Name: Brainstorm AI 2023: Shopping for New Uses of AI in Retail
Uploaded: 2023-12-12
Duration: 15 min 10 s
Description: Yasir Anwar, Chief Technology and Digital Officer, Williams-Sonoma Inc. Sravana Karnati, Senior Vice President and CTO, Walmart International, Walmart Moderator: Verne Kopytoff, Senior Editor, FORTUNE; Co-chair, Brainstorm AI

Yasir Anwar, Chief Technology and  Digital Officer, Williams-Sonoma Inc. Sravana Karnati,  Senior Vice President and CTO, Walmart International, Walmart Moderator: Verne Kopytoff, Senior Editor, FORTUNE; Co-chair, Brainstorm AI

Brainstorm AI 2023: Shopping for New Uses of AI in Retail

Conferences

Yasir Anwar, Chief Technology and  Digital Officer, Williams-Sonoma Inc. Sravana Karnati,  Senior Vice President and CTO, Walmart International, Walmart, Verne Kopytoff, Senior Editor, FORTUNE

Hello, everyone. And thank you, Yasser and Shabana for joining us. Good to be here. Thank you. And a reminder towards the end of our talk, we'll be taking questions from the audience. So please think up some good ones uh for our pan to start with. Uh Could you set the scene for, for the audience with a couple of examples? Your favorite examples of A I you're using at your company. We start with you, Mr Yeah, thanks. Um Yeah, I I represent Walmart. I work for Walmart International and the CTO for Walmart International. Um You know, we have uh you know A I is, is behind everything that we do. We've been using A I for a very long time including the transformer model that is core of chat GP T. Um You know, a couple of really good examples I can, I can highlight for this holiday season or in the way we are improving. Search for our customers. You can today, you can natural language type in the search box and um the the tool automatically understands the query and gives you the most appropriate search results. That's one example and the other one I can give you is in the way we are predicting demand at what granularity we are doing that and how we are disposition the demand, the inventory in our network across the supply chain. Those are two examples. Yeah, for us and for me specifically, it's about moving the right needle for the business. So besides doing a lot of R and D, we want to deploy A I to move the real business business outcomes. So two examples, recent ones were what is a personalized recommendation and personalization engine we built in house leveraging A I and uh parts of gen A I. And it has allowed us to generate a tremendous amount of uh incremental revenue and conversion lift, which has made a big dent in the in the in the digital revenue. At the same time, we also use personal uh we do a lot of personalization products in our brand like Mark and Gram. So monogramming and stuff, we get returns, we used to get returns and it's difficult sometimes to find the accurate cause of those returns because either there's a thread or there is a location of the monogram or A or or uh the machines that are using that. So we leveraged an A A I based solution, we built it, which is image based and we pass our returns to that solution and it accurately identifies what could have been the cause why a customer returned a particular product that helps us in closing the loop with our production manufacturing and tells us this has been a very good, very recent um exploration and deployment of A I in our company. And there are many more examples. Well, you may have heard it's we're in the busy holiday shopping season. Um When do you start preparing for that? And do you do anything special? Simply because it's the holiday season in terms of A I I go first. So in terms of the way I, so first of all, I would say at Walmart, we have every day is a holiday type of uh you know, operation. So uh we are super focused on our customer all through the year. Uh But we all know, so the holidays uh time frame is a very special one. We are sitting on a lot of inventory. We are, we are, we are bringing in a lot of inventory from overseas and, and domestic channels as well. Uh So being able to, you know, accurately predict the demand and uh and uh store to store the inventory at the right place at the right time is, is, is the most important thing we can do. And then really, you know, scaling our systems is another one that we do. Um You mentioned A IA I, like I said is in everything that we do. One of the things that we do in our infrastructure is enamel detection, you know, whether it is for fraud fraud detection or anomalous behavior in the infrastructure to see if workloads are not behaving the way they are supposed to behave and they need to be shifted around. So that's another example where we use ea I are, there are many such examples and you also holidays anything. Yeah, I think for the A I, it's also important to understand that A I is, you know, we talk about governance and it could also go rogue. It could also build biases over the period of time. So holiday when you are, you know, 40 30% of retailers, business happens in the Q four. It's important to kind of revisit your business rules, revisit your testing strategy on A I itself and make sure it's not going into other directions. So that's a general A I governance that is we focus on for preparation of holidays due to high volumes, prepare better data for it for its training of the MO models. And uh one other example from a functional perspective is uh you know, the promised date for the customers and finding the optimal path and supply chain to get the goods delivered to their homes. So we also leverage it there and with holiday with the different uh you know, allocation of the inventories in different locations and the and the holiday products. Um it has a major role to play to make sure we are serving to the customers in the most accurate manner and in terms of uh demand forecasting? What kind of data goes into that? Are you using like weather? And is there any kind of strange data you're pouring in there? Yeah. So obviously, you know, one of the things we did was to look at our like, like uh you know, most people look at past demand and then predict, you know, future demand. Uh But we found that over time, we need to incorporate all other signals as well, whether it is uh you know, weather data, social media channel, uh you know what people are searching on our site. Um And whatever else we are, we are seeing in, in, in the, in the market, in social media and other channels, incorporating all of that and being able to uh predict demand at a Q level by store level as well. So we've been able to apply the uh you know, deep neural network strategies to predict demand. And we, we, we did a I before I would say in this year, uh the, the fidelity of the data and the fidelity of the res results that we're seeing is much, much better compared to what we've seen in the past. So I think kind of the answer is kind of same and common because I think the dimensions of data are increasing day by day. So even if you have the models that we're running, you need to fit it more dimensional data. One you were talking about season one, it could be weather, it could be the supply chain answers that have been evolving from last year to now, things have changed. The cost of uh supply chain has changed, it has changed the dynamics of things making available. Your stored locations. Are there the events that are happening? So you try to tap into as many data insights you can bring in to be able to do better forecasting. Um Now, now yo your your supply chain is particularly uh so challenging because you do a lot of custom products. Uh you order a couch with 100 different options. There's no couch sitting in a warehouse with those options available. How how does that make uh sort of predicting delivery? Uh How do you go about predicting delivery under those circumstances? Well, I think um you know, a delivery from a order from a click all the way up to the uh houses the customer requires uh goes through multiple segments. One of them, as you mentioned is manufacturing. So we can talk about all of the segments, but we look at the different segments of our supply chain. But in this case to your specific to your question, we look at the within the four walls, productivity of that particular plant or a unit. Understand based on the volumes that are coming in uh what has been the historical production and what they can predict based on the raw materials that are available there. And that becomes a good data point into calculation of the delivery points for those uh custom products. And how about you? Uh Servan, you're not creating necessarily custom products, but you do have millions of products. Yeah, we have millions of products and you know, several notes in our supply chain, whether it is distribution centers or D CS or stores and every one of these things we call that is a node you could ship from either to the uh to the customer or if it's a DC to a store or directly to the customer as well. Um So one of the things we we look at is, you know, what is the optimal location for inventory? Right. So can you delay that as much as possible? So that you can actually have risk pooling and then hold the least amount of material in stock in your, in your network. Uh And then that, that also should consider, you know, your, your catchment areas for a, for a store, uh availability of your drivers, you know, when you're talking about, you know, uh distributing, you know, end products to customers. Uh And all of these supply chain signals are used not only in, in predicting what is the best place, but also what is the most optimal place to put it, right. So I make a distinction between, you know A I strategies um and you know, highly optimal mathematical strategies. So I think, you know, solving supply chain problems really involves blending both. It is not just A I or JA I, but it's also mathematical programming techniques such as operations research and, and so forth and, and the author uh obviously A I has an impact on jobs. Um How is William Sonoma uh thinking about that? Yeah, I think we, I mean, we are very excited about the advent of A I and especially the JN A that is coming and people talk and worry about and scared of losing jobs. I would put a question for ourselves and to everybody that I think I would be personally more worried and scared if in every five years, we don't have a big technology disruption that is coming because that actually brings jobs and it's not me again. We all love data. We are here for data. Data tells us historically, no matter whether it was from uh from horses to cars, to airplanes, to electric vehicles, you name it all the advance of technology came in. People might have been scared back then. But the new ecosystems, these technologies generate, generate millions of jobs. So if the key is about adoption of A I in a sustainable manner for the business outcomes, not just for, you know, shiny object running after that and also safe and secure A I. If you produce that and you scale it, I believe in the next five years, we'll have multi million jobs generated and a new ecosystem that will emerge, that will require jobs. Of course, it will require some training and transforming how we achieve our work and also redefining our priorities where we spend time uh in doing meaningful work. Uh Well, let's see if we have any audience questions out there, we'll have people uh with paddles coming with the microphone to uh we have somebody up here on front here. I am. Uh could you uh give your name and identify your affiliation? Sure. Um John Jessup, I'm CEO of 1440. Uh Yes sir. You guys are a big sales force shop. So I'm curious your thoughts on like how like data cloud like commerce, personalization and then Einstein, it's in the kind of overall shop we have partnership with sales force. I wouldn't necessarily call we are a sales force shop. We leverage some of their great technologies, especially their email marketing program and we build a lot of custom in house solutions and leveraging the open source A I and we continue to evaluate with them whatever they're bringing. I strongly believe that it cannot be just you're doing your own uh little A I and 27 solutions and broken different parts because in few years, if you do not uh integrate with the larger ecosystem, whether it's open A I, whether it's C Force Google uh API S, then uh the work we will do and solutions we will build. After a few years, you have to abandon them because they will not be able to catch up. So key is how do you leverage the ecosystem but also have the expertise in house to build and drive your own destiny versus depending too much on one vendor or a company. Another question over here. Thank you. I'm Sammy from a ARP. Um Could you, as you've, as you've ramped up your use of A I and to maximize your investment in it, Can you talk to us about the kind of roles and jobs that you've increased or you've created in your organization? Yeah. So, you know, uh one of the things we have done is to um educate all of our uh associates around use of gen A I and uh roll out, you know, incremental tools. For example, we rolled out a tool called my assistant that allows our associates to ask questions around their benefits or what have you. Um And so we are in the, in the stage where we are getting our, our, our personnel or associates trained on A I and advanced computation technologies. Uh When it comes to our supply chain, the kinds of work our supply chain uh associates do is quite different now than what they did a few years ago. It is not that, you know, we created new jobs from that, but it is the kinds of things that those personnel do that is quite dramatically changed. And we see this, you know, continuing this trend continuing um with our associates, you know, doing more of creative strategic work and less of mundane sort of manual type work. So anything that can be automated can be obviously automated. So using systems with A I, what we can do is to actually embed some intelligence, you know, in the systems as well. So they can mimic, you know, human like decision process. But when you are solving the problem accurately, as I said, you don't need to sort of emerge it with the mathematical sort of foundation. And many times our associates actually are asking for those solutions. And so we believe in designing the systems and solutions with our associates and with our customers as opposed to for them if you will. Yeah, I think a lot of the thing is about adoption and training and yes, making your, first of all, it starts with the top. So your CEO your board, I think it's very important to bring them along and explain them how you're gonna approach A I, what are the relevant use cases that you go? And then even in the in the building cycle, it's important. The role of a product manager in technology has dramatically changed leveraging A I. So they have to understand to be able to go to the outcomes, the way they are going to define the KPIS are very different. So there is training going on. So new you can transform a role of product managers have emerged. Then of course, you have to have A I engineers, data scientists, more and more of them. So they are attacking the problem in a way. So it has generated newer titles, newer jobs with newer skills, but it also requires the entire supporting ecosystem for them to come to fruition. Yeah, great. Well, I think we're out of time. I'd like to thank my Panelist, Servan and Yasser. Thank you for the uh fascinating chat.