1 00:00:00,200 --> 00:00:01,359 Speaker 1: There is a movie. 2 00:00:01,639 --> 00:00:05,000 Speaker 2: It is called Northside seven seven seven. It is of 3 00:00:05,040 --> 00:00:09,200 Speaker 2: a Chicago long ago. Jimmy Stewart takes a lens and 4 00:00:09,360 --> 00:00:14,160 Speaker 2: is fabulous in that movie until halfway through you realize 5 00:00:14,200 --> 00:00:16,560 Speaker 2: there's an old lady on her knees at the Wriggly 6 00:00:16,640 --> 00:00:21,119 Speaker 2: Building taking the movie, stealing the movie from Jimmy Stewart. 7 00:00:21,160 --> 00:00:24,400 Speaker 2: And of course that is the iconic grandmother and mother 8 00:00:24,760 --> 00:00:30,160 Speaker 2: within called Northside seven seven. This is an extraordinary book. 9 00:00:30,200 --> 00:00:31,720 Speaker 2: I really don't know what to say. 10 00:00:32,320 --> 00:00:35,760 Speaker 1: Jenny Rametti Romedy, as you know, it's a most mispronounced name. 11 00:00:36,320 --> 00:00:40,640 Speaker 1: Incorporate well, Jenny remedy with us this morning on a 12 00:00:40,840 --> 00:00:44,319 Speaker 1: hugely courageous book. And I want to start with the 13 00:00:44,400 --> 00:00:47,560 Speaker 1: blistering first fifty pages of your childhood. 14 00:00:47,960 --> 00:00:49,559 Speaker 2: And Baba came to the rescue. 15 00:00:49,600 --> 00:00:53,640 Speaker 3: Who is Baba my great grandmother? And you're right, and 16 00:00:53,840 --> 00:00:56,360 Speaker 3: you speak at the Wriggly Building because I was raised 17 00:00:56,360 --> 00:00:59,880 Speaker 3: by really strong women who all suffered great tragedies. And 18 00:01:00,320 --> 00:01:02,920 Speaker 3: that was my great grandmother who came here from Belarus, 19 00:01:02,920 --> 00:01:05,840 Speaker 3: the last person alive from her family, who worked third 20 00:01:05,840 --> 00:01:08,360 Speaker 3: shift in the Wrigley Building cleaning bathrooms, never spoke a 21 00:01:08,360 --> 00:01:12,120 Speaker 3: stitch of English English and saved every diamond US savings 22 00:01:12,120 --> 00:01:14,760 Speaker 3: bonds and one day it would would allow us to 23 00:01:14,760 --> 00:01:15,240 Speaker 3: have a car. 24 00:01:15,400 --> 00:01:18,640 Speaker 2: What was the first day? Like at Northwestern there were 25 00:01:18,760 --> 00:01:22,440 Speaker 2: fancy people from Nu Trier, debutantes. 26 00:01:21,720 --> 00:01:25,320 Speaker 4: From New Ja, you know that. And there I was, Yes, 27 00:01:25,360 --> 00:01:25,840 Speaker 4: there I was. 28 00:01:25,959 --> 00:01:28,720 Speaker 3: No I I was there, by the grace of God, right, 29 00:01:28,840 --> 00:01:32,200 Speaker 3: just lucky enough to get a scholarship. And what I 30 00:01:32,360 --> 00:01:34,319 Speaker 3: what I remember is having gone to the store and 31 00:01:34,319 --> 00:01:37,880 Speaker 3: bought one pair of jeans and topsiders to try. 32 00:01:37,680 --> 00:01:40,320 Speaker 4: To fit in a slide rule. I did use a 33 00:01:40,360 --> 00:01:42,560 Speaker 4: slide roll. You're dating this is like a nerd. 34 00:01:43,080 --> 00:01:45,640 Speaker 2: Yes, let's go to the woman from Chicago who wouldn't 35 00:01:45,640 --> 00:01:47,559 Speaker 2: know a slide rule if it hit or over the world. 36 00:01:47,720 --> 00:01:50,520 Speaker 5: From a slide roll to today, it's actually an appropriate 37 00:01:50,680 --> 00:01:53,720 Speaker 5: kind of pathway at a time. Win tech is at 38 00:01:53,760 --> 00:01:56,240 Speaker 5: the epicenter of how to get from the slide roll 39 00:01:56,400 --> 00:02:00,240 Speaker 5: to the graphing calculator and beyond right, how much much 40 00:02:00,280 --> 00:02:02,720 Speaker 5: can you see some of the lessons that you learned 41 00:02:02,760 --> 00:02:05,280 Speaker 5: at the helm of IBM of how to bring a 42 00:02:05,480 --> 00:02:09,240 Speaker 5: company up to speed with technology that moves more quickly 43 00:02:09,280 --> 00:02:10,240 Speaker 5: than anyone could imagine. 44 00:02:10,280 --> 00:02:12,520 Speaker 3: Yeah, this is it's timely because even though the book 45 00:02:12,560 --> 00:02:14,840 Speaker 3: took me two years to write, Good Power, there's a 46 00:02:14,840 --> 00:02:18,160 Speaker 3: whole section in it on AI because a decade ago 47 00:02:18,400 --> 00:02:20,720 Speaker 3: I spent quite a bit of time trying to bring 48 00:02:20,760 --> 00:02:23,080 Speaker 3: AI into the world to how really tough problems and 49 00:02:23,120 --> 00:02:26,560 Speaker 3: here we are now and to me the things I learned. 50 00:02:26,760 --> 00:02:29,280 Speaker 3: Everybody wants to talk about the technology, This is going 51 00:02:29,320 --> 00:02:32,000 Speaker 3: to be a people and a trust problem. That is 52 00:02:32,040 --> 00:02:35,079 Speaker 3: what I learned from all these years, and that you know, 53 00:02:35,320 --> 00:02:37,800 Speaker 3: with something like AI, I see it as two sides 54 00:02:37,840 --> 00:02:40,120 Speaker 3: of one coin, and if we don't manage the good 55 00:02:40,160 --> 00:02:42,560 Speaker 3: and the bad at the same time, we're going to 56 00:02:42,639 --> 00:02:43,240 Speaker 3: have trouble. 57 00:02:43,320 --> 00:02:45,720 Speaker 5: When you look back on your legacy at IBM and 58 00:02:45,760 --> 00:02:48,240 Speaker 5: you think about some of the advancements that perhaps were 59 00:02:48,280 --> 00:02:50,480 Speaker 5: shrugged off at the outset, whether it was cloud computing 60 00:02:50,560 --> 00:02:52,520 Speaker 5: or some of the others, what would you do differently, 61 00:02:52,639 --> 00:02:56,200 Speaker 5: or how do you understand what to identify as something 62 00:02:56,200 --> 00:03:00,120 Speaker 5: that holds promise versus the next, you know, false hope 63 00:03:00,160 --> 00:03:01,120 Speaker 5: that everyone's going to cling to. 64 00:03:01,400 --> 00:03:04,560 Speaker 3: Yeah, look, I think this is difficult. It's difficult because 65 00:03:04,600 --> 00:03:07,080 Speaker 3: actually many of the bets we placed were correct. Some 66 00:03:07,240 --> 00:03:10,720 Speaker 3: timing was not exactly right on those, But today everything 67 00:03:10,760 --> 00:03:13,240 Speaker 3: we did around AI and now it's hybrid cloud is 68 00:03:13,240 --> 00:03:16,760 Speaker 3: what the company is really growing from. So the idea 69 00:03:16,800 --> 00:03:18,680 Speaker 3: when you run a company, and IBM's the oldest tech 70 00:03:18,720 --> 00:03:20,600 Speaker 3: company and clearly I had to lead it through its 71 00:03:20,600 --> 00:03:24,360 Speaker 3: most tumultuous reinvention, is that you've just got to make 72 00:03:24,400 --> 00:03:26,760 Speaker 3: those decisions for the long term, even if you may 73 00:03:26,760 --> 00:03:28,400 Speaker 3: not see them play out during your tenure. 74 00:03:28,480 --> 00:03:31,600 Speaker 2: If you're joining us on radio and television, good power. 75 00:03:31,639 --> 00:03:34,160 Speaker 2: I can't say enough about it. It's summer read thin. 76 00:03:34,400 --> 00:03:37,760 Speaker 2: This is great but very dense. Two hundred and thirty 77 00:03:37,840 --> 00:03:41,840 Speaker 2: pages here on a life story that's extraordinary and also 78 00:03:41,920 --> 00:03:44,520 Speaker 2: the realities of IBM. I'm going to address it. You've 79 00:03:44,520 --> 00:03:48,160 Speaker 2: had critics like a Pinata here on the transformation of 80 00:03:48,240 --> 00:03:50,520 Speaker 2: I've been moved. I want to get to Gersner in 81 00:03:50,520 --> 00:03:52,320 Speaker 2: a minute. But the way you used to read the 82 00:03:52,360 --> 00:03:54,720 Speaker 2: statement from Sam Palmisano as you looked at the free 83 00:03:54,760 --> 00:03:57,040 Speaker 2: cash flow on page two of the on your report, 84 00:03:57,080 --> 00:04:00,240 Speaker 2: and you said, IBM to the moon. What happened? How 85 00:04:00,280 --> 00:04:01,920 Speaker 2: did Palmisano get this so wrong? 86 00:04:02,040 --> 00:04:04,080 Speaker 3: Look, I mean what we had done, we had done 87 00:04:04,240 --> 00:04:06,760 Speaker 3: very well to a certain point, but then the world changed. 88 00:04:06,840 --> 00:04:07,640 Speaker 2: The world changed. 89 00:04:07,760 --> 00:04:09,560 Speaker 4: There was not just one tech trend. 90 00:04:09,560 --> 00:04:12,080 Speaker 3: There was cloud, there was data, there was AI, mobility, 91 00:04:12,200 --> 00:04:14,680 Speaker 3: social media, and that's five trends. 92 00:04:14,720 --> 00:04:18,520 Speaker 2: Now in the meetings was it? Did you blame McKenzie? 93 00:04:18,560 --> 00:04:21,239 Speaker 2: Did you bring in consultants who screwed this up? Why 94 00:04:21,279 --> 00:04:24,360 Speaker 2: did you have such an inertial force where you couldn't 95 00:04:24,440 --> 00:04:25,040 Speaker 2: change faster? 96 00:04:25,240 --> 00:04:26,960 Speaker 3: Yeah, I think you would always had. When you're an 97 00:04:27,000 --> 00:04:30,200 Speaker 3: incumbnent in something, it is both you're trying to protect, 98 00:04:30,600 --> 00:04:32,640 Speaker 3: not just protect a pass. You've got customers that you've 99 00:04:32,680 --> 00:04:34,320 Speaker 3: got to serve, Yet you've got to move to the 100 00:04:34,320 --> 00:04:37,200 Speaker 3: future at the same time. And so that realized that 101 00:04:37,279 --> 00:04:39,240 Speaker 3: every one of us had a challenge, and mine was 102 00:04:39,320 --> 00:04:42,919 Speaker 3: to now prepare a whole new technology portfolio for the future. 103 00:04:43,200 --> 00:04:45,800 Speaker 3: But I had a double sort of challenge in that 104 00:04:45,880 --> 00:04:48,640 Speaker 3: I also had to reskill the workforce, and so when 105 00:04:48,640 --> 00:04:51,839 Speaker 3: I began, folks weren't prepared for future skills. 106 00:04:51,880 --> 00:04:53,760 Speaker 2: It's delate in the book, You're phenomenal on this. You 107 00:04:53,760 --> 00:04:55,719 Speaker 2: go to Brian Moyne at a Bank of America, who's 108 00:04:55,760 --> 00:04:57,880 Speaker 2: an animal. I mean, I love you know, Brian, you go, 109 00:04:58,120 --> 00:05:01,279 Speaker 2: what's the FED going to do? He's a bank animal, 110 00:05:01,320 --> 00:05:04,360 Speaker 2: he knows the depositive flows in Kansas City, and he 111 00:05:04,440 --> 00:05:07,280 Speaker 2: talks about hiring for skill. Yeah, does that mean you 112 00:05:07,400 --> 00:05:08,320 Speaker 2: only hire engineers? 113 00:05:08,520 --> 00:05:08,760 Speaker 4: Yeah. 114 00:05:08,800 --> 00:05:12,039 Speaker 3: Look, this is something that I work passionately on now, 115 00:05:12,080 --> 00:05:14,200 Speaker 3: this idea to hire for people for their skills, not 116 00:05:14,360 --> 00:05:17,480 Speaker 3: just their degrees. I came to it having had a mother. 117 00:05:17,760 --> 00:05:19,800 Speaker 3: You know, I was abandoned as a kid, and my 118 00:05:19,880 --> 00:05:23,920 Speaker 3: mother had no education. So but I quickly saw access 119 00:05:23,920 --> 00:05:26,719 Speaker 3: and aptitude were not equal, meaning she had aptitude no 120 00:05:26,800 --> 00:05:29,520 Speaker 3: access to education. So she got a little bit of 121 00:05:29,640 --> 00:05:31,680 Speaker 3: education and can get an hourly job, a little more, 122 00:05:31,680 --> 00:05:33,440 Speaker 3: a little better job to get us off of things 123 00:05:33,480 --> 00:05:37,039 Speaker 3: like food stamps. Fast forward, I would be searching for 124 00:05:37,080 --> 00:05:40,440 Speaker 3: people like Cyber. They're not in the marketplace. In twenty twelve, 125 00:05:40,800 --> 00:05:43,800 Speaker 3: we'd start to work with community colleges and high schools, 126 00:05:44,240 --> 00:05:46,280 Speaker 3: and I'd say, why aren't we hiring these people? They 127 00:05:46,279 --> 00:05:48,640 Speaker 3: can do the job while one hundred percent of our 128 00:05:48,720 --> 00:05:51,640 Speaker 3: requirements are college degrees. And it would lead me down 129 00:05:51,680 --> 00:05:55,120 Speaker 3: this path for really, now, almost two decades of a movement, 130 00:05:55,240 --> 00:05:57,960 Speaker 3: I think it's essential to democracy in this country that 131 00:05:58,000 --> 00:06:01,200 Speaker 3: we revisit having found fifth ffty percent of our jobs 132 00:06:01,200 --> 00:06:04,680 Speaker 3: were over credentialed, requiring a degree to start when you 133 00:06:04,720 --> 00:06:07,200 Speaker 3: didn't need one to start. This idea that where you 134 00:06:07,240 --> 00:06:10,600 Speaker 3: start shouldn't determine where you end. That skills first, you 135 00:06:10,760 --> 00:06:11,080 Speaker 3: join the. 136 00:06:11,080 --> 00:06:13,440 Speaker 5: Show at a really important point, and you're an important 137 00:06:13,520 --> 00:06:15,760 Speaker 5: voice at a time where we talk so much about 138 00:06:15,800 --> 00:06:18,120 Speaker 5: tech and how much that's a divergent story from the 139 00:06:18,160 --> 00:06:20,760 Speaker 5: rest of the economy. Do you think that the big 140 00:06:20,800 --> 00:06:23,880 Speaker 5: tech names that have really become the behemoths are doing 141 00:06:23,960 --> 00:06:28,760 Speaker 5: a good job managing the technological trends, managing to train 142 00:06:28,880 --> 00:06:32,000 Speaker 5: people in the appropriate ways, and creating a path for 143 00:06:32,040 --> 00:06:33,680 Speaker 5: a longer type of trajectory. 144 00:06:34,000 --> 00:06:35,280 Speaker 4: Let me answer that two ways. 145 00:06:35,320 --> 00:06:37,160 Speaker 3: One is there's a whole chapter of what I call 146 00:06:37,200 --> 00:06:39,960 Speaker 3: stort in good tech, and it isn't just the tech companies. 147 00:06:40,000 --> 00:06:42,840 Speaker 3: It's now all of us and has much efforts got 148 00:06:42,920 --> 00:06:45,839 Speaker 3: to be put on preparing people to see a better 149 00:06:45,880 --> 00:06:47,320 Speaker 3: future because of this technology. 150 00:06:47,520 --> 00:06:49,280 Speaker 4: I mean, my biggest learning with trying to. 151 00:06:49,240 --> 00:06:52,640 Speaker 3: Roll out AI, particular with professional people, they don't trust it. 152 00:06:53,120 --> 00:06:55,839 Speaker 3: They unless they're part of its co creation and how 153 00:06:55,839 --> 00:06:58,880 Speaker 3: it's used, won't use it. So as you go forward, 154 00:06:58,960 --> 00:07:02,080 Speaker 3: now this is about recent skilling, not just your current workers. 155 00:07:02,440 --> 00:07:04,440 Speaker 3: You know it will be multiple times in our lifetime 156 00:07:04,440 --> 00:07:07,479 Speaker 3: once and done. Education is over now and so that 157 00:07:07,680 --> 00:07:10,679 Speaker 3: idea that I think that side of the coin about 158 00:07:10,680 --> 00:07:14,440 Speaker 3: preparing the workforce is totally not paid attention to right now, 159 00:07:14,800 --> 00:07:17,160 Speaker 3: and to me, that is the biggest thing. And second 160 00:07:17,240 --> 00:07:20,920 Speaker 3: is is really getting people to trust the technology. You know, 161 00:07:21,000 --> 00:07:22,840 Speaker 3: I had a lot of experience with a big company 162 00:07:22,880 --> 00:07:26,040 Speaker 3: with a big brand, and when you introduce AI, there's 163 00:07:26,320 --> 00:07:28,720 Speaker 3: very little tolerance for it to be wrong. When you're 164 00:07:28,760 --> 00:07:31,520 Speaker 3: a big company, a startup, no problem. But when you 165 00:07:31,560 --> 00:07:34,440 Speaker 3: start using it for health financial services, you're in a 166 00:07:34,440 --> 00:07:36,800 Speaker 3: whole different ballgame with how people react to it. 167 00:07:37,000 --> 00:07:38,440 Speaker 4: So that issue of trust and. 168 00:07:38,480 --> 00:07:41,400 Speaker 3: Preparing people are what I think is not being acted 169 00:07:41,480 --> 00:07:41,960 Speaker 3: on enough. 170 00:07:42,080 --> 00:07:43,880 Speaker 2: I'm out of time, but I got to squeeze this in. 171 00:07:43,960 --> 00:07:46,640 Speaker 2: I got fourteen more questions. I got time for one question. 172 00:07:47,080 --> 00:07:49,800 Speaker 2: What was the best practice of lou gersoner. 173 00:07:51,680 --> 00:07:51,760 Speaker 4: My? 174 00:07:52,160 --> 00:07:53,840 Speaker 3: You know, I you know, I have a men's respect 175 00:07:53,880 --> 00:07:55,480 Speaker 3: for low and to this day is a very good 176 00:07:55,480 --> 00:07:58,480 Speaker 3: friend of mine. I think he was a brilliant both 177 00:07:58,520 --> 00:08:01,800 Speaker 3: strategist and execution leader both sides of the coin. 178 00:08:02,120 --> 00:08:03,640 Speaker 2: He got his hands dirty. 179 00:08:03,320 --> 00:08:06,080 Speaker 3: Issues absolutely, and to this day this is like gospel 180 00:08:06,120 --> 00:08:09,080 Speaker 3: to me. Yes, absolutely, and I share nothing. 181 00:08:08,840 --> 00:08:10,640 Speaker 2: On the show, everybody puts it together for me. 182 00:08:10,680 --> 00:08:11,280 Speaker 5: I just show up. 183 00:08:11,360 --> 00:08:13,960 Speaker 3: No, no, we share that admiration. He is able to 184 00:08:13,960 --> 00:08:16,080 Speaker 3: simplify the complex in an amazing way. 185 00:08:16,520 --> 00:08:19,840 Speaker 2: This is a great book, good Power leading positive change 186 00:08:19,840 --> 00:08:23,920 Speaker 2: in our life's work in world. The first fifty pages 187 00:08:24,840 --> 00:08:28,239 Speaker 2: is blistering. There's no other way to put it anything. 188 00:08:28,360 --> 00:08:29,840 Speaker 2: I'm not going to pronounce your last day. 189 00:08:30,080 --> 00:08:31,200 Speaker 4: That's all right. Remedy. 190 00:08:31,400 --> 00:08:40,000 Speaker 2: Remedy joins us good Power here this morning.