Data Solutions for Tailoring Agronomic Support to Meet Regional Needs - with Tami Craig Schilling of Bayer Crop Science (35 min)
ai-driven-innovation-economy ai-for-climate-modeling ai-global-economic-shifts ai-in-everyday-life ai-literacy-public-awareness ai-sustainable-agriculture
- Release date: 2026-01-06
- Listen on Spotify: Open episode
- 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!
Summary
- 🌾 Universal Crop Cycles, Local Chaos: Farming follows a consistent plan-plant-grow-harvest cycle worldwide, but success hinges on hyper-local factors like unique soil profiles, tillage practices, weather, pests, and genetics-environment interactions.
- 🧬 GenAI Triangulation Magic: Bayer’s ELI tool leverages zip codes and farmer prompts to integrate genetics, environment, pests, and products, delivering personalized recommendations beyond human spreadsheets or memory.
- 🤝 Farmers as AI Co-Pilots: Treating farmers as SMEs who supply contextual prompts builds partnerships, ensuring AI augments rather than replaces expertise while respecting data ownership.
- 📝 Prompting Mastery Unlocks Value: Training on detailed, jargon-aware prompts—covering crop stages, pest specifics, and labels—is crucial for optimal AI outputs, with guides bridging AI’s lack of generational context.
- 🌍 Scale-Neutral Global Impact: AI empowers smallholders via basic phones amid weather unpredictability, promoting sustainable yields, economic growth, and lessons for enterprise AI across industries.
Insights
How can generative AI deliver hyper-localized agronomic advice by triangulating genetics, environment, pests, and farmer practices?
Time: 7:29 – 12:42
Category: AI & Sustainable AgricultureAnswer: Bayer’s ELI tool uses zip code-based prompting to combine vast product data with local factors like soil history, tillage methods, weather, and pests, providing precise recommendations that surpass human capacity alone. This augments expertise, reduces inputs, and boosts crop yields amid unpredictable conditions. It matters for sustainable farming as it scales from commercial to smallholder operations worldwide. (Start at 7:29)
Why treat farmers as indispensable SMEs whose prompts unlock the full power of GenAI tools?
Time: 13:09 – 18:15
Category: AI in Everyday Life, AI-Driven Innovation EconomyAnswer: Farmers provide critical context on field-specific challenges, past outcomes, and practices, acting as the ‘prompt’ for AI to generate tailored solutions rather than generic advice. This partnership fosters trust and long-term relationships, translating farmer data into actionable insights without ownership claims. It’s key for AI adoption in expert-driven industries beyond agriculture. (Start at 13:09)
What makes prompting the secret sauce for maximizing GenAI in complex domains like pest control and crop stages?
Time: 23:41 – 27:56
Category: AI Literacy & Public AwarenessAnswer: Effective prompts must detail specifics like zip code, pest stage (e.g., larvae vs. adult), weed size, crop growth phase, and EPA label compliance to yield optimal, safe recommendations. Bayer provides guides and training, as humans supply context AI lacks, such as field history. This subtle skill shift drives better outcomes than spreadsheets or solo expertise. (Start at 23:41)
How is AI making agriculture scale-neutral, empowering smallholder farmers in developing regions?
Time: 28:29 – 29:26
Category: AI & Global Economic ShiftsAnswer: Tools like ELI work via basic cell phones, not requiring smartphones, to deliver advice amid variable weather and limited resources, building village economies and enabling education. This democratizes access to Bayer’s R&D insights, fostering global food security. It highlights AI’s role in equitable development beyond large-scale operations. (Start at 28:29)
In what ways does unpredictable weather amplify the need for flexible, real-time AI agronomy support?
Time: 29:26 – 30:05
Category: AI for Climate ModelingAnswer: Excessive rains in specific locales alter pest dynamics and planting timelines, demanding adaptive product choices that GenAI provides instantly, unlike delayed human consultations. This flexibility ensures success despite anomalies, reducing risks in outdoor farming. It parallels AI’s value in other climate-vulnerable sectors. (Start at 29:26)
Why does agriculture’s tech-savvy history position it as a model for cross-industry AI learning?
Time: 32:27 – 33:02
Category: AI-Driven Innovation EconomyAnswer: Farmers have long adopted innovations from cell phones to autonomous equipment, now extending to GenAI for advice, contrasting slower uptake elsewhere. Cross-industry dialogues reveal shared lessons in augmentation over replacement. This accelerates broader enterprise AI ROI through proven, practical deployment. (Start at 32:27)