This chapter examines the integration of algorithmic technologies in social service provision in Spain through a case study of Catalonia’s Self-Sufficiency Matrix (SSM-Cat), an evidence-based assessment tool adapted for social service governance that allows measuring a person’s ability to be self-sufficient, that is, to carry out daily activities independently. Based on this case study, the chapter reflects that while the adoption of artificial intelligence (AI) in public services grows, significant gaps persist in equity-oriented frameworks and participatory design methodologies. In addition, we demonstrate how Agent-Based Modelling (ABM) and gamification workshops can bridge these gaps by facilitating co-creation between policymakers, social workers, and vulnerable communities. Further, the ABM simulations are helpful because they reveal critical inconsistencies in how practitioners interpret standardized tools, thus highlighting the need for shared decision-making protocols to tackle such problems. Based on our research, we identify three systemic challenges: (1) inadequate science-policy interfaces for translating technical AI concepts into governance; (2) insufficient mechanisms for incorporating frontline practitioner knowledge into algorithmic design; and (3) inherent biases in administrative data that risk reinforcing structural inequalities. Through our case study and its extension, we propose a hybrid approach combining techniques such as clustering analysis, participatory ABM, and deliberative forums to create context-sensitive AI systems. Our findings reveal the importance of advancing towards a more transparent, stakeholder-driven AI governance approach that prioritizes participatory methodologies. The chapter concludes with policy recommendations for embedding dynamic evaluation frameworks, institutionalizing co-design processes, and promoting interpretive cultures around algorithmic tools as essential steps to ensure AI technologies serve as tools for seeking equity rather than exclusion in welfare provision.