That interest provides a starting point for agricultural technology developers, but it leaves an important question: what would make growers use one in practice?
KG2’s 2026 telephone survey of 350 grain growers explored where an AI assistant might fit into farm decision-making. The findings suggest that practical usefulness, local information and confidence in the advice would matter. They also highlight a challenge that product design cannot overlook: unreliable connectivity.
The research tells a connected story about the distance between interest in a new tool and a decision to adopt it.
Growers see potential, but reservations remain
Among surveyed growers, 30% said they would definitely use an AI farm assistant, while 40% said they probably would. Its perceived value averaged 6.8 out of 10, with 44% rating it eight or higher.
However, responses varied. One in ten rated its value at just one out of 10, and most of that group felt a tool could not replace practical farming experience.
The results show openness to the concept alongside reservations about its role. They measure stated willingness, rather than actual use. Whether that interest translates into adoption would need to be tested with a working product in farming conditions.
Time-sensitive decisions stand out
The strongest priority was the timing of input purchases, spraying and weather-dependent operations. This decision area received an average importance rating of 8.7 out of 10, the highest of any tested.
Spray and chemical timing was also the decision most frequently identified as taking the greatest time or effort, named by 30% of respondents.
Crop and variety selection remained important, averaging 7.7 out of 10. Yet no respondent identified it as the decision requiring the most effort.
This distinction helps explain where decision support might be useful. An important decision is not necessarily the one growers find most demanding. In this survey, timing stood out both for its importance and for the effort involved.
Information needs to reflect the farm
Weather and rainfall forecasting was the information growers most often found hardest to obtain, identified by 37%. Several respondents questioned the usefulness of a forecast for a location 40 kilometres from their property.
Pricing, market timing and forward contract decisions followed at 23%.
These responses point to a need for information relevant to a grower’s location and circumstances. Access to information alone may not resolve the problem if it does not help with the decision at hand.
For an AI farm assistant, this raises questions about the sources it uses and how clearly it communicates their limitations. The findings do not demonstrate that AI can improve weather forecasts or market predictions. Any such capability would need evidence from testing.
Digital tools are familiar, but access is uneven
Many respondents already used digital systems. Seventy per cent used a machine or GPS display, and 63% used farm management software. Only 10% reported having no formal system.
Connectivity ratings were less encouraging. Mobile reception averaged 5.1 out of 10, while internet connectivity averaged 5.5. Some 43% rated their mobile reception at four or below.
Existing use of digital tools therefore does not mean growers can reliably access an online assistant wherever they work. A product that depends on a continuous connection could be difficult to use during field operations.
These results make connectivity a practical consideration for agricultural AI development, alongside the quality of the information a tool provides.
Confidence comes from seeing the tool work
Reliability and accuracy were cited as reasons for hesitation by 33% of growers. Before committing, 34% said they would need to see other growers in their region using the tool and getting results. The same proportion wanted a free trial or demonstration.
These preferences suggest that growers want an opportunity to judge performance and relevance for themselves. Local examples and practical trials could provide that opportunity, although their effect on adoption would still need to be tested.
The research also found that willingness to share farm records with an AI assistant averaged 6.3 out of 10. This indicates some openness, without suggesting universal comfort with sharing data. Clear information about how records are accessed, stored and used would help growers make an informed choice.
Meanwhile, 43% of respondents had never used a chatbot or smart assistant. For these growers, assessing an agricultural AI assistant could also involve becoming familiar with a new type of tool.
The survey suggests a focused approach to product development: begin with a decision growers find demanding, then test whether an assistant provides useful support under real farm conditions.
Time-sensitive operations offer a potential starting point. Trials could examine whether the information is sufficiently local, whether advice is reliable, and whether growers can understand the uncertainty behind it.
Connectivity should also be part of testing. Developers could investigate which functions remain useful without reception, what information can be retained, and how the tool behaves when a connection returns.
Practical demonstrations would give growers a way to assess those capabilities before committing. Testing should also explore how an assistant can complement existing farming knowledge and what growers need to know before sharing records.
These are development priorities suggested by the findings, rather than proven drivers of adoption.
From interest to practical adoption
KG2’s research shows that many surveyed grain growers are willing to consider an AI farm assistant. It also gives a clearer picture of what deserves attention before that interest can become regular use.
The next step is to demonstrate useful, reliable support for the decisions growers already face. Local relevance, workable connectivity and visible results will be important areas to evaluate as agricultural AI products are developed and tested.
KG2 conducted a telephone survey of 350 Australian grain growers in 2026, drawing participants from its agricultural database. Respondents were predominantly located in the southern grain belt, and 87% operated mixed grain and livestock enterprises.
The findings describe the surveyed growers’ views and should not be read as nationally representative estimates. Stated willingness to use an assistant also differs from observed adoption.