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2026-05-13

Enterprise AI Translation: How to Scale Without Losing Control

On-demand webinar hosted by Petra Angeli, Head of Global Solutions at Acolad

Enterprise AI translation programs fail not because the technology is wrong, but because the operating model around it was never defined. This session covers the structural decisions that determine whether an AI translation program scales cleanly or generates rework, compliance gaps, and inconsistent output. 

Acolad Lia aspiration illustration woman dancing in front of logo

Meet Lia. Your AI-Powered Content Partner

From creation to translation and optimization, Lia blends advanced AI with human expertise to deliver fast, high-quality, brand-safe content—at scale, in any language.

Key Takeaways

Scaling AI translation is an operational problem, not a technology problem. These are the decisions that determine whether your program holds up under volume, compliance review, and cross-team scrutiny.

AI Does Not Perform Equally Across Content Types

Lia routes content to the right workflow automatically. No manual judgment calls, no uniform automation that creates compliance exposure.

Automation Decisions Need a Shared Classification Framework

Lia enforces routing across localization, legal, and compliance. One framework, consistently applied, with an audit trail behind every decision.

Terminology Controls Belong at the Engine, Not the Review Stage

Lia applies your term base before the AI generates output. Fewer errors to catch, less post-editing, consistent terminology across every language. 

Quality Compounds Through Translation Memory, Not AI Learning

Every validated translation in Lia expands your TM. Output improves over time because your history works for you, not because the AI is guessing better.