen before the next team ever sees the content, increasing the need for the right people to have the right information before the work travels too far.  

The fix? Greater overlap and understanding between roles. 

When roles overlap, errors get caught earlier – closer to where they're made. For instance: 

• When a Localisation lead understands enough of what LQA is watching for.  

• When stakeholders around a decision share a picture of the task, the goal, the risks, and how a call in one area lands in another. This allows teams to spot risk before it travels, instead of after. Good behaviours scale through the team the same way bad ones do. 

The term for this is shared mental models: enough adjacent-role awareness that no single decision has to survive the whole pipeline unchecked to be safe. 

Overlap is what turns a chain of handoffs into a system that can actually work with AI. It's also how ownership stops being accidental – when roles overlap, someone is always close enough to a decision to own it. 

Strong Localisation and LQA teams build overlap across five competency areas: language & cultural expertise, production & delivery, quality & validation, people skills, and business & player impact. The level of expertise changes by role, but adjacent-role awareness should exist throughout the team. 

5. Do We Have the Right Governance Around AI Decisions?

Strong AI governance defines where AI can be used, where its output needs validation, who approves it, and how downstream teams know when AI has influenced the work. 

Overlap is the guiding principle. A programme is how you run it at scale. 

To ensure proper governance, someone such as a localisation programme manager should have visibility and ownership across the whole pipeline. They decide where AI is allowed to make a call, where its output must be validated, and where it shouldn't be used at all. 

The programme layer sets those lines before production pressure hits. It embeds checkpoints while there's still room to catch things. It keeps the source of truth aligned with what delivery actually expects – so it's a decision someone made, not one that happened by default. 

It also drives mental modelling through the whole structure, so adjacent-role awareness isn't left to whoever happens to have it. All of it makes AI decisions visible instead of buried. 

AI Adoption Should Match the Pipeline 

AI adoption isn't all-or-nothing – it's a decision you can make per project, per content type, per point in the pipeline, matching the level of AI to what the work can carry.  

The teams getting this right are the ones who keep asking not just whether AI can do the job, but whether they can trust it too. 

This is the system Side is built to run. Across localisation, LQA, and localised audio, we provide both programme and production-level management across the entire pipeline, so AI decisions stay visible and delivery risk stays contained all the way to launch. 

Get that right and AI does what it was supposed to do all along: scale in a controlled way – not scale the risk along with it. 

If you're looking for an AI localisation programme and team you can trust at scale, get in touch with our team at Side.