AI & Localization: 6 Lessons from Hilton

For global brands, one of the hardest parts of localization is making sure content feels natural in every market. Guests expect each page and offer on your site to feel like it was created specifically for them. But for many enterprise teams, delivering that experience means navigating shifting content priorities across siloed systems and lengthy review cycles.

Some companies might jump to adopt an artificial intelligence (AI) solution as quickly as possible, often resulting in frustration and limited success. Savvy companies know that the answer isn’t “more AI,” it’s the right mix of AI and human expertise.

In our recent webinar, Hilton’s Playbook for Enterprise AI Adoption, I sat down with Curtis Pettit, Manager of AI Enablement and Measurement at Hilton, and Leah Wanta, Solutions Director at TransPerfect, to unpack how Hilton scaled AI across its global initiatives. Here are six lessons from that conversation to help shape your localization strategy.

1. Start with a Clear Vision and Measurable Goals

Before selecting technology, Hilton defined a simple, ambitious goal: for every hotel, make the full digital customer experience—discovery, browsing, and booking—available in all supported languages. Hilton’s scale, combined with tens of thousands of annual content updates and hundreds of new hotel openings, made manual translation alone impossible. AI wasn’t chosen because it was trendy; it was the only path to making the vision a reality.

The lesson for companies: define the goal first, so AI supports what the business actually needs. When planning your localization program, identify key languages and content types, set success metrics (customer satisfaction, cost savings, or time-to-market), and secure executive sponsorship to align stakeholders.

2. Benchmark and Select the Right AI Models

Not all translation technology is created equal. Hilton knew that fast neural machine translation (NMT) was essential for delivering instant translation on its site, but which engine would provide the best output? To find out, Hilton compared multiple engines using actual site content and asked native speakers to score the results for accuracy and fluency.

The takeaway: pilot different models with real content and choose the mix that meets your quality threshold. Use native speaker evaluations and keep separate tests for each language, as performance varies by language pair.

3. Customize with Translation Memory and Terminology

AI thrives on data. Hilton trained its NMT engines using decades of human-translated content, along with robust style guides and glossaries. Hilton created do-not-translate lists and term enforcement rules to ensure brand names like “Hilton Honors” remained untouched. This customization improved translation quality across languages. Organizations should build or collect translation memories, compile preferred terminology, and enforce style guides before deploying AI. These assets teach the machine to respect your brand voice and help prevent costly misinterpretations.

4. Build a Cross-Functional Team and AI Governance Framework

Technology alone doesn’t deliver global experiences—people do. Hilton’s project team included regional marketers, content strategists, developers, and executives from around the world. They met regularly to make decisions, align priorities, and quickly resolve issues. Senior stakeholders empowered the team to act and stepped in when strategic guidance was needed. To replicate this, assemble a diverse group representing all regions and functions. Establish a clear decision-making process, define roles and responsibilities, and foster collaboration through regular meetings. Document your roadmap and communicate capabilities clearly to set expectations.

5. Implement Continuous Quality Control and Feedback Loops

AI translation isn’t a set-and-forget solution. Hilton benchmarked quality before each language launch and continued auditing after go-live. It sampled content, crowdsourced feedback from hotel teams, and used translation memory to reuse common phrases like “one king bed” thousands of times. Structured forms allowed stakeholders to flag issues, which language leads categorized into mistranslations, source-text problems, or stylistic concerns. Trends informed global fixes such as adjusting model customizations or tweaking the source website code to avoid concatenated strings. To adopt this approach, implement sampling audits, assign reviewers, track metrics (error rates, edit distance), and create a formal feedback channel.

6. Train Your Team for AI-assisted Workflows

Many translation errors originate in the source language. For example, Hilton found that “play pool” (the game) translated as “play swimming pool.” To remedy this, HIlton trained copywriters to use the specific term “billiards,” resulting in reduced errors and improved AI output. The lesson:

• Provide writers with examples of problematic phrases and preferred alternatives.
• Encourage them to write with clarity and cultural neutrality in mind.
• When creative language is essential, plan for human post-editing.

Empowered writers become essential partners in delivering global experiences.

Final Thoughts: AI Is a Catalyst, Not a Cure-All

Hilton’s story shows that AI can bring global visions to life, but only when it’s grounded in clear goals, strong data, and human collaboration. By following these six lessons, organizations can scale translation while preserving brand voice and cultural relevance. AI doesn’t replace people; it empowers them, freeing teams to focus on creativity and strategy.

Embarking on your own AI-driven localization journey? Start by defining your vision, testing models, and building a foundation of the people and processes you need to support it.

Ready to take the first step? Connect with our team today.