Expert Panel: How Localization Leaders Are Implementing AI in 2025 | XTM
In 2024, the localization world buzzed with excitement over AI’s potential. But turning that excitement into real-world results? That’s been the tricky part.
Now, as we head into 2025, teams are rolling up their sleeves and figuring out how to actually make AI work in their day-to-day operations.
On Jan 30 2025, we sat down with four localization experts to tackle this head-on. Our panelists include:
- Alessandra Binazzi (Globalization Strategy Consultant)
- Alison Toon (Senior Analyst at CSA Research)
- Bruno Herrmann (Global Advisor at International Achievers Group)
- Ian Evans (CEO at XTM International)
Below, we’ve shared three of the biggest trends shaping localization in 2025, straight from our panel discussion and backed by our audience poll.
1. AI Moves from Theory to Practical Implementation
In 2024, localization teams tested AI’s potential by running pilot after pilot.
Conferences buzzed with theoretical discussions about AI’s impact. But something changed as the year progressed:
Alessandro Vinatzer explains:
“2024 was really the year of POC and pilots, organizations weren’t just experimenting – they were trying to keep up with a steep learning curve and find relevant applications for these technologies.”
That experimental phase has given way to something more practical.
More than 80% of the localization leaders we polled as part of the webinar identified “practical AI implementation and workflow integration” as their top priority for 2025.
Localization teams are prioritizing three key areas:
Operational Implementation
The time for experimentation is over. Leading organizations are implementing production-ready solutions.
This means integrating AI tools into existing workflows and measuring concrete business outcomes. The focus has shifted from what it could do to what it can reliably deliver today.
Start by selecting targeted use cases where AI can clearly improve your workflow – like preprocessing content or checking quality – rather than trying to transform everything overnight.
It’s about making AI work for you, not the other way around.
Evolving Job Roles
AI is reshaping human roles, not replacing them.
Bruno Herrmann, Global Advisor at International Achievers Group, emphasizes the need for “new toolsets, skillsets, and mindsets.”
Companies are investing in quality evaluation of AI outputs, prompt engineering, language data management, and AI project management.
These aren’t just job titles – they’re emerging specializations that bridge the gap between AI capabilities and business needs.
A Value-Driven Approach
The focus has shifted from AI capabilities to business impact.
Irma advocates for a “phased approach,” starting with awareness of AI’s capabilities and limitations, moving through organizational readiness, and finally measuring effectiveness through concrete business outcomes.
This methodical approach helps organizations avoid the hype cycle and focus on sustainable implementation.
2. The Reality of AI’s Language Capabilities
AI promised to handle any language pair flawlessly. But the reality is more complex.
Almost half of the language data used to train public large language models is English, then there’s a small percentage of top European languages, and it gets smaller and smaller for other languages.”
explains Alison Toon, Senior Analyst at CSA Research.
This imbalance creates significant challenges for global businesses.
Understanding the Language Gap
Popular AI models perform well for common language pairs like English to Spanish or French. But performance drops significantly for languages with less training data.
Enterprise localization often involves 60 to 70 languages, so companies can’t rely on a one-size-fits-all AI solution.
To overcome this challenge, leverage existing translation assets. This includes years of accumulated translation memories, terminology databases, and style guides.
The result?
Workflows tailored to your brand and customers.
Use AI-first approaches for high-resource languages, combine AI with traditional MT for mid-tier languages, and maintain traditional approaches for less common languages.
Why Traditional Machine Translation Still Matters
Traditional MT isn’t going away. It’s becoming part of a broader toolkit.
You can’t just declare traditional MT obsolete in favor of large language models – they’re not there yet, each technology has its strengths, particularly when you look beyond major European languages.”
Toon emphasizes.
The key is knowing when to use each tool:
- AI excels at creative content and context-heavy translations
- Traditional MT remains reliable for technical content and less-common languages
However, some projects require both. For example, a global website may combine straightforward support content with engaging customer stories.
The goal is to leverage each technology’s strengths while compensating for its weaknesses.
Making Smart Language Decisions
Rather than applying AI indiscriminately, leading organizations are taking a strategic approach to organizing resources.
Here’s how:
- Data-driven assets: Using existing translation assets – memories, glossaries, and style guides – to enhance AI performance for specific language pairs.
- Market-specific strategy: Not every market needs the same approach. Factors like content type, audience, and available language data determine the right mix of AI and traditional tools.
- Resource allocation: Organizations are investing more in high-priority language pairs while maintaining efficient solutions for lower-volume languages.
“The maturity of languages and language pairs is going to make a difference,” notes Ian Evans, CEO at XTM International. “You’ve got to be able to take intelligence and put that into your workflow.”
3. Quality Management Evolves Beyond Error Counting
The rise of AI has transformed how localization teams think about quality.
“We’re seeing a real change in focus,” explains Alison Toon. “Moving from counting spelling errors to real risk management as it applies to language and content.”
This shift reflects a deeper understanding of what quality means in 2025.
A New Approach to Quality
Traditional quality checks are limited to grammar and terminology. Today, a more comprehensive approach is needed:
- Evaluating content risk levels to determine appropriate translation workflows and review requirements
- Assessing brand consistency to ensure AI-generated content aligns with tone of voice and style across all markets
- Measuring business impact to track how localization quality affects customer engagement and sales performance
- Managing data security to protect sensitive information while leveraging AI translation tools
“Organizations are looking at quality from higher up,” Toon notes. “Helping evaluate which types of content should be processed through large language models, machine translation, human review, or purely human translation.”
Security Takes Center Stage
Data security has become a critical part of the quality management mix. According to Toon, security concerns now go far beyond basic data protection.
Companies must safeguard customer data and sensitive content during AI processing. Keeping clear records of content origins and processing methods is essential for security and compliance.
There’s also growing pressure around where and how data moves through AI systems. Organizations must comply with regional data residency requirements and provide complete transparency about how they’re using AI in their content workflows.
“A few years ago, security was just one box to check off,” Toon explains. “Now, you get 50 to 100-page questionnaires about information security when you’re a provider to a large enterprise.”
Balancing Speed with Brand Consistency
Companies face a growing challenge with AI language models.
Unlike traditional MT, these models can’t be easily trained to match brand voice.
“The output is harder to control, and it’s not as easy to train these models like we trained MT, you can’t just feed it your translation memory and expect it to start speaking like you.”
explains Alessandra Binazzi.
The solution lies in a balanced approach:
- Automated quality estimation for efficiency to quickly assess large volumes of content and determine appropriate review levels
- Human review for brand alignment to ensure AI outputs maintain brand voice, style, and cultural nuances
- Clear guidelines for AI usage to help teams make consistent decisions about when and how to apply AI tools
- Regular quality benchmarking to track performance trends and identify areas needing improvement
The Road Ahead for Localization in 2025
AI hype has created high expectations in the localization space. Today, it’s all about real business value rather than theoretical possibilities.
Companies that thrive will combine AI capabilities with human expertise, maintain strong quality management practices, and develop smart strategies for handling different language pairs.
A significant shift is happening, and it’s changing how organizations view localization. It’s no longer a support function, but a strategic pillar that shapes how companies expand into new markets.
Future-Proof Your Localization Operations with XTM
Get ahead of the shifts in localization. Built for global businesses, our platform helps you to scale your translation efforts, faster. Request a consultation to see how.