Speed vs Quality in AI Translation

There’s a widespread assumption that AI is a plug-and-play solution — something you can drop into your translation workflows and immediately see faster output, lower costs, and better results. But as John points out, that’s a product of hype, not reality.

“We haven’t completely finished the hype cycle of AI,” he says. “There’s this attitude that it’s going to solve all these problems — but it’s not magic.”

The root of the problem is a mismatch between expectation and execution. AI can deliver significant productivity gains, but only if it’s used thoughtfully. Simply applying it to all content indiscriminately often creates more work downstream, especially when teams find themselves having to correct or post-edit low-quality outputs.

The key, John says, is not in dismissing AI — but in understanding where and how it can actually deliver value. That starts with evaluating whether the technology is suited to the specific content type, audience, and risk level.

“AI opens new doors,” he explains, “but you still have to verify that it’s doing what you want it to do. You can’t skip that step.”

Companies that chase speed without putting the right checks in place risk introducing errors, undermining brand voice, and ultimately losing trust with global audiences. AI can be transformative — but only when deployed with clear goals and guardrails.

One of the biggest pitfalls in adopting AI for translation is treating all content the same. In reality, every content type carries its own expectations for quality, speed, and cost — and that means AI’s role needs to be carefully matched to the task.

“What we’ve learned over the past couple of decades in localization,” John explains, “is that different types of content require different approaches. And not all of them are a good fit for AI.”

This is where the concept of dynamic quality comes in — the idea that translation quality isn’t a single, fixed standard, but should flex depending on the content’s purpose. For example, translating legal documentation, marketing campaigns, and internal memos all demand very different levels of scrutiny and linguistic nuance.

According to John, the question companies should be asking isn’t “Is this translation good?” but “Is this translation good enough for the purpose it needs to serve?”

“Some of the biggest gains from machine translation and AI have come in content that no one would have paid a human to translate in the first place,” he says. “But now, people are trying to plug AI into everything without stepping back to assess whether it actually makes sense.”

That’s where risk becomes a more useful lens than quality. If the risk of getting a translation wrong is low — say, for FAQ pages or support content — AI might be a great fit. But when content carries regulatory implications or is critical to your brand voice, that same AI could introduce costly mistakes.

When companies talk about translation quality, the conversation often gets stuck on surface-level issues — things like grammatical accuracy, terminology consistency, or reviewer feedback. 

While these factors matter, they don’t tell the full story. According to John, the real measure of success begins before translation even happens.

This is a fundamental mindset shift. 

For example, if you’re producing a marketing email in English and translating it into ten languages, the question shouldn’t just be whether the translations are linguistically sound. The real question is whether those emails are generating clicks, conversions, or whatever KPI they were designed to support.

John emphasizes that success metrics will vary depending on the type of content and the team producing it. 

• A product team writing interface strings might measure success by user engagement or task completion
• A technical writer might look at support ticket deflection
• A marketer might care about lead volume

Each of these goals requires a different lens for assessing translation quality. And this is where the right technology becomes critical. 

A TMS like XTM doesn’t just help manage translation workflows — it helps organizations connect language performance with business outcomes. XTM provides visibility into quality scores, turnaround times, and costs, but it also integrates with broader enterprise systems to help companies close the feedback loop.

“We don’t know if your website is generating MQLs,” John notes, “but we give you the ability to plug into those systems and track how translated content is performing across markets.”

When you can tie translation activity to measurable outcomes, you move beyond debating whether AI output is “good enough.” Instead, you can start making decisions based on what’s working — and where to optimize further. That’s the real definition of quality in a modern localization strategy.

Speed is often the headline benefit of AI translation. It’s one of the first things companies look for — and one of the most misunderstood.

“People obsess over speed — always have,” John says. “But you’ve got to ask yourself: how long are you willing to wait for the right answer? That’s the real question.”

AI tools, especially generative ones, can produce content quickly. But “fast” doesn’t always mean efficient. In many cases, the outputs need significant review and rework — which not only slows things down, but also negates the productivity gains companies were chasing in the first place.

Take, for example, a marketing team translating a product launch campaign into six languages using a generative AI tool. The AI delivers translations within minutes — but the review team then spends two full days correcting tone, fixing terminology, and aligning the messaging to brand guidelines. What looked fast on paper ends up taking longer than if they had used a more targeted, rules-based system from the start.

This disconnect happens when teams measure speed at the wrong point in the process. As John explains, true efficiency comes from looking at end-to-end turnaround, not just the initial output.

To assess whether AI is actually saving time, teams should ask:

• Are we publishing content faster — or just reviewing it longer?

• Is the AI reducing work — or shifting it to another part of the process?

• How often are we having to backtrack or retranslate?

• Are we introducing errors that will need to be fixed later?

“AI is getting faster, no doubt,” says John. “But speed without control can just create a different kind of mess.”

The goal shouldn’t be speed at any cost. It should be reliable speed — where quality is consistent, workflows are predictable, and the content meets expectations the first time around.

With all the excitement around AI, it’s easy to feel pressure to implement it quickly — especially when competitors appear to be moving fast. But as John points out, the most successful companies aren’t necessarily the fastest adopters. They’re the most intentional.

“AI has huge potential,” he says, “but you need to align it to your brand, your goals, and your risk tolerance. Otherwise, you’re just creating more complexity.”

The companies that are thriving with AI in localization are taking a measured, strategic approach. They’re not relying on generic tools or rushing to automate everything. Instead, they’re focusing on creating a solid foundation for AI to succeed — and scale — in the right way.

Here’s what that looks like in practice:

• Start with clear goals — Know what success looks like for each content type and audience.
• Define your risk zones — Identify which content can be safely automated and which needs more control.
• Train your AI tools — Fine-tune systems using your own content, tone of voice, and brand standards.
• Centralize your workflows — Use platforms like XTM to connect AI with human oversight, QA, and analytics.
• Test, learn, and iterate — Build feedback loops that allow you to constantly improve your workflows and outcomes.

“Every enterprise app out there is trying to add AI — from Gmail to Jira,” John says. “But they don’t know your brand. That’s why alignment is so critical.”

Rather than relying on disconnected tools and hoping for the best, organizations should focus on building systems that are cohesive, flexible, and designed to evolve. With the right infrastructure — and the right mindset — AI can become a powerful engine for growth, not a source of risk.