Generative AI in Retail: Insights from NVIDIA’s AI Leaders

Embrace AI because especially genital AI is bringing so much intelligence. And you know, I think I quoted our CEO that said, you know, AI is not going to take your job, but someone using AI could take your job.

Hello and welcome to the Retail podcast. Now I'm it's really graced because Azita Martine, who is the lead or president or vice president, Vice President, I always keep promoting people. The CEO of NVIDIA directors, the vice president of retail, global retail for AI and NVIDIA was the keynote kick off speaker at NRF on day one. And as some of you know, I've already given you my thoughts on where we're going with AI. Basically, I position the fact that last year was about the use case.

This year we're hearing about AI transformation and Satya has already spoken about how the SAS models are going away and there's an AI layer that's coming on top. But hey, you're not here to listen to me, we're here to listen to you. So if you don't mind taking us through your because at your keynote, you sort of set the

scene about what's happening in the world of AI and the industry pretty much from a global context. And then you sort of board it to what it means to NVIDIA. So I was just wondering if you don't mind sharing that with the audience? Generative AI is now at a stage where it's moving from pilots to become real.

And the best way I would describe it is it's it's almost like an AI factory that takes your data, which is like the raw material in that factory, and it's generating digital intelligence in the form of AI agents. And these AI agents are making your employees more productive. They're delivering intelligence that just enables your company

to be much more productive. So now what customers are companies are doing is fine tuning these models with their data, whether it's PDFs, documents, customer data and images and videos and be able to create these digital agents that

are helping your employees become more productive. And so an example would be shopping advisor, which we're going to show to you a little bit later. And this shopping assistant is almost like replicating the knowledge of all your best sales associate and having that available 24 by 7 at scale on your e-commerce side to help your customers find exactly what they're looking for. What's been top of mind for the retailers that are coming to talk to you? You know what?

What are they saying as like their top of mind for 2025? You know, it ranges from everywhere where you know you have early adopters and you also have companies that it's like it has to be proven by 5 other companies or 10 other companies before I would do it. And I would certainly say that this is so much more real than e-commerce was in the year 2000. And the companies that are going to adopt it early are going to absolutely have a competitive advantage over the ones that are

going to be the laggards. Absolutely. And so, and the people that came to to to see some of our technology during NRAS, I think range from, you know,

commitments from the very top that, you know, we want to, you know, in the stores, prevent shrink or have a, you know, more optimised layout to make it easier for customers to find what they're looking for and be able to check out faster.

And that's all computer vision technology all the way to how can you help us improve throughput in our distribution centres and how to make fulfilment a lot faster in our fulfilment centres all the way to, you know, what are the top generated AI use cases that are going to bring business value

to, to our to the companies. And one of the areas that I highly recommend, even though 20%, less than 20% of retail revenue comes from e-commerce, I think generative AI has completely reinvented e-commerce and there was an opportunity to leverage it to improve search, to improve to have these shopping assistance on the website and to even create advertisement that retailers can make revenue from by selling it to to the consumer packaged good companies.

So I think there's a lot of opportunity to increase revenue leveraging generative AI on e-commerce and mobile sites. So. It sounds like there's a real mix of generate new revenue for retail media, help the customers have a better experience through the automation and bringing, and you said this earlier on, bringing all of that knowledge of your sales to the customers. In terms of Nvidia's focus in the retail space, I'm curious about where you see it going on.

NVIDIA, most companies know as far GPU. So our GPU is, is what companies like Open AI perplexity, Meta are using to train these real large language models and so forth. But, and there is actually an accelerated computing company. And what that means is not only our hardware, which involves our GPUs, our networking, even our own CPUs, but on top of it, we have a whole set of acceleration libraries and our application frameworks.

And basically those acceleration library and application frameworks are the acceleration software that developers use to build these AI application and to build their, to build and to

deploy, right. And so we're really a platform for building AI applications. And the blueprint that our CEO talked about doing his CES Kenu is basically step by step a workflow of how do you build a particular Gen AI application using our platform with open source models. So it's got step by step instructions. You know, how do you do fine tuning, how do you use RAC, How do you do the different components of building a general application? And it even includes the code.

And so that's what helps developers build these applications a lot faster and ensure that their performance from an inference perspective, which is the responsiveness of the generative AI application is the fastest and the most cost

efficient. So when you look to the future, when you look to the next two or three years, what fee do you see in from what you've taken from the show? What do you think will be the the sort of winning focus areas in AI? Is it in the business? Is it in front of house? Is it back? I'm just curious on on your opinion. I mean, I would say it's almost everywhere, but an area that's benefiting the most from AI is supply chain. Got you. And there NVIDIA is really

pioneering two things. One is called physics AI. And physics AI is AI that understands physics. It understands dimensions, volume and so forth, but more importantly, it understands how people and objects behave in a digital trend of a real space. So it allows you to create a physically accurate digital representation of a store or distribution centre, simulate different layouts and be able to predict accurately how people and objects behave.

And so that's being used for optimising throughput and distribution centres in your filming centres. It's used for optimising layout of stores for higher revenue. And so that's physics AI. Second thing that I talked about was physical AI. And physical AI is basically AI for the physical world. And it's a combination of computer vision, which is driven by smart cameras, robots and

models that understand physics. So it's a combination of our Omniverse simulation software, our robotics technology, and our computer vision technology. And it's all used to be able to train robots in a simulation environment. And the reality of it is that that in the physical world, you cannot even estimate how many situations can come up. But in the, in the, in the digital world, you can actually

simulate thousands of scenarios and be able to train those robots to behave a certain way during different situations. So I think we showed a video during the keynote that showed that an incident happened in one of the aisles and a bunch of a boxes fell. And so there what what was happening is the smart cameras were providing perception.

So it's like you're looking down, the AI is looking down to see what is going on. And then you have an agentic agent that is now going to has seen this and it's instructing another agent to reroute the forklift that was coming to go through a different aisle in order to ensure that, you know, no safety situation happens, right. So that's an example of physical AI, which is for the physical world. And I think that's a good way to

describe this. NVIDIA actually announced our Cosmo Nematron foundation model, and that's basically a foundation model that has been trained with billions of hours of video. Yeah, and it is to the physical world what tragedy BT has been to text and language. Yeah, I've been in technology all my life and I'm like struggling to keep up because my brain is exploding. Where what NVIDIA have announced and where you're taking it.

If you could go back in time and tell a younger you to come into this world, I think you've mentioned your career. Yeah, it was an aerospace. Engineer, that was. It you were an aerospace engineer and then you sort of shifted to NVIDIA and you you weren't really meant to end up

in rhythm. But anyway, my question being if it's OK to ask you about empowerment in terms of I'm looking at my career, what would be some sort of words of wisdom that you would if you could go back to a younger self? I think that really generative AI has democratised AI in many ways. So I want to encourage, maybe you don't need to code, but you can still use generative AI to

become so much smarter. So I want to encourage the younger generation to embrace AI because especially generative AI is bringing so much intelligence. And you know, I think I quoted our CEO that said, you know, AI is not going to take your job, that someone using AI could take your job. And so I want the younger generation to embrace AI and use it to, to be a lot more productive. At the end of the day, I would say even if you're in science, you got to love what you do.

So my first recommendation is really look around you, look

inside, really understand what your passion is. Learn as much as you can about what's happening in that area that you're interested in. Choose the top companies in that and aim high, aim big. Don't let your insecurity or your fear hold you back and get out of your comfort zone. Volunteer for roles that maybe you don't need 100% of the qualification, but you know, if you even read 60% of it, I'm sure you're smart enough to learn the rest of it and surround yourself by super smart people.

Because the more you get out of your comfort zone, the more you're going to grow and the more you're going to learn. I love it. And then what a beautiful way to end the interview. Thank you so much for giving me your time. Thank you. Thanks for having me. Hi Alex, I'm Cynthia Contours. I'm the Director of AI for retail CPG at NVIDIA as is it was talking to you about our AI blueprint for retail shopping

assistance. Yeah, I'm here to show you a little bit about it and and answer some questions. So yeah, we're demonstrating the AI assistant for a number of different categories. 1 is fashion, the other is furniture. Two really interesting categories, but also I can talk to you about grocery as another example. So let me go ahead and start with, let me go ahead and start with fashion, which is as an example.

Now one of the things that consumers have really been struggled, struggling with is it had to change their behaviour to an e-commerce site. I need to think about what are the keywords I need to search. And the reality is that's not the way we work, you know, as you mean this, you know, what are the upcoming fashion trends for this spring? Hey, do you have any purses or shoes to go with this skirt?

That's really the way we work. So I'm going to do an example here of I have an upcoming event that I'm going to a lunch and I'd like a recommendation for a dress or skirt so.

OK, Alex, see, as you can see, I asked for a dress and skirt appropriate for an outdoor event and it brought back a dress and skirt. But since it gave me a skirt, it also gave me some blouse ideas as well. Now typically in a search, you come back with just the products and then I need to start digging in, double click on the product, go and see more information, and then come back out, double click

again. So then the beautiful thing with the shopping assistance is I can now just ask questions. And so with that, let me go ahead and show you how that works. So I like the skirt, but of course the natural question is does it require dry cleaning? So typos and all. It's AI. It understands. So here we've got some great results coming back.

I didn't have to go in and look and hunt for that information is able to go and search for that information, bring it back makes it, it transforms it from my interactive with the e-commerce site too. I'm working with your stylist or your store associate just really naturally the next piece that's great is then being able to ask multiple questions, all right? Or being able to say, oh, I saw these pair of shoes online. Do you have something like that? So let me go ahead and show you

what that looks like. It's really an image search. So let me go ahead and transition and show you what that might look like. So I saw these pair of shoes online. They were really fabulous. And I want to see if you have something similar for me because of course they were a little expensive. So do you have any shoes like these?

And so in the background, we're going and saying, OK, what are the attributes of this image? Let me see what other products in the catalogue are similar to this? It could be red shoes, it could be strappy shoes. It could be shoes with embellishments. And in fact, you can see that's what we've got. We have one pair of shoes. We've got something strappy. Now this this shoe has an ankle component, has embellishment

components. So I have ankle components and embellishment components as well. So it's not a really nice job of finding similar things that also go with that skirt, right? I can then say, OK, I like these shoes, but how high are the heels? What's the price? What's the comfort level? What are how do people review them and rate them?

So all of those in all that information, I can just ask naturally, the next piece that I probably would like to show is the ability for integration, because this is a nice experience. But the next piece is, well, I want to be able to add to carts, right? So it's really simple the. E-commerce website. This is an e-commerce website. Website that I would have and so the images there are they catalogue images I as the

retailer provides. These are catalogue images that are in your retail catalogue right now. So we're keeping it very simple. I can do an example though of visualisation, right? And I'm happy to show that. And I'm doing that in a furniture example. So the other piece is being able to search for multiple things at once, right? You can go ahead and search for I want purses and shoes to go with a skirt. Do you have any tops? And to go with this, Kirk, all

of that is just very natural. So Azita may have mentioned a furniture demo where you're visualising furniture in your room because furniture is obviously considered purchase. There's challenges to oh, I like it, but how's it going to look in my room? The price point in that is lower, right? But I'm not sure about it if it's going to work and being able to play and actually visualise it. So let's go ahead and let's

transition to that next. OK, so as a consumer, I used your mobile app and I scanned my room to create a 3D room. So we're going to go ahead and ask to pull that information up. So I'm going to go on site. So what it's doing is it's saying, OK, this is Cynthia, she's got a living room. Let's take that 3D skin of the living room and we're putting it into Omniverse and VS 3D collaboration platform. So this is a, this is a 3D scan of my room. This is not a 2D photo of my room.

Yeah, right. And so I have objects in here. I have a couch, I have a table, I have a chair, for example. And so the first thing is obviously I don't have a case of luck, right? I want to, I want to now bring change my room and make a case. So do you have any modern couches? Let's start there. And very similar to the fashion example, it's going to go back. It's going to bring back some recommendations for me. It's going to give me information about those couches.

And so here we have 3 couches, right? I've got something that's very architectural. I have something that's a little softer. I like that one better. And then I've got a curve couch that's interesting, but I'm not sure about it. But the one I like that modern sectional is on the high end for me in price. The one that I don't like, but it's outside of my price range. And then there's the curb caps. That's much more affordable for me. So let me go ahead and explore it.

I might not necessarily explore it or be deterred that, you know, because of price points at this point. So let me go ahead and just explore replacing my couch with the curb couch. Now notice I'm not giving the product name or anything like that, right? The generatory I, the agent is understanding it and then it's going ahead and giving command back a tape. I've got a database right, of the 3D assets for my furniture. So I'm replacing the council was in there with this 3D asset of

furniture. So I can see it. What is it doing? Oh, now it's really transformed my room. It's making it more curved instead of squared, but it's still not crazy about it. It was right. We can do better, right? We can do better. So let me try that other. There's a couch that's kind of on the high end, but let me let me try that instead, right? So here's something that that I may not have explored it because of price points, right. And here we go. Oh, I like that.

Yeah, OK, I really like that. But I am going to need something round in the room. And I did see a table that I liked again, you know, I'm using examples outside of my price range or I just want to find something like this fast. OK, OK, so I've gone ahead and I've I've uploaded the image. Do you have a table like this? No, I'm not asking coffee table like this nesting coffee table like this marble top coffee

table like this. So I think as you may have talked to you about about the ability to search on images and not only did it give me the coffee table, but it also gave me some recommendations for chairs right as a part of this. So love it. It came really close to what I'm looking for so. And there we go. All right, I love it. That gives me some of the roundness, you know, for the room. And you can imagine going further. So let me go ahead. I think I talked about doing a

multiple query search. Let me show you what that was like. Because now that I got my couch and my table, right, maybe I want floor lamps, right? And alternative chairs or something of that nature, right? Now, more than likely in this scenario, it's going to give me similar chairs back, but let's see what it let's see what it does. OK, I came back with three floor lamps as different examples that would work in this environment, as well as one chair that it really highly recommended.

I'm not so crazy about the floor lamps. I'm not so sure about the chair, but let me go ahead and explore it, right. This is something that's you're putting control in the hands of the consumer to explore and visualise, to then say, oh, yeah, I do love it, or yeah, that didn't work out. So I hope this is giving you a sense of just the power of generative AI and AI agents for retail shopping assistance. So I'm sure the next question is, you know, how do I find out more?

So you come to nvidia.com and there's a full page on retail shopping assistance. So we create an AI blueprint that includes an AI agent. It includes Nemo components for guardrails, a pointer to Llama 3.370, BA large language model, being able to rank, being able to understand imagery, a an accelerated database, vector database. All of this is available in the blueprint for people to explore.

Try it out, Import your own product, catalogue information and imagery, play with it, Start to change out components that make sense for you, and then ultimately get to the point where you might want to deploy it for yourself. Where do people? Start a good question. The place to start is sign up to be notified. We have an upcoming package, Rev launchable blueprint, so it'll be available for your teams to click a button. It'll be unpacked and put on a clouds environment for them to

start starting with immediately. They don't need to know how to download and install it. We're making it as simple as possible for for your teams to get started. You can also work with a number of our partners as well.