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    (2026)
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    Harnessing AI and Data in African Libraries for Africa’s for Sustainable Development
    (2026)
    This paper explores the critical role African libraries can play in harnessing artificial intelligence and data to advance sustainable development across the continent. Positioned as trusted, equitable community institutions, libraries are uniquely equipped to mediate the adoption of AI technologies in ways that align with public good, ethical standards, and local needs. The analysis moves beyond a narrow focus on generative AI to consider a broader ecosystem, including machine learning for image recognition, big data analytics, and— crucially—AI-powered translation tools. This linguistic dimension is particularly vital in sub-Saharan Africa, where extreme linguistic diversity presents a significant barrier to information access. By leveraging opensource platforms and cloud-based services, libraries can deploy AI to translate and localize content, thereby dramatically widening access to educational, governmental, and cultural resources in local languages. The successful integration of AI depends on African libraries actively building agency and local capacity. This involves engaging in foundational work to develop regionally contextualized large language models, applying retrieval-augmented generation to enrich global models with local knowledge, and utilizing AI for enhanced discovery and classification of cultural heritage materials. While challenges such as funding constraints, data silos, and nascent digitization efforts exist, a pathway forward is demonstrated through emerging initiatives and international collaborations, such as the IFLA symposia held across African universities. Ultimately, by adopting a framework of governance, education, and experimentation, libraries can ensure AI serves as a multiplier for the visibility and accessibility of Africa’s rich research and cultural output, directly contributing to the continent’s inclusive and equitable development. Keywords: Artificial Intelligence, Big Data, Sub-Saharan Africa, Large Language models