Costa, Bernardo ForbesFerreira, Ricardo2026-05-272026-05-272026-01-072025-11-16http://hdl.handle.net/10362/203491Despite growing interest in artificial intelligence among nonprofits, 95% of AI initiatives fail due to inadequate data foundations. This challenge is particularly acute for federated nonprofit structures, organizations comprising multiple legally autonomous entities under a shared mission, where data fragmentation and inconsistent governance create implementation barriers.This research develops a comprehensive AI enablement framework specifically for federated nonprofits. The framework integrates: (1) a three-tier data governance architecture balancing centralized coordination with local autonomy; (2) a five-level maturity model establishing minimum AI readiness thresholds; and (3) a 24-month implementation roadmap. We adopt established corporate governance principles for nonprofit contexts where hierarchical enforcement is unavailable, emphasizing voluntary coordination and consensus-based decision-making. Validation through Cáritas Portuguesa, a federation of 20 autonomous dioceses, demonstrates that technical infrastructure alone does not ensure AI readiness without governance structures. The research addresses ethical considerations including human-centered design, GDPR compliance, transparency requirements, and multi-stakeholder co-creation for "AI for Good" initiatives. A proof-of-concept Streamlit application demonstrates practical implementation, providing tools for data standardization, KPI monitoring, and AI-assisted insights while maintaining confidentiality and human oversight. This work offers federated nonprofits a practical blueprint for building data infrastructure required for ethical AI implementation while preserving organizational autonomy and mission integrityengData governanceFederated nonprofit organizationsAI readinessMaturity modelHuman-centered AIGDPR complianceAI for goodMulti-stakeholder co-creationSocial sector digital transformationCáritas Portugalmaster thesis204242304