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?

Localizability has always been a challenge small issues in source content often lead to big problems later in translation. In this post, I explore how AI is giving localization teams a powerful new way to improve source quality, reduce friction, and create better content for every market right from the start.

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.

We all talk about the importance of localization metrics, but where do you actually get them?
This question hit me hard during a recent panel, and it made me realize something I had been overlooking.
If you’ve ever struggled to find the right data to prove localization’s value, this post is for you.

Localization has always evolved alongside technology, yet its core principles remain unchanged accuracy, efficiency, and speed. Each major shift, from translation memories to machine translation, and now AI-driven workflows, has sparked the same question: Has localization become an engineering problem? That’s for you to decide. But if you’re ready to embrace this new phase, there are plenty of skills to develop and resources available to help you stay ahead

Certain words in business settings push all my buttons: 'synergies,' 'strategic meetings,' and 'thought leadership.'

The worst offender? Synergy. Lately, though, another term has been creeping up my list: 'educated.' In localization, we often say, 'We need to educate stakeholders.' But do they really want to be educated? No, they want to understand how localization impacts them.

Instead of teaching, we need to translate localization into business value.

Be Careful What You Wish For.

Getting buy-in for localization feels like a big win, until the requests start piling up and it’s impossible to keep up. More teams want your help, more content needs translation, and suddenly, you’re overwhelmed. In this post, I share my experience and the signs that show it’s time to grow your localization team

With tools like LLMs and GPT, we’re at a turning point, and staying curious, planning carefully, and aligning efforts are key. Missing any crucial element could hinder progress. A "slow and steady" approach is essential to navigate the AI wave.

AI is everywhere, and localization teams feel the pressure to "do something with AI." However, without a clear strategy, AI adoption can become an expensive distraction rather than a real value driver. In this post, I explain how to define an AI localization strategy that enhances efficiency, improves quality, and delivers real business impact without just following the hype.

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.

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.

This blog explores how AI-driven post-editing frameworks, like ChatGPT, can redefine localization quality assurance by providing automated checks, real-time feedback, and prioritization of critical issues. If manual LQA feels impossible in your environment, this post will help you explore actionable AI solutions to bridge the gap.

"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.

Working with a coach has helped me see the bigger picture, stay focused, and find balance as a leader. I’ve discovered strengths I didn’t fully appreciate, faced blind spots I used to avoid, and learned how to manage stress while staying (kind of) on track with my goals. This post covers all that!