Agricultural artificial intelligence (AI) is expanding rapidly across Sub-Saharan Africa, yet the region lacks the legal frameworks, data protections, and accountability structures needed for smallholder farmers to safely adopt these tools. While 14 countries in the region have identified AI as a priority in national strategies, none have binding laws governing how AI is developed or used in farming, creating uncertainty that could undermine billions in potential investment and food security gains.

What's Driving Agricultural AI Investment in Africa?

Agriculture is the backbone of Sub-Saharan Africa's economy and survival. More than 60% of the region's population works in farming, with smallholder farmers operating roughly 80% of all farms. Climate change is intensifying the pressure: severe droughts, flooding, and extreme heat are becoming more frequent and severe, threatening food security and water access across the continent.

AI offers a practical solution. Farmers are already using AI-powered tools delivered through mobile apps, text messages, and voice calls to monitor crops, predict weather patterns, manage water and fertilizer use, and receive personalized farming advice. Investment in agricultural technology has surged from less than $10 million in 2014 to $206.9 million in 2024. However, the momentum stalled in 2025, with investments falling nearly 20% to under $170 million, signaling that the sector has not yet achieved the stable financing base needed for long-term growth.

Why Are Governance Gaps Becoming a Major Barrier?

The core problem is straightforward: there are no clear rules. When a farmer shares crop data through an AI tool, who owns that data and can profit from it? If an AI system gives wrong planting advice that leads to crop failure, who is responsible for the loss? These questions remain unanswered in both Rwanda and Nigeria, two countries that represent different governance approaches across the region.

Rwanda has taken a more centralized approach, embedding AI and digitization into its Strategic Plan for Agriculture Transformation 5 (PSTA 5) with clear budgets and defined responsibilities. Nigeria has a broader institutional base but less clearly defined boundaries between agencies, making coordination difficult in practice. Neither country has addressed critical governance issues around data ownership, fairness in how AI systems affect farmers, or liability when AI-driven tools fail.

These governance gaps create real costs. For AI developers and startups, unclear expectations around liability and data rights raise the financial and legal risks of entering the market. For investors, unpredictable regulatory conditions make it harder to assess opportunities and commit capital. For smallholder farmers, the absence of safeguards and clear rules erodes trust in the wider system, making them hesitant to share data or adopt new tools.

What Conditions Do Farmers Actually Need to Adopt AI?

Technology alone is not enough. For agricultural AI to deliver real benefits to smallholder farmers, several foundational conditions must be in place:

• Clear Governance Frameworks: Legal and regulatory structures that define who is responsible for implementation, how data is collected and used, what infrastructure is needed, and how public and private actors coordinate.
• Reliable Infrastructure: Functioning data centers, internet connectivity, and digital systems capable of supporting AI tools at scale. Rwanda has only a few data centers, while Nigeria has roughly twenty, illustrating the infrastructure gap across the region.
• Usable Data: High-quality, accessible data that AI systems can learn from and that farmers can trust is being used responsibly.
• Financing Pathways: Sustainable funding mechanisms that move beyond pilot programs and development grants to private investment and market-based solutions.
• Accessible Delivery: Ways for farmers to actually access AI tools, whether through mobile apps, SMS, or voice-based systems suited to areas with limited internet access.

Beyond these technical and operational requirements, farmers must be able to trust the system itself. This means clear rules around data use, industry standards, and safeguards against harm. Without these foundations, AI remains a theoretical promise rather than a practical tool farmers can rely on.

How Are Regional Institutions Responding?

The African Union has recognized the governance gap. Its Continental AI Strategy, Digital Transformation Strategy for Africa, and African Digital Compact have all called for stronger regulatory clarity around agricultural AI's development and use, particularly regarding data, ethics, and responsible innovation. However, concrete implementation guidance is still lacking.

Financing remains concentrated in research, pilots, and development programs. The AI4D Africa initiative supports AI research and development across the region, while the African Development Bank funds digital and tech-driven agricultural programs under its Feed Africa strategy. International tech giants like Google and Microsoft are investing in emerging AI startups, but broader private investment remains limited. This funding structure suggests that agricultural AI in Africa is still in an early stage, dependent on public and philanthropic support rather than self-sustaining market demand.

Steps Organizations Can Take to Build Trust in Agricultural AI Systems

• Develop Sector-Specific Data Governance Policies: Create clear rules defining who owns farmer data, how it can be used, who profits from it, and what happens if data is misused. These policies should be transparent and accessible to farmers.
• Establish Liability and Accountability Frameworks: Define responsibility when AI systems fail or cause harm. Clarify whether liability rests with AI developers, extension agents, governments, or farmers, and establish dispute resolution mechanisms.
• Invest in Infrastructure and Connectivity: Expand data centers, internet access, and digital systems in rural areas where most smallholder farmers operate. This is essential for both AI deployment and farmer participation.
• Create Multi-Stakeholder Governance Bodies: Bring together government agencies, AI developers, farmers, civil society, and international organizations to coordinate policy development and implementation across countries.
• Build Farmer Trust Through Transparency: Communicate clearly how AI tools work, what data they collect, how that data is protected, and what benefits farmers can expect. Trust is foundational to adoption.

The path forward requires coordinated action across multiple actors. Governments must establish clear legal frameworks that define rights, responsibilities, and safeguards. Private sector actors must invest in tools and infrastructure while respecting farmer data and interests. Development organizations must continue funding research and capacity building. And farmers themselves must have a voice in shaping how AI is developed and deployed in their communities.

Agricultural AI has enormous potential to help Sub-Saharan Africa adapt to climate change, increase crop yields, and build long-term food security. But realizing that potential depends on building the governance structures, infrastructure, and trust that allow farmers to safely and confidently adopt these tools. Without these foundations, even the most sophisticated AI systems will remain out of reach for the smallholder farmers who need them most.