The concept of data spaces has emerged as a structured, scalable solution to streamline and harmonize data sharing across established ecosystems. Simultaneously, the rise of AI services enhances the extraction of predictive insights, operational efficiency, and decision-making. Despite the potential of combining these two advancements, integration remains challenging: data spaces technology is still developing, and AI services require further refinement in areas like ML workflow orchestration and energy-efficient ML algorithms. In this paper, we introduce an integrated architectural framework, developed under the Green.Dat.AI project, that unifies the strengths of data spaces and AI to enable efficient, collaborative data sharing across sectors. A practical application is illustrated through a smart farming use case, showcasing how AI services within a data space can advance sustainable agricultural innovation. Integrating data spaces with AI services thus maximizes the value of decentralized data while enhancing efficiency through a powerful combination of data and AI capabilities.

Efficiency and interoperability are essential for the evolution of secure and scalable e-governance systems. This paper presents a novel framework for interoperable smart contract generation that integrates semantic technologies, and Layer-2 blockchain scaling solutions to enhance interoperability, security, and efficiency in e-governance applications. Using zero-knowledge proofs for privacy-preserving transactions and self-sovereign identity mechanisms for decentralized authentication, the proposed architecture ensures trust and compliance with international standards. Initially applied to e-voting, this framework is adaptable to broader public services, fostering a transparent, cost-effective, and sustainable digital governance ecosystem.