Discover how to balance humans and machines by infusing AI where it matters most to drive productivity, scale, and drive ROI.   

AI is an integral layer of how today’s organizations create, manage, and scale multilingual content. But infusing AI technology is more than just implementation. It’s about intelligent automation and the sweet spot where human and machine intelligence converge to create value. 

As part of DATAmundi’s three-part webinar series featuring Bruno Herrmann and Kathleen Bostick, we’ve been diving deep into how organizations can harness AI and generative AI within multilingual product and content ecosystems, and how human and machine intelligence can complement one another. 

In the third and final session, ‘Infusing AI into your Product and Content Management Systems’, Bruno spotlighted AI technologies in product, content, and translation management systems and shared two use cases where organizations had infused AI and demonstrated measurable progress and impact. 

“Infusing AI means finding and reaching the sweet spot. Intelligent automation is the balance between human intelligence and machine intelligence.”

Bruno Herrmann

Infusing AI: Build, Buy or Partner 

Successful AI infusion depends on a thoughtful and strategic approach to how technology is acquired and deployed and whether to build, buy, or partner. Few organizations can “do everything on their own.” Instead, Herrmann advocates for a best-of-breed model that strategically blends in-house expertise with trusted external partners. “The balance between build, buy, and partner,” he noted, “will determine how AI technology is used in your environment and ecosystem.” 

This foundation creates the conditions for an intelligent, AI-augmented ecosystem and one where people, processes, and technology work to enhance outcomes across product and content management. 

Watch Webinar Now: Infusing AI into your Ecosystem 

 

Use Case 1: A Creative Agency and the Rise of Agentic AI 

One use case Bruno covered in the webinar involved a pan-European creative agency that evolved from traditional, manual workflows to an AI-augmented environment. The agency began by integrating generative AI (GenAI) into content creation, validation, and localization phases, gradually introducing AI agents to automate repetitive coordination and quality assurance tasks. 

These “agents” weren’t replacements for humans. They were collaborators. Herrmann described them as “acting as an in-between layer between project managers and clients,” helping to manage workflow traffic and reduce operational friction. 

In fact, the agency redefined roles entirely. Former project managers became traffic managers i.e. orchestrators who supervise AI agents and ensure quality outputs. “You need some level of human supervision,” Herrmann explained. “To make sure what goes in and out of the traffic is relevant and valuable.” 

The results spoke for themselves: faster production cycles, improved client satisfaction, and empowered teams who could upskill and grow their AI capabilities.  

Use Case 2: Training for Multilingual Prompting 

The second use case featured in the webinar highlights the power and necessity of human expertise in multilingual GenAI environments. A global organization sought to optimize its prompting practices and evaluate the effectiveness of different large language models across languages. 

It starts, “not with the models, but with the data.” They united data scientists and language experts to ensure the AI systems were trained on trustworthy, relevant language data. 

This collaboration revealed something important: linguistic expertise matters as much as technical know-how. Language professionals consistently produced higher-quality prompts and outcomes.  

Multilingual prompting isn’t just about technology and writing an instruction. It has to be structured, based on syntax, grammar, and terminology. It’s about linguistic intelligence. Herrmann emphasized the growing need for collaboration between data scientists and language professionals.  

“Data scientists know how to structure and maintain data, but when it comes to generative AI, we are talking about language data. And in language data, there is the word language.” 

Data as the Foundation for AI Infusion 

Across both cases, the message was consistent: effective AI infusion begins with data quality and human collaboration. Whether training AI agents or designing multilingual prompts, the success of any AI system depends on balancing human and machines, and the quality, accuracy, and contextual depth of the data behind it. 

Herrmann summed it up simply: “Data is the lifeblood of AI. If there is no good data, there is no good AI.” 

That’s where organizations like DATAmundi come in, to ensure the human data that fuels AI is diverse, trustworthy, and expertly managed. From multilingual benchmarking to data annotation and validation, human-centered data training is what makes AI models intelligent. 

 

Explore More in the DATAmundi webinar series with Bruno Herrmann 

If you missed the earlier sessions, you can catch up here: 

For more information on DATAmundi and how we help organizations create and train high-quality human data to power better AI models, contact us HERE.