WEBVTT - Smart Talks with IBM: AI & the Productivity Paradox 0:00:00.160 --> 0:00:02.840 Hey everyone, it's Robert and Joe here. Today we've got 0:00:02.880 --> 0:00:04.760 something a little bit different to share with you. It 0:00:04.840 --> 0:00:08.039 is a new season of the Smart Talks with IBM 0:00:08.160 --> 0:00:09.160 podcast series. 0:00:09.640 --> 0:00:11.639 Today we are witnessed to one of those rare moments 0:00:11.680 --> 0:00:14.400 in history, the rise of an innovative technology with the 0:00:14.400 --> 0:00:18.680 potential to radically transform business and society forever. The technology, 0:00:18.760 --> 0:00:22.200 of course, is artificial intelligence, and it's the central focus 0:00:22.239 --> 0:00:24.840 for this new season of Smart Talks with IBM. 0:00:25.320 --> 0:00:28.400 Join hosts from your favorite Pushkin podcasts as they talk 0:00:28.480 --> 0:00:31.680 with industry experts and leaders to explore how businesses can 0:00:31.720 --> 0:00:35.400 integrate AI into their workflows and help drive real change 0:00:35.400 --> 0:00:38.240 in this new era of AI. And of course, host 0:00:38.280 --> 0:00:40.479 Malcolm Gladwell will be there to guide you through the 0:00:40.479 --> 0:00:42.640 season and throw in his two cents as well. 0:00:43.120 --> 0:00:46.200 Look out for new episodes of Smart Talks with IBM 0:00:46.400 --> 0:00:49.559 every other week on the iHeartRadio app, Apple Podcasts, or 0:00:49.600 --> 0:00:53.360 wherever you get your podcasts. And learn more at IBM 0:00:53.479 --> 0:00:57.720 dot com, slash smart Talks. 0:01:02.280 --> 0:01:03.160 Pushkin. 0:01:06.800 --> 0:01:14.200 Welcome, Welcome, Welcome to Smart Talks with IBM. 0:01:14.240 --> 0:01:17.880 Hello, Hello, Welcome to Smart Talks with IBM, a podcast 0:01:18.040 --> 0:01:23.920 from Pushkin Industries. iHeartRadio and IBM. I'm Malcolm Gladwell. This season, 0:01:24.160 --> 0:01:27.440 we're diving back into the world of artificial intelligence, but 0:01:27.520 --> 0:01:32.840 with a focus on the powerful concept of open its possibilities, implications, 0:01:33.200 --> 0:01:36.960 and misconceptions. We'll look at openness from a variety of 0:01:37.000 --> 0:01:40.880 angles and explore how the concept is already reshaping industries, 0:01:41.319 --> 0:01:44.920 ways of doing business, and our very notion of what's possible. 0:01:45.360 --> 0:01:47.880 And for the first episode of this season, we're bringing 0:01:47.920 --> 0:01:52.080 you a special conversation. I recently sat down with Rob Thomas. 0:01:52.560 --> 0:01:55.560 Rob is the senior vice president of Software and chief 0:01:55.600 --> 0:01:59.160 Commercial Officer of IBM. I spoke to him in front 0:01:59.160 --> 0:02:01.800 of a live audience as part of New York Tech Week. 0:02:02.440 --> 0:02:06.520 We discussed how businesses can harness the immense productivity benefits 0:02:06.560 --> 0:02:11.000 of AI while implementing it in a responsible and ethical manner. 0:02:11.520 --> 0:02:15.240 We also broke down a fascinating concept that Rob believes 0:02:15.280 --> 0:02:20.600 about AI, known as the productivity paradox. Okay, let's get 0:02:20.639 --> 0:02:31.120 to the conversation. How are we doing good? Rob? This 0:02:31.200 --> 0:02:35.280 is our our second time. We did one of these 0:02:35.560 --> 0:02:37.720 in the middle of the pandemic, but now it's all 0:02:37.760 --> 0:02:39.440 such a blur now that us can figure out when 0:02:39.440 --> 0:02:39.680 it was. 0:02:39.800 --> 0:02:42.160 I know it's hard to those are like a blurry years. 0:02:42.200 --> 0:02:43.600 You don't know what happened, right. 0:02:43.760 --> 0:02:46.680 But well, it's good to see you, to meet you again. 0:02:47.680 --> 0:02:50.240 I wanted to start by going back. You've been at 0:02:50.240 --> 0:02:52.560 IBM twenty years? Is that right? 0:02:52.960 --> 0:02:54.880 Twenty five in July, believe it or not. 0:02:55.040 --> 0:02:56.760 So you were a kid when you joined. 0:02:56.840 --> 0:02:57.360 I was four. 0:03:00.200 --> 0:03:04.639 W I want to contrast present day Rob and twenty 0:03:04.680 --> 0:03:09.560 five years ago Rob. When you arrive at IBM, what 0:03:09.639 --> 0:03:11.520 do you think your job is going to be? It, 0:03:11.600 --> 0:03:13.400 your career is going? Where do you think the kind 0:03:13.440 --> 0:03:15.760 of problems you're going to be addressing are? 0:03:17.560 --> 0:03:19.799 Well, it's kind of surreal because I I joined IBM 0:03:19.880 --> 0:03:25.600 Consulting and I'm coming out of school, and you quickly realize, wait, 0:03:25.639 --> 0:03:28.079 the job of a consultant is to tell other companies 0:03:28.120 --> 0:03:30.720 what to do. And I was like, I literally know nothing, 0:03:32.320 --> 0:03:34.160 and so you're immediately trying to figure out, so how 0:03:34.160 --> 0:03:35.760 am I going to be relevant given that I know 0:03:35.800 --> 0:03:39.160 absolutely nothing to advise other companies on what they should 0:03:39.200 --> 0:03:42.000 be doing. And I remember it well, like we were 0:03:42.040 --> 0:03:45.120 sitting in a room. When you're a consultant, you're waiting 0:03:45.160 --> 0:03:46.520 for somebody else to find work for you. 0:03:47.600 --> 0:03:48.240 A bunch of us. 0:03:48.160 --> 0:03:50.920 Sitting in a room and somebody walks in and says, 0:03:52.160 --> 0:03:53.560 we need somebody that knows visio. 0:03:53.760 --> 0:03:56.520 Does anybody know visio? I'd never heard of visio. 0:03:56.560 --> 0:03:59.040 I don't know if anybody in the room has. So 0:03:59.120 --> 0:04:02.800 everybody's likes sit around looking at their shoes. So finally 0:04:02.800 --> 0:04:05.640 I was like, I know it. So I raised my hand. 0:04:05.640 --> 0:04:08.000 They're like, great, we got a project for you next week. 0:04:08.880 --> 0:04:10.920 So I was like, all right, I have like three 0:04:11.000 --> 0:04:14.920 days to figure out what visio is, and I hope 0:04:14.920 --> 0:04:17.000 I can actually figure out how to use it now. Luckily, 0:04:17.520 --> 0:04:20.479 it wasn't like a programming language. I mean, it's pretty 0:04:20.520 --> 0:04:24.279 much a drag and drop capability. And so I literally 0:04:24.600 --> 0:04:25.800 left the office. 0:04:25.760 --> 0:04:29.080 Went to a bookstore, bought the first three books. 0:04:28.760 --> 0:04:31.080 On Visio I could find, spent the whole week in 0:04:31.120 --> 0:04:34.720 reading the books, and showed up and got their work 0:04:34.760 --> 0:04:37.080 on the project. And so it was a bit of 0:04:37.080 --> 0:04:40.760 a risky move, but I think that's kind of you 0:04:42.200 --> 0:04:44.400 doing this well. But if you don't take risk, you'll 0:04:44.480 --> 0:04:48.600 never you'll never achieve. And so does some extent. Everybody's 0:04:48.640 --> 0:04:51.320 making everything up all the time. It's like, can you 0:04:51.440 --> 0:04:54.880 learn faster than somebody else. Is what the difference is 0:04:55.320 --> 0:04:58.440 in almost every part of life. And so it was 0:04:58.440 --> 0:05:01.040 not planned, but it was an accident, but it kind 0:05:01.080 --> 0:05:02.840 of forced me to figure out that you're going to 0:05:02.920 --> 0:05:03.719 have to figure things out. 0:05:04.080 --> 0:05:06.560 You know, we're here to talk about AI, and I'm 0:05:06.600 --> 0:05:12.960 curious about the evolution of your understanding or IBM's understanding 0:05:13.000 --> 0:05:15.440 of my AI. At what point in the last twenty 0:05:15.440 --> 0:05:18.000 five years do you begin to think, oh, this is 0:05:18.040 --> 0:05:19.760 really going to be at the core of what we 0:05:20.120 --> 0:05:22.120 think about and work on at this company. 0:05:24.040 --> 0:05:28.200 The computer scientist John McCarthy, he was he's the person 0:05:28.240 --> 0:05:31.880 that's credited with coining the phrase artificial intelligence. It's like 0:05:31.920 --> 0:05:37.120 in the fifties, and he made an interesting comedy said 0:05:37.279 --> 0:05:39.600 he said, once it works, it's no longer called AI, 0:05:41.839 --> 0:05:45.040 and that then became it's called like the AI effect, 0:05:45.120 --> 0:05:48.880 which is it seems very difficult, very mysterious, but once 0:05:48.920 --> 0:05:53.080 it becomes commonplace, it's just no longer what it is. 0:05:53.440 --> 0:05:55.880 And so if you put that frame on it, I 0:05:55.880 --> 0:05:58.240 think we've always been doing AI at some level. And 0:05:58.320 --> 0:06:00.479 I even think back to when I joined I in 0:06:00.600 --> 0:06:05.120 ninety nine. At that point there was work on rules 0:06:05.160 --> 0:06:11.160 based engines, analytics, all of this was happening, So it 0:06:11.200 --> 0:06:14.280 all depends on how you really define that term. 0:06:14.520 --> 0:06:16.640 You could argue that elements of. 0:06:16.600 --> 0:06:21.719 Statistics, probability, it's not exactly AI, but it certainly feeds 0:06:21.760 --> 0:06:22.640 into it. 0:06:22.680 --> 0:06:23.840 And so I feel like. 0:06:23.839 --> 0:06:26.960 We've been working on this topic of how do we 0:06:27.040 --> 0:06:33.040 deliver better insights better automation since IBM was formed. If 0:06:33.040 --> 0:06:35.359 you read about what Thomas Watson Junior did, that was 0:06:35.440 --> 0:06:40.320 all about automating tasks that AI will probably certainly not 0:06:40.360 --> 0:06:44.320 by today's definition, but it's in the same zip code. 0:06:44.520 --> 0:06:46.680 So from your perspective, it feels a lot more like 0:06:46.720 --> 0:06:48.160 an evolution than a revolution. 0:06:48.279 --> 0:06:51.440 Is that a fair statement, yes, which I think most 0:06:51.520 --> 0:06:55.680 great things in technology tend to happen that way. Many 0:06:55.760 --> 0:06:58.760 of the revolutions, if you will, tend to fizzle out. 0:07:00.080 --> 0:07:02.360 Even given that is there, I guess what I'm asking is, 0:07:02.560 --> 0:07:05.720 I'm curious about whether there was a moment in that 0:07:05.760 --> 0:07:09.720 evolution when you had to readjust your expectations about what 0:07:11.160 --> 0:07:13.160 AI was going to be capable of. I mean, was there, 0:07:13.600 --> 0:07:18.000 you know, was there a particular innovation or a particular 0:07:18.640 --> 0:07:21.560 problem that was solved that made you think, oh, this 0:07:21.720 --> 0:07:24.360 is different than what I thought. 0:07:26.120 --> 0:07:29.800 I would say the moments that caught our attention certainly 0:07:31.120 --> 0:07:35.160 casper Off winning the chess tournament Nobody or Deep Blue 0:07:35.160 --> 0:07:38.080 beating casper Off. I should say, nobody really thought that 0:07:38.160 --> 0:07:43.760 was possible before that, and then it was Watson winning Jeopardy. 0:07:44.160 --> 0:07:46.840 These were moments that said, maybe there's more here than 0:07:46.840 --> 0:07:51.080 we even thought was possible. And so I do think 0:07:51.080 --> 0:07:57.440 there's points in time where we realized maybe way more could. 0:07:57.320 --> 0:07:59.400 Be done than we had even imagined. 0:08:00.120 --> 0:08:04.360 But I do think it's consistent progress every month and 0:08:04.400 --> 0:08:10.400 every year versus some seminal moment. Now, certainly large language 0:08:10.440 --> 0:08:13.040 models as of recent have caught everybody's attention because it 0:08:13.080 --> 0:08:17.600 has a direct consumer application, but I would almost think 0:08:17.640 --> 0:08:21.640 of that as what Netscape was for the for the 0:08:21.640 --> 0:08:25.520 web browser. Yeah, it brought the Internet to everybody, but 0:08:26.160 --> 0:08:28.400 that didn't become the Internet per se. 0:08:29.200 --> 0:08:29.520 Yeah. 0:08:29.840 --> 0:08:32.440 I have a cousin who worked for IBM for forty 0:08:32.480 --> 0:08:35.280 one years. I saw him this weekend. He's in Toronto. 0:08:35.880 --> 0:08:38.760 By the way, I said, do you work for Rob Thomas? 0:08:39.160 --> 0:08:44.400 He went like this, he goes, He said, I'm five 0:08:44.480 --> 0:08:48.200 layers down. But so I always whenever I see my 0:08:48.200 --> 0:08:50.280 cousin I ask him, can you tell me again what 0:08:50.320 --> 0:08:52.600 you do? Because it's always changing, right, I guess this 0:08:52.720 --> 0:08:55.880 is a function of working at IBM. So eventually he 0:08:56.000 --> 0:08:58.160 just gives up and says, you know, we're just solving problems. 0:08:58.240 --> 0:09:01.040 So what we're doing, which I sort of as a 0:09:01.120 --> 0:09:04.760 kind of frame, And I was curious, what's the coolest 0:09:04.800 --> 0:09:07.800 problem you ever worked on? Not biggest, not most important, 0:09:08.960 --> 0:09:11.800 but the coolest, the one that's like that sort of 0:09:11.800 --> 0:09:13.560 makes you smile when you think back on it. 0:09:13.640 --> 0:09:18.400 Probably when I was in microelectronics, because it was a 0:09:18.400 --> 0:09:21.480 world I had no exposure to. I hadn't studied computer science, 0:09:22.559 --> 0:09:28.679 and we were building a lot of high performance semiconductor technology, 0:09:28.800 --> 0:09:31.600 so just chips that do a really great job of 0:09:32.080 --> 0:09:36.080 processing something or other. And we figured out that there 0:09:36.160 --> 0:09:39.960 was a market in consumer gaming that was starting to happen, 0:09:40.720 --> 0:09:43.440 and we got to the point where we became the 0:09:43.520 --> 0:09:51.200 chip inside the Nintendo, We the Microsoft Xbox Sony PlayStation, 0:09:51.400 --> 0:09:54.440 so we basically had the entire gaming market running on 0:09:54.520 --> 0:09:55.400 ib AND chips. 0:09:56.080 --> 0:10:00.640 And to use every parent basically is pointing at you 0:10:00.679 --> 0:10:01.959 and saying you're the call. 0:10:03.120 --> 0:10:06.760 Probably well they would have found it from anybody. 0:10:06.559 --> 0:10:10.800 But it was the first time I could explain my 0:10:10.960 --> 0:10:12.920 job to my kids, who were quite young at that time, 0:10:13.080 --> 0:10:15.720 like what I did, Like it was more tangible for 0:10:15.760 --> 0:10:18.200 them than saying we solve problems or douce you know, 0:10:18.240 --> 0:10:22.560 build solutions like it became very tangible for them, and 0:10:22.920 --> 0:10:25.640 I think that's, you know, a rewarding part of the 0:10:25.720 --> 0:10:28.520 job is when you can help your family actually understand 0:10:28.559 --> 0:10:30.080 what you do. Most people can't do that. It's probably 0:10:30.120 --> 0:10:31.880 easier for you. They can they can see the books, 0:10:33.320 --> 0:10:36.160 but for for some of us in the business, the 0:10:36.160 --> 0:10:38.360 business world, it's not always as obvious. So that was 0:10:38.400 --> 0:10:41.200 like one example where the dots really connected. 0:10:42.160 --> 0:10:45.360 There were a couple's a couple of stuck about a 0:10:45.400 --> 0:10:48.120 little bit of this into context of of AI love 0:10:48.200 --> 0:10:51.360 because I love the frame of problem solving as a 0:10:51.400 --> 0:10:54.280 way of understanding what the function of the technology is. 0:10:54.600 --> 0:10:57.320 So I know that you guys did something, did some 0:10:57.400 --> 0:11:01.280 work with I never know how to announced it is 0:11:01.320 --> 0:11:05.280 it Sevia, Sevia, Sevia with the football club Sevia in Spain. 0:11:05.559 --> 0:11:08.160 Tell me about Tell me a little bit about that. 0:11:08.240 --> 0:11:11.040 What problem were they trying to solve and why did 0:11:11.120 --> 0:11:11.640 they call you? 0:11:11.720 --> 0:11:19.720 In every sports franchise is trying to get an advantage, right, 0:11:19.760 --> 0:11:25.760 Let's just be that clear. Everybody's how can I use data, analytics, insights, 0:11:25.840 --> 0:11:28.960 anything that will make us one percent better on the 0:11:29.000 --> 0:11:34.760 field at some point in the future. And Sevie reached 0:11:34.760 --> 0:11:36.959 out to us because they had. 0:11:36.800 --> 0:11:37.559 Seen some of that. 0:11:37.640 --> 0:11:39.840 We've done some work with the Toronto Raptors in the 0:11:39.880 --> 0:11:44.559 past and others, and their thought was maybe there's something 0:11:44.559 --> 0:11:48.000 we could do. They'd heard all about generative AI, that 0:11:48.080 --> 0:11:49.840 heard about large language models. 0:11:50.320 --> 0:11:52.800 And the problem, back to your point on. 0:11:53.120 --> 0:11:55.880 Solving problems, was we want to do a way better 0:11:55.960 --> 0:12:01.560 job of assessing talent, because really the lifeblood of a 0:12:01.559 --> 0:12:05.720 sports franchise is can you continue to cultivate talent? Can 0:12:05.800 --> 0:12:09.000 you find talent that others don't find? Can you see 0:12:09.040 --> 0:12:11.599 something in somebody that they don't see in themselves or 0:12:11.640 --> 0:12:12.240 maybe no other. 0:12:12.160 --> 0:12:13.280 Team season them. 0:12:13.640 --> 0:12:17.560 And we ended up building some of them called Scout Advisor, 0:12:18.440 --> 0:12:22.840 which is built on Watson X, which basically just ingests 0:12:24.000 --> 0:12:27.600 tons and tons of data, and we like to think 0:12:27.600 --> 0:12:29.800 of it as finding, you know, the needle in the 0:12:29.840 --> 0:12:34.280 haystack of you know, here's three players that aren't being considered. 0:12:34.600 --> 0:12:38.920 They're not on the top teams today, and I think 0:12:39.040 --> 0:12:41.360 working with them together, we found some pretty good insights 0:12:41.360 --> 0:12:43.600 that's helped them out how What was intriguing to. 0:12:43.559 --> 0:12:47.400 Me was we're not just talking about quantitative data. We're 0:12:47.400 --> 0:12:51.719 also talking about qualitative data. But that's the puzzle part 0:12:51.760 --> 0:12:54.239 of the thing that fastens me. How does what incorporate 0:12:54.360 --> 0:12:58.120 qualitative analysis into that sort of so you just feeding 0:12:58.160 --> 0:13:00.440 in scouting reports and things like that. 0:13:02.320 --> 0:13:03.959 I got to realize think about how much I can 0:13:04.000 --> 0:13:10.040 actually disclose it. But if you think about so, quantitative 0:13:10.160 --> 0:13:12.280 is relatively easy. 0:13:12.240 --> 0:13:13.480 Yeah, every team collects that. 0:13:14.920 --> 0:13:17.920 You know, what's the forty yard dashable think they use 0:13:17.960 --> 0:13:22.679 that term, certainly not in Spain. That's all quantitative. Qualitative 0:13:22.760 --> 0:13:26.839 is what's happening off the field. It could be diet, 0:13:27.160 --> 0:13:30.960 it could be habits, it could be behavior. You can 0:13:31.040 --> 0:13:33.720 imagine a range of things that would all feed into 0:13:34.640 --> 0:13:38.439 an athlete's performance and so relationships. 0:13:39.640 --> 0:13:41.160 There's many different aspects, and. 0:13:41.120 --> 0:13:44.480 So it's trying to figure out the right blend of 0:13:44.600 --> 0:13:47.720 quantitative and qualitative that gives you a unique insight. 0:13:48.360 --> 0:13:50.720 How transparent is that kind of system? I mean, is 0:13:50.720 --> 0:13:55.080 it telling you it's saying pick this guy, not this guy, 0:13:55.120 --> 0:13:56.920 But is it telling you why it prefers this guy 0:13:56.960 --> 0:13:57.520 to this guy. 0:13:57.520 --> 0:14:00.800 Is that I think for anything the realm of AI, 0:14:00.960 --> 0:14:04.480 you have to answer the why question. Otherwise you've fallen 0:14:04.480 --> 0:14:08.840 into the trap of the you know, the proverbial black box. 0:14:09.120 --> 0:14:11.880 And then wait, I made this decision, I'd never understood 0:14:11.880 --> 0:14:14.320 why it didn't work out, So you always have to 0:14:14.320 --> 0:14:15.959 answer why without a doubt? 0:14:16.880 --> 0:14:18.160 And how is why answered? 0:14:20.720 --> 0:14:24.720 Sources of data, the reasoning that went into it, and 0:14:24.840 --> 0:14:28.080 so it's basically just tracing back the chain of how 0:14:28.120 --> 0:14:31.000 you got to the answer. And in the case of 0:14:31.200 --> 0:14:33.680 what we do in Watson X is we have IBM models. 0:14:34.120 --> 0:14:36.760 We also use some other open source models, So it 0:14:36.760 --> 0:14:39.600 would be which model was used, what was the data 0:14:39.640 --> 0:14:41.840 set that was fed into that model, How is it 0:14:41.880 --> 0:14:42.640 making decisions? 0:14:42.640 --> 0:14:45.920 How is it performing? Is it robust? 0:14:46.080 --> 0:14:48.320 Meaning is it reliable in terms of if you feed 0:14:48.360 --> 0:14:50.120 it two of the same data set, do you get 0:14:50.160 --> 0:14:53.080 the same answer? These are all the you know, the 0:14:53.120 --> 0:14:55.080 technical aspects of understanding the why. 0:14:56.160 --> 0:15:00.320 How quickly do you expect all professional sports franchises to 0:15:00.360 --> 0:15:02.480 adopt some kind of are they already there? If I 0:15:02.520 --> 0:15:06.160 went out and pulled the general managers of the one 0:15:06.240 --> 0:15:09.120 hundred most valuable sports franchises in the world. How many 0:15:09.160 --> 0:15:11.760 of them would be using some kind of AI system 0:15:11.800 --> 0:15:13.000 to assist in their efforts. 0:15:14.920 --> 0:15:18.640 One hundred and twenty percent would meaning that everybody's doing it, 0:15:18.680 --> 0:15:20.560 and some think they're doing way more than they probably 0:15:20.560 --> 0:15:24.160 actually are. So everybody's doing it. I think what's weird 0:15:24.200 --> 0:15:29.720 about sports is everybody's so convinced that what they're doing 0:15:29.840 --> 0:15:34.160 is unique that they generally speaking, don't want to work 0:15:34.200 --> 0:15:36.600 with a third party to do it because they're afraid 0:15:36.720 --> 0:15:39.360 that that would expose them. But in reality, I think 0:15:39.400 --> 0:15:42.240 most are doing eighty to ninety percent of the same things. 0:15:43.880 --> 0:15:46.040 So but without a doubt, everybody's doing it. 0:15:46.440 --> 0:15:51.080 Yeah. Yeah. The other I say that I loved was 0:15:51.120 --> 0:15:54.240 there was one but a shipping line tricon on the 0:15:54.280 --> 0:15:57.560 Mississippi River. Tell me a little bit about that project. 0:15:57.640 --> 0:15:58.880 What problem were they trying to solve? 0:16:00.960 --> 0:16:04.680 Think about the problem that I would say everybody noticed 0:16:04.720 --> 0:16:08.280 if you go back to twenty twenty was things are 0:16:08.320 --> 0:16:10.920 getting hold held up in ports. It was actually an 0:16:10.960 --> 0:16:13.200 article in the paper this morning kind of tracing the 0:16:13.240 --> 0:16:17.000 history of what happened twenty twenty twenty one and why 0:16:17.120 --> 0:16:19.640 ships were basically sitting at seas for months at a 0:16:19.680 --> 0:16:23.320 time and at that stage, we just we had a 0:16:23.360 --> 0:16:28.840 massive throughput issue. But moving even beyond the pandemic, you 0:16:28.880 --> 0:16:33.800 can see it now with ships getting through like Panama Canal, 0:16:33.880 --> 0:16:36.640 there's like a narrow window where you can get through, 0:16:37.080 --> 0:16:40.800 and if you don't have your paperwork done, you don't 0:16:40.800 --> 0:16:42.880 have the right approvals, you're not going through and it 0:16:42.920 --> 0:16:44.320 may cost you a day or two and that's a 0:16:44.360 --> 0:16:48.200 lot of money. In the shipping industry and the tricon example, 0:16:48.840 --> 0:16:52.440 it's really just about when you're pulling into a port, 0:16:53.560 --> 0:16:56.840 if you have the right paperwork done, you can get 0:16:56.920 --> 0:17:01.880 goods off the ship very quickly. Ship a lot of food, 0:17:02.520 --> 0:17:06.240 which by definition, since it's not packaged food, it's fresh food. 0:17:06.640 --> 0:17:10.359 There is an expiration period and so if it takes 0:17:10.359 --> 0:17:15.680 them an extra two hours, certainly multiple hours or a day, 0:17:16.119 --> 0:17:17.879 they have a massive problem because then you're going to 0:17:17.920 --> 0:17:20.400 deal with spoilage and so it's going to set you back. 0:17:21.040 --> 0:17:24.400 And what we've worked with them on is using an 0:17:24.440 --> 0:17:28.440 assistant that we've built in Watson X called Orchestrate, which 0:17:28.480 --> 0:17:33.840 basically is just AI doing digital labor, so we can 0:17:34.000 --> 0:17:39.960 replicate nearly any repetitive task and do that with software. 0:17:39.760 --> 0:17:40.600 Instead of humans. 0:17:41.520 --> 0:17:45.000 So as you may imagine shipping industry still has a 0:17:45.040 --> 0:17:48.000 lot of paperwork that goes on and so being able 0:17:48.040 --> 0:17:51.040 to take forms that normally would be multiple hours of 0:17:51.119 --> 0:17:53.160 filling it out. Oh this isn't right, send it back. 0:17:53.680 --> 0:17:57.320 We've basically built that as a digital skill inside of 0:17:57.640 --> 0:18:02.080 WATSONEX orchestrate, and so now it's done in minutes. 0:18:03.119 --> 0:18:06.040 They did they realize that they could have that kind 0:18:06.080 --> 0:18:08.240 of efficiency by teaming up with you? Or is that 0:18:08.280 --> 0:18:12.320 something you came to them and said, guys, we can 0:18:12.359 --> 0:18:14.120 do this way better than you think. What's the. 0:18:15.960 --> 0:18:19.479 I'd say it's always, it's always both sides coming together 0:18:19.640 --> 0:18:22.560 at a moment that for some reason makes sense because 0:18:23.760 --> 0:18:25.560 you could say, why didn't this happen like five years ago, 0:18:25.640 --> 0:18:29.920 like this seems so obvious. Well, technology wasn't quite ready then, 0:18:30.440 --> 0:18:32.600 I would say, But they knew they had a need 0:18:33.080 --> 0:18:36.639 because I forget what the precise number is, but you know, 0:18:36.880 --> 0:18:40.680 reduction of spoilage has massive impact on their bottom line, 0:18:42.680 --> 0:18:44.160 and so they knew they had a need. 0:18:45.200 --> 0:18:48.120 We thought we could solve it and the two together. 0:18:48.960 --> 0:18:50.760 Who did you guys go to them? 0:18:50.800 --> 0:18:50.960 Though? 0:18:51.840 --> 0:18:52.560 Did they come to you? 0:18:52.840 --> 0:18:56.199 I recall that this one was an inbound meaning they 0:18:56.200 --> 0:18:59.520 had reached out to IBM and we'd like to solve 0:18:59.520 --> 0:19:01.359 this problem. I think it went into one of our 0:19:01.400 --> 0:19:04.440 digital centers if I recall it a literary phone. 0:19:04.200 --> 0:19:09.359 Call, but the other the reverse is more interesting to 0:19:09.440 --> 0:19:11.640 me because there seems to be a very, very large 0:19:11.720 --> 0:19:14.520 universe of people who have problems that could be solved 0:19:14.680 --> 0:19:19.480 this way and they don't realize it. What's your Is 0:19:19.520 --> 0:19:22.600 there a shining example of this of someone you just 0:19:22.680 --> 0:19:25.240 can't you just think could benefit so much and isn't 0:19:25.240 --> 0:19:26.160 benefiting right now? 0:19:28.920 --> 0:19:30.359 Maybe I'll answer it slightly differently. 0:19:30.520 --> 0:19:35.320 I'm I'm surprised by how many people can benefit that 0:19:35.400 --> 0:19:38.359 you wouldn't even logically think of. First, let me give 0:19:38.359 --> 0:19:46.200 you an example. There's a franchiser of hair salons, sport 0:19:46.240 --> 0:19:49.520 Clips is the name. My sons used to go there 0:19:49.520 --> 0:19:51.280 for haircuts because they have like TVs and you can 0:19:51.320 --> 0:19:54.080 watch sports, so they loved that they got entertained while 0:19:54.080 --> 0:19:57.080 they would get their haircut. I think the last place 0:19:57.080 --> 0:19:59.600 that you would think is using AI today would be 0:20:00.160 --> 0:20:01.880 a franchiser of hair salons. 0:20:03.840 --> 0:20:05.560 But just follow it through. 0:20:06.640 --> 0:20:09.639 The biggest part of how they run their business is 0:20:09.680 --> 0:20:12.840 can I get people to cut hair? And this is 0:20:12.840 --> 0:20:15.320 the high turnover industry because there's a lot of different 0:20:15.320 --> 0:20:16.840 places you can work if you want to cut hair. 0:20:17.240 --> 0:20:19.359 People actually get injured cutting hair because you're on your 0:20:19.400 --> 0:20:22.879 feet all day, that type of thing. And they're using 0:20:23.119 --> 0:20:28.240 same technology orchestrate as part of their recruiting process. How 0:20:28.240 --> 0:20:32.440 can they automate a lot of people submitting resumes, who 0:20:32.480 --> 0:20:35.800 they speak to, how they qualify them for the position. 0:20:36.560 --> 0:20:39.119 And so the reason I give that example is the 0:20:39.560 --> 0:20:45.000 opportunity for AI, which is unlike other technologies, is truly unlimited. 0:20:46.600 --> 0:20:50.200 It will touch every single business. It's not the realm 0:20:50.240 --> 0:20:53.040 of the fortune five hundred or the fortune one thousand. 0:20:53.840 --> 0:20:54.679 This is the. 0:20:54.880 --> 0:20:57.720 Fortune any size. And I think that may be one 0:20:57.760 --> 0:20:59.639 thing that people underestimate about. 0:21:00.680 --> 0:21:03.399 Yeah, what about I mean I was thinking about education 0:21:03.760 --> 0:21:07.159 as a kind of I mean, education is a perennial 0:21:09.800 --> 0:21:12.320 whipping boy for you guys that are living in the 0:21:12.400 --> 0:21:18.160 nineteenth century, right. I'm just curious about if a superintendent 0:21:18.160 --> 0:21:20.159 of a public school system or the president of the 0:21:20.240 --> 0:21:23.359 university sat down and had lunch with you and said, 0:21:25.000 --> 0:21:27.520 do the university first. My cost are out of control, 0:21:28.200 --> 0:21:34.000 my enrollment is down, my students hate me, and my 0:21:34.080 --> 0:21:40.719 board is revolting help. How would you think about helping 0:21:40.720 --> 0:21:41.680 someone in that situation. 0:21:43.280 --> 0:21:45.240 I spend some time with universities. 0:21:45.280 --> 0:21:48.720 I like to go back and visit Alma Maters, where 0:21:48.720 --> 0:21:50.720 I went to school, and so. 0:21:50.800 --> 0:21:51.680 I do that every year. 0:21:52.560 --> 0:21:55.320 The challenge I have hall of Seeming university is there 0:21:55.359 --> 0:21:58.240 has to be a will. Yeah, and I'm not sure 0:21:58.240 --> 0:22:03.840 the incentives are quite right today because bringing in new technology, 0:22:03.920 --> 0:22:05.720 let's say we want to go after we can help 0:22:05.840 --> 0:22:10.480 you figure out student recruiting or how you automate more 0:22:10.480 --> 0:22:15.160 of your education, everybody suddenly feels threatened that university. 0:22:15.720 --> 0:22:16.800 Hold on, that's my job. 0:22:17.359 --> 0:22:19.720 I'm the one that decides that, or I'm the one 0:22:19.760 --> 0:22:22.800 that wants to dictate the course. So there has to 0:22:22.800 --> 0:22:26.679 be a will. So I think it's very possible, and 0:22:27.560 --> 0:22:30.000 I do think over the next decade you will see 0:22:30.040 --> 0:22:32.400 some universities that jump all over this and they will 0:22:32.440 --> 0:22:34.800 move ahead, and you see others that do not. 0:22:35.680 --> 0:22:37.600 Because it's very possible. 0:22:39.200 --> 0:22:42.159 Where how does when you say there has to be 0:22:42.200 --> 0:22:44.760