Written by Aryan Gupta
Research Intern, Jindal Centre for the Global South, O.P. Jindal Global University, India
M.A. Diplomacy, Law and Business (2024-2026),
Jindal School of International Affairs, O.P. Jindal Global University
Email- 24jsia-agupta@jgu.edu.in
Imagine a farmer, Ama, checking her phone from a small village in Kenya. An app powered by Artificial Intelligence has sent her an alert about an impending drought, giving her the opportunity and time to adjust her planting plan and save her harvest. This aligns with the findings of Cornell University, where it is argued that AI-driven vegetation forecasting can strengthen drought early warning systems in Kenya, which can help farmers to save their harvest and use a different planting pattern. Meanwhile, in Lagos, Chidi, a young entrepreneur, has successfully gained access to a microloan because an AI system reviewed his mobile money transactions that no bank would have otherwise considered.
These examples are not far from reality, but rather early instances of AI’s emergence in Africa, and as this technology grows, an important question arises: Can AI help countries escape the cycle of poverty sustainably and inclusively, without exacerbating the divides that already exist in the region?
This blog examines AI’s potential role in addressing poverty in Africa by analysing its applications in agriculture, health, and financial inclusion, while also assessing ethical concerns, indigenous innovation, and digital infrastructure. Unlike optimistic narratives that list initiatives, this analysis weighs opportunities against systemic barriers, situating Africa’s AI adoption within broader debates on technological inequality and sustainable development (Bank, 2020). Ultimately, it boils down to the question of whether AI can promote an inclusive community development where no one is left behind?
AI in Africa: Where It Stands Today
Innovation centres in Kenya, Nigeria and Rwanda have become key sites for AI experimentation, tackling problems from food insecurity to healthcare access (Bank, 2020). For instance, FarmDrive in Kenya integrates satellite and farmer data through AI to provide tailored agricultural recommendations, helping smallholders boost yields. However, these innovations remain unevenly distributed. While urban-based start-ups thrive, rural areas-where poverty is most acute-face challenges of affordability, awareness, and connectivity (Ndemo, 2016).
In Rwanda, AI-powered drones deliver blood and vaccines to remote clinics, while diagnostic apps like Ada Health aim to expand healthcare access. Yet the long-term sustainability of drone delivery is debatable. High operational costs, dependence on foreign technology providers, and uncertain government subsidies cast doubt on whether such solutions can be scaled equitably (Ameso, 2024).
In Nigeria, fintech firms such as Carbon use AI algorithms to assess the creditworthiness of informal workers. While this broadens access to capital, it also introduces risks of algorithmic bias, especially against groups with limited digital footprints (Fred, 2024). Such examples demonstrate AI’s disruptive potential but also underscore systemic vulnerabilities-particularly the danger that benefits will concentrate among urban elites while rural populations remain marginalised.
Opportunities: A Tool for Inclusive Growth
AI’s value originates from its ability to solve problems at scale, offering real development opportunities: empowering the farmers with AI tools can alert farmers like Ama about the weather or detect crop diseases earlier, improving their productivity, and securing livelihoods (al., Cornell University , 2019). Amplifying the Health, is a tool that can minimise inequities in access to services. Vula Mobile helps connect rural patients with specialists in South Africa to help minimise lost lives due to delay (Vula Medical, n.d.).
Unlocking the quest of Finance using AI-driven credit scoring allows discontinuous individuals from formal banking to obtain capital to become entrepreneurs (Mbiti, 2010). Inclusive development here means providing the marginalised with tools to excel. AI can do just that by helping to operationalise key pain points around hunger, illness or exclusion in finances and deliver solutions that are capable of getting into the hands of all the families in the community. Yet the promise of inclusivity demands scrutiny. These solutions often succeed in pilot programs but face challenges when scaled to rural populations, who may lack smartphones, digital literacy, or stable electricity (Reality). Moreover, without parallel investments in infrastructure and education, AI risks serving as a short-term fix rather than a transformative driver of equitable development.
Challenges: Risks We Can’t Ignore
But its potential is matched by significant barriers, the biggest being connectivity gaps, where more than 60% of Africans have no stable internet (Sector, 2021). Without connectivity, rural populations are deprived of AI’s benefits, exacerbating city-countryside divides. The major focus should be on the availability to the deprived as well as the upper class. The skill shortages in developing AI necessitate expertise, but Africa graduates too few IT students (Bank, 2020). This threatens to keep the continent reliant on imported solutions. Along with that, the data centres in Africa should focus on the efficient use of AI, which will eventually trickle down to the deprived sections, but the data centres are limited, with concerns over costs and cybersecurity (OECD, 2021).
Bias and privacy might inflict harm. Suppose an algorithm rejected loans to women due to biased data, or a health application exposed confidential patient data. In order to tackle this, the professionals should focus on developing AI that cannot invade the privacy or create bias among the people.
If AI is going to promote inclusive development, these obstacles need to be addressed front-on, so that technology benefits all, not just a select privileged few.
Ethical AI: Establishing Trust and Fairness
Ethics is not discretionary, it is the foundation of the success of AI in Africa. This entails transparency, where citizens should be made aware of how AI decisions, such as loan approvals, affect them. Fairness, in which African diversity needs to be mirrored in AI. Initiatives such as Data Science Africa are advocating for datasets that integrate rural voices and local languages (Annual Report, 2024). Lastly, the protection of data, in which robust data privacy legislation is essential to protect users, particularly in nations with tax regulations.
An ethical solution makes sure AI not only fixes things but fixes them fairly, what the Global South demands in terms of equal progress.
Indigenous Innovation: Africa’s AI Revolution
Africa doesn’t have to bring in all its tech, it can produce it. Local innovators are already rising to the challenge:
(i) Language Solutions: Masakhane, a pan-African project, develops AI models for African languages such as Swahili and Yoruba, bringing tech to millions. (Wilhelmina Nekoto, 2020)
(ii) Frugal Tech: Startups are developing AI for low-cost phones, such as Ubenwa, a Nigerian app that can identify infant illnesses through cries, ideal for low-bandwidth environments (MILA, 2025; Joanna Wiaterek, 2025).
(iii) Local Expertise: South African and Ghanaian universities are developing AI researchers to address African issues, ranging from climate resilience to city planning (Brookings, n.d.).
Indigenous innovation makes AI relevant to Africa’s realities, fueling inclusive development by equipping local talent and addressing local needs.
Inclusive Digital Infrastructure: The Missing Link
Inclusive digital infrastructure requires:
(i) Widespread Connectivity: Governments must expand rural broadband via means of public-private partnerships, such as Kenya’s National Broadband Strategy.
(ii) Digital Skills: Initiatives such as Andela equip young people with coding and AI skills, helping them utilise and develop tech solutions.
(iii) Affordable Tools: AI must work on basic devices. So farmers can access apps on $20 phones, not just expensive ones. This infrastructure is the key to inclusive development, ensuring AI’s benefits aren’t confined to cities or the wealthy but reach every corner of the continent.
Conclusion: A Balanced Vision
Can AI set countries free from poverty? It may, but only if we do it right. Ama and Chidi’s stories illustrate the potential of AI to transform lives. It takes ethical rails, local solutions, and infrastructure with everyone included.
For us, this is a call to action: AI isn’t a tool-it’s a choice. If done well, it can power inclusive development that lifts millions out of poverty. If done poorly, it could leave them further behind. Africa’s future with AI is not predetermined-it’s ours to shape.
References:
- Aker, Jenny C., and Isaac M. Mbiti. 2010. “Mobile Phones and Economic Development in Africa.” Journal of Economic Perspectives 24 (3): 207–32.
- Ameso, E. A. (2024). Digital entanglements: Medical drones in African healthcare systems. Global Public Health, 19(1). https://doi.org/10.1080/17441692.2024.2405987
- AUC & OECD. (2024). Africa’s Development Dynamics 2024: Skills, Jobs and Productivity. OECD Publishing. https://www.oecd.org/en/publications/serials/africa-s-development-dynamics_2d9751bc.html
- Barrett, A. B., Duivenvoorden, S., Salakpi, E. E., Muthoka, J. M., Mwangi, J., Oliver, S., & Rowhani, P. (2019). Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1911.10339
- Data Science Africa (DSA). (2024). DSA annual activity report, 2024. Retrieved August 18, 2025, from https://www.datascienceafrica.org/dsa-annual-activity-reports/annual-activity-report-2024
- Digital Realty. (n.d.). Africa’s digital economy and digital transformation. Retrieved August 18, 2025, from https://www.digitalrealty.asia/resources/articles/africas-digital-economy
- Fred, Tommy. (2024). MACHINE LEARNING APPLICATIONS IN CREDIT SCORING FOR NIGERIAN FINANCIAL INSTITUTIONS. https://www.researchgate.net/publication/386177333_MACHINE_LEARNING_APPLICATIONS_IN_CREDIT_SCORING_FOR_NIGERIAN_FINANCIAL_INSTITUTIONS
- International Telecommunication Union. (2021). Measuring digital development: Facts and figures 2021 [PDF]. Retrieved August 18, 2025, from https://www.itu.int/en/ITU-D/Statistics/Documents/facts/FactsFigures2021.pdf
- Mapham, M. (2025, December 2). The Vula App – a massive force to improve patient access to specialist care. Vula Medical. Retrieved from https://www.vulamedical.com/news-media/medbriefafrica
- Mila. (2025, July 25). Ubenwa: The Doctor Will Hear You Now. Retrieved August 18, 2025, from https://www.mila.quebec/en/news/ubenwa-the-doctor-will-hear-you-now
- Ndemo, Bitange & Weiss, Tim. (2016). Digital Kenya: An Entrepreneurial Revolution in the Making. https://www.researchgate.net/publication/299469062_Digital_Kenya_An_Entrepreneurial_Revolution_in_the_Making/citation/download
- Nekoto, W., Marivate, V., Matsila, T., Fasubaa, T., Kolawole, T., Fagbohungbe, T., … Kamper, H. (2020). Participatory research for low-resourced machine translation: A case study in African languages (Version 2) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2010.02353
- Wiaterek, J., Abungu, C., & Okolo, C. T. (2025, May 7). Building regional capacity for AI safety and security in Africa. Brookings Institution. https://www.brookings.edu/articles/building-regional-capacity-for-ai-safety-and-security-in-africa/
- World Bank Group. (2020, January). Harnessing artificial intelligence for development: A new policy and regulatory framework [Policy brief]. World Bank Group. https://documents1.worldbank.org/curated/en/326191582688955286/pdf/Harnessing-Artificial-Intelligence-for-Development-A-New-Policy-and-Regulatory-Framework.pdf
The opinions expressed in this article are those of the author (s). They do not purport to reflect the opinions or views of the Jindal Centre for the Global South or its members.
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