How Accessible AI Lets Localization Teams Prototype Solutions and Demonstrate Value

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The accessibility of general-purpose AI tools has changed what non-technical professionals can accomplish in localization roles. Practitioners who once depended on engineering, design, and product teams to implement their ideas can now analyze workflows, identify gaps, and build functional prototypes independently—often within hours rather than across quarters.

This capability matters most at two stages of localization work. The first is analysis. An existing process can be described to a model such as Claude, ChatGPT, Gemini, or Devin, along with the desired outcome, and the tool can surface inefficiencies or propose a different approach. Work that once required coordinating multiple stakeholder groups across time zones can begin immediately, and entire steps of a process that took weeks to align on can be reconsidered in a single working session.

The second stage is prototyping. Rather than describing a proposed solution in a meeting and waiting on resource commitments, a working prototype can be built, deployed, and measured in advance. Stakeholders then collaborate on something tangible, and localization teams can demonstrate immediate, measurable impact—time saved and cost reduced—without first securing scarce engineering capacity. This reframes the relationship with partner teams: instead of arriving with a request, a localization lead arrives with a functioning solution.

“You want us to be using AI? Like, here’s how we’re using it, and here are the immediate benefits we’re seeing from it.”

The same approach strengthens the case for localization with leadership. Demonstrating concrete results in terms leaders already track—money saved, time recovered—is more persuasive than describing an expected outcome. A small, working example shows value rather than asserting it.

Effective use depends on matching the model to the task and on managing context deliberately. A structured, local note-taking environment—Obsidian is one example—can serve as a working directory that an AI tool reads from and writes to, preserving project context across sessions instead of overloading a single chat window. Thoughts, files, and project notes kept in that directory let the tool retain context as work is iterated over time.

“Find the right tool for the job and then use whatever your preferred project organizing tool would be alongside your tool.”

Sustained progress favors incremental delivery. A small proof of concept that demonstrates results builds leadership confidence more reliably than an unproven, large-scale overhaul. Smaller milestones, delivered consistently, establish credibility to drive larger initiatives forward.

“Start small, iterate, get it working, and then build on top of it.”

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Below is an automated transcript of this episode

Stephanie Harris-Yee: [00:00:00] Hello, I’m Stephanie Harris Yee, and I’m back today with another episode of “Global Ambitions.” So today we have Casey Garland, and he is the senior localization program manager at Toast.

So Casey, welcome to the show.

Casey Garland: Thank you very much. Very excited to be here. And today we’re gonna be talking about this idea of now not just being a scary time, but it’s actually an exciting time to be a non-technical person in a localization role just because of the access you have to these tools and these new things. So Casey, can you give us like a groundwork?

Stephanie Harris-Yee: Where are you coming from this?

Casey Garland: Yes, this is a very exciting topic for me personally, and something I’ve been tangentially connected to since the beginning of my career working alongside development teams and designers and product teams that are made up of all these technical people and yet having no experience in those domains myself.

And so in the [00:01:00] beginning of my career, I spent a lot of time going through like self-paced educational crash courses on coding and product, design and all that stuff. And so I’ve always had these like great we’ll say that in quotes, great ideas, grand ideas. But when it came to implementing them across the org that I was working in, I would always run into red tape with resourcing, availability for engineers or designers, et cetera.

And so, a lot of those ideas just kind of fizzled out and never saw the light of day. And I find myself very recently having access to tools that have enabled me to bring some of these ideas to light in my current org. And so that was kind of the inspiration for this topic that I was thinking of, was just how cool it is for me to be in a position 10 years later or 10 years from where I started with [00:02:00] the ability to bring these ideas to life and to be enabled by the AI tools that have been coming out.

And so that’s what I wanted to talk about because that’s the kind of work I’ve been diving into most recently. So it’s very top of mind.

Stephanie Harris-Yee: So let’s really try to dive in there then. Can you give us some examples?

Casey Garland: Tons of examples. Yeah, so Toast’s localization program is nascent, and so it, you know, we’re just getting off the ground with establishing processes and understanding the landscape of the tools that our partnering teams are working with, for example, across engineering and design.

And so we are putting together, we’re engineering our program to be able to support loads of different tools and teams. And so when we first got started, our program looked very different than it does today. And so [00:03:00] we’ve been acting with speed and accomplishing some really cool things in terms of localization at Toast.

But now we’re finding ourselves in a position where we have the opportunity to maybe redefine some of those processes that have either grown stale or just have become outdated. And as I find myself updating documentation for these processes, I’m also in the back of my mind wondering, like, is this the way– is this the most efficient way that we can approach this this issue or this challenge that we’re having from this maybe particular tech stack?

And what I have the ability now to do, instead of calling meetings across multiple stakeholder groups and trying to organize everybody’s schedules across the world to get together and talk about this very specific issue, I can just get started right away with Claude or Devin or ChatGPT or Gemini and say, “Hey, here’s the challenge, [00:04:00] here’s where we would like to be, and here’s what we’re currently doing. Or like, help us identify gaps where, that we can close in order to become more efficient. Is this entire approach, like, wrong? Is there a whole other way we can be doing this? And I’m finding areas where entire pieces of, of our process, which, you know, could take weeks at a time to get teams to commit to and, you know, filling out scoping exercises and, you know, all the stuff they own and trying to make sure that we’re doing our due diligence and, and documenting everything that this team owns so that we can ensure one hundred percent localization, internationalization of that product.

Well, instead of going through this lengthy process, AI tools are helping me identify ways that we can approach these product teams that are expected to be multilingual, and we have no idea where they’re at in, like, terms of localization maturity. Then I’m supposed to step into their [00:05:00] process and say, “Hey, by the way, you need to be multilingual by this day.”

Now I’m being enabled by these tools to approach teams from a different perspective and saying, “Hey. Instead of doing it this way that we did it before, I don’t need to tell them that. I just say, “Hey, this is how we do it now, and it’s very easy, and here’s what you need to do.” And that, but for one specific piece of our onboarding process, let’s say to, to the globalization workflow in general. But what I’m seeing is oh my gosh this is one tiny piece that I can alleviate a lot of friction between friction may be the wrong word, but, the challenges that we experience in localization across the industry with securing resources across, you know, partner, partnering groups, engineering, et cetera.

And so what I’m able to do now is approach these teams with a whole new concept of how to approach our existing workflows and say, “Hey, we’re seeing opportunities for improvement in the [00:06:00] way that we do this entire thing. What do you think about doing it this way?” And instead of, instead of just having to talk around it, I can prototype it and I can build, I can build it in an hour.

I can have an existing working prototype that I can bring to it. So instead of just setting a meeting with people and then going through notes and taking diligent notes, I can show up with an existing prototype and say, “Hey, I was looking ahead at this meeting. I knew we were gonna be talking about this. I went ahead and just designed and prototyped this solution I think that could work.” And then you can get everybody together, and then you can be collaborating in real time on that thing.

And so I’m just really, really excited about the future of being able to collaborate with my main stakeholders that I see. That’s my whole job is I’m working with big stakeholder groups, you know, across those departments that we mentioned. And instead of coming at them as a like “Oh, no here’s [00:07:00] localization needs something.” Instead of that, response, I wanna, I wanna make people excited about the opportunity to work together, and here’s how we can do it in a streamlined and efficient manner.

And I, and I finally feel like I’m enabled to do or to propose these solutions myself without being like, “That sounds great in practice. We’ve got… You know our engineers are booked through the quarter or through the year. Like we need to see some significant evidence on how this is going to improve things before we can commit to giving you resources to accomplish this thing.”

And so I can do that already. I can build a small scale prototype and then deploy it and then measure against it and then take it to them and say, “Hey, I did this small piece of the work already. Here’s how it is working. Imagine if we implemented this across the board.” And so I’m able to show more immediate impact and value from from my [00:08:00] role.

That’s just been a huge confidence booster personally, and I’m like really excited about the work I’m doing again, and I love that. Was always, happy to be working it, but excited to build and create and and share that, and that’s just what’s been so exciting.

Stephanie Harris-Yee: So it sounds like, the technology is really kind of helping even on two fronts. So one is that initial analysis phase, so can go in, can see what the gaps are, and then the second component is maybe with the same technology, maybe with different types of technology, is being able to create those prototypes to then be able to go to your folks within the company who maybe they’re doing their own translation on the side because suddenly they think it’s easy.

But showing them that you have this better way, you have this new thing that you’ve put together and that you can collaborate on, and it’s not gonna be dumped on their plate. You have it, you’re developing it, you can make tweaks depending on their inputs and that has really been a good [00:09:00] enabler.

Casey Garland: Exactly that, yes. And being able to showcase these things to leadership teams and say, “Hey, like, you want us to be using AI? Like, here’s how we’re using it, and here are the immediate benefits we’re seeing from it.” So it’s just another feather in, in the localization team’s cap in the eyes of leadership, and so being able to show that in easy-to-understand terms for leadership like, “Hey, this new tool is gonna save us this much money and this much time,” so like as easy as that, instead of having to, “Here’s what I think is gonna happen.” We can immediately, build tools to show.

Stephanie Harris-Yee: Yeah. Do you have any recommendations on someone listening to this who– I know you mentioned like Claude and Gemini, and there’s so many different ones out there. Do you have any recommendations of, hey, if you have to get one, start with this for this kind of thing or that for that kind of thing?

Casey Garland: That’s a great question, and there are answers to that especially if [00:10:00] you are more more technical or wanting to develop more technical solutions. There are better, better models for that. And, and people can look up that information. It, it– I think it’s worth researching the work you’re doing to the model that you want to use to help you accomplish that task.

And so I use a variety of models, and that’s why I bring, mention a bunch of them. So I would recommend that people based on the task they’re trying to accomplish, just research which tool. And it could be a question that you ask whatever your preferred LLM is. Like, “Hey, which, which model should I use across all models to accomplish this task?”

But my recommendation, this comes from my coworker Bill, who has been a, a mentor for me in the technical space, like wanting to become more technical space. Organizing my, my, my initiatives, my projects in a, a structured, like note-taking environment. I use, use Obsidian as a tool that I use, and it’s just like a free-to-use tool, and it [00:11:00] allows you to organize all of your notes and your directories, your thoughts, your, your files and folders, in a way that is local and shareable with your LLM.

And so especially if you’re using a tool like Claude via the terminal, you can point it very easily to those folders, the working folder that you want to work in, and then have Claude be building out the documentation for you as you’re going. Like, “Hey, like, let’s create a README for this project. Now I need you to update this, plan.”

And I can add notes to that folder and say, “Hey,” so instead of blowing up my context window in the chat with the LLM, I can, I can write out my thoughts on the side in a note in this project folder and say, “Here are all my thoughts on how I think this should look. Here’s the challenge that we’re currently facing. Here’s, what we want it to look like in the end. Based on all of my notes, build me a project implementation plan. I need it to be like this long, like break it into weeks-long chunks for [00:12:00] me,” et cetera.

And so being able to use the tool as a personal, like note-taking assistant and documentation assistant while I’m building the thing, it’s all about context, right? The more context you can add to a working space that your tool can live in and observe and just live within the boundaries of that working directory, the more you can provide context outside of the session window that you’re in, the more efficient the tool will be able to work with you in any way.

So, so my recommendation is find the right tool for the job and then use whatever your preferred project organizing tool would be alongside your tool and have it work within that space and help you organize your thoughts and your project within that space. It helps for multiple sessions. You’re coming back, you’re iterating on things. It’s able to [00:13:00] retain all of that context for your project.

Stephanie Harris-Yee: Yeah, no, it totally makes sense. Any last takeaways out of this that you want people to go home and remember that we haven’t already covered?

Casey Garland: Yes, thank you for for reminding me. There was one last piece of advice. Because I’ve got all these grand ideas like I mentioned, like, “Oh, I’m gonna totally change the landscape of, like, globalization at Toast.” Well, I gotta start small and build on one small concept at a time a proof of concept, right?

And show, hey, like it’s one thing to prototype this massive overhaul and be like, “See what we can do?” But being able to have deliverables and work up to that and, and gain confidence in your leadership team that you are capable of driving this massive project forward by delivering on smaller milestones at a time.

And so that’s another piece of advice I got from Bill when I was first getting started, was it can be easy to get lost in this oh my gosh, this, I have such a great idea on how I’m gonna do this. But [00:14:00] trying to build Rome in one day, like that’s not how it’s gonna happen. So start small, iterate, get it working, and then build on top of it.

Stephanie Harris-Yee: All right, Casey. Thank you so much for this conversation and some really good tips for folks out there.

Casey Garland: Thank you so much for having me.