The AI in Business Podcast
Data Solutions for Tailoring Agronomic Support to Meet Regional Needs - with Tami Craig Schilling of Bayer Crop Science
AI Summary
In this episode, Matthew DeMello interviews Tami Craig Schilling, Bayer Crop Science’s VP of Agronomic Digital Innovation, about how generative AI is used to deliver hyper‑local agronomic recommendations throughout the planting cycle. They discuss tools like the zip‑code‑based ELI platform that integrates genetics, environment, and pest data to generate precise, scalable advice for both large commercial farms and smallholder growers. The conversation highlights how AI augments traditional agronomy expertise, enabling tailored support that adapts to variable soils, practices, and weather conditions.
Episode Description
Today's guest is Tami Craig Schilling, Vice President of Agronomic Digital Innovation at Bayer Crop Science. Tami brings decades of expertise in agricultural sales, R&D, and digital tools for farmer support. Tami joins Emerj Editorial Director Matthew DeMello to explore how generative AI delivers localized recommendations across the plan-plant-grow-harvest cycle amid variable soil, practices, and weather conditions. Tami also shares practical takeaways like using zip code-based tools such as ELI for prompting that triangulates genetics, environment, and pests—augmenting human expertise with precise agronomy advice, prompt guides for optimal outputs, and scale-neutral support from commercial to smallholder farmers. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast!
Show Notes
Data Solutions for Tailoring Agronomic Support to Meet Regional Needs – with Tami Craig Schilling of Bayer Crop Science
January 6, 2026
Today’s guest is Tami Craig Schilling, Vice President of Agronomic Digital Innovation at Bayer Crop Science. Tami brings decades of expertise in agricultural sales, R&D, and digital tools for farmer support. She joins Emerj Editorial Director Matthew DeMello to explore how generative AI delivers localized recommendations across the plan‑plant‑grow‑harvest cycle amid variable soil, practices, and weather conditions.
Key takeaways include:
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Using zip‑code‑based tools such as ELI for prompting that triangulates genetics, environment, and pests.
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Augmenting human expertise with precise agronomy advice, prompt guides for optimal outputs, and scale‑neutral support that works for both commercial operations and smallholder farmers.
Want to share your AI adoption story with executive peers? Visit emerj.com/expert2 for more information and to be considered as a future guest on the “AI in Business” podcast.
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