The question most organizations bring to AI translation is whether it makes the work faster or cheaper. Antonio Tejada thinks that question is already obsolete.
Tejada, LanguageLine's Vice President of Translation and Localization, is one of the contributors to AI in Translation: How Experts Are Rethinking Language and Quality, a new eBook produced through a collaboration between LanguageLine Solutions, MultiLingual Media, and XTM.
The book gathers leaders from Adobe, TikTok, Uber, TED, Revolut, Cincinnati Children's Hospital, RWS, TAUS, Nimdzi Insights, and other organizations shaping multilingual communication. It doesn't offer a single vision of where AI is taking the field.
It captures an industry arguing with itself over the questions every business is now asking: How much should we automate? Where does human expertise actually earn its keep? Can you scale across languages without losing the thread of quality?
Tejada's contribution stands out because he isn't really talking about translation. He's talking about what happens to language itself when AI is everywhere. Here are four ideas from his interview worth sitting with.
For years the practical question has been simple: how does AI make translation faster or less expensive? Tejada argues that framing has become a trap.
"The real question is no longer how AI improves translation, but how language itself will function within an AI-native world,” he writes.
The shift is significant. AI isn't one more productivity tool bolted onto an existing process. As it gets embedded in products, customer experiences, and enterprise workflows, multilingual communication stops being a service you purchase and becomes part of how the business runs.
Most people still picture AI drafting a translation while a human checks it. Tejada describes something larger: specialized AI agents working in concert across the whole content lifecycle, estimating quality, routing content by risk, and flagging where human judgment is actually needed.
"The localization industry is now experiencing two transformations simultaneously," he writes. One is the reinvention of multilingual workflows. The other is the broader enterprise shift toward AI-enabled operations across every function. The point is that AI isn't just speeding translation up. It's rewiring how the work gets coordinated from beginning to end.
The familiar phrase "human in the loop" is starting to feel dated, and Tejada says so directly.
"The concept of 'human in the loop' is itself becoming too simplistic,” he writes.
Reviewing every translation was the old model. The emerging one asks language professionals to supervise the agents instead: writing the prompting strategies, setting the quality standards and cultural rules, and overseeing the frameworks that run multilingual work automatically. Human expertise moves from correcting outputs to designing the systems that produce them.
"The linguists of the future will increasingly operate alongside AI agents rather than merely reviewing their outputs,” Tejada writes.
As AI absorbs the routine production work, the human value moves upstream. The most valuable people in the field will pair linguistic skill with strategic thinking, governance, and quality management. They become architects of multilingual communication rather than processors of text. For any organization investing in AI, that makes workforce development as urgent as the technology itself.
After an interview full of agents, orchestration, and automation, Tejada lands somewhere human.
"The most important thing we must preserve is not necessarily the process, but the purpose,” he writes.
Translation was never only about swapping words between languages. Its purpose is to let people understand one another, get care, build trust, and take part fully in the life around them. AI can expand that mission enormously. But a company that optimizes only for speed and cost can lose the reason multilingual communication exists in the first place. Technology should serve human connection, not stand in for it.
The strength of AI in Translation is that it refuses to settle the debate. It puts leaders with different priorities in the same room, some focused on governance, others on quality, low-resource languages, accessibility, or risk, and lets the disagreement show. What they share is one conviction: meaningful communication still runs on trust.
If you're weighing how AI fits your organization's global communication strategy, the eBook offers a clear-eyed look from the people defining what comes next.
Download your free copy today to explore perspectives from Antonio Tejada and fellow leaders from Adobe, Uber, TED, TikTok, Revolut, RWS, TAUS, Cincinnati Children's Hospital, Nimdzi Insights, and other organizations shaping the future of multilingual communication.