Artificial intelligence is poised to transform agricultural R&D by dramatically accelerating the journey from invention to commercialisation, according to Syngenta’s research and digital technology leaders.
Speaking during a recent webinar, Martin Clough, Head of CP R&D Digital, Collaborations and Sustainability at Syngenta Crop Protection, said AI is helping address one of the industry’s longest-standing challenges: the length of time required to bring innovations to market.
From 15 years to “substantially sooner”
“Typically, from the first invention through to commercialisation takes 10-15 years,” Clough said. “That’s a hell of a long time.”
For farmers facing urgent challenges such as pest resistance, emerging diseases and changing climatic conditions, those timelines can make innovation feel disconnected from immediate needs.
Syngenta believes AI can significantly shorten that process through the use of predictive modelling, digital experimentation and generative design.
Designing molecules inside a computer
One of the most significant shifts is taking place in crop protection discovery.
Historically, researchers would identify a promising molecule and then spend years optimising it sequentially for efficacy, safety, manufacturing cost and sustainability.
Today, AI-powered generative design allows researchers to design molecules in a computer and optimise for multiple parameters simultaneously.
“We can optimise against efficacy, safety, cost of goods, sustainability and formulation requirements all at once,” said Clough.
He compared the process to solving a Rubik’s Cube.
“In the past, you might optimise one side of the cube, then another, then another. Generative models allow us to effectively solve all six sides at once.”
What previously required years of laboratory and field work can now be reduced to months, he said.
Syngenta currently uses around 50 predictive models to optimise roughly 15 parameters simultaneously during product development.
Building products around farmers’ needs
A central theme of the company’s AI strategy is ensuring future innovations are more closely aligned with growers’ real-world requirements.
Clough said Syngenta is increasingly focused on creating a direct flow of information from farmers back to researchers.
Historically, there have been multiple layers between product developers and growers, making it difficult to understand how products perform under commercial conditions and what future needs are emerging.
For the first time, digital tools are making that feedback loop possible.
“We want to understand how farmers are experiencing our products, whether they’re performing as expected and what challenges growers are facing,” he said.
The goal is not only to solve today’s problems but also anticipate the requirements of farmers a decade from now.
More sophisticated data collection, including imagery, sensor networks and digital farming platforms, is also helping generate what Clough described as higher-quality and more objective data for training AI systems.
Syngenta already has access to data from around half a million field trials dating back to the 1970s, but Clough argued that future datasets could be even more valuable because of their precision and objectivity.
Towards “safer-by-design” crop protection
The company believes AI can improve not just the speed of innovation but also its quality.
Rather than simply searching for effective chemistry, researchers can use AI to identify highly selective biological targets and design products that minimise impacts on people, beneficial organisms and the wider environment.
“We can have safer-by-design products that meet more farmers’ needs,” said Clough.
Technologies such as protein-folding models, genomic large language models and image analysis are helping scientists identify novel approaches to controlling pests, weeds and diseases, particularly where resistance is reducing the effectiveness of existing products.
AI already embedded across Cropwise
Andre Piza, Global Head of Digital AgTech at Syngenta, said AI is already deeply embedded throughout the company’s Cropwise platform.
The technology is being used to customise recommendations for different users, support precision agriculture, improve risk management and make agronomic knowledge more accessible.
“AI is already here,” he said. “The question is no longer whether it will arrive. It’s about adoption and trust.”
Piza highlighted the role AI could play in democratising agricultural advice.
In India, for example, Cropwise Grower has been downloaded more than four million times and provides agronomic guidance in thousands of languages. Growers can increasingly use simple prompts, images and questions to access information that previously required specialist expertise.
Humans will remain in the loop
Despite the excitement around AI, both speakers were adamant that scientists and farmers will remain central to decision-making.
Clough rejected the idea that R&D or farming could simply be handed over to autonomous systems.
“Scientists remain essential,” he said. “AI is a tool that assists scientists, not something we delegate everything to.”
The same principle applies on-farm.
“The farmers I know still want to make the final decision,” he added. “They’re happy to be AI-augmented, but they want to make the call.”
Rather than autonomous farming, Syngenta expects a “human-in-the-loop” model where AI generates recommendations but people retain responsibility for final decisions.
Piza agreed, saying AI should be viewed as a tool that augments human capabilities rather than replaces them.
Conscious of AI’s environmental footprint
The discussion also addressed concerns about AI’s sustainability credentials.
Clough acknowledged that data centres consume large amounts of energy and water and can have significant local impacts. However, he argued that AI’s land footprint remains small compared with global agricultural land use and that Syngenta’s own AI applications are relatively targeted rather than operating at hyperscale.
Piza said the company scrutinises the sustainability credentials of technology suppliers and works to ensure AI systems deliver greater agronomic and environmental benefits than the resources they consume.
“We invest heavily to make sure we are using AI in the most efficient way possible,” he said.
