1 00:00:04,160 --> 00:00:07,880 Speaker 1: Since the dawn of civilization, there have always been places 2 00:00:07,920 --> 00:00:12,120 Speaker 1: and markets where people gather to purchase essentials and luxuries. 3 00:00:12,880 --> 00:00:16,800 Speaker 1: The earliest bazaars are believed to have originated in ancient Persia, 4 00:00:16,880 --> 00:00:19,200 Speaker 1: and the first shopping mall is widely considered to be 5 00:00:19,200 --> 00:00:21,919 Speaker 1: a Trajan's market, which housed more than one hundred and 6 00:00:21,960 --> 00:00:25,279 Speaker 1: fifty shops and was built around one hundred AD, not 7 00:00:25,440 --> 00:00:29,360 Speaker 1: far from the Roman Colosseum. Of course, shopping has evolved 8 00:00:29,400 --> 00:00:32,239 Speaker 1: a lot since the days of ancient Rome, but the 9 00:00:32,240 --> 00:00:36,120 Speaker 1: appeal of visiting your favorite store has remained unchanged over time. 10 00:00:36,760 --> 00:00:39,800 Speaker 1: And despite the ways that online retail has transformed the 11 00:00:39,800 --> 00:00:43,440 Speaker 1: way we shop, new technology has revitalized the brick and 12 00:00:43,479 --> 00:00:48,000 Speaker 1: mortar shopping experience, with retailers eager to create unique spaces 13 00:00:48,080 --> 00:00:51,400 Speaker 1: where they can connect customers with products in person and 14 00:00:51,440 --> 00:00:55,000 Speaker 1: reduce product theft. But what does the future hold for 15 00:00:55,040 --> 00:00:58,560 Speaker 1: the shopping experience as we witness the rejuvenation of in 16 00:00:58,640 --> 00:01:02,360 Speaker 1: person retail, and what can retailers do to create a 17 00:01:02,400 --> 00:01:08,800 Speaker 1: more engaging and exciting experience for customers. Welcome to Technically Speak, 18 00:01:09,040 --> 00:01:13,720 Speaker 1: an Intel podcast produced by iHeartMedia's Ruby Studio in partnership 19 00:01:13,720 --> 00:01:17,920 Speaker 1: with Intel In every episode, we explore how AI innovations 20 00:01:18,040 --> 00:01:22,720 Speaker 1: are changing the world and revolutionizing the way we live. Hey, 21 00:01:22,760 --> 00:01:25,959 Speaker 1: then I'm grand class. Regular listeners know that in the 22 00:01:26,040 --> 00:01:29,959 Speaker 1: past we've covered topics like how AI impacts healthcare and 23 00:01:30,080 --> 00:01:33,000 Speaker 1: urban planning. This season, but now we're headed into the 24 00:01:33,000 --> 00:01:36,640 Speaker 1: world of retail, where online director consumer brands are seeking 25 00:01:36,680 --> 00:01:40,920 Speaker 1: innovative spaces to connect customers with products in person, and 26 00:01:40,959 --> 00:01:44,840 Speaker 1: retailers are eager to remain competitive by creating a frictionless 27 00:01:44,959 --> 00:01:49,040 Speaker 1: customer experience. We wanted to understand how technology is helping 28 00:01:49,120 --> 00:01:54,280 Speaker 1: to improve the retail space, making shopping easier, seamless, and exciting. 29 00:01:54,920 --> 00:01:57,560 Speaker 1: In this episode, we will focus on what shopping looks 30 00:01:57,640 --> 00:02:01,120 Speaker 1: like with an AI enhanced self service check out. Just 31 00:02:01,200 --> 00:02:04,760 Speaker 1: imagine a life without barcodes and smart technology that helps 32 00:02:04,840 --> 00:02:10,120 Speaker 1: retailers attract and retain consumers. Today, we'll continue our journey 33 00:02:10,120 --> 00:02:13,239 Speaker 1: of discovery into Intel's innovative ideas for the future and 34 00:02:13,400 --> 00:02:17,320 Speaker 1: how they will impact something as steadfast as retail. But 35 00:02:17,440 --> 00:02:22,320 Speaker 1: before we go any further, let's welcome our guests. Joining 36 00:02:22,360 --> 00:02:25,320 Speaker 1: us today is Shoalie's Childry, the general manager of Consumer 37 00:02:25,400 --> 00:02:28,520 Speaker 1: Industries at Intel and an expert in the intersection of 38 00:02:28,560 --> 00:02:34,160 Speaker 1: technology and the retail, banking, hospitality, and quick serve restaurant industries. 39 00:02:34,680 --> 00:02:38,120 Speaker 1: His team at Intel is responsible for delivering innovative technological 40 00:02:38,160 --> 00:02:41,960 Speaker 1: solutions and tools and building an ecosystem of partners to 41 00:02:42,040 --> 00:02:45,399 Speaker 1: scale those solutions. Welcome to the show, Shalish. 42 00:02:45,840 --> 00:02:48,000 Speaker 2: Thank you, Graham. I'm super excited to be here. 43 00:02:48,720 --> 00:02:51,680 Speaker 1: Also joining us is a Kert Denghi, the co founder 44 00:02:51,760 --> 00:02:56,160 Speaker 1: and CEO of Radius AI, a pioneer in transforming retail 45 00:02:56,200 --> 00:03:01,560 Speaker 1: operations with advanced human centric AI solutions. Acold also founded 46 00:03:01,600 --> 00:03:05,480 Speaker 1: the Internet of Things Collaboratory at Arizona State University in 47 00:03:05,520 --> 00:03:06,639 Speaker 1: twenty seventeen. 48 00:03:07,280 --> 00:03:10,280 Speaker 3: Welcome to you, too, Acold. Thank you Greg. It's a 49 00:03:10,320 --> 00:03:11,680 Speaker 3: pleasure to be part of the show. 50 00:03:15,520 --> 00:03:17,720 Speaker 1: I think we'll start with a little bit of background 51 00:03:17,800 --> 00:03:22,560 Speaker 1: on how retail has changed during the Internet era. Shelley 52 00:03:22,639 --> 00:03:25,040 Speaker 1: shall begin with you. What kind of shift have we 53 00:03:25,080 --> 00:03:27,880 Speaker 1: seen over the past two decades when it comes to 54 00:03:27,880 --> 00:03:32,280 Speaker 1: people opting for online shopping over in person shopping in 55 00:03:32,360 --> 00:03:33,440 Speaker 1: breaking mortar stores? 56 00:03:34,200 --> 00:03:35,120 Speaker 3: Thank you Graham. 57 00:03:35,280 --> 00:03:39,920 Speaker 2: Before starting about how Internet has changed retail, I like 58 00:03:40,000 --> 00:03:43,760 Speaker 2: to stab back at what I see as the core 59 00:03:43,800 --> 00:03:48,080 Speaker 2: retail experience. It's really a human experience. So from the 60 00:03:48,200 --> 00:03:52,920 Speaker 2: dawn of retail, there's something fundamental about retail that has 61 00:03:53,000 --> 00:03:56,920 Speaker 2: not changed, and that's about that experience. It's how we 62 00:03:57,200 --> 00:04:01,720 Speaker 2: feel after we have conducted the shopping, so the commerce 63 00:04:01,760 --> 00:04:05,680 Speaker 2: and the transaction that comes later. And as we have 64 00:04:05,760 --> 00:04:10,200 Speaker 2: progressed through all the developments, there have been new tools 65 00:04:10,240 --> 00:04:15,280 Speaker 2: and capabilities and technologies and those have shaped our expectations. 66 00:04:15,880 --> 00:04:18,960 Speaker 2: And that is exactly what has happened to Internet era as well. 67 00:04:19,400 --> 00:04:23,200 Speaker 2: So while we're happy shopping and tradition more Internet it 68 00:04:23,520 --> 00:04:28,120 Speaker 2: pvided a different technology which was pervasive in all aspects 69 00:04:28,160 --> 00:04:32,000 Speaker 2: of our life, and we carried those expectations in retail 70 00:04:32,080 --> 00:04:35,559 Speaker 2: as well. And for us it's just a different means 71 00:04:35,560 --> 00:04:40,160 Speaker 2: of shopping. And let's be honest, depending on our needs, 72 00:04:40,920 --> 00:04:44,560 Speaker 2: sometimes it's just way more convenient to shop online. And 73 00:04:44,600 --> 00:04:47,400 Speaker 2: it's not just about certain products that have better suit 74 00:04:47,440 --> 00:04:50,840 Speaker 2: it for online shopping. Same product at one moment, I'm 75 00:04:50,880 --> 00:04:53,880 Speaker 2: going to go buy in a physical restore, the other time, 76 00:04:53,920 --> 00:04:56,760 Speaker 2: I'm just going to go on internet. And as we've 77 00:04:56,839 --> 00:05:01,960 Speaker 2: all seen, retailers initially fail to see that. They saw 78 00:05:02,000 --> 00:05:05,360 Speaker 2: Internet just as a tool and technology that are separate. 79 00:05:05,839 --> 00:05:09,200 Speaker 2: They saw it as a competition as opposed to seeing 80 00:05:09,200 --> 00:05:13,000 Speaker 2: it as something that can enhance and augument retail experiences. 81 00:05:13,360 --> 00:05:16,960 Speaker 2: So initially a lot of us shifted to internet shopping 82 00:05:17,160 --> 00:05:20,640 Speaker 2: because that's where our needs are better met. But through 83 00:05:20,720 --> 00:05:25,240 Speaker 2: that twenty a decade, physical retailers started to see that's 84 00:05:25,279 --> 00:05:29,000 Speaker 2: not just a technology and they can just have a website, 85 00:05:29,440 --> 00:05:31,920 Speaker 2: and now they can compete with the online retailers. They 86 00:05:32,080 --> 00:05:37,400 Speaker 2: have integrated Internet into their physical operations. And now we've 87 00:05:37,600 --> 00:05:42,040 Speaker 2: also seen the retailers who started as pure prey online retailers, 88 00:05:42,080 --> 00:05:45,359 Speaker 2: they've started to open physical stores. So since then we 89 00:05:45,560 --> 00:05:49,240 Speaker 2: have reached what I call a steady state after initial 90 00:05:49,600 --> 00:05:53,120 Speaker 2: what I call the gloom and doom. I started in 91 00:05:53,240 --> 00:05:55,920 Speaker 2: retail industry in two thousand and eight, and I remember 92 00:05:56,160 --> 00:05:59,719 Speaker 2: everyone was predicting the death of physical retail, and even 93 00:05:59,760 --> 00:06:02,920 Speaker 2: then I had this conviction about this coat experience. And 94 00:06:02,960 --> 00:06:05,800 Speaker 2: then we started off at Intel. Our journey retail is 95 00:06:06,320 --> 00:06:10,039 Speaker 2: how we can bring all these great technologies and the 96 00:06:10,080 --> 00:06:14,479 Speaker 2: experience that are online, but deliver and working with retailers 97 00:06:14,520 --> 00:06:17,640 Speaker 2: and our technology partners that I could and others so 98 00:06:17,680 --> 00:06:21,560 Speaker 2: that the enhances and complements physical retail. And that's what's happening. 99 00:06:22,240 --> 00:06:24,560 Speaker 1: Yeah, and we're going to talk a little bit about 100 00:06:24,680 --> 00:06:27,839 Speaker 1: the technology side of things and particularly the role of AI. 101 00:06:28,400 --> 00:06:32,000 Speaker 1: But I would like your thoughts about the last two 102 00:06:32,040 --> 00:06:37,359 Speaker 1: decades of how we've evolved, you know, from brix and 103 00:06:37,400 --> 00:06:40,320 Speaker 1: mortar stores to online and there I think there is 104 00:06:40,320 --> 00:06:42,640 Speaker 1: probably a shift back to the brick and mortar stores. 105 00:06:43,360 --> 00:06:47,560 Speaker 3: I agree with Shilesh that we've seen exciting developments in retail. 106 00:06:47,600 --> 00:06:51,320 Speaker 3: Retail is one of the most complex human activities, and 107 00:06:51,680 --> 00:06:56,280 Speaker 3: I actually really enjoy being in retail because it's not 108 00:06:56,360 --> 00:07:00,560 Speaker 3: only human, it's also social. There is a complex web 109 00:07:00,640 --> 00:07:04,359 Speaker 3: of people who work together to create this experience. But 110 00:07:04,880 --> 00:07:09,200 Speaker 3: people missed, especially during the pandemic, they realized they missed 111 00:07:09,279 --> 00:07:13,520 Speaker 3: the social experience they missed going into a store interacting 112 00:07:13,600 --> 00:07:19,320 Speaker 3: with humans, and retailers also saw that the characteristics of 113 00:07:19,480 --> 00:07:23,920 Speaker 3: shopping in person may actually be more favorable for their 114 00:07:24,000 --> 00:07:27,880 Speaker 3: financial goals as well. People who shop in stores may 115 00:07:27,880 --> 00:07:31,600 Speaker 3: not return things as often as they do in detail, 116 00:07:32,160 --> 00:07:35,760 Speaker 3: and that is one of the biggest expenses in online shopping. 117 00:07:36,440 --> 00:07:40,280 Speaker 3: They may actually be more satisfied with their purchases because 118 00:07:40,320 --> 00:07:44,760 Speaker 3: they talked about it to store clerk and they interacted 119 00:07:44,840 --> 00:07:49,320 Speaker 3: with the other people in a store, So creating an experience, 120 00:07:49,440 --> 00:07:54,440 Speaker 3: a positive experience in the store has become more important 121 00:07:54,480 --> 00:08:00,440 Speaker 3: once again. Besides keeping the store efficient and profit fitable, 122 00:08:01,240 --> 00:08:05,960 Speaker 3: we see once again gaining speed the trend of making 123 00:08:06,080 --> 00:08:09,000 Speaker 3: customers want to go back every day. 124 00:08:11,520 --> 00:08:14,360 Speaker 1: Now you've heard Akould mention some of the social impacts 125 00:08:14,440 --> 00:08:17,080 Speaker 1: of the COVID nineteen pandemic, but I want to dig 126 00:08:17,120 --> 00:08:20,040 Speaker 1: a little deeper on its implications for the retail industry. 127 00:08:20,760 --> 00:08:23,840 Speaker 1: It's no surprise that some small retailers in your community 128 00:08:24,120 --> 00:08:27,960 Speaker 1: didn't survive the pandemic. Some of those businesses simply couldn't 129 00:08:28,000 --> 00:08:31,800 Speaker 1: pivot from their in person to online retail fast enough 130 00:08:31,840 --> 00:08:34,640 Speaker 1: to save their shop, or they didn't have the resources 131 00:08:34,640 --> 00:08:37,200 Speaker 1: to make the change at all. But the businesses that 132 00:08:37,280 --> 00:08:40,400 Speaker 1: did survive were able to invest in technology that allowed 133 00:08:40,400 --> 00:08:44,600 Speaker 1: them to make online retail easy and accessible. These companies 134 00:08:44,600 --> 00:08:46,800 Speaker 1: came out on the other side of the pandemic not 135 00:08:46,880 --> 00:08:50,840 Speaker 1: just alive, but stronger. With this in mind, I asked Akut, 136 00:08:50,840 --> 00:08:52,760 Speaker 1: what have you seen from companies that are investing in 137 00:08:52,800 --> 00:08:55,520 Speaker 1: technology to help fortify their retail business. 138 00:08:58,320 --> 00:09:04,400 Speaker 3: The pandemic changed human behavior, and we saw that impact 139 00:09:04,800 --> 00:09:09,440 Speaker 3: retailers differently and to rapidly adapt to behavioral change, you 140 00:09:09,520 --> 00:09:12,840 Speaker 3: need data and this is one of the ways that 141 00:09:12,960 --> 00:09:18,440 Speaker 3: technology help retailers. So if you have consistent, objective data 142 00:09:18,800 --> 00:09:25,200 Speaker 3: to guide your decisions, then you adapt faster, learning and 143 00:09:25,280 --> 00:09:30,839 Speaker 3: changing your processes and procedures, adapting to the market as 144 00:09:30,920 --> 00:09:34,040 Speaker 3: part of the system, as opposed to people saying now 145 00:09:34,120 --> 00:09:37,640 Speaker 3: we have to change. Change becomes part of your culture. 146 00:09:37,760 --> 00:09:41,160 Speaker 3: And as Charlie said, right now, the arrow is pointing 147 00:09:41,200 --> 00:09:46,360 Speaker 3: towards increasing the productivity of the people you have because 148 00:09:46,400 --> 00:09:49,920 Speaker 3: you cannot hire too many of them. Retailers that are 149 00:09:50,080 --> 00:09:53,400 Speaker 3: rapidly expanding right now, but one of the things they 150 00:09:53,440 --> 00:09:57,640 Speaker 3: don't want to drop is the customer experience. So you 151 00:09:57,679 --> 00:10:00,520 Speaker 3: want to automate things that are not visus to the 152 00:10:00,559 --> 00:10:04,720 Speaker 3: customer that is behind the desk, and you want to 153 00:10:04,760 --> 00:10:09,760 Speaker 3: allow the employee to personally interact with the customers so 154 00:10:09,800 --> 00:10:14,480 Speaker 3: that they get that in person experience so that they 155 00:10:14,520 --> 00:10:15,320 Speaker 3: want to come back. 156 00:10:15,920 --> 00:10:18,840 Speaker 1: Yeah, I would like to talk a little bit more 157 00:10:19,040 --> 00:10:24,040 Speaker 1: about the technology specifically, what would the customers feel, what 158 00:10:24,080 --> 00:10:27,000 Speaker 1: would they experience that would be different, whether it be price, 159 00:10:27,080 --> 00:10:31,360 Speaker 1: better information, better stock availability. What are some of the 160 00:10:31,440 --> 00:10:35,040 Speaker 1: tangible things that a person like myself just walking into 161 00:10:35,080 --> 00:10:38,680 Speaker 1: a store would actually experience. 162 00:10:38,960 --> 00:10:43,400 Speaker 2: So all of the questions you've post I think exactly 163 00:10:43,440 --> 00:10:46,720 Speaker 2: that's what the role of technology is. However, I see 164 00:10:46,760 --> 00:10:51,720 Speaker 2: the technology as something that will augment enhance existing experiences. 165 00:10:52,160 --> 00:10:55,440 Speaker 2: As an example, in all days, the only way I 166 00:10:55,480 --> 00:10:59,680 Speaker 2: would get introduced to products, new products was through stores 167 00:11:00,080 --> 00:11:03,440 Speaker 2: were the curators of the product. That is not true anymore. 168 00:11:04,280 --> 00:11:07,200 Speaker 2: We are introduced to new products through social media channels, 169 00:11:07,840 --> 00:11:11,120 Speaker 2: through all sorts of other technology. Then we go in 170 00:11:11,120 --> 00:11:13,400 Speaker 2: the stores. I'm not necessarily a lot of I'm looking 171 00:11:13,920 --> 00:11:16,480 Speaker 2: for that product in the store. I already know it. 172 00:11:17,080 --> 00:11:19,240 Speaker 2: I probably have done a lot of research as well, 173 00:11:19,880 --> 00:11:23,000 Speaker 2: so I'm not just relying on the associate to tell 174 00:11:23,040 --> 00:11:25,880 Speaker 2: me everything about too often because I'm only focused on 175 00:11:25,920 --> 00:11:28,560 Speaker 2: that one product. So I know a lot more than 176 00:11:28,880 --> 00:11:32,920 Speaker 2: an associate probably can because he or she has to 177 00:11:32,960 --> 00:11:35,840 Speaker 2: worry about thousands of fiscus in the store. So now 178 00:11:35,880 --> 00:11:39,599 Speaker 2: the focus is not so much about curating the products 179 00:11:39,600 --> 00:11:43,160 Speaker 2: more about meeting my needs there. So maybe when I 180 00:11:43,280 --> 00:11:45,600 Speaker 2: walk in the store, can you help me find the 181 00:11:45,640 --> 00:11:50,520 Speaker 2: product easily? Technology can help to make sure that they 182 00:11:50,559 --> 00:11:54,160 Speaker 2: have the right products in the store. Because now with 183 00:11:54,520 --> 00:12:00,000 Speaker 2: artificial intelligence supply chains will be way more efficient. Technology 184 00:12:00,160 --> 00:12:03,319 Speaker 2: like computer vision or RFID can help you to make 185 00:12:03,360 --> 00:12:06,920 Speaker 2: sure that you have products on the shelf. Technology can 186 00:12:07,000 --> 00:12:10,920 Speaker 2: also make sure that you are able to deliver more 187 00:12:10,960 --> 00:12:15,840 Speaker 2: personalized experience to shopper in all kinds of retail setting, 188 00:12:16,280 --> 00:12:17,520 Speaker 2: not just high end stores. 189 00:12:18,280 --> 00:12:20,880 Speaker 3: So I want to point out that there are different 190 00:12:20,920 --> 00:12:25,960 Speaker 3: types of customers and for some customers this omnichannel experience 191 00:12:26,000 --> 00:12:30,719 Speaker 3: where they enhance their experience through online knowledge is very important, 192 00:12:31,320 --> 00:12:35,560 Speaker 3: and sometimes we become a different customer ourselves at different days. 193 00:12:35,880 --> 00:12:38,560 Speaker 3: For instance, sometimes I'm in a hurry. I'm there just 194 00:12:38,600 --> 00:12:42,000 Speaker 3: to find one thing, and I really want to know 195 00:12:42,080 --> 00:12:44,720 Speaker 3: where exactly it's in the store, and I want to 196 00:12:44,800 --> 00:12:48,240 Speaker 3: just start there get it come out. There are other 197 00:12:48,360 --> 00:12:50,880 Speaker 3: times when I want to be at my leisure, I 198 00:12:50,920 --> 00:12:54,600 Speaker 3: want to discover new things. And for the retailer also, 199 00:12:55,000 --> 00:12:59,240 Speaker 3: they want to inspire their customers and lead them to 200 00:12:59,320 --> 00:13:04,000 Speaker 3: spontaneous purchases. So you want to create an experience for 201 00:13:04,040 --> 00:13:06,960 Speaker 3: those people who have time in their heads. It becomes 202 00:13:07,640 --> 00:13:11,400 Speaker 3: a challenge, of course to provide these different types of 203 00:13:11,480 --> 00:13:14,640 Speaker 3: experience all at the same time in the same store. 204 00:13:15,640 --> 00:13:20,400 Speaker 3: So given this challenge, you need to customize technology to 205 00:13:20,440 --> 00:13:23,320 Speaker 3: the different needs. I want to point out some of 206 00:13:23,320 --> 00:13:27,320 Speaker 3: the technology that you actually want to be invisible. You 207 00:13:27,400 --> 00:13:31,160 Speaker 3: put it there, it makes life easier, but the employees 208 00:13:31,600 --> 00:13:35,160 Speaker 3: or the customers don't even need to think about it. 209 00:13:35,720 --> 00:13:40,640 Speaker 3: So taking such a technology and making it disappear is 210 00:13:41,160 --> 00:13:45,280 Speaker 3: one of the goals of technology companies, and a lot 211 00:13:45,640 --> 00:13:50,120 Speaker 3: goes in the background of making that possible. Artificial intelligence 212 00:13:50,360 --> 00:13:54,720 Speaker 3: at its best is one of the empowering technologies that 213 00:13:54,840 --> 00:14:00,199 Speaker 3: makes it possible, and one of the interesting things ABOUTNOLO. 214 00:14:00,520 --> 00:14:03,679 Speaker 3: There are different ways it can become visible. For instance, 215 00:14:03,720 --> 00:14:08,199 Speaker 3: there's a lag, so you're doing something and you're expecting 216 00:14:08,240 --> 00:14:11,320 Speaker 3: your response and it takes forever for it to actually happen. 217 00:14:11,800 --> 00:14:14,000 Speaker 3: Why does that happen? Of course, as a customer, I 218 00:14:14,040 --> 00:14:17,120 Speaker 3: don't care. I'm just waiting there and it's not responding. 219 00:14:17,840 --> 00:14:21,880 Speaker 3: But from a technological point of view, that tells us, well, 220 00:14:22,040 --> 00:14:25,720 Speaker 3: maybe there's a communication link right between the store and 221 00:14:25,840 --> 00:14:28,560 Speaker 3: the cloud and that's taking a long time to go 222 00:14:28,600 --> 00:14:31,960 Speaker 3: and come back. Well, maybe I need to put things 223 00:14:32,040 --> 00:14:35,760 Speaker 3: at the edge, meaning the store, right where the customer is, 224 00:14:36,280 --> 00:14:40,280 Speaker 3: and then you can take that data that's happening. Whatever 225 00:14:40,320 --> 00:14:43,160 Speaker 3: the customer said, or the object they put on the 226 00:14:43,200 --> 00:14:49,320 Speaker 3: counter and process it, respond to it there and create 227 00:14:49,400 --> 00:14:53,280 Speaker 3: that instant experience so that it disappears because the lag 228 00:14:53,400 --> 00:14:56,200 Speaker 3: is the one that alerts to them that there's something 229 00:14:56,280 --> 00:15:00,680 Speaker 3: going on there. So that leads us into edge AI, 230 00:15:00,920 --> 00:15:05,880 Speaker 3: which is one of the things that huge industry working 231 00:15:05,920 --> 00:15:10,520 Speaker 3: all together, has been able to put together recently. It's 232 00:15:10,760 --> 00:15:15,479 Speaker 3: right now becoming feasible at actual locations. 233 00:15:18,720 --> 00:15:21,800 Speaker 1: Coming up next on Technically Speaking and Intel podcast. 234 00:15:22,720 --> 00:15:26,640 Speaker 3: Biggest advances in the last few years, and AI is 235 00:15:26,840 --> 00:15:29,040 Speaker 3: technology that learns by itself. 236 00:15:30,200 --> 00:15:32,280 Speaker 1: We'll be right back after a brief message from our 237 00:15:32,280 --> 00:15:46,520 Speaker 1: partners at Intel. Welcome back to Technically Speaking an Intel podcast. 238 00:15:46,920 --> 00:15:52,720 Speaker 1: I'm here now with Shailish Chowdhry and Akuttinghi. Chalie, I 239 00:15:52,760 --> 00:15:55,200 Speaker 1: know that you have the team there at Intel working 240 00:15:55,240 --> 00:15:58,920 Speaker 1: on these sorts of smart retail solutions at any that 241 00:15:59,080 --> 00:16:02,760 Speaker 1: you can share with us, ones that you're quite excited about. 242 00:16:03,960 --> 00:16:07,560 Speaker 2: Yes, we're actually working on a wide range of products 243 00:16:07,600 --> 00:16:11,960 Speaker 2: and like majority of them are AI. Every retailer today 244 00:16:12,000 --> 00:16:15,880 Speaker 2: is interested how they can use AI both to drive 245 00:16:16,120 --> 00:16:20,000 Speaker 2: or productivity but also to drive better experience for us 246 00:16:20,040 --> 00:16:23,920 Speaker 2: as shoppers. So I see two categories one is some 247 00:16:24,000 --> 00:16:28,200 Speaker 2: of the larger retailers, they are in the front edge 248 00:16:28,240 --> 00:16:30,880 Speaker 2: of innovation, so they are working on a lot of 249 00:16:30,920 --> 00:16:35,080 Speaker 2: what I call pilots and demonstration projects. One of the 250 00:16:35,080 --> 00:16:38,160 Speaker 2: examples at the checkout because checkout is one of the 251 00:16:38,200 --> 00:16:41,800 Speaker 2: most critical experiences. Also, we're working on the projects which 252 00:16:41,800 --> 00:16:45,520 Speaker 2: they call store automation, that has to do with better 253 00:16:45,600 --> 00:16:50,400 Speaker 2: visibility the inventory, also the improving receiving and the operations 254 00:16:50,440 --> 00:16:54,200 Speaker 2: the pack end. We're also engaged in the projects which 255 00:16:54,360 --> 00:16:57,240 Speaker 2: a lot of time gets referred to as last mile delivery, 256 00:16:57,480 --> 00:17:00,160 Speaker 2: which is a lot of times they're shopping online that 257 00:17:00,200 --> 00:17:05,160 Speaker 2: gets delivered through the physical retail and instead of opening 258 00:17:05,280 --> 00:17:08,840 Speaker 2: massive distribution centers, a lot of retailers already have stores 259 00:17:09,240 --> 00:17:12,040 Speaker 2: in all around neighborhoods and they're starting to use them 260 00:17:12,400 --> 00:17:15,600 Speaker 2: as their fulfillment centers, and they're upgrading a lot of 261 00:17:15,640 --> 00:17:18,920 Speaker 2: them to deliver similar experience that some of the larger 262 00:17:18,960 --> 00:17:22,720 Speaker 2: online retailers can within half an hour a you ordering 263 00:17:22,760 --> 00:17:25,800 Speaker 2: a product and showing up at your door. The third 264 00:17:26,040 --> 00:17:29,720 Speaker 2: area we're seeing a lot of interest is on personalization. 265 00:17:30,600 --> 00:17:34,680 Speaker 2: So it's just not about mass personalization, it's about individual personalization. 266 00:17:35,560 --> 00:17:39,359 Speaker 2: The one I'm really excited about. We cannot name who 267 00:17:39,359 --> 00:17:42,760 Speaker 2: that retailer is, but that has to do with search. 268 00:17:44,000 --> 00:17:47,080 Speaker 2: In spite of all the advances, Say, if I like 269 00:17:47,119 --> 00:17:50,320 Speaker 2: a shirt that you're wearing, grab I don't go buy it. 270 00:17:50,760 --> 00:17:52,760 Speaker 2: I have to go through like ten to fifteen searches 271 00:17:52,800 --> 00:17:55,560 Speaker 2: to be able to find the same shirt unless I 272 00:17:55,640 --> 00:18:00,680 Speaker 2: know exact brand and style. But with advances like Jenney AI, 273 00:18:01,320 --> 00:18:04,919 Speaker 2: I should be able to find exact same shirt with 274 00:18:05,119 --> 00:18:08,080 Speaker 2: literally one or two prompts. And that's going to be 275 00:18:08,119 --> 00:18:12,200 Speaker 2: a game changer on how we shop and where we shop, 276 00:18:12,680 --> 00:18:17,000 Speaker 2: but also how retailers are going to deliver the shopping 277 00:18:17,040 --> 00:18:18,120 Speaker 2: experience for us. 278 00:18:18,640 --> 00:18:21,680 Speaker 1: Yeah, so in that scenario, you might get your smartphone out, 279 00:18:21,760 --> 00:18:24,879 Speaker 1: take a photo of someone with that shirt, searches for 280 00:18:24,960 --> 00:18:28,960 Speaker 1: that vision to all the various local retail stores, and 281 00:18:29,000 --> 00:18:31,199 Speaker 1: then be able to deliver it, or you can go 282 00:18:31,240 --> 00:18:33,399 Speaker 1: pick it up and try it on and still have 283 00:18:33,520 --> 00:18:35,040 Speaker 1: that in person experience. 284 00:18:35,520 --> 00:18:39,760 Speaker 2: Absolutely in that scenario. The thing when I heard about it, 285 00:18:40,320 --> 00:18:42,840 Speaker 2: the word that came to my mind that each and 286 00:18:43,080 --> 00:18:45,880 Speaker 2: every one of us will be a walking model. Now, 287 00:18:46,680 --> 00:18:49,240 Speaker 2: the social media has made a lot of people influencers, 288 00:18:49,240 --> 00:18:52,480 Speaker 2: and that great, we are getting influence, but now everybody's 289 00:18:52,480 --> 00:18:53,960 Speaker 2: an influencer, everybody's a. 290 00:18:53,920 --> 00:18:58,760 Speaker 1: Model, and I could what are your thoughts around using 291 00:18:59,040 --> 00:19:04,520 Speaker 1: computer vision, particularly more towards I guess the checkout experience. 292 00:19:04,920 --> 00:19:07,800 Speaker 1: Do you think we could do away with barcodes or 293 00:19:08,480 --> 00:19:11,280 Speaker 1: do you think there could be a time where we 294 00:19:11,280 --> 00:19:14,960 Speaker 1: could almost go in and walk out and charge our cards. 295 00:19:15,640 --> 00:19:21,600 Speaker 3: Well, we're still experimenting as a technology and retailers together 296 00:19:21,720 --> 00:19:26,080 Speaker 3: as a team, and also customers as willing participants, are 297 00:19:26,119 --> 00:19:30,359 Speaker 3: giving us great feedback. On one end was the vision 298 00:19:30,520 --> 00:19:34,520 Speaker 3: of totally friction less checkout, right, so you walk in, 299 00:19:34,640 --> 00:19:39,000 Speaker 3: you walk out, and automatically you get charged for whatever 300 00:19:39,080 --> 00:19:43,320 Speaker 3: you bought. And we found that people did not respond 301 00:19:43,520 --> 00:19:49,440 Speaker 3: very positively to the totally friction less experience. What we 302 00:19:49,720 --> 00:19:53,240 Speaker 3: perceived as friction, like that hello from the employee was 303 00:19:53,320 --> 00:19:57,560 Speaker 3: not friction. That was actually a positive thing. Don't discard that. Yes, 304 00:19:57,800 --> 00:20:00,560 Speaker 3: and you might need help, and who's are to help? 305 00:20:00,600 --> 00:20:02,639 Speaker 3: There needs to be somebody to help. So there was 306 00:20:02,680 --> 00:20:06,359 Speaker 3: some adjustment there in terms of perception. What do customers 307 00:20:06,480 --> 00:20:09,880 Speaker 3: actually want? So you don't want to turn your brick 308 00:20:09,960 --> 00:20:14,919 Speaker 3: and mortar store into a glorified vending machine. So on 309 00:20:15,000 --> 00:20:18,320 Speaker 3: the other hand, people do not enjoy scanning items either, 310 00:20:18,840 --> 00:20:21,439 Speaker 3: so there needs to be some sort of middle ground, 311 00:20:21,880 --> 00:20:25,560 Speaker 3: and I think that's the hybrid experience now that is 312 00:20:25,760 --> 00:20:31,800 Speaker 3: gaining traction, where we mold the technology into the natural 313 00:20:31,920 --> 00:20:36,960 Speaker 3: flow of the store and make each of those steps frictionless. 314 00:20:37,400 --> 00:20:41,800 Speaker 3: So picking up the item is frictionless, you actually have 315 00:20:42,560 --> 00:20:47,160 Speaker 3: real time inventory, much faster inventory check, and it's accurate. 316 00:20:47,600 --> 00:20:50,760 Speaker 3: It starts there and then it goes towards putting your 317 00:20:50,800 --> 00:20:53,399 Speaker 3: items and you don't have to scan them. Computer vision 318 00:20:54,080 --> 00:20:59,240 Speaker 3: is used to recognize the items immediately, even if you 319 00:20:59,280 --> 00:21:02,120 Speaker 3: put twenty of it takes the same amount of time. 320 00:21:02,359 --> 00:21:04,640 Speaker 3: You just put it there, and then it may even 321 00:21:04,720 --> 00:21:09,160 Speaker 3: actually say hey, that's an age verified item, automatically alerts 322 00:21:09,200 --> 00:21:11,760 Speaker 3: the clerk. You don't have to call anybody, and then 323 00:21:12,160 --> 00:21:13,240 Speaker 3: they're already there. 324 00:21:15,640 --> 00:21:20,120 Speaker 1: What ACRED is describing this hybrid frictionalist experience that still 325 00:21:20,119 --> 00:21:23,879 Speaker 1: provides for human touch when necessary might be the future 326 00:21:23,920 --> 00:21:26,600 Speaker 1: of grocery shopping. And I think we can all agree 327 00:21:26,640 --> 00:21:29,679 Speaker 1: that an experience like this one will make running errands 328 00:21:29,800 --> 00:21:31,600 Speaker 1: or picking up milk and bread on the way home 329 00:21:31,640 --> 00:21:35,000 Speaker 1: from work simpler for all of us. But what about 330 00:21:35,000 --> 00:21:39,640 Speaker 1: the business owner's tasks? With implementing this frictionless experience. If 331 00:21:39,640 --> 00:21:42,600 Speaker 1: we take employees largely out of the equation, do we 332 00:21:42,640 --> 00:21:45,080 Speaker 1: also lose some of the oversight that keeps people from 333 00:21:45,080 --> 00:21:48,240 Speaker 1: forgetting to scan their items? Does this technology make it 334 00:21:48,280 --> 00:21:52,080 Speaker 1: easier for customers to steal from the store? Retail stores 335 00:21:52,119 --> 00:21:55,080 Speaker 1: lost and estimated eighty seven billion to theft in the 336 00:21:55,160 --> 00:21:58,200 Speaker 1: United States in twenty twenty two, and this could reach 337 00:21:58,200 --> 00:22:01,720 Speaker 1: one hundred and fifteen billion by twenty twenty five. So 338 00:22:01,760 --> 00:22:05,680 Speaker 1: I asked Shelish about the challenges businesses my face as 339 00:22:05,720 --> 00:22:08,800 Speaker 1: they adopt this frictionless experience when it comes to product 340 00:22:08,840 --> 00:22:10,320 Speaker 1: loss and product theft. 341 00:22:13,400 --> 00:22:17,399 Speaker 2: Very interesting topic and we've been seeing this debate as well. 342 00:22:17,560 --> 00:22:21,280 Speaker 2: And Graham, the product loss that a lot of time 343 00:22:21,359 --> 00:22:26,240 Speaker 2: gets referred to as shrink has always been part of industry, 344 00:22:26,800 --> 00:22:30,919 Speaker 2: So it includes not just the product loss due to theft, 345 00:22:31,560 --> 00:22:35,320 Speaker 2: but also a product that gets lost during shipping, during receiving, 346 00:22:35,880 --> 00:22:38,520 Speaker 2: so that a lot of different ways that product loss 347 00:22:38,560 --> 00:22:42,600 Speaker 2: can happen. Now when it comes to theft, there are 348 00:22:42,680 --> 00:22:45,720 Speaker 2: multiple elements there as well. So one is organized crime 349 00:22:46,200 --> 00:22:50,360 Speaker 2: and one is just consumer trying to sometimes mislabeled products. 350 00:22:51,160 --> 00:22:54,760 Speaker 2: Other times it's just not even scanning products. What does 351 00:22:54,840 --> 00:22:59,000 Speaker 2: computervision allows us to do is accurately see a product. 352 00:22:59,440 --> 00:23:01,800 Speaker 2: Now you can what product is. You're not just relying 353 00:23:01,840 --> 00:23:05,080 Speaker 2: on the barcode that somebody may have switched. So it's 354 00:23:05,160 --> 00:23:09,880 Speaker 2: actually in that instance will help you reduce the product loss. 355 00:23:10,040 --> 00:23:13,280 Speaker 2: If somebody doesn't scan a product, there's an alert that 356 00:23:13,320 --> 00:23:16,119 Speaker 2: gets sent to store associate. So what we are seeing 357 00:23:16,240 --> 00:23:18,359 Speaker 2: is in the back of the store. A lot of 358 00:23:18,400 --> 00:23:21,399 Speaker 2: loss happens there as well, and you'll be surprised how 359 00:23:21,880 --> 00:23:24,199 Speaker 2: in a creator receiving is a part of it, and 360 00:23:24,320 --> 00:23:28,160 Speaker 2: computervision technology can help drive the accuracy in the receiving 361 00:23:28,840 --> 00:23:31,760 Speaker 2: and further reducing the product class. So these are some 362 00:23:31,800 --> 00:23:34,480 Speaker 2: of the early reserves that we have seen in the 363 00:23:34,520 --> 00:23:37,520 Speaker 2: pilot and these are all read examples. 364 00:23:38,200 --> 00:23:40,960 Speaker 1: Yeah, because I was thinking of that trade off between 365 00:23:41,200 --> 00:23:44,520 Speaker 1: what you call the customer experience and this computer vision, 366 00:23:44,560 --> 00:23:48,840 Speaker 1: because I went to a new self checkout and I 367 00:23:48,880 --> 00:23:52,040 Speaker 1: think it detected my hand going over the scanner but 368 00:23:52,160 --> 00:23:55,080 Speaker 1: without a product, and it thinks that I didn't scan 369 00:23:55,160 --> 00:23:58,199 Speaker 1: it properly, and it kind of alerted the shop as 370 00:23:58,240 --> 00:24:01,600 Speaker 1: system next to me. So I guess, as you say, 371 00:24:01,760 --> 00:24:04,360 Speaker 1: it is a journey but the positive thing is that 372 00:24:04,400 --> 00:24:07,280 Speaker 1: there was a person there. It wasn't like the alarms 373 00:24:07,280 --> 00:24:10,480 Speaker 1: and everything and the security guards coming down bearing down 374 00:24:10,520 --> 00:24:14,880 Speaker 1: my neme. So is there any day that you've seen 375 00:24:15,000 --> 00:24:19,000 Speaker 1: and some of the trends around product loss and product theft, 376 00:24:19,359 --> 00:24:22,080 Speaker 1: particularly around that sort of self checkout area. 377 00:24:23,080 --> 00:24:26,760 Speaker 3: Yes, we hear from our customers that it is a 378 00:24:26,800 --> 00:24:31,359 Speaker 3: top concern of theirs loss in general and not just theft, 379 00:24:31,480 --> 00:24:36,600 Speaker 3: as Shylish very aptly described. But one of their biggest 380 00:24:36,600 --> 00:24:40,040 Speaker 3: problems is food waste, and it is part of their 381 00:24:40,080 --> 00:24:44,960 Speaker 3: business model. It can be improved massively by AI models, 382 00:24:45,200 --> 00:24:50,480 Speaker 3: but also alerting the store employees, Hey, that item there 383 00:24:50,520 --> 00:24:53,000 Speaker 3: has been sitting there for more than three hours. You 384 00:24:53,080 --> 00:24:56,040 Speaker 3: need to remove that so that the quality is preserved, 385 00:24:56,760 --> 00:25:01,399 Speaker 3: basically balancing waste and quality at the same time, but 386 00:25:01,560 --> 00:25:05,320 Speaker 3: also ringing up the right item. Not everything has a 387 00:25:05,359 --> 00:25:08,920 Speaker 3: barcode in a store. So you pick up a hot dog. There'sn't, 388 00:25:09,200 --> 00:25:13,320 Speaker 3: thank god, a barcode on the hot dog. So you 389 00:25:13,480 --> 00:25:16,400 Speaker 3: picked it up, maybe you put some condiments, maybe that's 390 00:25:16,600 --> 00:25:21,439 Speaker 3: price separately, right, ringing that up right now requires the 391 00:25:21,480 --> 00:25:24,040 Speaker 3: employee to ask you, what do you have there? Right, 392 00:25:24,480 --> 00:25:28,679 Speaker 3: so that already extends the process and the accuracy of 393 00:25:28,720 --> 00:25:31,919 Speaker 3: the reporting and so on. I mean, we need to 394 00:25:32,000 --> 00:25:36,160 Speaker 3: really for the environment too, we need to lower food waste. 395 00:25:36,400 --> 00:25:39,720 Speaker 3: So if you do accurate checkout and you know exactly 396 00:25:39,760 --> 00:25:42,480 Speaker 3: what was picked up and you ring it up, then 397 00:25:42,560 --> 00:25:47,399 Speaker 3: you improve on waste. So the important thing there is 398 00:25:47,440 --> 00:25:50,280 Speaker 3: doing the right thing at the right time, and that 399 00:25:50,400 --> 00:25:55,240 Speaker 3: helps your inventory controls, that helps your customer experience, that 400 00:25:55,359 --> 00:25:56,520 Speaker 3: helps your shrink. 401 00:25:57,040 --> 00:26:00,439 Speaker 1: And Shelly, I just wanted to get a like a 402 00:26:00,520 --> 00:26:05,399 Speaker 1: final thought from yourself if you were in the retail 403 00:26:05,480 --> 00:26:09,480 Speaker 1: industry and you're a manager there, what's the top issue 404 00:26:09,880 --> 00:26:14,760 Speaker 1: or consideration they should be thinking about if they want 405 00:26:14,800 --> 00:26:18,480 Speaker 1: to implement this sort of technology that we've been talking 406 00:26:18,520 --> 00:26:22,000 Speaker 1: about today. What are some of the challenges that they 407 00:26:22,080 --> 00:26:24,720 Speaker 1: need to be on the lookout for. What are some 408 00:26:24,760 --> 00:26:28,480 Speaker 1: of the intelligent questions they can ask their vendors to 409 00:26:28,560 --> 00:26:32,960 Speaker 1: make sure that they're getting the best value from their 410 00:26:33,000 --> 00:26:34,080 Speaker 1: time and money. 411 00:26:34,720 --> 00:26:38,080 Speaker 2: Now, that's a great question at a question that comes 412 00:26:38,160 --> 00:26:42,640 Speaker 2: up a lot. So I think everybody sees the potential 413 00:26:42,960 --> 00:26:46,840 Speaker 2: of what not just computer vision, but AI in general, 414 00:26:47,040 --> 00:26:53,520 Speaker 2: will bringing The challenge they're facing is threefour One is 415 00:26:53,920 --> 00:26:58,120 Speaker 2: the cost. So in this first stage. Majority of them, 416 00:26:58,440 --> 00:27:03,760 Speaker 2: especially with computer vision, are using specialize ecceletors for AI 417 00:27:04,080 --> 00:27:08,240 Speaker 2: that are very expensive. The second challenge that comes up 418 00:27:08,560 --> 00:27:14,080 Speaker 2: is availability of an ecosystem of technology providers. So while 419 00:27:14,160 --> 00:27:19,159 Speaker 2: large detaders have the resources both financial resources but also 420 00:27:19,640 --> 00:27:23,440 Speaker 2: the engineers and data scientists to go work on the 421 00:27:23,440 --> 00:27:27,280 Speaker 2: concepts that they can try understand the ROI, but majority 422 00:27:27,280 --> 00:27:29,960 Speaker 2: of the retailers do not have those resources and they 423 00:27:29,960 --> 00:27:34,120 Speaker 2: really need an ecosystem for partners. The third one is 424 00:27:34,840 --> 00:27:39,040 Speaker 2: the ROI the time to really get returns on their investment. 425 00:27:39,680 --> 00:27:42,800 Speaker 2: Those are the three questions. The good news is I 426 00:27:42,840 --> 00:27:46,320 Speaker 2: think they're starting to change and one of the technologies 427 00:27:46,359 --> 00:27:50,199 Speaker 2: that I'm excited about that will help with the cost 428 00:27:50,680 --> 00:27:56,600 Speaker 2: as well as with the ecosystem is AIPC concept. The 429 00:27:56,680 --> 00:28:00,160 Speaker 2: reason I'm excited about this because this is a technology 430 00:28:00,400 --> 00:28:03,800 Speaker 2: there's an extension of existing PC technologies and it is 431 00:28:03,880 --> 00:28:07,720 Speaker 2: not any specialized hardware. This already runs all of the 432 00:28:07,720 --> 00:28:11,840 Speaker 2: applications because in retail store, most of whether you're talking 433 00:28:11,840 --> 00:28:15,840 Speaker 2: about point of sale CHAOS or other instare devices. They're 434 00:28:15,840 --> 00:28:19,399 Speaker 2: all embedded PCs, so that will significantly reduce the cost 435 00:28:19,440 --> 00:28:22,920 Speaker 2: and att as first challenge, but that's also the technology 436 00:28:23,520 --> 00:28:26,200 Speaker 2: that a lot of technology products are very familiar with. 437 00:28:26,640 --> 00:28:30,959 Speaker 2: Now they have to augment what they're doing rather than reinvite. 438 00:28:32,320 --> 00:28:35,879 Speaker 1: Our regular listeners to Technically speaking, might recognize that term CHAI. 439 00:28:35,960 --> 00:28:39,640 Speaker 1: Let's just referred to the AIPC, which we covered in 440 00:28:39,640 --> 00:28:43,480 Speaker 1: episode three of this season. Aipcs I think represent the 441 00:28:43,480 --> 00:28:47,400 Speaker 1: future of computing. They'll incorporate cutting edge AI tech faster 442 00:28:47,600 --> 00:28:50,800 Speaker 1: and more effectively than anything we've seen before, and in 443 00:28:50,840 --> 00:28:54,280 Speaker 1: relation to today's conversation, they'll play a key role in 444 00:28:54,320 --> 00:28:58,040 Speaker 1: a faster, more efficient world of retail. And these aipcs 445 00:28:58,040 --> 00:29:01,640 Speaker 1: are relyingt on the Intel Core Ultrapersonessa Wish debuted last 446 00:29:01,680 --> 00:29:04,680 Speaker 1: year in twenty twenty three and offers the best AIPC 447 00:29:04,800 --> 00:29:08,000 Speaker 1: experience to mobile platforms and right out to the edge, 448 00:29:08,360 --> 00:29:10,120 Speaker 1: and it will power more than two hundred and thirty 449 00:29:10,120 --> 00:29:14,120 Speaker 1: models of the world's first aipcs. But given how much 450 00:29:14,120 --> 00:29:16,440 Speaker 1: of a game changer this Intel tech represents for the 451 00:29:16,480 --> 00:29:19,240 Speaker 1: retail industry, I want you to know some of the 452 00:29:19,560 --> 00:29:23,240 Speaker 1: challenges business owners would consider when attempting to adopt this. 453 00:29:24,200 --> 00:29:25,480 Speaker 1: He's acred with the. 454 00:29:25,400 --> 00:29:33,160 Speaker 3: Answer, So focus on the total cost and that will 455 00:29:33,440 --> 00:29:37,160 Speaker 3: lead you to Okay, this software vendor works only on 456 00:29:37,200 --> 00:29:40,720 Speaker 3: this hardware that is very expensive and custom and is 457 00:29:40,760 --> 00:29:44,280 Speaker 3: not available. That's not going to scale out in two 458 00:29:44,440 --> 00:29:48,200 Speaker 3: three years. So that's a very important part. And part 459 00:29:48,240 --> 00:29:53,000 Speaker 3: of that problem of scaling is adaptability. So a lot 460 00:29:53,040 --> 00:29:56,240 Speaker 3: of AI solutions they pilot very well, but then they 461 00:29:56,440 --> 00:30:01,760 Speaker 3: stumble on expanding two thousand locations. So pay attention to 462 00:30:02,360 --> 00:30:06,520 Speaker 3: whether the technology you're selecting has scaled out, has the 463 00:30:06,760 --> 00:30:11,480 Speaker 3: adaptive features that is prerequisite, and then how expensive is 464 00:30:11,520 --> 00:30:14,520 Speaker 3: it to this machine? Right? So let's say you have 465 00:30:14,560 --> 00:30:19,640 Speaker 3: a computer vision technology that recognizes products, but as a retailer, 466 00:30:19,800 --> 00:30:23,560 Speaker 3: you have new products coming in and their faces changes 467 00:30:23,600 --> 00:30:26,080 Speaker 3: all the time and so on, and why are you 468 00:30:26,120 --> 00:30:29,000 Speaker 3: going to have to have someone in the back constantly 469 00:30:29,120 --> 00:30:33,200 Speaker 3: scanning items and teaching the device so that it remains 470 00:30:33,280 --> 00:30:37,880 Speaker 3: accurate or does the technology automatically get to learn. So 471 00:30:37,920 --> 00:30:42,120 Speaker 3: that's the biggest advances in the last few years in 472 00:30:42,200 --> 00:30:46,840 Speaker 3: AI is technology that learns by itself and knows what 473 00:30:46,960 --> 00:30:51,200 Speaker 3: it doesn't look. So basically you need to evaluate the 474 00:30:51,240 --> 00:30:54,440 Speaker 3: technology what it tries to do, with all its failure 475 00:30:54,560 --> 00:30:59,440 Speaker 3: mechanisms and all the edge cases right, and select the 476 00:30:59,480 --> 00:31:03,880 Speaker 3: technology that adapts to the environment and during the pandemic, 477 00:31:04,000 --> 00:31:08,000 Speaker 3: we also learned things can change in a hurry, and 478 00:31:08,640 --> 00:31:13,120 Speaker 3: will your technology adapt with you with the circumstances. Can 479 00:31:13,200 --> 00:31:18,160 Speaker 3: it actually work outside of the very narrow path that 480 00:31:18,240 --> 00:31:22,720 Speaker 3: it was done for so that it continues being useful 481 00:31:22,960 --> 00:31:26,640 Speaker 3: over the lifetime of a product. Retailers have long lifetime 482 00:31:26,760 --> 00:31:31,520 Speaker 3: expectations for their technology. The technology has to last five 483 00:31:31,760 --> 00:31:35,840 Speaker 3: ten years. Both the hardware and the software needs to 484 00:31:35,880 --> 00:31:41,120 Speaker 3: be resilient work in a store environment, not be overly 485 00:31:41,160 --> 00:31:45,640 Speaker 3: fickle and robust. So that's very important to evaluate before 486 00:31:45,720 --> 00:31:47,520 Speaker 3: you actually choose the technology. 487 00:31:48,320 --> 00:31:50,800 Speaker 1: Okay, I think I've learned a lot, and hopefully all 488 00:31:50,800 --> 00:31:53,560 Speaker 1: listeners have as well. I could and Shaley, thanks very 489 00:31:53,640 --> 00:31:54,360 Speaker 1: much for your time. 490 00:31:54,600 --> 00:31:57,680 Speaker 3: Thank you Graham, Thank you Graham. 491 00:31:57,840 --> 00:32:01,880 Speaker 1: Thank you to Shalley's childry A Denghi for their expertise 492 00:32:01,960 --> 00:32:06,880 Speaker 1: in today's episode of Technically Speaking. The advent of trading 493 00:32:06,920 --> 00:32:09,959 Speaker 1: between each other has been the cornerstone of our civilization 494 00:32:10,640 --> 00:32:12,400 Speaker 1: and I think one of the main reasons for our 495 00:32:12,400 --> 00:32:16,000 Speaker 1: advancement as a human race. Retail will continue to be 496 00:32:16,000 --> 00:32:18,960 Speaker 1: a social experience, and any technology that can help bring 497 00:32:19,000 --> 00:32:23,160 Speaker 1: people together and harmoniously trade with one another will continue 498 00:32:23,200 --> 00:32:27,600 Speaker 1: to drive our society positively into the future. The advancement 499 00:32:27,720 --> 00:32:31,400 Speaker 1: in AI and in particular computer vision will help all 500 00:32:31,520 --> 00:32:34,720 Speaker 1: businesses large and small, be able to serve their customers 501 00:32:34,720 --> 00:32:39,280 Speaker 1: better by making retail interactions more meaningful for the owners, 502 00:32:39,640 --> 00:32:41,720 Speaker 1: it will allow them to serve their customers at a 503 00:32:41,800 --> 00:32:45,600 Speaker 1: higher standard, even if they are challenges with staff shortfalls. 504 00:32:46,400 --> 00:32:49,800 Speaker 1: For those with retail stores, the implementation of AI can 505 00:32:49,840 --> 00:32:53,080 Speaker 1: seem daunting, but you can always try to experiment with 506 00:32:53,120 --> 00:32:56,800 Speaker 1: the technology discussed in today's episode at a small scale. 507 00:32:57,240 --> 00:32:59,640 Speaker 1: This is so you can continue to build confidence and 508 00:32:59,680 --> 00:33:03,280 Speaker 1: continue need to learn. Remember you are the expert in 509 00:33:03,320 --> 00:33:06,240 Speaker 1: your field. AI is there to lift you to the 510 00:33:06,280 --> 00:33:10,760 Speaker 1: next level. In our next episode, we will look at 511 00:33:10,760 --> 00:33:14,600 Speaker 1: how AI is helping improve manufacturing holiday So join us 512 00:33:14,680 --> 00:33:17,880 Speaker 1: on June eighteenth or the next edition of Technically Speaking 513 00:33:18,000 --> 00:33:25,040 Speaker 1: and Intel podcast. Technically Speaking was produced by Ruby Studio 514 00:33:25,360 --> 00:33:28,920 Speaker 1: from iHeartRadio in partnership with Intel and hosted by me 515 00:33:29,280 --> 00:33:33,440 Speaker 1: Graham Class. Our executive producer is Molly Sosher, our EP 516 00:33:33,560 --> 00:33:37,400 Speaker 1: of Post Production is James Foster, and our supervising producer 517 00:33:37,640 --> 00:33:41,720 Speaker 1: is Nikia Swinton. This episode was edited by Sierra Spreen 518 00:33:42,200 --> 00:33:44,040 Speaker 1: and written by nick Firschel,