ZDNet caught up with Dr. Katia Walsh, Chief Strategy and AI Officer at Levi’s, to talk about implementing AI, machine learning and data science at a 168-year-old company during a pandemic. Here are a few highlights. The full conversation is in the video.
Where an AI and data science team sits in an organization. Walsh said that AI is a department that covers multiple units and is a horizontal function much like finance, technology and human resources.
She said:
The COVID-19 crash course. Like other technologies, the COVID-19 pandemic accelerated plans. Walsh said from March to August 2020, Levi’s saw a sprint where AI and data had to be used for “anything from improving the customer experience to delivering internal operating and operational efficiencies, and also possibly looking into new revenue models and business models for the company.”
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E-commerce and shipping. Walsh said Levi’s saw a surge in e-commerce sales and the company moved to ship from the stores closest to the consumer. She said:
Using 168 years of data. Levi’s has 168 years of data and the company considers itself one of San Francisco’s original startups. Walsh said that rich history and data set can inform what products will thrive in the future.
We were able to use data on climate and weather and epidemiology models and financial and market outlooks. So you know how I talked about the three parts of this flywheel; digital, data, and AI. What makes this particularly useful is that it uses more data than before, which gives us more points, more perspectives, more variables, and then we’re able to apply machine learning, which then makes the model even smarter and to deliver even better.
The role of algorithms in product design. Walsh said:
We are absolutely predicting right now what demand for products will be like. The further you go into time, the less accurate the model will be because there are just so many unknowns that happen to accumulate as time goes by. And we are looking to predict demand in the next half of the year, in the next month, in the next three months.
People, processes, privacy matter as much as the tech stack. Walsh said that building out AI capabilities has four building blocks.
Privacy is always important. It’s particularly important when you’re dealing with data and AI. We talk about responsible and ethical AI. So we have a code of conduct when it comes to data and AI in the company. Data of course, itself, very important. We now have more data than ever before, certainly internal data from our own operational systems, but also external data from partnerships or from mobility patterns or from social media, always with permissions in place. And of course technology is the fourth building block, also important. Yes, we use open source tools. We also partner with cloud providers, from AWS to Google Cloud Platform to make sure that we the most advanced tools that we can find.
