Introduction
Africa stands at a critical crossroads in its development journey. The continent’s agricultural sector, which employs nearly 60 per cent of its workforce and underpins the livelihoods of millions, faces unprecedented challenges. Climate variability, diminishing arable land, water scarcity, and rapid population growth threaten food security and economic stability. Yet, amid these challenges lies a powerful opportunity: artificial intelligence (AI). As a transformative technology, AI offers tools to leapfrog traditional development hurdles, enabling precision, efficiency, and resilience in farming and beyond.
Drawing lessons from global innovators who have harnessed AI to overcome similar constraints, African nations can craft tailored policies, strategies, and partnerships that unlock this potential. This article outlines a comprehensive framework to accelerate AI integration in Africa’s agriculture and broader development agenda. It emphasizes the urgent need for digital infrastructure, human capital development, data governance, innovation ecosystems, and multi-stakeholder collaboration.
- The promise of AI in African agriculture
Global demand for food is projected to surge by 50 per cent by 2050, requiring unprecedented increases in productivity amidst shrinking resources. Africa’s population is expected to double to nearly 2.5 billion within the same period, intensifying pressure on its agricultural systems. Current productivity gaps are significant: average cereal yields in sub-Saharan Africa hover around 1.8 tons per hectare, less than half the global average. These disparities arise from factors such as fragmented landholdings, limited mechanization, poor access to inputs, and vulnerability to pests and weather shocks.
AI offers a pathway to address these challenges by enabling precision agriculture — a method where farming practices are informed by detailed, real-time data rather than uniform guesswork. Through AI-powered tools, farmers can optimize irrigation schedules based on soil moisture sensors and weather forecasts, target fertilizer application precisely where nutrients are deficient, and detect pest infestations early via drone imagery and computer vision. Such interventions can increase yields by up to 30 per cent, reduce input costs, and conserve vital natural resources.
Moreover, AI-driven analytics facilitate improved supply chain management, market access, and financial services tailored to farmers’ needs, contributing to rural economic transformation. The potential of AI thus extends far beyond the field, touching every aspect of agricultural value chains.
2. Building the foundation: A policy framework for AI adoption
To realise AI’s promise, African governments must establish robust policy environments that lay the groundwork for sustainable digital transformation. This starts with ensuring that rural farmers—who make up most of the agricultural labour force—can access and benefit from digital technologies.
Primarily, digital infrastructure and access policies are critical. Reliable internet connectivity and electricity remain uneven across the continent, with the International Telecommunication Union estimating that only about 28 per cent of rural Africans have internet access as of 2023. Without addressing this digital divide, AI solutions cannot scale. Governments should therefore prioritize investments in broadband expansion and off-grid renewable energy projects. Subsidizing affordable smartphones and IoT sensors tailored for rural settings can accelerate adoption, ensuring that smallholder farmers are not left behind.
Equally important is the establishment of data governance and privacy regulations. AI systems thrive on data, but the sensitive nature of agricultural and personal data requires clear legal frameworks. Policies must delineate ownership rights, consent protocols, and ethical standards for data use to build farmer trust. Creating national agricultural data repositories that enable researchers, developers, and policymakers to access anonymized datasets will foster innovation while safeguarding privacy.
Governments should also incentivize innovation through research and development policies. Offering tax breaks, grants, and incubation support to agri-tech startups encourages homegrown solutions tailored to local agro-ecologies. Public research institutions must be funded to develop AI applications addressing region-specific challenges such as drought-resistant crops and pest control.
The human capital dimension cannot be overlooked. A capacity building and education policy focusing on digital literacy and AI proficiency is vital. This encompasses formal education reform, vocational training programs, and farmer extension services equipped to train rural populations on using AI tools effectively. African universities and technical institutes must be supported to create AI and data science curricula that nurture a skilled workforce.
Lastly, policies should embed inclusivity and sustainability clauses to ensure that AI benefits reach women, youth, and marginalized communities, and that environmental resources are conserved. With agriculture deeply entwined in cultural practices and livelihoods, policy must promote equitable access and monitor socio-economic impacts carefully.
3. Strategic roadmap: from pilot to scale
Policy alone is insufficient without a coherent strategic roadmap that guides phased implementation. A suggested multi-year approach allows African countries to build foundational capabilities, scale successful innovations, and institutionalize AI-driven practices.
Phase one (Years 1–2): foundation
Initial efforts should focus on comprehensive assessments to map digital infrastructure gaps, farming system characteristics, and data availability. Pilot projects deploying AI-enabled precision irrigation, crop monitoring, and digital twin models in diverse agro-ecological zones are crucial for learning and adaptation. Parallel to this, governments must formalize data governance frameworks and develop farmer consent mechanisms.
Capacity building must begin early, targeting extension agents, agronomists, and early adopter farmers through training programs and demonstration farms. Emphasis on community engagement will foster trust and create local champions for AI tools.
Phase two (Years 3–5): scaling and ecosystem development
Building on pilot success, infrastructure investment should expand aggressively to increase rural broadband coverage to at least 70 per cent, complemented by electrification efforts. Agri-tech innovation hubs and accelerators can nurture startups developing context-specific AI solutions, while interoperable data platforms will aggregate multisource agricultural data for broader use.
AI advisory services should be integrated into national extension programs, reaching a larger farmer base. Mechanisms for farmer feedback and continuous improvement of AI tools will ensure relevance and usability. Partnerships with mobile network operators and fintech providers can bundle AI advisory with credit and insurance services, enhancing farmer resilience.
Phase three (Years 5–10): institutionalisation and integration
In the long term, AI-driven decision-making must be embedded in agricultural policy, subsidy allocation, and disaster risk management frameworks. Cross-sectoral integration—linking AI applications in climate forecasting, supply chains, education, and health—will maximise developmental synergies.
Regional collaboration will facilitate knowledge exchange and harmonization of standards, enabling economies of scale and cross-border technology transfer. Continuous monitoring and evaluation systems will guide adaptive management and policy refinement, ensuring sustained impact.
Forging effective partnerships
No transformation can succeed without collaboration. Multi-stakeholder partnerships provide the necessary resources, expertise, and legitimacy to accelerate AI adoption.
Public-private partnerships (PPP) emerge as a key model. Governments provide enabling environments, infrastructure, and regulatory clarity, while private firms develop and deploy AI solutions suited to local realities. Shared-risk investment models encourage startups to innovate affordably for smallholder markets.
International collaborative research partnerships between African universities and experienced AI-agriculture centres globally are equally critical. These collaborations enable technology transfer, capacity building, and co-creation of solutions for Africa’s unique contexts. Initiatives like the Consultative Group on International Agricultural Research (CGIAR) have demonstrated the power of such partnerships in advancing agricultural science.
- At the grassroots, farmer cooperatives and digital platforms serve as vital conduits for dissemination and data collection. These organizations can aggregate demand for AI tools, facilitate training, and provide feedback loops, ensuring that innovations remain grounded in farmer needs.
- Finally, multilateral development banks and donor agencies play an indispensable role by mobilizing funding, technical assistance, and policy advocacy. Their involvement can help de-risk investments, ensure inclusivity, and uphold sustainability standards.
Challenges and the way forward
While the promise is immense, Africa’s path to AI-driven agriculture is not without hurdles. Affordability remains a significant concern—smallholder farmers often operate on razor-thin margins, and technologies must be accessible and user-friendly. Infrastructure deficits, particularly in electricity and connectivity, still hamper large swathes of rural areas. Data bias and quality issues can undermine AI reliability if models are trained on unrepresentative datasets. Cultural acceptance and behavioural change require continuous engagement and trust-building.
Addressing these challenges demands coordinated efforts across governments, the private sector, civil society, and international partners. Incremental wins in pilot areas can build momentum and demonstrate value. Governments must adopt flexible policies that encourage innovation while protecting farmers and ecosystems. Investment in education and extension services is paramount.
Conclusion
Artificial intelligence holds transformative potential to reshape African agriculture and catalyse wider socio-economic development. The experiences of pioneering nations offer valuable lessons on the importance of enabling policy environments, phased strategic implementation, and dynamic partnerships. By committing to a comprehensive approach that bridges infrastructure, innovation, capacity, and inclusivity, Africa can harness AI to secure food systems, empower its farmers, and foster resilient economies.
As the continent navigates the challenges of the 21st century, embracing AI is no longer optional but imperative. The difference between dreaming of a prosperous future and actualizing it lies in deliberate action—building frameworks that empower the continent to leapfrog and thrive in an increasingly digital world.
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Prof. Sarumi is the Chief Strategic Officer, LMS DT Consulting, Faculty, Prowess University, US, and ICLED Business School, and writes from Lagos, Nigeria Tel. 234 803 304 1421, Email: leadershipmgtservice@gmail.com.
