WEBVTT 1 00:00:00.600 --> 00:00:06.960 Inside Analysis dot Inside Analysis dot Com and now here's your host, Eric Kavanaugh. 2 00:00:12.400 --> 00:00:18.600 J All right, folks, welcome to the future. Indeed, you're 3 00:00:18.719 --> 00:00:22.640 truly Eric Kavanaugh here on the only coast to coast radio show that's all about 4 00:00:22.640 --> 00:00:26.239 the information economy. It's called Inside Analysis, and folks, we have an 5 00:00:26.239 --> 00:00:29.440 all star cast lineup for you today. I'm very excited. This is the 6 00:00:29.480 --> 00:00:33.479 first of a three part series of a pilot called The Foundry, and we've 7 00:00:33.520 --> 00:00:37.759 got the founder of the Foundry on the call right now, CEO Neil haunches 8 00:00:37.799 --> 00:00:42.560 with us today. And also we've got Alex Freed from Schematic Ventures, and 9 00:00:42.600 --> 00:00:47.359 we have Suketu Gandhi from at Kearney, a supply chain expert. So let's 10 00:00:47.359 --> 00:00:50.640 just go around the room and introduce ourselves real quick. Neil Haunch, welcome 11 00:00:50.679 --> 00:00:54.039 to the show. Thanks for joining the Fray. Pretty hot topic these days. 12 00:00:54.079 --> 00:00:57.280 What do you think, no question, Eric, Thanks for thanks again 13 00:00:57.320 --> 00:01:00.399 for having me back on the show and excited for the conversation. You know, 14 00:01:00.479 --> 00:01:04.640 just to provide the quick overview of Foundry, right, we were a 15 00:01:04.640 --> 00:01:10.400 business started ten years ago. We were acquired by Carney last summer and our 16 00:01:10.439 --> 00:01:15.719 core business is helping the Fortune thousand navigate external innovation and seeing you know, 17 00:01:15.760 --> 00:01:19.640 increasingly over the years, looking to understand what is happening with all of these 18 00:01:19.680 --> 00:01:23.439 emerging companies the world over. Doesn't matter what industry we're talking about or where 19 00:01:23.480 --> 00:01:29.159 these these large Fortune one thousand players are based, but to your point, 20 00:01:29.480 --> 00:01:33.640 figure out what's happening. And one of the through lines of all the corporates 21 00:01:33.640 --> 00:01:38.400 we work with supply chain, and so you know, it is a universal 22 00:01:38.400 --> 00:01:42.040 importance. It has been in particularly interesting over the last few years, which 23 00:01:42.079 --> 00:01:46.040 we'll get into over the course of this show. But thank you again for 24 00:01:46.040 --> 00:01:49.840 having me. Yeah. Absolutely, and Alex Freed dialing in, tell us 25 00:01:49.840 --> 00:01:53.079 a bit about yourself and what your role is in the world of supply chain. 26 00:01:53.719 --> 00:01:56.799 Absolutely and Eric also want to thank you for having me on the show 27 00:01:56.799 --> 00:02:00.159 and excited to be here with Neil and Sukett today. So I'm a partner 28 00:02:00.200 --> 00:02:05.120 at Schematic Ventures. We're in early stage venture capital fund based in San Francisco, 29 00:02:05.840 --> 00:02:09.080 and we are sector focused, so we invest in all things industrial technology 30 00:02:09.120 --> 00:02:15.360 related, in particular companies building in the areas of supply chain, logistics, 31 00:02:15.479 --> 00:02:22.280 manufacturing, transportation, decarbonization. So a lot of fun stuff going on in 32 00:02:22.319 --> 00:02:27.000 that world, and you know, we invest in brand new companies, so 33 00:02:27.039 --> 00:02:31.360 we see founders at the very beginning of their journey, sometimes at the ideation 34 00:02:31.520 --> 00:02:36.800 stage, and get to kind of see the latest and greatest of all the 35 00:02:36.840 --> 00:02:42.280 new technologies that folks are thinking about as applied to these categories. That's a 36 00:02:42.280 --> 00:02:44.360 lot of fun. That's a cool place to be. You know. Years 37 00:02:44.400 --> 00:02:47.000 ago, I spent I guess about five or six years on the advisory board 38 00:02:47.000 --> 00:02:52.680 for the south By Southwest Innovator. They had an accelerator accelerator program basically, 39 00:02:52.680 --> 00:02:54.439 which was all about new companies coming on and so it's kind of the same 40 00:02:54.479 --> 00:02:59.719 thing, right, I get to judge the different applicants, and it was 41 00:02:59.759 --> 00:03:02.080 all about learning like what the cutting edge is, what are people working out 42 00:03:02.120 --> 00:03:05.719 right at the racers That it's a cool place to be in. Last, 43 00:03:05.719 --> 00:03:07.919 but not least, you cet to Gandhi from at Kearney, you've written a 44 00:03:07.919 --> 00:03:12.800 book about supply chain. What's the title of the book. Well, the 45 00:03:13.199 --> 00:03:16.159 title of the book should have been things are changing fast, you need to 46 00:03:16.240 --> 00:03:21.639 know how to deal with them. But the publishers said, hey, you 47 00:03:21.719 --> 00:03:24.479 know what, that may not sell. So what we're doing is is really 48 00:03:24.560 --> 00:03:30.919 talking about supply chains and what are the fundamental principles in this dramatically changing world, 49 00:03:31.159 --> 00:03:35.759 and how do you act today, how do you act tomorrow, and 50 00:03:35.840 --> 00:03:39.120 more importantly, how do you plan for day after tomorrow where things could have 51 00:03:39.400 --> 00:03:46.080 even more variability in the way, you know, the various crises, but 52 00:03:46.199 --> 00:03:49.680 also opportunities. There's a lot of talk on crises, but I think there 53 00:03:49.719 --> 00:03:53.599 should be even more talk on the opportunities that are coming as a result of 54 00:03:53.639 --> 00:03:55.240 these disruptions. Well, you know, it's funny you should say that. 55 00:03:55.400 --> 00:04:01.039 I recall learning years ago that the Chinese character or chaos is very, very 56 00:04:01.080 --> 00:04:06.400 similar to the Chinese character for opportunity, because out of chaos, there's tremendous 57 00:04:06.439 --> 00:04:10.599 opportunity, right, And you look at the world now, and you know 58 00:04:10.639 --> 00:04:13.439 it's funny. So we talked before the show. I've got some background in 59 00:04:13.439 --> 00:04:17.759 supply chain, and supply chain is probably the most complex space in business. 60 00:04:17.959 --> 00:04:21.199 I mean absent. Maybe life science is or something if you really get into 61 00:04:21.240 --> 00:04:26.000 the human genome and things of that nature. But supply chain is so complicated 62 00:04:26.000 --> 00:04:30.160 because there are so many interdependencies, there are so many branches, there are 63 00:04:30.199 --> 00:04:35.000 so many bits of details about way people capture information. I'm a data guy, 64 00:04:35.079 --> 00:04:38.759 so I think in terms of how you persist data. And it's very 65 00:04:38.759 --> 00:04:44.000 difficult to even show a supply chain to any meaningful depth because you know, 66 00:04:44.040 --> 00:04:46.639 for an automobile, how many parts going to automobile? Thousands of parts and 67 00:04:46.720 --> 00:04:50.240 all these suppliers. And what's interesting, I'll throw it back to use Ketty, 68 00:04:50.360 --> 00:04:55.079 is that we had fair warning before COVID because we had all those tariffs, 69 00:04:55.480 --> 00:04:58.920 and I remember that was tremendously disruptive. I'm a cyclist, and you 70 00:04:58.959 --> 00:05:01.720 could not get a bike part for like a year all through whatever it was 71 00:05:01.759 --> 00:05:04.839 twenty eighteen or something like that into twenty nineteen. You could not get bicycle 72 00:05:04.879 --> 00:05:09.279 parts because they were all in China and we couldn't get them because the tariffs. 73 00:05:09.439 --> 00:05:12.480 So it's like we had advanced warning and then COVID hit, and then 74 00:05:12.560 --> 00:05:15.879 post COVID, So we're in a very strange place right now. But you 75 00:05:15.920 --> 00:05:18.360 know, what's your take on how to navigate through that? How do you 76 00:05:18.399 --> 00:05:21.639 advise your clients to just get a handle and make sure you're moving in the 77 00:05:21.720 --> 00:05:26.319 right direction. So to start with, you know, you were lucky you 78 00:05:26.360 --> 00:05:31.839 actually had a bike, because bicycles were really hot during COVID and then the 79 00:05:31.879 --> 00:05:34.839 parts that go with that. So the way I wanted to kind of put 80 00:05:34.839 --> 00:05:41.360 a simple definition of whether supply chains right. Supply chains are a mechanism where 81 00:05:41.480 --> 00:05:46.480 you deliver products and services that a customer wants, and then you bring in 82 00:05:46.639 --> 00:05:51.480 your internal resources, your factories, your distribution centers, your planning people, 83 00:05:51.680 --> 00:05:57.680 and your suppliers together to deliver it where they wanted, when they wanted, 84 00:05:58.120 --> 00:06:00.240 how they wanted, and for a fair price. So supply chain is the 85 00:06:00.360 --> 00:06:03.959 what I would call the heart of the business combined with the stomach, if 86 00:06:04.000 --> 00:06:08.560 you may, to make it very simple. So that's the really important part. 87 00:06:08.600 --> 00:06:13.839 Now, what happens is anytimes, anytime there is a small better disruption. 88 00:06:14.120 --> 00:06:16.600 For a long period of time, supply chains were broken down into silos 89 00:06:17.240 --> 00:06:20.279 that said, you're a product guy, so you think about products. You're 90 00:06:20.319 --> 00:06:24.920 a guy who plans what should be made. You're a guy who distributes, 91 00:06:25.279 --> 00:06:29.480 you're a galhu manufacturers, and you are here in somebody in procurement who deals 92 00:06:29.519 --> 00:06:33.439 with the suppliers. And everything was broken down into silos because we were in 93 00:06:33.439 --> 00:06:38.439 a game, or we ever fooled into a world that said everything will work 94 00:06:38.519 --> 00:06:42.839 as it's supposed to work. We know that doesn't happen. So one of 95 00:06:42.879 --> 00:06:46.160 the big things we are talking about are one, think about this end to 96 00:06:46.319 --> 00:06:51.160 end and second you said something really important, Eric, which is think about 97 00:06:51.160 --> 00:06:56.319 the data, because the data it gives is the one that gives you the 98 00:06:56.439 --> 00:07:00.759 light to understand what is happening in the whole end to a end. And 99 00:07:01.160 --> 00:07:06.759 the latest view is there are two factories in the world, the AI factory 100 00:07:06.759 --> 00:07:12.639 as Jensen Wong called them, and then the second one is factories that produce 101 00:07:12.720 --> 00:07:15.920 things and bring them together. And you have the supply chain of tomorrow. 102 00:07:15.920 --> 00:07:21.040 In simple words, Yeah, that's fascinating because with AI we can do lots 103 00:07:21.040 --> 00:07:26.399 of different very powerful things like predict for example, I mean, and just 104 00:07:26.399 --> 00:07:30.279 to give some background to the audience, as a procurement person, obviously you've 105 00:07:30.279 --> 00:07:34.399 got your bill of materials that you need to supply to your warehouse, to 106 00:07:34.519 --> 00:07:40.639 your manufacturing plant, et cetera. And being able to see who has which 107 00:07:40.680 --> 00:07:45.240 part and also how reliable those people are, because that's one of the key 108 00:07:45.279 --> 00:07:49.720 factors, right is understanding reliability over time. It's like Okay, XYZ provider 109 00:07:49.759 --> 00:07:53.759 has a better price, but are they reliable well, let's take a look. 110 00:07:53.759 --> 00:07:56.879 If you have the data and you can build a baseline, then even 111 00:07:56.959 --> 00:08:01.240 in difficult times, you can predict well maybe they're maybe we should just pay 112 00:08:01.240 --> 00:08:03.000 more and make sure we get that right. It's always a balancing act. 113 00:08:03.319 --> 00:08:07.199 But the thing is with the data and with the trained algorithms, and they 114 00:08:07.240 --> 00:08:09.839 have to be trained and they have to be focused and very on point. 115 00:08:11.319 --> 00:08:13.800 But if you have that, then you could do a much better job of 116 00:08:13.839 --> 00:08:16.199 managing this end to end process. And that's where the magic is, right, 117 00:08:18.040 --> 00:08:20.600 that is an absolute little where the magic is so the old days, 118 00:08:20.680 --> 00:08:22.879 you know, is to follow what we would call the plan and pray model. 119 00:08:24.279 --> 00:08:28.319 You plan for something and pray like heck that the products would be ready. 120 00:08:28.759 --> 00:08:31.720 But the world we are in that my friend Neil and I work a 121 00:08:31.759 --> 00:08:35.480 lot on is we call it a sense of pivot word, which is you're 122 00:08:35.519 --> 00:08:41.240 constantly sensing what's happening on the demand side, You're constantly sensing what's happening within 123 00:08:41.360 --> 00:08:46.120 your four walls, You're sensing what's happening on the supply side, and depending 124 00:08:46.159 --> 00:08:50.879 on the situation, you pivot, and you pivot. One of the complexities 125 00:08:50.919 --> 00:08:56.720 in supply chain is because it deals both with carbon based life forms and silicon 126 00:08:56.720 --> 00:09:01.080 based life forms. Silicon bileased life forms are depending on data and they can 127 00:09:01.120 --> 00:09:05.639 move quickly, whereas carbon based take time. So you can't just open a 128 00:09:05.720 --> 00:09:09.559 factory, you can't move logistics networks overnight, and so now you have to 129 00:09:09.600 --> 00:09:15.600 create those buffers in the middle that allow you to deal with these variances along 130 00:09:15.639 --> 00:09:18.799 the way, both on the supply site as well as the demand side. 131 00:09:18.919 --> 00:09:22.200 That's why we call it sense and pivot. Yeah, no, I like 132 00:09:22.240 --> 00:09:24.320 that a lot, and I'll throw one more question over you and then maybe 133 00:09:24.320 --> 00:09:28.559 bring you into the conversation. One of the real keys in supply chain and 134 00:09:28.600 --> 00:09:31.399 I think we're making good ground these days, but you can let me know 135 00:09:31.480 --> 00:09:37.159 your thoughts is having visibility not just into what you need, but into what 136 00:09:37.320 --> 00:09:41.960 all of your partners and suppliers have. And you know, consumers who who 137 00:09:41.320 --> 00:09:46.039 shop online have seen the leading indicators of this. Meaning you'll be stopping for 138 00:09:46.080 --> 00:09:50.440 a jack and I'll say fourteen left in stock, for example. There are 139 00:09:50.559 --> 00:09:54.919 systems of record being monitored to be able to deliver that kind of information. 140 00:09:54.399 --> 00:09:58.000 Because you're tracking the warehouse, you're tracking what's available, and so the more 141 00:09:58.080 --> 00:10:03.600 visibility you have into your suppliers and what their conditions are. And I mentioned, 142 00:10:03.600 --> 00:10:07.000 of course the score reliability score, but just knowing what they have and 143 00:10:07.039 --> 00:10:09.679 what you can get access to in a reasonable period of time, that's the 144 00:10:09.799 --> 00:10:13.679 key. But that's a very difficult thing to do because you need all of 145 00:10:13.720 --> 00:10:18.519 them to publish information in a format that you can then access and consume in 146 00:10:18.559 --> 00:10:24.120 your enterprise. Is that about right? You are spot on. And what 147 00:10:24.200 --> 00:10:28.960 is wonderful is both consumers and B to B customers actually send indication on what 148 00:10:30.000 --> 00:10:35.120 they're going to need in the next few weeks things. So for example, 149 00:10:35.159 --> 00:10:41.000 we have trained algorithms that tell us about twelve weeks out with ninety four percent 150 00:10:41.000 --> 00:10:45.159 accuracy on what somebody might buy. Right, So that's the first thing. 151 00:10:45.600 --> 00:10:50.600 The second thing is data is available, but we always look for definitive data. 152 00:10:50.759 --> 00:10:54.399 It means is it one hundred percent accurate and the realities it doesn't need 153 00:10:54.480 --> 00:10:58.840 to be. I need to apply statistical tools to them to understand what is 154 00:10:58.840 --> 00:11:05.519 the probability of something happening, and that is a significant mindset shift. So 155 00:11:05.639 --> 00:11:09.960 I can deal with partial data. Data may not be perfect, but at 156 00:11:09.039 --> 00:11:16.200 least it's a wonderful indicator and that is a significant mindset shift from the old 157 00:11:16.240 --> 00:11:20.320 days where we say everything had to be perfect, right. It doesn't have 158 00:11:20.440 --> 00:11:22.919 to be right now that that's a really interesting point you just made them. 159 00:11:24.039 --> 00:11:26.759 I'm reminded of Schrodinger's cat right, like is it in the boxes? It 160 00:11:26.799 --> 00:11:31.120 out of the box? And I explain to people, like, for sure, 161 00:11:31.240 --> 00:11:33.200 in a real world, it's either in the box or out of the 162 00:11:33.200 --> 00:11:35.240 box. Okay, there's no way it's in and out of the box. 163 00:11:35.399 --> 00:11:41.360 But for purposes of developing probabilistic algorithms and determinations, that's why you talk about 164 00:11:41.360 --> 00:11:43.000 it being maybe in the box, maybe outside of the box. But you 165 00:11:43.039 --> 00:11:46.720 make an excellent point. It doesn't have to be perfect. You just want 166 00:11:46.759 --> 00:11:50.759 a good a high score. I want it to be like ninety percent or 167 00:11:50.840 --> 00:11:54.000 higher to be able to make my plans because things change. To your point, 168 00:11:54.039 --> 00:11:56.720 I mean, I was joking with not really joking, but talking with 169 00:11:58.000 --> 00:12:01.840 the before the show about who the's having hyph brasonic missiles firing at ships in 170 00:12:01.879 --> 00:12:05.039 the Red Sea? Like who saw that coming? They don't want to have 171 00:12:05.039 --> 00:12:09.000 that in a Bingo card for this year. Not me. So you're right 172 00:12:09.039 --> 00:12:11.240 that you don't have to be perfect but you do want to have the data. 173 00:12:11.320 --> 00:12:13.919 You do want to score the data and then adjust it over time. 174 00:12:13.960 --> 00:12:16.120 Real quick final comment that I'll bring Neil and go ahead to get there. 175 00:12:18.039 --> 00:12:20.159 No, I think that's a great way to think about it, right, 176 00:12:20.200 --> 00:12:24.759 Shortinger's cat, whether it's alive or dead, really depends on the data. 177 00:12:26.120 --> 00:12:31.000 And now we have sensors for example, that allows you to connect everything from 178 00:12:31.080 --> 00:12:33.480 where the product is being used, what do the trucks say, what do 179 00:12:33.519 --> 00:12:37.799 the factories tell us, and what is happening at your suppliers. So you 180 00:12:37.840 --> 00:12:41.720 know, Shortinger's cat today would be fully instrumented, and we would use that 181 00:12:41.799 --> 00:12:46.840 information to drive our supply votes. That's a cool I love that. All 182 00:12:46.919 --> 00:12:48.360 right, Neil, I'm going to bring you back in and I'm going to 183 00:12:48.440 --> 00:12:50.679 laugh for a couple of minutes. Here Schroedeger's cat is now fully instrumented. 184 00:12:50.720 --> 00:12:54.360 That's brilliant. But Neil, you consult with companies all the time. Your 185 00:12:54.399 --> 00:12:58.279 clients look to you for information about how to innovate, how to stay on 186 00:12:58.279 --> 00:13:01.159 top of things. Thinks we kind of made an excellent point there. The 187 00:13:01.200 --> 00:13:07.960 supply chain now is very well instrumented, and that's wonderful stuff because these old 188 00:13:07.000 --> 00:13:11.759 devices. They tell the truth, right, they're seeing what's happening. You're 189 00:13:11.799 --> 00:13:13.200 not asking some of their opinion. You're like, device, are you on? 190 00:13:13.279 --> 00:13:16.279 Okay? You're on? Good? Go ahead? And Neil, yeah 191 00:13:16.519 --> 00:13:20.080 no. And I'm trying to see if I can keep building on the metaphor. 192 00:13:20.120 --> 00:13:22.919 I think I think I can't, but you know, you're reminded me. 193 00:13:22.200 --> 00:13:28.000 Actually last week's Suquto and I we had a two day Future of Supply 194 00:13:28.039 --> 00:13:30.799 Chain of that that we were hosting, you know, and so in the 195 00:13:30.840 --> 00:13:35.159 audience are a wide collection of global you know, supply chain officers, chief 196 00:13:35.159 --> 00:13:37.200 supply chain officers, and it reminded me of one of the comments and courses. 197 00:13:37.360 --> 00:13:41.039 There is no silver bullet, and there probably will never be a silver 198 00:13:41.120 --> 00:13:43.240 bullet, but you know, a bronze bullet would be incredible, right, 199 00:13:43.480 --> 00:13:46.960 It meaning and how do you get there? And it is it's all of 200 00:13:48.000 --> 00:13:52.840 these devices are right, data collection points, and then there's the next order, 201 00:13:52.879 --> 00:13:56.879 which is you know, and those will will do nothing more than continue 202 00:13:56.879 --> 00:13:58.799 to grow and grow and grow. But then there's the next order of Okay, 203 00:13:58.799 --> 00:14:00.480 but what do you do with the data right? Right? How do 204 00:14:00.480 --> 00:14:01.919 you make how you make it actionable? And at some point, how do 205 00:14:01.960 --> 00:14:07.879 you automate the actions that are taken from it? And and I think this 206 00:14:07.960 --> 00:14:11.039 is certainly what we see as we kind of bridge over to startup planned, 207 00:14:11.559 --> 00:14:16.679 right, which is either startups that are empowering the collection of data so industrial 208 00:14:16.720 --> 00:14:20.759 IoT you know, low worth orbit satellite networks. They can track the ships 209 00:14:20.759 --> 00:14:22.840 in the sea. So it's just on and on and on. You know. 210 00:14:22.120 --> 00:14:26.039 It also does kind of remind me and harkomeback to like blockchain is going 211 00:14:26.080 --> 00:14:28.960 to be a panacea, right, you know, big vendors would require a 212 00:14:30.000 --> 00:14:33.159 big buyers would require all their vendors to be on it and transparency in real 213 00:14:33.240 --> 00:14:35.399 time. Still a lot of work to be done there. I was also 214 00:14:35.480 --> 00:14:39.000 enjoying this is probably just the side of our age. You know. Back 215 00:14:39.039 --> 00:14:41.799 when r f I d tags and it was okay, we're going to RFID 216 00:14:41.879 --> 00:14:46.559 tag every single you know, toothbrush, it turned out the tag was more 217 00:14:46.600 --> 00:14:50.799 expensive than the brush itself. But you know, but again, just all 218 00:14:50.799 --> 00:14:56.200 of this is is is the mass increase in the data points that then can 219 00:14:56.240 --> 00:15:01.159 be parlayed into all of these these elements of what a supply chain professional has 220 00:15:01.159 --> 00:15:05.279 to worry about. You know, I think the other thing that's certainly the 221 00:15:05.080 --> 00:15:11.360 next word after data is all things AI. And if we if we look 222 00:15:11.360 --> 00:15:16.639 at the main categories that we're seeing and as we advise the corporations we work 223 00:15:16.679 --> 00:15:20.039 with on okay, if the intersection of the words supply chain AI, which 224 00:15:20.080 --> 00:15:22.240 would we be thinking about? Where are the ways it's going to impact positively 225 00:15:22.759 --> 00:15:26.360 our day to day job? You know, I think a handful that I 226 00:15:26.360 --> 00:15:28.240 would rattle off, you know, and there are in varying degrees of maturity, 227 00:15:28.600 --> 00:15:33.120 varying degrees of venture investment going into them, which I'm sure will be 228 00:15:33.200 --> 00:15:35.480 able to touch upon. But you know, it's things like back office automation. 229 00:15:35.600 --> 00:15:41.360 There's a lot of blocking tackling logistics automations, autonomous trucks and what have 230 00:15:41.480 --> 00:15:45.360 you. Warehouse automation think you know, robots, robots robots and computer vision 231 00:15:45.639 --> 00:15:50.759 automated quality checks coming down the line. That's also often computer vision inventory management. 232 00:15:50.000 --> 00:15:54.240 I think we've already touched a bit here about predictive analytics demand forecasting, 233 00:15:56.639 --> 00:16:00.759 and then there's even the region specific forecasts, right, you know, whether 234 00:16:00.799 --> 00:16:04.399 it's seasonality, weather right. So now you've got a lot more AI models 235 00:16:04.720 --> 00:16:10.039 trying to bake those and there's an endless series of data points, right that 236 00:16:10.080 --> 00:16:12.919 they can consume and once trained. So anyway, so I think this is 237 00:16:14.120 --> 00:16:17.960 and this is kind of indicative. There is so much innovation happening out there, 238 00:16:18.200 --> 00:16:22.039 much of it coming from the technology platform incumbents, but we might posit 239 00:16:22.240 --> 00:16:26.679 even more coming from companies that didn't exist two years ago or five years ago. 240 00:16:27.320 --> 00:16:30.200 And when we go back to that silver bullet comment and one of the 241 00:16:30.279 --> 00:16:33.759 questions we asked that there is no right answer, but where do you think 242 00:16:33.799 --> 00:16:36.679 that innovation is going to come from? The players that you've been buying from 243 00:16:37.519 --> 00:16:41.519 for the last ten, twenty thirty years or players that are just forming right 244 00:16:41.559 --> 00:16:44.639 now and you are our viewers, of course, is it's going to be 245 00:16:44.679 --> 00:16:47.480 a mix of boat right? That's right, And I mean you made a 246 00:16:47.519 --> 00:16:49.840 bunch of excellent points there, and just for the audience to understand. The 247 00:16:49.960 --> 00:16:53.720 key is to be able to string together the end to end process within your 248 00:16:53.840 --> 00:16:59.840 organization such that you're planning software, your logistics that the software your people are 249 00:16:59.840 --> 00:17:04.160 you using can consume, this information can consume these predictions, can understand and 250 00:17:04.240 --> 00:17:08.200 you have your your options right, you want to have options if this plant 251 00:17:08.480 --> 00:17:11.119 is on fire, somewhere in Ukraine, and we're not gonna be able to 252 00:17:11.160 --> 00:17:15.480 get that part anymore. What are other options? In the bottom line is 253 00:17:15.480 --> 00:17:19.680 that machine learning, algorithms and AI are much better at scanning across vast trobes 254 00:17:19.720 --> 00:17:22.839 of data points and human beings will ever be. So you want to be 255 00:17:22.880 --> 00:17:26.359 able to use these technologies to your advantage, but don't touch out. That'll 256 00:17:26.400 --> 00:17:38.440 be right back. You're listening to Inside Analysis. Welcome back to Inside Analysis. 257 00:17:38.960 --> 00:17:45.319 Here's your host, Eric Tabanac. All right, folks, back here 258 00:17:45.359 --> 00:17:51.240 on Inside Analysis talking to an all star cast of supply chain experts. We 259 00:17:51.319 --> 00:17:55.279 heard from Neil Haunch and Suketu Gandhi in the opening segment, and now up 260 00:17:55.319 --> 00:17:59.559 next Alex Freed. Alex, you're out there on the front lines trying to 261 00:17:59.559 --> 00:18:03.440 figure out where are the hot tanks, who has the right technology? And 262 00:18:03.480 --> 00:18:07.720 you know, the I guess the blessing and curse of the whole supply chain 263 00:18:07.759 --> 00:18:11.759 world is that there is an embarrassment of riches and opportunities, right. I 264 00:18:11.759 --> 00:18:15.559 mean, you have everything from the data collection, the instrumentation, and I 265 00:18:15.640 --> 00:18:19.720 think probably the hard is not to crack is the orchestration of all that data 266 00:18:19.799 --> 00:18:26.200 being able to bake policies in and rules and preferences and other such things to 267 00:18:26.359 --> 00:18:30.759 manage the data, because how do you even visualize this stuff? And what's 268 00:18:30.799 --> 00:18:36.079 interesting to me is no one seems to have really dominated the IoT space. 269 00:18:36.200 --> 00:18:38.599 I don't see any player who really owns everything. All the big guys have 270 00:18:38.720 --> 00:18:41.880 some some skin of the game, but no one really owns it yet. 271 00:18:41.880 --> 00:18:45.319 But Alex, what do you see out there and where are you focused? 272 00:18:47.680 --> 00:18:51.039 Well as it relates to IoT. You know, we definitely saw a large 273 00:18:51.039 --> 00:18:56.880 wave of new companies building IoT related devices. But let's say predictive analytics looking 274 00:18:56.920 --> 00:19:03.400 at predictive maintenance or perhaps to be able to track goods and provide visibility into 275 00:19:03.400 --> 00:19:07.839 where they are during transportation. So you know, a sensor that goes on 276 00:19:07.880 --> 00:19:11.559 a parcel or a palette or inside of a container, or whatever the case 277 00:19:11.599 --> 00:19:14.759 may be. I think what we found there is one a lot of value 278 00:19:14.960 --> 00:19:19.200 that those are providing, in particular for high value machines or high value goods. 279 00:19:19.200 --> 00:19:22.519 So for example, those sensors found that let's say, if it was 280 00:19:22.680 --> 00:19:32.440 high value electronics or perhaps things like jewelry or precious metals or even pharmaceuticals, 281 00:19:33.359 --> 00:19:37.839 which in that case are also temperature sensitive and sensitive to other things tons of 282 00:19:37.880 --> 00:19:44.000 value, but as you get down to other types of goods that are perhaps 283 00:19:44.079 --> 00:19:48.440 less valuable in in of themselves, it was hard to justify the ROI for 284 00:19:48.519 --> 00:19:52.240 companies. So I think that's always been a potential friction point of even though 285 00:19:52.240 --> 00:19:53.960 the technology is amazing and we'd love to have that, you know, how 286 00:19:55.039 --> 00:19:57.880 much does it cost and what is the ROI related to it? One just 287 00:19:57.920 --> 00:20:03.680 an example on that, and is in the warehouse people wanted to put better 288 00:20:03.400 --> 00:20:07.519 tags and trackers on every single item, but at the end of the day, 289 00:20:07.559 --> 00:20:12.839 barcodes are still what everyone uses because they're super cheap. So that's kind 290 00:20:12.839 --> 00:20:15.319 of on the IoT side of the world. And then I think one of 291 00:20:15.359 --> 00:20:18.359 your questions as well was what are we seeing now in terms of new technology 292 00:20:18.400 --> 00:20:22.319 and new companies being founded? And I think, to no surprise to anyone 293 00:20:22.359 --> 00:20:27.799 here, seeing a lot around LM's and generative AI. And I think in 294 00:20:27.880 --> 00:20:34.960 particular where we've seen that type of AI used to great effect as it relates 295 00:20:34.960 --> 00:20:42.279 to collaboration across multiple parties. So we know supply chains are highly fragmented multi 296 00:20:42.319 --> 00:20:48.680 agent systems, and today the common denominator for collaboration across parties is still email, 297 00:20:48.079 --> 00:20:52.799 phone, text, WhatsApp, we chat, kind of all these more 298 00:20:55.160 --> 00:20:59.599 traditional or horizontal methodologies. And we've seen in the past companies, let's say, 299 00:20:59.599 --> 00:21:03.319 create a supplier portal where the supplier has to come in and interact with 300 00:21:03.359 --> 00:21:08.440 that portal such that that communication then flows to the to the brand or the 301 00:21:08.440 --> 00:21:12.880 manufacturer of the OEM, and everything theoretically lives within that platform, but in 302 00:21:12.880 --> 00:21:17.599 reality, that supplier might have you know, ten clients are working with with 303 00:21:17.680 --> 00:21:22.400 ten different platforms, or perhaps the engagement on that platform is infrequent, and 304 00:21:22.440 --> 00:21:26.119 so they forget about and things go out of date. But you know, 305 00:21:26.200 --> 00:21:32.640 one really valuable application I think we've been seeing of LMS and generated AI is 306 00:21:33.720 --> 00:21:37.000 the fact that it can interpret emails, put them into your system, and 307 00:21:37.039 --> 00:21:41.519 then you know, communicate back to folks. And it's also an area where 308 00:21:41.799 --> 00:21:45.640 the cost of mistakes is relatively low. Like if you screw up a little 309 00:21:45.640 --> 00:21:48.839 bit on an email or something along those lines, usually it's not going to 310 00:21:48.880 --> 00:21:53.000 be the end of the world. It's not making a decision that is hyper 311 00:21:53.039 --> 00:22:00.440 critical, especially without a human in the loop for oversight. So when we 312 00:22:00.559 --> 00:22:03.359 when we look at that part of what we think about then, is we 313 00:22:03.440 --> 00:22:07.319 know that this technology is really effective with these modes of communication to both ingest 314 00:22:07.359 --> 00:22:11.839 them and then also draft something back out to communicate or automate that communication. 315 00:22:12.359 --> 00:22:17.759 And so what is the workflow that this company is putting together around that core 316 00:22:17.839 --> 00:22:21.839 technology such that it fits with the users on both sides? And to what 317 00:22:21.880 --> 00:22:29.880 extent did they understand the real life workflow of these this customer base in order 318 00:22:29.960 --> 00:22:32.839 to make sure that the chology is used to you know, the highest degree 319 00:22:32.839 --> 00:22:37.160 of effectiveness. Yeah, that's that's very very interesting stuff. I love that 320 00:22:37.200 --> 00:22:42.079 you brought in lms to consume emails and then parse, because one of the 321 00:22:42.200 --> 00:22:47.880 really cool things these engines can do is to summarize and to just serve as 322 00:22:47.920 --> 00:22:52.599 an agent with which you can communicate. So basically you can have every email 323 00:22:52.680 --> 00:22:56.359 coming in absorbed by the LM and also going to its target, so you 324 00:22:56.400 --> 00:22:59.880 get the target. But you know what I found, I'd be carey theory 325 00:23:00.279 --> 00:23:06.119 this alex AI is very very good at reminding people or making suggestions about things, 326 00:23:06.119 --> 00:23:10.640 and I think that's where probably eighty percent of AI's impact will take place 327 00:23:10.799 --> 00:23:14.160 is in suggestion. So you're in your console, you're doing your job of 328 00:23:14.200 --> 00:23:17.880 procurement and being a little message comes up that says, hey, we just 329 00:23:17.920 --> 00:23:22.279 saw an email from Bangalore and they're suggesting that this product is delayed. Do 330 00:23:22.359 --> 00:23:26.599 you want to use the other supplier? For example? And the user will 331 00:23:26.599 --> 00:23:30.640 go, well, gosh darn yeah, I will click yes, And then 332 00:23:30.680 --> 00:23:33.640 of course every time you do that, you're helping the model train theoretically. 333 00:23:33.680 --> 00:23:38.799 What do you think about that, Alex I agree, definitely a lot of 334 00:23:38.839 --> 00:23:44.559 application as relates to making suggestions for the user, for the end customer there. 335 00:23:44.880 --> 00:23:48.559 You know, one creative example that we've seen a company mentioned or be 336 00:23:48.640 --> 00:23:53.079 interested in, is you have an organization, but let's say and twenty one 337 00:23:53.160 --> 00:24:00.400 hundred operations folks who have their own suppliers or their own clients who they're speaking 338 00:24:00.440 --> 00:24:03.680 to, and there could be a supply chain chock or an event perhaps it's 339 00:24:03.680 --> 00:24:07.880 like a weather related event happening in Chicago that's going to affect multiple customers or 340 00:24:07.000 --> 00:24:11.880 multiple modes of transportation. But me, as an individual operator, I only 341 00:24:11.920 --> 00:24:17.400 have three clients or you know, three partners that would be impacted. But 342 00:24:18.119 --> 00:24:22.000 my company probably has one hundred, and if someone starts getting a few pings 343 00:24:22.039 --> 00:24:27.880 or a few emails. Then, because this you know AI agent or system 344 00:24:29.400 --> 00:24:33.039 has visibility across everyone's communication, everyone's emails, it can recommend to me to 345 00:24:33.039 --> 00:24:37.440 say, hey, by the way, we noticed other people in your organization 346 00:24:37.480 --> 00:24:41.559 are seeing this. You may want to proactively reach out to your partners or 347 00:24:41.559 --> 00:24:45.480 your customers and get ahead of this. So one interesting, I think example 348 00:24:45.480 --> 00:24:53.880 of this type of you know AI suggesting things that in a timely manner that 349 00:24:53.920 --> 00:25:00.920 we've seen that could be quite effective and very interesting across the organization. Bridging 350 00:25:00.960 --> 00:25:06.839 together people who have point and visibility and combining it all together. Yeah, 351 00:25:06.880 --> 00:25:11.039 that is just as an absolutely huge deal. And maybe Suqutto will bring you 352 00:25:11.079 --> 00:25:15.599 back into the conversation here to comment on this. One of the best things 353 00:25:15.640 --> 00:25:19.920 I've heard about AI is to treat it like a team member essentially and realize 354 00:25:19.920 --> 00:25:23.559 that it is an active force in the organization. Of course, you have 355 00:25:23.599 --> 00:25:26.240 to set it up properly, you have to manage it. All these things 356 00:25:26.359 --> 00:25:33.440 are kind of requirements, but nonetheless, as Alex just suggested here, if 357 00:25:33.480 --> 00:25:36.119 you set it up right, it has access to all these emails going around, 358 00:25:36.119 --> 00:25:40.920 so it can sense patterns that are occurring right now and give you feedback 359 00:25:40.960 --> 00:25:42.480 and say, hey, maybe you should jump on this. It looks like 360 00:25:42.559 --> 00:25:45.279 this is going to impact you as well. What do you think about that 361 00:25:45.359 --> 00:25:49.400 as a strategy, as a go forward strategy to at least start investigating how 362 00:25:49.400 --> 00:25:53.079 to use these kinds of technologies, because once you live in a world where 363 00:25:53.079 --> 00:25:56.200 you're getting those recommendations, it's really hard to go back to the darkness. 364 00:25:56.200 --> 00:26:00.680 What do you think, No, I think that it is you will put 365 00:26:00.720 --> 00:26:03.480 what Alex said and the way you surmised it, but what is I'll give 366 00:26:03.480 --> 00:26:10.640 you a practical example. We are using alms with the rags to understand what 367 00:26:10.799 --> 00:26:18.279 is happening in the world of some of the complex logistics procurement. You know, 368 00:26:18.720 --> 00:26:21.640 the LM might think that, hey, a load is a load. 369 00:26:21.680 --> 00:26:23.720 It might think of our financial load or things of that sort. But the 370 00:26:23.759 --> 00:26:26.319 moment you add a rag on top, it tells you, hey, in 371 00:26:26.359 --> 00:26:33.960 this context, a load is the goods you put on a truck or And 372 00:26:34.000 --> 00:26:38.440 then you take that information and now you have the massive ability to take all 373 00:26:38.519 --> 00:26:47.359 of that information and apply heuristics and logistics against that because you have a data 374 00:26:47.400 --> 00:26:51.200 set that is being collected over the last twenty to thirty forty years of time 375 00:26:51.440 --> 00:26:55.519 in terms of you know, reliability, costs, impact, things of that 376 00:26:55.559 --> 00:26:59.319 sort, and now you can say, here are the recommended actions, if 377 00:26:59.359 --> 00:27:03.839 you may. The second thing you talked about was agents, and it's really 378 00:27:03.880 --> 00:27:08.119 important. And had a conversation with chro for a very large company and they 379 00:27:08.119 --> 00:27:12.960 have a couple hundred thousand people. So do you start thinking about your agents 380 00:27:14.000 --> 00:27:19.680 as actual employees, because if you do, then you will start thinking about 381 00:27:19.920 --> 00:27:26.200 evaluating their actions, understanding their actions, and modifying them based on what they 382 00:27:26.240 --> 00:27:32.000 do good and areas that need improvement. Because that's a different way about thinking 383 00:27:32.359 --> 00:27:37.240 about this new technology that comes in and it brings together the concept we call 384 00:27:37.440 --> 00:27:42.119 the whole brain, that is human intelligence plus artificial intelligence should be the brain 385 00:27:42.200 --> 00:27:48.079 of the enterprise, which is thinking of them separately, and that's where the 386 00:27:48.160 --> 00:27:51.759 power we think is going to be unlocked over the next three to five years 387 00:27:51.759 --> 00:27:55.319 in a massive way. Yeah, I have to agree, and maybe I 388 00:27:55.559 --> 00:28:00.240 shall throw it back over to you. Figuring out how and where to deplay 389 00:28:00.279 --> 00:28:03.720 these kinds of technologies, that's one of the biggest challenges. Figuring out which 390 00:28:03.200 --> 00:28:07.440 vendors should be on your short list for a particular space is a big consideration 391 00:28:07.519 --> 00:28:11.160 as well. But I almost think that at this point in time, changing 392 00:28:11.240 --> 00:28:15.839 minds and opening minds is a big part of the conversation. And these llms, 393 00:28:17.119 --> 00:28:18.160 from what I've seen, I've been in this business a long time, 394 00:28:18.440 --> 00:28:22.839 these lms have absolutely opened the eyes of business leaders. They now see, 395 00:28:22.880 --> 00:28:26.319 oh wow, we really should be focused on this stuff. It's kind of 396 00:28:26.359 --> 00:28:30.079 like Cloud was ten years ago. I thought Cloud was going to take off 397 00:28:30.160 --> 00:28:33.680 like twenty years ago. It took about ten years longer than I thought, 398 00:28:33.720 --> 00:28:37.920 and then all of a sudden it just started really going in full steam. 399 00:28:37.960 --> 00:28:40.799 And I think a big part of that is a guy named Satya Nadella from 400 00:28:40.920 --> 00:28:45.880 Microsoft who somehow managed to pivot this aircraft carrier in a different direction a pretty 401 00:28:45.880 --> 00:28:48.920 short period of time. And I think we're in that big of a pivot. 402 00:28:48.960 --> 00:28:52.559 Speaking back to this concept of pivot right since and pivot, I think 403 00:28:52.680 --> 00:28:57.839 organizations have got to start pivoting to really understanding that these AI technologies are the 404 00:28:57.880 --> 00:29:02.240 game changers and if you don't investigate them, you're going to be in big 405 00:29:02.279 --> 00:29:06.640 trouble. What do you think, Alex No I one hundred percent agree. 406 00:29:06.640 --> 00:29:11.160 And I think there's two elements of that. One is the leadership of an 407 00:29:11.240 --> 00:29:18.119 organization needs to be forward thinking and thinking about the applic you know, how 408 00:29:18.119 --> 00:29:22.400 to apply technology within their business. And obviously Neil and Suketu do a lot 409 00:29:22.440 --> 00:29:29.240 of work with those corporations to help them structure. They're thinking around that aspect 410 00:29:29.240 --> 00:29:32.799 of it. And then the other part that we like to consider as well 411 00:29:32.839 --> 00:29:36.359 is then the implementation of that technology. And part that comes back to Eric. 412 00:29:36.359 --> 00:29:40.440 I think what you mentioned, you have different technology vendors and which one 413 00:29:40.599 --> 00:29:45.640 is going to work better for a particular organization, right, so, you 414 00:29:45.680 --> 00:29:48.440 know, we're talking a lot about LMS. One example, in the physical 415 00:29:48.440 --> 00:29:52.960 world, you would see sometimes let's say a small factory or a machine shop, 416 00:29:53.720 --> 00:29:57.240 they would want to adopt robotics and be kind of a top down initiative, 417 00:29:57.400 --> 00:30:03.880 and they would get a robot and everyone's very excited about it. And 418 00:30:03.920 --> 00:30:07.640 then once the executives go away and everyone goes back to producing the goods that 419 00:30:07.680 --> 00:30:11.319 they were producing, the robot is kind of pushed into a corner and forgotten 420 00:30:11.319 --> 00:30:18.759 about because it wasn't really implemented well, and it wasn't it wasn't running as 421 00:30:18.799 --> 00:30:23.200 well as as expected. Perhaps there was a lot of issues with the way 422 00:30:23.319 --> 00:30:27.279 was integrated into the line, or perhaps the other folks, you know, 423 00:30:27.319 --> 00:30:32.440 the people working there didn't trust it for one reason or another, and then 424 00:30:32.480 --> 00:30:33.880 it just gets wheeled out again when the executives come back in. So how 425 00:30:33.880 --> 00:30:37.920 do you avoid that, not just with a physical robot, but also with 426 00:30:37.000 --> 00:30:40.680 some of these AI related tools. And I think a lot of it comes 427 00:30:40.680 --> 00:30:47.359 down to ensuring that the way that they are implemented is truly valuable to the 428 00:30:47.400 --> 00:30:49.680 people who are going to interact with them, and also is done with an 429 00:30:49.759 --> 00:30:55.119 understanding of their actual day to day work. Right. It's not trying to 430 00:30:55.240 --> 00:30:59.599 force them into a structure that doesn't make sense or doesn't fit the realities of 431 00:30:59.640 --> 00:31:04.200 that or of that, you know, whatever function that that aigent is operating 432 00:31:04.240 --> 00:31:10.400 in, but very critical to actually fit in with the way that these people 433 00:31:10.440 --> 00:31:15.640 do their work and provide value to them, to promote that engagement and make 434 00:31:15.680 --> 00:31:18.920 sure it's not just sitting in the corner and you know, wheel that when 435 00:31:18.359 --> 00:31:22.359 they want to show it off to someone and then it goes right back back 436 00:31:22.400 --> 00:31:23.880 with no, that's exactly right. And you know, we only have two 437 00:31:23.880 --> 00:31:26.519 minutes left in this segment. But as you were talking, and this is 438 00:31:26.559 --> 00:31:30.160 what I love about doing these shows with smart people like we have on the 439 00:31:30.160 --> 00:31:33.279 show today. I have these epiphanies about things, and there's something going to 440 00:31:33.319 --> 00:31:37.319 tease this subject and sucqutta. I want you to just offer a quick answer 441 00:31:37.359 --> 00:31:40.240 and then in the next segment let's dive into this. I'll tell you something 442 00:31:40.279 --> 00:31:41.799 that's going to be very compelling. I just I sensed this. You can 443 00:31:41.839 --> 00:31:45.599 tell me if I'm wrong. There has been for years and years this ITOT 444 00:31:45.880 --> 00:31:52.720 divide right information technology operational technology, and very few people know how to bridge 445 00:31:52.759 --> 00:31:56.279 those two worlds. But I'll bet a smart AI engine can figure out how 446 00:31:56.319 --> 00:32:00.680 to bridge those two worlds because lllms can write not just an in French and 447 00:32:00.720 --> 00:32:04.079 Spanish, they can write in Cobol, they can write in Java. They 448 00:32:04.119 --> 00:32:07.839 can write in C sharp and C plus plus. Do you see got like 449 00:32:07.880 --> 00:32:13.279 sixty seconds, do you see LM's playing a significant role in infusing Finally, 450 00:32:13.319 --> 00:32:17.599 these roles of OT and IT communications, Well, so potato potato is what 451 00:32:17.799 --> 00:32:22.599 used to be with IT and OT andy both of them thought that they were 452 00:32:22.640 --> 00:32:27.920 special to be transparent. LLMS will play an important role, But there are 453 00:32:27.920 --> 00:32:31.200 even simpler technologies right like you talked about Eric in the past of hey, 454 00:32:31.440 --> 00:32:36.880 how do you get the underlying data in a simple, cohesive manner, So 455 00:32:36.920 --> 00:32:40.440 that allows you to interpret it through a machine very easily. So that's where 456 00:32:40.480 --> 00:32:45.799 the biggest help is gonna come it And r OT was real in realist, 457 00:32:45.559 --> 00:32:52.119 the real trumps if false dichotomy created because that's where the work was being done. 458 00:32:52.240 --> 00:32:54.319 Right, I work on the machine floor, so I am ot. 459 00:32:55.000 --> 00:32:59.279 I work on the planning side, so I am it. But it's all 460 00:32:59.319 --> 00:33:01.839 a supply chain, and the way we break it down is with this end 461 00:33:01.880 --> 00:33:07.599 to end silo and elellent scale help there sixty seconds. Okay, that was 462 00:33:07.640 --> 00:33:10.119 excellent, So folks, don't touch that doll. I'm like totally immersing this 463 00:33:10.160 --> 00:33:22.200 conversation. We'll be right back. You are listening to Inside Analysis. Welcome 464 00:33:22.240 --> 00:33:30.160 back to Inside Analysis. Here's your host, Eric Tabanac. All right, 465 00:33:30.319 --> 00:33:34.480 folks, welcome back to inside Analysis. What a fantastic conversation we were having 466 00:33:34.480 --> 00:33:38.599 here with Neil Haunch of Silicon Foundry, Alex Freed of some MARKI but did 467 00:33:38.640 --> 00:33:43.680 I get that right? What is it? Adventures? Schematic ventures? Schematic 468 00:33:43.839 --> 00:33:45.960 ventures. I'm sorry, that's my bad. Like there's a company SAMARKI it 469 00:33:46.119 --> 00:33:51.200 just jumped into my head. Schematic adventures like it right next time and suketto 470 00:33:51.279 --> 00:33:55.519 Gandhi from at Kearney all about the false dichotomy of it and oh tm Kenny. 471 00:33:55.559 --> 00:34:00.839 We're talking about supply chain today and we're talking about AI and where can 472 00:34:00.880 --> 00:34:04.519 you This is what every business person is wondering now, like where can I 473 00:34:04.640 --> 00:34:08.840 leverage this stuff? It looks great. Elms themselves not too good at lots 474 00:34:08.880 --> 00:34:12.960 of enterprise tasks. A lot of companies are kind of learning that now. 475 00:34:13.280 --> 00:34:15.519 Yes, it's good at writing copy, that's for sure. It's a lot 476 00:34:15.559 --> 00:34:19.679 of these engines know a lot of stuff. I mean. But the key 477 00:34:19.719 --> 00:34:23.480 to remember with these lms is that they are consensus engines because you vectorized this 478 00:34:23.599 --> 00:34:29.159 information. So dog has been vectorized in this particular fashioned cat. Schrodinger's cat 479 00:34:29.280 --> 00:34:31.159 is just a version of a cat, but the vector is very similar to 480 00:34:31.320 --> 00:34:36.960 just a cat. So they're they're giving you an average of something and that's 481 00:34:36.960 --> 00:34:38.960 good for a lot of different use cases. But business people want to know 482 00:34:39.079 --> 00:34:43.519 where can I leverage AI for business benefit? Today I'll throw it over to 483 00:34:43.559 --> 00:34:47.000 Neil Haunch. How do you counsel your clients when they're asking you these questions. 484 00:34:47.679 --> 00:34:50.480 Yes, and so I think, you know, a big chunk of 485 00:34:50.480 --> 00:34:52.639 the conversation on the show here today has been the intersection of AI the supply 486 00:34:52.760 --> 00:34:55.199 chain. But I think you know, if we back up and to your 487 00:34:55.239 --> 00:34:59.119 point, you know, the corporate executives are trying to figure out what's real, 488 00:34:59.199 --> 00:35:02.159 what's not, you know, what's actionable today. I think also in 489 00:35:02.199 --> 00:35:07.320 the context of what popular press is covering, so certainly things like ad tech 490 00:35:07.320 --> 00:35:10.719 and martech, right, content generation, uh, you know, video and 491 00:35:10.719 --> 00:35:14.599 what have you. But I think what we see is is the lowest hanging 492 00:35:14.599 --> 00:35:17.119 through that have the biggest impact and are already proven use cases. So things 493 00:35:17.159 --> 00:35:21.480 like, you know, if the company has software coders, right, you're 494 00:35:21.480 --> 00:35:24.920 going to use AI solutions to help on the coding side of things right off 495 00:35:24.920 --> 00:35:28.960 the bat, Uh, just knowledge management. I think a lot of the 496 00:35:29.000 --> 00:35:34.079 use cases we've seen here's a massive company with stores of knowledge spread throughout the 497 00:35:34.079 --> 00:35:38.199 classic you know, siloed disparate systems. It was a head of AI at 498 00:35:38.199 --> 00:35:43.119 a large finishing services institution who gave this example. It's not the most possible 499 00:35:43.159 --> 00:35:45.960 and if he said, you know, how many times a day do folks 500 00:35:45.440 --> 00:35:49.800 ask the question of you know, how do I fire somebody at the company, 501 00:35:50.039 --> 00:35:52.639 right and it takes them four hours to figure out? You know, 502 00:35:52.679 --> 00:35:54.199 I'm boie. I think put that in the chat GPT, your equivalent, 503 00:35:54.320 --> 00:36:00.360 that's pulling from the the internal sources. So just things like that. When 504 00:36:00.360 --> 00:36:05.159 you add it up at scale, you know, hundreds thousands knowledge worker hours 505 00:36:05.800 --> 00:36:08.360 areas like customer service, customer support, which that does get a lot of 506 00:36:08.400 --> 00:36:12.360 popular press. But you know, we're talking to organizations the other day and 507 00:36:12.360 --> 00:36:15.159 you look at it as a compliment to the agent, and yes, we 508 00:36:15.199 --> 00:36:17.639 recognize it also can be a replacement. Right how many text support calls? 509 00:36:17.719 --> 00:36:22.159 Or I forgot my password? And so I think these are you know, 510 00:36:22.199 --> 00:36:25.239 and then a lot of you know, a lot of the areas within supply 511 00:36:25.320 --> 00:36:30.320 chain this was seen obviously AI is well then into all the robotics automation solutions 512 00:36:30.360 --> 00:36:35.880 as roven, into all the computer revision really solutions. So but I think, 513 00:36:36.039 --> 00:36:38.000 you know, it's kind of the for some some of these large companies 514 00:36:38.079 --> 00:36:40.559 is the walk before you run. It's the work. And I absolutely get 515 00:36:40.599 --> 00:36:44.119 value out of this, and of course we always think about it, you 516 00:36:44.159 --> 00:36:46.119 know, regardless if it's on the leading edge, the bleeding edge, or 517 00:36:46.360 --> 00:36:50.719 what we've would define is right down the middle right now is how can I 518 00:36:50.840 --> 00:36:53.079 do the pilot and then do a successful roll out at scale? You know, 519 00:36:53.119 --> 00:36:57.400 for these companies that have thousands, if not tens of thousands, or 520 00:36:57.400 --> 00:37:01.199 in some cases hundreds of thousands of workers spread across the globe. Yeah, 521 00:37:01.239 --> 00:37:05.159 you know, you just brought up something very interesting and I haven't heard anyone 522 00:37:05.280 --> 00:37:07.800 say this yet, but I think it's it is truly inspired. This is 523 00:37:07.920 --> 00:37:12.760 knowledge management. Like well, years and years ago, we're talking thirty plus 524 00:37:12.840 --> 00:37:15.000 years ago, knowledge management was a big thing, but we just didn't really 525 00:37:15.039 --> 00:37:20.840 have the tech. We didn't have the capacity to absorb large amounts of unstructured 526 00:37:20.920 --> 00:37:25.880 data and then deliver some harmonized view of that information. Now we do. 527 00:37:27.000 --> 00:37:30.079 That's what these AI agents or that's what these llms do extremely well, is 528 00:37:30.239 --> 00:37:36.280 knowledge management. So to your point, if you have the confidence to point 529 00:37:36.320 --> 00:37:40.800 in LM to vectorize basically all your information. And it's my understanding that Google, 530 00:37:40.960 --> 00:37:44.199 this isn't pilot. I think. I don't think you can use it 531 00:37:44.239 --> 00:37:47.400 with your business account, but on your personal account, if you're using Gemini, 532 00:37:47.800 --> 00:37:52.000 you can type at drive and at Gmail, and it is they have 533 00:37:52.079 --> 00:37:58.360 already pre vectorized all of your content, which is like, that's pretty cool, 534 00:37:58.800 --> 00:38:00.559 right, So are you seeing companies yet do this or is that still 535 00:38:00.599 --> 00:38:04.639 kind of forward looking? Neil, go ahead, Uh, seeing companies do 536 00:38:04.760 --> 00:38:07.360 this today. And in terms of the arms providers, you know, a 537 00:38:07.360 --> 00:38:12.559 good example and as you were kind of referencing a company called Glean startup and 538 00:38:13.159 --> 00:38:17.119 it raised significant dollars. But we've brought them in to meet with a number 539 00:38:17.119 --> 00:38:20.960 of the companies that we work with, and we're hoping to work with across 540 00:38:21.000 --> 00:38:23.519 all different industries. And it's just that, right, And the founding team 541 00:38:23.519 --> 00:38:27.599 there believe they came from Google Search in the early days, right, But 542 00:38:27.639 --> 00:38:30.840 that's you call it enterprise knowledge management, enterprise search, and I think also 543 00:38:30.920 --> 00:38:35.559 to your point, it can search internal and external and the same at the 544 00:38:35.559 --> 00:38:40.920 same time, the same interface. And and whether it's the c suite of 545 00:38:40.920 --> 00:38:45.239 an oil and gas company or a cosmetics company or some people are company when 546 00:38:45.239 --> 00:38:47.360 they see the demo, you know, you can see the wheels turning immediately 547 00:38:47.400 --> 00:38:52.639 of oh gosh, what this could do for the efficiency of our people in 548 00:38:52.679 --> 00:38:57.639 their day to day roles. Sure, and just understanding policy for example, 549 00:38:58.000 --> 00:39:00.400 so you take the standard policy doc. And let's say it's thirty pages or 550 00:39:00.440 --> 00:39:04.800 forty pages. Okay, how many people think anyone ever sat down and read 551 00:39:04.800 --> 00:39:07.920 the whole forty page policy document. I'll bet you not many people did. 552 00:39:07.239 --> 00:39:12.280 But your very few employees ever did that. And search old fashioned search. 553 00:39:12.360 --> 00:39:15.199 I mean I actually talked to a very very smart lady who was at the 554 00:39:15.320 --> 00:39:21.119 Google search team years ago, and you know, I remember thinking jatbots came 555 00:39:21.159 --> 00:39:27.599 around largely because corporate search stinks, like on websites, Like if you ever 556 00:39:27.639 --> 00:39:30.480 go to a website five years ago, if you went to the corporate website 557 00:39:30.519 --> 00:39:32.000 and search for something you want to find, you would get absolute nonsense, 558 00:39:32.039 --> 00:39:37.119 press releases and stuff that just means nothing. That's not helpful. But to 559 00:39:37.159 --> 00:39:39.159 your point, I actually heard a story of just that, of using it 560 00:39:39.199 --> 00:39:42.760 to find out how to fire someone. But the suketto, what do you 561 00:39:42.800 --> 00:39:49.320 think that's a hard one to follow how to fire someone? Because I don't 562 00:39:49.360 --> 00:39:54.039 know. But to there's an aspect of this that you were pointing to both 563 00:39:54.159 --> 00:40:00.599 Neil and Etick is you got to understand how your business processes operate if you're 564 00:40:00.639 --> 00:40:06.280 gonna apply these new technologies, and that is a really critical part of it. 565 00:40:06.440 --> 00:40:08.920 So knowledge management only works if you know what you're gonna do with the 566 00:40:09.000 --> 00:40:15.719 knowledge, not otherwise. So you got to know where to apply these and 567 00:40:15.800 --> 00:40:17.719 how to apply this, and how do your companies operate. And what has 568 00:40:17.840 --> 00:40:22.039 happened is in larger and larger companies they started with a very simple process. 569 00:40:22.280 --> 00:40:25.480 Customer needs something, I'm gonna make it and find a way to deliver. 570 00:40:25.800 --> 00:40:30.800 And then we started adding layers on top, whether it was management layer, 571 00:40:30.079 --> 00:40:35.800 information layers, or process layers. And one of the big things that we 572 00:40:35.840 --> 00:40:40.360 see happening is you're stripping these down right. It is a reverse Mandel Brought 573 00:40:42.280 --> 00:40:44.760 I know, you said Schrodinger, So I had to go to Mandel Brought, 574 00:40:45.079 --> 00:40:47.320 but reverse manual broad where you're starting to clean everything up and go, 575 00:40:47.519 --> 00:40:52.400 this is how the process should run, and this is how a GENI will 576 00:40:52.440 --> 00:40:55.639 apply. So in the world of procurement, we created this thing. It's 577 00:40:55.679 --> 00:41:00.760 called seven Steps of procurement. Now we are working through and say can we 578 00:41:00.840 --> 00:41:05.440 do it in two And the answer is yes, and we're seeing it in 579 00:41:05.480 --> 00:41:09.000 action. Well that's the kind of rethinking that is needed. Yeah, that's 580 00:41:09.039 --> 00:41:12.360 awesome. I mean I have to say, maybe Neil will throw it over 581 00:41:12.400 --> 00:41:15.760 you to comment on it, and it's very encouraging because I go back to 582 00:41:15.800 --> 00:41:20.639 this mindset issue. People get into a certain mindset, they do things in 583 00:41:20.679 --> 00:41:23.239 certain ways how we've always done them. Now is the time, if ever, 584 00:41:23.480 --> 00:41:29.079 to challenge those presumptions because to Security's point, Okay, you've done this 585 00:41:29.519 --> 00:41:32.000 seven step process for years, do you really need all those seven steps? 586 00:41:32.039 --> 00:41:36.159 Like can't you get a predictive score on steps two, three, and four 587 00:41:36.199 --> 00:41:38.199 and then move right to step five? And that's kind of where we're seeing 588 00:41:38.199 --> 00:41:43.559 things go. That is innovation. That is where you save tremendous amounts of 589 00:41:43.599 --> 00:41:47.239 time and also keep your partners better informed, your employees better informed. And 590 00:41:47.360 --> 00:41:52.920 this gets to my soapbox topic morale. When morale is high, good things 591 00:41:52.960 --> 00:41:54.719 happen. When morale is low, you could have all the money and resource 592 00:41:54.760 --> 00:41:58.760 in the world and bad things are going to happen. Are you seeing this 593 00:41:58.800 --> 00:42:01.800 too, Neil, that companies are figuring out how to leap fraud over traditional 594 00:42:02.199 --> 00:42:07.800 sort of staid processes. Yes, I mean, and this is the classic 595 00:42:08.039 --> 00:42:14.199 you know, opportunity conundrum, right is the corporates are optimized around capital efficiency 596 00:42:14.280 --> 00:42:19.000 and risk reduction, and you know, if public obviously quarter to quarter, 597 00:42:19.320 --> 00:42:22.400 you know, metrics and numbers. You know, you're kind of reminding me. 598 00:42:22.559 --> 00:42:27.800 But then hey, sometimes that leap road disruption requires folks that are not 599 00:42:27.840 --> 00:42:30.800 from industry. You know, I'm generally the champion. Someone left industry because 600 00:42:30.800 --> 00:42:34.599 they saw an opportunity. It left their existing role to go start a company 601 00:42:34.840 --> 00:42:38.679 to solve you know, to address an opportunity or solve the challenge. But 602 00:42:38.719 --> 00:42:43.760 I remembering earlier, earlier in my career, we met with the founders of 603 00:42:44.880 --> 00:42:46.599 Square before Square existed, right, and it was, well, wait a 604 00:42:46.639 --> 00:42:50.920 minute, these founders have never worked in the financial services industries, you know, 605 00:42:50.960 --> 00:42:54.559 mership processing. You know, this can't work, right, the things 606 00:42:54.559 --> 00:42:58.119 that they're talking about doing. That's not the way it works, the industry 607 00:42:58.119 --> 00:43:00.159 works. But that's that is what it took, right, as someone who 608 00:43:00.239 --> 00:43:07.199 was not bounded in existing reality. Right. But obviously you know, and 609 00:43:07.559 --> 00:43:12.800 you know, maybe my last point would be just weaving in a culture of 610 00:43:12.800 --> 00:43:15.840 innovation, a culture of risk taking. Certainly we see it. I don't. 611 00:43:15.840 --> 00:43:19.119 I don't think there's a corporate we work with where the people that we 612 00:43:19.239 --> 00:43:22.639 serve specifically go gosh. You know, our massive organization, you know, 613 00:43:22.639 --> 00:43:27.679 we are nimble, we are flexible and you know instead it's you know, 614 00:43:28.119 --> 00:43:30.719 at times, got to beat the head against the wall, right in terms 615 00:43:30.719 --> 00:43:35.920 of to get changed to happen. And I think it's top down, bottoms 616 00:43:36.000 --> 00:43:38.760 up, but it's a universal challenge and it's it's also one born out of 617 00:43:38.800 --> 00:43:43.719 these very large companies that have been very successful in some caids, not just 618 00:43:43.760 --> 00:43:47.280 over years and decades, but centuries, march and centuries, and that'll always 619 00:43:47.280 --> 00:43:51.000 be, always be a challenge. Yeah, no, that's right, and 620 00:43:51.239 --> 00:43:52.360 I think you are seeing it at the big companies too. Of folks, 621 00:43:52.400 --> 00:43:59.079 podcast bonus segment is coming up next. You were listening to Inside Analysis, 622 00:44:00.199 --> 00:44:04.599 All right, folks back here on Inside Analysis. Time for the podcast bonus 623 00:44:04.679 --> 00:44:08.079 segment. And I'm talking to an excellent group of individuals here, Alex Freed, 624 00:44:08.440 --> 00:44:13.000 Neil hanch Suketu Gandhi Alex, I'm gonna throw it over to you because 625 00:44:13.000 --> 00:44:15.440 you're in the startup world. You talk to startups all the time from the 626 00:44:15.480 --> 00:44:20.760 ideation stage and beyond, and there's a lot of activity in the supply chain 627 00:44:20.800 --> 00:44:23.280 world because guess what, necessity is the mother of invention. When you have 628 00:44:23.480 --> 00:44:27.679 supply chain disruptions, there's a lot of money on the line. There are 629 00:44:27.679 --> 00:44:30.280 a lot of companies that are like, ooh, what are we gonna do? 630 00:44:30.840 --> 00:44:32.239 And that's where startups come in. What do you see happening, What 631 00:44:32.280 --> 00:44:36.360 are some of the more interesting ventures that you've come across, and where do 632 00:44:36.360 --> 00:44:39.719 you think things are going? Eric, I think I'll have to plug a 633 00:44:39.719 --> 00:44:44.639 couple of our portfolio companies that I think are doing really exciting things, especially 634 00:44:44.679 --> 00:44:49.440 as it relates to this topic. So one of them is Altana AI and 635 00:44:49.519 --> 00:44:52.920 so effectively they have a vast network of both public and private data. They 636 00:44:52.960 --> 00:44:57.960 believe it's one of the largest organized bodies of supply chain data in the world, 637 00:44:57.960 --> 00:45:02.079 calling it the altana at list, and it's helping to customers visualize kind 638 00:45:02.119 --> 00:45:07.920 of their global value chains. And so I think we mentioned earlier on the 639 00:45:07.920 --> 00:45:10.239 show kind of the difficulty of understanding, you know, you have your tier 640 00:45:10.239 --> 00:45:14.159 one supplier, but what about the next tier and the next tier and going 641 00:45:14.199 --> 00:45:15.280 all the way to the end tier. So that's one of the things that 642 00:45:15.280 --> 00:45:22.559 they're helping to solve through that at LISS and then working with both let's say, 643 00:45:22.599 --> 00:45:27.119 the US Customs and Border Protection to companies like marit to Boston Scientific to 644 00:45:27.199 --> 00:45:31.280 help them understand, you know, are they complying with trade laws, Are 645 00:45:31.320 --> 00:45:36.599 there any of their suppliers that are, you know, using forced labor or 646 00:45:36.639 --> 00:45:42.920 doing other things that bad actors do, or perhaps are there any potential risks 647 00:45:43.000 --> 00:45:46.280 or bottlenecks in the fourth tier their supply chain where they think they have a 648 00:45:46.280 --> 00:45:51.320 lot of supply redundancy or diversity less of the tier one or tier two stage, 649 00:45:51.360 --> 00:45:53.559 but they find out that there's actually this bottleneck at the fifth tier. 650 00:45:54.079 --> 00:46:01.960 So Altana helps identify those types of things. So in terms of visibility and 651 00:46:01.960 --> 00:46:07.119 an understanding of your overall global supply chain, I'd say that's one of the 652 00:46:07.159 --> 00:46:10.760 most exciting companies we've seen. Away from the software side, I think there's 653 00:46:10.800 --> 00:46:15.480 a lot these days that you see with let's say the robotics, the physical 654 00:46:15.599 --> 00:46:22.039 robotics and automation pieces as well. The there's a maturation happening with some of 655 00:46:22.079 --> 00:46:29.639 the existing technology, so articulated arms, autonomous mobile robots, asrs, autonomous 656 00:46:29.639 --> 00:46:32.320 storage and retrieval systems, and now you're also seeing some of these humanoid robots 657 00:46:32.679 --> 00:46:37.920 come in and there's a it's both exciting but also an open question of is 658 00:46:37.960 --> 00:46:45.440 the human robot form factor going to outperform some of these you know existing arm 659 00:46:45.719 --> 00:46:50.559 you know, mobile based style formats as well. And then the last piece 660 00:46:50.960 --> 00:46:53.440 that I would mention is just you know, we've talked about it before, 661 00:46:53.519 --> 00:47:00.159 but in terms of citing AI related technologies that are coming in the generative, 662 00:47:00.199 --> 00:47:06.079 aipieces and even just outside of let's say the LMS, understanding emails and so 663 00:47:06.159 --> 00:47:09.599 on, but helping design products and goods faster, more efficiently, to kind 664 00:47:09.599 --> 00:47:14.719 of increase the speed of innovation and the speed at which companies can release products. 665 00:47:14.960 --> 00:47:17.480 So yeah, three different areas, including one plug for one of our 666 00:47:17.480 --> 00:47:22.360 companies. Now that's good, And you actually hit on a real hot button 667 00:47:22.400 --> 00:47:25.840 issue for me, which is alternative data, or as I sometimes refer to 668 00:47:25.880 --> 00:47:31.480 it, real world data at scale, because when you can start capturing massive 669 00:47:31.480 --> 00:47:36.599 amounts of data, whether let's just say it's product data, understanding how many 670 00:47:36.639 --> 00:47:43.159 oranges came in yesterday, understanding how many computer parts arrived, if you can 671 00:47:43.199 --> 00:47:46.880 get a baseline of what is out there in TOTO and then map that against 672 00:47:46.880 --> 00:47:51.920 your internal data, Holy Christmas, that's where some amazing things can happen to 673 00:47:52.079 --> 00:47:53.320 ketto. I see you now at your head there, what do you think 674 00:47:53.320 --> 00:47:58.880 about that the marriage of what I call real world data at scale, which 675 00:47:58.920 --> 00:48:00.480 is all this alternative data, and you can buy it from all kinds of 676 00:48:00.519 --> 00:48:02.800 companies. You can buy it from credit card companies, you can buy it 677 00:48:02.800 --> 00:48:07.400 from states, from counties, from countries, and as long as it's quality 678 00:48:07.480 --> 00:48:10.519 data to be able to map that against your own view of the world. 679 00:48:10.840 --> 00:48:15.119 That's some pretty compelling stuff. What do you think, Yeah, no, 680 00:48:15.239 --> 00:48:19.239 it is. It is compelling and it is useful. But one of the 681 00:48:19.280 --> 00:48:23.440 structural things that you know, especially Neil and I talked to CEOs and boards 682 00:48:23.480 --> 00:48:30.400 all the time that they're asking is is this another transformation or what does this 683 00:48:30.679 --> 00:48:34.119 look like for the next three to five years? And you know, we 684 00:48:34.280 --> 00:48:37.760 tend to use the word regenerative, that you are going to be constantly regenerating. 685 00:48:37.079 --> 00:48:42.119 And CEO Client had this wonderful quote, I'm not trying to figure out 686 00:48:42.159 --> 00:48:45.920 the endgame. I'm trying to figure out that the game never ends. And 687 00:48:45.960 --> 00:48:51.559 in that kind of work, that's good. What happens is you are constantly 688 00:48:51.639 --> 00:48:54.119 looking and going, Okay, I got a new set of data, whether 689 00:48:54.159 --> 00:49:00.280 it's artificial or internal, wishing for a little bit more information. We all 690 00:49:00.280 --> 00:49:08.719 get it all here on KCAA Radio the most diversified radio station on the dial, 691 00:49:08.920 --> 00:49:22.039 KCAA. The information economy has a ride. What happens is you are 692 00:49:22.360 --> 00:49:25.199 constantly looking and going, Okay, I got a new set of data, 693 00:49:25.320 --> 00:49:30.559 whether it's artificial or internal. How do I use it? How does it 694 00:49:30.679 --> 00:49:34.719 change? So you go and run it, see the improvement, and then 695 00:49:34.880 --> 00:49:38.039 onto the next one. And this is a variation on you know, fail 696 00:49:38.119 --> 00:49:43.239 fast. This is learned fast model, not a fail fast model, because 697 00:49:43.280 --> 00:49:47.119 you have a lot of data coming in constantly and you can learn. And 698 00:49:47.480 --> 00:49:52.199 you know, one of the wonderful things we have always seen is that bugs 699 00:49:52.239 --> 00:49:58.199 are not failure. Bugs are an opportunity to improve your process. And that 700 00:49:58.400 --> 00:50:01.719 is the heart of regeneration that goes. Always keep the customer in mind, 701 00:50:02.159 --> 00:50:07.400 Always understand why do you exist, Always understand why your people are there, 702 00:50:07.559 --> 00:50:12.079 and then bringing these agents to constantly learn. So again, you know, 703 00:50:12.199 --> 00:50:15.719 fail fast wonderful, anybody can do that. But learn fast that's the company 704 00:50:15.760 --> 00:50:20.199 for the long term. I like that. I think that's brilliant. That's 705 00:50:20.239 --> 00:50:22.519 a very very clever Moniker because like fail fast, like why do you want 706 00:50:22.519 --> 00:50:27.280 to fail? They'll fail? Learn You're not failing, You're learning. I'll 707 00:50:27.280 --> 00:50:29.960 throw it over to Neil for final thoughts here, and I have to say, 708 00:50:30.000 --> 00:50:35.079 Neil, this regeneration of knowledge management, I think is absolutely spectacular. 709 00:50:35.159 --> 00:50:37.760 I think that's where a lot of the magic is going to come. But 710 00:50:37.920 --> 00:50:39.960 companies do need to be careful. You need to have some sort of governance 711 00:50:40.000 --> 00:50:45.360 practice in place. Now we can scan massive amounts of unstructured data. We 712 00:50:45.400 --> 00:50:49.280 can identify maybe data that's not so good, or maybe data that's personally identifiable 713 00:50:49.320 --> 00:50:54.119 information or sensitive. Do that first, but then absolutely leverage all that stuff. 714 00:50:54.159 --> 00:50:57.960 Now it comes back to life. It's been sitting on a shelf, 715 00:50:58.320 --> 00:51:00.719 you know, and share point from the last seven years. Maybe that's the 716 00:51:00.719 --> 00:51:05.559 best idea you've never acted on your company, and because of this new AI, 717 00:51:05.559 --> 00:51:07.719 we're gonna be able to find that. What do you think final thoughts? 718 00:51:07.760 --> 00:51:10.840 Yeah, No, I think it's I'm enjoying the fail fast. But 719 00:51:10.840 --> 00:51:15.519 if you're going to fail, learn from it and then iterate and integrate it 720 00:51:15.559 --> 00:51:17.239 in and don't be afraid to and you've got to take the risks at least 721 00:51:17.480 --> 00:51:22.159 to a degree. I mean, I think it's also consistent with just innovation 722 00:51:22.360 --> 00:51:24.880 and dare I say disruption is happening faster than ever right now. And so 723 00:51:24.920 --> 00:51:28.800 if you're the corporate executives like you've got to make some of these bets, 724 00:51:28.800 --> 00:51:34.000 you've got to release the resources to test and then integrate and then scale and 725 00:51:34.039 --> 00:51:36.880 solutions, so you can say, it's more exciting time than ever before. 726 00:51:37.400 --> 00:51:40.239 It's less about what are my direct competitors doing, It's what's happening out there 727 00:51:40.239 --> 00:51:45.440 that's relevant for my business, you know, And and take innovation disruption as 728 00:51:45.679 --> 00:51:50.360 a positive rather than negative, right because it can make you it more efficient, 729 00:51:51.519 --> 00:51:57.079 more opportunities for the top line. And it's an exciting and probably uniquely 730 00:51:57.119 --> 00:52:00.480 scary time to be an executive with endless decision and endless sets of new data 731 00:52:00.880 --> 00:52:05.440 they have to digest and then figure out what the action is to take against 732 00:52:05.480 --> 00:52:07.760 it. But you know, I would not be doing my job. I 733 00:52:07.800 --> 00:52:10.119 didn't say. That's why we're here. That's why Cartnis here, that's why 734 00:52:10.119 --> 00:52:15.599 Silicon's founder here to help be a partner, a thought partner, a guide 735 00:52:15.920 --> 00:52:19.840 through all of this. That's great stuff. We'll look all these folks up 736 00:52:19.880 --> 00:52:23.760 online, ladies and gentlemen. We've talked to Neil Haunch of Silicon Foundry and 737 00:52:24.039 --> 00:52:30.840 Suquetu Gandhi of at Kearney and of course Alex Freed of Schematic Adventures and send 738 00:52:30.840 --> 00:52:31.480 me an email if I want to be in the show. Info at inside 739 00:52:31.480 --> 00:52:35.679 analysis dot com. That Go, Go's our show you've been listening to Inside 740 00:52:35.719 --> 00:52:42.719 Analysis. Redlands Ranch Market is a unique full service international or grocery store that 741 00:52:42.800 --> 00:52:46.960 specializes in authentic food items from Mexico, India, and from many Mediterranean and 742 00:52:47.000 --> 00:52:52.599 Asian countries, including popular items from the US. They offer fresh baked items 743 00:52:52.599 --> 00:52:57.039 from their in house bakery, housemade tortillas from their tortilla area, a delicious 744 00:52:57.119 --> 00:53:00.320 array of prepared Mexican foods, a terrific fresh food and juice bar, and 745 00:53:00.400 --> 00:53:05.480 a large selection of meats, seafoods and deli sandwiches, salads, and hellal 746 00:53:05.599 --> 00:53:08.159 meats. 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The complete website is to ebot club dot com or 763 00:54:23.360 --> 00:54:28.559 call us at eight one eight sixty one zero eight zero eight eight Monday through 764 00:54:28.599 --> 00:54:31.880 Saturday nine am to five pm California time. That's eight one eight sixty one 765 00:54:31.960 --> 00:54:38.440 zero eight zero eight eight t Ebot Club dot com with sixty years of fascinating 766 00:54:38.480 --> 00:54:45.320 facts. This is the man from yesterday and back in time. We go 767 00:54:45.400 --> 00:54:49.199 to this time in two thousand and two. Phil Donahue is back with the 768 00:54:49.239 --> 00:54:53.079 new talk show, this one on MSNBC. His older syndicated show went off 769 00:54:53.119 --> 00:54:59.239 the air after being cloppered by Oprah Winfrey a few years ago. I want 770 00:54:59.239 --> 00:55:01.320 to show him you Gracie at work. This is just a bit of a 771 00:55:01.360 --> 00:55:07.880 clip. I mean, okay, if this isn't history, this was at 772 00:55:07.880 --> 00:55:10.880 the center of our popular culture and from this time in nineteen ninety four, 773 00:55:12.039 --> 00:55:16.039 passing Gary Killdall, He's the guy who created the first popular operating system for 774 00:55:16.119 --> 00:55:22.320 PCs and he lost out to Microsoft's new ms DASS standard. Gary Killdall was 775 00:55:22.519 --> 00:55:27.440 only fifty two. So what's so important about the work that Gary did was 776 00:55:27.480 --> 00:55:31.719 the fact that he was one of the first to introduce an operating system for 777 00:55:32.079 --> 00:55:37.280 personal computers. And from this time. In nineteen sixty one, astronaut Gus 778 00:55:37.320 --> 00:55:42.840 Grissom, America's second man in space, makes a successful sixteen minute flight, 779 00:55:43.280 --> 00:55:47.119 but Grissom gets into big trouble when his Liberty Bell seven capsule sinks in the 780 00:55:47.159 --> 00:55:53.159 Atlantic Ocean, Grisom managed to escape the astronaut himself. Air Force Captain Virgil 781 00:55:53.159 --> 00:55:58.840 Grissom was recovered, is now aboard the USS Randolph in the Atlantic and will 782 00:55:58.880 --> 00:56:01.960 soon be steaming towards mom Island. He is undergoing the first of his medical 783 00:56:01.960 --> 00:56:13.480 examinations with more at man from yesterday dot com. Palm Springs Dispensary reminds everyone 784 00:56:13.559 --> 00:56:17.119 during these challenging times they understand the importance of mental health. Offering daily deals 785 00:56:17.119 --> 00:56:22.519 on everything from top quality flowers to edibles and discounts on CBD products, Palm 786 00:56:22.559 --> 00:56:24.840 Springs Dispensary's goal is to help you win the battle within. As one of 787 00:56:24.840 --> 00:56:30.079 the area's most beautiful wellness shops. Located on Garnet Avenue, visit the Palm 788 00:56:30.119 --> 00:56:36.039 Springs Dispensary dot com. That's the Palm Springs Dispensary dot com. The segment 789 00:56:36.079 --> 00:56:39.119 brought to you by Habitat for Humanity. Habitat presents hops for houses at the 790 00:56:39.239 --> 00:56:43.840 Ritual Brewing Company to this Wednesday, June twenty six, from six to nine 791 00:56:43.920 --> 00:56:47.000 pm, and it's all for just twenty dollars. It's at thirteen fifteen Research 792 00:56:47.119 --> 00:56:52.719 Drive in Redlands that's thirteen fifty Research Drive in Redlands. It's the Ritual Brewing 793 00:56:52.760 --> 00:56:55.559 Company. 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Make 802 00:57:30.239 --> 00:57:38.840 your life fun for de Listina KCAA Loma Linda at one O six point five 803 00:57:39.000 --> 00:57:43.920 FM K two ninety three c f Brito Valley. Don't listen admitted of the 804 00:57:43.960 --> 00:57:49.000 action. Check out the podcast. It's at www. KCAA radio dot com. 805 00:57:49.039 --> 00:57:55.679 The station that leaves no listener. Behind KCAA NBC News Radio, I'm 806 00:57:55.760 --> 00:58:00.360 Chris Karagio Tomorrow marks two years since the Supreme Court overturn Roby Wade, which 807 00:58:00.440 --> 00:58:05.639 unraveled the constitutional right to an abortion. In an interview with ABC's This Week, 808 00:58:05.880 --> 00:58:09.480 Massachusetts Senator Elizabeth Warren, warrened about the impact a second Trump presidency would 809 00:58:09.559 --> 00:58:15.159 have on reproductive rights. If Donald Trump is elected to the presidency, he 810 00:58:15.480 --> 00:58:22.559 and the extremist Republicans are coming after abortion, contraception, and IVF in every 811 00:58:22.599 --> 00:58:28.039 single state in this country. Warren added, Democrats will try and add protections 812 00:58:28.079 --> 00:58:30.039 to all three if they win both Chambers of Congress and the White House in 813 00:58:30.079 --> 00:58:36.000 November. President Biden and former President Trump are gearing up for Thursday's televised debate 814 00:58:36.039 --> 00:58:38.400 in Atlanta. The White House says Biden has been meeting with advisors at Camp 815 00:58:38.480 --> 00:58:42.559 David over the weekend. Meanwhile, Trump has been hitting the campaign trail. 816 00:58:42.840 --> 00:58:45.639 He spoke to a gathering of Christian conservatives in Washington, d C. Yesterday 817 00:58:45.840 --> 00:58:50.440 before holding a rally in Philadelphia. Trump also hinted that he's settled on a 818 00:58:50.519 --> 00:58:53.719 running mate and will likely announce his pick at the GOP convention in Milwaukee. 819 00:58:53.760 --> 00:59:00.119 Senator Lindsay Graham is slamming President Biden over his latest action allowing certain undocumented immigrants 820 00:59:00.360 --> 00:59:04.079 married to US citizens to stay in the country. In an interview on Fox 821 00:59:04.119 --> 00:59:07.599 New Sunday, the South Carolina Republican called Biden's decision a disaster, adding that 822 00:59:07.639 --> 00:59:14.079 Biden unilaterally gave five hundred thousand people legal status. This is beyond lawless and 823 00:59:14.119 --> 00:59:19.000 beyond dangers. So the idea is spreading around the world. Biden just gave 824 00:59:19.239 --> 00:59:22.880 amnesty legally to have of me and people. Graham argued the move would incentivize 825 00:59:22.880 --> 00:59:28.559 more migrants to cross into the US illegally. The executive order allows non citizens 826 00:59:28.639 --> 00:59:30.880 who have been in the country for at least ten years and are married to 827 00:59:30.920 --> 00:59:36.360 a US citizen to apply for permanent residents without leaving the country. Emergency room 828 00:59:36.440 --> 00:59:39.079 visits have spiked in many cities around the country this weekend due to the extreme 829 00:59:39.199 --> 00:59:44.760 heat. Health officials in New York say heat related er visits are up actually 830 00:59:44.800 --> 00:59:49.199 five hundred percent over what they normally see on a typical June day. I'm 831 00:59:49.239 --> 00:59:57.199 Chris Caragio, NBC News Radio, NBC News on CACAA lovel sponsored by Teamsters 832 00:59:57.239 --> 01:00:00.880 Local nineteen thirty two, protecting the future of war working Families. Teamsters nineteen 833 01:00:01.000 --> 01:00:02.039 thirty two, dot org