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The Localization Manager’s AI Cheat Sheet


1 min read

Written by


Argos Multilingual

Published on

31 May 2024

You may be feeling the pressure from higher up to figure out how to make AI work in your localization program. Or you’ve decided that your team needs to be proactive about AI and get busy with hands-on experimentation. There are many reasons for wanting to learn about and start testing AI. 

Regardless of how you got here, you need to do your due diligence about large language models (LLMs) and generative AI — if only to determine that they may not be a good fit for the current iteration of your localization setup, which may be one of the potential outcomes. But where do you start?

You will want to know where you are on your AI trajectory and what you need to do to reach a point where AI-powered solutions become an integral part of the process. You want to be prepared to ask the right questions. 

We prepared a cheat sheet of crucial questions for you to answer to start being AI-active.


It's about asking the right questions at the right time!
Gauge where you are on your Al trajectory and what you must do to integrate
Al solutions into your localization process.

Gauge where you are
1. Are you using AI?
2. Does your boss want you to use AI?
3. Are you planning to use AI in the future?
4. What are your current priorities?
5. Where do you want to push your program in the next 12-18 months?
6. Which parts of the process today are too costly or time-intensive?
7. Who drives AI internally?
8. Has the localization team been part of the internal AI debate?
9. Does the company have guidelines for responsible and safe use of AI?
10. Are there any limits to the use of AI?
11. Do you have someone on the team who is proficient with AI?
12. Can you hire someone to deal with AI?

Determine what you need to do
1. Identify short-term priorities while you plan your long-term strategy
2. Learn as much as you can about AI
3. Upskill yourself and your team
4. Use AI for the low-hanging fruit — the time- or labor-intensive tasks few fancy doing
5. Focus on getting the reps: the experimentation is more important than the results
6. Gather data: about localization, about AI, about AI in localization.
7. With data in hand, make your case and decide on pursuing AI… or not

Talk to us

At Argos, we understand the complexity of integrating a new piece of transformative technology into the process — never mind something as transformative (and daunting) as AI. We’ve been researching and helping our clients navigate their transition into the AI-augmented future. 

Get in touch today!

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