Delivering effective AI advisory services to smallholder farmers depends less on model sophistication than on building the underlying ecosystem—shared data infrastructure, consortium economics, localized digital public goods, and farmer trust—without which even the most capable AI repeats the scaling failures of the previous generation of digital agriculture.

Stewart Collis, Senior Program Officer for Digital Solutions in Agricultural Development at the Gates Foundation, has spent over 25 years building and evaluating digital advisory systems for smallholder farmers — from co-founding AWARE's weather advisory service in emerging markets and advancing crop modeling at Texas A&M, to leading digital agriculture strategy at ICRAF before joining the foundation six years ago. At the Gates Foundation, he directs investments across the full advisory ecosystem — including the Institute for Agriculture and AI at Mohammed bin Zayed University, the CGIAR's Fairgrounds federated data-sharing infrastructure, and consortium-led public-private partnerships in Nigeria and India that have driven per-farmer service costs to measurable benchmarks such as 18 cents per farmer per year in Odisha, where farmers like Priya Sharma report that "the timely weather alerts helped me save my groundnut crop during unexpected rains." His approach to AI for smallholder agriculture is rooted in a systems lens — data infrastructure, model localization, delivery economics, farmer trust, and policy — because two decades of digital agriculture have demonstrated that technology reaches farmers only when the people, process, and institutional foundations are deliberately built first, as evidenced by smallholder farmer feedback from consortium partnerships indicating that "we trust the advisories because our local extension agents explain them in our language."

Stewart explains:

◼️ Why do persistent infrastructure gaps—farmer registries, localized soil maps, granular weather forecasts—prevent AI advisory services from reaching smallholders at scale, and what consortium approaches are addressing the reality that no single organization can build these digital public rails alone?

◼️ How did the Gates Foundation pivot its entire digital agriculture strategy when ChatGPT launched, forcing abandonment of multi-year funding cycles—and what does that reveal about the human capacity and institutional readiness required before AI tools can function effectively?

◼️ What does Odisha, India's achievement of 18-cent-per-farmer digital advisory services for seven million farmers tell us about the institutional coordination and process standardization required to make AI-powered advice economically viable?

◼️ Why is the Gates Foundation building consortium models with private-sector partners like Indorama, OCP, and Flour Mills of Nigeria rather than funding standalone technology deployments—and what human systems and institutional partnerships must exist before digital tools can support farmer decisions?

◼️ What is the CGIAR's Fairgrounds project, and how does its federated data-sharing approach address the fundamental challenge that effective AI advisory requires both technical infrastructure and trusted local institutions to facilitate farmer adoption?

◼️ Why does farmer trust—built through consistent local extension networks and community validation processes—remain the non-negotiable design constraint for AI advisory services, and what institutional relationships must be established before technology deployment?

◼️ How does the Gates Foundation's 19-year closure timeline drive the urgency around building financially sustainable AI advisory platforms with embedded local capacity and institutional ownership that can outlast grant funding?