Jay Schroeder, the chief technology officer of CNH, was recently in Brazil, discussing artificial intelligence use cases to support the company’s research and development team. The agricultural equipment maker has put a lot of effort into governance and assessing certified tools from vendors, but the actual application of the technology within its own research division remains very nascent.
“It’s great that we’ve got people engaged in AI,” says Schroeder, who quickly pivots to ask rhetorically, “How do we measure success? What are the things that we can measure to say, ‘This has been a worthwhile investment for CNH?’”
Schroeder, a three-decade veteran of CNH who began his career as an engineer focused on mid-range tractor transmission design, isn’t completely cautious when it comes to leveraging AI. The tech has been deployed across broad corners of the business including software development, to assist with drafting contracts, producing R&D database queries, and content management.
CNH has scored some wins that Schroeder has been able to track. The company is leaning on AI to assist software engineers who are focused on precision agricultural technology and the FieldOps farm management systems, where AI, machine learning, and sensors are applied to digitally enhance farming. Early data has shown that these engineers are reducing the time needed for documentation by 60%, giving them more time to write new code.
Another project underway involves AI-enabled spraying systems that use cameras and machine learning to detect the difference between weeds and planted crops when applying chemicals in the field. Farmers who use AI in this manner can reduce the amount of herbicides they use by 80%.
Other applications have more nebulous gains. CNH’s engineers participated in a pilot program where they were able to use AI to pull field reports from dispersed datasets across the company. Within three minutes, this generative AI tool can produce a report that includes details about a design project, CNH’s standards for developing the gear, field test reports, and other key information.
“Can you measure that?” asks Schroeder. He says that many hours are being saved from the work that would have gone into developing one of those reports manually in the past. But putting an exact figure on the time savings is more difficult.
Only 3% of the world’s land is suitable for crops, but the global population is growing by 35 million each year, according to CNH. Farmers have to squeeze out more efficiencies in the field to meet that rising demand.
“The solutions we’re developing for AG [agriculture] are really helping to feed the world,” says Schroeder. “I grew up on a family farm…so for me, it’s personal.”
The company’s precision agricultural tech AI projects, Schroeder says, are still “mostly in the pilot phase. We have a long hill to climb.”
One AI tool that CNH launched externally at the beginning of 2025 is the “AI Tech Assistant,” which was deployed to hundreds of agricultural and construction dealer groups to field questions about any issues for CNH-branded machinery and propose a repair plan.
Marx says that every member of CNH’s global leadership team is running at least one generative or agentic AI pilot program within their respective fields.
He says CNH is looking for tangible benefits to business outcomes. One area of increased focus is the application of generative AI to produce conversational, real-time insights that can connect the dots between the seeds, fertilizers, and equipment a farmer has at their disposal, as well as current and near-future weather patterns, to help improve crop management and planning.
“Tomorrow, the agronomist will become a set of agentic AIs that help the farmer to make bigger decisions better,” says Marx.
John Kell
