Farmers are using AI, but not where you might expect

When most people picture AI on a farm, they imagine drones scanning paddocks, sensors predicting crop yields, or autonomous tractors navigating rows of grain. That image is not wrong, exactly. Those technologies exist. But a survey released this week suggests that the reality of how farmers are actually using AI in 2026 looks quite different from the brochure.

Bushel, a US agricultural technology company, published its 2026 State of the Farm report this month, drawing on responses from more than 1,400 farmers across the United States and Canada. For the first time, the survey asked farmers directly about AI. The headline number is that 14 per cent of respondents said they are using AI tools on their farm today. That is a meaningful minority, not a majority, and not negligible either.

But the more revealing finding is what those farmers are using AI for. Among larger operations that have adopted AI, half said they use it for business or financial analysis. Only a quarter said they use it for yield prediction or agronomy. The tool that was supposed to transform paddock-level decision-making is, in practice, being used more often to help with the books.

Why this pattern makes sense

This should not be surprising, even if it contradicts the narrative that dominates most agricultural technology coverage. The business side of farming is where AI tools are easiest to adopt right now, for several practical reasons.

Financial analysis, cash flow forecasting, and market research are text-based tasks. They involve reading documents, summarising information, comparing scenarios, and drafting reports. These are precisely the tasks that generalist AI tools like ChatGPT, Claude, and Copilot handle well today, without any specialist agricultural software, without sensors, and without connectivity in the paddock. A farmer can sit at a kitchen table with a laptop and use AI to help model a machinery purchase, compare input pricing, or draft a loan application.

Yield prediction and precision agronomy, by contrast, require a stack of supporting infrastructure: soil sensors, weather station data, satellite imagery, paddock-level historical records in a digital format, and software that can integrate all of it. Most Australian grain operations, let alone mixed farming or livestock enterprises, do not have that stack in place. The AI is ready. The data pipeline is not.

This is something we hear consistently at ARAIN workshops across regional Victoria and beyond. When farmers try AI for the first time, the tasks that click immediately are the ones that feel like having a smart offsider for the paperwork: drafting compliance documents, summarising market reports, writing chemical application records, or preparing for a bank meeting. The paddock-level precision work is further away, not because the AI cannot do it, but because the data foundations are not there yet for most operations.

The financial pressure connection

The Bushel report reveals another trend worth reading alongside the AI data. Farmers are under increasing financial pressure. Equipment financing rose to 39 per cent of respondents in 2026, up from 28 per cent the year before. Operating loans jumped to nearly 39 per cent from 30 per cent. Real estate loans climbed to 31 per cent from 22 per cent.

Those numbers are from North America, but the dynamic is familiar to Australian producers. Input costs remain high. Interest rates, while easing, have squeezed margins for several years. The cost of machinery, chemicals, and fertiliser continues to climb.

In that environment, it makes complete sense that the farmers who are experimenting with AI would reach for tools that help them manage the financial side of the operation. If you are trying to decide whether to finance a new header or stretch another season out of the old one, an AI tool that can help you model the cash flow implications is immediately useful. It does not require a sensor network or a precision agriculture platform. It requires a conversation with a chatbot and a spreadsheet.

What this means for Australian producers

The Bushel survey covers North American farmers, and there are obvious differences between a 5,000-hectare grain operation in Saskatchewan and a mixed farm in the Wimmera. But the underlying dynamics translate well.

Australian agriculture faces the same gap between the AI vision and the AI reality. The vision is paddock-level precision, automated variable-rate applications, and real-time yield mapping driven by machine learning. The reality, for most operations, is that the first useful AI application is more likely to involve drafting a Freshcare audit response than optimising a spray map.

That is not a failure. It is a sensible starting point. Business administration, compliance documentation, market analysis, and financial planning consume a significant share of a farm operator's time, particularly on smaller operations where the same person driving the tractor is also doing the BAS and responding to the agronomist. If AI can save several hours a week on those tasks, the return is immediate and requires no capital investment beyond a subscription.

The digital marketing data in the Bushel report reinforces this point. Digital tools for grain marketing rose from 21 per cent of respondents in 2024 to more than 31 per cent in 2026. Farmers are adopting digital tools where the value is clear and the barrier to entry is low. AI adoption appears to be following the same pattern.

The practical path forward

For Australian farmers and regional businesses who have not yet tried AI, this data suggests a straightforward starting point. Do not begin with the most complex, infrastructure-heavy application. Begin with the task that costs you the most time at the desk.

That might be preparing financial documents for your accountant or bank manager. It might be drafting a chemical usage record or summarising a market outlook from ABARES. It might be writing a funding application for an NRM grant or preparing a submission for your local council. These are real tasks that take real time, and current AI tools can assist with them today, without any special agricultural technology.

The 14 per cent figure from the Bushel report will grow. And as it does, the balance between business-side and paddock-side AI use will shift. Sensor networks are getting cheaper. Farm management software is getting better at integrating data sources. Satellite imagery is becoming more accessible. The precision agriculture vision is not wrong. It is just arriving after the spreadsheet, not before it.

For now, the farmers who are using AI have found a sensible place to start. The rest of the industry can learn from that.