The future of language intelligence: A conversation with XTM CEO Lorcan Malone

Managing global content isn't getting any easier. If anything, it's becoming more complex by the day. More languages, more content, more systems to connect. And somehow, you're expected to do it all faster and more efficiently than ever before.

Sound familiar?

We sat down with Lorcan Malone, the CEO of XTM, to talk about how companies are navigating this complexity.

What we discovered is that the most successful localization teams aren't just translating content. They're orchestrating entire ecosystems. And that shift in thinking changes everything.

Prefer video? You can watch the full interview below!

It's Not Just Translation. It's a Supply Chain.

Here's something that might surprise you: your localization operation has more in common with a manufacturing supply chain than you might think.

"There's the content creation side of it. There's the product development side, there's the vendors, there's your internal people. There's your reviewers, there's the translators," Lorcan explains. "So there's an entire ecosystem out there for all of this.”

Think about it. You've got content coming from multiple sources: your CMS, your product teams, your marketing department, maybe even GitHub. That content needs to flow through translators, reviewers, and quality checks. Then it needs to land in the right place at the other end, whether that's your website, your app, or your product documentation.

And you need all of this to happen predictably, with proper governance, and at a consistent quality level.

That's not simple. In fact, Lorcan says it's one of the things that struck him most since joining XTM: "What they do is difficult."

The problem with a fragmented workstack

Here's where many companies run into trouble. They've assembled a collection of really exciting tools. Maybe a great translation memory system here, a shiny new AI tool there, a content management system somewhere else.

But if these tools don't talk to each other? You've got a problem.

"The problem with a series of disconnected tools is it becomes very inefficient and you lose the benefit of a well-orchestrated ecosystem," Lorcan points out. "You may have some very exciting tools out there, but if they don't play a part in a connected ecosystem, then you've got an issue."

This is where the concept of "composability" comes in. You should be able to choose the best tools for your needs, and they should all work together seamlessly.

Whether you're using one product or ten, whether they're all from the same vendor or from different companies entirely, they need to stick together. XTM integrates with CMS solutions, GitHub, machine translation engines, and large language models.

Why? Because that's what real-world localization operations look like.

Finding the sweet spot: Automation vs. human control

Let's talk about AI for a minute. There's a lot of hype out there about what AI can do. And yes, it's changing the game in localization.

But here's the thing: humans are still in control.

"It augments what humans do, but the human is still in control," Lorcan emphasizes. "Even where you automate a lot, our philosophy is that humans still control the outcome."

That philosophy is shaping how XTM is building its latest AI innovations.

Intelligent Post-Editing

Take Intelligent Post-Editing, for example.

Instead of routing every machine translation through full human review, XTM uses AI-driven quality scoring to assess translation output automatically.

Based on configurable thresholds, content that meets quality standards can move forward without unnecessary human intervention. Lower-scoring content is intelligently routed to expert linguists for review.

The result? Human expertise is focused where it adds value. Not wasted where it doesn’t.

For organizations operating in regulated industries (financial services, healthcare, legal) this balance is critical. AI can accelerate workflows and reduce repetitive effort, but governance and accountability remain firmly in human hands.

XTM Agent

And then there’s XTM Agent.

If Intelligent Post-Editing improves the quality and efficiency of translation workflows, XTM Agent transforms how teams interact with the entire localization ecosystem.

Instead of navigating complex workflows manually, teams can interact conversationally with the system. XTM Agent understands context across projects, vendors, quality thresholds, and integrations. It turns fragmented operational data into clear next steps.

This is where localization shifts from task management to orchestration.

AI in XTM isn’t about replacing people. It’s about eliminating bottlenecks, automating decision layers, and enabling globalization teams to operate at enterprise scale without adding headcount.

And in a world where content volumes are exploding, “good enough” AI isn’t enough. You need intelligent automation embedded directly into your workflows, governed by clear policies, and adaptable to your risk profile.

That’s what XTM is building.

What does value actually look like?

Okay, so what does this actually mean in practice? Let's look at some real examples.

1. The productivity play

One XTM customer recently adopted new AI capabilities, particularly around quality scoring. The system uses multiple large language models to evaluate translation quality and give it a score.

Here's the clever part: you can set different acceptable scores for different language pairs based on your risk profile. Maybe certain languages need to hit 100%, while others can be 95%.

The result? This customer believes they can save 40-50% of their translation costs.

But they're not pocketing that money. They're using it to translate way more content with the same resources they already have.

That's important because content is exploding.

Adobe predicts there will be five times more content produced in the coming years, largely thanks to AI agents creating content. You can't just spend five times more money on localization. You need to get more productive.

2. The speed play

Another customer (a consumer goods manufacturer) needed to get training videos and product videos out to global markets faster.

Using XTM's Video Creation Cloud, they can now localize video content in a much more automated way.

It's not primarily about cost savings for them. It's about time to market. They can get material out to their global teams and customers incredibly quickly.

In a world that moves this fast, that speed advantage matters.

More languages, higher expectations

Here's an interesting trend Lorcan is seeing: customers are asking for more languages than ever before.

Why? Because consumer expectations have changed.

"I think it's an expectation of customers around the world to deal in their own language," Lorcan explains. "The bar for globalization is raising because these technologies have set expectations across the consumer base that they should deal in their own language."

Large language models can handle more and more language pairs. There's more training data available. And customers around the world increasingly expect to interact with your product or content in their native language.

If you're only localizing into your top five languages, you might be leaving opportunity on the table.

Building the future: What's next?

So what's coming in 2026 and beyond?

Lorcan's prediction centers on something called "agentic experiences". Basically, a fundamental shift in how we interact with software.

Imagine this: you're in Slack or Microsoft Teams, and you type, "Take my latest marketing campaign and translate it into these five languages. If it passes the quality test, publish it to our website."

In the background, XTM orchestrates everything. It talks to your various systems, brings in human translators if needed through automated workflows, and delivers the result. All from a simple conversational request.

"Over the next couple of years, you're going to see a fundamental change in how people interact with systems," Lorcan predicts. "Any system. And I include XTM in that."

Build with customers, not for them

One thing that stood out in our conversation was Lorcan's approach to product development.

"I always like to build products in conjunction with a customer," he says. XTM currently has design partnerships running with customers in different areas, sitting down together for days at a time, iterating through how AI will impact their work, and building it directly into the system.

But there's a balance. Sometimes you need to work closely with customers. Other times, you need to lead the way and innovate ahead of what customers are asking for.

The key is working with customers who are themselves innovators. The best localization teams in the world who are leading the industry forward.

The bottom line

Managing global content at scale isn't getting simpler. But it is getting more possible.

The companies that succeed are the ones who stop thinking about localization as a series of disconnected translation tasks and start thinking about it as an orchestrated supply chain.

They're the ones who embrace composability, choosing the right tools and making sure they work together seamlessly.

They're the ones who use AI to augment human capabilities, not replace them.

And they're the ones who recognize that in a world of content explosion and rising global expectations, productivity and speed aren't nice-to-haves—they're essential.

The future of localization isn't just about translating more content faster. It's about creating an intelligent, connected ecosystem that helps you deliver the right content, in the right language, at the right time, with the right level of quality.

That's language intelligence. And that's where the industry is headed.