Bayer’s 117bn data points and decade-old data culture drive AI advantage, says CIO

It’sDespite growing scrutiny of MIT’s infamous claim that 95% of enterprise generative AI pilots fail, anecdotal evidence suggests many large corporations are still struggling to successfully deploy AI across their businesses.

A variety of things are to blame, from the wrong corporate culture to ineffective talent to data hygiene and infrastructure. One anonymous COO captured the prevailing sentiment to MIT researchers: “The hype on LinkedIn says everything has changed, but in our operations, nothing fundamental has shifted.”

By contrast, Bayer Crop Science appears to be deploying GenAI tools at scale, resulting in measurable operational improvements: Bayer’s E.L.Y. system, which helps agronomists access product knowledge, shows 60% productivity improvements and is being used by over 1,500 frontline employees across North America,  according to Bayer chief information officer Amanda McClerren. 

This difference may lie in a 12-year-old acquisition that most competitors lack.

The 12-year build

The foundation for Bayer’s current AI capabilities was laid in 2013, when Monsanto acquired The Climate Corporation for $930 million. That deal brought more than just the FieldView precision agriculture platform—it brought a data culture, technical talent, and, crucially, lessons about digital product development that shaped everything that followed, McClerren tells AFN.

Fast forward to today and Bayer’s leading GenAI tool E.L.Y. recently won ‘AI-based AgTech Solution of the Year‘ at the AgTech Breakthrough Awards.

“One of the reasons we chose to develop [E.L.Y.] is because we have unique data, and we have unique insights about that data…[such as] the richness of our R&D and product supply and commercial field testing data,” says McClerren, who started her career as a biochemist at Monsanto and spent nearly eight years in biotech before moving into breeding and eventually IT.

The Climate acquisition taught critical lessons, McClerren notes: understanding “how different it is to bring a digital product to market as opposed to a physical product” and grasping “the value proposition between the interface of those two things.” The company spent years building data infrastructure—accumulating field-testing data, creating semantic tools to make that data discoverable, and establishing, as McClerren describes, a mature data warehouse platform.

The moat

The data moat is substantial: 117 billion data points on seed performance.

“We have one of the largest and most complete data sets in the industry. We have decades of field testing data on our products, both the ones that made it to market, and the ones that were in the pipeline…and failed, as well as the genetic information about those products, so that we can start to explore and understand the relationship between what genetic combinations are most successful in what environments.”

But this foundation was built first for traditional AI. The company has been using machine learning and deep learning in R&D “for a long time,” McClerren notes—well before the GenAI hype cycle began.

The results have been tangible: AI technology has “cut overall product delivery time by two years” through accelerated crop breeding cycles. In an industry where product development traditionally takes seven to 10 years, that represents a significant competitive advantage.

The proven track record has helped produce what McClerren describes as “the leading pipeline in the industry”—worth $32 billion on roughly $2.4 billion in annual R&D investment.

A key innovation has been the digital twin project: “A literal digital twin of our field testing network…a replica of millions of potential farming acres,” McClerren explains. “By leveraging this high-fidelity twin, we can simulate the performance of things that are coming through the pipeline.”

The value here is speed and the ability to predict under untested conditions. 

“You’re subject to the weather. In real life, you’re subject to, ‘did it rain in July, or was it cool in July?’” she notes. “In any given year, you can only test what the weather gives you. And by having this digital twin, we can really start to understand product performance across environments that they may not have experienced yet.”

GenAI: Test and learn at scale

E.L.Y. launched as what McClerren calls “a test-and-learn opportunity” to explore both business utility and technical strategy. The company ran a rigorous validation: over 1,500 agronomists tested it for about a year to ensure it met customer needs.

When pressed on how they balance “test and learn” with urgency, McClerren emphasizes iteration: “AI has been accelerating in both its capabilities and adoption at an extremely fast pace, so we’ve embraced iterative methodology that allows us to pilot new technologies while continuously gathering data and insights to inform our next steps.”

The system aggregates what McClerren describes as “all of our agronomic knowledge, all of our product recommendation sheets”—contextual information about how to use Bayer’s products most effectively. “We were able to develop this tool, this product. We’ve got it deployed in North America today. And so our field-facing agronomists are seeing about a 60% increase in productivity…saving them about four hours a week that they don’t have to spend searching for all of this knowledge.”

Those time savings translate directly to more customer engagement.

McClerren outlines three key pillars for broader AI deployment: sales and service (where E.L.Y. sits), supply chain and logistics, and R&D. “The future is multiple agents that work together,” she envisions. She sees potential for GenAI to integrate with FieldView to eventually provide growers with direct advice. She acknowledges, “it’s not something that we have initiated yet.”

The data moat in practice

Where the decade of infrastructure building becomes tangible is in specific product applications. McClerren points to PRECEON, Bayer’s short-stature corn product, as an example of how digital and physical products intersect.

“In order for farmers to really fully leverage that innovation and see the most productive output on-farm, they need to marry that with the right hybrid selection, and they need to marry that to the right density, planting density of that hybrid on their farm,” she explains. “That isn’t possible without a platform like FieldView that helps us understand that on-farm acre and can help make those precise recommendations.”

This integration of proprietary germplasm, decades of performance data, and digital tools represents a moat that’s difficult to replicate. As McClerren notes, “agronomy is deeply unique to farming and agriculture,” unlike more commoditizable AI applications like customer service that “go across many different types of industries.”

Growing up on a farm herself—her father is a farmer—McClerren brings personal context to the digitization challenge. “I think you’re seeing a new generation of farmers that grew up differently…and have a different [approach to] the complexity of the decisions and the various types of data that need to come together to make a good decision,” she observes. “Digital is the obvious choice for how to manage all of these on-farm decisions over the course of a season.”

Bayer’s approach aligns with what MIT researchers found differentiates the successful 5% of AI implementations: “They pick one pain point, execute well, and partner smartly.” The 1,500-person E.L.Y. pilot, the emphasis on iterative methodology, and the focus on specific use cases all reflect a disciplined strategy.

But the real differentiator may be simpler: Bayer had a 12-year head start. 

“Reimagining work”

The Climate acquisition brought not just technology but also a data culture that takes years to build and has been blamed for stalling corporate AI rollouts more broadly.

Perhaps the most revealing insight comes when McClerren discusses change management for agentic AI. “We not only have to be prepared to work differently and maybe have agents do tasks that people did in the past and people do different types of tasks, but we also have to reimagine the work,” she says. “If a digital agent can do something that before could only be done with people or a team of people, maybe the whole business process needs to look different.”

This suggests a company thinking beyond simple automation to fundamental business-process redesign—ambitious, but still largely hypothetical. “It’s something we’re still very early on, the learning journey of, but it’s something we’re paying a lot of attention to,” she acknowledges.

When asked about quantifying ROI, McClerren frames it as necessarily complex: “Quantifying ROI is a multifaceted process, and ultimately our goal is to create AI solutions that not only drive financial performance but also contribute to sustainable agricultural practices.” 

The response reflects a company balancing short-term productivity gains with longer-term strategic positioning around sustainability—whether that represents sophisticated thinking or hedging against harder financial questions remains to be seen.

More on AI in agribusiness:

Are agrifood corporates pushing their AI agendas enough?

Corteva’s planned separation raises questions about AI and data split

Corteva: AI can transform crop protection to replace ‘randomness and chance’ with ‘prediction, specificity and design’

Where are we in the AI bubble?

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