This chapter discusses additional critical success factors pivotal to the effective deployment of AI to achieve the SDGs by ensuring that AI systems are technically viable, socially, ethically, and contextually relevant. These factors include access to high-quality data, availability of skilled human capital, ethical and inclusive AI governance, and policy and regulatory support. For instance, access to localised, unbiased, and representative datasets is a critical success factor for AI applications aimed at achieving SDG 3 (Good Health and Well-Being), as healthcare AI models must be trained on diverse datasets to ensure accurate disease detection and diagnosis for all populations. Similarly, capacity building through education and skills development is essential for achieving SDG 4 (Quality Education), as communities must have the technical skills to co-create and operate AI-powered educational platforms. Ethical AI governance and strong regulatory frameworks are essential success factors in ensuring that AI systems respect human rights and avoid reinforcing biases, especially when addressing SDG 10 (Reduced Inequalities). Leapfrogging is a critical success factor for deploying AI to achieve the SDGs. It enables countries, particularly in the Global South, to bypass traditional, costly, and time-consuming development stages. Decoloniality ensures that AI systems are inclusive, culturally relevant, and community-driven, enabling local ownership and knowledge sovereignty. The democratisation of AI broadens access to AI tools, education, and data, empowering marginalised communities and local innovators to co-create solutions.