Written by
Argos Multilingual
Published on
24 Feb 2026

Join Stephanie Harris-Yee and Erik Vogt for a critical discussion about why subject matter experts (SMEs) have become the most significant constraint in modern localization workflows. While AI has made translation faster and more accessible than ever, the real challenge lies in validating accuracy, intent, and domain expertise—and that requires human SMEs who are increasingly scarce and difficult to engage effectively.

Key topics covered:

  • Why SME feedback loops are now the primary bottleneck in localization
  • The disconnect between translator expertise and market value
  • How procurement practices are inadvertently devaluing the experts we need most
  • The theory of constraints applied to localization workflows
  • Practical strategies for optimizing around your most valuable resources
  • Why trust and accountability matter more than speed and cost in the AI era

 

More “Field Notes” Episodes

Explore more topics with Stephanie and Erik in our Field Notes series, where we break down complex localization concepts, ideas, and experiments for industry professionals. Check out our other discussions below:

Field Notes – Episode 9: SME Bottleneck

Erik Vogt: Hello. I’m Stephanie with Argos back here for another episodes with Field Notes, and today I’m with Erik, who’s an independent advisor with Vogt Strategy and he’s an expert in a lot of things in the field, so we love having him on to talk about all these cool concepts and today’s concept we’re gonna be talking about SMEs.

So Erik, you’ve said that the real bottleneck in localization isn’t AI or resources. It’s subject matter expert feedback loops. What is really the core issue here?

Yeah, the challenge is that AI is now ubiquitous. Translation is no longer a constraint, so it’s relatively easy to make a translation happen. The challenge is in knowing what is truth, what is intent, and the accountability. We’ve talked about that before, that accountability in the risk aspects of quality are an important part of it, but without somebody who actually knows what the product does in the market and in the context localization of any kind of automatic solution is largely guesswork and that can open up some significant risk for organizations.

Stephanie Harris-Yee: Yeah, and I’d imagine, especially in the enterprise space, so maybe let’s focus there a little bit. Why are SMEs such a bottleneck, especially in that space?

Erik Vogt: Yeah, and this is probably a good idea to differentiate between the different kinds of subject matter experts, but there’s internal ones and there’s partner SMEs. So the internal ones could be within an organization who really deeply understand the product. They have a constraint in that they’re a full-time resource often.

And that they often have other jobs to do. So reviewing things might not be their primary responsibility. Even translation might not even be their formal responsibility. Then there’s partner SMEs and I’m thinking about these as sort of the extension of the sales engines. Sometimes you might have in country sales reps or partner entities that know about their product, what is available in their country and what those services offer.

But there’s another branch of SMEs and that is a subject… a linguist who has invested in the time to really understand deeply a product or domain space. And they are restrained in regards to time and capacity, largely because they’re being plugged in via an external business ecosystem.

So I think in general, either localization is not their priority or they are not fully plugged into the full expert ecosystem. There’s just not a good ownership model for this. I think that we don’t buy experts.

We buy words and we buy hours, and that transaction tends to bury what the value of that subject matter is and how to inject that expertise into a, into the workflow where it’s needed.

Stephanie Harris-Yee: Okay, so then when we’re looking at this, of course AI comes into the picture and AI, it’s made that translation bit, or at least the words part, very fast. It’s helped out a lot maybe versus traditional machine translation. The fluency is better. Shouldn’t it also kind of help out somehow to reduce the burden on these SMEs?

Erik Vogt: It could, if the data is available for the model to produce accurate results, which is and it’s interesting in the sense that now you could go to AI to get answers to become an expert in a topic that the LLM has some information about. But the challenge here is that you’re still going back to the trust and

the context, both of which are generally not something that you can outsource to a large language model. So you have the hallucination, you have subject meaning drift, you have product misrepresentation and you’ll see that AI when it’s doing translations generally can’t really do fuzzy matches very well, and it tends to focus more on kind of the most probable outcome, the most probable unit, the entire unit.

And that can sometimes create tonal errors and that can lead to brand damage. There’s variety of different risks that come up here, but here’s what’s really interesting. Because AI makes fewer ugly and easily identifiable errors. It’s making more believable mistakes that are harder to detect. So you have this kind of two different forces going on.

One is, yes, you can easily produce a lot of content to help subject matter experts to be better informed, and it becomes even more intense and more difficult to find the actual mistakes because the way that it’s being presented is so believable.

Stephanie Harris-Yee: So then let’s like zoom out into the other end of the spectrum, right? So we have translators who are one of these SMEs not necessarily the in-country reviewer type of SMEs, but that second type of SME that you’ve been talking about. And they’re saying that work is evaporating or we’ve seen many like educational programs for translators, interpreters, et cetera going under these days and the rates keep dropping.

Where’s the disconnect here? We’re saying these are very valuable. They have that kind of expertise, that knowledge, and it’s a rare resource, but then we’re not seeing that reflected in the market. Where’s the disconnect?

Erik Vogt: Yeah, I think this is where the system is differentiating human contributions. So it’s really, really easy to produce AI deployments. You can spin up an AI tool in a matter of days. It can produce an enormous amount of outcome, but how do we manage that output? With… with SMEs, it’s hard to focus their time in a meaningful way.

All industries say that they want expertise. They want to have the translations done by somebody who knows what they’re doing. It’s obvious, and yet translators are being commoditized at the exact moment, we need them to be acting as domain experts. And so we have, SMEs are missing from the equation because they’re either too busy or they’re not engaged enough, as we talked about before.

So that increases risk. Procurement engines, and and that happens at multiple layers within the organization, tend to drive unit prices down. Then that pushes out experts, right? So you end up buying the cheapest of a type of a resource and, and less people are motivated to get into the industry as translators or subject matter experts.

Because of this, then quality becomes harder to manage because you have fewer experts there looking at the process. And that generally will, if you think of the U-shaped cost quality curve that tends to increase rework and the consequences of bad, like missing these translations.

So enterprise complains about inconsistency, blames a vendor, and the cycle continues. And so you end up with a kind of a treadmill of diminishing returns for the entire system ’cause you’re isolating the, the vendor of the, the best, most expert eyeballs, the extra brains that we want to have associated with, with the review of this.

It is harder and harder to focus our time in the most meaningful way, so we end up devaluing the exact people who are the most critical to reduce risk.

Stephanie Harris-Yee: So what, or I guess I could say is procurement not seeing this issue? Or how come we aren’t seeing them yet trying to like, fix this issue? Looking in into the future, it could get much worse.

Erik Vogt: Yeah, for sure. So I think there’s several different things. Some procurement folks are realizing that you need to make sure that you’ve got the individuals who know what they’re doing associated with your work and they’re protecting the erosion of those costs. So there’s a group of this industry where procurement is, in fact, I wanna acknowledge that ’cause it’s an important part of the equation.

However, when you’re driving for cost reduction. Then the problem becomes, people are shopping around for lower price and they tend to believe that AI plus humans can, can and should deliver a savings that lowers the top line expense, and that creates some distortions within the supply chain.

But the essence of it is what you can measure. How many conversations are there about scoring or quantifying domain familiarity or some credentialing system. Like we know, okay, a translator has a degree, that’s enough, or they do a test, that’s enough, but we really don’t have a good mechanism within our organization as a whole to measure performance as tied to business risk.

And the pricing model for decision making is not there. We tend to think in terms of productivity, efficiency, how many words per hour can you do? How fast is this? And yet when you talk to real translators who know what they’re doing, they’re like, well, this is not making my life easier. This is, you know, expertise isn’t, isn’t measurable.

If expertise isn’t measurable and it, and it’s hard to measure, then it becomes unpurchasable. Like just trying to put a procurement line item in there of like, please rate the skill level of your resources. Now, there are some ways around this, and we can talk about that in a minute but we really are constrained

by 20 years of buying habits oriented around a unit rate, specifically the word rate.

Stephanie Harris-Yee: So then maybe let’s pull it back down to, okay, what can we do. The actionable insights here, hopefully? So as localization leaders, experts, if they’re in their own company, what can we do differently in order to try to help the situation?

Erik Vogt: The theory of constraints is interesting, there’s a book about this. You can look it up. It’s an interesting theory. It basically treats the most valuable entity and most expensive entity in the loop, in a different kind of a way, right? You’re not trying to optimize down for unit price for that

most expensive component, you’re trying to make the utilization of that component as valuable as possible. So you tend to, for example, create a backlog so they can manage their efficiency and they are continually busy. So they’re optimized around keeping them busy and you focus on making them as a effective as possible.

So just park that for a second as a mindset. Then we have something that a lot of organizations are doing, which is differentiating by content type. And I think this is really helping to accordion out the different risk profiles of the, of the work that they’re doing.

So you have high risk and low risk types of activities and you create processes that are appropriate for that. But I think at a more substantive level we really need to rethink how we’re using AI and how you’re building workflows to optimize around the skills that you’re trying to look for.

So how, how, for example, could you look for consequences of failure? And then tie that into what you’re paying to make sure that you’re mitigating those costs for failure. You think about that value of that step of that human in there. What are they actually delivering and what are the consequences of not doing it?

Don’t just commoditize everything. It tends not to work. So now you have the serious players in the room. They’re doing things like. Routing things on different workflows based on those risk profiles, and that really helps to make sure that you’re putting the human effort in the most valuable place.

I think also it means, maybe thinking about what expertise is. What is it? It’s knowledge about the product that, that they’re supporting. It’s understanding the style guide intuitively, understanding the tools, workflows, the preferences of a particular workflow, and thinking about how valuable that is.

Now, when you do that, you start making different trade-offs and you start seeing, instead of optimizing around word price, you start optimizing around, the scheduling of these valuable resources. So you say, I would rather have this person who does know what they’re doing. I want them in the loop, as opposed to, I want it when I deliver it because it’s more convenient for me to fit that into my cycle.

That makes the most valuable resource, essentially not available because they are valuable on other projects or other things. So you plan your workflow with plenty of heads up, plenty of managing the prioritization that you’re really putting into these specialists. So I, I think also we need to think about you know, if we’re over indexing on price and turnaround time.

And this isn’t just the buyers, it’s the LSPs, it’s also the linguists. Like the whole system is biased around a certain way of thinking about this. That’s confidence is what you’re selling.

Trust is what you’re selling. So the degree to which you can amplify reasons to trust, or accountability for individuals, we’re almost getting back to the world of, trust your translator. Like know who they are. That individual person knows what they’re doing for your product and are the best fit as opposed to just rapidly deploying a number of random translators as quickly as possible. So I think in general, when we think about what’s happening with this market, where the translation is plentiful, there’s tons of it. It’s just saturating our market. And that’s eroding. It’s causing margin compression for everybody in the loop.

And not to mention macroeconomic factors like weaker dollar, which is affecting our whole industry as well. It’s another thing, but if I had a, one less, one thing I’d wanna say to everybody in the system is.

We need to not forget that SMEs scarcity and the limited number of people who actually know your product and actually know how to fix it, and the investment in them and the management of their, you know, critical time is really the new localization tax. And, and it’s gonna be even the, the closer and closer we get to AI

delivering more and more close enough content, the ability of those subject matter experts to validate truthiness or truthfulness more important than ever. That’s kind of the main takeaway I think, of this whole, this whole topic.

Stephanie Harris-Yee: We’re out of time for today, but thank you so much, Erik, and yeah, this was an interesting one.

Erik Vogt: I hope this spawns some conversation. I think this is an important topic for, it’s a, it’s affecting the entire industry at the same time right now, so it’s an important one for us to be talking about. Steph, thanks so much for the time. It’s a pleasure as always.

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