Authors: Dr. Monisha Lakshminarayan, Zeba Siddiqui
The possibilities of AI within agriculture is real, however its greatest value will emerge when technological innovation is synergised with strong evidence, inclusive design, and responsive systems that ensure farmers - especially smallholders - can benefit from it.
Editor’s note:This article is written by Dr. Monisha Lakshminarayan and Zeba Siddiqui from Athena Infonomics. The team at Athena Infonomics recently presented findings from research commissioned through the RCC at EvalFest 2026[BR4.1] and the India AI Impact Summit in New Delhi. In this piece, Dr. Monisha and Zeba reflects on emerging evidence on artificial intelligence in food and agriculture systems and what it will take to translate innovation into meaningful impact for farmers in low- and middle-income countries.
Artificial intelligence (AI) is increasingly shaping conversations about the future of agriculture. From predictive analytics to crop diagnostics, AI-enabled tools are opening up new possibilities for supporting farmers and strengthening food systems. But the real question is no longer whether these technologies can work, but whether they can deliver meaningful and equitable impact, particularly for farmers in low- and middle-income countries (LMICs).
That question sat at the centre of discussions at EvalFest 2026 and the India AI Impact Summit, where Athena Infonomics shared insights from our landscape analysis and rapid evidence review. Across both events, the conversation went beyond excitement about innovation to something more grounded: where AI is already showing promise, and where gaps in evidence and implementation still hold it back.
One idea came up again and again: It’s not enough to showcase what AI can do, what matters is how it works in the real world.
AI tools can offer powerful capabilities, from detecting crop diseases to supporting farm-level decision-making. But they don’t operate in a vacuum. Their effectiveness depends on the systems around them, extension services, data infrastructure, financing, and public delivery mechanisms, that ultimately determine whether farmers can access and benefit from these tools.
The possibilities of AI within agriculture are real, however its greatest value will emerge when technological innovation is synergised with strong evidence, inclusive design, and responsive systems that ensure farmers, especially smallholders, can benefit from it.
Our rapid review of 51 studies on AI-enabled agricultural solutions across LMICs highlights both progress and important gaps. Much of the existing research focuses on predictive modelling and simulation-based studies, useful for demonstrating technical potential, but limited in what they tell us about real-world impact. Far fewer studies look at outcomes that matter most to farmers and policymakers, such as income, food security, sustained adoption or who actually benefits.
In other words, we are getting better at understanding what AI can do, but we still know far less about what it actually changes.
There are, of course, encouraging examples. AI-powered crop diagnostics are helping farmers identify diseases earlier and reduce losses. Predictive tools are improving advisory services, supporting decisions on planting, irrigation and fertiliser use. These innovations point to the real potential of AI to strengthen agricultural decision-making.
But moving from potential to impact, and from pilots to scale, remains a challenge. Many promising tools stall because they are not embedded in the systems farmers already rely on, such as extension services, insurance schemes or supply chains. At the same time, persistent barriers like limited connectivity and digital literacy continue to shape who can access and use these technologies.
Across the discussions, three challenges surfaced repeatedly: fragmented agricultural data ecosystems, gaps in digital infrastructure, and institutional systems that struggle to keep pace with rapid technological change. But just as important are the questions of equity and context that sit beneath these challenges. Without careful design, digital technologies risk benefiting farmers who already have access to infrastructure while leaving others further behind.
Agriculture is inherently very native. It is highly dependent on the local ecological conditions, socio-economic norms and cultural realities. This is what makes scaling particularly complex. Solutions that work in one setting cannot simply be transplanted into another. Instead, context-sensitive design, developed in partnership with farmers, extension workers and local institutions, is essential to ensuring that AI-enabled innovations are not only adopted, but sustained and equitable in their impact.
All of this leads to a bigger question that came up across both events: how do we move beyond isolated pilots and towards system-wide adoption? How do governments and development partners create the conditions for AI tools to deliver public value at scale?
Part of the answer lies in strengthening the evidence base. As AI technologies evolve quickly, keeping pace with high-quality, up-to-date evidence will be critical for guiding policy and investment decisions. Tools like Evidence Gap Maps can play an important role in identifying where rigorous research is most needed and helping direct resources accordingly.
Ultimately, the conversations at EvalFest and the India AI Impact Summit reinforced a simple but important lesson. Innovation alone does not transform agriculture. Real impact emerges when technology, evidence and institutions evolve together, and when farmers are at the centre of that process with deliberate attention to equity, including gender and the barriers faced by marginalised farmers in accessing and using these tools. Otherwise, even the most advanced innovations risk remaining pilots rather than pathways to public value.
RIDE: generating evidence for sustainable transport in developing economies Share Post : Scene from Al Nnuhoud Crop Market, North Kordofan, Sudan. Photo: Salahaldeen Nadir / World Bank A major new international research and policy partnership is helping to shape the future of sustainable transport across low and middle-income countries. Launched during the first UN Decade […]
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