How, when, and why to use AI for global content

At Acclaro, we’re always looking for ways to help teams work smarter—not just faster—when it comes to creating impactful global content. That’s why we brought together a panel of industry experts to have a frank, practical conversation about where AI fits into the content lifecycle today, called AI for global content: how to measure its impact and minimize its risks.

We were joined by three industry leaders who brought a wealth of experience in global content strategy, localization, and AI innovation:

Coral Diez Carbajo is the MT & AI Strategist at Acclaro. A machine translation pioneer, Coral blends practical translation workflow experience with hands‑on generative AI and micro‑localization tactics—perfect for pushing campaigns to perform better at scale.
Bruno Herrmann is the Global Content & Digital Experience Advisor and Vice‑Chairman at LT‑Innovate. With over 30 years of helping companies scale content and customer experiences internationally, Bruno grounded our conversation around clean data, thoughtful processes, and cultural context.
Matt Rodano is the VP of Strategic Accounts at Acclaro. With deep roots in localization strategy and client success, Matt spoke about realistic ROI, personalization models, and how teams are evolving roles as AI becomes part of their workflow.

This wasn’t a conversation about hype or silver bullets. It was about what’s working, what’s still evolving, and how AI can support, not replace, the people and processes behind high-performing multilingual content. From upstream planning to measurement and iteration, the insights shared in this session are already helping teams rethink their approach.

While you may want to watch the webinar in full, here we recap the top points the experts made.

AI can support the entire content lifecycle, not just translation

AI’s value goes far beyond translation. As Coral Diaz Caro highlighted, AI is quietly transforming every stage of the content lifecycle:

Campaign planning with local insights: AI tools can analyze regional trends, local holidays, and social conversations to give marketers crucial context when designing campaigns.
Cultural moment detection: Spotting upcoming events or cultural signals (e.g., a local sports final or festival) helps provide timely, resonant messaging.
Real-time performance monitoring: Instead of waiting weeks, teams can now track multilingual content live, allowing them to optimize underperforming assets quickly.
Micro-localization: AI supports nuanced phrasing adjustments, such as tailoring French for Quebec vs. France, at scale

Matt Rodano added that personalization is no longer expensive. AI democratizes custom content, allowing brands to speak directly to local audiences without overshooting budgets.

Your content strategy becomes smarter when AI reads market signals, spots opportunities, and helps teams adapt messaging before, during, and after launch. Think of it as shifting from a static plan to a dynamic content flow, driven by data, creativity, and lasting impact.

Start with the big picture before generating any multilingual content

One of the core takeaways from the webinar was a reminder that you shouldn’t begin with the content output. As Bruno Herrmann emphasized, it’s crucial to “zoom out” and look at upstream processes long before generating multilingual content. For example, if the source data you use to generate multilingual content is messy, filled with inconsistent terminology, unclear phrasing, or formatting issues, even the most advanced AI won’t deliver accurate translations or reliable insights.

Clean data matters because AI that uses disorganized or untagged data can produce translations with tone inconsistencies, cultural misinterpretations, or dropped nuances. The well-worn principle “garbage in, garbage out” couldn’t be clearer in this context, especially with multilingual content that requires precision across languages and cultures.

That’s why prompt engineering should be a collaborative effort, not a siloed data science activity. Bruno and the panel underscored that linguists and cultural specialists must be involved from day one, helping to shape prompts that reflect tone, regional nuance, and cultural context. This multi-disciplinary input ensures AI doesn’t just interpret meaning but also communicates it effectively.

Before asking AI to generate your next multilingual asset, whether it’s a landing page, campaign brief, or product manual, prioritize the quality of both your data and your source content. Set up collaborative workflows involving data engineers, prompt experts, and linguists. Leverage services like Acclaro’s Data Services to ensure your AI has the clean, annotated, culturally sensitive inputs it needs to perform reliably.

Your tech stack might not be ready for AI yet

A recurring caution at the webinar was that AI can’t simply be dropped into legacy systems. Without proper integration, it risks creating process chaos, security gaps, and broken workflows.

Coral shared an eye-opening case: handling a mountain of scanned PDFs, handwritten notes, and images in 55 languages. Technology played a part, including OCR, LLMs, and secure platforms, but the success of the project stemmed from assembling a robust cross-functional team of project managers, linguists, engineers, and prompt experts from day one.

That approach echoes Acclaro’s broader philosophy: you must design the end-to-end process, not just select tools. Without defined file handoffs, version control, security protocols, and integrated workflows, AI becomes more of a loose bolt than a gear.

Pro tip: Audit your tech stack early. Do you have secure storage? Versioning? TMS or CMS integration? These are key factors that determine whether AI will integrate effectively into an existing tech stack. AI is precious, but only if it works in concert with your existing systems.

People are still important and might be even more important now

AI is a powerful enabler, but it doesn’t make humans obsolete; in fact, its success depends on human input and collaboration.

Coral shared that the best teams viewed AI as an experiment, not a guarantor. Those teams were:

Embracing small tests
Gathering feedback from all relevant teams
Iterating frequently

Bruno’s metaphor echoed this view: AI is like tea, you don’t dump it; you infuse it. You decide the strength, upstream sources, and levels of human guidance. Matt reinforced the shift in roles: translators becoming strategists, project managers becoming data owners. AI doesn’t replace people, it retools them.

Takeaway: Build cross-functional teams early: mix linguists, strategy, engineering, and data. Encourage pilots. Promote feedback loops. AI implementations work best within a corporate culture that respects human insight and involves experts at each step.

Measuring success is about more than just cost savings

How do you know AI is working? Here’s how our panel suggested measuring it:

Bruno: Tie AI to real problems, be it metrics like faster translation turnaround, higher engagement, and/or lower error rates? Those are measurable wins.
Coral: Measure internal adoption. If teams use it gladly and ask for it, it’s helping.
Matt: Track total investment, training, integration, and human time, not just dollars per word or MT cost. Does overall efficiency and ROI improve?

These measures line up with best practices in the industry and AI localization: evaluate AI using cost, quality, speed, and human satisfaction. AI implementations should be seen as more of a strategic initiative than a short-lived cost play.

The hype is cooling, and that’s a good thing

Finally, the panel welcomed what they called the “cooling” phase of AI adoption. They are seeing fewer instances of companies rushing to implement ChatGPT across everything, without a clear strategy. Instead:

Many brands now kick off with machine translation (MT) as a proven base, and then experiment with AI layers for QA, summarization, and planning.
There’s growing interest in open, modular, interoperable systems, where tools like quality estimation, glossaries, and prompt templates can be combined or swapped, not chained to a single platform.
Early projects promising 85–90% cost savings “overnight”? Those fell short. Now, AI projects are more measured, iterative, and realistic.

Takeaway: We’re past the flash—now it’s strategy. AI’s value lies in scalable foundations, thoughtfully layered tools, and flexibility to evolve. Slow down, experiment, measure, and refine.

Your next steps for AI implementations

At Acclaro, we operate with the view that building a successful AI strategy for global content isn’t about chasing trends—it’s about creating a strong, adaptable foundation. This webinar underscored the importance of clean source data, collaborative processes, cross-functional teams, and thoughtful measurement. AI has the potential to amplify human expertise, but only when it’s implemented with care, clarity, and purpose.

If you’re looking to explore how AI can fit into your global content strategy or if you’re ready to take your first steps, we’re here to help. Let’s start a conversation about how to build an AI-enhanced approach that aligns with your goals and puts people at the center. Let’s chat: [email protected]