Google Cloud Next 2025 | Reggie Booker, Macy's & Saurabh Mishra, Quantiphi

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Reggie Booker

Director of Engineering

Macy's

Saurabh Mishra

Global Leader - Google Cloud Business

Quantiphi

play_circle_outline The Evolution of GenAI in Retail: Common Use Cases and Trends in Artificial Intelligence

play_circle_outline Enhancing Retail Efficiency Through AI Adoption and Tech Stack Modernization

play_circle_outline Maximizing Sales with Real-Time AI Analytics for Enhanced Customer Experience

play_circle_outline Moving from software as a service to results as a service

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Reggie Booker, Macy's & Saurabh Mishra, Quantiphi

Reggie Booker

Director of Engineering Macy's

Saurabh Mishra

Global Leader - Google Cloud Business Quantiphi

Exploring the Role of Generative AI in Retail - Insights from Industry Experts

Join this insightful discussion with Reggie Booker of Macy's and Saurabh Mishra of Quantiphi as they delve into the transformative potential of generative artificial intelligence within the retail sector. Hosted by Savannah Peterson and John Furrier on theCUBE, this segment provides a comprehensive overview of AI's evolving landscape at the Google Cloud Next event.

In this video, we introduce Reggie Booker, renowned for their expertise in retail operations at Macy... Read more

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What are the three phases that can be identified in the evolution of GenAI in retail, according to the speaker? add

What is necessary for retailers to take full advantage of AI technology in their value chain? add

What are some ways that companies like Macy's can use data generated from customer interactions to improve their overall customer experience and increase sales? add

What do you think the future of technology will hold in terms of moving from software as a service to results as a service, particularly with the use of AI? add

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Reggie Booker, Macy's & Saurabh Mishra, Quantiphi

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>> Good afternoon, nerd fam and welcome back to Google Cloud Next here in Las Vegas, Nevada. My name's Savannah Peterson, joined by John Furrier for our last segment with guests. We've just got some analysts after this.

>> Yeah. And this is GenAI for retail, which we covered heavily in our NRF coverage earlier in the year.

>> Yeah.

>> So again, so much change. The future's here. It's upon us. Navigating it, big conversation here in retail.

>> And with our friends and VIP alums. Saurabh, thank you so much for coming to hang out with us again. This is now our Google Cloud Next tradition.

>> Yes, I think we love coming here every year with both of you, doing a session, inviting our customers like Reggie.

>> And Reggie, welcome to the show.

>> Thank you. Thank you. First time here. Glad to be here.

>> First time caller, long time listener hopefully. No, I'm kidding. I got to say, I was really excited when I saw that you work at Macy's and we're coming on the show, because I am a Macy's customer. This is from Paris just to flex a little bit, but most of my outfits, a lot of my clothes on the show are all from Macy's.

>> Awesome.

>> So I'm super excited to get your retail hot take on everything that's going on. Actually, so Saurabh, since you've been on the show, I'm going to start with you and you get my first question.

>> Okay.

>> Talk to me about how GenAI has evolved, what some of the common use cases are that you're seeing, and what's changed in even just the last year since we spoke last.

>> Great. So I think, GenAI became a hot buzzword towards the end of 2022. Over the last three, three and a half years, we've seen a big evolution in terms of how GenAI in retail has evolved, and how we are witnessing the change that customers are taking. So if I have to basically break down the evolution into three phases, I would say, the first phase is what we call as a discovery phase, in which most organizations were basically asking how can this technology help them? What can they do without it? While there was a lot of interest from across the board, 70% of our conversations focused around POCs, mainly around customer service, and generating content, those use cases. So that was phase one. The second phase is what started, I would say, last year, at the beginning of 2024, where customers started from why to how. They wanted to basically understand how to build and implement these technologies in their use cases. They started building business cases, talking about ROI. And what was the most interesting aspect is how security and enterprise wide adoption became center of the discussion with the customers. The third phase, which is what we are envisioning now, or we are observing now, is, what I call, enterprise grade maturity with GenAI, where customers are implementing these use cases in a scalable way, in a production environment, connected with their core offerings. So that's, I would say, a third phase in terms of how we have seen the evolution. One of the paddles that I will draw is how the adoption of cloud happened decades ago. It started with skepticism, went to experimentation, and then broad adoption. We are seeing a similar trend in terms of how GenAI is being adopted by customers. It's just at the pace of adoption. And, innovation is much faster than what we observed a couple of decades ago with cloud.

>> Yes it is. Yes it is. That was very well articulated. Thank you. That was a perfect little landscape. Reggie, what are you seeing? Let's talk AI in retail. I'm so curious. What are the trends you've seen? What's working? What's not?

>> Absolutely. So one of the things that I noticed starting out is we are jumping into certain things around knowledge summarization, right? So think about that, right? And we can even double click on knowledge summarization and what is it, right?

>> Please do.

>> So think about your employee base, and we'll give you a use case on, I want to file for go on leave of absence. So today, I can go to the HR website, I can click on the HR tab, and then double click on another thing, and guess what? I can maybe pop up a search. And then, the search comes up, I'll type in leave of absence, right? And then, the leave of absence will now pop up based off of the keywords. You have a long list of articles. In fact, it might be prioritized maybe at night. When I click on the first one, then it pops up a PDF. Now I got to try to read through it, right? Now, with AI layered on top of it, now I can just put a bot in front of that, free type, "I want to go on leave of absence, what do I need to do?" And it pops back a summarized tab there that lists out, "Here are the three steps you can do to go on leave of absence." It might even allow you to double click and get some additional information, right? Another example, another use case of that would be if I want to create some go on vacation... "How many vacation days do I have? What are my benefits?" All right. You can ask the bot anything that you want to ask it and it provides it up in a summarized manner. So I think, the summarization piece is something that we're seeing that's really coming along pretty well. How do you increase sales, right? Conversion rates.

>> Yeah, yeah, yeah.

>> Super critical. How do you do targeted marketing?

>> I was going to say, the personalization on marketing side has got to be pretty big for you too.

>> Exactly, exactly. The personalization is huge. So those are some of the things that I think that are really coming around the corner within the retail space. And not even just retail, but I think in a lot of other companies in all the industry verticals are doing that.

>> And the customer expectations on the user interface, any crossover there on the external side? Because the internal, great use case, finding for things fast, searches the killer app.

>> Absolutely, absolutely. So if you actually even think about it, you can actually use the same chat widget and turn it straight to your customer. The customer may want to say, "Hey, I bought a product six months ago, can I still return it?" And then, the chat widget goes and it goes through all the data policies, and procedures, and props back out a summarized answer. Wow, imagine that experience that companies can provide to their customers.

>> You can use this to get your stresses based on the colors of the brands we cover.

>> Oh, yeah. My closet is already bursting, and this is going to certainly continue that trend. Yeah, yeah. It's fun to think about. What's been the most surprising thing in your discovery process for the retail space in the AI side?

>> I think a little bit about what Amit said, it's been the pace, right?

>> We are moving at a serious velocity right now.

>> A serious velocity. I mean, three years ago, like I said, it was way off in the distance. You could see it coming. But, fast-forward a year later, and it's in the room with you. The company's coming around, "How are we going to leverage it? How can make this put it in the best use?" And now, you have with Avid and the pace of it has changed from AI to generative AI. So now, I can generate videos, I can generate media, I can generate music, and then it transformed again to adjective AI, right? So now, I can-

>> Quite quickly....

>> Very quickly.

>> A couple months.

>> How quickly is that, right?

>> Yeah, yeah.

>> So now, I have an agent, what do I do with that? That agent can now take the information that you can pump into it and actually make human decisions. So I'm really stoked about that. I mean, it's something that I think the pace that is coming is just breakneck, right? I think another thing that I think is paramount as well is just the speed to market.

>> Mm-hmm.

>> So if you think about delivering applications, things like that, how fast that we're able to do it now using AI. Before you would think in terms of 12 months, 18 months. Just deliver something now using AI, generative AI, six weeks, eight weeks, it's months to delivery.

>> That's huge time savings right there.

>> Absolutely.

>> And you get data too. Once you publish it out, you've got more-

>> It's powerful....

>> Powerful data coming in.

>> Exactly.

>> It's very powerful. So Saurabh, how do you help Reggie do this and others in the retail space?

>> Okay. So for customers like Reggie, what we have seen is we've held a lot of retailers adopt AI across their value chain, similar to what we are envisioning doing with Reggie and his team as well. But to basically take full advantage of AI as a technology for any use cases, it requires a modernization of the entire tech stack, from infrastructure, to data, to intelligence layer, and then to application layer. I think an example of generating insights from communication recordings or call recordings that any retailer that may have, right? And this from infrastructure stack layer, there has to be change from having hard-line phones to a platform that can process and record call recordings, so that's one. From the data perspective, the calls need to be transcribed. That is a second one. The third one, from intelligence perspective, there needs to be insights that need to be generated using different tools. And then, the last one, from application perspective, everything that we basically do has to get connected to downstream applications in terms of marketing activation and other business processes. Now, continuing on the same example about basically a retailer like Macy's, which has a lot of customer interactions, different touch points through their websites, through call recordings, et cetera. A large amount of data is generated. Now, using GenAI, this data that we have generated can be used to understand customer grievances, it can be about products, it can be about pricing strategies, it can be about their experience that they have in the stores, right? Now, when we basically put all of this together, we are able to gather insights that we can basically use to see how we can provide a better experience to our customers, how do we upsell based on how the customer is engaging with different omni-channel platforms that a customer has. So these are some of the ways that we are helping customers like Macy's adopt GenAI, and basically, take the next step.

>> Well, I can see why this is working so well and why you're both sitting here. I would actually love to ask both of you this question. How are you advising... Reggie, I'll start with you. How are you preparing yourself? And, I mean, it's not even preparing really, because it's not like we're sprinting, and then resting for a little bit, sprinting and resting. We're just all out sprinting right now, I think, as an industry. So I'm curious, how do you stay on top of things and how do you two work together to make sure that you are paying attention both to the retail industry and all the other industries that you work with? Reggie, I'll start with you.

>> Well, I think, for us, it starts with starting small.

>> It's fine. They can't hear it.

>> Okay. So it starts with starting small. And then, building on scalability, right? And then, identifying someone that can really help you bring that to fruition at a scalable level. And I think that's what we've really been able to do. And so, I think that's been a real clear point for us, in starting small and building on what we have at least emerged with, right?

>> Yeah. Well, and then you can have that confidence when you make the bigger investments and scale up. So, Saurabh, what about you? What are you telling people to do? I mean, like you said, "We're moving so fast."

>> Yes, we are moving so fast. So I think what we have seen is from our customer perspective, there are two large cohorts of customers. The first cohort is where customers started adopting AI very early and they started experimenting back in 2023, 2024. Now, for those customers, our advice is that they should think about not just implementing isolated use cases, but have a more platform-centric approach, so they are able to have a more comprehensive adoption and maturity from the AI perspective, right? And, based on our experience, and this is across industry, not just for retailers, right, we envision basically three phases. The first one is around building the foundation, where you're basically building the core thinking about company's AI center of excellence, what is going to be the governance framework, what is going to be the talent strategy? Which is basically around getting your basics, right? The second phase is around how you are building the platform and building AI applications across the value chain. This is where it gets interesting, because you are now talking about use cases that can be across the value chain. Now for a retailer, it can be a customer-specific or a external-facing use case, which is with customers around personalization, about upselling, and basically recommending new things to them. Whereas, internal use case can be around employee satisfaction or automating tasks. So those are the use cases that we are basically helping customers advice built across the value chain on a platform. And the last one, which is where actually it gets very interesting, or I would say, this is where what people call music to the ears, is around integrating and scaling, where all these different use cases that you have built across the value chain get integrated together to have real-time analytics and automated decision-making. So I think, the analogy that I use when I advise customers who fall into this cohort is you can either build roads one at a time or you can plan a proper highway in a connected way. While the second approach may take time, but it will give you better results. So this is an advice for customers who basically started adopting AI early. Now, there are some customers who did not buy into the AI hype, GenAI hype, and wanted to see other companies first adopt and then move. The advice to those customers that I have is, I would say, don't get paralyzed by the fact that you need to get a perfect strategy, start moving, because technology is moving so fast. So what we implement today, right, will slowly and slowly evolve, right? So those are the two different types of customers that we are seeing. And based on where they stand on the AI maturity curve, we are advising them to act in a very different way.

>> I believe that. I love that highway and road analogy. I'm just thinking of the agents now as the autonomous vehicles, the driverless vehicles on those roads, right? I mean, I guess, they have some driving wherewithal. But anyway, love that one. All right, I have one final question for you both and I'm super curious to hear your answers to this. I know I asked you this last year too. We had a slightly different conversation. But, what do you hope to be able to say when we're at Google Cloud Next 2026, that you can't yet say today? I'll give you a second to noodle that.

>> Wow, that's a great question.

>> Thank you. So Saurabh, I'm going to start with you since you've been here before.

>> Yeah.

>> Yeah, I will pass that to you now.

>> So I would say, I feel what I envision we will be able to hear next year is, this year, I think the conversation around agents has become very, very predominant, right? As people are building different agents to automate different tasks, whether it be internal, external across the value chain. I feel when we talk next year, I think it will be, agents are integrated, right? Now, how are we using these different agents to basically solve for different workflows? And there will be lot of customer stories that we will hear how customers have been able to integrate these agents to solve for different business processes and have actually transformed their value chain using AI. There will be lot of different use cases of adoption across the board that we will hear when we come back again in the next 12 months.

>> Awesome. I look forward to helping you tell those stories. That's awesome. Okay, Reggie, now you've had some time.

>> Amazing. Hey, I'll keep mine straight to the point. I think this time next year, you're going to be able to say, "We have actually migrated from software as a service, to actually results as a service."

>> Ooh, punchy.

>> Yeah. So I think that's what we're going to be able to see, and particularly with AI and how it's going to be leveraged within the companies, all the things that we've talked about today, I think that's what's going to be the driving factor is, what results are we going to see leveraging AI in any industry, right? Whether it's shaving time, increasing productivity, resolving customer pain points, I think that is going to be the driving factor we're going to be able to say next year is that, "Hey, we actually implemented something that took us from software as a service to results as a service."

>> Results as a service. That's the first time I've heard that. I'm going to give you credit every time I repeat it, because I'm confident that will be quite a few times. That is awesome.

>> And I think that's a very interesting concept of results as a service, right? Because, right now, when we talk to customers, we talk about building applications, we don't talk about the results those applications are going to deliver. I think this will bring in a new paradigm where with the customers we are going to talk about what outcomes will we deliver, based on that we basically interact or engage with them, rather than in terms of what steps we will do to get to the outcome.

>> The results of this interview were a smashing success.

>> Yes, absolutely.

>> Great service.

>> Yes, so thank you both so much. This was a blast as usual.

>> Love it. Thank you.

>> And Reggie, really appreciate your insights.

>> Happy to be here.

>> Thank you, John. And thank all of you for tuning in. We're here in Las Vegas, Nevada for Google Cloud Next, my name's Savannah Peterson, you're watching theCUBE, the leading source for enterprise tech news.