Shadow Localization: An Organizational Perspective

Translation is no longer a single lane that runs through one department. We are watching localization spread into marketing stacks, product releases, support tools, and AI features like chatbots, sometimes without any coordination at all. That shift can feel empowering and fast, but it also creates a new question that companies cannot dodge: who owns quality when everyone can ship multilingual content?

We dig into the forces behind “shadow localization,” from executive pressure for velocity to the growing ease of plugging AI translation into any workflow. When teams can route work around traditional processes, the old model of centralized control breaks down. The risks are not just technical fragmentation or duplicated effort. The bigger problem is governance: inconsistent terminology, unclear accountability, and unmanaged risk that stays hidden until it becomes a customer facing failure.

We also talk about what actually works in practice. Instead of trying to re centralize everything, we explore connective governance: shared standards, clearer rules of engagement, and an assessment layer that helps teams move quickly while still getting feedback on quality. We discuss where a human in the loop matters most, how to think about content rubrics by risk level, and why localization is becoming distributed infrastructure rather than a standalone service. If you are seeing AI localization pop up across your org, subscribe, share this with a teammate, and leave a review. Where is shadow localization showing up in your world?

Transcript

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Welcome And Companion Series Setup

Stephanie Harris-Yee 0:00

Hello, I'm Stephanie, and here for another episode of Field Notes. And today it's kind of exciting because, as many of you may know, last week we just started our companion field notes series with Giulia Greco. And today we're going to do a companion episode with Erik talking about the similar topic. So this topic is something that we've been hearing about a lot. And this is where teams are building their own translation solutions, sometimes without even involving localization

Localization Escapes The Department

Stephanie Harris-Yee 0:30

at all. So, Erik, from your perspective, what's going on?

Erik Vogt 0:33

Yeah, it was really amazing seeing Julia's input on this because she's raising something that I've also been noticing as well, which is this decentralization pressure that we're seeing. So, because localization can be in embedded, this is not necessarily a brand new problem, but organizations would either have different teams doing their own localization vendors and handling things their own way. This is a legacy of MA activity or different sort of business purposes. You might have marketing and product working with different vendors, then they consolidate and then they break it apart again. It's a it's a not that uncommon of a phenomenon. But because we're actually seeing a new thing happening here where you can not only direct localization traffic via different channels, which we talked about last time, but also we're seeing this pressure to add this capability in a much faster, more agile way in different places within an organization at the same time. So it's a very interesting phenomenon and it's hitting a much broader range of companies than I ever expected that would happen, that's happening faster than I would have expected.

Stephanie Harris-Yee 1:40

So we have now teams are somewhat, you know, localization is escaping the localization

Why Teams Build Translation Fast

Stephanie Harris-Yee 1:48

department. Why are teams doing this, do you think, instead of going through the typical localization routes?

Erik Vogt 1:54

Yeah, I think what we're seeing is that demand is there, right? So in I think Julia pointed this out as well, but that there's a demand and the capability is there. So the these teams are just reacting to these fundamental demands. And so the marketing team, for example, has a need to get things out very quickly. You know that. Sometimes it can be very powerful to use AI to create content or help shape the understanding of what the market signal is and being able to bring that signal in and respond to it in a much more effective way. Meanwhile, the product teams are sometimes being empowered to, or sometimes executives are demanding that they add a chatbot into there. So now they have to add an AI capabilities like with some translation functions directly into the product. And then you might have support teams that are dealing with auto-translating content, or which is sometimes end-user uh conversations with their users and and their support ecosystems, or sometimes their knowledge base. And then, of course, we have on top of that this crazy vibe coding reality where teams are needing to implement lightweight scripts or plug and play tools. But we hear people joking about vibe coding a CMS over the weekend. Like it's a little bit of a joke, obviously. It's not real realistic. Please don't try this at home. But the mindset is real that that now that we have these powerful automation tools and we have individuals who are building hundred million dollar businesses with no code experience, they're building an app themselves and then generating like a hundred million in revenue. It's it's uh it's unbelievable how um how fast this is all changing. So executives are definitely expecting fast and demand demanding this velocity. The business is demanding that velocity. So I think it's just realistically, it's not okay to create an arbitrary bottleneck if there isn't a legitimate business reason to do. So I think yes, localization is escaping the localization department, but it's actually it's actually being driven by very meaningful business drivers underneath.

When Shadow Localization Creates Risk

Stephanie Harris-Yee 3:57

So then where does this become an issue? So people are doing it, is it an issue? When does it become an issue? What do you think?

Erik Vogt 4:05

Yeah, I think it is when you have a distributed production system, then anybody who understands the consequences of poor terminology control will instantly see that as a major area of concern. Um, you have fragmented systems, but I think these are technical things that we've been familiar with for decades. But I think one of the key problems is the ownership and the governance aspects of it, which it's not clear who owns quality in this case. So if you're thinking about this as a localization manager and somebody's vibe coding some new tools and then packing in maybe your content without even talking to you about it as part of some AI solution that they're building, and then it spits out some less than ideal output. What happens to the localization owners of this? How do they handle this ambiguity? Who's accountable for fixing it? So without that clear ownership, then you're gonna end up with inconsistent terminology and there's of course duplicated effort, but the big one that ties into what we've talked about before is the unmanaged risk. They're not consciously managing that risk. So you have this demand force which is driving towards less regulated, less controlled output. And then you don't really know or understand how you're managing that risk. So what ends up happening is that even though the technology could be owned locally or by individual teams, the accountability ultimately gurgles up to the CEO at the top, right? So when we're trying to align the ownership and the governance around each of these kind of emerging systems, and you don't really have the clear feedback loops, the consequences are gonna pop up later and you're gonna end up with a lot of uncomfortable surprises. So I think this is really what we're talking about, what Julia was calling shadow localization. And I think it's not necessarily that people are trying to break the system on purpose. None of this is intentional. But because the market demands that we talked about earlier are forcing innovation essentially, trial and error, sometimes very just run fast and break things mindset, that you don't necessarily see the damage you're doing until later. But I think what we're seeing is this kind of need to think about that responsive layer uh to support business need that's

Connective Governance Without Bottlenecks

Erik Vogt 6:31

emerging.

Stephanie Harris-Yee 6:31

So then as an organization or a company, what do you do?

Erik Vogt 6:35

Yeah, well, I don't think that it's gonna work to go back to a fully centralized system. It's just not gonna happen. That creates an arbitrary bottleneck. But I think having a conversation about connective governance is like your starting point. So I think each of the, and this is putting myself into the to the shoes of individual owners of different systems, they often don't feel empowered to really have that broad level of conversation. So I just want to be realistic here. This isn't this is an easy thing to say, but publishing standards, for example, centralizing knowledge. One thought would be to make your content consumable by LLMs directly. So that's bring your own LLM model. So there's a new emerging thing here where you're basically trying to adapt or allow your content to be adapted to in this new AI ecosystem, in which case you as the team are saying, hey, here's the here's the markdown files you should use when you're running your model that we're gonna shape for you so that you're gonna have the best chance of getting better results from this. There could be better, clearer rules of engagement. I think some companies have said, go ahead and do whatever localization you want, but we will be an assessment layer. We're not gonna be a gatekeeper, but we'll be maybe an assessment layer. So if you're not sure what the quality is, we can do an assessment. Maybe we'll plug in Taos or Model Front or something like that as part of kind of this monitoring layer. Or we can have human reviewers look at the content and say, Are you gonna hit any red flags here? You can also be an integrator, and playing an integration role means actively looking for ways of supporting those folks out there. Like, how can you supply them with the best chance of being successful without creating this stuff? So basically, you're offering yourself to be um kind of an advisor and accountability partner, and then being an expert in the loop. And then, of course, that if you want to get it right, and that's again where the human arbitrage piece comes into it, that's where we want the human in the loop to be. And the team needs to make sure that is getting the right answer. That's for all the gold standard output, that's all for all the training corpora. That's another piece of metadata, by the way, that I think we've had or should be having in the loop here, which is is this content consumable by any learning efforts, or is this an output that we don't want to learn from? So empty output is generally not something you want to train on. So the accountability layer is about delivering high quality content that you can stand behind as training corporate.

Stephanie Harris-Yee 9:17

Okay.

Speed With Accountability Takeaways

Stephanie Harris-Yee 9:18

So you've gone over like several points throughout this, but what would be your top takeaway? Remember this one thing.

Erik Vogt 9:25

Localization isn't going anywhere, but it is becoming distributed infrastructure. And as we talked about last time, there's these two tracks. One is the facilitating the automated translation to get the best available option with a human in the loop accountability layer. And then you've got just both of them. There's the speed and there's accountability. So make sure you have both of these supported. Think differently about this. And then some companies are making a rubric of their content with maybe even three or four layers deep of different content types and different sort of accountability layers, and they'll create intentional workflows for each of those work types across the different teams. So you're you're not saying everything needs to go through this process and get to 100%. You're mitigating the risk with the right approach that balances cost and quality outcomes. So you're managing risk, you recognize that shadow localization is going to happen if you create unrealistic barriers to managing that content, uh, and then you end up with a governance failure. So accept the reality that this is the direction things are going and think about ways of shifting towards connecting systems, deciding decision ownership, and anchoring accountability.

Stephanie Harris-Yee 10:31

Okay. All right, Erik. Well, thank you very much.

Erik Vogt 10:34

It's always a pleasure, Steph.