Usually, when we want to improve something in our Localization strategy, we first set an ambitious goal. From there, we design a strategy to reach it as quickly as possible. If we don't see rapid changes, we feel like we're not improving. To break this cycle, we can try changing the way we set goals—smaller, but consistent objectives that, when we look back, show us that we are indeed making progress. This progress can be the driving force for us every week, every month, every year.

Here are some ideas on how we could apply this Kaizen methodology in the Localization world.

"The Localization Accountability Ladder" explains how to go from avoiding responsibility to becoming a leader in localization. The blog covers seven simple steps, showing key behaviors, skills, and tasks to help teams improve.

I'm passionate about Localization metrics, but it can be frustrating. We live in a data-rich world, allowing us to measure our impact better. However, this data abundance brings challenges when building a Localization metrics system.

In my experience, two main pitfalls emerge (but they are not the only ones 😅). One is overemphasizing Localization ROI, which can lead to unproductive discussions. The other is tracking effort-related KPIs without translating their impact on company success into terms that matter to leadership and product owners.

Several factors affect the duration of localization for a digital product, including translators' availability and the technology setup level. This blog post explores the various elements that impact the amount of time needed for product localization.

Seven years ago, my twin Maria and I stood on the LocWorld stage in Warsaw and talked about Cultural Intelligence ….how culture shapes trust, feedback, leadership, and collaboration. Back then, the big question was: How can humans work better across cultures?

Today, that same question still matters but with a twist:
How can AI LLMs can work better across cultures?

In an AI-driven world, we’re seeing that language alone isn’t enough. Multilingual doesn’t mean multicultural. And as we rush to scale AI globally, cultural nuance is too often left behind.

Maybe it’s time to rethink how we build. Maybe it’s time for Cultural LLMs.

Before any great translation comes great source content. In this post, I compare what I’ve learned from golf lessons to the localization world: success depends on how well you set up from the start. I share real examples, red flags to watch out for, and four practical pillars : governance, clarity, style, and global mindset that can make or break your localization process. If you want smoother workflows, better quality, and fewer headaches, the fix might not be downstream… it might be in your stance.

Talking to executives is never easy and feeling ignored makes it even harder. I’ve been there…..pitched a localization project I truly believed in… and never heard back. That experience stayed with me. Over time, I’ve learned what works (and what doesn’t) when trying to influence decision-makers.

In this post, I share 5 practical tactics that helped me go from feeling overlooked to getting buy-in (without changing the heart of what we do in Localization)

What if inclusive localization isn’t just the right thing to do but also the smartest way to break free from outdated pricing models? In this post, I explore the core elements of an inclusive localization strategy and why it might be the key to escaping the trap of price-per-word thinking.

Much is said about AI's threat to professionals in the localization industry. But the truth is that the AI + human combo has a lot of potential. In this blog post, you can learn about the role of an internal Localization team in the AI era.

Understanding in which areas AI is really powerful, and the ones that a human professional is superior is critical nowadays to maximize our impact and possibilities to stay relevant in the generative content world we are living in nowadays

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.

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?