Data Solutions for Tailoring Agronomic Support to Meet Regional Needs - with Tami Craig Schilling of Bayer Crop Science

2026-01-06 · Source: The AI in Business Podcast · Field: Agriculture & Food Systems — Precision Agriculture & Smart Farming, Crop Science & Plant Technology · Depth: Intermediate, extended

Summary

Bayer Crop Science's Vice President of Agronomic Digital Innovation, Tami Craig Schilling, discusses how generative AI is transforming agricultural support by providing localized recommendations across the plan-plant-grow-harvest cycle. The company utilizes tools like ELI, which employs zip code-based prompting to triangulate genetics, environment, and pests, augmenting human expertise with precise agronomy advice. This approach addresses the unique challenges farmers face due to variable soil conditions, diverse farming practices, and unpredictable weather. The technology offers scale-neutral support, benefiting both commercial and smallholder farmers, and emphasizes the importance of farmer input as subject matter experts to refine AI-driven solutions.

Key takeaway

For AI Product Managers developing solutions for agriculture, you should prioritize building tools that treat farmers as subject matter experts, integrating their localized data to fuel generative AI. Focus on creating intuitive prompting mechanisms and comprehensive guides, like Bayer's ELI, to ensure precise, personalized recommendations that adapt to unique field conditions and diverse farmer needs, ultimately boosting crop success and reducing input requirements.

Key insights

Generative AI provides localized, precise agronomic advice by integrating diverse environmental and genetic data.

Principles

• Every field is unique due to soil, history, and practices.
• Genetics by Environment (GxE) is a critical success factor.
• Farmer input is essential for effective AI recommendations.

Method

Utilize zip code-based prompting tools like ELI to triangulate genetics, environment, and pests, generating precise, label-compliant agronomic advice that augments human expertise and adapts to local conditions.

In practice

• Implement zip code-based prompting for localized recommendations.
• Develop prompt guides to optimize user interaction with AI tools.
• Integrate farmer-provided data for personalized advice.

Topics

• Generative AI
• Agronomic Digital Innovation
• Localized Agronomic Support
• AI Prompt Engineering
• Bayer Crop Science

Best for: AI Product Manager, Director of AI/ML, VP of Engineering/Data, Executive

Related on AIssential

• Think You Know AI? 25 Startups Prove You Wrong — AI is transforming physical world industries by enabling precise, scalable solutions across diverse sectors.
• Rainbow Crops raises €9.7M to scale AI-powered crop engineering — AI-powered genome editing and precision breeding accelerate development of resilient, high-performing crop varieties.
• Prioritizing neglected food species in nutritional studies using expert-knowledge and explainable AI — Explainable AI and expert knowledge can effectively prioritize neglected food species for nutritional research.
• I built a church for AI agents to fund a tree planting project.. and now "they" want me to build a reforestation robot dog. Boston Dynamics, call me. — AI-driven biomimicry can scale reforestation efforts by automating complex tasks with specialized robotic systems.
• The End of One-Size-Fits-All Enterprise Software — Generative AI enables deep software customization, shifting enterprise strategy from adapting to tools to owning distinctive workflows.
• The AI Algorithm Deciding What You’ll Eat in 2030 — AI algorithms are centralizing global agricultural decisions, replacing traditional farming knowledge with data-driven directives.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.