\n<\/p>\n
It\u2019sDespite growing scrutiny of MIT\u2019s 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.<\/span><\/p>\n A variety of things are to blame, from the wrong corporate culture to ineffective talent to data hygiene and infrastructure. <\/span>One anonymous COO captured the prevailing sentiment to MIT researchers: \u201cThe hype on LinkedIn says everything has changed, but in our operations, nothing fundamental has shifted.\u201d<\/span><\/p>\n By contrast, Bayer Crop Science appears to be deploying GenAI tools at scale, resulting in measurable operational improvements: Bayer\u2019s 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,\u00a0 according to Bayer chief information officer Amanda McClerren.\u00a0<\/span><\/p>\n This difference may lie in a 12-year-old acquisition that most competitors lack.<\/span><\/p>\n The foundation for Bayer\u2019s current AI capabilities was laid in 2013, when Monsanto acquired The Climate Corporation for $930 million. That deal brought more than just the FieldView<\/a> precision agriculture platform\u2014it brought a data culture, technical talent, and, crucially, lessons about digital product development that shaped everything that followed, McClerren tells <\/span>AFN<\/span><\/i>.<\/span><\/p>\n Fast forward to today and Bayer\u2019s leading GenAI tool E.L.Y. recently won \u2018AI-based AgTech Solution of the Year<\/a>\u2018 at the AgTech Breakthrough Awards.<\/p>\n \u201cOne of the reasons we chose to develop [E.L.Y.] is because we have unique data, and we have unique insights about that data\u2026[such as] the richness of our R&D and product supply and commercial field testing data,\u201d 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. <\/span><\/p>\n The Climate acquisition taught critical lessons, McClerren notes: understanding \u201chow different it is to bring a digital product to market as opposed to a physical product\u201d and grasping \u201cthe value proposition between the interface of those two things.\u201d The company spent years building data infrastructure\u2014accumulating field-testing data, creating semantic tools to make that data discoverable, and establishing, as McClerren describes, a mature data warehouse platform.<\/span><\/p>\n The data moat is substantial: 117 billion data points on seed performance.<\/span><\/p>\n \u201cWe 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\u2026and 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.\u201d<\/span><\/p>\n But this foundation was built first for traditional AI. The company has been using machine learning and deep learning in R&D \u201cfor a long time,\u201d McClerren notes\u2014well before the GenAI hype cycle began.<\/span><\/p>\n The results have been tangible: AI technology has \u201ccut overall product delivery time by two years\u201d through accelerated crop breeding cycles. In an industry where product development traditionally takes seven to 10 years, that represents a significant competitive advantage. <\/span><\/p>\n The proven track record has helped produce what McClerren describes as \u201cthe leading pipeline in the industry\u201d\u2014worth $32 billion on roughly $2.4 billion in annual R&D investment.<\/span><\/p>\n A key innovation has been the digital twin project: \u201cA literal digital twin of our field testing network\u2026a replica of millions of potential farming acres,\u201d McClerren explains. \u201cBy leveraging this high-fidelity twin, we can simulate the performance of things that are coming through the pipeline.\u201d<\/span><\/p>\n The value here is speed and the ability to predict under untested conditions.\u00a0<\/span><\/p>\n \u201cYou\u2019re subject to the weather. In real life, you\u2019re subject to, \u2018did it rain in July, or was it cool in July?\u2019\u201d she notes. \u201cIn 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.\u201d<\/span><\/p>\n E.L.Y. launched as what McClerren calls \u201ca test-and-learn opportunity\u201d 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.<\/span><\/p>\n When pressed on how they balance \u201ctest and learn\u201d with urgency, McClerren emphasizes iteration: \u201cAI has been accelerating in both its capabilities and adoption at an extremely fast pace, so we\u2019ve embraced iterative methodology that allows us to pilot new technologies while continuously gathering data and insights to inform our next steps.\u201d<\/span><\/p>\n The system aggregates what McClerren describes as \u201call of our agronomic knowledge, all of our product recommendation sheets\u201d\u2014contextual information about how to use Bayer\u2019s products most effectively. \u201cWe were able to develop this tool, this product. We\u2019ve got it deployed in North America today. And so our field-facing agronomists are seeing about a 60% increase in productivity\u2026saving them about four hours a week that they don\u2019t have to spend searching for all of this knowledge.\u201d<\/span><\/p>\n Those time savings translate directly to more customer engagement.<\/span><\/p>\n McClerren outlines three key pillars for broader AI deployment: sales and service (where E.L.Y. sits), supply chain and logistics, and R&D. \u201cThe future is multiple agents that work together,\u201d she envisions. She sees potential for GenAI to integrate with FieldView to eventually provide growers with direct advice. She acknowledges, \u201cit\u2019s not something that we have initiated yet.\u201d<\/span><\/p>\n Where the decade of infrastructure building becomes tangible is in specific product applications. McClerren points to PRECEON<\/a>, Bayer\u2019s short-stature corn product, as an example of how digital and physical products intersect. <\/span><\/p>\n \u201cIn 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,\u201d she explains. \u201cThat isn\u2019t possible without a platform like FieldView that helps us understand that on-farm acre and can help make those precise recommendations.\u201d<\/span><\/p>\n This integration of proprietary germplasm, decades of performance data, and digital tools represents a moat that\u2019s difficult to replicate. As McClerren notes, \u201cagronomy is deeply unique to farming and agriculture,\u201d unlike more commoditizable AI applications like customer service that \u201cgo across many different types of industries.\u201d<\/span><\/p>\n Growing up on a farm herself\u2014her father is a farmer\u2014McClerren brings personal context to the digitization challenge. \u201cI think you\u2019re seeing a new generation of farmers that grew up differently\u2026and 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,\u201d<\/span> <\/b>she observes. \u201cDigital is the obvious choice for how to manage all of these on-farm decisions over the course of a season.\u201d<\/span><\/p>\n Bayer\u2019s approach aligns with what MIT researchers found differentiates the successful 5% of AI implementations: \u201cThey pick one pain point, execute well, and partner smartly.\u201d 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.<\/span><\/p>\n But the real differentiator may be simpler: Bayer had a 12-year head start.\u00a0<\/span><\/p>\n The Climate acquisition brought not just technology but\u00a0<\/span>also a data culture that takes years to build and has been\u00a0blamed for stalling corporate AI\u00a0<\/a>rollouts more broadly<\/span>.<\/span><\/p>\n Perhaps the most revealing insight comes when McClerren discusses change management for agentic AI. \u201cWe 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,\u201d she says. \u201cIf 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.\u201d <\/span><\/p>\n This suggests a company thinking beyond simple automation to fundamental business-process redesign\u2014ambitious, but still largely hypothetical. \u201cIt\u2019s something we\u2019re still very early on, the learning journey of, but it\u2019s something we\u2019re paying a lot of attention to,\u201d she acknowledges.<\/span><\/p>\n When asked about quantifying ROI, McClerren frames it as necessarily complex: \u201cQuantifying 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.\u201d\u00a0<\/span><\/p>\n The response reflects a company balancing short-term productivity gains with longer-term strategic positioning around sustainability\u2014whether that represents sophisticated thinking or hedging against harder financial questions remains to be seen.<\/span><\/p>\n More on AI in agribusiness:<\/em><\/p>\n Are agrifood corporates pushing their AI agendas enough?<\/a><\/p>\n Corteva\u2019s planned separation raises questions about AI and data split<\/a><\/p>\nThe 12-year build<\/b><\/h4>\n
The moat<\/strong><\/h4>\n
GenAI: Test and learn at scale<\/b><\/h4>\n
The data moat in practice<\/b><\/h4>\n
\u201cReimagining work\u201d<\/b><\/h4>\n