AI-EFFECT will establish a European Testing Experimentation Facility (TEF) for developing, testing, and validating AI applications in the energy sector. It will be distributed across nodes, virtually connecting existing European facilities. The solution includes a digital platform leveraging European building blocks for interoperability, flexibility, and scalability. AI-EFFECT aims to be a central hub for testing energy sector AI algorithms, fostering collaboration across utilities, industry, academia, and regulatory authorities. Resilience is ensured through a decentralized design, aligning with the EU Energy Data Spaces framework. The project involves developing 4 use cases/nodes addressing key energy challenges, focusing on district heating, transmission congestion management, DERs integration, and energy communities. The framework involves utilities proposing challenges, vendors developing algorithms, and researchers contributing solutions. Each use case has evaluation criteria, baselines, and benchmarks. AI certification procedures, including interpretability and verification, will be implemented, and the evaluation process will be automated. Benchmarks and certifications are publicly available, encouraging open-source contributions. The project breaks sector barriers, leveraging existing infrastructures and technologies for cross-sectoral collaboration. The platform enforces policies for data quality, integrity, and privacy, promoting controlled data sharing and collaboration. Secure APIs ensure controlled interactions, including risk and security assessments. The consortium explores certification, standardization, and quality requirements in line with the EU AI Act. Governance and business models for the enduring AI-EFFECT will be examined, considering the EU AI Act. The consortium aims to make AI-EFFECT a sustained business beyond initial funding, seeking input from members, other TEFs, and regulatory authorities for the preferred model.