Is your enterprise ready for AI in your localization program? 9 questions to ask

AI promises faster localization and fewer manual bottlenecks, and that’s exactly what enterprise teams need when demand keeps growing but budgets and bandwidth don’t. So, it’s no wonder you’re eager to put it to work.

But is your business really ready to implement AI in your localization program?

We ask because we’ve seen it go sideways if you’re not: when AI is rolled out prematurely, it may make things worse instead of better. Translation quality can become unpredictable. Review teams might spend more time fixing errors than progressing on projects. And product launches can get delayed while teams try to troubleshoot new tools and processes.

If all of that sounds like a risk you can’t afford, it’s time for an AI readiness check. Here, we share the 9 key questions to ask before you consider adding AI tools to your localization arsenal.

1. What’s your “why”?

Before you invest in AI tools or run a pilot, you need to know what you’re actually trying to fix.

Vague ambitions like “do more with less” sound good in a slide deck, but they don’t lead to effective implementation. If you can’t clearly articulate the problem you’re trying to solve, AI won’t solve it for you. You need clear use cases.

The first and most common use case for most businesses is AI translation and others include processes like terminology extraction, content prep, and automated QA checks.

But it’s best to start small by naming one or two specific problems you want to solve. For example, are you localizing more content than your current team can handle? Are you under pressure to speed up delivery or lower costs? Is inconsistent quality dragging down your brand experience in key markets?

Your goals will shape every decision that follows, from which tools you use to which metrics you track.

First, get specific. Only then can you get strategic.

2. Have you documented your current workflows?

Before AI can improve your localization process, you need to know in detail what that process actually is. Many teams operate with undocumented workflows, outdated assumptions, or processes that vary from team to team. If you don’t fully understand your current state, you won’t know where AI can help, or where it might cause problems.

Start by identifying your key content types and tracing what happens to them from intake to delivery. Ask yourself:

Where do delays happen?
Where are people doing repetitive, manual work that could be automated or accelerated?
What tools are you already using and where in the process?

This doesn’t need to be an enterprise-wide audit. Even a simple visual map of your most common workflows can reveal patterns, inefficiencies, and quick wins. And it sets a baseline that will help you compare performance once AI is in play.

3. Have you assessed your localization maturity level?

AI implementation depends on more than just tools. It’s shaped by how consistent and scalable your localization workflows already are.

Localization maturity isn’t about headcount or budget. It’s about whether your team can repeat processes, measure results, and integrate new systems without disruption. The more structured your program, the easier it is to apply AI in ways that deliver value quickly.

Here’s how to think about where you stand, and what makes sense at each stage:

Maturity level: Reactive

At this level, you have minimal process documentation and depend on ad hoc, inconsistent workflows.

Here’s how AI can help:

Start with low-risk internal content using MT-only workflows
Use AI to extract terminology and build glossaries or style guides

Maturity level: Developing

Here, you have some workflows defined, but your team isn’t always consistent and there’s room for optimization.

Here’s how to start getting the most out of AI:

Introduce MT + post-editing for quick-turn content
Use AI for source cleanup, segmentation, or quality scoring

Maturity level: Mature

Your team has standardized localization workflows and strong asset management.

You’re ready to use AI for scale and efficiency across more complex workflows by:

Automating content routing and review workflows
Using LLMs for content variants and quality estimation to speed up human review

Your maturity level helps you set realistic expectations and choose the right starting point.

4. Have you benchmarked your current performance?

Once you’ve identified how localization processes work in your business, it’s time to figure out how well it works.

That means capturing real, measurable performance data.

For example:

How long does it take to move content from request to delivery?
How much does it cost per word, per language, or per content type?
How often do reviewers catch issues that should’ve been flagged earlier?

Without this baseline, you won’t be able to tell if AI is improving anything or just making things more complicated.

Don’t wait for perfection. Even rough numbers are better than none. Choose a few metrics that tie directly to your goals and track them consistently. When it’s time to run a pilot, these benchmarks will show you what’s working. They’ll also help you defend future investments.

5. Is your team ready?

AI can’t make a localization program successful all by itself. For that, you need capable and engaged people.

Before you introduce new technology, make sure your team is aligned and equipped to manage the change. That means assigning ownership:

Who’s responsible for evaluating AI output?
Who updates glossaries or handles integration points?
Who sets the bar for translation quality, and who monitors and enforces it?

You’ll also need buy-in from across the organization. Content leads, engineers, localization managers, and procurement all have a stake in how AI affects their work. If even one group isn’t aligned, your rollout can stall midstream.

Two other things to consider related to your team:

Training matters, too. AI translation output isn’t always easy to assess—it may look polished but include subtle issues. Reviewers need clear guidance on when to trust it, when to edit, and when to flag patterns. Project managers also need to know what to escalate and who to involve when quality drops or workflows break down.
Make sure you have a plan for keeping humans in the loop for critical content (whether for post-editing and QA) and workflows (for making decisions only a human can make).

AI can support your team. But first, your team needs to be ready to support AI.

6. Are your other tools in sync?

If your team is already struggling with your current set of tools, AI can become just another ball to juggle. If your current toolset is largely working for you and AI is just about leveling up, then you probably don’t need an expensive rebuild or an unwieldy, one-size-fits-all platform. But you do need to understand and optimize the capabilities of your current tech stack.

Start by checking whether your CMS, TMS, and QA systems can handle basic automation.

Can you route content automatically?
Can your TMS support pre-processing, or surface segments that need human review?
Are files passed smoothly between systems, or does your team still need to manually upload and download?

Even a few targeted improvements, like adding API connections or automating file cleanup, can make AI easier to implement. And if you’re evaluating new platforms, now is the time to make sure they support the AI-enabled workflows you want to build.

7. Do you have content for a pilot?

Piloting an AI implementation for translation lets you compare performance against your current process, make targeted improvements, and avoid the noise of one-off exceptions.

You’ll also learn how well your tools and teams adapt to new workflows without putting your brand voice or timelines on the line.

Look for pilot content, whether text, audio, video, or a UI, that is:

Low risk: If something goes wrong, it won’t impact customer experience or brand perception.
High volume: You need enough content to measure impact and identify patterns.
Structured and predictable: Consistent formatting and tone make it easier for AI to perform well.
Frequently updated: The more cycles you run, the faster you can learn and adjust.
Not customer-facing (yet): Internal content like support articles or onboarding materials is ideal.

Start with content that lets you test and learn without putting deadlines or quality at risk. The goal is to gather useful data and build confidence before expanding to higher-stakes work.

8. Do you have inputs to train an AI translation model?

AI translation is only as good as the input you give it. If you want consistent, high-quality output, you need to feed your tools the right data from the start.

That means preparing foundational resources like translation memory, glossaries, and style guides. These assets teach AI how your brand sounds, which terms to use, and how to handle context that’s specific to your business or industry.

If you don’t have these materials yet, now is the time to create them. And if they’re outdated or incomplete, cleaning them up before launch will save your team hours down the line.

You should also be prepared to review and fine-tune AI output on an ongoing basis. Feedback loops—whether through post-editing, quality estimation, or manual checks—help your tools improve over time.

AI can learn to translate your brand well. But only if you teach it.

9. Do you have the right partner?

Partnering with the right Language Services Provider (LSP) will help you improve what’s already working and fix what isn’t. They can also help you identify where targeted AI solutions can deliver value fast, whether it’s cleaning up your translation memory, automating prep work, or reducing manual review through smarter quality checks.

Note that we specified an LSP partner, not AI technology vendor who just sells software. A tech-savvy LSP will generally outperform a traditional tech vendor that may want to lock you into a complex platform or expensive custom build. LSPs bring more than one tool and experience with tech implementations of that tool; they bring linguistic expertise, content strategy, and real-world operational experience. They can help you assess various choices and apply the latest tech in a way that improves speed and efficiency.

Our modular approach allows you to start with one service, add more over time, and stay focused on what drives impact. And since we’re tech-agnostic, you can keep the tools and systems that are working for you. Our tech consulting team works within your current systems to help you get measurable results without unnecessary disruption.

If you’re considering AI, don’t go it alone. The right support makes all the difference between experimenting and making real progress.

Ready to make AI work for you?

If you’ve worked through these questions and identified a few gaps, that’s a good thing. It means you now know where your focus should be as you move forward.

You don’t have to figure it all out on your own. Whether you’re just getting started or ready to scale, we’re here to help you build a localization program that makes smart use of AI.

To go deeper, download our full guide: Scaling Your Localization Program with AI: A Practical Playbook

It’s packed with real use cases, workflow examples, and tips to help you turn AI from a trend into a working part of your localization strategy.