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Journal of Ethical Innovation and Impact (ISSN 3105-0808)
Article . 2025 . Peer-reviewed
License: CC BY
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License: CC BY
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Article . 2025 . Peer-reviewed
Data sources: Crossref
ZENODO
Other literature type . 2025
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2025
License: CC BY
Data sources: Datacite
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Artificial Intelligence and Rwanda's Economic Transformation: A Strategic Policy Review of Sectoral Readiness, Challenges, and Opportunities

Authors: SANGWA, Sixbert; MUTABAZI, Placide;

Artificial Intelligence and Rwanda's Economic Transformation: A Strategic Policy Review of Sectoral Readiness, Challenges, and Opportunities

Abstract

This strategic policy review critically examines the role of artificial intelligence (AI) in Rwanda’s broader economic transformation agenda, focusing on the alignment between national development goals and sectoral realities. Rwanda, still largely agrarian with 82% of employment in informal sectors, is pursuing an ambitious shift toward a knowledge-based economy as outlined in Vision 2050, the Smart Rwanda Master Plan, and the National Artificial Intelligence Policy (2023). These policy frameworks position AI as a catalyst for socioeconomic development, targeting key sectors such as agriculture, healthcare, education, finance, and public administration. The paper synthesizes labor force statistics, digital economy data, and sector-specific AI case studies to assess Rwanda’s current readiness for AI integration. While flagship initiatives—such as VIEBEG’s AI-enabled health logistics and Farmonaut’s precision agriculture tools—demonstrate localized impact, national-level adoption remains uneven. Key challenges include inadequate data infrastructure, limited AI skills, low awareness among SMEs, and a persistent rural-urban digital divide. Comparisons with Kenya and Ghana further contextualize Rwanda’s progress and limitations within the regional AI ecosystem. This review highlights that despite Rwanda’s strong political will and high-level coordination, significant barriers must be addressed to achieve inclusive AI adoption. It concludes with forward-looking policy recommendations centered on human capital development, regulatory frameworks, public-private innovation, and regional collaboration. The findings aim to inform policymakers, researchers, and development stakeholders seeking to leverage AI for inclusive growth and to align Rwanda’s digital trajectory with its Vision 2050 goals.

Related Organizations
Keywords

AI adoption barriers Rwanda, digital transformation Africa, Smart Rwanda Master Plan, informal economy and automation, Artificial intelligence in Rwanda, sectoral AI readiness, Vision 2050 policy review

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Top 10%
Average
Average
Green
hybrid