1 00:00:00,120 --> 00:00:02,440 Hello and welcome to the retail podcast. 2 00:00:02,720 --> 00:00:06,560 Now I've had the pleasure of seeing Keith actually was I 3 00:00:06,560 --> 00:00:10,800 think last year at Shop Talk Spring when Microsoft had a, a 4 00:00:10,800 --> 00:00:14,240 room to the one of the sides and you were one of the, the, the 5 00:00:14,240 --> 00:00:18,040 speakers there, Keith. And for Full disclosure, I have 6 00:00:18,040 --> 00:00:21,480 known Keith from my previous life at Microsoft, but that 7 00:00:21,480 --> 00:00:25,200 shouldn't stop us talking about some of the awesome things that 8 00:00:25,200 --> 00:00:28,480 I know Keith has been travelling the world telling people what 9 00:00:28,480 --> 00:00:31,520 about what Microsoft is doing. But Keith, for those people who 10 00:00:31,520 --> 00:00:34,080 don't have that intimate knowledge of who you are and 11 00:00:34,080 --> 00:00:36,280 what do you do, How about you tell us who you are and what do 12 00:00:36,280 --> 00:00:38,080 you do at Microsoft? Of course. 13 00:00:38,080 --> 00:00:41,080 And first of all, thank you for Alex for having me on the retail 14 00:00:41,080 --> 00:00:43,480 podcast. So excited to be here. 15 00:00:44,160 --> 00:00:47,200 I grew up in retail. I spent 27 years working for 16 00:00:47,200 --> 00:00:48,800 Gap. It's in my blood. 17 00:00:48,800 --> 00:00:51,520 So I love having retail conversations. 18 00:00:51,840 --> 00:00:54,960 But for the rest of you, I am the Vice President of the 19 00:00:54,960 --> 00:00:58,160 worldwide retail and consumer goods industry team at Microsoft 20 00:00:58,480 --> 00:01:03,160 and our role is to work with our top retail and consumer goods 21 00:01:03,160 --> 00:01:06,840 customers globally. We are all ex industry folks. 22 00:01:06,840 --> 00:01:11,120 We I always call us the great translators of customer business 23 00:01:11,120 --> 00:01:14,560 problems into ways that Microsoft technology and 24 00:01:14,560 --> 00:01:17,440 Microsoft partners can help solve those problems. 25 00:01:18,040 --> 00:01:20,000 So thanks for having. Yeah, I know. 26 00:01:20,000 --> 00:01:23,640 And I and I think it's a testament to how well Microsoft 27 00:01:23,640 --> 00:01:27,320 has grown within the retail community, how your team has 28 00:01:27,320 --> 00:01:31,280 basically been able to address those major issues and what a 29 00:01:31,280 --> 00:01:34,120 time it is for those major issues. 30 00:01:34,120 --> 00:01:36,800 So I mean, I'm, I'm really curious. 31 00:01:36,800 --> 00:01:39,320 You, you meet executives day in, day out. 32 00:01:39,320 --> 00:01:41,320 I know. I'm surprised you're at home, to 33 00:01:41,320 --> 00:01:42,400 be honest, right? You're not. 34 00:01:42,600 --> 00:01:44,520 It's, I mean, the airport somewhere. 35 00:01:44,560 --> 00:01:46,160 Yeah. What's well, what's what's the 36 00:01:46,160 --> 00:01:47,640 thing that's top of mind right now? 37 00:01:47,640 --> 00:01:49,080 What are what are people coming to you? 38 00:01:49,080 --> 00:01:51,640 And talking to you about or wanting to talk to you about. 39 00:01:51,880 --> 00:01:56,280 I mean, it's really about a gentic AII mean the the pace at 40 00:01:56,280 --> 00:02:02,000 which this technology is changing and the importance that 41 00:02:02,280 --> 00:02:05,600 executives realise that it can have on their business is 42 00:02:05,600 --> 00:02:08,759 probably unparalleled than any other technology that I've ever 43 00:02:08,759 --> 00:02:11,680 seen. So they really want to 44 00:02:11,680 --> 00:02:15,800 understand how can they uniquely adopt these capabilities to 45 00:02:15,800 --> 00:02:20,520 reduce cost, to give better customer experiences, to build a 46 00:02:20,520 --> 00:02:24,560 more agile supply chain, and honestly make use of the 47 00:02:24,560 --> 00:02:27,480 incredible value of data that they sit upon. 48 00:02:27,840 --> 00:02:30,920 And retailers have an incredible amount of data. 49 00:02:30,920 --> 00:02:34,520 And for years they generate data, but they haven't been able 50 00:02:34,520 --> 00:02:38,440 to really unlock the value. So with generative AI, agentic 51 00:02:38,520 --> 00:02:41,840 AI, they're seeing finally this uniqueness of how they can take 52 00:02:41,840 --> 00:02:45,680 that competitive advantage and put it to work to change and 53 00:02:45,680 --> 00:02:50,600 transform their organisations. If you don't mind, if we do like 54 00:02:50,600 --> 00:02:55,840 a quick one O 1 on what a gentic is, because I think sometimes I 55 00:02:55,840 --> 00:02:59,160 mean, it was the IT was sort of the on on stage at shop talk 56 00:02:59,160 --> 00:03:01,840 this year, shop talk spring. It was like, you know, a gentic 57 00:03:01,840 --> 00:03:04,840 is just from from the person who's in data and AI. 58 00:03:05,040 --> 00:03:06,920 You you tell us what is a gentic. 59 00:03:07,040 --> 00:03:09,320 AI yeah, yeah. And I'll start back from when 60 00:03:09,320 --> 00:03:13,120 generative A kind of first came on, you know, came in the market 61 00:03:13,120 --> 00:03:18,040 with GPT, which was this natural language interface, which which 62 00:03:18,040 --> 00:03:21,320 you could ask questions, generate content. 63 00:03:21,320 --> 00:03:22,680 And that's that's where we started. 64 00:03:22,720 --> 00:03:27,120 And it taught us all how to have the power of the prompt, right. 65 00:03:27,360 --> 00:03:30,520 The output was only as good as the input. 66 00:03:30,560 --> 00:03:33,520 And we all had to learn how to do these inputs. 67 00:03:33,520 --> 00:03:36,800 So at Microsoft we took that capability and started launching 68 00:03:36,800 --> 00:03:40,880 Co pilots and Co pilots were the ways you could access your data. 69 00:03:40,880 --> 00:03:43,840 It's the way you could help generate that first draught of 70 00:03:43,840 --> 00:03:45,440 an e-mail. And that's really where we 71 00:03:45,440 --> 00:03:48,600 started. Now you layer on a gentech and 72 00:03:48,600 --> 00:03:52,760 the good news is we believe Co pilot is the UI for all AI. 73 00:03:52,960 --> 00:03:56,600 So the way we will communicate with agent with agents in the 74 00:03:56,600 --> 00:03:59,800 future is the same way we've been communicating in the last 75 00:03:59,800 --> 00:04:01,560 year and a half by using prompts. 76 00:04:01,920 --> 00:04:06,760 But agents now can do more than where we started. 77 00:04:06,920 --> 00:04:10,120 So the first thing is more about retrieval, going out and finding 78 00:04:10,120 --> 00:04:12,160 a piece of information and bringing that back to you. 79 00:04:12,160 --> 00:04:14,880 You're still directing it using natural language. 80 00:04:15,280 --> 00:04:18,519 Then it will move to tasks, so performing a task on your 81 00:04:18,519 --> 00:04:20,800 behalf. So I think about the marketing 82 00:04:20,800 --> 00:04:25,360 function and every campaign starts with a brief and we used 83 00:04:25,360 --> 00:04:28,000 to use Co pilots to write that first draught of the brief. 84 00:04:28,440 --> 00:04:31,960 Now we can have an agent write the first draught of the brief, 85 00:04:32,600 --> 00:04:36,360 and once we build confidence in that agent, over time we will 86 00:04:36,360 --> 00:04:40,360 see those agents act autonomously and start to 87 00:04:40,360 --> 00:04:44,560 generate briefs on their own based on the insights the agents 88 00:04:44,560 --> 00:04:47,520 are seeing in the business. And that is really the unlock of 89 00:04:47,520 --> 00:04:50,520 value because now the human intervention is still always 90 00:04:50,520 --> 00:04:55,120 going to be there, but it will be significantly less than it is 91 00:04:55,120 --> 00:04:58,880 today, which means we as humans, we'll be able to do higher level 92 00:04:58,880 --> 00:05:02,320 thinking, think more strategically, and take a lot of 93 00:05:02,320 --> 00:05:05,960 that kind of lower level work, like brief generation and 94 00:05:06,200 --> 00:05:08,800 business insight, you know, analysis. 95 00:05:09,000 --> 00:05:12,160 The agents will be doing that on our behalf and surfacing those 96 00:05:12,160 --> 00:05:14,720 insights and recommendations to the humans. 97 00:05:14,920 --> 00:05:17,040 And we could just say, Yep, keep moving forward. 98 00:05:17,040 --> 00:05:19,760 So that is really going to change the game in terms of 99 00:05:19,920 --> 00:05:22,280 humans and agents working alongside of each other. 100 00:05:23,200 --> 00:05:27,880 I know Satya, maybe one month or two months ago, he gave an 101 00:05:27,880 --> 00:05:29,760 interview where he said it was the death death. 102 00:05:30,080 --> 00:05:33,560 I can't remember the death of Sass or Sass is gone or I don't 103 00:05:33,560 --> 00:05:34,840 know. I'm just curious. 104 00:05:35,200 --> 00:05:38,680 Was that just, was that like a sound bite someone took or is 105 00:05:38,680 --> 00:05:41,640 that genuinely how Microsoft is actually seeing it because you 106 00:05:41,640 --> 00:05:45,640 have so many products underneath your agentic just from a 107 00:05:45,640 --> 00:05:48,640 Microsoft perspective, if I'm the Microsoft customer? 108 00:05:48,640 --> 00:05:52,200 And in the spirit of that comment is around if you think 109 00:05:52,200 --> 00:05:56,280 about the number of business applications that we use to do 110 00:05:56,280 --> 00:05:59,040 our jobs everyday. So if you are in a marketing 111 00:05:59,040 --> 00:06:03,440 function, you probably have some data that's sitting in Excel, 112 00:06:03,440 --> 00:06:06,000 you have some data that's sitting in some type of web 113 00:06:06,000 --> 00:06:08,960 analytics tool. You have other data sets that 114 00:06:08,960 --> 00:06:11,400 are sitting in in your advertising tools. 115 00:06:11,760 --> 00:06:14,480 And all of those are individual business applications that 116 00:06:14,480 --> 00:06:17,920 you're going in and out of every day to gather data. 117 00:06:18,080 --> 00:06:20,960 So you can do some analysis potentially in a fourth tool 118 00:06:20,960 --> 00:06:22,440 like Excel. Yeah. 119 00:06:22,600 --> 00:06:27,200 And what we're, what we believe is that agents will now be the, 120 00:06:27,560 --> 00:06:30,200 the systems going in and interacting with that. 121 00:06:30,200 --> 00:06:32,440 So we don't have to do that any longer. 122 00:06:32,880 --> 00:06:36,920 And we'll simply use natural language as our communication 123 00:06:36,920 --> 00:06:40,800 and UI through a Co pilot. So you no longer have to go in 124 00:06:40,800 --> 00:06:43,000 and out of all of these different business applications 125 00:06:43,160 --> 00:06:45,320 to get your insights. The agents will be doing that on 126 00:06:45,320 --> 00:06:48,040 our behalf and we'll just be asking questions. 127 00:06:48,280 --> 00:06:50,920 And it doesn't matter where the data sits any longer, right? 128 00:06:51,040 --> 00:06:53,000 It doesn't matter if it's in this tool or that tool, as long 129 00:06:53,000 --> 00:06:56,000 as you trust the data, as long as the data is secure and 130 00:06:56,000 --> 00:06:59,560 compliant, then we'll let the agents do that work on our 131 00:06:59,560 --> 00:07:01,320 behalf. So I think the spear of that is 132 00:07:01,320 --> 00:07:05,480 more about natural language will be the UI throughout your Co 133 00:07:05,480 --> 00:07:08,440 pilot, which was how we'll start to interact with disparate data 134 00:07:08,440 --> 00:07:10,560 sources across the entire company. 135 00:07:11,160 --> 00:07:13,960 Yeah, I mean we, I mean that makes perfect sense. 136 00:07:13,960 --> 00:07:15,880 So thank you for. Yeah, of course. 137 00:07:15,920 --> 00:07:19,920 Clarifying that, I remember from days gone by, we used to talk 138 00:07:19,920 --> 00:07:24,080 about retail operating at 2 speeds in terms of, you know, 139 00:07:24,080 --> 00:07:26,880 almost people that have structured the data, people who 140 00:07:26,880 --> 00:07:31,680 have spent time, energy, money and effort over over the years 141 00:07:31,680 --> 00:07:34,480 getting some form of structured data. 142 00:07:34,880 --> 00:07:38,480 And then you've got this other sort of wheelhouse of retailers 143 00:07:38,480 --> 00:07:42,680 that haven't done anything and everything lives in silos. 144 00:07:43,120 --> 00:07:45,760 I'm just curious. I I guess this this question 145 00:07:45,760 --> 00:07:48,120 breaks into a couple of points and I'd love to know if I'm a 146 00:07:48,120 --> 00:07:52,960 Microsoft customer and I sit in in those two worlds, does a 147 00:07:52,960 --> 00:07:57,680 gentic AI actually help me or no, sorry, you still need to get 148 00:07:57,680 --> 00:08:01,560 your data into some order. That's the first question. 149 00:08:01,720 --> 00:08:04,680 And then if I'm not a Microsoft customer, but I'm, you know, I'm 150 00:08:04,720 --> 00:08:07,240 saying here, listening, going, holy cow, this really sounds 151 00:08:07,240 --> 00:08:09,720 like, you know, I I'll be with the dinosaurs. 152 00:08:09,720 --> 00:08:15,240 If I'm not going ahead, I'm doing, you know, Where did you 153 00:08:15,240 --> 00:08:16,440 start? What does it look like? 154 00:08:16,440 --> 00:08:17,720 Well, I love. It we're having a data 155 00:08:17,720 --> 00:08:22,320 conversation, because it is, I always say every generative AI 156 00:08:22,320 --> 00:08:25,120 conversation always leads to a data conversation. 157 00:08:25,240 --> 00:08:26,760 You can't have one with the other, right? 158 00:08:27,160 --> 00:08:29,800 You can't. And you, you said it, Alex, like 159 00:08:30,160 --> 00:08:33,960 retailers traditionally have siloed data. 160 00:08:33,960 --> 00:08:35,520 It's just how they've been built. 161 00:08:35,520 --> 00:08:39,799 You know, many started as brick and mortar and built entire 162 00:08:39,960 --> 00:08:42,080 organisation and tech stack around that. 163 00:08:42,080 --> 00:08:44,320 Then they launched e-commerce and that was a different data 164 00:08:44,320 --> 00:08:46,160 set. Maybe they went into a different 165 00:08:46,160 --> 00:08:50,800 model or wholesale, whatever. But the disparate data silos 166 00:08:50,800 --> 00:08:54,240 have been a challenge in the industry and for years. 167 00:08:54,240 --> 00:08:58,720 You're right, many retailers have been working to bring these 168 00:08:58,760 --> 00:09:03,000 this data together. It's hard, it's expensive, it's 169 00:09:03,000 --> 00:09:05,440 slow. It's really a process. 170 00:09:05,440 --> 00:09:11,120 And what we're seeing now is we've launched the data platform 171 00:09:11,120 --> 00:09:14,720 that Microsoft called Fabric that is less about breaking down 172 00:09:14,720 --> 00:09:16,960 the silos. It's leave the data where it is. 173 00:09:16,960 --> 00:09:19,440 We can just help you extract what you need. 174 00:09:19,960 --> 00:09:22,560 Well, so you can build that first AI use case. 175 00:09:22,560 --> 00:09:26,600 And I'd say what we're seeing in the industry is this like I've 176 00:09:26,600 --> 00:09:29,400 got to get my data house in order before I can even start. 177 00:09:29,600 --> 00:09:32,600 And yes, that's true. But we have ways to help you 178 00:09:32,600 --> 00:09:36,080 fast track that. And we're, that's where we're 179 00:09:36,080 --> 00:09:39,200 spending a lot of time with our customers today is let's pick a 180 00:09:39,200 --> 00:09:41,960 use case. Let's look at the data sources 181 00:09:41,960 --> 00:09:44,480 that drive that particular use case. 182 00:09:44,800 --> 00:09:48,000 Let's unlock those first. Let's feel good about the data. 183 00:09:48,000 --> 00:09:51,440 Let's make sure it's secure and compliant, and then let's launch 184 00:09:51,440 --> 00:09:53,280 a use case. And I think that has been 185 00:09:53,280 --> 00:09:55,200 actually one of the barriers we've seen. 186 00:09:55,200 --> 00:09:58,400 We've seen a lot of our customers launch a lot of Pocs 187 00:09:58,480 --> 00:10:01,600 last year, but they've kind of got stuck in this pilot 188 00:10:01,600 --> 00:10:04,480 purgatory where they have been able to move to scale. 189 00:10:04,880 --> 00:10:08,320 And we really want to help them take those use cases to scale. 190 00:10:08,440 --> 00:10:10,920 And the data unlock is is key to that. 191 00:10:11,880 --> 00:10:15,200 I mean that that's a wonderful segue in terms of obviously we 192 00:10:15,200 --> 00:10:19,680 can dwell on the whole data and AI element, but I can on the 193 00:10:19,680 --> 00:10:23,680 second speed where people are moving at blistering speeds. 194 00:10:23,920 --> 00:10:26,440 What are some of the use cases that have really sort of 195 00:10:26,640 --> 00:10:29,360 impressed you? Yeah, on your travels around the 196 00:10:29,360 --> 00:10:31,320 world. Well, you know, there's two that 197 00:10:31,400 --> 00:10:34,200 we're seeing a lot of traction and there's many, but I'll, I'll 198 00:10:34,200 --> 00:10:36,680 talk about two. And I like to talk about these 199 00:10:36,680 --> 00:10:40,040 two in conjunction because one of my roles many, many years 200 00:10:40,040 --> 00:10:43,760 ago, that gap was when I first started, I was a sales 201 00:10:43,760 --> 00:10:45,240 associate. It was my first job. 202 00:10:45,640 --> 00:10:48,520 I was in high school and I was folding jeans and I was, you 203 00:10:48,520 --> 00:10:50,640 know, I had to learn everything about our products. 204 00:10:50,640 --> 00:10:53,760 We had this printed manual and we studied it every season. 205 00:10:53,760 --> 00:10:57,320 So I could talk about features and benefits, but we didn't sell 206 00:10:57,320 --> 00:10:59,480 very much. It was jeans and T-shirts and 207 00:10:59,480 --> 00:11:02,080 sweaters. It was relatively easy, but I 208 00:11:02,080 --> 00:11:06,320 think about the complex nature of the sales associate today in 209 00:11:06,320 --> 00:11:11,680 retail and some of the products that customers and offer and how 210 00:11:11,680 --> 00:11:16,360 do you learn everything about all your products in a confident 211 00:11:16,360 --> 00:11:19,240 way where you can just help a customer on the sales floor. 212 00:11:19,440 --> 00:11:23,320 And I've always said, you know, retail brick and mortar is very 213 00:11:23,320 --> 00:11:29,360 service rich but very data poor. And this is where agentic AI is 214 00:11:29,360 --> 00:11:32,280 going to help bring the necessary data into the 215 00:11:32,280 --> 00:11:36,000 fingertips to the associate at the moment they need it, which 216 00:11:36,000 --> 00:11:38,320 is the moment they that matters with that customer. 217 00:11:38,320 --> 00:11:41,240 And I'll I'll tell you a great customer story with me to mark 218 00:11:41,240 --> 00:11:42,920 Saturn. If you don't know them, they're 219 00:11:42,920 --> 00:11:45,320 yes, I'll see 3 Taylor in Germany. 220 00:11:45,680 --> 00:11:50,560 I'm in that carry a very broad range of a highly complex and 221 00:11:50,560 --> 00:11:53,320 very similar products across many brands. 222 00:11:53,600 --> 00:11:58,400 And they've launched this my buddy in in my ear Co pilot that 223 00:11:58,400 --> 00:12:03,680 is a on demand basically phone a friend to ask a product question 224 00:12:03,920 --> 00:12:07,760 and have it answered in your ear while you're working live with 225 00:12:07,760 --> 00:12:09,760 the customer. And it really is changing the 226 00:12:09,760 --> 00:12:13,120 game of how they can answer questions about think about, you 227 00:12:13,120 --> 00:12:16,680 know, big screen televisions. I have 5 of them that look very 228 00:12:16,680 --> 00:12:18,520 similar. They're different brands. 229 00:12:18,520 --> 00:12:21,520 The price point's very similar. How do I explain to a customer 230 00:12:21,520 --> 00:12:24,600 what the differences are? So yeah, my buddy has been a way 231 00:12:24,600 --> 00:12:28,200 to help bring that information into the front line worker at 232 00:12:28,200 --> 00:12:31,520 the moment they need it. So bringing that that data along 233 00:12:31,520 --> 00:12:34,320 with the service, now I'm going to take you to the e-commerce 234 00:12:34,320 --> 00:12:36,080 world. And that was a role I also had 235 00:12:36,080 --> 00:12:38,280 at gaps like I have to run the e-commerce business for a while. 236 00:12:38,520 --> 00:12:40,120 And you kind of have the opposite problem. 237 00:12:40,200 --> 00:12:42,000 It's actually very data rich, right? 238 00:12:42,200 --> 00:12:46,160 Data is just a click away on the web, but it's very service poor. 239 00:12:46,320 --> 00:12:48,000 There's no one there. You're you're kind of 240 00:12:48,000 --> 00:12:50,800 self-guided, right? And e-commerce hasn't even 241 00:12:50,800 --> 00:12:53,920 changed very much in 20 years. You've got your navigation 242 00:12:53,920 --> 00:12:58,000 across the top, some categories down the left, your search box, 243 00:12:58,240 --> 00:13:02,720 but that's kind of it. So conversational commerce is 244 00:13:02,720 --> 00:13:07,200 changing how customers solve problems. 245 00:13:07,400 --> 00:13:13,080 So if you're trying to throw a World Cup watch party for 10 246 00:13:13,080 --> 00:13:18,880 friends, what should I buy? Well, you might need drinks, you 247 00:13:18,880 --> 00:13:22,680 might need snacks, you might need a large screen television 248 00:13:22,680 --> 00:13:26,880 and a great sound system. So companies like Walmart are 249 00:13:26,880 --> 00:13:29,600 bringing that type of conversational commerce 250 00:13:29,840 --> 00:13:34,080 experience to the web. And what they're seeing is it's 251 00:13:34,080 --> 00:13:37,080 their customers are moving from this kind of scroll based 252 00:13:37,080 --> 00:13:41,120 shopping to goal based shopping. And we're seeing other brands 253 00:13:41,120 --> 00:13:44,320 launch this and we're seeing increases in basket size. 254 00:13:44,320 --> 00:13:47,800 We're seeing speed through checkout, which are all great 255 00:13:47,800 --> 00:13:51,640 signal that consumers are engaging on this and they're 256 00:13:51,640 --> 00:13:55,120 finally able to shop by solving their real problem versus kind 257 00:13:55,120 --> 00:13:57,840 of searching for things. So those are two of my 258 00:13:57,840 --> 00:13:59,120 favourites and that we're seeing today. 259 00:14:00,040 --> 00:14:02,760 And and so just just to sort of understand that in terms of the 260 00:14:02,760 --> 00:14:05,600 Walmart use case that you were saying you were talking about 261 00:14:05,600 --> 00:14:08,680 there. So just so I've got a clear in 262 00:14:08,680 --> 00:14:11,800 my head, I'm I'm having a BBQ. Is that me? 263 00:14:11,920 --> 00:14:17,920 Instead of typing with the e-commerce page or site I'm 264 00:14:17,920 --> 00:14:19,840 talking to, it is that is that it's. 265 00:14:19,960 --> 00:14:21,440 Oh, can you just. Yeah, it's right. 266 00:14:21,760 --> 00:14:24,840 You're actually seeing not only, and now they haven't launched 267 00:14:24,840 --> 00:14:27,760 this yet, but we're actually seeing multimodal where you're 268 00:14:27,760 --> 00:14:31,200 saying, I've got this a hiking shoe. 269 00:14:31,880 --> 00:14:34,040 I, I want something just like it. 270 00:14:34,040 --> 00:14:38,720 And you're holding it up to the camera and have an agent behind 271 00:14:38,720 --> 00:14:41,800 that camera who's recognising that and comparing that to your 272 00:14:41,800 --> 00:14:45,440 product catalogue. So it's text, it's voice. 273 00:14:45,440 --> 00:14:48,880 And very soon you'll start to see multimodal commerce where 274 00:14:49,280 --> 00:14:52,680 you as a user can interact with different ways that are most 275 00:14:52,680 --> 00:14:56,040 comfortable to you and helpful. So where Walmart is starting 276 00:14:56,040 --> 00:15:00,080 right now is they've added more conversation to their search, 277 00:15:00,360 --> 00:15:03,480 but we'll eventually move to a full conversational commerce 278 00:15:03,480 --> 00:15:06,800 experience. Other examples are Bath and Body 279 00:15:06,800 --> 00:15:09,800 Works in the US has launched full conversational commerce 280 00:15:09,800 --> 00:15:13,560 where you can literally ask a natural language question around 281 00:15:13,960 --> 00:15:17,440 I'm, I'm looking for a gift from Mother's Day. 282 00:15:17,600 --> 00:15:20,880 What ideas do you have? I mean something like ask a 283 00:15:20,880 --> 00:15:22,600 sales associate in the physical store. 284 00:15:23,400 --> 00:15:25,560 I think, I mean, that sounds a little bit like because I saw 285 00:15:26,080 --> 00:15:31,360 the CEO founder of Perplexity sort of saying that's what 286 00:15:31,360 --> 00:15:34,560 they're trying to build or building or training their 287 00:15:34,560 --> 00:15:37,400 models to do. But obviously as a retailer, I 288 00:15:37,400 --> 00:15:39,960 want to own and have as much control of that. 289 00:15:39,960 --> 00:15:42,720 Why would I want some? I mean, obviously I'll, I'll be 290 00:15:42,720 --> 00:15:45,600 open to all channels, but why would I want someone else to 291 00:15:45,600 --> 00:15:47,920 have that data on, on my business? 292 00:15:47,920 --> 00:15:51,040 So that makes so much sense because I I, yeah, I remember. 293 00:15:51,120 --> 00:15:53,400 The point on data is important, one, because what we haven't 294 00:15:53,400 --> 00:15:56,680 talked about in the case of conversational commerce, imagine 295 00:15:56,680 --> 00:16:00,760 if you're a fashion retailer and you offer that experience and a 296 00:16:00,760 --> 00:16:05,000 woman comes and types in. I'm going to a wedding in Greece 297 00:16:05,280 --> 00:16:08,120 in June. Can you suggest an outfit? 298 00:16:08,720 --> 00:16:09,760 Yeah, of. Course. 299 00:16:09,760 --> 00:16:15,080 And now as a marketer, I now know it's, you know, it's early 300 00:16:15,080 --> 00:16:17,720 May and next month you're going to Greece in June. 301 00:16:18,440 --> 00:16:22,200 I've got some real context to start marketing to you about. 302 00:16:22,400 --> 00:16:25,120 Well, here are some great items you should buy for sightseeing 303 00:16:25,120 --> 00:16:27,240 while you're in Greece. And have you thought about a 304 00:16:27,280 --> 00:16:29,680 swimsuit for the beach in addition to the out that we 305 00:16:29,680 --> 00:16:31,680 recommended for the wedding you're going to be attending? 306 00:16:31,680 --> 00:16:34,800 So you could start to see how the data capture of 307 00:16:34,800 --> 00:16:38,480 conversational commerce is very strategic for retailers because 308 00:16:38,480 --> 00:16:42,520 I'm getting more context about you as a shopper, I could hand 309 00:16:42,520 --> 00:16:46,000 that back to your customer data profile so I can use that in the 310 00:16:46,000 --> 00:16:48,800 future. So it's really opening up this 311 00:16:48,800 --> 00:16:51,760 opportunity for true personalization that we haven't 312 00:16:51,760 --> 00:16:54,280 seen before. And I think that's another 313 00:16:54,280 --> 00:16:56,400 unlocked. It's not just the the the 314 00:16:56,400 --> 00:16:58,920 existing customer experience that we're going to see, but 315 00:16:58,920 --> 00:17:01,400 it's this incredible data capture that's going to come out 316 00:17:01,400 --> 00:17:03,880 of it. Yeah, and it makes so much 317 00:17:03,880 --> 00:17:06,200 sense. What do you see as the blocker? 318 00:17:06,200 --> 00:17:08,040 What's the barrier? If you were going to do a CHEAT 319 00:17:08,040 --> 00:17:11,520 SHEET, you know what? What's holding people back? 320 00:17:12,599 --> 00:17:15,359 I think there's a few things. I think 1 is I talked about, we 321 00:17:15,359 --> 00:17:19,800 talked about data is like how do I start to think about my data 322 00:17:19,800 --> 00:17:22,440 strategically? And, and it was a this, this 323 00:17:22,440 --> 00:17:25,280 isn't the CI OS problem. This is the CEO and the 324 00:17:25,280 --> 00:17:28,480 executive leadership team is a retailer's data is very 325 00:17:28,480 --> 00:17:31,480 strategic because it's incredible about their customer, 326 00:17:31,480 --> 00:17:33,280 about their products. It's unique to them. 327 00:17:33,280 --> 00:17:36,160 So there's that. There's the second part is how 328 00:17:36,160 --> 00:17:41,080 do I start to get off and running with with a use case? 329 00:17:41,080 --> 00:17:43,360 And part of that is assigning some business value. 330 00:17:43,360 --> 00:17:46,400 So we do spend a lot of time with our customers helping them 331 00:17:46,400 --> 00:17:49,520 prioritise use cases around business values. 332 00:17:49,520 --> 00:17:52,640 They know which ones that are going to, you know, offer the 333 00:17:52,640 --> 00:17:56,000 most cost savings or revenue uplift, whatever the KPIs are 334 00:17:56,000 --> 00:17:58,800 they're trying to drive. So I think that's the second 1/3 335 00:17:58,800 --> 00:18:02,360 is organizationally, you know, it's these are going to change 336 00:18:02,360 --> 00:18:05,400 the way we work, right, right. And if you, if you've had a 337 00:18:05,400 --> 00:18:08,520 chance to use a Co pilot everyday, I know I do in my job, 338 00:18:08,560 --> 00:18:12,440 I'd have to change the way I work everyday because I now have 339 00:18:12,440 --> 00:18:16,480 a Co pilot that I can go to first to ask questions and 340 00:18:16,520 --> 00:18:20,160 summarise my emails and tell me what I missed when I was out 341 00:18:20,160 --> 00:18:23,400 last week on vacation. That's very different way of 342 00:18:23,400 --> 00:18:25,760 working than I used to. So yeah, it's going to change 343 00:18:25,760 --> 00:18:28,960 the way teams work together. It's going to change the way 344 00:18:28,960 --> 00:18:32,480 functions work. So I think that kind of cultural 345 00:18:32,480 --> 00:18:35,000 change and organisational change is also another one. 346 00:18:37,440 --> 00:18:40,760 It touches on in terms of you were talking about this bimodal 347 00:18:40,760 --> 00:18:45,480 or, you know, trimodal in terms of voice, text and image. 348 00:18:45,480 --> 00:18:47,600 Yeah. In terms of when you look at the 349 00:18:47,600 --> 00:18:51,720 future then I don't know if you talk about quantum computing and 350 00:18:51,720 --> 00:18:56,680 the chip, but forgetting that for one second, practically when 351 00:18:56,680 --> 00:19:00,160 you then think about the future of retail, it feels like you're 352 00:19:00,160 --> 00:19:05,680 describing this sort of multi sensory e-commerce world, right. 353 00:19:05,680 --> 00:19:06,920 Is that, is that where we're going? 354 00:19:07,800 --> 00:19:12,560 But I think it's a world where shoppers are finally going to 355 00:19:12,560 --> 00:19:16,560 get the level of service in stores that they that they 356 00:19:16,560 --> 00:19:20,000 crave. And I think as frontline workers 357 00:19:20,000 --> 00:19:23,040 are finally going to get the data that they want at the 358 00:19:23,040 --> 00:19:26,040 moment that matters. It's going to help their jobs be 359 00:19:26,040 --> 00:19:27,480 more enjoyable. It's going to make them feel 360 00:19:27,480 --> 00:19:29,320 more confident when they work with customers. 361 00:19:29,320 --> 00:19:32,840 So I think we're going to start to really see those things come 362 00:19:32,840 --> 00:19:36,320 together in ways that keep associates happy in their jobs 363 00:19:36,320 --> 00:19:38,040 that make cut. Edwards and associates. 364 00:19:38,280 --> 00:19:40,920 You think today, when a customer and a sales associate has a 365 00:19:40,920 --> 00:19:45,240 conversation that is dark data, it goes away and there's some 366 00:19:45,240 --> 00:19:47,480 really valuable information in there. 367 00:19:47,480 --> 00:19:50,720 So I think in the future, when you think about conversational 368 00:19:50,720 --> 00:19:54,160 commerce capabilities, we'll capture that data. 369 00:19:54,160 --> 00:19:57,040 And finally, retailers will be able to deliver on that promise 370 00:19:57,040 --> 00:20:00,120 of personalization that they've been really going after for 371 00:20:00,120 --> 00:20:02,360 years. So I'm excited because I think 372 00:20:02,360 --> 00:20:06,000 as a shopper, our experiences are going to be elevated. 373 00:20:06,440 --> 00:20:09,480 It's just going to happen. As a retailer, our ability to 374 00:20:09,480 --> 00:20:13,000 learn more about you and use that with your permission to 375 00:20:13,000 --> 00:20:17,200 make your future shopping experiences better is finally 376 00:20:17,200 --> 00:20:20,160 going to happen. And, and that frontline worker, 377 00:20:20,160 --> 00:20:23,160 you know, who's been so underserved for so many years as 378 00:20:23,160 --> 00:20:26,800 a persona is going to be happy doing their jobs because they're 379 00:20:26,800 --> 00:20:30,240 going to have these incredible digital tools in their hands or 380 00:20:30,240 --> 00:20:33,320 in their ear that are going to bring that information that they 381 00:20:33,320 --> 00:20:36,520 need and just help them get through their day, just like we 382 00:20:36,520 --> 00:20:40,440 are every day in the office. So I, I'm excited by, you know, 383 00:20:40,640 --> 00:20:44,680 about how it really is going to raise the ceiling for everybody, 384 00:20:44,680 --> 00:20:46,960 but also raise your floor for everybody. 385 00:20:46,960 --> 00:20:48,200 So I think that's really exciting. 386 00:20:48,760 --> 00:20:51,040 Do you think I mean you, you touched on a really important 387 00:20:51,040 --> 00:20:53,480 point and I don't know how many read because just going back to 388 00:20:53,480 --> 00:20:57,800 the perplexity or, or search, so many, I mean, when Alexa came 389 00:20:57,800 --> 00:21:00,480 out, people were like, oh, this is fantastic until they, and a 390 00:21:00,480 --> 00:21:04,160 couple of US retailers, large ones realised that all of those 391 00:21:04,160 --> 00:21:08,560 search terms invoice, they had no idea. 392 00:21:08,720 --> 00:21:12,960 They, they had absolutely 0 idea what people were searching for. 393 00:21:13,240 --> 00:21:16,360 And it feels like people or retailers haven't woke up to the 394 00:21:16,360 --> 00:21:20,160 fact that, yes, there's lots of great platforms out there, but 395 00:21:20,160 --> 00:21:22,480 that data isn't yours. You're not going to be able to 396 00:21:22,560 --> 00:21:26,440 get context on that. And, and I remember from years 397 00:21:26,440 --> 00:21:29,600 gone by, Microsoft was really sort of it's your data, you, 398 00:21:29,680 --> 00:21:31,840 you, you get access to all of that. 399 00:21:32,040 --> 00:21:33,720 We don't do anything. We just give you the cable. 400 00:21:33,840 --> 00:21:36,560 Is that still the case? Is that like because it's such a 401 00:21:36,600 --> 00:21:38,600 fundamental point? Yeah, definitely. 402 00:21:38,600 --> 00:21:43,960 Like we're not training our models at 1 customer and taking 403 00:21:43,960 --> 00:21:47,440 them to another. I always say each instance of AI 404 00:21:47,440 --> 00:21:50,640 is kind of like your own child that you're raising with your 405 00:21:50,640 --> 00:21:51,280 data. Got you. 406 00:21:51,680 --> 00:21:53,720 And that stays? But it's a really important 407 00:21:53,760 --> 00:21:55,960 point. I think it's so important. 408 00:21:56,280 --> 00:22:00,840 Yeah, we, we definitely obviously having like, like 409 00:22:00,840 --> 00:22:03,680 retailers must have a trusting relationship with their 410 00:22:03,920 --> 00:22:06,080 customers. We need to do the save. 411 00:22:06,080 --> 00:22:10,360 So when we talk about our AI that runs in Azure, that is 412 00:22:10,360 --> 00:22:13,160 being utilised by retailers to build conversational commerce 413 00:22:13,160 --> 00:22:16,840 experiences or build frontline worker experiences, that is all 414 00:22:16,840 --> 00:22:20,440 trained on the retailer's data. That is their unique first party 415 00:22:20,440 --> 00:22:24,280 strategic advantage. So obviously that stays there. 416 00:22:24,280 --> 00:22:28,720 So every platform is unique. Just like today when a customer 417 00:22:28,720 --> 00:22:32,200 runs on Azure that that data is not commingled with any other 418 00:22:32,360 --> 00:22:36,000 customers that run on Azure. It's very walled off that way. 419 00:22:36,280 --> 00:22:39,400 It's critical. Yeah, no, OK, that's, it's good 420 00:22:39,400 --> 00:22:42,120 to hear because I think that's that's something that people are 421 00:22:42,120 --> 00:22:45,520 sleepwalking into, if I'm honest, you know, well on that 422 00:22:45,520 --> 00:22:46,920 point. Though I would say what is 423 00:22:46,920 --> 00:22:50,680 really becoming critical security, because if you think 424 00:22:50,680 --> 00:22:54,640 about democratising data and putting it into the hands of 425 00:22:54,640 --> 00:22:57,440 folks that might not normally have it, but you're doing it 426 00:22:57,440 --> 00:23:00,280 because it's going to bring value, it's going to save money 427 00:23:00,280 --> 00:23:03,680 and time, it's going to allow for new experiences. 428 00:23:04,080 --> 00:23:08,640 How you secure that data is now more critical than ever because 429 00:23:08,640 --> 00:23:11,600 obviously we know when when there's a trust break between a 430 00:23:11,600 --> 00:23:15,480 data breach at a retailer takes many years for trust to be built 431 00:23:15,480 --> 00:23:18,400 back. So we always talk a lot about 432 00:23:18,400 --> 00:23:22,280 security as well, or I always say get us your chief, get your 433 00:23:22,280 --> 00:23:26,080 your chief information security officer involved early and often 434 00:23:26,120 --> 00:23:29,760 when you're doing AI use cases because you want to make sure 435 00:23:29,760 --> 00:23:33,080 that everything you do is still within the guide. 436 00:23:33,120 --> 00:23:36,680 The guise of like secure and responsible AI, which is the 437 00:23:36,680 --> 00:23:40,680 other bookend of that is using this capability in a responsible 438 00:23:40,680 --> 00:23:43,760 way that make it your customers feel confident it doesn't 439 00:23:43,760 --> 00:23:48,040 violate privacy laws. So all of that is in critical to 440 00:23:48,040 --> 00:23:50,960 all of these use cases as well. It's foundational, honestly. 441 00:23:51,720 --> 00:23:54,520 I think, well, I mean, look, we've had so many public cases 442 00:23:54,800 --> 00:23:58,560 recently in the UK with two major retailers having outages. 443 00:23:58,840 --> 00:24:03,120 I mean, there's, there's it's like who hasn't had a security 444 00:24:03,120 --> 00:24:06,120 or cyber event? I get, I guess it gets 445 00:24:06,120 --> 00:24:09,040 overlooked because people sort of almost think that the Ciso's 446 00:24:09,040 --> 00:24:13,120 taking care of it where, you know, it's a culture thing as 447 00:24:13,120 --> 00:24:14,880 well, right? You need everyone on the same 448 00:24:14,880 --> 00:24:17,080 page. Yeah, but in this world of 449 00:24:17,080 --> 00:24:21,040 privacy, GDPR and everything else, there's a whole another, 450 00:24:21,080 --> 00:24:25,520 you know, how are you protecting the critical data that you're 451 00:24:25,520 --> 00:24:27,440 gathering on, on your on your customers? 452 00:24:27,440 --> 00:24:31,560 No one wants to know that their their AI data set has been 453 00:24:31,560 --> 00:24:34,080 stolen. I think that's that's another 454 00:24:34,080 --> 00:24:38,720 level from your credit card. But again, that's like we're 455 00:24:38,720 --> 00:24:41,320 getting into I think that's a little bit further down the line 456 00:24:41,320 --> 00:24:44,800 that people, people need to be aware of it in, in terms of 457 00:24:45,360 --> 00:24:48,840 where you are going to be focused on for the rest of the 458 00:24:48,840 --> 00:24:50,960 year. Is that due? 459 00:24:50,960 --> 00:24:53,280 Because obviously Microsoft again operates in, you know, 460 00:24:53,280 --> 00:24:56,280 four or five different product areas, right? 461 00:24:56,520 --> 00:25:01,400 Cloud security teams, what a workforce in playing what, what 462 00:25:01,400 --> 00:25:02,880 are the things that you're going to be like? 463 00:25:02,880 --> 00:25:06,160 How is the rest of the 2025 going to be for you? 464 00:25:06,840 --> 00:25:10,960 I'd say we're seeing this adoption of a Gentek AI mean 465 00:25:10,960 --> 00:25:15,640 this is going to, as I said earlier is helping our customers 466 00:25:16,320 --> 00:25:19,720 think about natural places to adopt. 467 00:25:19,720 --> 00:25:22,800 And I think, you know, marketing content creation is a great 468 00:25:22,800 --> 00:25:24,720 example. I talked about that earlier. 469 00:25:25,000 --> 00:25:28,120 You know, I think about business analysts, the merchandising and 470 00:25:28,120 --> 00:25:30,520 the, and the inventory management sides of the world. 471 00:25:30,520 --> 00:25:34,120 I remember, you know, my days at Gap, every Monday morning, our 472 00:25:34,120 --> 00:25:37,320 inventory folks would come in, our merchants would come in, 473 00:25:37,320 --> 00:25:40,840 They'd spent 4 hours kind of diagnosing the business. 474 00:25:41,480 --> 00:25:44,440 And that was about 20 people doing that, right? 475 00:25:44,440 --> 00:25:48,920 So there was like 80 hours, 80 human hours every Monday 476 00:25:49,240 --> 00:25:52,720 diagnosing the business. And then in the afternoon, my 477 00:25:52,720 --> 00:25:55,600 leadership team and I, there's five of us, we would decide what 478 00:25:55,600 --> 00:25:57,880 we were going to do about the business in the course of an 479 00:25:57,880 --> 00:25:59,720 hour. And I think about the imbalance, 480 00:25:59,720 --> 00:26:04,760 80 hours spending, diagnosing 5 hours spending making strategic 481 00:26:04,760 --> 00:26:08,600 business decisions. That's an imbalance, right? 482 00:26:08,600 --> 00:26:12,160 We we need to unlock more of that 80 and shift it to more 483 00:26:12,160 --> 00:26:15,240 strategic thinking. And that is where a gentek is 484 00:26:15,240 --> 00:26:18,800 going to make a real impact. You know, maybe those eight out 485 00:26:19,040 --> 00:26:22,400 those eight, you know, 80 hours become 20 hours because over the 486 00:26:22,400 --> 00:26:25,280 weekend agents have been diagnosing the business. 487 00:26:25,280 --> 00:26:28,320 So when when the merchandising team comes in and Monday morning 488 00:26:28,800 --> 00:26:33,040 the insights are ready, some recommendations are already teed 489 00:26:33,040 --> 00:26:38,240 up and maybe even in the future of autonomous, the first draught 490 00:26:38,240 --> 00:26:41,040 of the marketing brief has already been written on the 491 00:26:41,040 --> 00:26:44,280 promo that needs to run as a result of last week's business. 492 00:26:44,280 --> 00:26:48,800 Like imagine that happening and being kind of ready on the 493 00:26:48,800 --> 00:26:52,920 desktop Monday morning. And that is really game changing 494 00:26:52,920 --> 00:26:54,840 when you think about running your, your, your retail 495 00:26:54,840 --> 00:26:56,960 business. Yeah, I mean, you, you, you 496 00:26:56,960 --> 00:27:01,200 touch on a this isn't, this isn't going to be a self 497 00:27:01,200 --> 00:27:05,040 promotion about a book, But I, I like many people, I've written a 498 00:27:05,040 --> 00:27:08,160 book about the future of retail, but I talk about the store being 499 00:27:08,160 --> 00:27:11,680 your signal generator, right? So all of the signals that the 500 00:27:11,680 --> 00:27:15,320 store's generating that you then need to respond to, whether 501 00:27:15,320 --> 00:27:20,200 that's a, a TikTok trend that has been, you know, going up and 502 00:27:20,200 --> 00:27:22,720 up and up. And if you don't tap into it or 503 00:27:22,720 --> 00:27:26,040 it's a Netflix movie that has, I don't know, blown people away 504 00:27:26,040 --> 00:27:28,080 and people want that jumper in that colour. 505 00:27:28,440 --> 00:27:32,400 Those signals are currently, you know, again, we're going to that 506 00:27:32,400 --> 00:27:35,400 people are trying to sort of chip away at those types of 507 00:27:35,400 --> 00:27:36,400 problems. Yep. 508 00:27:36,400 --> 00:27:39,280 But the store is a is this is a data centre. 509 00:27:39,480 --> 00:27:41,800 There's petrol bytes of data that's going to come out of that 510 00:27:41,800 --> 00:27:44,280 store and and how do you tap into it? 511 00:27:44,520 --> 00:27:49,560 So I think the signal story is is brilliant because it's also a 512 00:27:49,560 --> 00:27:54,280 data problem, right? So if you know they're right, 513 00:27:54,360 --> 00:27:58,280 traditional store signal sits in rows and columns, right? 514 00:27:58,720 --> 00:28:01,240 That's it. A TikTok video is an 515 00:28:01,240 --> 00:28:03,920 unstructured data source. An Instagram feed is an 516 00:28:03,920 --> 00:28:07,240 unstructured data source. So something that's trending on 517 00:28:07,240 --> 00:28:11,040 a social platform is mostly unstructured data. 518 00:28:11,360 --> 00:28:14,200 So really it's how do you take both? 519 00:28:14,200 --> 00:28:17,200 How do you take the traditional structured data from the store? 520 00:28:18,080 --> 00:28:23,160 And by the way, you multiply that by miscue count, by region, 521 00:28:23,160 --> 00:28:27,960 by store, right, by how many countries you operate in, plus 522 00:28:28,000 --> 00:28:31,360 all the unstructured data. And how do you take all of that 523 00:28:31,360 --> 00:28:35,160 signal and turn that into some recommendations on what you 524 00:28:35,160 --> 00:28:38,400 should do next, whether that's what you should be buying next 525 00:28:38,400 --> 00:28:41,760 year because there's a trend that's emerging that you want to 526 00:28:41,760 --> 00:28:45,840 make sure you capture on or whether it's responding to out 527 00:28:45,840 --> 00:28:49,920 of stocks in a location based on a significant change in weather 528 00:28:50,600 --> 00:28:54,960 and all of that signal and resiliency is where generative 529 00:28:54,960 --> 00:28:57,360 AI, a gentic AI is going to play a role. 530 00:28:57,360 --> 00:29:00,000 It's it's you just can't throw enough humans at it. 531 00:29:00,320 --> 00:29:04,120 It's just impossible. It's costly, it's slow, it's 532 00:29:04,320 --> 00:29:07,600 filled with bias. Like this is where I think we're 533 00:29:07,600 --> 00:29:10,360 going to see such that this really step function changes. 534 00:29:10,600 --> 00:29:14,560 Retailers will finally be able to kind of take all that data 535 00:29:14,560 --> 00:29:17,600 signal back in even from your commerce, like all that data 536 00:29:17,600 --> 00:29:21,200 exhaust that comes from all the clicks and the movement and 537 00:29:21,200 --> 00:29:26,560 really make sense of it in real time and start to act on it in 538 00:29:26,560 --> 00:29:28,480 real time. And that's going to be really 539 00:29:28,480 --> 00:29:32,040 incredible to watch. Where, where, where is is mine? 540 00:29:32,040 --> 00:29:34,360 Where's Mike? I know Microsoft has the biggest 541 00:29:34,360 --> 00:29:38,840 presence at NRF, which is also, I don't know are you anywhere 542 00:29:38,840 --> 00:29:41,600 else throughout the year is Microsoft, are you at any other 543 00:29:41,600 --> 00:29:43,280 events or is there anywhere else people? 544 00:29:43,280 --> 00:29:45,360 Could still definitely it's one. More plan have a. 545 00:29:45,920 --> 00:29:49,600 So retail NRF in New York is definitely a big one. 546 00:29:49,600 --> 00:29:53,120 NRF Asia, we have presence there in June, say cool. 547 00:29:53,120 --> 00:29:56,160 Those who are going to be there with definitely be there 548 00:29:56,160 --> 00:29:57,480 talking. Stage Yeah. 549 00:29:59,080 --> 00:30:02,400 And then we also on the consumer goods side of the business, we 550 00:30:02,400 --> 00:30:05,080 go to the consumer goods forum every year and present. 551 00:30:05,480 --> 00:30:08,720 And then this year, I'll be at camp, there's a lot of our 552 00:30:08,720 --> 00:30:12,240 customers, marketers go to Can and we have some events planned 553 00:30:12,240 --> 00:30:16,720 around that, specifically around how generated by Agentic AI is 554 00:30:16,720 --> 00:30:18,840 transforming the marketing room of the house. 555 00:30:19,200 --> 00:30:21,920 That's fantastic. Well, Keith, thank you so much 556 00:30:21,920 --> 00:30:24,320 for giving up some time to speak to us. 557 00:30:24,320 --> 00:30:26,200 It's probably early in the morning for you there, right? 558 00:30:27,200 --> 00:30:28,240 It's not so bad. It's not so bad. 559 00:30:28,600 --> 00:30:30,360 That's wonderful. Not bad at all. 560 00:30:30,440 --> 00:30:33,120 And thank you Alex. It's just a pleasure always to 561 00:30:33,120 --> 00:30:35,560 chat with you. We've I've always liked chatting 562 00:30:35,560 --> 00:30:37,000 with you. We've never had a problem 563 00:30:37,000 --> 00:30:39,680 talking. So in six months, let's do it 564 00:30:39,680 --> 00:30:43,440 again because a lot will change as we know and we'll we can give 565 00:30:43,440 --> 00:30:45,960 an update. Well, hopefully, maybe maybe 566 00:30:46,360 --> 00:30:49,640 joking aside at NRF 2026 with. Less 2. 567 00:30:50,160 --> 00:30:51,920 Yeah, wonderful. Thank you.