Artificial intelligence is moving deeper into agriculture, with applications spanning research, production, supply chains and on-farm operations, but challenges around trust, data ownership and skills gaps remain, speakers said at Expana’s Women in Food and Agriculture (WFA) event, which took place a day before Agri-Food Europe 2026 in Amsterdam.
The session, “How AI Is Going to Revolutionize the Sector,” featured Anna Fensel, AI Group Chair at Wageningen University & Research, and Lori Edwards, Head of IT and Digital Europe at Syngenta. The session was moderated by Mike Burgess, Vice President of Product at Expana.
Why AI in agriculture is different
Edwards said AI is being integrated across business functions and regions at Syngenta, but agriculture presents distinct complexities.
“AI in agriculture is different due to the biological aspect, living organism and the business is cyclical. That’s also where we can make the most value,” she said. She identified fragmented data ownership as a key barrier, particularly for manufacturers that depend on farm-level data. “It’s fragmented. We don’t always get it as manufacturers which we need in manufacturing,” she said, adding that varying levels of digital literacy among farmers also slow adoption. “We need to bridge that gap.”
Fensel described AI as a broad field with multiple sub-disciplines and limited understanding across the sector.
“There’s a lot of fragmentation, and not enough skills or understanding in the field,” she said. However, she noted that AI is already delivering results in areas such as computer vision, where for instance automated fruit-picking systems are increasingly deployed. “AI is having a positive impact in agriculture in computer vision, where robots collect fruits is pretty much automated,” she said.
The impacts of AI
Edwards said AI is creating impact across R&D, production, supply chains, and on-farm tools. In production and supply, she cited demand forecasting and collaboration with channel partners. On farms, she pointed to precision spraying and planting technologies as among the most impactful applications. “It’s simple things that are the most impactful. Ultimately, the farmer,” she said.
Despite rapid deployment, trust remains central to adoption.
“Trust is the core,” Fensel said, adding that many agri-food users lack transparency into how AI systems function. “The focus is the collaboration between us and AI. This level of trust we’re still learning.”
Edwards said AI at Syngenta has largely been positioned as augmenting, rather than replacing, employees. “We’ve implemented agents in our work that help people understand that the robots are helping with the mundane housekeeping work,” she said. However, she cautioned that implementation requires realistic expectations.
Both speakers emphasized that AI success depends on clear objectives and strong underlying processes. “You can’t implement AI in a space where the process is broken,” Edwards said, noting that the most successful initiatives were built on structured data and defined workflows. Fensel added that the pace of development requires continuous engagement. “There are new versions of tools every day. It’s useful to engage little by little every day,” she said, pointing to growing integration of AI into education and data science.
AI is also being deployed to support sustainability goals, particularly in yield optimization and resource efficiency.
Edwards said tools that support precision agriculture and sustainable farming practices are a growing investment focus, with regulatory frameworks, particularly in the US, influencing deployment strategies.
Written by Fei Thompson
