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Localization as a Product means building solutions that make it easier for others to work with localization by turning our expertise into systems that are scalable and reusable.This shift doesn’t replace the service mindset. It builds on it.
We still support teams but we also design tools, workflows, and automations that reduce repetitive work and increase impact. Instead of translating on request, we enable content flows.
Instead of fixing the same problems again and again, we build systems that prevent them. This is how localization grows from being reactive support to becoming part of how the company builds and scales globally.
Just like the flywheel on a spinning bike, it’s hard to get going at first , there’s resistance. But once you build speed, it gets easier to keep the momentum.
In Globalization, it works the same way. You can enter at any point quality, user feedback, tooling, content…. and that push will start turning the wheel.
Each action builds on the next, and little by little, it helps the company move faster toward market expansion.
Consistently posting about our accomplishments on platforms like LinkedIn can quickly become tiresome for others. At the same time, not giving visibility to what we do or achieve can cause our work to go unnoticed, causing us to miss out on certain professional opportunities to advance our careers further.
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.
What if AI could help us make localization more accessible without losing the human touch?
In this post, I share some ideas on how AI can support the work we already do to make digital products easier to use and understand. From simplifying source content to spotting UI risks and improving inclusive language. Because in the end, I truly believe that together might be better.
Localization isn’t just a side task focused on adapting content. It actually plays a key role in accessibility. This is not only because it makes content available in other languages (that’s the part we’re best known for) but also because what we do, just to name two examples, helps to improve readability and understandability
We often think that showing up at the right time, explaining localization’s value, and communicating clearly will be enough. And while that helps, it’s not a guarantee.
For busy product teams, localization is often just one more item in a long list of priorities.
When that’s the case, we can either feel frustrated or shift our mindset, protect our mental health, and keep refining how we advocate for localization.
As a Globalization/Localization Manager to be effective and rise to the various challenges our teams encounter we must be fundamentally flexible. We must be able to have the right conversation with the different stakeholders we interact with. We must understand the unique ways in which each conversation should be handled.
To achieve our goals, we need to bring 5 different conversations into the workplace
Before jumping on the AI bandwagon: What localization problem are you trying to solve? AI is everywhere right now, including in localization.
But before jumping on the bandwagon, we need to stop and ask:
Are we solving the right problem?
If we want to treat localization like a product, we need to measure it like one. Traditional metrics like cost per word and delivery time still matter, but they don’t capture the full picture. In this post, I share four categories of product-style metrics: adoption, automation impact, strategic influence, and internal visibility that they can help you track the real value your localization team is creating across the company.