The AI-native Pharma Innovator Case Study

​Putting the next generation data operating system to work

​Life sciences organizations have invested heavily in data, from warehouses and catalogs to decades of research, development, and commercial data. As LLMs become more capable and affordable, and agentic AI opens up new possibilities, how can organizations unlock the value of these investments without another costly cycle of re-platforming?

​In Episode 4 of Nextdata’s AI-Native learning series, Omar Khawaja, Global Head of Data & AI at argenx, shares a different approach to building an AI-ready data foundation.

​At argenx, meaning is owned where data is produced. Autonomous data product teams define priorities around business outcomes, creating FAIR and AI-ready data products on a federated enterprise data platform, with Nextdata OS as a key component.

​These data products bring together meaning, data, and access policies, with data quality contracts and policy compliance computationally enforced at the point of access. Through MCP, any agentic harness or LLM provider can discover, understand, and query data products directly.

​The result? A trusted data layer that makes enterprise data usable by AI agents without moving everything into a single technology stack or changing existing data storage technologies.

​Join us to explore how this approach puts data domains in control, connects enterprise context to AI, and helps life sciences organizations turn their existing data investments into real business outcomes.

​See you there!