Why SMEs Are The Real Bottleneck (Not Resources. Not AI)

Translation is getting faster every month, yet localization risk keeps rising. That’s not a paradox, it’s a signal that the bottleneck has moved. Stephanie from Argos sits down with Erik, an independent advisor at Vogt Strategy, to name the real constraint most enterprise teams are feeling: subject matter expert feedback loops that can’t keep up with AI-driven volume. 

We dig into what SMEs actually mean in a modern localization program, from internal product experts to partner teams in-country to linguists who’ve built deep domain knowledge over years. Erik explains why “buying words and hours” hides the value of expertise, and why accountability for truth, intent, and market context is the piece automation can’t safely replace. We also talk about the new failure modes of large language models: hallucinations, meaning drift, product misrepresentation, and the most dangerous category of all, believable mistakes that look perfectly fluent. 

From there, we get practical. We unpack how procurement habits and word-rate economics commoditize experts right when organizations need them most, and why measuring productivity without measuring risk leads to rework and inconsistency. Eric shares approaches localization leaders can use now: content triage by risk profile, workflow routing that puts humans where consequences are highest, and planning that protects scarce SME capacity. 

If you’re building an AI localization workflow, managing enterprise translation quality, or trying to justify expert review, this conversation will help you make the case with clearer logic and better incentives. Subscribe, share this with your localization team, and leave a review with the biggest quality risk you’re trying to solve right now.

Transcript

Welcome And The SME Bottleneck

Stephanie Harris-Yee 0:00

Hello, I'm Stephanie with Argos, back here for another episode 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 going to 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?

Translation Is Easy Accountability Is Not

Erik Vogt 0:33

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, any kind of automatic solution is largely guesswork. And that can open up some significant risk for organizations.

Stephanie Harris-Yee 1:08

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

Internal Partner And Linguist SMEs

Stephanie Harris-Yee 1:18

space?

Erik Vogt 1:18

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. But 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 engine 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 with 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.

AI Fluency Creates Harder Mistakes

Stephanie Harris-Yee 2:47

Okay. So then when we're looking at this, of course, AI comes into the picture. And AI, it's made that that translation bit or at least the words part very fast. It's helped out a lot with 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 3:09

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 brain damage. So there's a variety of different uh 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.

Why Expertise Gets Priced Out

Stephanie Harris-Yee 4:30

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 um 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 5:04

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 in with SMEs, it's hard to focus their time in a meaningful way. And all industries say that they want expertise. You know, 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. The curement engines and the tendency, and that happens at multiple layers within the organization, tends to drive unit prices down, and then that pushes out experts, right? So you end up buying the cheapest of a type of a resource, and then 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. You're isolating the vendor of the best, most expert eyeballs, the extra brains that we want to have associated with the review of this. It's harder and harder to focus our time in the most meaningful way. So we end up devaluing the exact people who are the most

Procurement Measures The Wrong Things

Erik Vogt 6:55

critical to reduce risk.

Stephanie Harris-Yee 6:56

So what, or I guess I guess 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 7:07

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 want to acknowledge that because 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 sh 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, it's a translator that 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 manageable. If expertise isn't measurable and it and it's hard to measure, then it becomes unpers purchasable. 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 8:59

So then maybe let's pull it back down to okay, what can we

Build Risk Based Workflow And Capacity

Stephanie Harris-Yee 9:02

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 9:13

The theory of constraints is an 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 as 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 they are continually busy, so they're optimized around keeping them busy, and you focus on making them as uh effective as possible. So let's just park that for a second of 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 you 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, 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 sale of 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 uh 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. You know, what is it? It's knowledge about the product 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, it when you do that, you start making different trade-offs and you start seeing instead of optimizing around work 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 sky 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 think also we need to think about, you know, if we're over-indexing on price and turnaround time, uh, and this isn't just the buyers, it's the LCPs, 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 N 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. Yeah, we do, 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, is another thing. I if I had a one less one thing I'd want to say to everybody in this system is we need to not forget that SME 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 critical time is really the new localization tax. And it's going to be even 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 is more important than ever. That's kind of the main takeaway, I think, of this whole this

The New Localization Tax

Erik Vogt 13:40

whole topic.

Stephanie Harris-Yee 13:40

We're out of time for today, but thank you so much, Erik. And yeah, this was an interesting one.

Erik Vogt 13:46

I 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.

Stephanie Harris-Yee 13:59

Pleasure.