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Artificial Intelligence and Startups in Sub-Saharan Africa: A Strategic Approach to Frugal Innovation in the Face of Challenges
[Intelligence Artificielle et startups en Afrique subsaharienne : une approche stratégique d'innovation frugale face aux défis]

Author

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  • Mohammed Rida Nour

    (USMBA - Université Sidi Mohamed Ben Abdellah)

  • Lamiae Tazi

    (USMBA - Université Sidi Mohamed Ben Abdellah)

Abstract

This article explores how African startups leverage artificial intelligence (AI) within a frugal innovation approach to address structural local constraints (lack of infrastructure, unequal technological access, and economic limitations). Using a qualitative and exploratory methodology, the study analyzes six case studies located in West and East Africa, covering diverse sectors (healthcare, agriculture, education, logistics, finance, and environment). Each case is examined through common criteria: targeted constraint, frugal lever employed, AI technology used, and observed impact. The analysis identifies several recurring drivers, including the strategic reuse of available infrastructure, prioritization of accessible and user- friendly technologies, deployment of streamlined AI tools, deep integration within local contexts, and positive ripple effects on ecological sustainability and social equity. The article concludes that frugal innovation may constitute a sovereign technological strategy for Africa, provided it is anchored in long-term public policies, guided by strategic governance, and supported by robust local innovation ecosystems. However, it also cautions against the risks of a techno-solutionist approach disconnected from ground realities.

Suggested Citation

  • Mohammed Rida Nour & Lamiae Tazi, 2025. "Artificial Intelligence and Startups in Sub-Saharan Africa: A Strategic Approach to Frugal Innovation in the Face of Challenges [Intelligence Artificielle et startups en Afrique subsaharienne : une," Post-Print hal-05215257, HAL.
  • Handle: RePEc:hal:journl:hal-05215257
    DOI: 10.5281/zenodo.16646068
    Note: View the original document on HAL open archive server: https://hal.science/hal-05215257v1
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