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Building AI Systems that Support Employee & Business Goals

Consider a horse and a tractor – both can be used on farms to complete work. Either can plow, harvest, or move supplies, but one can do more in less time and has fewer limitations. When farmers moved from horses to tractors, there might have been push back but the efficiencies gained proved too valuable to ignore.

Technology in the workplace is at a similar crossroad – the introduction of AI has the ability to bring greater speed, accuracy, and ability to the business. Just like the tractor, it needs to be guided so it’s used correctly and responsibly, which is where the role of human experts will continue to be important.

“It’s healthy to be skeptical about new technology like AI, but the reality is it’s here to stay,” said Brandon Leander, Head IT and Digital Vegetables at Syngenta. “Our people are still at the foundation of this technology. It takes years to build a quality background system of processes and data to feed AI programs so their output can be trusted.”

Data quality is the foundation of successful AI for any business and over the past 8+ years at Syngenta, data quality engineers have worked to create a system that provides meaningful data interpretation.

“We want repeatable AI model/data environments that have rich background data to account for variables important to our business,” Leander said. “We know that across functions there are many different reporting methods, so unifying data process and presentation will be key for like-to-like comparison.”

Through this process, the company has identified key human touchpoints, data and technical owners.

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