Farmers Embrace AI, But Trust Remains the Biggest Barrier
MorganMyers survey finds nearly half of farmers are using AI tools, yet most remain cautious about letting artificial intelligence influence operational decisions
According to the 2026 MorganMyers AI & Agriculture Report (link), artificial intelligence has moved beyond theory and is already becoming part of day-to-day farm management, with 48% of surveyed farmers and ranchers reporting they use AI tools such as ChatGPT, Gemini, Claude, or Copilot with some regularity. However, widespread skepticism remains, as concerns about accuracy, data privacy, and biased recommendations continue to limit trust and slow broader adoption.
The report suggests agriculture is further along the AI adoption curve than many outside observers might assume. AI-powered technologies have been embedded in precision agriculture, robotics, sensing systems, and satellite imagery analysis for years. What has changed is accessibility. Generative AI tools are now directly available to farmers and ranchers, putting powerful analytical capabilities into the hands of producers rather than limiting them to equipment manufacturers and service providers.
The survey found that AI adoption is meaningful but still largely experimental. While 48% of farmers and ranchers use AI at least occasionally, only 26% report using it several times per week or daily. That frequent-use rate is roughly comparable to the broader U.S. workforce, indicating that agriculture is moving alongside, rather than ahead of, other industries in integrating AI into routine business practices.
One notable finding is that general-purpose AI tools are being adopted faster than AI-enabled agricultural platforms. While nearly half of farmers use tools like ChatGPT, only 39% use AI features embedded within farm management systems, equipment platforms, or livestock-monitoring applications on a weekly basis or more. This suggests awareness, ease of use, cost, and trust may be limiting adoption of specialized agricultural AI applications.
Adoption is not evenly distributed across agriculture. Dairy producers, younger farmers, and larger operations are the most active users. Dairy farmers and producers under age 35 each recorded AI usage rates of 64%, well above the survey average. Larger operations showed some of the strongest engagement, reflecting their greater reliance on data-driven management systems. In contrast, smaller operations, older farmers, and row-crop producers were considerably less likely to use AI regularly.
The practical applications being pursued by farmers reveal where AI is currently delivering value. Nearly half of users employ AI for research and drafting tasks, while others use it for crop planning, livestock health and nutrition insights, and business management. Importantly, 84% of respondents reported that AI provides at least some value to their operations. Producers cited time savings and improved confidence in decision-making as the most significant benefits.
Yet value does not automatically translate into trust. Only 24% of farmers and ranchers said they somewhat or fully trust AI-generated recommendations for operational decisions, while 39% expressed little or no trust. Another 37% remain undecided. Even more telling, 45% said they are uncomfortable allowing AI to directly influence decisions involving product selection, timing, or resource allocation.
The roots of that skepticism are clear. Accuracy of recommendations ranked as the top concern, cited by 72% of respondents. Data privacy and ownership followed at 57%, while 51% worried about biased or brand-influenced recommendations. More than one-third feared AI could eventually diminish the role of experience and judgment that remain central to successful farming.
The report highlights an important reality often overlooked in technology discussions: farming remains both a science and an art. Producers operate in environments shaped by weather, biological variability, policy shifts, and volatile markets. Even among large commercial operations, decision-making blends analytics with intuition. As a result, many farmers appear to view AI not as a replacement for expertise but as a tool to augment human judgment.
What would increase trust? Farmers overwhelmingly pointed to proof rather than marketing. Sixty-two percent said real-world farm results would strengthen confidence in AI systems. Other important factors included the ability to audit or override recommendations, transparent data sources, and recommendations validated by agronomists or veterinarians. Brand reputation ranked surprisingly low, suggesting that performance and transparency will matter more than corporate messaging.
The survey also identified agricultural retailers as a potential bottleneck in AI adoption. Retailers lag farmers in usage, trust, and perceived value, making them less likely to recommend AI-powered products and services. Because retailers play a critical role in introducing new technologies to producers, their caution could slow the pace of adoption across the industry.
Despite the reservations, the long-term outlook remains positive. Nearly 70% of farmers and ranchers expect to increase their use of AI over the next one to two years. Producers see opportunities for AI to improve marketing decisions, recordkeeping, product comparisons, and recommendation accuracy. However, the message throughout the report is consistent: farmers want AI proven on real farms before they entrust it with real decisions.
The broader implication for agriculture technology companies is straightforward. AI adoption is no longer the primary challenge — trust is. The companies most likely to succeed will be those that demonstrate measurable on-farm results, maintain transparency about data and recommendations, and position AI as a partner to human expertise rather than a substitute for it. As the report notes, the ultimate question in agriculture remains the same one producers ask of any new technology: “Does it pay?”
