An AI Integration Framework for Social Innovation: A Cross-Case Analysis of AI System Configurations for the Triple-Zero Agenda

Abstract

Artificial Intelligence (AI) systems are increasingly being applied to complex social and environmental challenges, but their implementation across different application domains requires appropriate combinations of computational techniques, data sources, deployment environments, user interfaces, and responsible-AI mechanisms. This study proposes an AI Integration Framework for examining and configuring AI-enabled social innovation systems in support of the Triple-Zero Agenda: zero poverty, zero unemployment, and zero net carbon emissions. The framework is derived from a qualitative multi-case analysis of five social enterprises operating in agriculture, healthcare, education, clean energy, and employment. The analysis examines the AI technologies employed, their development and deployment environments, accessibility mechanisms, data-collection approaches, privacy mechanisms, explainability practices, human-in-the-loop controls, ethical review mechanisms, and measurable impact indicators. The cases demonstrate the use of heterogeneous AI configurations, including predictive machine learning in mobile and cloud environments, natural language processing in application and SMS-based systems, adaptive learning algorithms in web platforms, AI-based energy optimization integrated with IoT and edge devices, and AI-driven skill-job matching through mobile applications. The cross-case analysis indicates that effective AI deployment depends not only on the choice of AI technique but also on the configuration of supporting data, infrastructure, interaction, privacy, explainability, and human-oversight mechanisms. Based on these observations, the study organizes these dimensions into an AI Integration Framework that provides a structured basis for analyzing and configuring AI systems for socially oriented applications. The framework also connects system configurations with operational, social, economic, and environmental outcomes, thereby providing a computer science-oriented perspective on the design, deployment, and scalability of AI-powered social innovation.