AI is transforming localization at an incredible pace, yet access to clear, relevant, and confidence-building education about AI hasn’t always been equal. One of the missions of Women in Localization is to empower women in the industry to lead in the AI era by breaking down structural barriers, amplifying global voices, and accelerating women-driven innovation. That’s why we also created WeShape AI and are building WeShape AI Labs, a structured innovation environment designed to help our members.
At the heart of our mission is supporting our community. But to truly support them, we first need to understand how they are navigating this unprecedented moment. We reached out to members of Women in Localization to learn how AI is affecting their day-to-day responsibilities and to capture perspectives from across the industry—whether from the vendor, client, or consultant side—so we can build a clearer picture of how the field is evolving.
Dorota Pawlak, Localization Consultant and AI Trainer
How has your role changed with the rise of AI?
Just before the big wave of LLMs such as ChatGPT in late 2022, I discovered Midjourney, an AI image generator. That’s how my adventure with AI began. From experiments with AI-based image localization, my curiosity led me to large language models and hundreds of other tools that could enhance my everyday work.
Those experiments gradually shifted my focus from “just” translation and localization to generating culturally relevant visuals for clients, teaching AI-driven localization practices, speaking about AI in localization, and ultimately training and consulting on the responsible use of AI.
Over the past three years, my role has evolved from a translator specializing in localization and localization training to an AI-in-localization trainer and consultant. Now I run courses and workshops on AI in localization and help freelancers and small localization teams find the right dose of AI for their daily workflows.
Are traditional quality frameworks still sufficient?
Traditional frameworks are still relevant: linguistic accuracy, consistency, and terminology management remain essential foundations. However, as the way content is created changes, it makes sense to modify these frameworks to ensure they reflect AI-enhanced processes. That’s especially important for projects where content is partially or fully generated by AI.
How must localization leaders evolve to remain strategic in an AI-first world?
Without AI literacy, localization leaders won’t be able to keep up with the pace of change. What’s also crucial is a mindset of experimentation, but it must be anchored in critical thinking and a responsible approach to AI. For example, localization leaders need to be able to differentiate when it’s a good idea to automate, when it’s better to rely on humans only, and how to create workflows that balance efficiency with cultural responsibility.
Anastasia Vestfal, Localization Program Manager
How has your role changed with the rise of AI?
The most significant shift is transitioning from managing manual translation workflows to orchestrating large-scale AI ecosystems without compromising quality. We no longer just manage strings; we leverage Large Language Models to fine-tune Neural Machine Translation outputs. My role focuses on identifying repetitive tasks that AI can automate to increase efficiency and cost-effectiveness. We are moving toward ‘always-on’ localization across all 55+ languages simultaneously, enabling AI to rapidly scale and process high-volume data in real time.
Are traditional quality frameworks still sufficient?
Traditional frameworks remain vital for high-impact content, but they are no longer sufficient on their own in an AI-first world. We must implement differentiated quality standards: while AI is ideal for massive quantities of data that do not require hyper-specificity, it cannot yet be applied universally. A significant modern nightmare is the risk of a polluted Translation Memory. If the careless use of AI leads to a polluted TM, it creates a mess that compromises all future strings. Leaders must prioritize protecting linguistic assets while integrating AI, ensuring continuous evaluation of emerging models to maintain the integrity of our global company voice.
How must localization leaders evolve to remain strategic in an AI-first world?
Leaders must evolve from back-end service providers into Product Localization Evangelizers. We can no longer rely on outdated waterfall methods; instead, we must integrate localization early in the design and product development phases to achieve agile localization-as-a-service. To remain strategic, one must stay up to date on how to utilize new resources and technologies in this fast-moving landscape. This requires a commitment to continuously evaluating emerging AI models as they are released, rather than becoming attached to a single solution. Leadership in this era necessitates staying ahead of AI trends and bridging the gap between design and development, ensuring that our technical infrastructure is as robust as our linguistic expertise.
Damián Fernández, International Experience Optimization Manager at Notion
How has your role changed with the rise of AI?
The role has shifted from managing processes to designing systems. AI hasn’t replaced the work, but it has raised the stakes for getting the setup right. When you’re shipping multilingual content at speed, the question is no longer just “is this translated correctly?” It’s “do we have the right workflows, the right human touchpoints, and the right guardrails to ensure quality doesn’t get lost in the velocity?” My focus now is much more upstream: making sure that when AI is in the loop, it’s there intentionally, not just because it’s fast, but because we’ve built around it properly.
Are traditional quality frameworks still sufficient?
They’re the foundation, but honestly: were they ever fully sufficient? Traditional frameworks gave us consistency and a shared language for quality, which still matters. But they were built for a different pace and a different content reality. Today, quality needs to be more flexible. Not lower, but contextual. Otherwise, teams lose efficiency without gaining anything. If frameworks don’t evolve, they become bureaucracy.
How must localization leaders evolve to remain strategic in an AI-first world?
By staying close to what actually matters to the business. Localization becomes strategic when it’s understood as a growth lever, not a cost center, and that requires leaders who speak the language of their stakeholders, not just the language of translation. That means staying curious: if the business is excited about a new market, a new channel, or a new technology, localization should already be curious about that conversation, bringing perspective on what it takes to make it land globally. The leaders who remain relevant are the ones who position localization as a trusted partner in business decisions, not just an executor at the end of the pipeline.
Martyna Pakula, Sr. Global Program Director at a large LSP
How has your role changed with the rise of AI?
My core responsibilities haven’t fundamentally shifted, but the lens through which I view them certainly has. My role has always leaned heavily on leadership, critical thinking, and decisive action to lead large, multicultural operations and solve complex client challenges. These core competencies remain my “north star,” providing the essential human judgment that AI cannot replicate. However, the integration of AI has triggered a significant evolution in how I execute this strategy.
For years, localization workflows remained relatively stagnant, centered on traditional Machine Translation and post-editing. The rise of LLMs changed the game. To remain a trusted advisor, I have moved beyond the surface, diving deep into the mechanics of LLMs to understand their impact on quality and the broader ecosystem.
What is more, we are seeing a new market standard: a diminishing appetite for leaders who aren’t “AI-first.” To stay relevant, I now bridge the gap between traditional localization and these emerging technologies.
Personally, I view AI not as a replacement, but as an unstoppable force multiplier. It has become my most patient and skilled consultant, available 24/7 to help me brainstorm solutions or pressure-test new strategies. My role isn’t directly replaceable, but it is infinitely enhanced by the tools now at my disposal.
Are traditional quality frameworks still sufficient?
Traditional quality frameworks are increasingly insufficient because they focus on error counts and linguistic severity rather than content purpose and associated performance. Traditionally, quality has been assessed by language specialists in a vacuum, often ignoring the actual end-users for whom the content is intended.
As technology matures, consumer acceptance of lower translation quality is higher, while expectations are more nuanced than in previous years. The next challenge for our industry will be to adapt these frameworks to account for AI-enhanced outputs, moving beyond flawless translation to measure actual outcomes. We must evaluate localized content by its ability to drive specific user actions and business results, shifting the metric from “error-free” to “impact-driven.”
How must localization leaders evolve to remain strategic in an AI-first world?
To lead in an AI-first world, leaders must first gain a deep technical understanding of how AI works—knowing what it excels at and where it falls short. This literacy is critical to remaining a credible advisor within any organization. Leaders should then pilot solutions on small content subsets before making an informed decision to scale, ensuring AI deployment is a well-thought-out move rather than a reaction to hype. Finally, as AI eliminates manual coordination tasks, localization teams must pivot. The future of the role lies in high-level consultancy and stakeholder engagement, moving from managing processes to driving global strategy.
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