Generic or Specialized AI for Global Communication: Real-World Implementations and Best Practices

Many teams already use tools like ChatGPT or Copilot to create, translate, and adapt content for global markets.

But when multilingual communication becomes part of real business operations, new questions emerge: How do you control quality? How do you protect sensitive data? How do you scale across teams and markets?

This on-demand webinar, hosted by Leena Peltomaa, Global Solutions Manager at Acolad, features real-world insights on implementing AI from Hanna Heinonen, Digital Content Lead at KONE, and Nelli Iivanainen, Language Technology Lead at OP Pohjola.

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.

There Is No One-Size-Fits-All Tool for Multilingual Communication

Different content types carry different risk levels. A quick email translation and a field maintenance manual are not the same decision. Knowing which workflow requires human oversight, and building that into your process, is what separates pilots from production.

Customization Is What Makes Generic AI Actually Useful

Out-of-the-box models produce generic results. Glossaries, translation memories, and domain-specific fine-tuning are what allow AI to perform reliably in specialized industries. If your organization has been building terminology assets for years, that data is already your competitive advantage.

Legal and Compliance Should Be in the Room From Day One

Both KONE and OP involved legal and compliance before moving anything to production - not after. Treating governance as a final gate creates delays and rework. Building it into the process early is what enables scale.

Shadow AI Is a Bigger Risk Than External Threats

The more likely source of data exposure is an employee pasting confidential content into a public tool, not an external breach. Clear policies, approved tooling, and internal visibility into what teams are actually using are the practical controls that matter.