Generative artificial intelligence (gen AI) is gaining traction in agriculture. Conversational agents such as chatbots and voice assistants are being explored as digital extension solutions, promising scalable, personalized advice for farmers that can supplement and support the work of human extension agents. Yet designing and deploying such AI tools presents many challenges.
The needs of smallholder farmers have often been among the last considered in rollouts of new agricultural technologies, resulting in uneven rates of adoption, where some farmers benefit while others are left behind. These disparities are amplified in the digital landscape. Some farmers may not have the necessary access to mobile phone technologies, internet connectivity, or electricity; among those who do, some may be comfortable with new AI tools and others confounded.
At the same time, the potential rapid deployment of gen AI in agricultural risks entrenching a common deficit-based assumption about new technologies: That farmers lack knowledge and that this gap can be “fixed” from the outside with the right content or nudges engineered by AI developers. This approach downplays or even ignores those key farmer needs, overlooking social, economic, and technical context, as well as the community-based knowledge systems that farmers already rely on.