A big part of the customer-centric experience is related to the language, culture, the localized price per market, and payment methods
A company that really wants to reach a diverse audience has a potent weapon through hyper localization.
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AI will not eliminate (initially) localization roles, but it is reducing the time spent on certain tasks. What once took hours can now take minutes. That creates capacity.We can treat that time as a cost savings or reinvest it. If nothing meaningful replaces it, the value of the role will eventually be called into question.Jobs do not disappear because tasks are automated. They disappear when the value is not redefined.
So the real question is: what can you do now with the time AI gives you that wasn't possible before?
The world of localization is full of small, hidden details.
Some things are deeper than they seem, and I often see between in-context review and LQA in the world of Localization. They might seem the same, but if we scratch beneath the surface, we'll see they're not what they seem.
In this post, I want to focus on explaining the differences between in-context review and LQA, which is something I see being confused quite frequently, and although the tasks are similar ... they are not the same.
AI is not eliminating localization. But it is removing the illusion that execution alone is enough.
Layer 1 accuracy, delivery, quality was our playfield. Now AI scales it faster and cheaper. And when value is framed only around execution, the conversation shifts to cost and headcount.
Meanwhile, executives focus on retention and growth.
That’s Layer 2 cultural impact.
In the age of AI, localization must operate in both.
You need a solid localization tech stack before you can build a global digital product. Tools that help manage content, automate workflows, ensure consistency, handle volume, control quality, and scale across languages are essential. Without them, everything becomes slower, more expensive, and harder to manage. Today, we have great tools to support all of this. And yet, despite all these changes, something fundamental hasn’t changed in the ingredients that define good localization.
Outside the circles of localization and globalization, translation is still seen as the step to go global. As if adapting the language automatically creates a global product. As if users in new markets will suddenly feel at home just because the words are no longer in English. In reality, that’s rarely how it works. Users don’t experience products in pieces. They experience prices, payments, support, content, and expectations all at once. Adapting the language is an important start. Still, users experience the product as a whole. If only the words change, they will naturally notice the parts that didn’t.
For a long time, localization was treated as a pure execution task: translate fast, deliver on time, and stay invisible. That model worked when content volumes were lower and speed was the main challenge. As AI becomes part of everyday workflows, this approach is no longer enough. Translation itself is not the hardest part anymore. The real challenge is deciding what content deserves attention and how AI fits into the broader content ecosystem. This shift highlights a deeper change: moving from simply translating content to actively managing it.
Localization professionals often focus on translation quality and best practices, but decision-makers care about customer impact and revenue. If we frame localization as a cost, it risks being deprioritized. Instead, we must highlight its value driving engagement, trust, and business growth.
At a New Year’s Eve dinner with my family, I saw a familiar situation play out: someone speaking with strong confidence about something they only partly understood. In Spain, we call this el efecto cuñado. What I didn’t realize for a long time is that this behavior has a name in psychology too. It’s the Dunning–Kruger effect, and it shows up just as often in localization and AI conversations as it does at Christmas dinner tables.
This feels like a pivotal moment. Localization teams are being asked to support more markets, move faster, use AI responsibly, and show impact, not just output. Expectations are higher than ever, but many teams are still trained mainly for execution. We are strong at delivering localization work, yet we often struggle to move from output to outcome and to clearly explain the impact of what we do.
Defining localization metrics is relatively easy. In many cases, a team can write a reasonable list during a workshop, like the ones I mentioned above, or during a strategy session. The conceptual part of what to track and why rarely takes long. The real difficulty appears later: HOW you actually obtain those metrics.