Building control and trust for farmers in agriculture’s generative AI transition
In the Global North, digital agriculture emerged in the form of precision systems built around tangible technologies such as sensors and connected machinery operating alongside farm management software and other digital tools.
- Artificial Intelligence
- governance
- farmers
By Katarzyna KosiorSeptember 22, 2026
Key takeaways
- Generative AI can deepen power imbalances. Farmers see simple tools, but providers often control the data, infrastructure, and knowledge generated behind the scenes.
- ·Current governance protections are weak. Many agricultural AI services provide limited transparency, weak user control, and insufficient safeguards for farmers’ data and privacy.
- ·Farmer-centered governance is essential. Farmers need stronger data rights, meaningful choices over data use, and a greater role in shaping how agricultural AI systems are designed and governed.
In the Global North, digital agriculture emerged in the form of precision systems built around tangible technologies such as sensors and connected machinery operating alongside farm management software and other digital tools. Farmers often approached these systems cautiously, given the sustained investments required and the challenges of integrating new technologies into everyday farm operations, and voluntary initiatives on data governance, including sector-specific codes of conduct on agricultural data sharing, emerged gradually.
Generative AI applications for agriculture operate differently and are being deployed much more quickly. Many take the form of chatbots that ask questions such as “How can I help?” The conversational interface creates an impression of immediacy and simplicity that earlier agricultural technologies rarely offered.
Yet the ease of use also obscures the infrastructure, incentives, and data flows operating behind these systems—making governance far more challenging, particularly in low-income countries.