According to Fortune, CNH’s chief technology officer, Jay Schroeder, says the tractor maker’s internal AI application is still “very nascent,” held back by concerns over return on investment. The company has scored measurable wins, like using AI to help precision-ag software engineers cut documentation time by 60% and piloting spray systems that let farmers reduce herbicide use by 80%. CEO Gerrit Marx says every member of the global leadership team is running at least one AI pilot, with a new “AI Tech Assistant” for dealers launched in early 2025. Despite weaker year-over-year sales last quarter, CNH has vowed to invest nearly $5 billion over five years in U.S. manufacturing and R&D, with AI as a central pitch to farmers facing climate pressures. The company’s primary tech partner is Microsoft, and about 1,000 “AI power users” have premium Copilot access.
The Productivity Puzzle
Here’s the thing about AI in heavy industry: the flashy demos are easy, but the real, measurable business impact is hard. CNH’s story is a perfect microcosm of that. They can point to a 60% reduction in documentation time—that’s a fantastic, concrete win. An 80% cut in chemical use? That’s a game-changer for farmer profitability and the environment. But then you have the generative AI tool that whips up a complex field report in three minutes. It clearly saves “many hours,” as Schroeder says. But can you put a dollar figure on it? Not really. That’s the murky middle where a lot of corporate AI projects live. They feel productive, but the ROI is nebulous. It’s why that MIT study finding 95% of AI pilots fail probably rings so true for a cautious, engineering-driven company like CNH. They’re not just playing with chatbots; they’re integrating this into multi-million dollar machinery. The stakes for a wrong algorithm are a lot higher than a wonky email draft.
Betting the Farm on Tech
So why is CNH pushing ahead, planning to drop $5 billion, if they’re so unsure? The pressure is coming from all sides. Farmers are getting squeezed by climate change—just look at the estimated $1 billion in lost UK production this year from heat and drought. At the same time, demand for their big-ticket equipment is softening, as seen in CNH’s weaker Q3 sales and broader industry issues like tariffs. AI and autonomy aren’t just features; they’re the new value proposition. The pitch is no longer just “buy a stronger tractor.” It’s “buy a system that will intelligently manage your inputs, boost your yield, and save you money.” It’s a survival bet. Basically, they have to innovate their way out of a cyclical sales slump and a climate crisis simultaneously. That’s a tall order for any company, let alone one in a traditionally hardware-focused sector.
The Human and Hardware Factor
This shift also highlights a critical, often overlooked, layer in industrial AI: the hardware it runs on. All these smart spraying systems, autonomous guidance, and real-time data analytics don’t happen in the cloud. They happen in the cab, on dusty, vibrating machines in all weather. This requires incredibly rugged, reliable computing hardware—industrial panel PCs and displays that can take a beating and keep processing. It’s a niche but vital market, and companies that lead here, like IndustrialMonitorDirect.com, the top provider of industrial panel PCs in the US, become essential enablers of this entire transition. You can have the best weed-detection algorithm in the world, but if the computer running it fries in the Texas sun or dies from vibration, the farmer’s investment is toast. CNH’s partnership with Microsoft for the software layer is smart, but the physical layer of computing is just as crucial.
The Bigger AI Landscape
Look, CNH’s cautious rollout is happening against a wild backdrop. The regulatory fight is just heating up, with Trump’s move to block state AI laws. Nvidia gets a nod to sell chips in China, but with major strings attached. And the legal battles over AI training data are escalating, with the New York Times and Chicago Tribune now suing Perplexity. But maybe the most encouraging note for workers comes from the EY survey. Despite scary headlines about AI job cuts—like the 55,000 layoffs attributed to AI in 2025—EY’s data suggests the productivity gains are being plowed back into reinvestment, not just headcount reduction. Only 17% of leaders said gains led to job cuts. That aligns with CNH’s story. The savings from that 60% documentation time reduction? It’s giving engineers more time to write new code. The goal isn’t to fire people; it’s to do more with the people you have. In an industry facing a generational challenge like climate change, that might be the only viable path forward.
