WEBVTT - Smart Talks with IBM: AI & the Productivity Paradox 0:00:04.480 --> 0:00:12.680 Welcome to Tech Stuff, a production from iHeartRadio. Today, we 0:00:12.720 --> 0:00:15.680 are witnessed to one of those rare moments in history, 0:00:16.040 --> 0:00:19.279 the rise of an innovative technology with the potential to 0:00:19.400 --> 0:00:24.120 radically transform business in society forever. That technology, of course, 0:00:24.600 --> 0:00:28.159 is artificial intelligence, and it's the central focus for this 0:00:28.360 --> 0:00:32.320 new season of Smart Talks with IBM. Join hosts from 0:00:32.400 --> 0:00:36.120 your favorite Pushkin podcasts as they talk with industry experts 0:00:36.120 --> 0:00:39.720 and leaders to explore how businesses can integrate AI into 0:00:39.760 --> 0:00:43.080 their workflows and help drive real change in this new 0:00:43.200 --> 0:00:46.839 era of AI, and of course, host Malcolm Gladwell will 0:00:46.880 --> 0:00:49.200 be there to guide you through the season and throw 0:00:49.280 --> 0:00:52.199 in his two cents as well. Look out for new 0:00:52.200 --> 0:00:55.080 episodes of Smart Talks with IBM every other week on 0:00:55.160 --> 0:00:59.360 the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts, 0:00:59.560 --> 0:01:14.720 and learn more at IBM dot com, slash smart Talks. 0:01:11.600 --> 0:01:12.479 Pushkin. 0:01:16.120 --> 0:01:23.520 Welcome, Welcome, Welcome to Smart Talks with IBM. 0:01:23.560 --> 0:01:27.199 Hello, Hello, Welcome to Smart Talks with IBM, a podcast 0:01:27.360 --> 0:01:33.240 from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season, 0:01:33.480 --> 0:01:36.760 we're diving back into the world of artificial intelligence, but 0:01:36.840 --> 0:01:42.199 with a focus on the powerful concept of open its possibilities, implications, 0:01:42.520 --> 0:01:46.280 and misconceptions. We'll look at openness from a variety of 0:01:46.319 --> 0:01:50.200 angles and explore how the concept is already reshaping industries, 0:01:50.680 --> 0:01:54.240 ways of doing business, and our very notion of what's possible. 0:01:54.680 --> 0:01:57.200 And for the first episode of this season, we're bringing 0:01:57.240 --> 0:01:58.680 you a special conversation. 0:01:59.320 --> 0:02:00.200 I recently said. 0:02:00.080 --> 0:02:03.120 That down with Rob Thomas. Rob is the senior vice 0:02:03.120 --> 0:02:07.200 president of Software and chief Commercial Officer of IBM. We 0:02:07.280 --> 0:02:09.919 spoke to him in front of a live audience as 0:02:09.960 --> 0:02:13.480 part of New York Tech Week. We discussed how businesses 0:02:13.560 --> 0:02:17.799 can harness the immense productivity benefits of AI while implementing 0:02:17.840 --> 0:02:21.840 it in a responsible and ethical manner. We also broke 0:02:21.880 --> 0:02:25.960 down a fascinating concept that Rob believes about AI, known 0:02:26.080 --> 0:02:30.720 as the productivity paradox. Okay, let's get to the conversation. 0:02:38.160 --> 0:02:40.280 How are we doing good, Rob? 0:02:40.320 --> 0:02:44.600 This is our second time. We did one of these 0:02:44.880 --> 0:02:47.040 in the middle of the pandemic. But now it's all 0:02:47.080 --> 0:02:48.760 such a blur now that us can figure out when 0:02:48.760 --> 0:02:49.000 it was. 0:02:49.120 --> 0:02:51.480 I know, it's hard to those are like a blurry years. 0:02:51.520 --> 0:02:53.320 You don't know what happened, right, But. 0:02:54.120 --> 0:02:56.000 Well, it's good to see you, to meet you again. 0:02:57.000 --> 0:03:00.160 I wanted to start by going back. You've been an IBM. 0:02:59.840 --> 0:03:04.280 Two years right, twenty five in July, believe it or not. 0:03:04.400 --> 0:03:06.040 So you were you were a kid when you joined. 0:03:06.160 --> 0:03:06.680 I was four. 0:03:09.080 --> 0:03:13.959 So I want to contrast present day Rob and twenty 0:03:14.000 --> 0:03:18.880 five years ago Rob. When you arrive at IBM, what 0:03:18.960 --> 0:03:20.640 do you think your job is going to be? 0:03:20.680 --> 0:03:21.480 It, your career is going? 0:03:21.800 --> 0:03:23.800 Where do you think the kind of problems you're going 0:03:23.840 --> 0:03:25.079 to be addressing are? 0:03:26.880 --> 0:03:29.160 Well, it's kind of surreal because I joined IBM a 0:03:29.200 --> 0:03:34.920 consulting and I'm coming out of school, and you quickly realize, wait, 0:03:34.960 --> 0:03:37.400 the job of a consultant is to tell other companies 0:03:37.440 --> 0:03:40.080 what to do. And I was like, I literally know nothing, 0:03:41.640 --> 0:03:43.480 and so you're immediately trying to figure out, so how 0:03:43.480 --> 0:03:45.080 am I going to be relevant given that I know 0:03:45.120 --> 0:03:48.480 absolutely nothing to advise other companies on what they should 0:03:48.520 --> 0:03:51.320 be doing. And I remember it well, like we were 0:03:51.360 --> 0:03:54.440 sitting in a room. When you're a consultant, you're waiting 0:03:54.480 --> 0:03:57.200 for somebody else to find work for you. A bunch 0:03:57.240 --> 0:03:59.680 of us sitting in a room and somebody walks in 0:03:59.760 --> 0:04:03.680 and as we need somebody that knows visio. Does anybody 0:04:03.680 --> 0:04:06.240 know Visio? I'd never heard of visio. I don't know 0:04:06.360 --> 0:04:09.680 if anybody in the room has. So everybody's like sitting 0:04:09.680 --> 0:04:12.440 around looking at their shoes. So finally I was like, 0:04:12.880 --> 0:04:15.600 I know it. So I raised my hand. They're like, great, 0:04:15.800 --> 0:04:18.440 we got a project for you next week. So I 0:04:18.480 --> 0:04:20.640 was like, all right, I have like three days to 0:04:20.640 --> 0:04:24.440 figure out what visio is, and I hope I can 0:04:24.520 --> 0:04:26.960 actually figure out how to use it now. Luckily, it 0:04:27.040 --> 0:04:30.000 wasn't like a programming language. I mean, it's pretty much 0:04:30.000 --> 0:04:34.160 a drag and drop capability. And so I literally left 0:04:34.200 --> 0:04:37.760 the office, went to a bookstore bought the first three 0:04:37.760 --> 0:04:40.320 books on Visio I could find, spent the whole week 0:04:40.360 --> 0:04:43.880 in reading the books, and showed up and got their 0:04:43.880 --> 0:04:46.280 work on the project. And so it was a bit 0:04:46.320 --> 0:04:49.960 of a risky move, but I think that's kind of 0:04:50.000 --> 0:04:53.760 you this well, But if you don't take risk, you'll 0:04:53.800 --> 0:04:57.920 never you'll never achieve. And so does some extent. Everybody's 0:04:57.960 --> 0:05:00.640 making everything up all the time. It's like, can you 0:05:00.760 --> 0:05:04.200 learn faster than somebody else? Is what the difference is 0:05:04.680 --> 0:05:07.760 in almost every part of life. And so it was 0:05:07.760 --> 0:05:10.360 not planned, but it was an accident, but it kind 0:05:10.360 --> 0:05:12.160 of forced me to figure out that you're going to 0:05:12.240 --> 0:05:13.039 have to figure things out. 0:05:13.400 --> 0:05:15.880 You know, we're here to talk about AI, and I'm 0:05:15.920 --> 0:05:22.279 curious about the evolution of your understanding or IBM's understanding 0:05:22.320 --> 0:05:24.760 of my AI. At what point in the last twenty 0:05:24.760 --> 0:05:27.320 five years do you begin to think, oh, this is 0:05:27.360 --> 0:05:29.080 really going to be at the core of what we 0:05:29.440 --> 0:05:31.440 think about and work on at this company. 0:05:33.360 --> 0:05:37.520 The computer scientist John McCarthy, he was he's the person 0:05:37.560 --> 0:05:41.200 that's credited with coining the phrase artificial intelligence. It's like 0:05:41.240 --> 0:05:46.440 in the fifties, and he made an interesting comedy said 0:05:46.600 --> 0:05:48.920 he said, once it works, it's no longer called AI, 0:05:51.160 --> 0:05:54.360 and that then became it's called like the AI effect, 0:05:54.440 --> 0:05:58.200 which is it seems very difficult, very mysterious, but once 0:05:58.240 --> 0:06:02.400 it becomes commonplace, it's just no longer what it is. 0:06:02.760 --> 0:06:05.200 And so if you put that frame on it, I 0:06:05.200 --> 0:06:07.520 think we've always been doing AI at some level. And 0:06:07.680 --> 0:06:09.680 I even think back to when I joined IBM in 0:06:09.920 --> 0:06:14.440 ninety nine. At that point there was work on rules 0:06:14.480 --> 0:06:20.480 based engines, analytics, all of this was happening. So it 0:06:20.520 --> 0:06:23.880 all depends on how you really define that term. You 0:06:23.920 --> 0:06:29.120 could argue that you know, elements of statistics, probability. It's 0:06:29.120 --> 0:06:32.120 not exactly AI, but it certainly feeds into it, and 0:06:32.200 --> 0:06:35.680 so I feel like we've been working on this topic 0:06:35.720 --> 0:06:41.120 of how do we deliver better insights better automation since 0:06:41.400 --> 0:06:43.880 IBM was formed. If you read about what Thomas Watson 0:06:43.920 --> 0:06:48.440 Junior did, that was all about automating tasks, is that AI? 0:06:48.600 --> 0:06:52.719 Well probably certainly not by today's definition, but it's in 0:06:52.760 --> 0:06:53.640 the same zip code. 0:06:53.839 --> 0:06:56.000 So from your perspective, it feels a lot more like 0:06:56.040 --> 0:06:57.480 an evolution than a revolution. 0:06:57.600 --> 0:06:58.400 Is that a fair statement? 0:06:58.520 --> 0:07:02.200 Yes, yeah, which I think most great things in technology 0:07:03.120 --> 0:07:06.640 tend to happen that way. Yeah, many of the revolutions, 0:07:06.680 --> 0:07:08.080 if you will, tend to fizzle out. 0:07:09.120 --> 0:07:11.160 But even given that is there, I guess what I'm 0:07:11.160 --> 0:07:14.440 asking is, I'm curious about whether there was a moment 0:07:14.680 --> 0:07:18.520 in that evolution when you had to readjust your expectations 0:07:18.560 --> 0:07:21.840 about what AI was going to be capable of. 0:07:21.960 --> 0:07:24.080 I mean, was there, you know, was. 0:07:24.040 --> 0:07:28.600 There a particular innovation or a particular problem that was 0:07:28.600 --> 0:07:31.800 solved that made you think, oh, this is different than 0:07:31.800 --> 0:07:32.680 what I thought. 0:07:35.440 --> 0:07:39.120 I would say the moments that caught our attention certainly 0:07:40.440 --> 0:07:44.480 casper Off winning the chess tournament, nobody or Deep Blue 0:07:44.480 --> 0:07:47.400 beating casper Off. I should say nobody really thought that 0:07:47.480 --> 0:07:53.080 was possible before that, and then it was Watson winning Jeopardy. 0:07:53.480 --> 0:07:56.160 These were moments that said, maybe there's more here than 0:07:56.160 --> 0:08:00.400 we even thought was possible. And so I do think 0:08:00.400 --> 0:08:06.160 there's there's points in time where we realized maybe way 0:08:06.200 --> 0:08:09.520 more could be done than we had even imagined. But 0:08:09.960 --> 0:08:13.880 I do think it's consistent progress every month and every 0:08:14.000 --> 0:08:20.120 year versus some seminal moment. Now, certainly large language models 0:08:20.120 --> 0:08:22.560 as of recent have caught everybody's attention because it has 0:08:22.600 --> 0:08:27.040 a direct consumer application. But I would almost think of 0:08:27.080 --> 0:08:32.440 that as what Netscape was for the for the web browser. Yeah, 0:08:32.520 --> 0:08:36.280 it brought the Internet to everybody, but that didn't become 0:08:36.760 --> 0:08:38.920 the Internet per se. Yeah. 0:08:39.160 --> 0:08:41.400 I have a cousin who worked for I'd be up 0:08:41.440 --> 0:08:44.079 for forty one years. I saw him this weekend. He's 0:08:44.080 --> 0:08:47.280 in Toronto. By the way, I said, you work for 0:08:47.440 --> 0:08:49.600 Rob Thomas. He would like this, he goes. 0:08:52.880 --> 0:08:54.360 He said, I'm five layers down. 0:08:55.679 --> 0:08:57.920 But so I always whenever I see my cousin, I 0:08:57.960 --> 0:08:59.719 ask him, can you tell me again what you do? 0:08:59.760 --> 0:09:02.680 Because always changing? Right, I guess this is a function 0:09:02.720 --> 0:09:03.800 of working at IBM. 0:09:04.200 --> 0:09:06.520 So eventually he just gives up and says, you know, 0:09:06.520 --> 0:09:08.960 we're just solving problems, so what we're doing, which I 0:09:08.960 --> 0:09:11.880 sort of loved as a kind of frame, And I 0:09:12.000 --> 0:09:15.120 was curious, what's the coolest problem you ever worked on? 0:09:15.240 --> 0:09:19.440 Not biggest, not most important, but the coolest, the one 0:09:19.440 --> 0:09:21.880 that's like that sort of makes you smile when you 0:09:21.880 --> 0:09:22.880 think back on it. 0:09:22.960 --> 0:09:27.720 Probably when I was in microelectronics, because it was a 0:09:27.720 --> 0:09:30.800 world I had no exposure to. I hadn't studied computer science, 0:09:31.880 --> 0:09:38.000 and we were building a lot of high performance semiconductor technology, 0:09:38.120 --> 0:09:40.880 so just chips that do a really great job of 0:09:41.400 --> 0:09:45.400 processing something or other. And we figured out that there 0:09:45.480 --> 0:09:49.280 was a market in consumer gaming that was starting to happen, 0:09:50.040 --> 0:09:52.760 and we got to the point where we became the 0:09:52.840 --> 0:10:00.559 chip inside the Nintendo, We the Microsoft Xbox Sony PlayStation, 0:10:00.720 --> 0:10:03.720 so we basically had the entire gaming market running on 0:10:03.840 --> 0:10:04.720 ib AND chips. 0:10:05.400 --> 0:10:09.960 And to use every parent basically is pointing at you 0:10:10.000 --> 0:10:11.280 and saying you're the call. 0:10:12.440 --> 0:10:16.000 Probably well they would have found it from anybody, but 0:10:16.320 --> 0:10:20.480 it was the first time I could explain my job 0:10:20.559 --> 0:10:22.240 to my kids, who were quite young at that time, 0:10:22.400 --> 0:10:25.040 like what I did like it was more tangible for 0:10:25.080 --> 0:10:27.520 them than saying we solve problems or douce you know, 0:10:27.559 --> 0:10:31.880 build solutions like it became very tangible for them, and 0:10:32.240 --> 0:10:34.959 I think that's, you know, a rewarding part of the 0:10:35.040 --> 0:10:37.840 job is when you can help your family actually understand 0:10:37.880 --> 0:10:39.400 what you do. Most people can't do that. It's probably 0:10:39.440 --> 0:10:41.200 easier for you. They can, they can see the books, 0:10:42.640 --> 0:10:45.480 but for for some of us in the business, the 0:10:45.480 --> 0:10:47.680 business world, it's not always as obvious. So that was 0:10:47.720 --> 0:10:50.520 like one example where the dots really connected. 0:10:51.480 --> 0:10:54.719 There were a couple's a couple of stuck about a 0:10:54.720 --> 0:10:57.240 little bit of this into context of of AI. I 0:10:57.280 --> 0:11:00.600 love because I love the frame of problem solving as 0:11:00.600 --> 0:11:03.600 a way of understanding what the function of the technology is. 0:11:03.920 --> 0:11:06.640 So I know that you guys did something, did some 0:11:06.720 --> 0:11:10.640 work with I never know how to pronounce it. Is 0:11:10.640 --> 0:11:14.600 it Seville Sevia, Sevia with the football club Sevia in Spain? 0:11:14.880 --> 0:11:17.480 Tell me about Tell me a little bit about that. 0:11:17.559 --> 0:11:20.360 What problem were they trying to solve and why did 0:11:20.440 --> 0:11:20.960 they call you? 0:11:21.040 --> 0:11:29.040 In? Every sports franchise is trying to get an advantage, right, 0:11:29.120 --> 0:11:35.079 Let's just be that clear. Everybody's how can I use data? Analytics, insights, 0:11:35.160 --> 0:11:38.280 anything that will make us one percent better on the 0:11:38.320 --> 0:11:44.080 field at some point in the future. And Seville reached 0:11:44.080 --> 0:11:46.880 out to us because they had seen some of that. 0:11:46.960 --> 0:11:49.160 We've done some work with the Toronto Raptors in the 0:11:49.200 --> 0:11:53.840 past and others, and their thought was maybe there's something 0:11:53.880 --> 0:11:57.319 we could do. They'd heard all about generative AI, that 0:11:57.400 --> 0:12:00.720 heard about large language models, and the problem, back to 0:12:00.720 --> 0:12:04.600 your point on solving problems, was we want to do 0:12:04.640 --> 0:12:09.480 a way better job of assessing talent, because really the 0:12:10.160 --> 0:12:13.120 lifeblood of a sports franchise is can you continue to 0:12:13.160 --> 0:12:17.160 cultivate talent, can you find talent that others don't find? 0:12:17.800 --> 0:12:20.280 Can you see something in somebody that they don't see 0:12:20.280 --> 0:12:23.120 in themselves or maybe no other team season them. And 0:12:23.440 --> 0:12:26.880 we ended up building somebody with them called Scout Advisor, 0:12:27.760 --> 0:12:32.160 which is built on Watson X, which basically just ingests 0:12:33.320 --> 0:12:36.880 tons and tons of data and we like to think 0:12:36.920 --> 0:12:39.960 of it as finding the needle in the haystack of 0:12:40.640 --> 0:12:44.160 you know, here's three players that aren't being considered. They're 0:12:44.160 --> 0:12:48.640 not on the top teams today, and I think working 0:12:48.679 --> 0:12:50.839 with them together, we found some pretty good insights that's 0:12:50.840 --> 0:12:51.319 helped them out. 0:12:51.520 --> 0:12:52.920 How what was intriguing to. 0:12:52.880 --> 0:12:56.719 Me was we're not just talking about quantitative data. We're 0:12:56.720 --> 0:13:01.120 also talking about qualitative data. That's the puzzle part of 0:13:01.160 --> 0:13:04.319 the thing that fastens me. How does what incorporate qualitative 0:13:04.320 --> 0:13:07.600 analysis into that sort of so you just feeding in 0:13:08.160 --> 0:13:09.800 scouting reports and things like that. 0:13:11.640 --> 0:13:13.280 I got to realize think about how much I can 0:13:13.320 --> 0:13:19.360 actually disclose it. But if you think about so, quantitative 0:13:19.480 --> 0:13:25.280 is relatively easy. Every team collects that, you know, what's 0:13:25.360 --> 0:13:28.080 their forty yard dashable think they use that term, certainly 0:13:28.080 --> 0:13:33.280 not in Spain. That's all quantitative. Qualitative is what's happening 0:13:33.320 --> 0:13:37.439 off the field. It could be diet, it could be habits, 0:13:37.640 --> 0:13:41.360 it could be behavior. You can imagine a range of 0:13:41.400 --> 0:13:46.160 things that would all feed into an athlete's performance, and 0:13:46.240 --> 0:13:51.240 so relationships. There's many different aspects, and so it's trying 0:13:51.240 --> 0:13:55.400 to figure out the right blend of quantitative and qualitative 0:13:55.679 --> 0:13:57.040 that gives you a unique insight. 0:13:57.679 --> 0:14:01.640 How transparent is that kind of system telling you? It's 0:14:01.679 --> 0:14:04.720 saying pick this guy, not this guy, But is it 0:14:04.760 --> 0:14:06.679 telling you why it prefers this guy to this guy? 0:14:06.840 --> 0:14:07.160 Was it? 0:14:08.240 --> 0:14:10.360 I think for anything in the realm of AI, you 0:14:10.440 --> 0:14:13.800 have to answer the why question. Yeah, otherwise you've fallen 0:14:13.800 --> 0:14:18.160 into the trap of the you know, the proverbial black box, 0:14:18.440 --> 0:14:21.200 and then wait, I made this decision, I'd never understood 0:14:21.200 --> 0:14:23.640 why it didn't work out, So you always have to 0:14:23.640 --> 0:14:25.080 answer why without a. 0:14:25.040 --> 0:14:27.480 Doubt and how is why answered? 0:14:30.040 --> 0:14:34.080 Sources of data, the reasoning that went into it, and 0:14:34.160 --> 0:14:37.400 so it's basically just tracing back the chain of how 0:14:37.440 --> 0:14:40.320 you got to the answer. And in the case of 0:14:40.520 --> 0:14:42.960 what we do in Watson X is we have IBM models. 0:14:43.440 --> 0:14:46.080 We also use some other open source models, so it 0:14:46.080 --> 0:14:48.920 would be which model was used, what was the data 0:14:48.960 --> 0:14:51.160 set that was fed into that model, How is it 0:14:51.200 --> 0:14:55.680 making decisions? How is it performing? Is it robust? Meaning 0:14:55.760 --> 0:14:57.800 is it reliable in terms of if you feed it 0:14:58.000 --> 0:14:59.520 two of the same data set, do you get the 0:14:59.520 --> 0:15:02.920 same answer? These are all the you know, the technical 0:15:02.960 --> 0:15:05.440 aspects of understanding the why. 0:15:05.520 --> 0:15:09.640 How quickly do you expect all professional sports franchises to 0:15:09.680 --> 0:15:11.800 adopt some kind of are they already there? If I 0:15:11.840 --> 0:15:15.520 went out and pulled the general managers of the one 0:15:15.560 --> 0:15:18.440 hundred most valuable sports franchises in the world, how many 0:15:18.480 --> 0:15:21.080 of them would be using some kind of AI system 0:15:21.120 --> 0:15:22.360 to assist in their efforts? 0:15:24.240 --> 0:15:27.960 One hundred and twenty percent would meaning that everybody's doing it, 0:15:28.000 --> 0:15:29.880 and some think they're doing way more than they probably 0:15:29.880 --> 0:15:33.480 actually are. So everybody's doing it. I think what's weird 0:15:33.520 --> 0:15:39.080 about sports is everybody's so convinced that what they're doing 0:15:39.160 --> 0:15:43.520 is unique that they generally speaking don't want to work 0:15:43.520 --> 0:15:45.680 with a third party to do it because they're afraid 0:15:46.040 --> 0:15:48.680 that that would expose them. But in reality, I think 0:15:48.720 --> 0:15:51.560 most are doing eighty to ninety percent of the same things. 0:15:53.200 --> 0:15:55.320 So but without a doubt, everybody's doing it. 0:15:55.760 --> 0:15:58.600 Yeah. Yeah. 0:15:58.080 --> 0:16:01.680 The other that love was there was one but a 0:16:01.720 --> 0:16:05.760 shipping line tri gun on the Mississippi River. Tell me 0:16:05.760 --> 0:16:07.600 a little bit about that project. What problem were they 0:16:07.640 --> 0:16:08.200 trying to solve? 0:16:10.280 --> 0:16:14.000 Think about the problem that I would say everybody noticed 0:16:14.040 --> 0:16:17.600 if you go back to twenty twenty was things are 0:16:17.640 --> 0:16:20.240 getting hold held up in ports. It was actually an 0:16:20.280 --> 0:16:22.520 article in the paper this morning kind of tracing the 0:16:22.560 --> 0:16:26.320 history of what happened twenty twenty twenty one and why 0:16:26.440 --> 0:16:29.320 ships were basically sitting at seas for months at a time. 0:16:30.000 --> 0:16:33.040 And at that stage we just we had a massive 0:16:33.040 --> 0:16:38.360 throughput issue. But moving even beyond the pandemic, you can 0:16:38.360 --> 0:16:43.120 see it now with ships getting through like Panama Canal. 0:16:43.200 --> 0:16:46.000 There's like a narrow window where you can get through, 0:16:46.440 --> 0:16:50.120 and if you don't have your paperwork done, you don't 0:16:50.120 --> 0:16:52.200 have the right approvals, you're not going through and it 0:16:52.240 --> 0:16:53.640 may cost you a day or two and that's a 0:16:53.680 --> 0:16:57.520 lot of money. In the shipping industry and the tricon example, 0:16:58.160 --> 0:17:01.760 it's really just about when you're pulled into a port, 0:17:02.880 --> 0:17:06.160 if you have the right paperwork done, you can get 0:17:06.240 --> 0:17:10.720 goods off the ship very quickly. They ship a lot 0:17:10.760 --> 0:17:14.760 of food which by definition, since it's not packaged food, 0:17:14.800 --> 0:17:18.719 it's fresh food, there is an expiration period and so 0:17:19.160 --> 0:17:24.040 if it takes them an extra two hours, certainly multiple 0:17:24.040 --> 0:17:26.760 hours or a day, they have a massive problem because 0:17:26.760 --> 0:17:28.880 then you're going to deal with spoilage and so it's 0:17:28.880 --> 0:17:31.600 going to set you back. And what we've worked with 0:17:31.600 --> 0:17:35.159 them on is using an assistant that we've built in 0:17:35.240 --> 0:17:40.560 Watson X called Orchestrate, which basically is just AI doing 0:17:40.920 --> 0:17:46.439 digital labor, so we can replicate nearly any repetitive task 0:17:47.560 --> 0:17:51.119 and do that with software instead of humans. So, as 0:17:51.160 --> 0:17:54.600 you may imagine, shipping industry still has a lot of 0:17:54.640 --> 0:17:57.720 paperwork that goes on and so being able to take 0:17:57.800 --> 0:18:01.040 forms that normally would be multiple hours of filling it out. 0:18:01.080 --> 0:18:04.160 Oh this isn't right, send it back. We've basically built 0:18:04.160 --> 0:18:08.200 that as a digital skill inside of Watson X orchestraate 0:18:08.720 --> 0:18:11.400 and so now it's done in minutes. 0:18:12.440 --> 0:18:15.360 They did they realize that they could have that kind 0:18:15.400 --> 0:18:17.560 of efficiency by teaming up with you? Or is that 0:18:17.600 --> 0:18:21.639 something you came to them and said, guys, we can 0:18:21.680 --> 0:18:22.840 do this way better than you think. 0:18:23.000 --> 0:18:23.440 What's the. 0:18:25.280 --> 0:18:28.800 I'd say it's always it's always both sides coming together 0:18:28.960 --> 0:18:31.880 at a moment that for some reason makes sense because 0:18:33.080 --> 0:18:34.880 you could say, why didn't this happen like five years ago, 0:18:34.960 --> 0:18:39.240 like this seems so obvious. Well, technology wasn't quite ready then, 0:18:39.760 --> 0:18:41.880 I would say, But they knew they had a need 0:18:42.400 --> 0:18:45.959 because I forget what the precise number is, but you know, 0:18:46.200 --> 0:18:50.080 reduction of spoilage has massive impact on their bottom line, 0:18:52.000 --> 0:18:54.880 and so they knew they had a need. We thought 0:18:54.880 --> 0:18:57.840 we could solve it and the two together. 0:18:58.280 --> 0:19:01.399 Who did you guys go to the Now? Did they 0:19:01.400 --> 0:19:01.840 come to you? 0:19:02.160 --> 0:19:05.520 I recall that this one was an inbound meaning they 0:19:05.520 --> 0:19:08.840 had reached out to IBM and we'd like to solve 0:19:08.840 --> 0:19:10.679 this problem. I think it went into one of our 0:19:10.720 --> 0:19:13.760 digital centers if I recall it a literary phone. 0:19:13.520 --> 0:19:18.720 Call, but the other the reverse is more interesting to 0:19:18.760 --> 0:19:20.960 me because there seems to be a very very large 0:19:21.040 --> 0:19:23.840 universe of people who have problems that could be solved 0:19:24.000 --> 0:19:25.600 this way and they don't realize it. 0:19:26.480 --> 0:19:27.359 What's your. 0:19:28.720 --> 0:19:31.679 Is there a shining example of this of someone you 0:19:31.800 --> 0:19:34.159 just can't you just think could benefit so much and 0:19:34.320 --> 0:19:35.480 isn't benefiting right now? 0:19:38.280 --> 0:19:42.960 Maybe I'll answer it slightly differently. I'm I'm surprised by 0:19:43.240 --> 0:19:46.040 how many people can benefit that you wouldn't even logically 0:19:46.080 --> 0:19:49.600 think of. First, let me give you an example. There's 0:19:49.840 --> 0:19:56.360 a franchiser of hair salons. Sport Clips is the name. 0:19:57.560 --> 0:19:59.679 My sons used to go there for haircuts because they 0:19:59.680 --> 0:20:02.479 have like and you can watch sports, so they loved that. 0:20:02.480 --> 0:20:05.439 They got entertained while they would get their haircut. I 0:20:05.440 --> 0:20:07.600 think the last place that you would think is using 0:20:07.640 --> 0:20:13.240 AI today would be a franchiser of hair salons. But 0:20:14.080 --> 0:20:17.880 just follow it through. The biggest part of how they 0:20:17.960 --> 0:20:20.280 run their business is can I get people to cut hair? 0:20:21.720 --> 0:20:24.200 And this is the high turnover industry because there's a 0:20:24.200 --> 0:20:25.720 lot of different places you can work if you want 0:20:25.720 --> 0:20:28.320 to cut hair. People actually get injured cutting hair because 0:20:28.320 --> 0:20:29.920 you're on your feet all day, that type of thing. 0:20:30.600 --> 0:20:35.320 And they're using same technology orchestrate as part of their 0:20:35.680 --> 0:20:39.320 recruiting process. How can they automate a lot of people 0:20:39.760 --> 0:20:44.320 submitting resumes, who they speak to, how they qualify them 0:20:44.359 --> 0:20:47.639 for the position. And so the reason I give that 0:20:47.680 --> 0:20:52.520 example is the opportunity for AI, which is unlike other technologies, 0:20:53.240 --> 0:20:58.760 is truly unlimited. It will touch every single business. It's 0:20:58.800 --> 0:21:01.359 not the realm of the fun five hundred or the 0:21:01.400 --> 0:21:06.159 fortune one thousand. This is the fortune any size. And 0:21:06.240 --> 0:21:08.480 I think that may be one thing that people underestimate 0:21:08.680 --> 0:21:09.520 about AI. 0:21:11.080 --> 0:21:13.560 What about I mean, I was thinking about education as 0:21:13.600 --> 0:21:19.520 a kind of I mean, education is a perennial whipping 0:21:19.520 --> 0:21:22.720 boy for you guys that are living in the nineteenth century, right. 0:21:23.200 --> 0:21:27.920 I'm just curious about if a superintendent of a public 0:21:27.920 --> 0:21:31.320 school system or the president of the university sat down 0:21:31.320 --> 0:21:35.359 and had lunch with you and said, do the university first. 0:21:35.720 --> 0:21:40.600 My cost are out of control, my enrollment is down, 0:21:41.440 --> 0:21:44.560 my students hate me, and my board is revolting. 0:21:44.720 --> 0:21:45.000 Help. 0:21:46.920 --> 0:21:50.240 How would you how would you think about helping someone 0:21:50.280 --> 0:21:51.000 in that situation. 0:21:52.600 --> 0:21:55.080 I spend some time with universities. I like to go 0:21:55.160 --> 0:21:58.760 back and visit Alma Maters where I went to school, 0:21:59.080 --> 0:22:03.040 and so I do that every year. The challenge I 0:22:03.040 --> 0:22:05.080 have hall is Seeming university is there has to be 0:22:05.119 --> 0:22:08.320 a will. Yeah, and I'm not sure the incentives are 0:22:08.400 --> 0:22:13.600 quite right today because bringing in new technology, let's say 0:22:13.600 --> 0:22:15.600 we want to go after we can help you figure 0:22:15.640 --> 0:22:20.560 out student recruiting or how you automate more of your education, 0:22:22.520 --> 0:22:26.120 everybody suddenly feels threatened that university. Hold on, that's my job. 0:22:26.680 --> 0:22:29.040 I'm the one that decides that, or I'm the one 0:22:29.080 --> 0:22:32.119 that wants to dictate the course. So there has to 0:22:32.119 --> 0:22:36.000 be a will. So I think it's very possible, and 0:22:36.880 --> 0:22:39.320 I do think over the next decade you will see 0:22:39.359 --> 0:22:41.720 some universities that jump all over this and they will 0:22:41.760 --> 0:22:45.400 move ahead, and you see others that do not, because 0:22:46.000 --> 0:22:46.920 it's very possible. 0:22:48.520 --> 0:22:51.480 Where how does when you say there has to be 0:22:51.520 --> 0:22:54.080 a will? Is that the kind of Is that a 0:22:54.200 --> 0:22:56.639 kind of thing that that people at IBM think about, 0:22:57.359 --> 0:23:00.639 Like when in this conversation you hype type a conversation 0:23:00.680 --> 0:23:03.200 you might have with the university president, would you give 0:23:03.240 --> 0:23:08.680 advice on where the will comes from? 0:23:08.760 --> 0:23:11.080 I don't do that as much in a university context. 0:23:11.080 --> 0:23:14.880 I do that every day in a business context because 0:23:15.640 --> 0:23:17.680 if you can find the right person in a business 0:23:17.680 --> 0:23:21.800