1 00:00:00,440 --> 00:00:03,520 I think AI is going to be a capability, it's going to be an 2 00:00:03,520 --> 00:00:07,360 enabler that is going to be embedded in everything you do. 3 00:00:18,480 --> 00:00:20,840 Hello and welcome to the Retail Podcast. 4 00:00:21,360 --> 00:00:25,600 We are now into the post NRF retail podcast guests that I 5 00:00:25,600 --> 00:00:29,960 couldn't get together with at NRF just due to the sheer volume 6 00:00:29,960 --> 00:00:32,119 for for for the guests as well as myself. 7 00:00:32,439 --> 00:00:36,480 Jane Chung is one of such guests the from the IBM Institute. 8 00:00:36,880 --> 00:00:41,000 Sorry, the I'll start. Well, actually we could keep it 9 00:00:41,040 --> 00:00:45,520 from IBM, the institute business value part of our game, which is 10 00:00:45,520 --> 00:00:48,600 ultimately the thought leadership and the research and 11 00:00:48,600 --> 00:00:53,080 they had some research they had a quick look at I think it came 12 00:00:53,080 --> 00:00:55,920 out it was just before or during NRF. 13 00:00:55,920 --> 00:00:59,400 But rather than me butcher all of the details about the 14 00:00:59,400 --> 00:01:04,560 research, Jane, what give us an give us a view of what you do 15 00:01:04,560 --> 00:01:09,600 within IBM or the IBV and then what is the research that we're 16 00:01:09,600 --> 00:01:11,440 going to be talking about? Sure. 17 00:01:11,440 --> 00:01:12,720 Thank you. Thank you, Alex. 18 00:01:12,720 --> 00:01:15,600 It's a pleasure to be here. You know, like you said, it's 19 00:01:15,600 --> 00:01:19,040 post NRF, although it feels like long ago, but it's only a couple 20 00:01:19,040 --> 00:01:21,960 weeks ago. So thank you for inviting me to 21 00:01:21,960 --> 00:01:27,440 have a chat about the research. My role within the Institute of 22 00:01:27,440 --> 00:01:32,280 Business Value, which is part of IBM, is to work with the key 23 00:01:32,280 --> 00:01:35,440 stakeholder within IBM to determine the research agenda 24 00:01:35,520 --> 00:01:38,520 every year. So every other year we work on a 25 00:01:38,560 --> 00:01:41,600 global consumer research study and then every and then the 26 00:01:41,600 --> 00:01:45,840 other every other year we work on executive research study. 27 00:01:46,120 --> 00:01:53,000 So I launched the latest IBB consumer research study a few 28 00:01:53,000 --> 00:01:55,520 days before NRF, so on January 8th. 29 00:01:55,520 --> 00:01:59,840 And no surprise is, is about AI, but it's, it's a research based 30 00:01:59,840 --> 00:02:03,960 on five that based on 5000 executive who, who are 31 00:02:03,960 --> 00:02:08,400 responsible of AI initiative. So we do have a screening 32 00:02:08,400 --> 00:02:10,440 process for people who participate. 33 00:02:10,520 --> 00:02:15,040 We want to make sure that the executive, the leaders of our 34 00:02:15,040 --> 00:02:19,600 participants, not only are they knowledgeable, but they actually 35 00:02:19,600 --> 00:02:22,280 are responsible of AI initiative. 36 00:02:22,280 --> 00:02:27,720 This year's study is very broad. We talked to we interviewed 37 00:02:28,320 --> 00:02:31,440 executive across 13 different area, Alex. 38 00:02:31,440 --> 00:02:35,120 So is from the front end of the house, which is customer 39 00:02:35,120 --> 00:02:40,160 experience stores to product design, product development, 40 00:02:40,160 --> 00:02:44,280 merchandising, inventory management to supply chain 41 00:02:44,280 --> 00:02:48,560 operation and then also the corporate function like HR, 42 00:02:48,600 --> 00:02:52,400 finance and IT. So very rich study. 43 00:02:52,720 --> 00:02:56,840 One of the challenge is so much data, but to actually drive 44 00:02:57,120 --> 00:03:01,240 insight that really matters. So that's, that's what, that's 45 00:03:01,240 --> 00:03:05,400 what the study is is about. I'm already fascinated. 46 00:03:05,600 --> 00:03:08,000 First question, It's not one that I send you. 47 00:03:08,160 --> 00:03:10,240 What's the one? Yeah, let's go. 48 00:03:10,520 --> 00:03:12,840 What's the one? Because I, I, I mean, what's the 49 00:03:12,840 --> 00:03:16,160 one thing that surprised you? Because you've got a fantastic 50 00:03:16,240 --> 00:03:20,480 pool of people to, to, to have data and you've got data coming 51 00:03:20,480 --> 00:03:23,480 from every angle. What, what was the one thing 52 00:03:23,480 --> 00:03:26,480 that you thought, oh wow, what's the oh wow piece of data? 53 00:03:26,480 --> 00:03:30,040 It's. So I've I worked in retail for 54 00:03:30,040 --> 00:03:32,920 30 years. So between in the industry as a 55 00:03:32,920 --> 00:03:35,640 consultant and now with the doing research. 56 00:03:35,880 --> 00:03:40,320 What surprised me the most with this study is how uniform and 57 00:03:40,320 --> 00:03:44,520 we're talking about data and I love data, how uniform the data 58 00:03:44,760 --> 00:03:50,720 are across all area. So for example, you know, I 59 00:03:50,720 --> 00:03:53,640 started working in retail in the 90s. 60 00:03:53,920 --> 00:03:58,240 I remember Y2K. So there is the E com wave, 61 00:03:58,240 --> 00:04:00,400 right? So it's the E com and then E com 62 00:04:00,400 --> 00:04:03,680 is really about the front, the selling of it, of the, of the 63 00:04:03,680 --> 00:04:06,720 products, right? And then there's the mobile 64 00:04:06,720 --> 00:04:10,280 commerce, social commerce. So then the anchor is always 65 00:04:10,280 --> 00:04:12,720 around selling the product, not that the back end is not 66 00:04:12,720 --> 00:04:14,520 important, not that operation is not important. 67 00:04:14,960 --> 00:04:18,640 And then you know, COVID hit the world and then also by supply 68 00:04:18,640 --> 00:04:20,920 chain disruption. So then the supply chain 69 00:04:20,920 --> 00:04:25,920 inventory become really, really critical and everybody become 70 00:04:25,920 --> 00:04:27,960 more, you have to be digital, right? 71 00:04:27,960 --> 00:04:31,200 And I think that just shifted everybody's behaviour. 72 00:04:31,480 --> 00:04:34,480 So then it's OK. How do you engage with consumer 73 00:04:34,680 --> 00:04:37,600 and how do you get the product? This always seems like a 74 00:04:37,600 --> 00:04:41,440 dominant area where we need to focus on this year. 75 00:04:41,640 --> 00:04:43,520 I keep going back to look at the data. 76 00:04:43,520 --> 00:04:46,760 Initially it's like, am I seeing the right number, big number 77 00:04:46,760 --> 00:04:50,520 across all areas. So, you know, if you look at the 78 00:04:50,520 --> 00:04:54,800 report, we're talking about organisation that are expanding 79 00:04:54,800 --> 00:05:02,120 the use of AI and across 14 area almost the same unison like 80 00:05:02,400 --> 00:05:06,240 right, right. So very big number between 80 to 81 00:05:06,240 --> 00:05:09,440 90%. That means everybody is 82 00:05:09,440 --> 00:05:12,440 expanding the use of AI. And then it's like, OK, what do 83 00:05:12,440 --> 00:05:15,400 you oh, you know, where's the focus, where's the trend? 84 00:05:16,040 --> 00:05:19,800 So that was the big surprise initially when I just look at 85 00:05:19,800 --> 00:05:23,440 the number, well, when I think about it, does that make sense? 86 00:05:23,680 --> 00:05:25,760 Yeah. So that's quite interesting then 87 00:05:25,920 --> 00:05:30,600 is that mean? So it has it moved away from AI, 88 00:05:30,920 --> 00:05:37,320 evolve it's evolving role in retail as a tool or because 89 00:05:37,320 --> 00:05:40,400 obviously first you do some experimentation, then you look 90 00:05:40,400 --> 00:05:43,640 at your business process and you know, how do I optimise the 91 00:05:43,640 --> 00:05:48,400 business process and put AI as part of that optimization. 92 00:05:48,680 --> 00:05:51,600 So what do you, what's the research showing? 93 00:05:51,680 --> 00:05:55,120 Are people actually integrating AI? 94 00:05:55,560 --> 00:05:59,720 What is the evolving role of AI? That's a very good question, 95 00:05:59,720 --> 00:06:02,400 Alex. So if you think about it, if 96 00:06:02,400 --> 00:06:04,920 just look at it and that helps us like at the end, we took a 97 00:06:04,920 --> 00:06:08,640 step back and, and we, we decided on the title, which is 98 00:06:08,640 --> 00:06:14,040 embedding AI in your brand's DNAAI is going to be embedded in 99 00:06:14,040 --> 00:06:16,960 everything you do. It's almost like, it's almost 100 00:06:16,960 --> 00:06:20,360 like the Internet without even knowing it, you will be using 101 00:06:20,440 --> 00:06:22,080 AI. The reason why I said that is 102 00:06:22,080 --> 00:06:25,960 because not only our company consciously making investments. 103 00:06:25,960 --> 00:06:28,240 So if you look at the budget, we talk about the budget, right? 104 00:06:28,480 --> 00:06:33,320 AI as a budget as a best part of the IT budget as part of the 105 00:06:33,320 --> 00:06:36,480 technology tool. What we're seeing is the growth 106 00:06:36,480 --> 00:06:41,040 of AI spend actually is higher in the line of business. 107 00:06:41,480 --> 00:06:46,080 So, so then I talked to a lot of our leaders who are working with 108 00:06:46,080 --> 00:06:49,040 our client. We have partners like Microsoft 109 00:06:49,040 --> 00:06:52,680 is a partner of ours, Adobe is a partner of ours and our 110 00:06:52,680 --> 00:06:57,120 partners, all these solution are have AI embedded in it. 111 00:06:57,120 --> 00:07:01,080 So whether you know it or not, you're going to be using AI, 112 00:07:01,120 --> 00:07:03,760 right? So, so AI is going to be 113 00:07:03,760 --> 00:07:07,480 embedded in everything we do, just like everyone knows how to 114 00:07:07,480 --> 00:07:08,840 Google. If you want to find something, 115 00:07:08,880 --> 00:07:13,040 it's going to be Google. But here now is you will be able 116 00:07:13,040 --> 00:07:15,720 to get richer in like a Gemini, right? 117 00:07:15,880 --> 00:07:19,040 Like you will be able to get richer information. 118 00:07:19,040 --> 00:07:23,880 It's almost like it has expanded your search capability and it 119 00:07:23,880 --> 00:07:27,720 has offered your capability using ChatGPT. 120 00:07:27,880 --> 00:07:31,400 Students are using it, right, in summarising knowledge, in 121 00:07:31,400 --> 00:07:34,880 augmenting knowledge. So I think AI is going to be a 122 00:07:34,880 --> 00:07:37,960 capability. It's going to be an enabler that 123 00:07:38,080 --> 00:07:40,160 is going to be embedded in everything you do. 124 00:07:40,480 --> 00:07:43,040 Yeah, I, I want 100% agree with that. 125 00:07:43,040 --> 00:07:46,440 And, and in the sense that most of the SAS providers have now 126 00:07:46,440 --> 00:07:51,160 got an AI bot as the interface at once or they've got one or 127 00:07:51,160 --> 00:07:55,000 they're building 1 or there's one in, in beta that basically 128 00:07:55,000 --> 00:07:58,160 you don't go to the to the software anymore, You go to the 129 00:07:58,160 --> 00:08:00,960 agent. And I think even ChatGPT has 130 00:08:00,960 --> 00:08:03,160 released an agent. What's it called? 131 00:08:03,360 --> 00:08:05,560 Automation, I can't remember. It was only released a couple of 132 00:08:05,560 --> 00:08:10,040 days ago that basically it goes off as an agent and helps you in 133 00:08:10,040 --> 00:08:13,720 the real world, I don't know, buy flowers and, and stuff like 134 00:08:13,720 --> 00:08:15,640 that. So I, I can definitely see that 135 00:08:15,880 --> 00:08:21,720 when you look at your research for retailers and in what areas, 136 00:08:21,880 --> 00:08:24,160 because everyone, you know, I think the, the statistics are 137 00:08:24,160 --> 00:08:28,400 52% projected increase in spending AI. 138 00:08:28,480 --> 00:08:31,800 What are those applications retailers are thinking about? 139 00:08:31,960 --> 00:08:35,039 I think this links to the first question that you said that 140 00:08:35,039 --> 00:08:37,200 surprised you that they're all the same. 141 00:08:37,280 --> 00:08:38,640 What? But what parts of the business 142 00:08:38,640 --> 00:08:41,039 are they? Yeah, so that's a very good 143 00:08:41,039 --> 00:08:46,560 question because after I got I, I was preparing to talk to you. 144 00:08:46,880 --> 00:08:52,240 I actually went to look at the details and it's very uniform. 145 00:08:53,000 --> 00:08:56,360 What I mean by that is, you know, on and on average, when 146 00:08:56,360 --> 00:08:59,160 you look at the aggregate of average, you look at the AI 147 00:08:59,160 --> 00:09:02,200 spend outside of IT was 52% increase. 148 00:09:02,920 --> 00:09:07,280 When I look through the 13 different area, the percentage 149 00:09:07,280 --> 00:09:13,800 is within 5%, meaning it's about 45% in some area, like in some 150 00:09:13,800 --> 00:09:19,320 area is a little bit over 52%. So the the deviation of this is 151 00:09:19,480 --> 00:09:21,880 is very close. So it's it's a little bit hard 152 00:09:22,280 --> 00:09:28,040 to say which area is is going to be more important in in a in AI 153 00:09:28,040 --> 00:09:30,240 application. Very. 154 00:09:30,280 --> 00:09:32,400 Yeah. So you didn't get that because 155 00:09:32,400 --> 00:09:34,120 again, you were saying, you know, whether it goes into 156 00:09:34,120 --> 00:09:40,760 supply chain or marketing or customer experience, you don't 157 00:09:40,760 --> 00:09:44,440 have a sense of which one will get the lion's share of 158 00:09:44,440 --> 00:09:48,000 investment from your research. No, no. 159 00:09:48,160 --> 00:09:51,840 And, and one of the one of you know, you think about the data 160 00:09:51,840 --> 00:09:55,600 and one of we always provide an, an action guide, right? 161 00:09:55,960 --> 00:09:59,640 So one of the important, important action guy that 162 00:09:59,640 --> 00:10:03,520 retailers, brands and retailers, vertical retailers too, they 163 00:10:03,520 --> 00:10:07,920 need to decide what is the most important priority because 164 00:10:08,040 --> 00:10:10,840 obviously AI is a capability that can do everything. 165 00:10:11,280 --> 00:10:13,880 Yeah. So what's your core, what's 166 00:10:13,880 --> 00:10:15,960 what's the most important for you? 167 00:10:16,160 --> 00:10:20,840 And you also need to balance the investment versus return of 168 00:10:20,840 --> 00:10:23,760 investment, right. So you really need to, I think 169 00:10:23,760 --> 00:10:26,120 it's highly adaptable, it's a good way. 170 00:10:26,120 --> 00:10:30,040 But then you also need to think about the way can it give you 171 00:10:30,040 --> 00:10:34,840 the most in ROI? And then what's your brand DNA? 172 00:10:34,960 --> 00:10:38,720 So for example, I used to work for Nike and Nike is all about 173 00:10:38,720 --> 00:10:41,200 performance. So any product that they 174 00:10:41,200 --> 00:10:44,680 produce, it has to enhance the athlete performance. 175 00:10:44,680 --> 00:10:48,280 So if you know that, then guess what product is very important. 176 00:10:48,640 --> 00:10:50,960 Even though they even though they have stores, right? 177 00:10:50,960 --> 00:10:54,160 They have flagship. So when I work at 90, Nike in 178 00:10:54,160 --> 00:11:00,120 the 90s Nike store was very small percentage of of the total 179 00:11:00,120 --> 00:11:02,520 business. But then the store has evolved, 180 00:11:02,520 --> 00:11:06,240 right? So for them the, the product 181 00:11:06,360 --> 00:11:08,040 performance is the most important. 182 00:11:08,040 --> 00:11:09,840 So that's where you want to honed in. 183 00:11:10,440 --> 00:11:12,680 If you look at the company like Starbucks, right? 184 00:11:12,920 --> 00:11:15,920 So what's up? Obviously coffee, a Starbucks is 185 00:11:15,920 --> 00:11:19,760 trying to create a community, but they somehow lost that, lost 186 00:11:19,760 --> 00:11:22,040 that direction a little bit, right. 187 00:11:22,200 --> 00:11:26,760 So if, if, if the, the coffee experience and you want to bring 188 00:11:26,760 --> 00:11:31,080 people in, then you need to focus on how do you create that 189 00:11:31,080 --> 00:11:34,360 local community, How do you create that coffee experience 190 00:11:34,640 --> 00:11:38,480 that are unique for you? So it, I think in some ways 191 00:11:38,600 --> 00:11:41,840 brands and retailers have to go back to the fundamental, what 192 00:11:41,840 --> 00:11:44,480 business are you in and what are you known for? 193 00:11:44,720 --> 00:11:48,560 That's where you need to use AI. In the meantime, we talked about 194 00:11:48,960 --> 00:11:51,400 this is a marathon with a Sprint. 195 00:11:51,680 --> 00:11:54,520 Yeah, right. You need to have the strategy in 196 00:11:54,520 --> 00:11:58,000 who you want to be, but then you also need to have Sprint 197 00:11:58,000 --> 00:12:01,440 investment to get you to to your endpoint. 198 00:12:01,440 --> 00:12:04,320 So I'd be so. Sorry, go on. 199 00:12:04,720 --> 00:12:06,000 I hope you'd answer your question. 200 00:12:06,000 --> 00:12:09,280 I know. Does the research then go in go 201 00:12:09,280 --> 00:12:13,920 into, for example, a workforce transition in upskilling them 202 00:12:14,160 --> 00:12:19,160 for AI skills? Did you get any data on what 203 00:12:19,160 --> 00:12:22,960 that looks like in terms of then the number of organisations that 204 00:12:22,960 --> 00:12:26,680 are looking at upskilling or how they get the workforce ready for 205 00:12:26,800 --> 00:12:30,520 AI or AI related AI related skills? 206 00:12:31,040 --> 00:12:37,080 So IBV, we have specific workforce study that that 207 00:12:37,160 --> 00:12:39,920 actually get into the how do you do that? 208 00:12:39,920 --> 00:12:43,640 In my study, I wanted to find out where the responsibility, 209 00:12:43,640 --> 00:12:46,840 who's training, meaning who's driving the training. 210 00:12:47,240 --> 00:12:50,600 Because for me, I'm more on the business side, growing up in 211 00:12:50,600 --> 00:12:54,640 retail. For me, business has to has to 212 00:12:54,640 --> 00:12:58,880 determine the agenda because if if the application that I'm 213 00:12:58,880 --> 00:13:01,960 using day-to-day have AI, yes, you're going to have new way to 214 00:13:01,960 --> 00:13:04,560 learn, you have a new way of doing things. 215 00:13:04,800 --> 00:13:07,600 But ultimately I'm the one, right, Because the study also 216 00:13:07,600 --> 00:13:09,960 talked about most of the activities going to be 217 00:13:09,960 --> 00:13:12,720 augmented. That means the people in the 218 00:13:12,720 --> 00:13:16,640 role will be augmented by AI. So it's, it's a little bit 219 00:13:16,640 --> 00:13:18,520 tricky. It's not like automation where 220 00:13:18,520 --> 00:13:20,040 things going to be automated for you. 221 00:13:20,240 --> 00:13:23,600 You're going to have to decide, you know, let's say generative 222 00:13:23,600 --> 00:13:26,480 AI. The most valuable capability of 223 00:13:26,480 --> 00:13:30,280 generative AI is to augment knowledge is to summarise 224 00:13:30,400 --> 00:13:34,560 information for you. So I think the business need to 225 00:13:34,560 --> 00:13:39,040 drive, but then HR has to manage the culture change, right, 226 00:13:39,120 --> 00:13:40,760 because it's a change in process. 227 00:13:41,080 --> 00:13:45,520 And then it actually have have to have a say in it because it 228 00:13:45,520 --> 00:13:48,960 is ultimately a technology tool. You think about AI governance 229 00:13:49,040 --> 00:13:54,600 AI, you know, AI data is, is. So that's why in my study, in 230 00:13:54,600 --> 00:13:57,920 this study that I've, I've LED, we, we look at who's 231 00:13:57,920 --> 00:14:01,000 responsible. And very interestingly, there is 232 00:14:01,000 --> 00:14:05,440 AAI centre of excellence, AI centre of competency. 233 00:14:05,600 --> 00:14:08,680 And, and that's pretty high rated. 234 00:14:08,680 --> 00:14:12,840 So the interpreting when we interpreted the data of that is 235 00:14:13,120 --> 00:14:15,800 OK. So organisation are thinking 236 00:14:15,800 --> 00:14:20,360 about they need to have an AI centre that monitor all the 237 00:14:20,360 --> 00:14:26,360 training, but then ITHR and line of business will almost have an 238 00:14:26,360 --> 00:14:28,480 even number of seats at the table. 239 00:14:28,640 --> 00:14:33,320 So from my study, it looks like where they are in this journey 240 00:14:33,320 --> 00:14:36,440 is still pretty early. People have a concept of doing 241 00:14:36,440 --> 00:14:39,520 that. People have identified who's 242 00:14:39,520 --> 00:14:42,240 going to be organising it, who's going to be providing the 243 00:14:42,240 --> 00:14:46,240 content, who's going to be actually executing it, but it's 244 00:14:46,240 --> 00:14:48,880 not set in stone yet, meaning people are still trying to 245 00:14:48,880 --> 00:14:51,520 figure out. Yeah, I think that your research 246 00:14:51,520 --> 00:14:56,120 said that 87% of executives had some form of AI governance 247 00:14:56,120 --> 00:15:01,520 framework that fewer than 25% fully had implemented them. 248 00:15:01,960 --> 00:15:04,640 Yes. And is that why is that? 249 00:15:04,640 --> 00:15:08,320 Because it's a good thing to do, but really difficult to to? 250 00:15:08,480 --> 00:15:10,160 It's a good thing to say that you're doing, but really 251 00:15:10,160 --> 00:15:11,360 difficult to implement? What? 252 00:15:11,760 --> 00:15:13,160 What? Why is the discrepancy? 253 00:15:13,520 --> 00:15:16,360 Yes, so great question. Again, Alex, you asked a lot of 254 00:15:16,360 --> 00:15:19,640 great questions. You're very kind. 255 00:15:22,360 --> 00:15:27,520 So we look at AI governance together with the barrier to 256 00:15:27,520 --> 00:15:29,880 progress. So we ask question about, you 257 00:15:29,880 --> 00:15:32,240 know, so where are you with an AI journey? 258 00:15:32,240 --> 00:15:36,240 Where do you see as the biggest obstacle to progress, to really 259 00:15:36,240 --> 00:15:42,280 scale and to really transform AI and privacy, explainability, 260 00:15:42,280 --> 00:15:46,960 transparency, all these are on top of mind of business leader. 261 00:15:47,320 --> 00:15:51,760 And then we connect that insight with, OK, so are you thinking 262 00:15:51,760 --> 00:15:54,320 about it to find that many people are thinking about it, 263 00:15:54,520 --> 00:15:57,920 They have actually done work to define the roles and 264 00:15:57,920 --> 00:16:03,200 responsibility to, to establish governance, but they are still a 265 00:16:03,200 --> 00:16:06,760 little bit lagging in getting the tool, but they are following 266 00:16:06,760 --> 00:16:09,840 the same steps, right? So first you need to assess what 267 00:16:09,840 --> 00:16:14,600 you have, then you need to be be clear about, you know, how do 268 00:16:14,600 --> 00:16:17,920 you establish roles and responsibility along come the 269 00:16:17,920 --> 00:16:21,000 governance right now is to actually doing it. 270 00:16:21,000 --> 00:16:26,840 So how we look at the data and interpreted the data is that a 271 00:16:26,840 --> 00:16:32,160 lot in a very high percentage, 8090% are doing something, 272 00:16:32,160 --> 00:16:34,920 they're thinking about it, they're establishing it, but now 273 00:16:35,000 --> 00:16:38,440 they are now have to move into the doing phase, right. 274 00:16:38,720 --> 00:16:42,400 So if you think about or for IBM, there is a Watson 275 00:16:42,400 --> 00:16:44,680 governance. So you need a framework, a 276 00:16:44,680 --> 00:16:49,720 platform to help you monitor, to help you purge bias and to help 277 00:16:49,720 --> 00:16:52,920 you assess your risk on an ongoing basis. 278 00:16:52,920 --> 00:16:56,560 This is this is the the challenge that pretty much 279 00:16:56,560 --> 00:16:59,920 everybody again, Alex, I'm telling you, this is the first 280 00:16:59,960 --> 00:17:05,280 time I see the data moving together so closely with each 281 00:17:05,280 --> 00:17:08,480 other. And and we cover we cover all 282 00:17:08,480 --> 00:17:11,400 the continents. We have the UK, we have the US, 283 00:17:11,400 --> 00:17:14,359 we have Japan, we have China, we have Southeast Asia with 284 00:17:14,359 --> 00:17:18,520 Australia, we have Germany, we have, you know, Switzerland. 285 00:17:18,760 --> 00:17:23,200 So very interesting norm we're seeing here. 286 00:17:23,720 --> 00:17:26,520 Yeah, I, I. Mean my, my, my take away from 287 00:17:26,520 --> 00:17:31,840 that is the, the, the fact that the, the skills needed are 288 00:17:31,840 --> 00:17:37,680 embedded in, let's say 3 or 4 areas, supply chain, consumer 289 00:17:37,680 --> 00:17:40,640 marketing, you know, data and analysts. 290 00:17:40,880 --> 00:17:44,560 And that's probably why I think people can't go off the beats 291 00:17:44,560 --> 00:17:48,120 and track because there's not that many off the beaten track 292 00:17:48,120 --> 00:17:50,240 type data outcomes that you can get. 293 00:17:50,520 --> 00:17:55,160 And plus how many data silos exist within businesses and how 294 00:17:55,240 --> 00:17:58,640 unstructured data, you know, there's so many different areas 295 00:17:58,640 --> 00:18:02,040 that you need good quality data to be able to get that AI 296 00:18:02,040 --> 00:18:05,840 outcome that doesn't exist. And so therefore, probably you 297 00:18:05,840 --> 00:18:11,320 have data or all retailers have data, yes, have data in HR. 298 00:18:11,600 --> 00:18:15,680 And that's, I can see why the real, the real leaders out there 299 00:18:15,880 --> 00:18:19,560 will be thinking in that way. How do I get this new data? 300 00:18:19,720 --> 00:18:22,880 What do I do to drive that? Three quick questions to sort of 301 00:18:22,880 --> 00:18:26,800 close off with as you've been, as you, this is your baby, this 302 00:18:26,800 --> 00:18:30,320 is your research. When you are presenting this to 303 00:18:30,320 --> 00:18:34,880 executives, what do you find? Where do they go to with this 304 00:18:35,480 --> 00:18:37,680 research? What's been the common theme 305 00:18:37,680 --> 00:18:39,920 from the people receiving what you're telling them? 306 00:18:39,920 --> 00:18:42,840 Is there like one thing that people keep coming back to and 307 00:18:42,840 --> 00:18:44,400 asking you about? Yes, so. 308 00:18:44,400 --> 00:18:50,280 People wants details was it was just those right? 309 00:18:50,480 --> 00:18:52,480 I was just. It depends on the area. 310 00:18:52,480 --> 00:18:57,440 So I presented the study to a group of, of marketers, you 311 00:18:57,440 --> 00:18:59,720 know, more of the front end of the house. 312 00:19:00,080 --> 00:19:02,920 And what they really want is they're not surprised at the 313 00:19:02,920 --> 00:19:05,960 data, but they want to understand what's behind the 314 00:19:05,960 --> 00:19:07,720 marketing and customer experience. 315 00:19:07,720 --> 00:19:11,760 They want to know because the study that I, you know, I LED, 316 00:19:12,040 --> 00:19:16,200 we have data. So for every area we have 5 key, 317 00:19:16,360 --> 00:19:20,520 5 or 6 key activities. So people want the data and it's 318 00:19:20,520 --> 00:19:23,200 great because then I can give them the follow up because it's 319 00:19:23,200 --> 00:19:25,960 impossible to cram everything in there. 320 00:19:26,320 --> 00:19:30,200 Absolutely right. So I get so number one, I get a 321 00:19:30,200 --> 00:19:32,560 lot of head knots. So that's always good. 322 00:19:33,440 --> 00:19:37,240 People raise their eyebrows to see the magnitude, not just 323 00:19:37,240 --> 00:19:41,800 their area, but all areas. The second thing they asked for 324 00:19:41,800 --> 00:19:45,720 is do you have the underlying data for this aggregated data? 325 00:19:45,760 --> 00:19:47,840 So there's a lot of follow up from there. 326 00:19:48,240 --> 00:19:53,440 And then the third is really, how do they interpret this data? 327 00:19:53,720 --> 00:19:56,240 How do they share it with their team, right? 328 00:19:56,480 --> 00:20:00,600 Because many times when I meet, I meet with the, the business 329 00:20:00,600 --> 00:20:03,440 leader. So a lot of time they say, well, 330 00:20:03,440 --> 00:20:06,200 you know, how do I, how, where do I start? 331 00:20:06,960 --> 00:20:10,920 Question with, you know, you asked initially is there's 332 00:20:10,920 --> 00:20:14,000 there's so much so where, where, where do I start and that's 333 00:20:14,000 --> 00:20:18,360 where the action guys becomes important and I often use it as 334 00:20:18,360 --> 00:20:21,200 a way to close is you have to start somewhere. 335 00:20:22,120 --> 00:20:27,440 For example, within IBM we have, but we have our own tool. 336 00:20:27,440 --> 00:20:30,240 So I say make it fun. Make it fun for your employees. 337 00:20:30,240 --> 00:20:33,040 We have competition. You want innovation, you drive 338 00:20:33,040 --> 00:20:36,080 innovation with people that are currently working in the job. 339 00:20:36,320 --> 00:20:39,720 If you know that their job is going to be augmented, in what 340 00:20:39,720 --> 00:20:44,160 way can this tool really help them do their job to give you 341 00:20:44,160 --> 00:20:46,800 better results as a as a product designer? 342 00:20:47,040 --> 00:20:49,480 People are talented. They want to design product 343 00:20:49,480 --> 00:20:51,760 that's of good quality. You know, that's what they be 344 00:20:51,760 --> 00:20:53,640 measured, right? But instead what happened is 345 00:20:53,640 --> 00:20:55,960 people are going through Excel spreadsheet, right? 346 00:20:56,040 --> 00:20:58,560 I used to be a merchandiser, I used to be a planner. 347 00:20:58,640 --> 00:21:02,200 You know what, open to buy monthly, month, weekly, Monday 348 00:21:02,200 --> 00:21:06,000 meeting really take up a lot of my time because I'm working with 349 00:21:06,000 --> 00:21:08,360 Excel spreadsheets. If you're in supply chain, 350 00:21:08,520 --> 00:21:12,800 right, oftentimes planners are frustrated because everybody's 351 00:21:12,800 --> 00:21:15,040 in the reactive mode because you can't be proactive because 352 00:21:15,040 --> 00:21:18,040 there's just not enough time between the close of the week to 353 00:21:18,040 --> 00:21:20,920 the morning you show up and you have Monday review meeting. 354 00:21:21,240 --> 00:21:24,880 So I say start somewhere and start with your employee to get 355 00:21:24,880 --> 00:21:30,400 ideas in how to drive innovation to better serve the area that 356 00:21:30,400 --> 00:21:32,040 you serve. So chances are you have very 357 00:21:32,040 --> 00:21:34,160 good employee, they're very good at what they do, but they're 358 00:21:34,160 --> 00:21:38,920 being bogged down right now. I mean, to be fair, that sounds 359 00:21:38,920 --> 00:21:42,640 like a, a lovely place to, to sort of end because that's like 360 00:21:42,640 --> 00:21:46,040 the, the nugget of wisdom that you would give to the executives 361 00:21:46,320 --> 00:21:50,880 receiving the, the thing my, my own curiosity in terms of data 362 00:21:50,880 --> 00:21:53,080 points, because you're probably a, you love data. 363 00:21:53,320 --> 00:21:56,280 What I think I mean, it could be the, the, the data points I've 364 00:21:56,280 --> 00:21:59,480 already mentioned in terms of 52% projected increase in AI 365 00:21:59,480 --> 00:22:03,920 spend or 82% planned increase in AIG and integrated business 366 00:22:04,160 --> 00:22:06,440 models. What's the one data? 367 00:22:06,440 --> 00:22:10,360 The one data outnumber that you like in your report? 368 00:22:10,360 --> 00:22:12,080 What's the one that you keep going back to? 369 00:22:12,120 --> 00:22:15,720 I love your. Question, I would say that for 370 00:22:15,720 --> 00:22:22,320 me, I as excited to see AI as an enabler to drive growth instead 371 00:22:22,320 --> 00:22:24,520 of cutting costs, Save, yeah. Got you. 372 00:22:24,920 --> 00:22:26,520 Yeah, Yeah. So so. 373 00:22:27,320 --> 00:22:29,200 Yeah, right. So, you know, if you read the 374 00:22:29,200 --> 00:22:32,880 report, there are some stats about because we, we ask because 375 00:22:32,880 --> 00:22:34,680 I've been doing this for a few years. 376 00:22:34,880 --> 00:22:38,960 So we do have longitudinal analysis and we last question 377 00:22:38,960 --> 00:22:41,440 every year for a five year horizon. 378 00:22:41,680 --> 00:22:46,040 So where do you see AI contribute as a driver to save 379 00:22:46,040 --> 00:22:48,960 costs? And then how do you see AI 380 00:22:48,960 --> 00:22:53,320 contribute to revenue growth? And I was really, really excited 381 00:22:53,360 --> 00:22:57,320 this year to see when we asked last year was where do they say 382 00:22:57,320 --> 00:23:00,520 they see AI as a contribution to revenue growth. 383 00:23:00,800 --> 00:23:04,200 And we asked them 20 last year, this year and the next year, 384 00:23:04,200 --> 00:23:07,920 right, Yeah. And what I see is the percentage 385 00:23:08,640 --> 00:23:13,240 of AI contribution to revenue growth is going to crossover. 386 00:23:13,240 --> 00:23:17,000 It's going to crossover, meaning it's going to go higher in the 387 00:23:17,000 --> 00:23:21,160 next in the next three years. So I know we have to save costs 388 00:23:21,200 --> 00:23:24,160 definitely, you know, otherwise, you know, where's the margin 389 00:23:24,160 --> 00:23:27,000 going to come from? But do understand that AI is a 390 00:23:27,000 --> 00:23:30,800 tool that help you improve productivity gain right now. 391 00:23:30,880 --> 00:23:35,760 Once you're ready to scale, it has the opportunity to drive 392 00:23:35,800 --> 00:23:38,560 revenue growth. That's the one thing that get me 393 00:23:38,560 --> 00:23:42,640 really excited is, is like, other than looking at 394 00:23:42,640 --> 00:23:45,960 productivity gain, saving cost, be more efficient. 395 00:23:46,200 --> 00:23:50,160 This is a transformational tool that can help you try growth. 396 00:23:50,560 --> 00:23:53,400 That's and is there is. There one question that you 397 00:23:53,400 --> 00:23:55,560 people are missing when they look at the data. 398 00:23:55,880 --> 00:23:58,120 Is there like something that you think you you, you were 399 00:23:58,120 --> 00:24:00,800 expecting people to ask you about, but they don't ask you 400 00:24:00,800 --> 00:24:03,040 about? Just again, from having done 401 00:24:03,040 --> 00:24:06,360 this before, are there things that people are fundamentally 402 00:24:06,360 --> 00:24:08,880 missing in their approach to AII guess? 403 00:24:09,360 --> 00:24:11,600 I think that's the last question that you asked me. 404 00:24:12,160 --> 00:24:15,320 That's the question that people don't don't really ask people. 405 00:24:15,440 --> 00:24:18,520 I think, I think people are in an interesting place right now 406 00:24:18,520 --> 00:24:22,280 because 2024 is the year where people experiment, right. 407 00:24:22,680 --> 00:24:27,640 A lot of whether you are in it, HR, customer service, everybody, 408 00:24:27,640 --> 00:24:30,120 whether you're a store, everybody is experimenting it. 409 00:24:30,200 --> 00:24:33,080 And then this year is how do you scale it? 410 00:24:33,200 --> 00:24:36,920 When you look at scaling, you need investment, Yeah, right. 411 00:24:36,920 --> 00:24:40,160 You need dollar investment. And we see the investment in it. 412 00:24:40,200 --> 00:24:42,560 You know, people are dedicated in it, but people want to know 413 00:24:42,560 --> 00:24:44,440 where's the return? Yeah, yeah. 414 00:24:44,680 --> 00:24:49,760 And I think, I think I mean, I when I work in retail also very, 415 00:24:49,800 --> 00:24:54,600 very, very often we think about technology tool to help with 416 00:24:54,600 --> 00:24:57,040 efficiency. That's where we see the margin 417 00:24:57,040 --> 00:24:58,520 game, right? So you look at price, you look 418 00:24:58,520 --> 00:25:03,520 at, but I think AI has has the opportunity to, to turn the 419 00:25:03,520 --> 00:25:06,760 table around. Think about, don't think about 420 00:25:06,760 --> 00:25:11,240 how do you do markdown better think about how do you get your 421 00:25:11,240 --> 00:25:15,800 product right, so you can sell it at full price because because 422 00:25:15,800 --> 00:25:19,400 people are willing to pay for it, whether it's because the 423 00:25:19,400 --> 00:25:23,240 quality is there, whether because it's more convenient for 424 00:25:23,240 --> 00:25:26,560 them, whether it's because it aligned with their value. 425 00:25:26,560 --> 00:25:30,520 You know, if if you if you know their brand purpose, right, 426 00:25:30,520 --> 00:25:31,960 whether it's to support the purpose. 427 00:25:32,400 --> 00:25:37,440 I think AI has the opportunity for brands and retailer to 428 00:25:37,440 --> 00:25:41,640 rethink how how do they get because how and to build 429 00:25:41,640 --> 00:25:45,120 customer loyalty. Yeah, you know what I mean. 430 00:25:45,120 --> 00:25:47,600 So I I. Think I think they're the rabbit 431 00:25:47,600 --> 00:25:51,760 holes they, they can depending on who within the business is 432 00:25:51,760 --> 00:25:55,840 looking at it. So if it's marketing or product 433 00:25:56,080 --> 00:26:01,200 in the sense of personalization, am I personalising on, you know, 434 00:26:01,400 --> 00:26:05,960 discount and and whatever am I personalising to maximise the 435 00:26:06,360 --> 00:26:08,440 recommended retail price that we're putting out there? 436 00:26:08,440 --> 00:26:10,360 Right. Well, and I don't know, there's 437 00:26:10,360 --> 00:26:14,080 a lot of, you know, I've heard people say people are just 438 00:26:14,080 --> 00:26:16,960 giving discounts where they don't need to be discounting, 439 00:26:17,200 --> 00:26:20,360 they're marking down where they don't need to be marking down. 440 00:26:20,800 --> 00:26:24,160 But I guess data has been always lacking to do that effectively, 441 00:26:24,640 --> 00:26:25,200 right? So. 442 00:26:25,200 --> 00:26:28,880 So a retailer that I love and I'm a loyal customer. 443 00:26:28,960 --> 00:26:31,360 I mean because, because I used to live in the Pacific 444 00:26:31,360 --> 00:26:33,200 Northwest. Is REI OK? 445 00:26:33,600 --> 00:26:37,960 If I think about REI as as a retailer, if you can provide 446 00:26:37,960 --> 00:26:43,280 them services, if you, if you can leverage data to extend your 447 00:26:43,280 --> 00:26:46,240 services. Let's say I got into biking, I 448 00:26:46,240 --> 00:26:49,280 got into cycling, but I'm useless in putting a bike 449 00:26:49,280 --> 00:26:52,640 together. It's actually more complicated 450 00:26:52,640 --> 00:26:55,080 than I thought and actually needed help. 451 00:26:55,320 --> 00:27:01,080 So if I purchased a bike like a fat tyre bike, then you know, a 452 00:27:01,080 --> 00:27:05,080 purse a person may not have to in may not have enough time, 453 00:27:05,080 --> 00:27:08,120 like the store associates may not have enough time to look at 454 00:27:08,120 --> 00:27:11,840 my profile to see I'm a beginner and I live in the Pacific 455 00:27:11,840 --> 00:27:16,000 Northwest and I would love to have the opportunity to engage 456 00:27:16,000 --> 00:27:18,440 in activities with my local community, right? 457 00:27:18,600 --> 00:27:22,680 If you have all this in your, you know, the store associates 458 00:27:22,760 --> 00:27:26,440 all that at the fingertip. Not only can they help me with 459 00:27:26,440 --> 00:27:31,200 my bike, they can offer me how to enrich my experience. 460 00:27:31,440 --> 00:27:34,640 There's a better chance that I would just keep going back for 461 00:27:34,640 --> 00:27:37,840 my gear because, you know, I need a helmet, I need hands, you 462 00:27:37,840 --> 00:27:42,200 know what I mean? In a way you sort of expand the 463 00:27:42,200 --> 00:27:47,000 experience, your product experience because your store 464 00:27:47,000 --> 00:27:50,760 associate or the customer service person who's helping you 465 00:27:50,960 --> 00:27:53,040 have all this information available. 466 00:27:53,480 --> 00:27:57,560 By the way, just to just to end with like a number that 467 00:27:57,560 --> 00:28:00,760 personal. I responds with follow up action 468 00:28:00,760 --> 00:28:04,640 in customer service. The investment of AI is going to 469 00:28:04,640 --> 00:28:10,320 double this year because everybody you know is have 470 00:28:10,320 --> 00:28:12,480 virtual assistant, they have a chat box and help you. 471 00:28:12,720 --> 00:28:16,880 But now 2025 is the year where they think about how do you 472 00:28:16,880 --> 00:28:20,520 personalise it, not just for promotion, right, Personalise 473 00:28:20,520 --> 00:28:26,440 and follow up action. So think beyond pricing, think 474 00:28:26,440 --> 00:28:29,760 about services. That's one of the the high stat, 475 00:28:29,760 --> 00:28:35,960 which is 89% of executive are expecting AI to innovate product 476 00:28:35,960 --> 00:28:38,200 and services. And this is just one simple 477 00:28:38,200 --> 00:28:40,440 example. You have all the data, you have 478 00:28:40,440 --> 00:28:42,600 my purchasing history, you know where I live. 479 00:28:42,960 --> 00:28:48,360 But it's a matter of connecting data to create insight, to make 480 00:28:48,360 --> 00:28:51,400 it matters, to make an impact. Yeah. 481 00:28:51,560 --> 00:28:53,720 I think that's a wonderful. I mean, obviously I can keep 482 00:28:53,720 --> 00:28:55,920 going. I've got 100 questions I can ask 483 00:28:55,920 --> 00:28:57,840 you. But Jane, thank you so much for 484 00:28:57,840 --> 00:29:00,520 sharing those insights with us. I'm really grateful. 485 00:29:00,880 --> 00:29:02,920 Thank you for. Having me and have a great day. 486 00:29:02,920 --> 00:29:03,400 Thank you.