Syngenta continues to deliver on its digital strategy by boosting adoption of its crop management tool Cropwise by adding new capabilities and investing in bleeding-edge technologies like quantum computing to solve research and development problems, Feroz Sheikh, CIO and CDO at Syngenta, shared with AgTechNavigator during an interview at the 2026 World Agri-Tech Innovation Summit in San Francisco.
Over the years, the ag supplier expanded Cropwise’s capabilities, launching a GenAI system to improve agronomic advice at the 2024 World Agri-Tech Innovation Summit in London. More recently, Syngenta opened the Cropwise platform to third-party developers, enabling them to integrate Cropwise into new tools and applications using its APIs.
Since the start, Syngenta worked with farmers to ensure that Cropwise was an easy fit into their existing workflows and that growers trusted the platform and the agronomic advice it provides, Sheikh said. “Cropwise has grown to 80 million hectares globally,” Sheikh said.
“Farmers have to trust the technology. If they don’t trust, they won’t use it. ... The trust comes from being transparent, being clear about what data has gone into training these AI models, what data is used to give them the response or answers, and how [they can] benefit from it,” he elaborated.
Syngenta explores tech’s bleeding-edge
Syngenta is not only using technology to help farmers better plan and manage their crops, but the ag supplier is also solving research and development (R&D) problems with bleeding-edge technologies, such as quantum computing.
In March, Syngenta announced a partnership with QuantumBasel to develop next-generation crop protection products with quantum computing, as AgTechNavigator reported.
Quantum computing shows promise in understanding structural chemistry, even if the technology is still nascent, Sheikh explained. Syngenta plans to use quantum computing alongside other technologies to solve R&D challenges, utilizing the best tool for the task, he added.
“If we take any problem, whether it’s some of the research problems we are taking — like predicting chemical structures or image recognition problems — you can break down the problem into different parts. Some parts are still suitable [to solve with] traditional AI and neural network architectures. And some parts are more about predicting many different outcomes and then finding out what works best,” Sheikh elaborated.
