# Bridging the AI divide in sub-Saharan Africa: Challenges and opportunities for inclusivity

arXiv: https://arxiv.org/abs/2601.06145 · HTML: https://mlsanity.com/paper/2601.06145/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [Empowering Africa: An In-depth Exploration of the Adoption of Artificial Intelligence Across the Continent](https://mlsanity.com/paper/2401.09457/) (2023)
- [Connectivity for AI enabled cities -- A field survey based study of emerging economies](https://mlsanity.com/paper/2501.09479/) (2025)

## Built on by (most cited first, 1 of 1)

- [Mapping the Artificial Intelligence Divide in Africa: Infrastructure, Accessibility and Capacity](https://mlsanity.com/paper/2606.30656/) (2026)

## Abstract (arXiv)

The artificial intelligence (AI) digital divide in sub-Saharan Africa (SSA) presents significant disparities in AI access, adoption, and development due to varying levels of infrastructure, education, and policy support. This study investigates the extent of AI readiness among the top SSA countries using the 2024 Government AI Readiness Index, alongside an analysis of AI initiatives to foster inclusivity. A comparative analysis of AI readiness scores highlights disparities across nations, with Mauritius (53.94) and South Africa (52.91) leading, while Zambia (42.58) and Uganda (43.32) lag. Quartile analysis reveals a concentration of AI preparedness among a few nations, suggesting uneven AI development. The study further examines the relationship between AI readiness and economic indicators, identifying instances where AI progress does not strictly correlate with Gross Domestic Product per capita, as seen in Rwanda and Uganda. Using case studies of AI initiatives across SSA, this research contextualises quantitative findings, identifying key strategies contributing to AI inclusivity, including talent development programs, research networks, and policy interventions. The study concludes with recommendations to bridge the AI digital divide, emphasising investments in AI education, localised AI solutions, and cross-country collaborations to accelerate AI adoption in SSA.