Artificial Intelligence (AI) is transforming the way nations approach productivity, governance, and innovation. Across the world, governments are turning to AI not merely as a technological trend but as a cornerstone for national development. From streamlining public services to advancing medical research and enhancing agricultural productivity, AI is being touted as a transformative force for the future of work and welfare. It has moved from the fringes of innovation into the heart of policy discourse.
In the United States, for instance, the Department of Health and Human Services has released a national AI strategy outlining how AI will be used in public health and medical research. Similarly, Canada’s Pan-Canadian Artificial Intelligence Strategy highlights the government’s commitment to integrating AI into national innovation, research, and public-sector systems.
In Africa, this optimism is beginning to take root. Countries such as Nigeria, Ghana, Kenya, and Rwanda are developing national AI strategies, establishing data centres, and integrating automation into their public policy planning. The World Bank’s 2024 report on “The Future of Work in Africa” underscores this growing shift, noting that digital technologies, especially AI, hold the potential to boost productivity, create new forms of employment, and expand access to essential services across the continent.
Governments across the Global South are positioning it as a pathway to development, one that could fast-track productivity, streamline governance, and expand access to opportunity.
Nigeria’s National Artificial Intelligence Strategy (NAIS), for instance, envisions AI as a driver of job creation, improved healthcare, and precision agriculture. Similarly, Ghana’s recently launched National Artificial Intelligence Strategy outlines an ambitious plan to harness AI for inclusive growth across key sectors, including agriculture, education, healthcare, and public services, aiming to position the country as a regional hub for responsible and innovative AI adoption. Across policy circles, AI is being sold as the technology that can bridge the development gap between Africa and the rest of the world.
Learning from Others: How AI Works in Practice
Source: NSW Liberal
For African governments, inspiration often comes from abroad. In India, AI is already transforming the agriculture sector. The government’s Digital Agriculture Mission (DAM) is a key initiative to modernise the industry through digital technologies. This includes using AI-powered tools, often delivered via mobile platforms like the AI Sowing App, which utilise satellite imagery, real-time weather data, and soil analysis to give precision farming advice to farmers. These systems enable data-driven decisions on when to sow, irrigate, or apply fertiliser, dramatically reducing costs and resource waste while achieving reported yield increases of 10% to 30% in pilot projects. This model proves AI can be made accessible and impactful for smallholder farmers.
Similarly, Singapore’s Smart Nation initiative integrates AI into public transportation, city planning, and healthcare, creating real-time systems that enhance service delivery and the citizen experience.
A notable example of AI in governance at scale comes from China, where systems engineered with generative AI are deployed as “digital personnel” or AI copilots in local government. In districts like Shenzhen’s Futian, these AI systems handle tasks such as document processing, correcting document formats with over 95% accuracy, and optimising interdepartmental coordination. By automating vast amounts of administrative work, these tools significantly reduce processing times (sometimes by as much as 90%) across hundreds of governance scenarios, from civil services to crisis response.
Source: Ciobulletin
Even within Africa, there are success stories worth studying. South Africa’s AI strategy has facilitated collaborations between academia and government, leading to innovations in medical imaging and early disease detection. Kenya’s use of AI in wildlife conservation and climate adaptation also shows how public-sector-led innovation can align technology with national priorities.
These examples demonstrate that AI achieves its full potential only when data, infrastructure, and policy coherence are in place. For Nigeria and Ghana, the key question is not whether AI can deliver, but whether the environment needed for it to deliver is being built.
Sector Spotlights: Medicine, Research, and Agriculture
Source: frontiersin
Medicine and Research
Source: ecrcommunity
Healthcare represents one of AI’s most promising frontiers for Africa. AI can analyse medical images faster than human specialists, identify early-stage diseases, and even accelerate drug discovery. Globally, AI-assisted research has already shortened the time between drug design and market approval, saving billions in development costs.
For Africa, where underfunded health systems grapple with delayed diagnoses and limited medical expertise, AI offers transformative potential. In Nigeria, Ubenwa, an AI startup founded by Charles Onu, uses machine learning to detect birth asphyxia through a baby’s cry. This locally developed innovation exemplifies the power of context-driven AI. Across the continent, universities and research institutes are also beginning to explore AI-assisted genomic research to better understand and address diseases such as malaria and sickle cell anaemia.
Agriculture
Source: Olimpum
Agriculture employs more than half of Africa’s population, yet productivity remains low. AI applications such as predictive weather systems, satellite-based crop monitoring, and pest detection could help farmers make more informed decisions. In Ghana, pilot projects are already using AI to advise cocoa farmers on optimal planting and irrigation schedules. In Kenya, AI tools are predicting crop disease outbreaks weeks in advance.
These examples are not futuristic; they are happening now. The question is how governments can scale such initiatives beyond pilot phases, ensuring they become part of a national productivity framework rather than isolated success stories.
The Policy and Ethical Crossroads
Yet, even as AI promises efficiency, it also raises difficult ethical and regulatory questions. Around the world, governments are grappling with what “responsible AI” truly means. Europe’s AI Act seeks to ensure transparency and accountability. In the United States, antitrust debates surrounding big data companies highlight growing unease over unchecked power and privacy violations.
For Africa, the challenge is more fundamental: how to push forward the good while keeping a watchful eye on the bad. Should countries pause until they can regulate perfectly, or should they innovate now and correct as they go? Regulatory lag remains one of the biggest risks, as laws and institutions often move more slowly than the technologies they aim to govern.
Representation, Bias, and the Global Data Divide
Perhaps the more urgent ethical challenge lies not in law, but in data. Most of the world’s AI systems are trained on datasets from the Global North that do not reflect African realities. When AI models are built on Western data, they can produce biased or even dangerous outcomes when applied elsewhere.
A clear example lies in healthcare. Much of global medical research is based on data from men rather than women, a long-standing bias that continues to shape medical knowledge and practice. Studies have shown that males remain the default subjects in both clinical and pre-clinical research, leaving major gaps in understanding how diseases, treatments, and even medications affect women differently. If those biases are then baked into AI diagnostic systems, women, particularly African women, are disproportionately affected.
In the UK, for instance, data show that Black and Asian women experience higher rates of emergency caesarean sections and maternal complications compared to White women (Nuffield Trust, 2023). While the reasons are complex, involving social, clinical, and systemic factors, such disparities highlight how medical standards and risk assessments can fail to reflect population diversity. Now imagine AI tools trained primarily on Western biomedical data being used to predict pregnancy risks in Nigeria or Ghana. These systems could inadvertently misclassify normal conditions as dangerous, leading to unnecessary interventions and reinforcing long-standing inequalities in women’s healthcare.
Source: Nuffield Trust
This illustrates a deeper problem: AI trained on unrepresentative data risks exporting inequality at scale. It is not just about technology; it is about whose realities inform it.
Infrastructure and Local Data Production
AI cannot thrive on borrowed data. For African countries, investing in local data infrastructure is not optional; it is foundational. According to a 2025 report by GIZ and AfriLabs, African nations need datasets that accurately reflect local demographics, climate, health patterns, and social behaviours. Without such contextually relevant data, even the most advanced AI systems risk delivering solutions that do not fit.
This means governments must look beyond the excitement of “AI for productivity” and focus on the less glamorous but essential work. The African Union’s Continental AI Strategy (2024) and initiatives like openAFRICA emphasise the importance of funding local research, digitising records, and building open data platforms. Only by strengthening these foundations can AI‑driven solutions be truly relevant and equitable.
Put simply, AI will not fix what it does not understand.
Learning from Others: Managing Ethical and Policy Challenges
The experiences of other countries offer useful lessons. In Europe, robust data protection laws, such as the GDPR, provide a framework for AI accountability. In India, public-private collaborations have created flexible yet transparent governance structures for AI in agriculture and healthcare.
Outcomes are often visible, but the processes behind them are not. This lack of transparency in AI use and its governance is itself a risk, representative of the divide and inequality across continents. There is a need for greater openness and global knowledge sharing, to show not just the what but the how. If Africa is to learn from global successes, countries must be willing to share not only outcomes but also the ethics, oversight, and values that make those systems trustworthy.
A balanced approach does not mean slowing progress; it means grounding it in reality. Africa can learn from what has worked elsewhere, but must adapt those lessons to its own realities. For instance, if India’s AI-powered agricultural models succeed due to robust digital infrastructure and farmer education programs, similar outcomes in Ghana or Nigeria would depend on building those same foundations. If Singapore’s healthcare AI thrives under strict data governance, African countries must invest in data ethics frameworks before deploying similar systems. The task is not to simply copy the technology delivering the outcomes, but to build the runway that will enable the technology to deliver useful outcomes in local contexts.
The key is contextual adaptation: utilising global lessons without blindly copying global models, and ensuring that Africa’s unique circumstances inform both policy and practice.
Conclusion
AI holds immense promise for African development. It can enhance decision-making, accelerate research, and improve the efficiency of governance. But these benefits will only come to life if Africa invests in the essentials: local data, ethical frameworks, and inclusive policies.
The goal should not simply be to adopt AI, but to shape it, to ensure that the intelligence driving national productivity truly understands its people, their needs, and their realities.
As the continent stands on the edge of a new technological era, the real question is not whether AI can transform Africa, but whether Africa can transform AI into a tool that genuinely serves its people.
