On a farm, the hardest problems rarely arrive as neat questions. They show up as a yellowing patch in a field corner, a new pest pressure after a warm winter, or a stubborn yield gap that refuses to explain itself. The advice a grower needs is often buried in a mix of field history, product labels, trial data, and the kind of “local knowledge” that lives in an agronomist’s head.
Land O’Lakes and Microsoft want to turn that messy reality into a conversation.
In mid-November, the two companies announced a multiyear partnership to build AI tools for agriculture, including a digital assistant called Oz—a custom “copilot” designed to answer farm questions quickly, on mobile, using Land O’Lakes’ own agronomic data and Microsoft’s AI stack. The name is a wink: in this Oz, the wizard is not a man behind a curtain, but a search-and-reasoning layer sitting on top of a very large knowledge base.
The ambition is straightforward: help retail agronomists and farmers make faster, more confident decisions—at a time when costs are high, time is tight, and data is everywhere. The harder question is whether a chat interface can do something agriculture has struggled to do for decades: convert information into better outcomes at scale, without breaking trust.
From binders to copilots
Oz is not being sold as a robot agronomist. It is being positioned as a shortcut through complexity.
Land O’Lakes and Microsoft say the assistant draws on a longstanding internal resource: an 800-page crop protection guide built on 20 years of data and “millions of data points,” historically used by retailers to make product recommendations. In other words, the “answer” has existed for years—just not in a form that fits the pace of modern farming.
That matters because the front line of decision-making is often not a corporate data scientist. It is the local adviser, the co-op agronomist, the retailer rep—people who have to make calls in-season, with imperfect information, while balancing agronomy, economics, and logistics.
Land O’Lakes has an unusually direct path to those advisers. The cooperative says it is owned by farmers and ag retailers, with 800+ retail owners, 1,200+ dairy producers, and 500+ ag producers, supported by 9,000+ employees. A regional newspaper report adds that the co-op “touches” roughly half of America’s harvested acres through its crop inputs and animal nutrition businesses. If Oz becomes part of that retail workflow, it could spread faster than many farm apps that struggle to escape pilot mode.
The product is not yet widely available. Land O’Lakes says Oz is currently in beta, with plans to expand access to retail agronomists in the coming year.
Why a chat interface matters (even if AI hype doesn’t)
Agriculture is not new to “digital transformation.” Precision tools have been around since the 1990s: yield monitors, guidance systems, variable-rate application, soil mapping. Yet adoption has been uneven—and often concentrated among larger operations.
A USDA analysis found that technologies such as yield monitors, yield maps, and soil maps were used on 68% of large-scale crop-producing farms, with adoption rising sharply with farm size. A GAO report, using USDA information, noted that only 27% of U.S. farms or ranches used precision agriculture practices to manage crops or livestock in 2023. That gap is telling: many farms still do not have a “data exhaust” rich enough for advanced analytics, and even when they do, the tools are not always easy to use.
This is where a chat-based layer could be meaningful. A conversational interface does not require growers to learn a new dashboard language. It reduces friction: ask a question in plain English, get an answer, move on. In theory, it helps two groups at once:
- Experienced advisers who need faster retrieval—less time hunting through manuals, labels, and internal guides.
- Early-career agronomists who need training wheels—structured guidance, and consistent recommendations, while they build field intuition.
Land O’Lakes itself framed Oz as a way to streamline access to a large knowledge base so retailers can recommend “the best solution” during the season. Microsoft framed it as a copilot that can help “control costs” and “boost crop yields.”
The promise lands in a tough economic moment. USDA forecasts put farm sector production expenses at $467.4 billion in 2025. A separate USDA summary of 2024 farm production expenditures shows combined crop inputs—chemicals, fertilizers, and seed—at $72.2 billion, accounting for 28.6% of crop farms’ total expenses. When nearly a third of the expense stack sits in the “input decision” bucket, even small improvements in timing, product choice, or application rates can matter.
But agriculture is also a business where the “right” decision is often probabilistic. Weather changes, pests evolve, and what looks optimal on paper can fail on a specific field. That reality is why the next part—trust—matters more than interface.
The trust problem: accuracy, incentives, and data rights
If Oz is to become more than a novelty, it will need to clear three hurdles that have tripped many agtech tools: reliability, incentives, and ownership.
1) Reliability: farmers don’t tolerate confident nonsense
Generative AI can hallucinate. In agriculture, hallucinations are not just embarrassing—they are expensive. A wrong answer on a herbicide restriction, a misread on resistance management, or an incorrect mixing instruction can have real consequences.
Land O’Lakes and Microsoft emphasize that Oz is built on Land O’Lakes’ own agronomic data and guidance resources. That helps, because a model “grounded” in vetted documents is less likely to invent facts than one that is simply guessing based on internet patterns. But the standard should be higher than “less likely.”
A farm copilot should behave like a cautious adviser: show its work, cite the source it relied on (label, internal guide, trial summary), and flag uncertainty. The fastest way to lose credibility in a co-op network is to sound certain when you should be asking a follow-up question.
2) Incentives: is this decision support—or a smarter sales funnel?
Oz is being built from a crop protection guide that has historically supported product recommendations. That is not inherently bad—retail agronomy already sits at the intersection of advice and commerce. But AI can make that intersection sharper.
If a copilot becomes the default interface for recommendations, it can quietly shape what “good agronomy” looks like. Does it optimize for profitability on that field? For yield? For risk reduction? For input volume? For environmental impact? The assistant’s outputs will reflect the priorities embedded in the data, the prompt design, and the guardrails.
This is where transparency becomes a feature, not a compliance checkbox. If Oz recommends a product or practice, it should be explicit about why—trial evidence, conditions, constraints—and clear about alternatives. Otherwise, farmers may treat it as a black box built to move inventory.
3) Ownership: who controls the farm’s data trail?
Agriculture’s data debate predates generative AI. Farmers have long worried that field data—yields, prescriptions, practices—could be used against them (in pricing, land rents, or competitive positioning) or shared without clear consent.
One reference point is the Ag Data Transparent principles, which emphasize that farmers “own” information generated on their operations and that use and sharing should be governed by clear agreements. The AI era raises the stakes: data is not only stored; it is used to fine-tune systems, train models, and improve recommendation engines.
Land O’Lakes has a strong trust brand in co-op country, but trust is not infinite. If Oz is to scale, farmers and retailers will want plain answers: What data is used? Where is it stored? Who can access it? Can it be deleted? Can it be used to train future models? These questions are not obstacles—they are the price of admission.
Why Microsoft wants agriculture—and why co-ops are attractive AI platforms
The Oz partnership also says something about the direction of enterprise AI.
Microsoft does not need agriculture to prove it can run models. It needs industry workflows where AI becomes sticky and defensible. Farming is an unusually complex workflow: biology, chemistry, logistics, risk, regulation, and local variation. If a copilot can be made useful here, it can be made useful almost anywhere.
Land O’Lakes, for its part, is trying to modernize its own operations. In the partnership announcement, the co-op said it has migrated more than two-thirds of its IT environment to Azure and has piloted Microsoft Copilot internally, including custom tuning using agriculture-specific data. Oz is framed as the first product in a broader pipeline of AI offerings.
There is also a strategic logic specific to co-ops. Co-ops sit between farmer and market. They have recurring relationships, seasonal touchpoints, and a service model that already blends advice with products. In a world where many farm apps struggle with engagement, a co-op can embed a tool like Oz where decisions actually happen: in the retail counter conversation, the agronomist visit, and the in-season troubleshooting call.
What success would look like (and what would count as failure)
It is easy to imagine the best version of Oz: an assistant that speeds up agronomy, helps new advisers learn faster, reduces costly mistakes, and nudges practices that protect soil and water while maintaining profitability.
It is just as easy to imagine the worst version: a chat window that confidently repeats generic advice, pushes biased recommendations, and becomes another system farmers tolerate only because it is tied to their supplier.
So what should we watch?
First: measurable time savings. If Oz truly compresses the “search” portion of agronomy—finding label rules, checking constraints, recalling trial learnings—it can free advisers to do the human part: walking fields, asking better questions, building trust.
Second: fewer errors, not just faster answers. Speed is only valuable if it improves decisions. A copilot that includes source citations, uncertainty flags, and clear constraints could reduce the kind of mistakes that happen when people are rushed.
Third: adoption beyond the early enthusiasts. Precision ag has often been a “large farm” story. A conversational layer could broaden reach, but only if it works on patchy connectivity, fits the season’s cadence, and respects the realities of smaller operators.
Finally: clarity on data rights and incentives. In the next phase of agtech, the winner will not be the company with the smartest model. It will be the one that can convince farmers that the tool is working for them—not on them.
Oz is in beta today. That is exactly when these questions should be asked, before “copilot” becomes another buzzword stapled to agriculture’s long list of dashboards.
Because in farming, the magic trick is not producing an answer. It is earning the right to be believed.
