Beyond the technical challenges of incorporating artificial intelligence into their internal systems, companies face another quandary: how to get employees to buy into the changes that A.I. can bring.
At Fortunes Brainstorm A.I. conference in San Francisco on Monday, Teddy Bekele, CTO of agricultural cooperative Land OLakes, and Fiona Tan, CTO of online furniture retailer Wayfair LLC, compared and contrasted how workers at their companies have embracedor raised an eyebrow atefforts to introduce A.I. into the supply chain.
Land OLakes is using A.I. to approximate the supply and demand of different products at different times of year. The technology has become a tool used directly by the companys farmers. Farmers see it as assisting their decisions, not replacing their expertise, Bekele says. Yet getting the farmers fully on-board takes some convincing, he says, since planting and harvesting are high-stakes decisions. Farmers will always try things, theyre entrepreneurs at heart, Bekele explained. However, to fully adopt it in their operation, they want to make sure the solution really works.
Some A.I. models can seem counterintuitive to farmers at first. Bekele brought up the example of using A.I. models to determine the best locations to plant crops based on climate, topography, and soil. At times, the A.I. suggestion differs from where farmers have planted crops in the past. On paper, [the A.I. model] doesnt sound right, Bekele says. But with some explanation, farmers come around to the idea.
A.I. can also serve as a sort of second opinion for farmers. They input their own data into the A.I. tools and use the system to confirm their own instincts.
Wayfair is a digitally-native company so its employees are fairly open to adopting new tech, yet Tan says that a tech-savvy workforce can become frustrated that A.I. doesnt move faster. Sometimes theres impatience for the models to work immediately, Tan said. Its not like its deployed today and its all going to work magically, she said.
When Wayfair adds A.I. to internal processes, it starts with low-stakes tasks to mitigate the risk of errors and ensures humans are still checking the technologys work, Tan says. For example, in marketing, the worst that can happen is you pay too much for a bid, so thats something we can tolerate, she said. Yet other areas, like when looking at images or text for the product to ascertain the quality of the furniture, well have models give a suggestion or recommendation, and humans can go back and make sure it looks good, she said.
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