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- Ag Tech leaders are encouraging farmers and agribusinesses to focus on measurable returns on investment (ROI) when adopting AI, rather than following industry hype.
- Industry experts say AI is delivering the most value in areas such as data management, logistics optimisation, operational efficiency, and decision support.
- Speakers at a recent Ag Tech webinar stressed that human expertise remains essential, with AI serving as a tool to enhance rather than replace farm and agribusiness decision-making.
- The discussion reflects a broader shift across agriculture toward practical, results-driven AI adoption as businesses seek solutions to rising costs, labour shortages, and growing data complexity.
Ag Tech leaders are calling for a more practical approach to artificial intelligence adoption in agriculture, arguing that the technology’s greatest value lies not in replacing people but in solving specific business challenges, improving productivity, and helping farmers make better use of data.
The message emerged during a Women in Ag Tech webinar held on June 4, where industry experts discussed how artificial intelligence is being applied across agriculture and where expectations surrounding the technology may need to be tempered.
The discussion featured Tami Craig Schilling, founder of DeepRoots Strategy; Mara Jorgensen, enterprise product leader at Strategic Consulting Corp. and an Iowa farmer; and Trisha Rentschler, software product manager at Kahler Automation.
The conversation comes as agricultural businesses increasingly invest in AI-powered tools to improve decision-making, streamline operations, and address challenges such as labour shortages, rising costs, and growing data complexity. However, speakers stressed that successful adoption depends on delivering measurable business outcomes rather than pursuing technology for its own sake.
“If you can’t track the ROI, it’s not a real AI win,” Jorgensen said, highlighting the need for clear performance metrics when evaluating AI investments. She pointed to logistics and supply chain optimisation as areas where artificial intelligence is already demonstrating tangible value.
Schilling noted that while AI has existed in agriculture for years, recent advances in large language models have made the technology more accessible to non-technical users. She cited Bayer’s internal AI platform, E.L.Y. (Expert Learning for You), as an example of how organisations are using AI to improve access to agronomic and business information.
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A recurring theme throughout the discussion was agriculture’s growing data challenge.
Industry experts said valuable information often remains scattered across spreadsheets, PDFs, handwritten records, and disconnected software systems, limiting its usefulness. AI’s emerging role, they argued, is to organise and surface that information in ways that support faster and more informed decision-making.
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Despite rapid technological advances, the panelists cautioned against viewing AI as a replacement for human expertise. They argued that contextual understanding, relationship-building, and judgement remain critical in farming and agribusiness operations.
AI performs best as a support tool that enhances human decision-making rather than replacing it.
The discussion reflects a broader shift occurring across the agricultural technology sector, where companies are increasingly focusing on practical applications of AI in precision agriculture, farm management, crop intelligence, and operational planning. Industry research shows that adoption is accelerating as farmers seek tools that improve efficiency and profitability while reducing resource waste.
However, experts say data quality, system integration, and user trust remain significant barriers to wider adoption.
As agricultural businesses continue experimenting with AI-powered solutions, the industry’s challenge may be less about developing new technologies and more about ensuring they deliver measurable value in real-world farming environments.
