1 00:00:00,200 --> 00:00:03,280 Speaker 1: Because you're a subscriber to this Bloomberg podcast, we thought 2 00:00:03,320 --> 00:00:07,160 Speaker 1: you'd be interested in a sponsored podcast called The Ceo Radar, 3 00:00:07,480 --> 00:00:11,560 Speaker 1: produced by BCG and Bloomberg Media Studios. It analyzes more 4 00:00:11,560 --> 00:00:15,040 Speaker 1: than forty eight hundred Q three earnings calls worldwide to 5 00:00:15,080 --> 00:00:18,960 Speaker 1: assess what topics merit a CEO's time and attention. Here's 6 00:00:19,079 --> 00:00:25,920 Speaker 1: a recent episode when the company is investing in these 7 00:00:25,960 --> 00:00:29,520 Speaker 1: AI pilots. Are they spending too long on them? Are 8 00:00:29,560 --> 00:00:31,920 Speaker 1: they giving up too quickly on them? Or is it 9 00:00:32,040 --> 00:00:35,080 Speaker 1: just right? I wish it was just right. It's both. 10 00:00:35,120 --> 00:00:37,920 Speaker 2: Actually they're closing some of them too soon and some 11 00:00:38,000 --> 00:00:39,920 Speaker 2: of them they're just stinking on for way too long. 12 00:00:40,040 --> 00:00:43,120 Speaker 3: But I think that focus on value creation and particularly 13 00:00:43,120 --> 00:00:46,519 Speaker 3: where you can build distinctive value creations, that is the 14 00:00:46,560 --> 00:00:48,839 Speaker 3: critical way to be thinking about where and how to 15 00:00:48,880 --> 00:00:50,040 Speaker 3: apply AI right now. 16 00:00:51,320 --> 00:00:55,080 Speaker 1: The Ceo Radar reviewed earnings calls at forty eight hundred 17 00:00:55,080 --> 00:00:58,160 Speaker 1: companies worldwide to see what's on the agendas of both 18 00:00:58,240 --> 00:01:05,080 Speaker 1: CEOs and the market as a whole. I'm Edward Adams 19 00:01:05,120 --> 00:01:08,479 Speaker 1: of Bloomberg Media Studios on this episode. I'm joined by 20 00:01:08,480 --> 00:01:12,760 Speaker 1: BCG Global Chair Rich Lesser and Lad Lukic BCG Global 21 00:01:12,840 --> 00:01:16,959 Speaker 1: Leader Tech and Digital Advantage. Rich and Blad Welcome to 22 00:01:16,959 --> 00:01:17,520 Speaker 1: the podcast. 23 00:01:17,800 --> 00:01:20,039 Speaker 4: Great to be here, ed Nice to be here. 24 00:01:20,520 --> 00:01:24,360 Speaker 1: In Q three, tariffs remain the top one or two 25 00:01:24,600 --> 00:01:27,720 Speaker 1: topics that were most mentioned during earning's calls, both by 26 00:01:27,760 --> 00:01:31,720 Speaker 1: CEOs and by analysts, but the number of mentions went 27 00:01:31,760 --> 00:01:32,880 Speaker 1: down from Q two. 28 00:01:33,280 --> 00:01:35,440 Speaker 4: There is a shift that I thought was really interesting. 29 00:01:35,520 --> 00:01:37,560 Speaker 3: And when we did this a quarter ago, all of 30 00:01:37,560 --> 00:01:40,320 Speaker 3: the discussions are about policy related topics that will happen. 31 00:01:40,360 --> 00:01:42,560 Speaker 3: What will happen. Now it's about, Okay, what are we 32 00:01:42,600 --> 00:01:45,480 Speaker 3: going to do? There is still policy uncertainty. It's not 33 00:01:45,520 --> 00:01:48,000 Speaker 3: that things won't shift, but now I think everybody realizes 34 00:01:48,400 --> 00:01:52,960 Speaker 3: substantial tariffs versus anything we've dealt with historically in decades 35 00:01:53,400 --> 00:01:56,320 Speaker 3: are now most likely here to stay, certainly as it 36 00:01:56,360 --> 00:01:59,520 Speaker 3: relates to the US, and now we have to navigate it. 37 00:01:59,560 --> 00:02:01,960 Speaker 3: And I thought the interesting shift in the types of 38 00:02:02,440 --> 00:02:05,760 Speaker 3: questions and conversations that CEOs and ANAUS were having was 39 00:02:05,840 --> 00:02:07,680 Speaker 3: really quite reflective of that shift. 40 00:02:07,840 --> 00:02:10,320 Speaker 1: And along those lines, they were talking far more about 41 00:02:10,320 --> 00:02:14,200 Speaker 1: growth topics we saw this year, particularly in AI, a 42 00:02:14,280 --> 00:02:17,359 Speaker 1: topic we didn't think necessarily could be mentioned any more often. 43 00:02:17,360 --> 00:02:18,040 Speaker 1: Than it had been. 44 00:02:18,280 --> 00:02:21,280 Speaker 3: What we observe in our research is a bell curve 45 00:02:21,520 --> 00:02:24,120 Speaker 3: of behaviors where you've got about five percent of CEOs 46 00:02:24,160 --> 00:02:25,920 Speaker 3: that are really at the cutting edge about how to 47 00:02:25,960 --> 00:02:29,320 Speaker 3: embed AI into their core business, reshape how they operate 48 00:02:29,360 --> 00:02:32,240 Speaker 3: and invent new business models drive the business. You've got 49 00:02:32,240 --> 00:02:35,240 Speaker 3: another thirty or forty percent of CEOs that are well 50 00:02:35,280 --> 00:02:37,520 Speaker 3: on that journey, and you've got about sixty percent of 51 00:02:37,560 --> 00:02:40,560 Speaker 3: CEOs that are really struggling to get on that curve, 52 00:02:40,760 --> 00:02:46,240 Speaker 3: often either six zeros six percent. Right, No, it's a 53 00:02:46,280 --> 00:02:49,080 Speaker 3: hard it's a hard transition to make because on the 54 00:02:49,080 --> 00:02:52,280 Speaker 3: one hand, you have to change your mindset about how 55 00:02:52,320 --> 00:02:55,200 Speaker 3: you're going to translate AI into impact at scale, not 56 00:02:55,400 --> 00:02:58,000 Speaker 3: just do interesting pilots, and then you have to change 57 00:02:58,000 --> 00:03:00,720 Speaker 3: your mindset to raise Yes, there is an enormous amount 58 00:03:00,720 --> 00:03:03,440 Speaker 3: of interesting and important tech in here, but the hardest 59 00:03:03,440 --> 00:03:05,320 Speaker 3: part of that change is often the human part of 60 00:03:05,320 --> 00:03:08,520 Speaker 3: that change, how you embedded in and processes, leadership skills, 61 00:03:08,960 --> 00:03:12,560 Speaker 3: and companies just find that journey to be a bit hard, 62 00:03:12,639 --> 00:03:16,080 Speaker 3: and so every year webs are more and more moving 63 00:03:16,200 --> 00:03:18,520 Speaker 3: toward the more advanced and their ability to do it, 64 00:03:18,560 --> 00:03:20,760 Speaker 3: but it's still a substantial percentage of CEOs that are 65 00:03:20,800 --> 00:03:23,560 Speaker 3: struggling on that journey. But for the ones at the 66 00:03:23,600 --> 00:03:26,200 Speaker 3: top of the list, I think they're thinking about it 67 00:03:26,240 --> 00:03:29,000 Speaker 3: in very sophisticated ways. How do you pick fewer things 68 00:03:29,080 --> 00:03:31,240 Speaker 3: rather than more and really deliver them? 69 00:03:31,800 --> 00:03:34,440 Speaker 1: LAD, you're oftentimes down in the trenches with these companies, 70 00:03:34,480 --> 00:03:37,280 Speaker 1: not necessarily always at the CEO level. Are you seeing 71 00:03:37,320 --> 00:03:39,800 Speaker 1: any disconnect between what rich is reporting at the top 72 00:03:40,240 --> 00:03:42,400 Speaker 1: and what you're seeing when you're talking to people two 73 00:03:42,440 --> 00:03:43,240 Speaker 1: or three levels down. 74 00:03:43,360 --> 00:03:45,800 Speaker 2: Many times you could have the direction lined up by 75 00:03:45,800 --> 00:03:47,400 Speaker 2: the CEO and fully aligned. 76 00:03:47,000 --> 00:03:48,480 Speaker 4: By the executives. 77 00:03:48,520 --> 00:03:50,440 Speaker 2: But if you're running HR and now, you need to 78 00:03:50,560 --> 00:03:54,960 Speaker 2: hire igentic engineers that know how to wire these workflows together. 79 00:03:55,720 --> 00:03:58,480 Speaker 2: You need to change job descriptions, you need to introduce 80 00:03:58,560 --> 00:04:01,080 Speaker 2: new titles, and many times they they don't know how 81 00:04:01,120 --> 00:04:02,760 Speaker 2: to do that. They haven't done it before. 82 00:04:03,200 --> 00:04:05,040 Speaker 1: Richard told us that they are About sixty percent of 83 00:04:05,040 --> 00:04:07,880 Speaker 1: companies are still struggling to get out of the incubation phase. 84 00:04:08,000 --> 00:04:11,080 Speaker 1: Right with AI, If we're on the outside of a company, 85 00:04:11,320 --> 00:04:14,800 Speaker 1: how do we recognize a company has moved beyond and 86 00:04:14,840 --> 00:04:18,400 Speaker 1: it's begun to really get some ROI from from its 87 00:04:18,440 --> 00:04:19,560 Speaker 1: implementation of that. 88 00:04:20,000 --> 00:04:22,279 Speaker 2: I mean, I would say focused on the bottom line results, 89 00:04:22,279 --> 00:04:25,159 Speaker 2: like the really good ones immediately. However, we cut the 90 00:04:25,240 --> 00:04:27,359 Speaker 2: data and when we look at it, the ones that 91 00:04:27,400 --> 00:04:31,080 Speaker 2: have really leaned into AI are growing faster the numbers 92 00:04:31,120 --> 00:04:31,479 Speaker 2: are there. 93 00:04:31,680 --> 00:04:34,159 Speaker 1: Are you finding that that CEOs are willing to talk 94 00:04:34,200 --> 00:04:36,719 Speaker 1: about that on earnings calls to sort of be public 95 00:04:36,760 --> 00:04:39,320 Speaker 1: about yes, we're improving in AI is one of the 96 00:04:39,320 --> 00:04:40,400 Speaker 1: reasons why. 97 00:04:40,240 --> 00:04:42,960 Speaker 3: I think some are starting. One of the leading insurers 98 00:04:43,080 --> 00:04:46,479 Speaker 3: talked at length about underwriting and how they've really thought 99 00:04:46,480 --> 00:04:48,960 Speaker 3: about underwriting differently to be able to go faster and 100 00:04:49,080 --> 00:04:52,839 Speaker 3: more effectively. And in commercial underwritings it's often a speed game. 101 00:04:52,880 --> 00:04:55,479 Speaker 3: The ability to get to a proposal very soon for 102 00:04:55,520 --> 00:04:58,040 Speaker 3: a client is critical to whether you're going to win 103 00:04:58,120 --> 00:05:01,640 Speaker 3: the win the bid or not. And so a leading 104 00:05:01,720 --> 00:05:04,599 Speaker 3: mining company has talked about how it's automated. A huge 105 00:05:04,640 --> 00:05:07,320 Speaker 3: part of Western Australia runs it out of a control 106 00:05:07,400 --> 00:05:10,760 Speaker 3: center with TED people uses predictive and enerve AI to 107 00:05:10,760 --> 00:05:13,120 Speaker 3: be able to make much smarter decisions about what time 108 00:05:13,160 --> 00:05:15,720 Speaker 3: of data fill different kinds of ships, how to run 109 00:05:15,760 --> 00:05:19,680 Speaker 3: the whole process. Loreal has talked about beauty Genius. It's 110 00:05:19,760 --> 00:05:23,240 Speaker 3: desired to create a more agentic beauty platform that people 111 00:05:23,320 --> 00:05:27,200 Speaker 3: can use online and get recommendations, but also fulfilled orders 112 00:05:27,240 --> 00:05:30,320 Speaker 3: and do things. And how they just did a major 113 00:05:30,400 --> 00:05:32,839 Speaker 3: rollout here in the US and some other places this 114 00:05:32,960 --> 00:05:36,080 Speaker 3: year that it's a learning mode, but those are real businesses, 115 00:05:36,200 --> 00:05:37,880 Speaker 3: not just a small pilots. 116 00:05:37,960 --> 00:05:40,039 Speaker 2: It's a test to build off of that. There is 117 00:05:40,400 --> 00:05:42,960 Speaker 2: need to communicate for several reasons. Son, you need to 118 00:05:42,960 --> 00:05:45,840 Speaker 2: get new talent. The talent is now looking at am 119 00:05:45,880 --> 00:05:47,600 Speaker 2: I joining a company that will be a winner in 120 00:05:47,640 --> 00:05:49,240 Speaker 2: the space, and will I get all the tools in 121 00:05:49,279 --> 00:05:53,320 Speaker 2: my hands to be successful. The buyers are looking at 122 00:05:53,320 --> 00:05:55,680 Speaker 2: their suppliers and saying are you going to be cutting edge? 123 00:05:55,720 --> 00:05:57,800 Speaker 2: And are you going to be pushing on the innovation 124 00:05:57,920 --> 00:06:00,919 Speaker 2: curve and on the cost curve fast enough to remain 125 00:06:01,040 --> 00:06:03,839 Speaker 2: so the CEOs need to signal both to their employees 126 00:06:03,880 --> 00:06:06,400 Speaker 2: and partners in the broader ecosystem that they're on top 127 00:06:06,480 --> 00:06:06,680 Speaker 2: of this. 128 00:06:06,920 --> 00:06:08,640 Speaker 1: I want to find out whether or not you're seeing 129 00:06:08,680 --> 00:06:11,599 Speaker 1: AI as a job creator or a job killer, at 130 00:06:11,680 --> 00:06:13,840 Speaker 1: least at this stage of the process. Lad, you're on 131 00:06:13,880 --> 00:06:14,880 Speaker 1: the ground, what are you seeing? 132 00:06:14,960 --> 00:06:15,159 Speaker 4: Yeah? 133 00:06:15,279 --> 00:06:17,640 Speaker 2: Counter to like what we might hear in the media, right. 134 00:06:17,720 --> 00:06:20,560 Speaker 2: I'm actively engaged with probably twenty engagements right now. Not 135 00:06:20,600 --> 00:06:22,800 Speaker 2: a single one of them has a thesis on reducing 136 00:06:22,839 --> 00:06:26,919 Speaker 2: the number of labor. It is about growth opportunities and 137 00:06:26,960 --> 00:06:29,680 Speaker 2: growing the business. Now that is in the short term. 138 00:06:29,680 --> 00:06:32,480 Speaker 2: Does that mean it doesn't have longer term implications for sure? 139 00:06:32,880 --> 00:06:33,040 Speaker 4: Right? 140 00:06:33,080 --> 00:06:35,080 Speaker 2: But where I'm seeing the winning convoys when you get 141 00:06:35,080 --> 00:06:38,160 Speaker 2: the efficiency and then translate it into new value propositions 142 00:06:38,160 --> 00:06:41,080 Speaker 2: to customers, right and grow the business. So that's the 143 00:06:41,120 --> 00:06:42,800 Speaker 2: conbo that seems to be winning so far. 144 00:06:43,960 --> 00:06:46,760 Speaker 3: I agree with that, but I would add the two things. 145 00:06:46,760 --> 00:06:50,000 Speaker 3: One is, we have seen examples I mentioned earlier, the 146 00:06:50,120 --> 00:06:54,000 Speaker 3: Australian example where a a mining company can control an 147 00:06:54,120 --> 00:06:57,719 Speaker 3: enormous amount of land area autonomously have a control center 148 00:06:57,760 --> 00:06:59,520 Speaker 3: with a very small number of people in it. I 149 00:06:59,640 --> 00:07:02,120 Speaker 3: mean obviously that has labor displacement from the way they 150 00:07:02,160 --> 00:07:03,080 Speaker 3: would have used to. 151 00:07:03,160 --> 00:07:04,440 Speaker 4: Formal we would have operated. 152 00:07:04,920 --> 00:07:06,840 Speaker 3: And then the second thing I'd say is there is 153 00:07:06,920 --> 00:07:10,280 Speaker 3: so much capital going into AI and data centers now 154 00:07:10,720 --> 00:07:12,880 Speaker 3: it's hard to see how that pays out in the 155 00:07:12,920 --> 00:07:16,640 Speaker 3: long term if labor displacement isn't a part of that. 156 00:07:16,880 --> 00:07:19,560 Speaker 2: I'm optim is that it's going to generally result in 157 00:07:19,640 --> 00:07:22,080 Speaker 2: net positive number of jobs, but they're not going to 158 00:07:22,160 --> 00:07:25,080 Speaker 2: be the same jobs. People will have to be reskilled 159 00:07:25,080 --> 00:07:28,720 Speaker 2: and moving to new into new fields, et cetera. And 160 00:07:28,760 --> 00:07:30,400 Speaker 2: the ones that embrace this are going to be more 161 00:07:30,440 --> 00:07:32,480 Speaker 2: relevant than ever. If you embrace this and do it, 162 00:07:32,520 --> 00:07:34,960 Speaker 2: you'd get a superpower and you can be more relevant. 163 00:07:35,360 --> 00:07:37,200 Speaker 1: But at the beginning of the year, there's a lot 164 00:07:37,200 --> 00:07:40,800 Speaker 1: of talk about agentic AI AI being able to accomplish 165 00:07:40,840 --> 00:07:43,520 Speaker 1: tasks to a degree on its own right, to be 166 00:07:43,560 --> 00:07:46,360 Speaker 1: able to book my flight to La get me a hotel, 167 00:07:46,480 --> 00:07:49,040 Speaker 1: get me a dinner reservation without me having to get 168 00:07:49,080 --> 00:07:52,000 Speaker 1: involved with it, as just one example. Are you still 169 00:07:52,000 --> 00:07:54,520 Speaker 1: seeing that emphasis on agentic at the as we begin 170 00:07:54,600 --> 00:07:56,320 Speaker 1: to close out the year, or has it has it 171 00:07:56,360 --> 00:07:57,000 Speaker 1: shifted somewhat? 172 00:07:57,320 --> 00:08:01,600 Speaker 2: For sure, it's it's still there, and it's in two 173 00:08:01,680 --> 00:08:05,160 Speaker 2: specific flavors. One is articulating, are of the possible? Oh boy, 174 00:08:05,200 --> 00:08:07,200 Speaker 2: wouldn't it be cool if we could combine all of 175 00:08:07,240 --> 00:08:09,760 Speaker 2: these things together? Right, the reality is we're just not 176 00:08:09,920 --> 00:08:12,760 Speaker 2: there to pull it off. So where we're seeing agentic 177 00:08:12,800 --> 00:08:15,480 Speaker 2: being deployed is in a specific workflow end to end, 178 00:08:16,400 --> 00:08:19,240 Speaker 2: but then with a human to act on the decision. 179 00:08:19,360 --> 00:08:21,160 Speaker 2: What we're not seeing is and what a lot of 180 00:08:21,200 --> 00:08:23,680 Speaker 2: folks are hoping for, is agents talking to agents and 181 00:08:23,720 --> 00:08:26,920 Speaker 2: like stringing together a bunch of processes because you still 182 00:08:26,920 --> 00:08:31,040 Speaker 2: have accuracies in the eighty to ninety percent range. So 183 00:08:31,080 --> 00:08:33,559 Speaker 2: that means if I string together ten steps and each 184 00:08:33,640 --> 00:08:35,520 Speaker 2: one of them is eighty percent accurate, at the end 185 00:08:35,520 --> 00:08:37,679 Speaker 2: of it, I'm at less than fifty percent accuracy. So 186 00:08:37,679 --> 00:08:40,400 Speaker 2: you do need a human in between that is interpreting 187 00:08:40,400 --> 00:08:42,120 Speaker 2: and fine tuning the agents within. 188 00:08:41,960 --> 00:08:43,320 Speaker 4: Those specific workflows. 189 00:08:43,520 --> 00:08:45,440 Speaker 2: That's where we are, and for us to enable the 190 00:08:45,480 --> 00:08:48,959 Speaker 2: next level, we'll need better data, we'll need better infrastructure. 191 00:08:49,120 --> 00:08:51,760 Speaker 2: It was a fantastic catalyst to actually surface a lot 192 00:08:51,760 --> 00:08:54,800 Speaker 2: of the basic needs that the companies need to rewire 193 00:08:54,840 --> 00:08:57,679 Speaker 2: and rethink on their textag. So it's there as a 194 00:08:57,679 --> 00:09:00,559 Speaker 2: catalyst and the promise of what it was can happen, 195 00:09:00,559 --> 00:09:03,000 Speaker 2: and companies are leaning in and are committed to that 196 00:09:03,080 --> 00:09:04,280 Speaker 2: motion for the years to come. 197 00:09:04,240 --> 00:09:05,200 Speaker 4: Which I don't need to tell you. 198 00:09:05,240 --> 00:09:08,760 Speaker 1: The valuations of the Magnificent seven and other AI related 199 00:09:08,800 --> 00:09:12,520 Speaker 1: stocks are scott high. If those valuations were to tank, 200 00:09:12,800 --> 00:09:15,480 Speaker 1: much as we saw the valuations of dot com companies 201 00:09:15,520 --> 00:09:18,760 Speaker 1: tank during the dot com bubble. Do you think that 202 00:09:19,240 --> 00:09:22,840 Speaker 1: companies will continue to invest in their own AI initiatives 203 00:09:22,960 --> 00:09:24,400 Speaker 1: or do you think that may have a knock on 204 00:09:24,440 --> 00:09:28,400 Speaker 1: effect in terms of how the worldwide corporate environment looks 205 00:09:28,400 --> 00:09:28,800 Speaker 1: at AI. 206 00:09:29,559 --> 00:09:31,720 Speaker 3: I think it will have very little effect on the 207 00:09:31,760 --> 00:09:34,800 Speaker 3: real economy. It'll obviously have a massive effect on the 208 00:09:34,880 --> 00:09:37,680 Speaker 3: stock market, as they represent a high share of total 209 00:09:37,720 --> 00:09:38,800 Speaker 3: stock market valuations. 210 00:09:38,800 --> 00:09:40,160 Speaker 4: If that's an aarrea where will occur. 211 00:09:40,480 --> 00:09:43,880 Speaker 3: But I actually think the more companies see the kinds 212 00:09:43,880 --> 00:09:45,880 Speaker 3: of impacts they can drive, and the more they can 213 00:09:45,920 --> 00:09:50,920 Speaker 3: point to examples even if it's of competitors or in 214 00:09:51,080 --> 00:09:54,000 Speaker 3: other industries that they can rise, they need to take action, 215 00:09:54,360 --> 00:09:57,040 Speaker 3: that will be what drives momentum. I actually think that 216 00:09:58,440 --> 00:10:02,840 Speaker 3: companies are realizing you can fundamentally operate differently with these technologies. 217 00:10:03,160 --> 00:10:06,559 Speaker 3: Even if the market is overvaluated, there's no question AI's 218 00:10:06,600 --> 00:10:08,319 Speaker 3: capabilities will continue to grow. 219 00:10:08,640 --> 00:10:11,200 Speaker 1: Rich Are you finding that companies are investing in AI 220 00:10:11,320 --> 00:10:12,520 Speaker 1: in the right kind of way? 221 00:10:13,000 --> 00:10:16,840 Speaker 3: Obviously a subset are, but I'd say too many more 222 00:10:16,880 --> 00:10:19,520 Speaker 3: than half are still at the stage where they're either 223 00:10:19,600 --> 00:10:22,120 Speaker 3: investing may need to do pilots and not thinking about 224 00:10:22,120 --> 00:10:25,959 Speaker 3: scaling or Equally importantly, they're doing the kinds of things 225 00:10:25,960 --> 00:10:28,880 Speaker 3: that are important to stay competitive, to be more productive 226 00:10:29,200 --> 00:10:31,559 Speaker 3: all of the many AI tools that come along from 227 00:10:31,679 --> 00:10:35,640 Speaker 3: tech players and others, but insufficiently investing to what will 228 00:10:35,679 --> 00:10:39,000 Speaker 3: really lead to competitive advantage, which is where you're either 229 00:10:39,080 --> 00:10:44,800 Speaker 3: reshaping entire workflows or functions, where you're building new business models. 230 00:10:44,880 --> 00:10:49,000 Speaker 1: So lad when a company is investing in these AI pilots, 231 00:10:49,080 --> 00:10:52,080 Speaker 1: are they spending too long on them? Are they giving 232 00:10:52,160 --> 00:10:54,960 Speaker 1: up too quickly on them? Or is it just right? 233 00:10:55,600 --> 00:10:57,199 Speaker 1: I wish it was just right. It's both. 234 00:10:57,240 --> 00:10:59,920 Speaker 2: Actually they're closing some of them too soon, and it's 235 00:11:00,000 --> 00:11:02,040 Speaker 2: some of them they're just sticking on for way too long. 236 00:11:02,200 --> 00:11:04,199 Speaker 2: We had a client that for three months was running 237 00:11:04,200 --> 00:11:06,880 Speaker 2: a pilot and in an area that had so much 238 00:11:06,960 --> 00:11:09,880 Speaker 2: value right and they were evaluating a tool that was 239 00:11:09,920 --> 00:11:11,920 Speaker 2: available three months ago, and at the end of it 240 00:11:11,960 --> 00:11:15,120 Speaker 2: was lukewarm results and they said, Okay, we're done. Well, 241 00:11:15,280 --> 00:11:17,160 Speaker 2: there's so many new tools that got released in the 242 00:11:17,200 --> 00:11:19,160 Speaker 2: last three months that can get the performance to the 243 00:11:19,160 --> 00:11:21,440 Speaker 2: next level, but they gave up. So if you know 244 00:11:21,559 --> 00:11:23,800 Speaker 2: that there is a lot of value in that workflow, 245 00:11:24,280 --> 00:11:26,559 Speaker 2: sweat it out, stick with it, don't abandon it too 246 00:11:26,600 --> 00:11:28,640 Speaker 2: soon because the pace at which the new tools are 247 00:11:28,679 --> 00:11:32,760 Speaker 2: coming is so high. Right on the flip side, there 248 00:11:32,800 --> 00:11:35,000 Speaker 2: are a number of them that are seeing good, interesting 249 00:11:35,040 --> 00:11:37,680 Speaker 2: results from the pilots, but it's in a process that's 250 00:11:37,720 --> 00:11:39,880 Speaker 2: not a bottleneck, and even if you solve it from 251 00:11:39,880 --> 00:11:42,800 Speaker 2: ten days to minutes doesn't change the overall outcome. You're 252 00:11:42,840 --> 00:11:45,160 Speaker 2: just increasing your cost. But they stick with it because 253 00:11:45,160 --> 00:11:47,680 Speaker 2: the technologies, they can show cool demos and it's moving, 254 00:11:48,160 --> 00:11:50,720 Speaker 2: but it ends up being a distraction. Stop stop those 255 00:11:50,800 --> 00:11:53,559 Speaker 2: much sooner than kind of instead of creating zombies in 256 00:11:53,600 --> 00:11:54,240 Speaker 2: the organization. 257 00:11:54,360 --> 00:11:57,319 Speaker 1: So apply AI to the core of your business operations. 258 00:11:56,960 --> 00:12:00,840 Speaker 3: Where there's business value in a very distinct and measurable way, 259 00:12:01,360 --> 00:12:04,080 Speaker 3: as opposed to where you can do really interesting stuff 260 00:12:04,160 --> 00:12:06,640 Speaker 3: that looks really cool. I mean not that we don't 261 00:12:06,640 --> 00:12:07,800 Speaker 3: all like to have a little bit of cool. You 262 00:12:07,840 --> 00:12:09,880 Speaker 3: want a little bit of pizaz in whatever you're doing. 263 00:12:10,120 --> 00:12:13,200 Speaker 3: But I think that focus on value creation and particularly 264 00:12:13,200 --> 00:12:16,600 Speaker 3: where you can build distinctive value creation that is the 265 00:12:16,640 --> 00:12:18,880 Speaker 3: critical way to be thinking about where and how to 266 00:12:18,920 --> 00:12:20,160 Speaker 3: apply AI right now. 267 00:12:20,440 --> 00:12:22,720 Speaker 1: Since we're almost at the end of twenty twenty five. 268 00:12:23,000 --> 00:12:24,560 Speaker 1: One of the questions we want to ask you was 269 00:12:24,640 --> 00:12:28,160 Speaker 1: what were the kinds of attributes that CEOs needed this 270 00:12:28,320 --> 00:12:29,319 Speaker 1: year to be. 271 00:12:29,320 --> 00:12:34,880 Speaker 3: Effective rich resilience? Like this is another year that could 272 00:12:34,880 --> 00:12:38,120 Speaker 3: be characteristic over this entire decade. But I just think 273 00:12:38,160 --> 00:12:42,120 Speaker 3: this ability to take unexpected things and figure out how 274 00:12:42,120 --> 00:12:44,200 Speaker 3: to navigate them and do it in a way that 275 00:12:44,200 --> 00:12:47,959 Speaker 3: both anticipates as best you can, but then can respond 276 00:12:48,040 --> 00:12:51,320 Speaker 3: quickly and adapt and then reimagine that set of attributes 277 00:12:51,400 --> 00:12:53,800 Speaker 3: is turning out to be over and over again really 278 00:12:53,800 --> 00:12:54,800 Speaker 3: critical in this world. 279 00:12:55,360 --> 00:12:58,400 Speaker 1: Are you finding that most CEOs have that skill set 280 00:12:58,600 --> 00:13:00,640 Speaker 1: or is it something that any of them are still 281 00:13:00,679 --> 00:13:01,120 Speaker 1: reaching for. 282 00:13:02,679 --> 00:13:04,680 Speaker 3: I think they've had to develop it a lot more. 283 00:13:05,360 --> 00:13:07,560 Speaker 3: In fact, when I meet new CEOs that I do 284 00:13:07,640 --> 00:13:09,520 Speaker 3: a lot of sessions with them, I always say, you 285 00:13:09,559 --> 00:13:12,400 Speaker 3: have no idea how much this generation of CEOs is 286 00:13:12,520 --> 00:13:15,959 Speaker 3: able to handle resilience. More than six years ago, pre COVID, 287 00:13:16,640 --> 00:13:19,280 Speaker 3: it had been so steady for so long, I think 288 00:13:19,320 --> 00:13:20,040 Speaker 3: people sort. 289 00:13:19,840 --> 00:13:20,760 Speaker 4: Of lost that muscle. 290 00:13:20,840 --> 00:13:23,400 Speaker 3: But now one thing after another, so I do think 291 00:13:23,440 --> 00:13:26,760 Speaker 3: CEOs are generally more capable, but each shock requires something different, 292 00:13:26,800 --> 00:13:29,920 Speaker 3: and the tear of challenges that we discussed on earlier 293 00:13:30,280 --> 00:13:33,080 Speaker 3: podcasts this year I think required a new set of 294 00:13:33,080 --> 00:13:36,200 Speaker 3: capabilities to be built. But people are more comfortable building 295 00:13:36,240 --> 00:13:38,720 Speaker 3: new capabilities in this world, Blaed. 296 00:13:38,400 --> 00:13:40,360 Speaker 4: What do you think really good ones not? 297 00:13:40,440 --> 00:13:43,079 Speaker 2: As they were resilient, they were not shying away from 298 00:13:43,120 --> 00:13:45,760 Speaker 2: asking some of the basic questions. I see too many 299 00:13:45,800 --> 00:13:48,880 Speaker 2: CEOs that sometimes don't want to ask a basic question 300 00:13:48,960 --> 00:13:50,880 Speaker 2: so they don't look silly in front of their technical 301 00:13:50,880 --> 00:13:53,080 Speaker 2: staff or in front of the juniors. Right, and the 302 00:13:53,200 --> 00:13:55,800 Speaker 2: really good ones went back to the basics. Okay, what 303 00:13:55,880 --> 00:13:58,840 Speaker 2: problem are we solving for each customer? Where what will 304 00:13:58,880 --> 00:14:01,960 Speaker 2: be the value created? So those the combination of resilience 305 00:14:02,200 --> 00:14:04,280 Speaker 2: and going back to the basics was the winning combo 306 00:14:04,360 --> 00:14:04,720 Speaker 2: this year. 307 00:14:04,920 --> 00:14:06,920 Speaker 1: What was the best thing to come out of this 308 00:14:06,960 --> 00:14:08,960 Speaker 1: whole tariff chaos that we saw this year? 309 00:14:09,120 --> 00:14:11,040 Speaker 2: It forced people to step back and think, Okay, how 310 00:14:11,040 --> 00:14:12,280 Speaker 2: do we make money in this business? 311 00:14:12,320 --> 00:14:14,760 Speaker 4: Where are we irrelevant? What is the value we're delivering? 312 00:14:14,880 --> 00:14:18,040 Speaker 2: And it really had them look at the business and 313 00:14:18,120 --> 00:14:19,680 Speaker 2: I would argue for a lot of them to fall 314 00:14:19,680 --> 00:14:21,200 Speaker 2: back in love with the business because there was a 315 00:14:21,200 --> 00:14:22,840 Speaker 2: momentum they were just building off of. 316 00:14:23,120 --> 00:14:25,760 Speaker 1: Interesting are you seeing companies focus their attention? 317 00:14:26,120 --> 00:14:30,080 Speaker 3: I think this year pushed people to build geopolitical muscle 318 00:14:30,520 --> 00:14:33,000 Speaker 3: that they probably needed to build in the long term anyway, 319 00:14:33,240 --> 00:14:35,640 Speaker 3: but it really accelerated it. I mean often the people 320 00:14:35,760 --> 00:14:38,840 Speaker 3: understood how to deal with tariffs and trade possees were 321 00:14:38,880 --> 00:14:42,760 Speaker 3: three and four and five levels down understanding supply chain risks, 322 00:14:42,800 --> 00:14:45,840 Speaker 3: not just in your own manufacturing, but deep into supply chains. 323 00:14:46,280 --> 00:14:49,400 Speaker 3: We're really not getting the attention that they deserved. And 324 00:14:49,440 --> 00:14:53,120 Speaker 3: I think the sharp impact of tariffs, the increases that 325 00:14:53,160 --> 00:14:56,720 Speaker 3: are so substantial versus the last eighty ninety years of history, 326 00:14:57,400 --> 00:15:00,000 Speaker 3: I think that's caused people to build muscle that will 327 00:15:00,080 --> 00:15:02,800 Speaker 3: served them well in the long term, but had not 328 00:15:03,040 --> 00:15:05,880 Speaker 3: received as much attention in years prior to this one. 329 00:15:06,200 --> 00:15:08,560 Speaker 1: Rich and lad thanks for your insights today. 330 00:15:08,920 --> 00:15:10,680 Speaker 4: Great to be here, Nice to be here. 331 00:15:11,200 --> 00:15:12,960 Speaker 1: Those of you who would like to learn more about 332 00:15:12,960 --> 00:15:15,640 Speaker 1: the CEO Radar can read the full report at Bloomberg 333 00:15:15,680 --> 00:15:19,480 Speaker 1: dot com slash CEO Radar, and if you liked what 334 00:15:19,520 --> 00:15:22,120 Speaker 1: you heard, we encourage you to subscribe on YouTube or 335 00:15:22,160 --> 00:15:26,600 Speaker 1: your favorite podcast platform. I'm Edward Adams of Bloomberg Media Studios. 336 00:15:26,920 --> 00:15:27,760 Speaker 1: Thanks for listening.