There’s no getting around it—the localization industry is ripe for a shakeup, and it’s not only AI that’s doing the shaking. From geopolitics to new regulations, the way we help companies communicate changed throughout 2025, and the business decisions behind those changes are moving just as quickly.

At Argos, we think it’s just as important to listen as it is to broadcast. That’s why we spend a good part of the year at conferences, events, and talking with partners and clients. So we’re taking a moment to look back at a year where the ground continued to shift.

2025 was the year AI work stopped being reactive. We heard from teams that finished pilots and spent the year proactively building, testing, and fixing AI systems in real production. They described how hard it was to keep those systems running. Small changes in format or content often caused unexpected behavior. When users engaged with the output in ways the system didn’t expect, things broke.

Quality became a real pressure point. Reviewing models that were built to mimic human output didn’t always hold up. Some AI rewrites were so unpredictable that teams said they spent more time figuring out what the system was doing than actually reviewing the content. This meant even more work fell on people already stretched thin, without much room for error.

It’s pretty obvious that these aren’t isolated issues. If you look through our blogs and recaps from this year, you’ll see a pattern emerge.

Teams all over were running into similar problems, even when their tools and use cases looked nothing alike. Most were dealing with unstable systems, unclear review processes, and fixes that depended too much on individual effort. That’s where the real story comes into focus around AI, quality, and the people in the middle of both.

The Year AI Moved From Reactionary to Operationalization

From being at LocWorld, TAUS, and other events this year, it’s clear that AI is now being further incorporated into everyday production work. We talked to teams testing AI translation, automating checks, extracting terminology, and performing in-product evaluation on actual releases. They showed actual applications and deployments, as well as real failure points.

However, several people described their setups as brittle because older systems aren’t built for how LLMs behave. In many cases, localization teams are adjusting their processes on the fly just to keep the AI usable. Because these issues were emerging on actual projects, not just pilot tests, they often required human intervention. More often, they required teams to redesign or replace older systems not wired for LLM integration.

On the flip side, experimentation became a credibility marker in some camps. Everybody wants to show that they understand where AI helps, where it adds risk, and what it takes to keep it running. Often, we heard teams were experimenting because leadership wanted evidence that they understood the tools well enough to make decisions about them.

Meanwhile, what most localization pros need right now is time to understand all these new bells and whistles. They’re doing a lot of talking with their colleagues about what we’re all experiencing.

“Long, intimate conversations with AI are cool, but only by talking with other people do we get to validate or change what we believe, and how genius ideas and connections get generated,” says Dr. Belén Agulló García, who serves on the AI Localization Think Tank.

Of course, once AI entered production, the next problem everyone ran into was quality.

Quality Needs a New Recipe

We heard from a few professionals that their quality steps stopped working once AI became part of the production work. Those processes were built for human translation, where reviewers can see how one change leads to the next. AI often changed content in ways that reviewers could not trace, which forced them to research the system’s behavior before they could evaluate the result.

Often, this issue emerged in work that involved more than text. In those cases, quality effectively became a risk management function because changes to timing or visual structure introduced consequences that standard scoring frameworks weren’t designed to catch.

To cope, teams refocused their review efforts on the content most at risk of harm and added small checkpoints or created new internal rules because the older frameworks didn’t stretch far enough. These fixes worked, but they remained informal, relying on individual ingenuity instead of a clear, shareable system.

Several conference speakers described rising pressure from leaders who want faster work and higher quality at once, even though the strongest results still needed a reviewer. In other words, everybody wants translations that are faster, better, and cheaper. Sound familiar?

Humans Held It All Together

As AI-powered solutions continue gaining momentum, we’re seeing localization move upstream into product development and content creation. That doesn’t mean that translation and all the processes around it are losing importance. If anything, it’s the opposite. But the pressure is taking its toll on people, who really were the last line of defense this year for getting the work done.

This year felt different because the pressure came from the systems, not the volume. AI’s arrival in production changed processes in ways teams could feel immediately. Established checks no longer behaved the way they were supposed to, and reviewers had to dig into the model’s choices before they could evaluate the output. Some of the issues came from language, while others came from timing, layout or structure shifting at the same time. Older frameworks could not account for those combinations, so people adjusted what they could and kept moving.

Many of the conference talks focused on what the AI tools could do without showing how the content landed with real readers. This missing answer matters to those who have to explain results to leadership or decide how much human review is still necessary.

We also heard some good questions about how teams keep expertise in place as roles change. Several people pointed out that younger professionals may not get the hands-on practice they need if more work moves into automated paths without enough visibility into how decisions are made. Others described concerns about how teams will maintain judgment-heavy tasks when the tools behave unpredictably.

It became clear that a lot of the problems were the natural result of tools that still behave in ways no one can fully predict. Knowing that didn’t fix anything, but it made the work easier to face together.

Our own Gabriel Karandyšovský had high praise for his fellow humans.

“LocWorld is really just a sideshow for meeting people,” he says. “You get to meet your personal heroes, discover kindred souls, and hear their stories. You learn from them and get inspired by them. Beneath the buzzwords and corporate speak, it’s really just humans trying to do good work.”

What’s Next? Build Back Better​

When the ground is shifting beneath you, sometimes the best thing to do is to hold on. Movement makes a lot of situations unclear, and there are many forces shaking localization right now. But freezing in place isn’t an option; we all have to continue figuring out new ways of working, and quickly.

We should be proud of ourselves this year. The work got done, and more importantly, we didn’t fall apart, even when the systems we use didn’t always perform as expected.

As we close the books on 2025, the biggest takeaway is the resilience of people in the face of change. AI work moved from being theoretical to being used in everyday production, forcing localization teams to become agile troubleshooters in real time. The experience reinforced that AI is still a developing tool, one that requires human intervention and quick fixes just to keep things usable.

The next phase is to build back better. At Argos, we believe the next stage is to continue to embrace this new reality proactively. This means designing systems and quality processes that are wired for LLM integration, ensuring we maintain necessary human expertise, and learning from our shared challenges as we move forward together.

Contact Argos to learn how human expertise can strengthen your LLM quality frameworks.

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