Data Spaces and Foundation Models for AI

This white paper examines how the integration of data spaces and foundation models can enhance the effectiveness of artificial intelligence (AI) in various industries. It outlines the challenges faced by AI, particularly the reliance on public internet data for training models, which often leads to issues with data quality, bias, and compliance risks. The paper emphasizes the importance of high-quality, industry-specific data for AI to drive competitiveness and innovation. It discusses regulatory requirements, such as the EU AI Act, that necessitate transparency in AI-driven decision-making. The document details practical approaches, including retrieval-augmented generation (RAG) and fine-tuning, which can help AI models incorporate trusted knowledge and improve their reliability for real-world applications. Furthermore, it highlights the role of data spaces in facilitating secure data sharing while allowing organizations to maintain control over their proprietary information. By leveraging these strategies, businesses can unlock new opportunities and enhance AI performance while ensuring compliance with regulatory standards.