WEBVTT - AI & The Productivity Paradox 0:00:02.759 --> 0:00:10.160 Welcome, Welcome, Welcome to Smart Talks with IBM. 0:00:10.200 --> 0:00:13.840 Hello, Hello, Welcome to Smart Talks with IBM, a podcast 0:00:14.000 --> 0:00:19.880 from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season, 0:00:20.120 --> 0:00:23.400 we're diving back into the world of artificial intelligence, but 0:00:23.480 --> 0:00:29.120 with a focus on the powerful concept of open its possibilities, implications, 0:00:29.160 --> 0:00:32.920 and misconceptions. We'll look at openness from a variety of 0:00:32.960 --> 0:00:36.840 angles and explore how the concept is already reshaping industries, 0:00:37.280 --> 0:00:40.879 ways of doing business and our very notion of what's possible. 0:00:41.320 --> 0:00:43.839 And for the first episode of this season, we're bringing 0:00:43.880 --> 0:00:48.040 you a special conversation. I recently sat down with Rob Thomas. 0:00:48.520 --> 0:00:51.520 Rob is the senior vice president of Software and chief 0:00:51.560 --> 0:00:55.120 Commercial Officer of IBM. I spoke to him in front 0:00:55.160 --> 0:00:57.800 of a live audience as part of New York Tech Week. 0:00:58.400 --> 0:01:02.040 We discussed how business is can harness the immense productivity 0:01:02.080 --> 0:01:06.080 benefits of AI while implementing it in a responsible and 0:01:06.160 --> 0:01:10.399 ethical manner. We also broke down a fascinating concept that 0:01:10.520 --> 0:01:15.800 Rob believes about AI, known as the productivity paradox. Okay, 0:01:16.040 --> 0:01:26.320 let's get to the conversation. How are we doing good? 0:01:26.720 --> 0:01:26.920 Rob? 0:01:26.959 --> 0:01:30.959 This is our our second time. We did one of 0:01:31.000 --> 0:01:33.560 these in the middle of the pandemic. But now it's 0:01:33.560 --> 0:01:35.200 all such a blur now that us can figure out 0:01:35.200 --> 0:01:35.640 when it was. 0:01:35.760 --> 0:01:38.120 I know it's hard to those are like a blurry years. 0:01:38.160 --> 0:01:39.560 You don't know what happened, right. 0:01:39.720 --> 0:01:42.640 But well, it's good to see you, to meet you again. 0:01:44.000 --> 0:01:46.200 I wanted to start by going back. You've been at 0:01:46.200 --> 0:01:47.440 IBM twenty years. 0:01:48.080 --> 0:01:50.840 Is that right? Twenty five in July, believe it or not. 0:01:51.040 --> 0:01:52.720 So you were a kid when you joined. 0:01:52.800 --> 0:01:53.280 I was four. 0:01:53.760 --> 0:01:59.560 Yeah, So I want to contrast present day Rob and 0:02:00.280 --> 0:02:04.920 twenty five years ago. Rob. When you arrive at IBM, 0:02:05.360 --> 0:02:07.280 what do you think your job is going to be? 0:02:07.320 --> 0:02:08.120 It, your career is going. 0:02:08.440 --> 0:02:10.400 Where do you think the kind of problems you're going 0:02:10.480 --> 0:02:11.680 to be addressing are? 0:02:13.520 --> 0:02:16.280 Well, it's kind of surreal because I joined IBM Consulting 0:02:16.960 --> 0:02:21.359 and I'm coming out of school and you quickly realize 0:02:21.400 --> 0:02:23.560 what the job of a consultant is to tell other 0:02:23.600 --> 0:02:26.239 companies what to do. And I was like, I literally 0:02:26.280 --> 0:02:29.880 know nothing, and so you're immediately trying to figure out, 0:02:29.880 --> 0:02:31.480 so how am I going to be relevant given that 0:02:31.520 --> 0:02:34.720 I know absolutely nothing to advise other companies on what 0:02:34.720 --> 0:02:37.400 they should be doing. And I remember it well, like 0:02:37.639 --> 0:02:40.680 we were sitting in a room. When you're a consultant, 0:02:40.680 --> 0:02:42.480 you're waiting for somebody else to find work for you. 0:02:43.560 --> 0:02:45.799 A bunch of us sitting in a room, and somebody 0:02:45.880 --> 0:02:49.520 walks in and says, we need somebody that knows Visio. 0:02:49.720 --> 0:02:52.480 Does anybody know Visio? I'd never heard of Visio. 0:02:52.520 --> 0:02:55.000 I don't know if anybody in the room has. So 0:02:55.080 --> 0:02:58.720 everybody's like sitting around looking at their shoes. So finally 0:02:58.760 --> 0:03:01.600 I was like, I know it. So I raised my hand. 0:03:01.600 --> 0:03:03.960 They're like, great, we got a project for you next week. 0:03:04.840 --> 0:03:06.880 So I was like, all right, I have like three 0:03:06.960 --> 0:03:10.880 days to figure out what visio is, and I hope 0:03:10.880 --> 0:03:12.480 I can actually figure out how to use it now. 0:03:12.560 --> 0:03:14.320 Luckily, it wasn't like. 0:03:14.480 --> 0:03:17.040 A programming language. I mean, it's pretty much a drag 0:03:17.120 --> 0:03:21.360 and drop capability. And so I literally left the office, 0:03:21.720 --> 0:03:24.800 went to a bookstore, bought the first three books on 0:03:24.880 --> 0:03:27.360 Visio I could find, spent the whole week in reading 0:03:27.360 --> 0:03:30.800 the books, and showed up and got to work on 0:03:30.840 --> 0:03:31.399 the project. 0:03:31.520 --> 0:03:33.840 And so it was a bit of a risky move, 0:03:33.960 --> 0:03:36.800 but I think that's kind of you. 0:03:38.400 --> 0:03:40.560 This well, but if you don't take risk you'll never 0:03:40.920 --> 0:03:44.880 you'll never achieve, and so does some extent. Everybody's making 0:03:44.920 --> 0:03:47.640 everything up all the time. It's like, can you learn 0:03:47.680 --> 0:03:51.360 faster than somebody else? Is what the difference is in 0:03:51.440 --> 0:03:55.000 almost every part of life. And so it was not planned, 0:03:55.040 --> 0:03:57.400 but it was an accident, but it kind of forced 0:03:57.440 --> 0:03:59.240 me to figure out that you're gonna have to figure 0:03:59.280 --> 0:03:59.680 things out. 0:04:00.080 --> 0:04:02.520 You know, we're here to talk about AI. And I'm 0:04:02.560 --> 0:04:08.920 curious about the evolution of your understanding or IBM's understanding 0:04:08.960 --> 0:04:11.400 of my AI. At what point in the last twenty 0:04:11.400 --> 0:04:13.960 five years do you begin to think, oh, this is 0:04:14.000 --> 0:04:15.720 really going to be at the core of what we 0:04:16.080 --> 0:04:19.760 think about and work on at this company. 0:04:20.000 --> 0:04:24.159 The computer scientist John McCarthy, he was he's the person 0:04:24.200 --> 0:04:27.839 that's credited with coining the phrase artificial intelligence. It's like 0:04:27.880 --> 0:04:33.039 in the fifties, and he made an interesting comedy said 0:04:33.240 --> 0:04:35.560 he said, once it works, it's no longer called AI, 0:04:37.800 --> 0:04:41.000 and that then became it's called like the AI effect, 0:04:41.080 --> 0:04:44.839 which is it seems very difficult, very mysterious, but once 0:04:44.880 --> 0:04:49.040 it becomes commonplace, it's just no longer what it is. 0:04:49.400 --> 0:04:51.839 And so if you put that frame on it, I 0:04:51.839 --> 0:04:54.160 think we've always been doing AI at some level, and 0:04:54.320 --> 0:04:56.320 I even think back to when I joined IBM in 0:04:56.560 --> 0:04:57.200 ninety nine. 0:04:57.839 --> 0:04:59.120 At that point there. 0:04:59.080 --> 0:05:04.200 Was work on rules based engines, analytics. 0:05:04.360 --> 0:05:05.440 All of this was happening. 0:05:05.960 --> 0:05:10.200 So it all depends on how you really define that term. 0:05:10.480 --> 0:05:15.880 You could argue that elements of statistics, probability, it's not 0:05:15.920 --> 0:05:18.920 exactly AI, but it certainly feeds into it. And so 0:05:19.240 --> 0:05:22.480 I feel like we've been working on this topic of 0:05:22.520 --> 0:05:28.400 how do we deliver better insights better automation since IBM 0:05:28.480 --> 0:05:31.040 was formed. If you read about what Thomas Watson Junior did, 0:05:31.040 --> 0:05:35.719 that was all about automating tasks that AI well, probably 0:05:35.880 --> 0:05:39.680 certainly not by today's definition, but it's in the same 0:05:39.760 --> 0:05:40.360 zip code. 0:05:40.480 --> 0:05:42.640 So from your perspective, it feels a lot more like 0:05:42.680 --> 0:05:44.080 an evolution than a revolution. 0:05:44.240 --> 0:05:47.400 Is that a fair statement, yes, which I think most 0:05:47.480 --> 0:05:51.680 great things in technology tend to happen that way. Many 0:05:51.720 --> 0:05:54.720 of the revolutions, if you will, tend to fizzle out. 0:05:55.760 --> 0:05:57.760 But even given that is there, I guess what I'm 0:05:57.800 --> 0:06:00.560 asking is, I'm curious about whether there was a a 0:06:00.680 --> 0:06:04.480 moment in that evolution when you had to readjust your 0:06:04.480 --> 0:06:08.479 expectations about what AI was going to be capable of. 0:06:08.600 --> 0:06:12.000 I mean, was there, you know, was there a particular 0:06:12.360 --> 0:06:15.800 innovation or a particular problem that was solved that made 0:06:15.800 --> 0:06:18.960 you think, oh, this is different than what I thought. 0:06:22.080 --> 0:06:25.760 I would say the moments that caught our attention certainly 0:06:27.080 --> 0:06:31.119 casper Off winning the chess tournament Nobody or Deep Blue 0:06:31.120 --> 0:06:34.039 beating casper Off. I should say, nobody really thought that 0:06:34.120 --> 0:06:39.719 was possible before that, and then it was Watson winning Jeopardy. 0:06:40.120 --> 0:06:42.800 These were moments that said, maybe there's more here than 0:06:42.800 --> 0:06:47.000 we even thought was possible. And so I do think 0:06:47.040 --> 0:06:53.400 there's points in time where we realized maybe way more could. 0:06:53.279 --> 0:06:55.359 Be done than we had even imagined. 0:06:56.040 --> 0:07:00.280 But I do think it's consistent progress every month and 0:07:00.360 --> 0:07:04.000 every year versus some seminal moment. 0:07:04.320 --> 0:07:04.520 Now. 0:07:04.600 --> 0:07:08.200 Certainly, large language models as of recent have caught everybody's 0:07:08.200 --> 0:07:12.760 attention because it has a direct consumer application. But I 0:07:12.760 --> 0:07:16.920 would almost think of that as what Netscape was for 0:07:16.960 --> 0:07:20.000 the for the web browser. Yeah, it brought the Internet 0:07:20.040 --> 0:07:24.360 to everybody, but that didn't become the Internet per se. 0:07:25.160 --> 0:07:25.400 Yeah. 0:07:25.800 --> 0:07:28.400 I have a cousin who worked for IBM for forty 0:07:28.440 --> 0:07:31.239 one years. I saw him this weekend. He's in Toronto. 0:07:31.840 --> 0:07:34.720 By the way, I said, do you work for Rob Thomas. 0:07:34.840 --> 0:07:35.640 He went like this. 0:07:35.800 --> 0:07:43.120 He goes, he said, I'm five layers down. But so 0:07:43.160 --> 0:07:45.080 I always whenever I see my cousin, I ask him, 0:07:45.360 --> 0:07:46.880 can you tell me again what you do? Because it's 0:07:46.920 --> 0:07:49.400 always changing, right, I guess this is a function of 0:07:49.440 --> 0:07:52.920 working at IBM. So eventually he just gives up and says, 0:07:53.000 --> 0:07:54.920 you know, we're just solving problems. So what we're doing, 0:07:55.240 --> 0:07:57.800 which I sort of loved as a kind of frame, 0:07:58.320 --> 0:08:01.160 And I was curious, What's what's the coolest problem you 0:08:01.240 --> 0:08:05.760 ever worked on? Not biggest, not most important, but the coolest, 0:08:05.760 --> 0:08:08.280 the one that's like that sort of makes you smile 0:08:08.320 --> 0:08:09.520 when you think back on it. 0:08:09.600 --> 0:08:14.360 Probably when I was in microelectronics, because it was a 0:08:14.360 --> 0:08:17.440 world I had no exposure to. I hadn't studied computer science, 0:08:18.520 --> 0:08:24.640 and we were building a lot of high performance semiconductor technology, 0:08:24.720 --> 0:08:27.520 so just chips that do a really great job of 0:08:28.040 --> 0:08:32.040 processing something or other. And we figured out that there 0:08:32.120 --> 0:08:35.920 was a market in consumer gaming that was starting to happen, 0:08:36.679 --> 0:08:39.400 and we got to the point where we became the 0:08:39.480 --> 0:08:47.160 chip inside the Nintendo. We the Microsoft Xbox Sony PlayStation, 0:08:47.360 --> 0:08:50.360 so we basically had the entire gaming market running on 0:08:50.480 --> 0:08:52.360 IBM chips and. 0:08:52.520 --> 0:08:57.359 To use every parent basically is pointing at you and saying. 0:08:57.440 --> 0:09:02.200 You're the Probably well, they would have found it from anybody. 0:09:02.520 --> 0:09:06.760 But it was the first time I could explain my 0:09:06.920 --> 0:09:08.880 job to my kids, who were quite young at that time, 0:09:09.040 --> 0:09:11.680 like what I did, Like it was more tangible for 0:09:11.720 --> 0:09:14.160 them than saying we solve problems or douce you know, 0:09:14.200 --> 0:09:18.520 build solutions like it became very tangible for them, and 0:09:18.880 --> 0:09:21.599 I think that's, you know, a rewarding part of the 0:09:21.679 --> 0:09:24.480 job is when you can help your family actually understand 0:09:24.520 --> 0:09:26.040 what you do. Most people can't do that. It's probably 0:09:26.080 --> 0:09:27.840 easier for you. They can, they can see the books, 0:09:29.280 --> 0:09:32.040 but for for some of us in the business the 0:09:32.120 --> 0:09:34.320 business world, it's not always as obvious. So that was 0:09:34.360 --> 0:09:37.160 like one example where the dots really connected. 0:09:38.120 --> 0:09:41.240 There were a couple there's a couple of stuck about 0:09:41.280 --> 0:09:43.760 a little bit of this in the context of of AI. 0:09:43.840 --> 0:09:47.040 I love because I love the frame of problem solving 0:09:47.080 --> 0:09:49.400 as a way of understanding what the function of the 0:09:49.440 --> 0:09:52.480 technology is. So I know that you guys did something, 0:09:52.920 --> 0:09:56.360 did some work with I never know how to pronounce 0:09:56.400 --> 0:10:00.400 it is it Sevilla Sevilla with the football club Severe 0:10:00.679 --> 0:10:03.840 in Spain. Tell me about Tell me a little bit 0:10:03.840 --> 0:10:06.640 about that. What problem were they trying to solve and 0:10:06.679 --> 0:10:07.600 why did they call you? 0:10:07.679 --> 0:10:15.679 In Every sports franchise is trying to get an advantage, right, 0:10:15.720 --> 0:10:21.719 Let's just be that clear. Everybody's how can I use data, analytics, insights, 0:10:21.800 --> 0:10:24.920 anything that will make us one percent better on the 0:10:24.960 --> 0:10:30.720 field at some point in the future. And Seville reached 0:10:30.720 --> 0:10:33.520 out to us because they had seen some of the 0:10:33.600 --> 0:10:35.800 We've done some work with the Toronto Raptors in the 0:10:35.800 --> 0:10:40.520 past and others, and their thought was maybe there's something 0:10:40.520 --> 0:10:44.000 we could do. They'd heard all about generative AI, they 0:10:44.040 --> 0:10:47.319 heard about large language models. And the problem, back to 0:10:47.360 --> 0:10:51.240 your point on solving problems, was we want to do 0:10:51.280 --> 0:10:56.120 a way better job of assessing talent, because really the 0:10:56.800 --> 0:10:59.719 lifeblood of a sports franchise is can you continue to 0:10:59.720 --> 0:11:03.800 cult a talent, Can you find talent that others don't find? 0:11:04.440 --> 0:11:06.920 Can you see something in somebody that they don't see 0:11:06.920 --> 0:11:08.200 in themselves or maybe no other. 0:11:08.120 --> 0:11:09.240 Team season them. 0:11:09.600 --> 0:11:13.520 And we ended up building somebody with them called Scout Advisor, 0:11:14.400 --> 0:11:18.800 which is built on Watson X, which basically just ingests 0:11:19.960 --> 0:11:23.559 tons and tons of data, and we like to think 0:11:23.559 --> 0:11:25.760 of it as finding you know, the needle in the 0:11:25.800 --> 0:11:30.240 haystack of you know, here's three players that aren't being considered. 0:11:30.559 --> 0:11:34.880 They're not on the top teams today, and I think 0:11:35.000 --> 0:11:37.320 working with them together we found some pretty good insights 0:11:37.320 --> 0:11:37.959 that's helped them out. 0:11:38.160 --> 0:11:40.720 How What was intriguing to me was we're not just 0:11:40.760 --> 0:11:45.840 talking about quantitative data. We're also talking about qualitative data. 0:11:46.480 --> 0:11:48.839 But that's the puzzle part of the thing that fastens me. 0:11:49.080 --> 0:11:52.720 How does one incorporate qualitative analysis into that sort of 0:11:53.320 --> 0:11:56.400 so you just feeding in scouting reports and things like that. 0:11:58.280 --> 0:11:59.760 I got to realize, think about how much I can 0:11:59.800 --> 0:12:05.080 act actually disclosed it. But if you think about so, 0:12:05.280 --> 0:12:11.080 quantitative is relatively easy. Every team collects that, you know, 0:12:11.640 --> 0:12:14.680 what's their forty yard dash? They use that term, certainly 0:12:14.720 --> 0:12:19.920 not in Spain. That's all quantitative. Qualitative is what's happening 0:12:19.960 --> 0:12:24.119 off the field. It could be diet, it could be habits, 0:12:24.280 --> 0:12:28.000 it could be behavior. You can imagine a range of 0:12:28.040 --> 0:12:32.800 things that would all feed into an athlete's performance and 0:12:32.880 --> 0:12:34.400 so relationships. 0:12:35.600 --> 0:12:37.120 There's many different aspects, and. 0:12:37.080 --> 0:12:40.439 So it's trying to figure out the right blend of 0:12:40.559 --> 0:12:43.680 quantitative and qualitative that gives you a unique insight. 0:12:44.320 --> 0:12:46.680 How transparent is that kind of system? I mean, is 0:12:46.679 --> 0:12:51.040 it telling you it's saying pick this guy not this guy, 0:12:51.080 --> 0:12:52.880 But is it telling you why it prefers this guy 0:12:52.920 --> 0:12:53.319 to this guy? 0:12:53.480 --> 0:12:53.599 Is that? 0:12:54.840 --> 0:12:57.000 I think for anything in the realm of AI, you 0:12:57.080 --> 0:13:00.640 have to answer the why question, otherwise you fall into 0:13:00.640 --> 0:13:04.800 the trap of the you know, the proverbial black box, 0:13:05.080 --> 0:13:07.840 and then wait, I made this decision, I'd never understood 0:13:07.840 --> 0:13:09.280 why it didn't work out. 0:13:09.520 --> 0:13:11.880 So you always have to answer why without a doubt? 0:13:12.840 --> 0:13:14.160 And how is why? Answered? 0:13:16.679 --> 0:13:20.679 Sources of data, the reasoning that went into it, and 0:13:20.800 --> 0:13:24.040 so it's basically just tracing back the chain of how 0:13:24.080 --> 0:13:26.960 you got to the answer. And in the case of 0:13:27.160 --> 0:13:29.640 what we do in Watson X is we have IBM models. 0:13:30.080 --> 0:13:32.719 We also use some other open source models, So it 0:13:32.720 --> 0:13:35.560 would be which model was used, what was the data 0:13:35.600 --> 0:13:37.800 set that was fed into that model, How is it 0:13:37.840 --> 0:13:38.600 making decisions? 0:13:38.600 --> 0:13:41.840 How is it performing? Is it robust? 0:13:42.040 --> 0:13:44.280 Meaning is it reliable in terms of if you feed 0:13:44.320 --> 0:13:46.080 it two of the same data set, do you get 0:13:46.120 --> 0:13:49.040 the same answer. These are all the you know, the 0:13:49.080 --> 0:13:51.040 technical aspects of understanding the why. 0:13:52.120 --> 0:13:56.240 How quickly do you expect all professional sports franchises to 0:13:56.320 --> 0:13:58.440 adopt some kind of are they already there? If I 0:13:58.480 --> 0:14:02.120 went out and pulled the general managers of the one 0:14:02.200 --> 0:14:05.080 hundred most valuable sports franchises in the world, how many 0:14:05.120 --> 0:14:07.720 of them would be using some kind of AI system 0:14:07.760 --> 0:14:09.000 to assist in their efforts. 0:14:10.880 --> 0:14:14.600 One hundred and twenty percent would, meaning that everybody's doing it, 0:14:14.640 --> 0:14:16.480 and some think they're doing way more than they probably 0:14:16.520 --> 0:14:20.120 actually are. So everybody's doing it. I think what's weird 0:14:20.160 --> 0:14:25.680 about sports is everybody's so convinced that what they're doing 0:14:25.800 --> 0:14:30.120 is unique that they generally speaking, don't want to work 0:14:30.160 --> 0:14:32.520 with a third party to do it because they're afraid 0:14:32.680 --> 0:14:35.320 that that would expose them. But in reality, I think 0:14:35.360 --> 0:14:38.200 most are doing eighty to ninety percent of the same things. 0:14:39.840 --> 0:14:42.640 So but without a doubt, everybody's doing it. Yeah. 0:14:43.240 --> 0:14:47.200 Yeah. The other I say that I loved was there 0:14:47.240 --> 0:14:51.120 was one but a shipping line tricon on the Mississippi River. 0:14:52.120 --> 0:14:53.920 Tell me a little bit about that project. What problem 0:14:54.000 --> 0:14:54.840 were they trying to solve? 0:14:56.920 --> 0:15:00.280 Think about the problem that I would say every body 0:15:00.360 --> 0:15:04.080 noticed if you go back to twenty twenty was things 0:15:04.120 --> 0:15:06.760 are getting hold held up in ports. It was actually 0:15:06.760 --> 0:15:09.040 an article in the paper this morning kind of tracing 0:15:09.040 --> 0:15:12.520 the history of what happened twenty twenty twenty one and 0:15:12.760 --> 0:15:15.520 why ships were basically sitting at seas for months at 0:15:15.520 --> 0:15:19.160 a time. And at that stage we just we had 0:15:19.200 --> 0:15:24.640 a massive throughput issue. But moving even beyond the pandemic, 0:15:24.720 --> 0:15:28.880 you can see it now with ships getting through like 0:15:28.960 --> 0:15:32.160 Panama Canal, there's like a narrow window where you can 0:15:32.200 --> 0:15:35.760 get through, and if you don't have your paperwork done, 0:15:36.440 --> 0:15:38.600 you don't have the right approvals, you're not going through 0:15:38.640 --> 0:15:40.080 and it may cost you a day or two and 0:15:40.080 --> 0:15:43.000 that's a lot of money. In the shipping industry and 0:15:43.080 --> 0:15:46.800 the Tricon example, it's really just about when you're pulling 0:15:46.840 --> 0:15:51.520 into a port, if you have the right paperwork done, 0:15:52.200 --> 0:15:56.040 you can get goods off the ship very quickly. They 0:15:56.120 --> 0:16:00.600 ship a lot of food, which by definition, since it's 0:16:00.600 --> 0:16:04.080 not packaged food, it's fresh food, there is an expiration 0:16:04.160 --> 0:16:08.240 period and so if it takes them an extra two hours, 0:16:09.320 --> 0:16:12.800 certainly multiple hours or a day, they have a massive 0:16:12.840 --> 0:16:15.200 problem because then you're going to deal with spoilage and 0:16:15.240 --> 0:16:17.840 so it's going to set you back. And what we've 0:16:17.840 --> 0:16:21.280 worked with them on is using an assistant that we've 0:16:21.280 --> 0:16:25.680 built in Watson X called orchestrate, which basically is just 0:16:26.360 --> 0:16:31.960 AI doing digital labor, so we can replicate nearly any 0:16:32.080 --> 0:16:35.920 repetitive task and do that with software. 0:16:35.720 --> 0:16:36.560 Instead of humans. 0:16:37.480 --> 0:16:40.960 So, as you may imagine, shipping industry still has a 0:16:40.960 --> 0:16:43.920 lot of paperwork that goes on, and so being able 0:16:44.000 --> 0:16:47.000 to take forms that normally would be multiple hours of 0:16:47.080 --> 0:16:49.120 filling it out, Oh this isn't right, send it back. 0:16:49.640 --> 0:16:53.280 We've basically built that as a digital skill inside of 0:16:53.600 --> 0:16:58.040 watsonex orchestrate, and so now it's done in minutes. 0:16:59.040 --> 0:17:01.720 They did Did they realize that they could have that 0:17:01.800 --> 0:17:04.080 kind of efficiency by teaming up with you or is 0:17:04.119 --> 0:17:08.119 that something you came to them and said, guys, we 0:17:08.160 --> 0:17:09.480 can do this way better than you think. 0:17:09.640 --> 0:17:10.080 What's the. 0:17:11.920 --> 0:17:15.439 I'd say it's always, it's always both sides coming together 0:17:15.600 --> 0:17:18.520 at a moment that for some reason makes sense because 0:17:19.720 --> 0:17:21.520 you could say, why didn't this happen like five years ago, 0:17:21.600 --> 0:17:25.880 like seems so obvious. Well, technology wasn't quite ready then, 0:17:26.400 --> 0:17:28.560 I would say, But they knew they had a need 0:17:29.040 --> 0:17:32.600 because I forget what the precise number is, but you know, 0:17:32.840 --> 0:17:36.639 reduction of spoilage has massive impact on their bottom line, 0:17:38.640 --> 0:17:41.320 and so they knew they had a need, we. 0:17:41.280 --> 0:17:44.480 Thought we could solve it, and the two together. 0:17:44.920 --> 0:17:47.879 Who did you guys go to them thought? Or did 0:17:47.880 --> 0:17:48.520 they come to you? 0:17:48.800 --> 0:17:52.159 I recall that this one was an inbound meaning they 0:17:52.160 --> 0:17:55.199 had reached out to IBM and that we'd like to 0:17:55.200 --> 0:17:57.159 solve this problem. I think it went into one of 0:17:57.200 --> 0:17:59.800 our digital centers, if I if I recall so literary, 0:17:59.800 --> 0:18:01.200 I call yeah. 0:18:01.240 --> 0:18:05.480 But the other the reverse is more interesting to me 0:18:05.840 --> 0:18:08.000 because there seems to be a very very large universe 0:18:08.040 --> 0:18:10.800 of people who have problems that could be solved this 0:18:10.840 --> 0:18:12.240 way and they don't realize it. 0:18:13.119 --> 0:18:13.800 What's your. 0:18:15.359 --> 0:18:18.320 Is there a shining example of this of someone you 0:18:18.440 --> 0:18:20.760 just can't you just think could benefit so much and 0:18:20.960 --> 0:18:22.120 isn't benefiting right now? 0:18:24.880 --> 0:18:26.320 Maybe I'll answer it slightly differently. 0:18:26.480 --> 0:18:31.280 I'm I'm surprised by how many people can benefit that 0:18:31.359 --> 0:18:33.080 you wouldn't even logically think of. 0:18:33.520 --> 0:18:34.920 First, let me give you an example. 0:18:35.960 --> 0:18:43.000 There's a franchiser of hair salons, sport Clips is the name. 0:18:44.200 --> 0:18:46.359 My sons used to go there for haircuts because they 0:18:46.359 --> 0:18:48.719 have like TVs and you can watch sports, so they 0:18:48.760 --> 0:18:50.959 loved that they got entertained while they would get their haircut. 0:18:51.960 --> 0:18:53.879 I think the last place that you would think is 0:18:53.960 --> 0:19:00.320 using AI today would be a franchiser of hair salons. Yeah, 0:18:59.760 --> 0:19:04.280 but just follow it through. The biggest part of how 0:19:04.320 --> 0:19:06.440 they run their business is can I get people to 0:19:06.480 --> 0:19:10.600 cut hair? And this is the high turnover industry because 0:19:10.600 --> 0:19:12.080 there's a lot of different places you can work if 0:19:12.119 --> 0:19:14.560 you want to cut hair. People actually get injured cutting 0:19:14.560 --> 0:19:16.280 hair because you're on your feet all day, that type 0:19:16.280 --> 0:19:21.480 of thing. And they're using same technology orchestrate as part 0:19:21.560 --> 0:19:25.360 of their recruiting process. How can they automate a lot 0:19:25.400 --> 0:19:31.240 of people submitting resumes, who they speak to, how they qualify. 0:19:30.800 --> 0:19:31.760 Them for the position. 0:19:32.520 --> 0:19:35.080 And so the reason I give that example is the 0:19:35.520 --> 0:19:40.960 opportunity for AI, which is unlike other technologies, is truly unlimited. 0:19:42.560 --> 0:19:46.159 It will touch every single business. It's not the realm 0:19:46.200 --> 0:19:49.000 of the fortune five hundred or the fortune one thousand. 0:19:49.800 --> 0:19:53.240 This is the fortune any size. And I think that 0:19:53.280 --> 0:19:56.119 may be one thing that people underestimate about AI. 0:19:56.640 --> 0:19:59.359 Yeah, what about I mean I was thinking about education 0:19:59.680 --> 0:20:02.480 as as a kind of I mean, education is a 0:20:02.560 --> 0:20:08.240 perennial whipping boy for you guys that are living in 0:20:08.240 --> 0:20:11.760 the nineteenth century, right. I'm just curious about if a 0:20:13.400 --> 0:20:16.040 superintendent of a public school system or the president of 0:20:16.080 --> 0:20:19.320 the university sat down and had lunch with you and said, 0:20:21.000 --> 0:20:23.480 do the university first. My cost are out of control, 0:20:24.160 --> 0:20:29.960 my enrollment is down, my students hate me, and my 0:20:30.040 --> 0:20:31.200 board is revolting. 0:20:31.359 --> 0:20:31.639 Help. 0:20:33.560 --> 0:20:37.640 How would you think about helping someone in that situation. 0:20:39.240 --> 0:20:41.720 I spend some time with universities. I like to go 0:20:41.800 --> 0:20:42.520 back and there's. 0:20:42.359 --> 0:20:46.840 Alma maters where I went to school, and so I 0:20:46.880 --> 0:20:50.000 do that every year. The challenge I have hall of 0:20:50.040 --> 0:20:53.200 Seming University is there has to be a will. Yeah, 0:20:53.680 --> 0:20:55.840 and I'm not sure the incentives are quite right today 0:20:57.040 --> 0:21:00.679 because bringing in new technology, say we want to go 0:21:00.720 --> 0:21:05.080 after we can help you figure out student recruiting or 0:21:05.119 --> 0:21:10.119 how you automate more of your education, everybody suddenly feels 0:21:10.119 --> 0:21:11.120 threatened that university. 0:21:11.680 --> 0:21:12.760 Hold on, that's my job. 0:21:13.320 --> 0:21:15.680 I'm the one that decides that, or I'm the one 0:21:15.720 --> 0:21:18.760 that wants to dictate the course. So there has to 0:21:18.760 --> 0:21:22.639 be a will. So I think it's very possible, and 0:21:23.520 --> 0:21:25.960 I do think over the next decade you will see 0:21:26.000 --> 0:21:28.360 some universities that jump all over this and they will 0:21:28.400 --> 0:21:30.760 move ahead, and you see others that do not. 0:21:31.640 --> 0:21:33.560 Because it's very possible. 0:21:35.160 --> 0:21:38.119 Where how does when you say there has to be 0:21:38.160 --> 0:21:41.040 a will? Is that the kind is that a kind 0:21:41.080 --> 0:21:43.280 of thing that that people that IBM to think about, 0:21:44.000 --> 0:21:47.560 Like when in this conversation you hypothetical conversation you might 0:21:47.600 --> 0:21:51.320 have with the university president, would you give advice on 0:21:51.920 --> 0:21:55.320 where the will comes from? 0:21:55.400 --> 0:21:57.720 I don't do that as much in a university context. 0:21:57.720 --> 0:22:01.520 I do that every day in a business context, because 0:22:02.280 --> 0:22:04.320 if you can find the right person in a business 0:22:04.320 --> 0:22:08.440 that wants to focus on growth or the bottom line 0:22:08.960 --> 0:22:11.560 or how do you create more productivity. Yes, it's going 0:22:11.560 --> 0:22:15.679 to create a lot of organizational resistance potentially, but you 0:22:15.720 --> 0:22:17.840 can find somebody that will figure out how to push 0:22:17.920 --> 0:22:23.000 that through. I think for universities, I think that's also possible. 0:22:23.280 --> 0:22:26.160 I'm not sure there's there's there's a return on investment for. 0:22:26.160 --> 0:22:26.679 Us to do that. 0:22:27.000 --> 0:22:34.000