Bayer Crop Science’s AI breakthrough accelerates agricultural innovation with its E.L.Y. system

Bayer Crop Science’s E.L.Y. platform, backed by a decade of data-driven innovation and strategic partnerships, demonstrates how generative AI can deliver significant productivity gains and reshape the future of farming.

Bayer Crop Science is emerging as a standout example in the often challenging corporate deployment of generative AI, demonstrating tangible productivity gains and operational innovation where many enterprises continue to struggle. This success is under...

scored by Bayer’s E.L.Y. system, an AI-driven tool that has already achieved a 60% increase in productivity for over 1,500 frontline agronomists across North America, saving them around four hours per week previously spent searching for critical agronomic knowledge. The system was recently honoured with the ‘AI-based AgTech Solution of the Year’ award at the AgTech Breakthrough Awards, reflecting its impact on advancing agricultural technology.

Central to Bayer’s AI leadership is a data culture and digital foundation established over the last twelve years, initially catalysed by Monsanto’s 2013 acquisition of The Climate Corporation for $930 million. This landmark deal brought not only the FieldView precision agriculture platform but also a wealth of unique insights, data, and technical prowess that have proven crucial to Bayer’s AI advancements. Amanda McClerren, Bayer’s Chief Information Officer, who has a deep background in biotech and agronomy, highlights that this acquisition introduced essential lessons on the distinct challenges and value propositions involved in developing digital versus physical products for agriculture.

At the heart of Bayer’s AI differentiation is its vast reservoir of data, 117 billion data points encompassing decades of field testing, genetic information, and product performance data, including those products that never reached market. This data richness enables advanced machine learning and deep learning models that have historically accelerated crop breeding, cutting product delivery times by two years in an industry where development typically takes seven to ten years, a significant competitive edge. Bayer’s annual investment of approximately €2.3 billion in crop science R&D underpins the company’s strong innovation pipeline, valued at an estimated peak of €30 billion.

One notable technological advance is Bayer’s digital twin project, which creates a precise simulation of millions of acres for crop testing under various environmental conditions. This innovation allows Bayer to predict product performance beyond the constraints of real-world variables like weather, enabling faster and more accurate product development decisions.

E.L.Y. embodies a disciplined, test-and-learn approach that echoes research findings from MIT, which identified successful AI implementations as those targeting specific business pain points with rigorous execution and smart partnership. Bayer’s year-long pilot with over 1,500 agronomists ensured the system met user needs before broader deployment. The platform aggregates comprehensive agronomic knowledge, product guidance, and field trial data to provide timely, context-aware responses to complex natural language queries, enhancing efficiency and customer engagement.

Partnerships with Microsoft and Ernst & Young have been instrumental in building E.L.Y., leveraging Microsoft’s Azure AI Foundry and a large language model trained on Bayer’s proprietary agronomy content. This collaboration earned Bayer additional recognition through the ‘Artificial Intelligence for Good’ award at the 2025 AI Breakthrough Awards, recognising AI’s role in improving agricultural knowledge delivery and farmer support.

Bayer’s integration of physical and digital innovation is exemplified by products like PRECEON, a short-stature corn variety. Maximising its effectiveness requires precise agronomic recommendations from digital platforms like FieldView, which helps tailor hybrid selection and planting density to specific farm conditions, a synergy difficult for competitors to replicate due to the unique and complex nature of agronomy data compared to more commoditised AI applications.

Beyond immediate AI productivity gains, Bayer is also contemplating broader organisational and process transformations brought about by agentic AI, where digital agents execute tasks traditionally performed by humans, prompting a fundamental reimagining of workflows and business processes. McClerren describes this as an ongoing learning journey, balancing innovation with sustainable agricultural practices and complexity in quantifying return on investment.

Bayer’s approach contrasts sharply with the broader corporate experience of AI adoption, which MIT research has famously described as having a 95% failure rate in enterprise generative AI pilots. Many organisations face roadblocks ranging from cultural resistance and inadequate talent to poor data hygiene and infrastructure. In comparison, Bayer’s decade-plus head start on digital transformation and a robust data culture provide a strong moat supporting its generative AI advantage.

This case reinforces the critical importance of long-term data strategy and iterative development in building AI capabilities that can yield significant operational improvements and business value in complex sectors like agriculture. As Bayer looks ahead, the potential to extend AI-driven insights directly to growers through integration with platforms like FieldView hints at a future where generative AI not only supports internal teams but also empowers farmers on the ground, though that vision remains at an early stage.

Overall, Bayer exemplifies how sustained investments in data, digital culture, and targeted AI applications can transform traditional industries, turning generative AI hype into productive and sustainable outcomes that ripple through both business operations and agricultural ecosystems.

Source: Noah Wire Services