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.

AI isn’t just changing tools, it’s changing expectations

Three years into working with AI in localization, I’ve seen the pressure build: automate faster, scale more, do it now.

But the real challenge isn’t the tech itself. It’s the gap between hype and reality , and what happens when teams are expected to act like everything’s already working.

In this post, I break down five common challenges that keep showing up, and what we can actually do about them.

Words have the power to shape perceptions and influence actions, which is why reframing is such a powerful tool. In localization, we can reframe our role from simply translating to driving alignment across the company. By ensuring content is consistent, culturally relevant, and strategically aligned with business goals, localization professionals play a key role in helping businesses grow globally. This post explores how we create that alignment and why our work is much more than just translation.

Choosing between a single-vendor or multi-vendor localization model is not only about price. The real difference shows up in the day-to-day metrics: turnaround time, quality stability, cost control, and how much coordination your teams need to do. In this post, I walk through the core metrics that tend to improve with a single-vendor setup, and how they can help you understand which model fits your localization strategy right now

Even when localization teams aren’t directly affected by a company reorg, everything around them can change fast sponsors move, teams merge, budgets shift.

That’s when weak governance shows its cracks.

This post explores how strong localization governance keeps programs stable, visible, and credible no matter how often the org chart moves.

Over the years, I’ve noticed that localization buyers, like the emotions in Inside Out, are often driven by one dominant need. Some are in survival mode, juggling too much. Others are just getting started and need guidance. Some care about speed, others about cost, and some are laser-focused on how things feel in-market.

Most localization discussions stop at per-word rates. However, what appears inexpensive on paper can often turn into costly delays, rework, and frustrated teams.

Total Cost of Ownership (TCO) provides a more comprehensive view, one that encompasses engineering fixes, QA cycles, support tickets, and brand impact.

When procurement and leadership focus only on price, localization becomes a cost center. When they consider TCO, it becomes a key driver of quality and efficiency. And this post explains how to approach that

Many teams treat localization vendors as interchangeable compare rates, sign the contract, move on. But every switch comes with invisible costs: lost context, quality dips, and team fatigue. In this latest post, I unpack what really happens behind a vendor change ( and how to prepare for it like a continuity project, not a quick fix)

Localization teams collect tons of data, edit distance, LQA errors, fuzzy matches, deadlines met, but too often, that data doesn’t speak the language of business. This post explains how to change that. By linking each localization metric to one of the classic project management levers, quality, time, or cost, and showing its real-world impact, we can turn linguistic KPIs into insights that leaders understand, value, and act on.

Let’s be honest. Our industry has not done a great job explaining the cost behind a good translation. What does the translation cost? Is it by word, per page, per type of document? We've been opaque for years about this topic. There are many reasons behind the cost of translation. This post will help you understand what you should take into consideration when purchasing a localization job.

Many companies claim to be customer-centric, but too often that promise stops at English. Surveys and focus groups are run in the US or UK and treated as if they represent the entire customer base, leaving most global customers unheard. In this post, I share a practical framework 5 audit questions to help leaders test if their strategy is truly customer-centric or just a slogan.

Many companies claim to be customer-centric, but in practice their listening stops at English. Most research is based on customers in the US or UK and then applied everywhere else. The result is predictable: cultural blind spots, costly rework, and frustrated global customers. In this post, I explore why true global listening begins when localization insights are treated as part of the customer voice