Digitalization and the Application of Artificial Intelligence in Priority Sector among Resource-Endowed African Countries

Digitalization and artificial intelligence possess significant potential to enhance the inefficient utilization of natural resources. Nonetheless, research on this subject remains limited in many resource-abundant African nations. This study draws upon economic growth theory, which posits that AI can serve as a substitute for conventional factors of production and foster the generation of innovative ideas, thereby facilitating long-term growth trajectories. The results indicate that digital initiatives in resource-rich African countries predominantly concentrate on sectors such as financial technology, agriculture, manufacturing, public services, healthcare, education, and general service provision, while largely neglecting their primary resource industries, including oil, gas, and minerals. Notably, South Africa has prioritized the integration of digital technologies within its mining sector. Overall, the adoption of AI across Africa is still in a nascent stage, even among countries endowed with substantial resources. However, nations like Algeria, South Africa, and Zimbabwe have demonstrated some progress in implementing AI solutions within their petroleum and mining industries. The study recommends that other resource-rich African countries intentionally incorporate digital technologies and AI into their resource sectors to improve efficiency, enhance competitiveness, and boost productivity.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-31
DOI
https://doi.org/10.5281/zenodo.18397502
Primary Topic
Natural Resources and Economic Development
Type
article
Field-Weighted Citation Impact
0.00
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article

Digitalization and the Application of Artificial Intelligence in Priority Sector among Resource-Endowed African Countries

Opeyemi Nathaniel Oladunjoye
Zenodo (CERN European Organization for Nuclear Research)
Natural Resources and Economic Development
article

Digitalization and the Application of Artificial Intelligence in Priority Sector among Resource-Endowed African Countries

Opeyemi Nathaniel Oladunjoye
article en

Abstract

Digitalization and artificial intelligence possess significant potential to enhance the inefficient utilization of natural resources. Nonetheless, research on this subject remains limited in many resource-abundant African nations. This study draws upon economic growth theory, which posits that AI can serve as a substitute for conventional factors of production and foster the generation of innovative ideas, thereby facilitating long-term growth trajectories. The results indicate that digital initiatives in resource-rich African countries predominantly concentrate on sectors such as financial technology, agriculture, manufacturing, public services, healthcare, education, and general service provision, while largely neglecting their primary resource industries, including oil, gas, and minerals. Notably, South Africa has prioritized the integration of digital technologies within its mining sector. Overall, the adoption of AI across Africa is still in a nascent stage, even among countries endowed with substantial resources. However, nations like Algeria, South Africa, and Zimbabwe have demonstrated some progress in implementing AI solutions within their petroleum and mining industries. The study recommends that other resource-rich African countries intentionally incorporate digital technologies and AI into their resource sectors to improve efficiency, enhance competitiveness, and boost productivity.

Zenodo (CERN European Organization for Nuclear Research)
Obafemi Awolowo University (NG)
Decent work and economic growth
Openalex Percentile: Top 21%
Natural Resources and Economic Development
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Digitalization and the Application of Artificial Intelligence in Priority Sector among Resource-Endowed African Countries — Opeyemi Nathaniel Oladunjoye · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS