Language AI you can trust: How to manage your brand reputation at scale

In today's rapidly evolving AI landscape, translation technology presents both extraordinary opportunities and significant risks for global businesses. To explore how organizations can harness Language AI to stay in control of translation quality and protect brand integrity, we hosted a webinar with industry experts Kathleen Pierce, Principal Analyst at Forrester, and Morana Perić, Director of Localization at DeepL. 

Kathleen brings over 25 years of experience across content strategy, sales enablement, localization, UI/UX, process optimization, compliance, and taxonomy, while Morana has spent 15 years building scalable localization and content operations in tech.  Here are some of the highlights.

Wielding AI safely: From chainsaw effect to trust premium

"Localization AI feels like learning to use a chainsaw," Kathleen explained during the webinar. "You need skill, you need strength, you need to know how to use it. It can do a lot for you, but it can also really backfire."

While Language AI offers unprecedented capabilities to scale global communications, the risks of getting it wrong can be severe.

Which brings us to the heart of the issue: trust. Forrester's research demonstrates what they call the "trust premium"—the tangible business benefits that come from customer trust. When customers trust a brand, they're significantly more willing to purchase additional products, continue doing business, and even pay premium prices. Yet when it comes to AI implementation, nine of the top-15 organizational concerns pertain directly to trust, whether it’s data privacy and compliance or overconfidence and inaccurate outputs. 

The challenge is clear: How do you harness the power of Language AI without compromising the trust you’ve worked so hard to earn?

Redefining quality as “fit for purpose”

Building that trust starts with how you define quality. One of the strongest insights from our discussion was that, in the AI era,  translation quality can’t be a universal standard anymore.

"Quality should be redefined as fit for purpose," Kathleen advised. "A promo video needs different standards than a user form or legal contract. Quality must be fit for the audience, fit for the interaction."

Defining quality this way also clarifies where human expertise still matters most: idioms, cultural nuance, business-specific terminology, and interpreting context, style, and tone. Depending on the content and its audience, organizations can decide what level of control they need—and whether human review or full transcreation is necessary. 

This more strategic approach to quality allows teams to strike the right balance between speed, scale, and precision, and enables them to allocate resources where they matter most, rather than applying the same standards everywhere.

We’re already seeing this approach in action. Weglot, for example, applies a hybrid AI-and-human model for website translation, combining automated quality checks with human oversight and aligning quality controls with content importance.

Morana echoed this from her hands-on experience at DeepL: “We all want speed, quality, and lower costs. You can get your content out quickly, but you have to be comfortable with different levels of quality and investment. You might invest in transcreation, review, and research for certain types of content—but not all of it. That’s simply not possible.”

Morana also talked about how we implement this philosophy at DeepL. For example, all our Help Center communications are AI-generated and sent out without human review. “We employ all of our tools – the glossary, the customization – so we're confident that this is fit for purpose. But there's a lot of thought behind that approach; we don't just automatically take the same action for every type of content."

Ultimately, building trust in your AI output requires strong control mechanisms within your translation system —  as well as continued human oversight where it truly matters. After all, who understands linguistic complexity better than your own in-house localization experts?

Elevate, don't eliminate your localization teams

One concerning trend Kathleen observed is organizations laying off localization teams because of AI advancements. "That’s a big mistake," she emphasized. "We need to elevate our localization teams, not decimate them!"

After all, language is complex, and human expertise is essential to guide AI implementation.  While translators understand audience differences, context, cultural nuances, brand voice, and industry-specific terminology, AI must be taught these things explicitly. That’s where localization teams come in—moving beyond execution to become strategic advisors.

Instead of simply delivering translations, localization teams can help design the end-to-end translation workflow—defining processes, tools, and quality controls across the organization. As Kathleen explained, "It used to be that localization teams were order-takers. They would receive orders to get a translation. Now, you should be positioning them as advisors so that they are helping all these other functions build a pipeline, building the process and technology and quality controls."

Why you need to pick up that chainsaw

Kathleen’s warning was clear: AI translation is inevitable. "This is water coming up through the floorboards. This is not something that we can escape."

Without controlled, customized AI translation solutions, customers will turn to generic translators—risking brand voice, miscommunication, and costly mistakes. "How much do you care about your brand voice and people understanding what you’re saying?" Kathleen noted. Advanced, complex services and tech demand more than a generic LLM.

Morana reinforced this from her experience with localization teams. "In this day and age, when we have all of this content coming in, you really need to know how to do this balancing act – where to invest into training your models, when to invest into having your people look at it. I don't think there's escaping working with technology today."

Control: the new frontier for enterprise AI adoption

AI adoption brings undeniable opportunity but also significant risk. To manage it, organizations need robust processes that clean up translation data, introduce the right controls, and ensure that their AI translation technologies accurately reflect brand voice across global markets.

At DeepL, we've responded to these needs by recently launching our Customization Hub, providing organizations with powerful tools like glossaries, style rules, translation memory, and style profiles to ensure their unique voice and terminology are preserved across languages.

By defining quality expectations by content type, elevating localization teams to strategic partners, and implementing clear guardrails, organizations can confidently expand their global communications without compromising trust.

Take charge of your AI-powered translations

The future isn't about choosing between human expertise and AI, but rather finding the right synergy between them. 

In our latest webinar, “Translation AI you can trust: How to manage your brand reputation at scale,” we break down how leading brands are balancing speed, scale, and quality to take charge of their AI translations and protect their brand voice.

So you can harness a smarter, more controlled approach to your localization workflows. One where quality is built in at every stage. 

Watch the webinar in full here.