• Why our business needs a FAIR data ecosystem (data products)
• What we aim to build - Our data mesh architecture and setup
• How we aim to inner-source and drive adoption of components – develop services together and for the many
• Learnings so far – Key learnings from our journey so far

AI is taking an ever more important role in modern data-driven enterprises and the importance of having high quality and available data increases constantly. Until now the architectural and governance aspects of data and AI development were rather separate, since AI was developed through separate data science projects. However, with the new era of LLMs and ever-increasing complexity and demands on continuous development that might need to change in the future. In this discussion we talk about:

• How AI projects are developed, what data requirements are needed and communication between various stake holders
• The increasing demands and complexity in the AI strategy, the need for continuous development of LLM models and how this impacts the requirements on the data infrastructure
• Communication between AI leaders and Data Architecture leaders, common understandings and mindset shifts
• After the hype of Gen AI, what are the next steps in ensuring a proper data and governance foundation for implementing a long-term vision for an AI-driven company, which includes regulations (AI Act) and rules for ethical and responsible use