Why language professionals are indispensable as AI training grows more specialized

As artificial intelligence (AI) continues to reshape the language industry, many multilingual professionals have wondered where they fit into the future. A common assumption is that AI will reduce the need for linguists as automation improves. According to MK Blake, VP of Delivery Services at Welo Data, the opposite is happening. Human judgment remains fundamental to building effective AI systems, and multilingual experts are helping shape those systems from the very beginning. 

“That judgment and what the model sounds like comes from a human. And that’s embedded right from the beginning.” 
— MK Blake, VP of Delivery Services, Welo Data 

Hear MK explain why multilingual professionals are foundational to AI training in the full episode of the Lang Talent Podcast.  

Speaking on the Lang Talent Podcast with host Eddie Arrieta, MK explained that multilingual professionals are not simply supporting AI after it has been built. Instead, they play a foundational role in shaping how models understand language, culture, context, and human intent from the very beginning. 

That shift has important implications for translators, localization specialists, subject matter experts, and anyone considering a career in AI data. As models become more specialized and more capable, the demand for human judgment continues to grow. 

The First Mile of AI 

Much of the conversation around multilingual AI focuses on localization. How can an AI model speak another language? How can content be translated accurately after the model has already been developed? MK argues that this perspective overlooks where multilingual expertise has the greatest impact. 

Rather than thinking about the “last mile” of AI, she encourages organizations to focus on the “first mile,” where models are initially trained. At this stage, human contributors help teach AI systems what good responses look like across languages, regions, and cultures. Whether a user is interacting with an AI assistant in French, Spanish, Urdu, or another language, the quality of that experience depends on judgments made by multilingual professionals during training. Those human decisions become embedded within the model itself. 

In other words, multilingual contributors define AI outputs. They shape those decisions long before a model ever responds. 

Human Judgment Matters 

During the podcast, MK identified two capabilities that have become especially valuable: pragmatic competence and the ability to navigate ambiguity. Pragmatic competence involves understanding what people actually mean, not simply what they say. That includes recognizing humor, sarcasm, tone, implied meaning, and conversational context. AI training often requires contributors to evaluate conversations spanning multiple exchanges or even multiple forms of communication. Human reviewers must understand not only individual words but also the broader context surrounding them. 

Equally important is navigating ambiguity. AI projects frequently provide extensive guidelines, yet contributors still encounter situations that do not fit neatly into predefined rules. Professionals must build a consistent mental framework that allows them to make thousands of high-quality decisions while handling edge cases thoughtfully. That consistency becomes an important part of producing reliable training data. 

Specialization Creates Opportunity 

One of the biggest changes MK highlighted is the growing importance of domain expertise. Early AI development focused on building broad foundational knowledge. Today’s models, however, increasingly support specialized consumer and enterprise use cases. As a result, organizations need contributors who bring expertise beyond language itself. MK encouraged multilingual professionals to think about the industries, hobbies, and subjects they know best. 

A multilingual contributor with deep knowledge of healthcare, finance, travel, sports, retail, or another field brings unique value because they understand both the language and the context users expect. During the podcast, MK used the example of a French-speaking expert on Senegal’s World Cup team. That combination of language ability and subject knowledge creates highly valuable training data for AI systems serving those users. Rather than competing solely on language skills, professionals can distinguish themselves by combining multilingual expertise with specialized knowledge. 

Culture Goes Beyond Translation

Another theme throughout the conversation was the importance of cultural understanding. Organizations often request support for broad language categories such as Arabic or Spanish, but MK explained that successful AI models require much greater precision. Dialects, regional vocabulary, and cultural expectations all influence how users experience AI. A model trained for one region may not resonate with users somewhere else. 

For that reason, Welo Data emphasizes detailed assessments to understand contributors’ regional expertise, dialect knowledge, and specialization before matching them to projects. Client discussions also begin by identifying where a product will launch and how users will interact with it, allowing training to reflect the appropriate audience and use case. 

The result is AI that better reflects the cultures and communities it is designed to serve. 

AI Skills Continue to Evolve 

Beyond language and subject expertise, MK discussed how AI work itself is changing. As models become more agentic and capable of completing complex tasks, contributors increasingly need to explain their reasoning, not simply provide an answer. Teams are looking for professionals who can articulate why they made a particular decision, what information influenced their judgment, and how they evaluated different options. 

This emphasis on reasoning supports newer forms of AI training, including systems that learn from human decision-making processes rather than just completed outcomes. 

To help contributors develop these capabilities, MK also highlighted Welo Works, Welo Data’s platform for multilingual professionals. In addition to connecting contributors with projects, the platform offers AI-focused learning opportunities that introduce concepts such as prompting, model behavior, and how AI systems function in real-world workflows. 

New Career Paths Are Emerging 

The conversation also explored career opportunities that barely existed a few years ago. One example MK shared is the role of cultural safety reviewers. These professionals evaluate whether AI systems respond appropriately within specific cultural contexts, helping models avoid insensitive or inaccurate recommendations while remaining aligned with local customs and expectations. 

She also discussed the growing need for agentic systems testers, professionals who document the reasoning behind their decisions so AI systems can better understand how humans approach complex tasks. As AI agents become more capable, these contributors help evaluate both safety and decision-making throughout multi-step workflows. 

Both roles illustrate how AI development increasingly depends on human expertise rather than replacing it. 

Looking Beyond Translation 

MK concluded the podcast with advice for multilingual professionals considering the next stage of their careers. 

She encouraged listeners to explore opportunities beyond traditional localization and translation while continuing to value those foundations. More importantly, she urged professionals to recognize the value of their own interests and domain expertise. Whether someone’s passion is shopping, travel, healthcare, sports, or another field, those experiences can become important assets in AI training. Combining multilingual ability with specialized knowledge helps shape the next generation of AI systems. 

Listen to the Full Conversation 

Want to hear the complete discussion with MK Blake? 

The full episode of the Lang Talent Podcast explores the evolving role of multilingual professionals in AI, emerging career opportunities, and why language expertise remains central to building high-quality AI systems. 

Hear the full conversation between MK Blake and host Eddie Arrieta on the Lang Talent Podcast.