1 00:00:02,520 --> 00:00:11,520 Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Masters in 2 00:00:11,600 --> 00:00:15,120 Speaker 1: Business with Barry Ritholt on Bloomberg Radio. 3 00:00:17,160 --> 00:00:21,160 Speaker 2: This week on the podcast, yet another extra special guest. 4 00:00:21,560 --> 00:00:25,479 Speaker 2: Vimal Kapoor is CEO and chairman of Honeywell. He's worked 5 00:00:25,480 --> 00:00:29,760 Speaker 2: there for the past thirty seven years, and not only 6 00:00:29,920 --> 00:00:34,600 Speaker 2: has he been overseeing a fascinating transition, Honeywell is in 7 00:00:34,600 --> 00:00:38,680 Speaker 2: the midst of breaking itself up into three distinct parts. 8 00:00:39,080 --> 00:00:41,640 Speaker 2: I thought this conversation was fascinating and I think you 9 00:00:41,720 --> 00:00:46,440 Speaker 2: will also with no further ado, my conversation with Honeywell's 10 00:00:46,960 --> 00:00:47,800 Speaker 2: Vimal Kapoor. 11 00:00:48,760 --> 00:00:50,559 Speaker 3: Pleasure Berry, thanks for hosting me. 12 00:00:50,720 --> 00:00:52,880 Speaker 2: Well, my pleasure to have you here. It's not very 13 00:00:52,920 --> 00:00:57,720 Speaker 2: often we get a member of the down industrials as 14 00:00:57,800 --> 00:01:01,920 Speaker 2: part of our guests. Let's start out a little bit 15 00:01:01,960 --> 00:01:06,759 Speaker 2: with your background. You received a degree in electronics engineering 16 00:01:07,120 --> 00:01:10,640 Speaker 2: from the the par Institute of Engineering in in India. 17 00:01:10,760 --> 00:01:12,280 Speaker 2: What was the original career plan? 18 00:01:13,440 --> 00:01:16,040 Speaker 3: Original carrier plan was to work then get a job. 19 00:01:16,200 --> 00:01:17,920 Speaker 3: That that was a carrier plan. Yeah, that was a 20 00:01:17,959 --> 00:01:21,160 Speaker 3: carrier plan. And then you know, first I did two 21 00:01:21,200 --> 00:01:24,000 Speaker 3: small stints of a job and then I joined Honeywell 22 00:01:24,040 --> 00:01:27,080 Speaker 3: in early eighty nine. It was a new company in India, 23 00:01:27,560 --> 00:01:30,240 Speaker 3: so set up. So I ended up joining a startup 24 00:01:31,600 --> 00:01:33,800 Speaker 3: because it was set up as a joint venture between 25 00:01:33,800 --> 00:01:36,760 Speaker 3: two large companies. There's a large Indian company called Tata Group. 26 00:01:37,400 --> 00:01:41,960 Speaker 3: They joined the automobiles everything, so they invested in this venture. 27 00:01:42,560 --> 00:01:44,400 Speaker 3: It's a big Honeywell with a lot of tech, and 28 00:01:44,440 --> 00:01:46,959 Speaker 3: then they created this joint venture in which you show 29 00:01:47,040 --> 00:01:51,600 Speaker 3: up and it's basically creating something from scratch. We had 30 00:01:51,640 --> 00:01:54,040 Speaker 3: no revenue when I started. Our revenue was zero points 31 00:01:54,120 --> 00:01:57,600 Speaker 3: zero zero. So you learn how to build a company, 32 00:01:57,880 --> 00:02:00,880 Speaker 3: how you scale, You wear much people hats like in 33 00:02:00,920 --> 00:02:04,920 Speaker 3: a startup, you don't have a very defined role. So 34 00:02:05,040 --> 00:02:08,799 Speaker 3: thenk that early experience of high flexibility and you know, 35 00:02:09,040 --> 00:02:11,520 Speaker 3: going through a very high pace in a short period 36 00:02:11,560 --> 00:02:14,440 Speaker 3: of time. That laid some very strong foundations. You know. 37 00:02:15,360 --> 00:02:18,800 Speaker 2: So in the United States, out in Silicon Valley, we 38 00:02:18,960 --> 00:02:22,480 Speaker 2: noticed a lot of these startups. Where they end up 39 00:02:22,760 --> 00:02:25,840 Speaker 2: certainly isn't where they began. There's usually a pivot or 40 00:02:26,040 --> 00:02:29,280 Speaker 2: three or four. What was the original idea and the 41 00:02:29,360 --> 00:02:31,920 Speaker 2: joint venture and what did that eventually turn into? 42 00:02:32,200 --> 00:02:35,200 Speaker 3: They turned into what it was planned for because Honeywell, 43 00:02:35,400 --> 00:02:38,080 Speaker 3: they did not have its automation business footprint in India 44 00:02:38,080 --> 00:02:40,720 Speaker 3: at that time you're talking forty years back. So they 45 00:02:40,840 --> 00:02:43,000 Speaker 3: partnered with the local company to scale the business. They 46 00:02:43,040 --> 00:02:46,120 Speaker 3: already had those products and capabilities in US and they 47 00:02:46,120 --> 00:02:49,200 Speaker 3: were trying to get into Asia and they formed partnerships 48 00:02:49,240 --> 00:02:52,400 Speaker 3: in few countries, India being one of them. And the 49 00:02:52,480 --> 00:02:55,760 Speaker 3: strategy was to penetrate the local market, develop the local capability, 50 00:02:55,760 --> 00:02:59,079 Speaker 3: and we were able to do that quite well. So 51 00:02:59,160 --> 00:03:01,679 Speaker 3: it's not that we have to change our product strategy, 52 00:03:01,720 --> 00:03:04,040 Speaker 3: but we have to run learn as we go through. 53 00:03:04,040 --> 00:03:07,200 Speaker 3: We had intense local competition. How do you beat that? 54 00:03:07,360 --> 00:03:11,800 Speaker 3: How we create our own our own avenue stream there. 55 00:03:12,280 --> 00:03:14,120 Speaker 3: So it was a very successful story. 56 00:03:14,280 --> 00:03:17,240 Speaker 2: So you come up through the operating side, not so 57 00:03:17,360 --> 00:03:22,320 Speaker 2: much the Harvard Business School DeVos theory side. How much 58 00:03:22,360 --> 00:03:26,480 Speaker 2: of an advantage has that been as your career clicked 59 00:03:26,480 --> 00:03:28,440 Speaker 2: through all these different divisions. 60 00:03:29,000 --> 00:03:31,680 Speaker 3: I mean, I think it's an advantage to in a 61 00:03:31,720 --> 00:03:34,640 Speaker 3: way to work in a practical business because you have 62 00:03:34,639 --> 00:03:37,240 Speaker 3: to deal with actual problems which the business deal with. 63 00:03:37,960 --> 00:03:41,200 Speaker 3: And having worked in different businesses gave me an opportunity 64 00:03:41,200 --> 00:03:44,520 Speaker 3: to deal with a different customer situation, different end markets, 65 00:03:45,080 --> 00:03:49,680 Speaker 3: operational issue, commercial issue, product development issue, supply chain. So 66 00:03:49,720 --> 00:03:53,560 Speaker 3: I would say, I mean, there's no replacement of formal education. One. 67 00:03:53,640 --> 00:03:58,120 Speaker 3: I'm not suggesting that having a higher degree is a disadvantage, 68 00:03:58,480 --> 00:04:00,920 Speaker 3: but I would say that it is amount of advantage 69 00:04:00,960 --> 00:04:04,840 Speaker 3: to get practical experience. And I was benefiting from a 70 00:04:04,920 --> 00:04:08,200 Speaker 3: variety of experiences I got in my long career in Honeywell. 71 00:04:08,800 --> 00:04:14,840 Speaker 2: And you ran three very different businesses before becoming CEO 72 00:04:15,160 --> 00:04:21,000 Speaker 2: Process Solutions, Building Technologies, Performance Materials. Tell us, I mean 73 00:04:21,000 --> 00:04:23,960 Speaker 2: those names seem sort of ambiguous, right, tell us a 74 00:04:24,000 --> 00:04:26,479 Speaker 2: little bit about what each of those three divisions get. 75 00:04:26,640 --> 00:04:31,000 Speaker 3: So process solution business is. You know, it provides automation 76 00:04:31,160 --> 00:04:35,640 Speaker 3: system in the energy sector. So energy sector, think about it, refining, 77 00:04:36,880 --> 00:04:42,039 Speaker 3: petrochemical plants, other oil and gas facilities, pipeline terminals, even 78 00:04:42,080 --> 00:04:46,960 Speaker 3: I would say facilities like which may paper metals and mining. 79 00:04:47,000 --> 00:04:49,680 Speaker 3: So these facilities are very complex in terms of their 80 00:04:49,720 --> 00:04:54,560 Speaker 3: operating procedures, and if they're not automated, it's nearly impossible 81 00:04:54,560 --> 00:04:57,760 Speaker 3: to run them. So this business provides a sophisticated automation 82 00:04:57,920 --> 00:05:01,840 Speaker 3: system to these large companies. So think about Axon and 83 00:05:01,920 --> 00:05:06,280 Speaker 3: Shell and BP as kind of a typical customer or 84 00:05:06,360 --> 00:05:08,919 Speaker 3: a Ramco in Middle East and at NOx, so this 85 00:05:09,400 --> 00:05:14,000 Speaker 3: serving these customer So this business was very global, is 86 00:05:14,080 --> 00:05:16,560 Speaker 3: very global even today the business still is very successful. 87 00:05:17,200 --> 00:05:20,680 Speaker 3: And I became CEO in twenty fourteen of this business 88 00:05:22,000 --> 00:05:25,040 Speaker 3: and Oil Downtown happened within six months of I becoming 89 00:05:25,040 --> 00:05:29,120 Speaker 3: the leader of the business. So you learn through tough experiences. 90 00:05:29,200 --> 00:05:32,800 Speaker 3: Oil price was from whatever one hundred and forty hundred 91 00:05:32,800 --> 00:05:36,600 Speaker 3: and fifty dollars to like a big nose dive, and 92 00:05:36,640 --> 00:05:38,760 Speaker 3: we did a lot of work in that downturn. Learned 93 00:05:38,760 --> 00:05:42,760 Speaker 3: a lot, but primarily or question, this business is all 94 00:05:42,760 --> 00:05:46,360 Speaker 3: of our sophisticated automation and complex facilities. And then I 95 00:05:46,400 --> 00:05:50,000 Speaker 3: moved to the building automation business, where we still do automation, 96 00:05:50,160 --> 00:05:54,000 Speaker 3: but now in this case buildings of different type hospitals, airports, schools, 97 00:05:55,120 --> 00:06:02,120 Speaker 3: university campuses, data centers. There the business model was very different. 98 00:06:02,240 --> 00:06:06,360 Speaker 3: Now you serve multiple building through variety of channel partners 99 00:06:06,400 --> 00:06:11,160 Speaker 3: across the world, and so our strength comes through product innovation, 100 00:06:11,520 --> 00:06:16,320 Speaker 3: our strength comes through channel management. Way different business model 101 00:06:17,360 --> 00:06:20,640 Speaker 3: compared to what I did in you know, in my 102 00:06:21,120 --> 00:06:25,240 Speaker 3: in my process automation days. And then performance material and technology, 103 00:06:25,880 --> 00:06:29,840 Speaker 3: very interesting business. They build technology. They build energy infrastructure. 104 00:06:29,920 --> 00:06:32,719 Speaker 3: So if you want to build a UH, if you 105 00:06:32,720 --> 00:06:35,880 Speaker 3: are a refiner, you buy crude, which we all hear 106 00:06:35,920 --> 00:06:39,320 Speaker 3: a lot about today to do you know, ongoing around conflict. 107 00:06:40,320 --> 00:06:43,080 Speaker 3: You don't sell crude, you sell product. You sell gasoline, 108 00:06:43,200 --> 00:06:46,159 Speaker 3: you sell diesel, you sell jet fuel. So they have 109 00:06:46,200 --> 00:06:50,520 Speaker 3: options to make multiple products, and as the input changes 110 00:06:50,600 --> 00:06:52,800 Speaker 3: on a market needs changes, they need to decide what 111 00:06:52,839 --> 00:06:56,080 Speaker 3: are the options they have to build different offering. From 112 00:06:56,120 --> 00:07:00,160 Speaker 3: their perspective, this business provides technology to energy company to 113 00:07:00,200 --> 00:07:07,120 Speaker 3: build energy infrastructure. Because it's a molecule transformation converting one 114 00:07:07,120 --> 00:07:10,400 Speaker 3: molecule to another molecule. That's heavy technology involved behind it. 115 00:07:10,800 --> 00:07:15,640 Speaker 3: So performance material and technology provides technology to the customer 116 00:07:15,760 --> 00:07:20,960 Speaker 3: to build tech energy infrastructure. So very high technology or 117 00:07:21,000 --> 00:07:25,320 Speaker 3: research oriented business. You have a lot of chemical engineers 118 00:07:25,320 --> 00:07:27,520 Speaker 3: who are going to invent the next best technology and 119 00:07:27,560 --> 00:07:31,360 Speaker 3: you provide their technology to some very large companies. And 120 00:07:31,440 --> 00:07:34,160 Speaker 3: that was fascinating to lead that business, to see that 121 00:07:34,280 --> 00:07:38,480 Speaker 3: cycle of nomination and work in that business. So you 122 00:07:38,520 --> 00:07:43,040 Speaker 3: have very diverse experiences in variety of sectors, different business models, 123 00:07:44,400 --> 00:07:47,560 Speaker 3: which I'm benefiting today because now I have experience of 124 00:07:48,480 --> 00:07:53,120 Speaker 3: dealing with different markets in different situations, and that practical 125 00:07:53,200 --> 00:07:55,560 Speaker 3: experience helps you a lot as you really get into 126 00:07:55,600 --> 00:07:56,600 Speaker 3: your CEO job. 127 00:07:56,920 --> 00:08:00,920 Speaker 2: So in twenty twenty two, you were name chiefing officer. 128 00:08:01,520 --> 00:08:05,440 Speaker 2: We were just coming out of the pandemic. What was 129 00:08:05,440 --> 00:08:08,680 Speaker 2: that environment like? How did you take your experience at 130 00:08:08,720 --> 00:08:12,480 Speaker 2: these three prior divisions where you were either president or 131 00:08:12,520 --> 00:08:16,280 Speaker 2: president and CEO? Howd that affect running operations? 132 00:08:16,560 --> 00:08:21,040 Speaker 3: I mean, I think at that time. The biggest challenge 133 00:08:21,040 --> 00:08:24,560 Speaker 3: that time actually was the chip shortages and how do 134 00:08:24,600 --> 00:08:28,200 Speaker 3: we really redesign our products because chips are simply not available, 135 00:08:28,280 --> 00:08:30,920 Speaker 3: So we really had to learn how do we redesign 136 00:08:30,920 --> 00:08:32,840 Speaker 3: our products in a much shorter period of time. So 137 00:08:32,920 --> 00:08:35,000 Speaker 3: think about if we designed a product in one year, 138 00:08:35,440 --> 00:08:38,040 Speaker 3: we had to do that in two months because there's 139 00:08:38,080 --> 00:08:40,040 Speaker 3: no other option. If we don't do that, we can't 140 00:08:40,040 --> 00:08:43,760 Speaker 3: have an alternative source of supply and we can't shape 141 00:08:43,800 --> 00:08:46,720 Speaker 3: our product. So I use a lot of experiences on 142 00:08:47,040 --> 00:08:51,560 Speaker 3: dealing with such a different scenario in earlier jobs, and 143 00:08:51,640 --> 00:08:55,439 Speaker 3: we were able to successfully deal with that. That was 144 00:08:55,480 --> 00:08:58,000 Speaker 3: also a job. I also got exposure to the businesses 145 00:08:58,040 --> 00:09:02,040 Speaker 3: of Honeyweale, which I hadn't done before. Aerospace being the 146 00:09:02,040 --> 00:09:06,000 Speaker 3: biggest one, so that goll ed into my responsibility. So 147 00:09:06,000 --> 00:09:08,280 Speaker 3: there was a sort of learning there on how that 148 00:09:08,320 --> 00:09:10,920 Speaker 3: industry works, which is totally different from everything else I 149 00:09:10,960 --> 00:09:11,400 Speaker 3: had done. 150 00:09:11,640 --> 00:09:16,800 Speaker 2: Is there a through put through materials, processes, technologies and 151 00:09:16,960 --> 00:09:20,280 Speaker 2: aerospace or are these all completely different animals? 152 00:09:20,800 --> 00:09:23,040 Speaker 3: Different animals in the sense of the n market says 153 00:09:23,120 --> 00:09:26,120 Speaker 3: Serve right, there are some commonality of the business models, 154 00:09:26,440 --> 00:09:31,560 Speaker 3: and you know there there's a common denominator, but there 155 00:09:31,559 --> 00:09:35,400 Speaker 3: are differences which really led me to think about whether 156 00:09:35,440 --> 00:09:37,760 Speaker 3: we are good to be one company or multiple companies 157 00:09:37,760 --> 00:09:40,199 Speaker 3: when I started as a CEO, and part of it 158 00:09:40,360 --> 00:09:42,440 Speaker 3: was the differences between them, but part of it was 159 00:09:42,559 --> 00:09:46,559 Speaker 3: opportunities which is ahead of us, that how these businesses 160 00:09:46,679 --> 00:09:51,560 Speaker 3: independently could shape or scale much differently versus when we 161 00:09:51,600 --> 00:09:54,679 Speaker 3: are together, which led us to do a lot of 162 00:09:54,679 --> 00:09:57,560 Speaker 3: work to think about optionality and pros and cons of 163 00:09:57,600 --> 00:10:01,679 Speaker 3: each option, which led us to make a decision that 164 00:10:01,720 --> 00:10:05,400 Speaker 3: we are better off to split into three companies, and. 165 00:10:05,320 --> 00:10:07,720 Speaker 2: We're going to spend some time delving into those three 166 00:10:07,760 --> 00:10:11,520 Speaker 2: companies and the thinking behind it. Before we get to that, 167 00:10:12,080 --> 00:10:16,040 Speaker 2: I want to ask you a couple of more general 168 00:10:16,120 --> 00:10:21,200 Speaker 2: questions about the firm. You've been there so long since 169 00:10:21,240 --> 00:10:25,560 Speaker 2: the nineteen eighties. I'm curious, how has the culture of 170 00:10:25,640 --> 00:10:31,640 Speaker 2: Honeywell changed. It's almost forty years, three and a half decades. 171 00:10:32,480 --> 00:10:36,680 Speaker 2: Is it still essentially the same company or has everything 172 00:10:36,760 --> 00:10:38,200 Speaker 2: changed like so many other companies. 173 00:10:38,280 --> 00:10:41,080 Speaker 3: Yeah, it evolved a lot, I would say. You know, 174 00:10:42,160 --> 00:10:44,360 Speaker 3: there was a big change moment in early two thousand 175 00:10:44,360 --> 00:10:48,679 Speaker 3: and when Honeywell and a Light Signal merged together. Recall yep, 176 00:10:48,760 --> 00:10:52,680 Speaker 3: so a little bit of fun fact, A Light Signal 177 00:10:52,679 --> 00:10:56,520 Speaker 3: acquired Honeywell and changed its name to Honeywell, which doesn't 178 00:10:56,559 --> 00:10:59,640 Speaker 3: happen the acquator keeps name because they figured Honeywell brand 179 00:10:59,720 --> 00:11:02,800 Speaker 3: was so powerful, i was more impactful, so they changed 180 00:11:02,840 --> 00:11:04,520 Speaker 3: their own name. So that was a big moment to 181 00:11:04,559 --> 00:11:08,640 Speaker 3: your queshion on cultural assimilation of two large companies. It 182 00:11:08,679 --> 00:11:11,920 Speaker 3: was kind of merger of equals, and it did go 183 00:11:12,040 --> 00:11:15,880 Speaker 3: through its own motion of ups and downs. And that's 184 00:11:15,920 --> 00:11:18,880 Speaker 3: when Dave Cody came in as chairman and CEO of Honeywell, 185 00:11:19,559 --> 00:11:21,640 Speaker 3: and Dave did a great job to rebuild the County 186 00:11:21,679 --> 00:11:25,480 Speaker 3: Honeywell culture, which was much more one company mindset. We 187 00:11:25,520 --> 00:11:28,040 Speaker 3: are not two companies. We have one company. We're going 188 00:11:28,080 --> 00:11:31,599 Speaker 3: to work towards one stock, one Honeywell mindset, put a 189 00:11:31,600 --> 00:11:35,800 Speaker 3: lot of operational culture in the organization. So that was 190 00:11:35,840 --> 00:11:41,240 Speaker 3: one phase of you know, under her leadership. Then my predecessor, 191 00:11:41,320 --> 00:11:46,199 Speaker 3: Darius Ademchek, he became CEO in twenty seventeen. He further 192 00:11:47,120 --> 00:11:49,880 Speaker 3: enhanced our operational excellent skill. He invested a lot of 193 00:11:49,960 --> 00:11:53,640 Speaker 3: effort to build more digital backbone of the company, simplifying 194 00:11:53,640 --> 00:11:57,560 Speaker 3: Honeywell in terms of internal systems we have. Darius was 195 00:11:57,640 --> 00:12:00,480 Speaker 3: very passionate about digital on how to mind data and 196 00:12:00,520 --> 00:12:03,600 Speaker 3: create more capability for our customers. So he created a 197 00:12:03,640 --> 00:12:09,240 Speaker 3: culture of more operational excellence, more operational rigor, while Dave 198 00:12:09,400 --> 00:12:13,199 Speaker 3: was much more focused on one Honeymoon mindset, cultural integration, 199 00:12:13,679 --> 00:12:16,960 Speaker 3: not multiple companies. And as my tenure comes in over 200 00:12:16,960 --> 00:12:19,680 Speaker 3: the last no two plus years, we are not pivoting 201 00:12:19,760 --> 00:12:23,400 Speaker 3: from the more growth oriented company. And the reason that's 202 00:12:23,440 --> 00:12:26,360 Speaker 3: important is that over a period of time, our margin 203 00:12:26,440 --> 00:12:29,920 Speaker 3: rates have grown up and we were sub ten percent 204 00:12:29,960 --> 00:12:32,400 Speaker 3: margin company in two thousand and five, two thand and 205 00:12:32,440 --> 00:12:35,360 Speaker 3: six last day to us twenty three percent. So our 206 00:12:35,400 --> 00:12:37,280 Speaker 3: earnings growth is going to come more from the top 207 00:12:37,360 --> 00:12:40,199 Speaker 3: line growth versus margin expansion. Not that we want to 208 00:12:40,240 --> 00:12:44,400 Speaker 3: do margin expansion, but we can't get from another fifteen percent. 209 00:12:44,520 --> 00:12:48,560 Speaker 3: There's no headroom. So growth culture is important, which means 210 00:12:48,559 --> 00:12:50,600 Speaker 3: we have to be more externally focused. Now we need 211 00:12:50,640 --> 00:12:53,280 Speaker 3: to understand our markets. We need to understand our customers, 212 00:12:53,520 --> 00:12:57,600 Speaker 3: what's changing, need to understand our competition. So our company, 213 00:12:57,679 --> 00:13:01,960 Speaker 3: even though name preserves it sell as a heriitage, but 214 00:13:02,080 --> 00:13:05,199 Speaker 3: it has been constantly evolving itself. And that's one of 215 00:13:05,240 --> 00:13:07,280 Speaker 3: the reasons this company has survived on hundred and twenty 216 00:13:07,360 --> 00:13:11,800 Speaker 3: years because it has courage to reinvent itself versus being 217 00:13:11,800 --> 00:13:14,800 Speaker 3: inward looking and always saying that Okay, we are what 218 00:13:14,880 --> 00:13:16,240 Speaker 3: we are and we aren't going to change. 219 00:13:16,840 --> 00:13:20,720 Speaker 2: Really really interesting. So I used to hear people talk 220 00:13:20,760 --> 00:13:26,520 Speaker 2: about automation pretty regularly as just the process of moving 221 00:13:26,559 --> 00:13:31,000 Speaker 2: more and more things to machines. We kind of hear 222 00:13:31,040 --> 00:13:35,600 Speaker 2: people using the phrase artificial intelligence and AI the same way. 223 00:13:35,760 --> 00:13:40,440 Speaker 2: Kind of bluntly. I'm curious from the Honeywell perspective when 224 00:13:40,440 --> 00:13:44,000 Speaker 2: it comes to automation and AI, what are the customers buying? 225 00:13:44,559 --> 00:13:48,280 Speaker 2: Is it productivity gains, is it safety improvements? Is it 226 00:13:48,440 --> 00:13:52,160 Speaker 2: cheaper labor or substitute for labor? What is the key 227 00:13:52,679 --> 00:13:55,040 Speaker 2: selling point for your customers. 228 00:13:55,040 --> 00:13:57,520 Speaker 3: So I would say the we have to go back 229 00:13:57,520 --> 00:14:00,880 Speaker 3: to where the automation industry started from to better appreciate 230 00:14:01,000 --> 00:14:06,240 Speaker 3: how will AI impact automation offerings or automation products. Go 231 00:14:06,360 --> 00:14:09,120 Speaker 3: back to mid seventies when this industry got created. Somewhere 232 00:14:09,160 --> 00:14:15,840 Speaker 3: in mid seventy five timeframe, when computing was invented, chips 233 00:14:15,840 --> 00:14:20,400 Speaker 3: were invented. There came the need to say, the word 234 00:14:20,440 --> 00:14:24,160 Speaker 3: has a lot of these expensive assets. Those assets are 235 00:14:24,160 --> 00:14:27,160 Speaker 3: not running very efficiently. So can we move from the 236 00:14:27,200 --> 00:14:29,920 Speaker 3: older technologies which were kind of World War one Word 237 00:14:29,960 --> 00:14:33,760 Speaker 3: War two war era to more modern digital technologies. And 238 00:14:33,840 --> 00:14:38,240 Speaker 3: the way automation system was created was that you sense 239 00:14:38,760 --> 00:14:41,320 Speaker 3: a set of properties on how a particular equipment or 240 00:14:41,360 --> 00:14:44,440 Speaker 3: a machine or a processor is running, and then you 241 00:14:44,560 --> 00:14:48,120 Speaker 3: have a software program running in a computer which is 242 00:14:48,200 --> 00:14:50,760 Speaker 3: going to make sure that it gets back to the 243 00:14:50,840 --> 00:14:52,880 Speaker 3: desired condition what it wants it to be. So just 244 00:14:52,920 --> 00:14:57,440 Speaker 3: a logic based, pre defined system, and the assumption was 245 00:14:57,480 --> 00:15:01,760 Speaker 3: most of the time this will work in a normal situation. 246 00:15:01,840 --> 00:15:06,040 Speaker 3: When exception occur, human will take a call. So automation 247 00:15:06,200 --> 00:15:09,040 Speaker 3: systems were always designed with the human in the loop, 248 00:15:09,320 --> 00:15:12,000 Speaker 3: and human was supposed to take care of change in 249 00:15:12,000 --> 00:15:16,960 Speaker 3: input condition, change in output conditions. Maintain the equipment, take 250 00:15:17,000 --> 00:15:21,520 Speaker 3: care of maintenance requirement down the line. Now you fast 251 00:15:21,520 --> 00:15:24,400 Speaker 3: forward fifty years before AI and data science came in. 252 00:15:25,440 --> 00:15:30,120 Speaker 3: The people who were running these equipment or automation system 253 00:15:30,280 --> 00:15:33,600 Speaker 3: or different facilities in different environment think of a former 254 00:15:33,680 --> 00:15:37,840 Speaker 3: manufacturing facility or a data center. They acquired in knowledge 255 00:15:37,880 --> 00:15:40,560 Speaker 3: on the exceptions which were occurring in those operating conditions. 256 00:15:41,000 --> 00:15:44,120 Speaker 3: But when they retire or they move on, their knowledge 257 00:15:44,400 --> 00:15:46,840 Speaker 3: went along with them. So when the next set of 258 00:15:46,880 --> 00:15:49,720 Speaker 3: people came in, they kind of have the same learning cycle. 259 00:15:50,200 --> 00:15:53,000 Speaker 3: Maybe some of it was captured in some documents and manuals, 260 00:15:53,000 --> 00:15:56,000 Speaker 3: but not a lot. So what AI is solving for 261 00:15:56,200 --> 00:16:00,320 Speaker 3: is our systems have no intelligence layer on top of 262 00:16:00,360 --> 00:16:03,400 Speaker 3: the core automation layer, so that when the next human 263 00:16:03,440 --> 00:16:06,120 Speaker 3: being comes in, they're not starting from scratch. They have 264 00:16:06,120 --> 00:16:08,880 Speaker 3: an advantage of all the learning over the last twenty 265 00:16:08,920 --> 00:16:11,840 Speaker 3: five years all built in, so they get to say, 266 00:16:11,840 --> 00:16:14,840 Speaker 3: when this condition occurred, nine out of ten times this 267 00:16:15,000 --> 00:16:17,560 Speaker 3: was done, it always worked. So you are the human 268 00:16:17,600 --> 00:16:19,560 Speaker 3: being and say, okay, I think I will choose this 269 00:16:19,680 --> 00:16:22,360 Speaker 3: logic mix. So human still needs to make a decision 270 00:16:22,960 --> 00:16:25,920 Speaker 3: so I think it's a changing the human and making 271 00:16:25,960 --> 00:16:29,560 Speaker 3: them more capable at the heart of it. And the 272 00:16:29,600 --> 00:16:32,480 Speaker 3: reason it becomes even more compelling now is the shortage 273 00:16:32,480 --> 00:16:35,280 Speaker 3: of skills which are happening in the industrial sector for 274 00:16:35,400 --> 00:16:38,120 Speaker 3: performing these kind of tasks. So I would say it's 275 00:16:38,160 --> 00:16:43,440 Speaker 3: a perfect convergence of the situation that more capability is 276 00:16:43,440 --> 00:16:47,120 Speaker 3: coming into our system because of availability of data science, 277 00:16:48,720 --> 00:16:52,240 Speaker 3: and at the same time, situation requires this capability to 278 00:16:52,240 --> 00:16:55,560 Speaker 3: be there because less people are available to do this work, 279 00:16:56,080 --> 00:16:59,400 Speaker 3: and that's going to create more capability in automation system. 280 00:17:00,000 --> 00:17:03,760 Speaker 3: Animasion system remains Intelligencelyer is on top of it, so 281 00:17:03,800 --> 00:17:07,080 Speaker 3: it makes its automation system better in terms of what 282 00:17:07,200 --> 00:17:10,480 Speaker 3: it can do by preserving its capability. 283 00:17:10,600 --> 00:17:13,479 Speaker 2: Coming up, we continue our conversation with vim L. Kapoor, 284 00:17:14,040 --> 00:17:21,200 Speaker 2: CEO of Honeywell, discussing turning Honeywell into three standalone companies. 285 00:17:21,480 --> 00:17:25,040 Speaker 2: I'm Barry Ridults. You're listening to Masters in Business. I'm 286 00:17:25,080 --> 00:17:41,200 Speaker 2: Bloomberg Radio. I'm Barry Ridults. You're listening to Masters in 287 00:17:41,280 --> 00:17:44,880 Speaker 2: Business on Bloomberg Radio. My extra special guest this week 288 00:17:44,960 --> 00:17:49,000 Speaker 2: is vim Al Kapoor. He is CEO and chairman of 289 00:17:49,080 --> 00:17:53,640 Speaker 2: Honeywell International, he's been with the firm for thirty seven years. 290 00:17:54,080 --> 00:18:00,200 Speaker 2: Honeywell is a highly regarded automation and industrial company. So 291 00:18:00,680 --> 00:18:04,440 Speaker 2: let's start out with plans to break the firm up. 292 00:18:04,960 --> 00:18:11,959 Speaker 2: You have three distinct entities Honeywell Automation, Honeywell Aerospace, and 293 00:18:12,000 --> 00:18:17,160 Speaker 2: then Solstice Advanced Materials. So let's talk about that split. 294 00:18:18,320 --> 00:18:24,760 Speaker 2: That sounds fairly natural, breakup based on industry. Tell us 295 00:18:24,760 --> 00:18:26,560 Speaker 2: a little bit about the thinking behind that. 296 00:18:26,680 --> 00:18:29,320 Speaker 3: So thinking behind that was when I started as a CEO. 297 00:18:29,880 --> 00:18:33,080 Speaker 3: My incoming thesis was that we have to simplify this company. 298 00:18:33,480 --> 00:18:37,440 Speaker 3: It's performed extremely well, greater return to shareholder, great service 299 00:18:37,480 --> 00:18:39,639 Speaker 3: to our customer, but what will we do for the 300 00:18:39,640 --> 00:18:41,600 Speaker 3: next twenty five to thirty years? Are we set up 301 00:18:41,640 --> 00:18:45,199 Speaker 3: for that? And my thesis was that we need to 302 00:18:45,200 --> 00:18:47,800 Speaker 3: simplify this into few things where we have a scale. 303 00:18:49,160 --> 00:18:51,239 Speaker 3: But I started the job in middle of twenty three 304 00:18:51,320 --> 00:18:54,040 Speaker 3: as the CEO of the company. Two things happened in 305 00:18:54,080 --> 00:18:55,760 Speaker 3: the year of twenty three, which is good to kind 306 00:18:55,760 --> 00:18:58,280 Speaker 3: of reflect back just three years back. That was the 307 00:18:58,359 --> 00:19:01,280 Speaker 3: first year when the aerospace cycle really became very strong. 308 00:19:01,920 --> 00:19:04,040 Speaker 3: That was a year one where everybody said, oh, this 309 00:19:04,119 --> 00:19:07,000 Speaker 3: industry is growing a lot, let's pay more attention to it. 310 00:19:07,600 --> 00:19:10,480 Speaker 3: And this was also the first year when something called 311 00:19:10,520 --> 00:19:13,680 Speaker 3: AI was talcked, right, So if we were sitting here 312 00:19:13,800 --> 00:19:15,960 Speaker 3: three years back, we wouldn't be talking AI. So it's 313 00:19:15,960 --> 00:19:18,720 Speaker 3: that recent phenomena. So the question we had to really 314 00:19:18,760 --> 00:19:21,560 Speaker 3: ask ourselves that if we have to simplify as a 315 00:19:21,600 --> 00:19:26,560 Speaker 3: company and these two external drivers are occurring simultaneously a 316 00:19:26,640 --> 00:19:30,360 Speaker 3: huge demand in our largest business, which is aerospace, automation, 317 00:19:30,400 --> 00:19:33,959 Speaker 3: which is core to Honeywell, is going to probably redefine 318 00:19:33,960 --> 00:19:38,280 Speaker 3: itself with AI. Should we do it as one company 319 00:19:38,480 --> 00:19:41,760 Speaker 3: or should we do it as in a different construct, 320 00:19:42,240 --> 00:19:47,240 Speaker 3: And that question get into a problem solving by early 321 00:19:47,280 --> 00:19:49,359 Speaker 3: twenty four, to say, let's look at all the scenarios, 322 00:19:49,359 --> 00:19:53,480 Speaker 3: what's possibilities, what others are doing. And as we did 323 00:19:53,480 --> 00:19:56,560 Speaker 3: the work over twenty twenty four, we got more and 324 00:19:56,600 --> 00:20:00,760 Speaker 3: more conviction it's better to separate automation and aerospace into 325 00:20:00,760 --> 00:20:04,320 Speaker 3: two separate companies. But we ended up making three decisions 326 00:20:04,359 --> 00:20:08,560 Speaker 3: because specialty chemical is extremely good business which neither fitted 327 00:20:08,560 --> 00:20:11,040 Speaker 3: in any one of these two, and we said it's 328 00:20:11,080 --> 00:20:14,200 Speaker 3: compelling to also spin that off as a separate company. 329 00:20:14,560 --> 00:20:18,000 Speaker 3: So rather than you know, two ended up becoming three. 330 00:20:18,400 --> 00:20:21,160 Speaker 3: So they became a standalone business in October of last year, 331 00:20:21,520 --> 00:20:24,520 Speaker 3: doing extremely well since we spun it off now for 332 00:20:24,600 --> 00:20:27,439 Speaker 3: six months. Very proud of the management team and the 333 00:20:27,440 --> 00:20:30,520 Speaker 3: board which is running this company. Aerospace will become a 334 00:20:30,560 --> 00:20:32,960 Speaker 3: standalone company in about six to eight weeks from now. 335 00:20:33,040 --> 00:20:36,520 Speaker 3: Six weeks actually as we speak today, twenty nine is 336 00:20:36,560 --> 00:20:39,480 Speaker 3: a date and data is firm. We quite committed to 337 00:20:39,520 --> 00:20:42,040 Speaker 3: that and it's going to be a leader in its 338 00:20:42,040 --> 00:20:45,199 Speaker 3: segment in aerospace. And the remain core will be a 339 00:20:45,200 --> 00:20:48,399 Speaker 3: pure play automation company which will be probably one of 340 00:20:48,400 --> 00:20:51,119 Speaker 3: the largest, if not the largest automation company in the world. 341 00:20:51,880 --> 00:20:56,840 Speaker 2: So advanced materials does that include building technologies. 342 00:20:57,080 --> 00:21:00,439 Speaker 3: It's a pure play chemicals business, straight up chemical chemical business. 343 00:21:00,480 --> 00:21:03,040 Speaker 3: They make refrigerant which goes into your car, which goes 344 00:21:03,080 --> 00:21:06,080 Speaker 3: into your home. They have some other technologies which are 345 00:21:06,119 --> 00:21:10,440 Speaker 3: related to chemicals. That business is doing extremely well as 346 00:21:10,480 --> 00:21:14,200 Speaker 3: a standalone company. The automation which you mentioned building automation 347 00:21:14,560 --> 00:21:18,320 Speaker 3: or automation of industrial facilities, that's part of the remaining Honeywell, 348 00:21:19,000 --> 00:21:21,360 Speaker 3: which is Honeywell Automation. Now we won'll not be called 349 00:21:21,359 --> 00:21:25,200 Speaker 3: Honeywell Automation. We're using as just as an equal descriptor 350 00:21:25,280 --> 00:21:28,320 Speaker 3: on what the business will be. We will reimagine our 351 00:21:28,400 --> 00:21:30,560 Speaker 3: name as we go by in a couple of weeks 352 00:21:30,600 --> 00:21:32,919 Speaker 3: from now and will reveal that name what it should be. 353 00:21:34,119 --> 00:21:38,360 Speaker 3: But for sake of simplicity, a chemicals business, an aerospace business, 354 00:21:38,359 --> 00:21:39,800 Speaker 3: and an automation business. 355 00:21:39,880 --> 00:21:42,720 Speaker 2: And performance materials and technology. 356 00:21:42,280 --> 00:21:45,800 Speaker 3: Is so part of it became into Advanced Material Advanced 357 00:21:45,800 --> 00:21:49,280 Speaker 3: Material Solstice, and then part of it is retained within Honeywell, 358 00:21:49,320 --> 00:21:51,320 Speaker 3: so it split into kind of two. 359 00:21:51,520 --> 00:21:55,440 Speaker 2: Because this is really everybody thinks of these very broadly, 360 00:21:55,560 --> 00:21:59,240 Speaker 2: but there are some really narrow specific use cases for 361 00:21:59,359 --> 00:22:03,400 Speaker 2: different groups. So I was trying to figure out what 362 00:22:03,400 --> 00:22:04,520 Speaker 2: would align with what. 363 00:22:05,080 --> 00:22:09,680 Speaker 3: So so think about. Automation business serves three large end markets, 364 00:22:10,240 --> 00:22:13,560 Speaker 3: all types of buildings, all types of energy facilities, and 365 00:22:13,640 --> 00:22:17,000 Speaker 3: all types of industrial facilities. That's what we have kept 366 00:22:17,160 --> 00:22:20,159 Speaker 3: in the automation and we also are conscious that we 367 00:22:20,160 --> 00:22:23,879 Speaker 3: should not make automation business serving so many segments that 368 00:22:23,960 --> 00:22:27,120 Speaker 3: it becomes confusing again, so we want to narrow down 369 00:22:27,160 --> 00:22:30,560 Speaker 3: to a few very large and impactful segments. The market 370 00:22:30,600 --> 00:22:33,439 Speaker 3: size is about two hundred billion dollars. We will be 371 00:22:33,520 --> 00:22:35,399 Speaker 3: just shy off twenty billion of revenues, we have a 372 00:22:35,400 --> 00:22:38,119 Speaker 3: lot of runway to think about creatively, what more we 373 00:22:38,119 --> 00:22:40,840 Speaker 3: can do, how do we grow more so we're not 374 00:22:40,960 --> 00:22:46,520 Speaker 3: shortage off runway. Secularly, automation is a naturally high growth 375 00:22:47,320 --> 00:22:51,240 Speaker 3: segment because it's something which is so basic to existence 376 00:22:51,400 --> 00:22:54,600 Speaker 3: of an industrial facility or on an asset, and then 377 00:22:54,640 --> 00:22:58,160 Speaker 3: when you add the AI story coming on top of it, 378 00:22:58,160 --> 00:23:02,479 Speaker 3: it's going to have increasingly more growth momentum. So all 379 00:23:02,560 --> 00:23:04,720 Speaker 3: things being said, yeah, it's very well positioned for a 380 00:23:04,720 --> 00:23:05,560 Speaker 3: compelling future. 381 00:23:05,840 --> 00:23:09,080 Speaker 2: And what does the Aerospace Group do? Not unlike GE, 382 00:23:09,240 --> 00:23:12,399 Speaker 2: you're not making aircraft eins. 383 00:23:11,920 --> 00:23:14,560 Speaker 3: Right, So we do make aircraft engine for the business jet, 384 00:23:14,640 --> 00:23:17,680 Speaker 3: some more mid size, smaller and smaller engine the business 385 00:23:17,760 --> 00:23:19,919 Speaker 3: jet engines make. We don't make the big engines. But 386 00:23:19,960 --> 00:23:22,920 Speaker 3: we are a systems company. We make different components from 387 00:23:22,920 --> 00:23:26,879 Speaker 3: the nose to tail of the plane, so our components 388 00:23:26,920 --> 00:23:31,120 Speaker 3: are right in the cockpit. Our components. We make radars, 389 00:23:31,119 --> 00:23:34,600 Speaker 3: we make navigation system we make brakes for the plane, 390 00:23:34,720 --> 00:23:37,520 Speaker 3: we make environmental controls in the plane. So we are 391 00:23:37,560 --> 00:23:41,000 Speaker 3: a systems company. We make engines, we make APUs, so 392 00:23:41,600 --> 00:23:46,000 Speaker 3: our approach is system designed for a new platform. So 393 00:23:46,080 --> 00:23:49,359 Speaker 3: every platform comes in and it could be a commercial plane, 394 00:23:49,400 --> 00:23:51,600 Speaker 3: could be a business jet, could be a defense platform. 395 00:23:52,119 --> 00:23:55,320 Speaker 3: We will pitch in different components and systems of honeywere 396 00:23:55,359 --> 00:23:58,400 Speaker 3: customers will select many of them, some of them. Then 397 00:23:58,440 --> 00:24:02,199 Speaker 3: that will become part of that you know that fleet 398 00:24:02,280 --> 00:24:08,000 Speaker 3: for decades and decades. So it's a multi product business, 399 00:24:08,000 --> 00:24:11,720 Speaker 3: not constrained to one particular product line. And the business 400 00:24:11,760 --> 00:24:15,399 Speaker 3: model is more powerful because it's a system's approach and 401 00:24:15,440 --> 00:24:18,040 Speaker 3: not a component approach. So you're right in the heart 402 00:24:18,040 --> 00:24:20,680 Speaker 3: of the systems. You understand how the whole mechanics work 403 00:24:21,840 --> 00:24:23,679 Speaker 3: and really add more value for our customers. 404 00:24:23,880 --> 00:24:26,800 Speaker 2: So over the past let's call it ten years, there 405 00:24:26,840 --> 00:24:30,840 Speaker 2: have been a number of activist investors like Alliott Management 406 00:24:31,400 --> 00:24:34,280 Speaker 2: that not just Honeywell, but lots and lots of other 407 00:24:34,400 --> 00:24:40,679 Speaker 2: large conglomerates. They often agitate for share buybacks or increased dividends, 408 00:24:41,920 --> 00:24:46,280 Speaker 2: or sometimes just break the company into pieces. You seem 409 00:24:46,359 --> 00:24:50,480 Speaker 2: to have landed pretty much in a similar space as 410 00:24:50,480 --> 00:24:54,960 Speaker 2: some of these activists. First, were they at all influential 411 00:24:55,000 --> 00:24:59,240 Speaker 2: in your thinking or was this something that Hey, these 412 00:24:59,240 --> 00:25:03,439 Speaker 2: are such diferent businesses, is no longer scale advantages of 413 00:25:03,440 --> 00:25:04,840 Speaker 2: having them under one room. 414 00:25:05,080 --> 00:25:07,920 Speaker 3: I would say the situation in art case was a 415 00:25:07,920 --> 00:25:11,200 Speaker 3: bit unique because we started doing work to investigate our 416 00:25:11,240 --> 00:25:14,760 Speaker 3: future optionality early twenty twenty four and did a lot 417 00:25:14,800 --> 00:25:18,639 Speaker 3: of work and actually even announced the separation of chemicals business. 418 00:25:18,640 --> 00:25:22,479 Speaker 3: In October. Elliott wrote a letter which was in public 419 00:25:22,480 --> 00:25:24,640 Speaker 3: domain and I got to see it at the same time, 420 00:25:24,680 --> 00:25:26,600 Speaker 3: and if everybody else saw it to say we should 421 00:25:26,600 --> 00:25:29,720 Speaker 3: further split aerospace and rest of Honeywell too. That was 422 00:25:29,760 --> 00:25:33,199 Speaker 3: their argument, is a more value to be created. The 423 00:25:33,280 --> 00:25:35,640 Speaker 3: good news was that we already had done the work 424 00:25:35,640 --> 00:25:37,239 Speaker 3: and we were convinced that's the right thing to do, 425 00:25:37,280 --> 00:25:42,120 Speaker 3: but we had not announced anything, so we treated them 426 00:25:42,119 --> 00:25:45,399 Speaker 3: as another shareholder who has a point of view and 427 00:25:45,480 --> 00:25:47,800 Speaker 3: we have to articulate our strategy. So there were strong 428 00:25:47,840 --> 00:25:51,720 Speaker 3: convergence on the thinking, and I think we worked with 429 00:25:51,760 --> 00:25:56,320 Speaker 3: them very collaboratively on path forward. And I would say 430 00:25:56,359 --> 00:26:00,399 Speaker 3: that there's lots being said on activist shareholder, but my 431 00:26:00,480 --> 00:26:04,600 Speaker 3: experience is that they are like any other shareholder who 432 00:26:04,600 --> 00:26:08,879 Speaker 3: have a logical argument. If you have a counterpoint, you 433 00:26:08,920 --> 00:26:11,919 Speaker 3: should support this with the facts and data. Or if 434 00:26:11,960 --> 00:26:15,120 Speaker 3: you support their point, then you have to execute it, 435 00:26:15,200 --> 00:26:17,600 Speaker 3: and in that case it just becomes much more of 436 00:26:18,119 --> 00:26:20,560 Speaker 3: not what to do, but how to do it. So 437 00:26:20,640 --> 00:26:23,840 Speaker 3: our conversation with Aliot, like any of the shareholder, was 438 00:26:23,920 --> 00:26:26,560 Speaker 3: this is the situation, here are the paths, this is 439 00:26:26,560 --> 00:26:29,080 Speaker 3: how we are thinking about it. And we benefited from 440 00:26:29,160 --> 00:26:32,720 Speaker 3: their expertise in capital markets how the shareholders will react, 441 00:26:32,760 --> 00:26:35,560 Speaker 3: and definitely that helped us to shape our decision in 442 00:26:35,600 --> 00:26:38,160 Speaker 3: terms of in a certain way, which was very constructive, 443 00:26:38,600 --> 00:26:39,760 Speaker 3: really really interesting. 444 00:26:40,280 --> 00:26:44,120 Speaker 2: So we seem to go through these long phases where 445 00:26:44,800 --> 00:26:49,919 Speaker 2: conglomerates kind of become in style, they become favored. You 446 00:26:50,240 --> 00:26:54,520 Speaker 2: oversaw fourteen billion dollars in M and A, which sounds 447 00:26:54,560 --> 00:26:57,040 Speaker 2: like a lot of money, but we know really isn't 448 00:26:57,440 --> 00:27:01,080 Speaker 2: You know? That's a that's not a mega buying spray. 449 00:27:02,720 --> 00:27:05,080 Speaker 2: And for long, for the longest time, it seemed like 450 00:27:05,200 --> 00:27:10,280 Speaker 2: there was a financial advantage to being a conglomerate. At 451 00:27:10,320 --> 00:27:14,080 Speaker 2: what point does that structure stop being an advantage? Is 452 00:27:14,600 --> 00:27:17,240 Speaker 2: what is being part throwing all these different pieces under 453 00:27:17,240 --> 00:27:20,960 Speaker 2: one roof, What does that prevent the company from doing? 454 00:27:21,359 --> 00:27:24,000 Speaker 3: I think every business model has an era, So I 455 00:27:24,000 --> 00:27:27,560 Speaker 3: think we have to go back to what created this 456 00:27:27,640 --> 00:27:33,680 Speaker 3: era of conglomerate or larger companies. It really started from 457 00:27:33,680 --> 00:27:36,080 Speaker 3: them when the world was started becoming more globalized. After 458 00:27:36,119 --> 00:27:39,560 Speaker 3: two thousand, China came into wto the world became more 459 00:27:39,600 --> 00:27:44,919 Speaker 3: global and there was much more global trade, which became 460 00:27:44,960 --> 00:27:48,080 Speaker 3: the norm on how companies were growing. So all US 461 00:27:48,160 --> 00:27:51,800 Speaker 3: companies started growing globally. But at the same time they 462 00:27:51,800 --> 00:27:54,040 Speaker 3: were able to drive a lot of productivity by taking 463 00:27:54,080 --> 00:27:57,680 Speaker 3: manufacturing into Asia, a lot of you know, man park 464 00:27:57,720 --> 00:28:00,520 Speaker 3: productivity by doing work in different virtual with a lot 465 00:28:00,520 --> 00:28:03,679 Speaker 3: of IT skills coming in. So there was a case 466 00:28:03,880 --> 00:28:08,040 Speaker 3: to make bigger companies bigger because they had the unique 467 00:28:08,080 --> 00:28:11,000 Speaker 3: know how to drive a lot of productivity and scale 468 00:28:11,040 --> 00:28:14,080 Speaker 3: at a global scale because they were already present there. 469 00:28:14,520 --> 00:28:18,000 Speaker 3: And that cycle persisted for almost fifteen years till the 470 00:28:18,040 --> 00:28:23,679 Speaker 3: time that value was captured, and that value capture became 471 00:28:23,760 --> 00:28:28,240 Speaker 3: generally known. Therefore, the question started asking to say, is 472 00:28:28,359 --> 00:28:31,639 Speaker 3: creating this complex company worth it? Or simplification or a 473 00:28:31,680 --> 00:28:34,280 Speaker 3: sector focus is a better way to do it. So 474 00:28:34,320 --> 00:28:37,400 Speaker 3: I think there was a reason that proposition really worked 475 00:28:37,400 --> 00:28:39,120 Speaker 3: well and created a lot of value. Take a case 476 00:28:39,160 --> 00:28:42,280 Speaker 3: of Honeywell, a shareholder value creation from a time of 477 00:28:42,320 --> 00:28:44,640 Speaker 3: two thousand to twenty seventeen eighteen, one of the best 478 00:28:44,680 --> 00:28:48,200 Speaker 3: in class and the entire SMP. So it's not that 479 00:28:48,280 --> 00:28:51,680 Speaker 3: anything was wrong. We created a tremendous shareholder value. But 480 00:28:51,800 --> 00:28:54,680 Speaker 3: now this point of saturation comes in and then it 481 00:28:54,760 --> 00:28:58,160 Speaker 3: really brings you to the point of specialization. If their 482 00:28:58,200 --> 00:29:02,440 Speaker 3: markets have scale and you can preserve scale while you 483 00:29:02,440 --> 00:29:05,120 Speaker 3: are specialist, that's best of the both words. And that's 484 00:29:05,160 --> 00:29:08,880 Speaker 3: what we are doing now to create a scale aerospace company, 485 00:29:09,200 --> 00:29:12,240 Speaker 3: a scale automation company. We're still very global, we still 486 00:29:12,280 --> 00:29:15,440 Speaker 3: have very mature processes, but at the same time, you're 487 00:29:15,480 --> 00:29:19,520 Speaker 3: focused on singular segment. So I guess, like in everything else, 488 00:29:19,560 --> 00:29:22,800 Speaker 3: you learn through cycles, and this cycle is all about 489 00:29:22,800 --> 00:29:26,800 Speaker 3: having the mix of scale and specialization. This will persist 490 00:29:26,800 --> 00:29:29,320 Speaker 3: it something else comes in now where there's a case 491 00:29:29,360 --> 00:29:33,120 Speaker 3: to do something else, and I feel good about where 492 00:29:33,120 --> 00:29:35,360 Speaker 3: we are in opposition, and it is going to create 493 00:29:35,440 --> 00:29:36,720 Speaker 3: much more shareholder value. 494 00:29:36,840 --> 00:29:41,600 Speaker 2: So twenty years before you started talking about breaking into 495 00:29:41,640 --> 00:29:46,720 Speaker 2: three pieces, your fellow Dell component General Electric, went through 496 00:29:46,760 --> 00:29:52,160 Speaker 2: the same process, arguably with not a whole lot of success. 497 00:29:52,720 --> 00:29:56,560 Speaker 2: They started out fairly richly valued. There wasn't a whole 498 00:29:56,560 --> 00:30:02,040 Speaker 2: lot of room to grow and cre When you're thinking 499 00:30:02,080 --> 00:30:05,600 Speaker 2: about breaking into three are you looking at other companies 500 00:30:05,600 --> 00:30:08,200 Speaker 2: like General Electric and saying what can we learn from 501 00:30:08,200 --> 00:30:11,040 Speaker 2: what they did right, what they got wrong, what missteps 502 00:30:11,040 --> 00:30:11,600 Speaker 2: they made. 503 00:30:11,760 --> 00:30:14,200 Speaker 3: I think the situation for each company is very different 504 00:30:14,280 --> 00:30:19,120 Speaker 3: because separation cannot create value alone by itself. We have 505 00:30:19,160 --> 00:30:22,440 Speaker 3: to be convicted that the standalone asset has enough growth 506 00:30:22,480 --> 00:30:26,400 Speaker 3: potential and invest and asset base which is going to grow, 507 00:30:26,400 --> 00:30:29,160 Speaker 3: which is going to create value. So I think comparing 508 00:30:30,000 --> 00:30:34,040 Speaker 3: example you gave versus Honeywell is absolutely very different. Portfolios 509 00:30:34,840 --> 00:30:37,200 Speaker 3: way very different. I mean, so I would say that 510 00:30:37,320 --> 00:30:41,120 Speaker 3: our drivers were more around what I talked about. Our 511 00:30:41,240 --> 00:30:46,120 Speaker 3: stock price are more static, We were more We did 512 00:30:46,120 --> 00:30:49,240 Speaker 3: not destroy any shareholder value, so our question was how 513 00:30:49,280 --> 00:30:52,360 Speaker 3: do we create more shareholder value with external factors coming in, 514 00:30:52,400 --> 00:30:56,480 Speaker 3: growth of aerospace, growth of AI. Is that inflection point 515 00:30:56,520 --> 00:30:58,760 Speaker 3: for us to make a different decision. So we did 516 00:30:58,800 --> 00:31:01,720 Speaker 3: it more from a point of strength versus we have 517 00:31:01,800 --> 00:31:04,520 Speaker 3: some crisis coming in. So sometimes you use your point 518 00:31:04,520 --> 00:31:08,120 Speaker 3: of strength to make the right decisions. And we did 519 00:31:08,160 --> 00:31:11,080 Speaker 3: it fast and we did it right. I think every 520 00:31:11,080 --> 00:31:15,440 Speaker 3: other company came from a different circumstances, but the decision 521 00:31:15,480 --> 00:31:17,880 Speaker 3: on the outwardy looked very similar. They look like they 522 00:31:17,920 --> 00:31:20,440 Speaker 3: all did the same thing, but they all came from 523 00:31:20,480 --> 00:31:25,120 Speaker 3: a different backgrounds and you know, different set of assets 524 00:31:26,000 --> 00:31:29,360 Speaker 3: when we started looking at it. Some people believe that 525 00:31:29,760 --> 00:31:34,600 Speaker 3: we got influenced by success of GE. I want to 526 00:31:34,600 --> 00:31:38,600 Speaker 3: remind that G success came post our decision. That was 527 00:31:38,600 --> 00:31:41,280 Speaker 3: a process which was occurring. So yeah, you have that's 528 00:31:41,280 --> 00:31:43,200 Speaker 3: a data point to say they are also doing it. 529 00:31:43,240 --> 00:31:45,920 Speaker 3: But some of the success we have observed some outstanding 530 00:31:45,920 --> 00:31:48,920 Speaker 3: work by the G leadership team that really started happening 531 00:31:48,960 --> 00:31:51,520 Speaker 3: twenty four to twenty five time frame. We were far 532 00:31:51,560 --> 00:31:53,600 Speaker 3: along the way in our own analysis by that time, 533 00:31:53,640 --> 00:31:57,120 Speaker 3: so I think those are parallel things happening. So there's 534 00:31:57,160 --> 00:31:59,160 Speaker 3: no one thing you can attribute to say that this 535 00:31:59,240 --> 00:32:01,880 Speaker 3: thing influenced. It's a combination of the reason which all 536 00:32:01,880 --> 00:32:04,680 Speaker 3: come together, and that's what really brings us to where 537 00:32:04,680 --> 00:32:05,240 Speaker 3: we are today. 538 00:32:05,560 --> 00:32:09,240 Speaker 2: I like this phrase in your thesis of the current 539 00:32:09,280 --> 00:32:15,200 Speaker 2: transition from automation to autonomy with artificial intelligence as the 540 00:32:15,240 --> 00:32:21,280 Speaker 2: dividing line. How far along that process are we as 541 00:32:21,320 --> 00:32:27,320 Speaker 2: a country are the industrial sector and honeywell, so. 542 00:32:27,280 --> 00:32:31,520 Speaker 3: I would say that we as a country have an 543 00:32:31,560 --> 00:32:35,480 Speaker 3: advantage of being the leader in the space of cloud 544 00:32:35,560 --> 00:32:40,040 Speaker 3: and data science and companies like Honeywell has responsibility to 545 00:32:40,120 --> 00:32:42,680 Speaker 3: take that know how which the tech sector is creating. 546 00:32:42,800 --> 00:32:48,360 Speaker 3: Beat Microsoft, beat Google, Beatenvidia and all the very capable 547 00:32:48,440 --> 00:32:51,640 Speaker 3: tech companies. How do we bring that capability into our 548 00:32:51,720 --> 00:32:57,400 Speaker 3: sector Because our customer is not going to go and 549 00:32:57,440 --> 00:32:59,800 Speaker 3: they are not looking to buy a cloud capability or 550 00:32:59,840 --> 00:33:03,600 Speaker 3: they not looking to buy AI LM. They want to 551 00:33:03,600 --> 00:33:05,479 Speaker 3: solve a problem. They want to run a business, they 552 00:33:05,520 --> 00:33:07,760 Speaker 3: want to run an operation, they want to have more uptime, 553 00:33:07,800 --> 00:33:12,080 Speaker 3: they want to have more profitability. So our job is 554 00:33:12,120 --> 00:33:15,480 Speaker 3: to take our system to what I mentioned before and 555 00:33:15,560 --> 00:33:18,640 Speaker 3: add this intelligence layer. And what this intelligence layer is 556 00:33:18,680 --> 00:33:21,880 Speaker 3: all about. Taking capability from the tech companies, take large 557 00:33:21,920 --> 00:33:25,560 Speaker 3: language models from the likes of Google and Nvidia, use 558 00:33:25,600 --> 00:33:28,160 Speaker 3: a cloud power which is there from Amazon and Microsoft, 559 00:33:28,480 --> 00:33:31,760 Speaker 3: but really build a purpose offering from the industrial sector. 560 00:33:32,360 --> 00:33:34,120 Speaker 3: And as we are doing that, we are able to 561 00:33:34,160 --> 00:33:37,880 Speaker 3: create the agentic models for our customers. And that's what 562 00:33:38,000 --> 00:33:41,320 Speaker 3: they buy from us. The underlying plumbing what we have, 563 00:33:41,520 --> 00:33:42,960 Speaker 3: they don't want to know it, They don't want to 564 00:33:42,960 --> 00:33:45,520 Speaker 3: know how this is built. Say yeah, so you're automating 565 00:33:45,560 --> 00:33:47,960 Speaker 3: this piece of my work. That's great, So I'm going 566 00:33:48,040 --> 00:33:50,240 Speaker 3: to get more productivity for that how much I should 567 00:33:50,280 --> 00:33:52,840 Speaker 3: pay you for it? Right, So I would say we 568 00:33:52,920 --> 00:33:55,800 Speaker 3: are in the state that this is no more hypothesis. 569 00:33:57,280 --> 00:34:00,680 Speaker 3: We are in the not in the early but we 570 00:34:00,720 --> 00:34:03,640 Speaker 3: are in the stage of deployment of these capabilities across 571 00:34:03,720 --> 00:34:08,279 Speaker 3: different customer base. Why it is not taken up at 572 00:34:08,320 --> 00:34:10,440 Speaker 3: scale is because our customers have to go through a 573 00:34:10,480 --> 00:34:15,800 Speaker 3: significant change management in their organization because fundamentally the roles 574 00:34:15,840 --> 00:34:20,839 Speaker 3: of people are changing. Some roles require skills which are 575 00:34:20,920 --> 00:34:24,280 Speaker 3: less important today and some more new skills are required. 576 00:34:24,640 --> 00:34:26,800 Speaker 3: And they can't do that overnight. Just because I created 577 00:34:26,800 --> 00:34:29,880 Speaker 3: a new set of technology. They have to absorb it, 578 00:34:29,960 --> 00:34:32,880 Speaker 3: they have to ingest it. But we have some fabulous 579 00:34:32,920 --> 00:34:36,840 Speaker 3: examples on customer useating and scale in different sectors like 580 00:34:36,920 --> 00:34:41,120 Speaker 3: university systems, quick service restaurants. People are using some of 581 00:34:41,160 --> 00:34:44,560 Speaker 3: our technologies at a very large scale in refineries, etc. 582 00:34:44,920 --> 00:34:47,560 Speaker 3: So I would say that if I'm sitting with you 583 00:34:48,400 --> 00:34:51,280 Speaker 3: it twelve months back, I would have said very modest deployment. 584 00:34:51,680 --> 00:34:54,640 Speaker 3: Sitting today, I would say I'm very excited on about 585 00:34:54,680 --> 00:34:57,840 Speaker 3: opportunity we see a year from now. I would argue 586 00:34:57,840 --> 00:35:00,600 Speaker 3: that the penetration will go up substantially up because there's 587 00:35:00,600 --> 00:35:03,840 Speaker 3: a real economic value creation from what were really properity. 588 00:35:03,880 --> 00:35:07,000 Speaker 3: And we as a country are leading because we have 589 00:35:07,280 --> 00:35:11,719 Speaker 3: the core components of this technology and now we have to, 590 00:35:12,120 --> 00:35:14,520 Speaker 3: you know, take this cability across the world. When our 591 00:35:14,520 --> 00:35:16,640 Speaker 3: customers excited, they really like what we are doing. 592 00:35:17,080 --> 00:35:23,359 Speaker 2: Earlier, you mentioned restaurant automation. What does hauntingwell do for 593 00:35:23,640 --> 00:35:25,880 Speaker 2: either fast food service or casual dining? 594 00:35:26,360 --> 00:35:28,000 Speaker 3: They say, think about it. I mean, when you look 595 00:35:28,040 --> 00:35:31,160 Speaker 3: at a small fast food dining restaurant, there's not much 596 00:35:31,200 --> 00:35:35,200 Speaker 3: automation in that, but it consumes energy for sure. I mean, 597 00:35:35,320 --> 00:35:39,160 Speaker 3: let's take a typical McDonald's restaurant as just as an example. 598 00:35:39,800 --> 00:35:43,279 Speaker 3: There's a kitchen there, there's a fryer, there's a refrigeration. 599 00:35:43,440 --> 00:35:45,960 Speaker 3: It's is keeping a lot of products there. There's of 600 00:35:45,960 --> 00:35:50,080 Speaker 3: course lights going on. These assets were never thought as 601 00:35:50,080 --> 00:35:52,960 Speaker 3: a way to improve energy efficiencies by companies like us. 602 00:35:53,000 --> 00:35:56,320 Speaker 3: We say we should automate a large hospital. It's massive, 603 00:35:56,320 --> 00:35:58,320 Speaker 3: there's a lot of opportunity there, or a large building. 604 00:35:59,040 --> 00:36:02,680 Speaker 3: These assets were never paid attention by us because there 605 00:36:02,719 --> 00:36:06,520 Speaker 3: was no technology available. But when the cloud technology came in, 606 00:36:07,120 --> 00:36:10,520 Speaker 3: we are able to connect these assets flawlessly. You know, 607 00:36:10,560 --> 00:36:12,920 Speaker 3: in a matter of hours, and then you able to 608 00:36:13,000 --> 00:36:16,040 Speaker 3: use a lot of AI based rules set to understand 609 00:36:16,520 --> 00:36:19,359 Speaker 3: what should be the energy consumption actual versus what it 610 00:36:19,440 --> 00:36:22,440 Speaker 3: is today and give that tools to the owner to say, 611 00:36:22,800 --> 00:36:25,279 Speaker 3: you know, an example, we connected a quick service chain 612 00:36:25,360 --> 00:36:27,759 Speaker 3: in UK, I think something like five hundred plus of 613 00:36:27,800 --> 00:36:31,719 Speaker 3: their restaurants into a single operating system and they are 614 00:36:31,760 --> 00:36:35,400 Speaker 3: observing thirty to forty percent energy reduction. Like anything else, 615 00:36:35,520 --> 00:36:38,319 Speaker 3: the good old management principle, what do you inspect is 616 00:36:38,360 --> 00:36:42,040 Speaker 3: what you get. Once these were thinking running off my own, 617 00:36:42,200 --> 00:36:45,520 Speaker 3: nobody paid attention even though they desired to do something, 618 00:36:45,560 --> 00:36:48,759 Speaker 3: there was no mechanism. So we created an easy mechanism 619 00:36:49,040 --> 00:36:51,480 Speaker 3: to make this available to the customer. So all of 620 00:36:51,480 --> 00:36:53,120 Speaker 3: a sudden they were able to generate a lot more 621 00:36:53,120 --> 00:36:56,480 Speaker 3: productivity without adding too much of cost. And that's a 622 00:36:56,480 --> 00:36:58,919 Speaker 3: part of the new tools which is coming in which 623 00:36:59,000 --> 00:37:01,719 Speaker 3: was not possible, and that gives me a lot of 624 00:37:01,760 --> 00:37:05,120 Speaker 3: excitement that this is going to be much more level 625 00:37:05,160 --> 00:37:09,759 Speaker 3: of productivity efficiency which is less talked about. You know, 626 00:37:09,760 --> 00:37:13,000 Speaker 3: whenever there's AI dialogue, it's jobs going to cut jobs. 627 00:37:13,400 --> 00:37:16,520 Speaker 3: Nobody talks about economic value creation. It is doing a 628 00:37:16,600 --> 00:37:20,080 Speaker 3: real value for our customer base, making people more productive. 629 00:37:21,520 --> 00:37:24,280 Speaker 3: That's the story of the industrial side, which is probably 630 00:37:24,320 --> 00:37:26,080 Speaker 3: requires more more amplification. 631 00:37:26,360 --> 00:37:29,319 Speaker 2: So what's the Pewter Drucker quote. You can't manage what 632 00:37:29,400 --> 00:37:33,200 Speaker 2: you can't measure. So forget five hundred restaurants, what is 633 00:37:33,560 --> 00:37:36,680 Speaker 2: Starbucks thirty thousand? McDonald's forty thousand. 634 00:37:36,360 --> 00:37:38,480 Speaker 3: Applies to all of these kind of accid and many 635 00:37:38,480 --> 00:37:40,160 Speaker 3: people have done this work. So it's not that we 636 00:37:40,239 --> 00:37:42,359 Speaker 3: have created some new inventions. Some of them have done 637 00:37:42,400 --> 00:37:46,680 Speaker 3: this kind of discovery, but this effort was not very standardized. 638 00:37:46,719 --> 00:37:49,560 Speaker 3: It's like a custom made thing somebody will do. Because 639 00:37:49,680 --> 00:37:51,799 Speaker 3: you are a big company, you can afford it. But 640 00:37:51,840 --> 00:37:54,800 Speaker 3: when you do it a large scale, there are hundreds 641 00:37:54,800 --> 00:37:58,120 Speaker 3: of these chains. There are hundreds of retail stores. We're 642 00:37:58,120 --> 00:38:01,120 Speaker 3: also doing similar work around the big retail store chains, 643 00:38:01,360 --> 00:38:06,759 Speaker 3: very similar example. So these distributed assets are becoming a 644 00:38:06,800 --> 00:38:09,400 Speaker 3: way of capturing value at one end of the equation 645 00:38:10,120 --> 00:38:12,040 Speaker 3: or the other end of the equation. When you have 646 00:38:13,360 --> 00:38:16,880 Speaker 3: retirals coming and our customers are worried about knowledge going 647 00:38:16,920 --> 00:38:19,440 Speaker 3: out of the door, they're looking at a mechanism of 648 00:38:19,560 --> 00:38:23,240 Speaker 3: knowledge capture so they can perform their task. That's also 649 00:38:23,280 --> 00:38:27,080 Speaker 3: penetrating very rapidly so scenarios are different. Some scenarios are 650 00:38:27,080 --> 00:38:30,200 Speaker 3: looking at I never paid attention and now I can 651 00:38:30,239 --> 00:38:32,759 Speaker 3: do it. Some are saying I have less people do 652 00:38:32,920 --> 00:38:36,399 Speaker 3: something about it. But the capability is fundamentally the same. 653 00:38:36,440 --> 00:38:39,000 Speaker 3: It's the same capability which solves both the problem. 654 00:38:39,320 --> 00:38:43,600 Speaker 2: Coming up, we continue our conversation with Vimal Kapor, CEO 655 00:38:43,680 --> 00:38:49,719 Speaker 2: and chairman of Honeywell, discussing the state of automated technology today. 656 00:38:50,200 --> 00:38:56,520 Speaker 2: I'm Barry Richults. You're listening to Master's Business on Bloomberg Radio. 657 00:39:06,880 --> 00:39:09,800 Speaker 2: I'm Barry Ridolts. You're listening to Masters in Business on 658 00:39:09,840 --> 00:39:13,160 Speaker 2: Bloomberg Radio. My extra special guest this week is vim 659 00:39:13,160 --> 00:39:17,440 Speaker 2: Al Kapoor. He is CEO and chairman at Honeywell, the 660 00:39:17,480 --> 00:39:20,040 Speaker 2: company he has worked at for the past thirty seven 661 00:39:20,120 --> 00:39:24,280 Speaker 2: years since starting there as an engineer. So I'm curious 662 00:39:24,320 --> 00:39:29,280 Speaker 2: as to how some technologies seem to just take forever 663 00:39:29,360 --> 00:39:33,719 Speaker 2: to find their way into the real world, you know, 664 00:39:33,760 --> 00:39:38,680 Speaker 2: if you travel around the world. I remember the first 665 00:39:38,719 --> 00:39:42,560 Speaker 2: time I saw one of the point of sale handheld 666 00:39:42,640 --> 00:39:45,919 Speaker 2: units in a restaurant in Europe. I don't know, maybe 667 00:39:45,920 --> 00:39:49,480 Speaker 2: it was fifteen years ago and I was astonished. Wait, 668 00:39:49,520 --> 00:39:52,080 Speaker 2: I don't have to request the check. They come, then 669 00:39:52,120 --> 00:39:54,200 Speaker 2: they have to give him the key, the card, they 670 00:39:54,239 --> 00:39:56,839 Speaker 2: go away, like I will take a check they come 671 00:39:56,880 --> 00:40:00,080 Speaker 2: by it. It seems to have taken a decade to 672 00:40:00,080 --> 00:40:03,720 Speaker 2: make its way here. What are some of the impediments 673 00:40:03,760 --> 00:40:07,600 Speaker 2: to some of this some of the cutting edge technologies 674 00:40:07,640 --> 00:40:11,560 Speaker 2: that's obviously using a bunch of tech that already existed. 675 00:40:13,360 --> 00:40:17,360 Speaker 2: Is this a problem getting adaptation even though it's clearly 676 00:40:18,040 --> 00:40:22,359 Speaker 2: more productive, more efficient, faster turn of tables, Like I 677 00:40:22,400 --> 00:40:25,320 Speaker 2: was astonished how long it took for the United States 678 00:40:25,320 --> 00:40:26,600 Speaker 2: to implement that fure. 679 00:40:26,719 --> 00:40:30,239 Speaker 3: I think there's a scenario in your example because it's 680 00:40:30,280 --> 00:40:33,919 Speaker 3: a technology displacement of some old method versus a more 681 00:40:34,080 --> 00:40:38,120 Speaker 3: new method. But the reason I believe more bullish about 682 00:40:38,160 --> 00:40:40,200 Speaker 3: it is that we are solving a known problem. And 683 00:40:40,239 --> 00:40:43,799 Speaker 3: the known problem is word has less people to do 684 00:40:43,840 --> 00:40:48,040 Speaker 3: a lot of work around skill labor in the industrial world. 685 00:40:48,080 --> 00:40:50,919 Speaker 3: That's a real problem. So our solution is not trying 686 00:40:50,920 --> 00:40:53,160 Speaker 3: to find a problem. We are finding we are giving 687 00:40:53,200 --> 00:40:57,200 Speaker 3: a solution to a known problem. Adoption rates are lower 688 00:40:57,239 --> 00:40:59,800 Speaker 3: because of the change management issue, But this is the 689 00:41:00,239 --> 00:41:02,960 Speaker 3: monument of the order of eighteen months, twenty four months, 690 00:41:03,000 --> 00:41:06,480 Speaker 3: thirty months, that's not a decade, So I remain very 691 00:41:06,480 --> 00:41:11,520 Speaker 3: optimistic given my experiencing these sectors, the adoption rates are 692 00:41:11,560 --> 00:41:13,600 Speaker 3: going to be much more quicker because the problem is real. 693 00:41:14,320 --> 00:41:17,799 Speaker 3: We are not inventing the problem. This problem exists for it. 694 00:41:18,239 --> 00:41:20,480 Speaker 3: And by the way, this problem is everywhere in the world. 695 00:41:21,400 --> 00:41:22,239 Speaker 3: This is not a. 696 00:41:22,320 --> 00:41:23,960 Speaker 2: US problem only skilled labor. 697 00:41:24,080 --> 00:41:28,040 Speaker 3: Skilled labor. Europe has more population shrinkage than US. Go 698 00:41:28,080 --> 00:41:30,760 Speaker 3: to Japan and Korea, they have the same problem. China 699 00:41:30,800 --> 00:41:34,560 Speaker 3: has population shrinkage. So this is a universal issue. This 700 00:41:34,680 --> 00:41:38,239 Speaker 3: is not invented here. Now we get excited on the 701 00:41:38,360 --> 00:41:42,960 Speaker 3: job displacement happening with robots and humanoids. That's a small 702 00:41:43,000 --> 00:41:48,040 Speaker 3: portion of a manufacturing industry that probably is also displacing 703 00:41:48,080 --> 00:41:50,160 Speaker 3: some tasks which humans are not willing to do, like 704 00:41:50,200 --> 00:41:54,080 Speaker 3: lifting boxes. Right yeah, I mean, okay, it's not very interesting. 705 00:41:55,400 --> 00:41:58,560 Speaker 3: But then there are other jobs with other sectors which 706 00:41:58,600 --> 00:42:03,200 Speaker 3: we address. A physical AI or intelligence layer is going 707 00:42:03,239 --> 00:42:05,480 Speaker 3: to create a tremendous amount of economic value. 708 00:42:05,640 --> 00:42:10,560 Speaker 2: So I keep hearing people compare that intelligence layer of 709 00:42:10,719 --> 00:42:15,000 Speaker 2: artificial intelligence to the Internet. I'm wondering, and you seem 710 00:42:15,120 --> 00:42:19,680 Speaker 2: very bullish and excited about everything AI can do. Is 711 00:42:19,719 --> 00:42:24,680 Speaker 2: there a better comparison. Is the Industrial Revolution a better 712 00:42:24,880 --> 00:42:28,719 Speaker 2: framework for thinking about the impact of AI over the 713 00:42:28,760 --> 00:42:31,200 Speaker 2: next ten to fifty one hundred years. 714 00:42:31,200 --> 00:42:33,760 Speaker 3: I think the AI impact will be different in each sector, 715 00:42:34,239 --> 00:42:38,040 Speaker 3: and I think if we make it too broadbrush, we 716 00:42:38,160 --> 00:42:40,879 Speaker 3: are losing the bigger picture. But when we are making 717 00:42:40,960 --> 00:42:44,520 Speaker 3: it specific to a segment, then you're being more precise 718 00:42:44,600 --> 00:42:48,120 Speaker 3: to say in context of the end markets we serve 719 00:42:48,160 --> 00:42:52,040 Speaker 3: of the industrial sector. I talked about examples there. It's 720 00:42:52,080 --> 00:42:54,360 Speaker 3: all about the skill shortage issue, which is very different 721 00:42:54,440 --> 00:42:58,000 Speaker 3: from if we are using AI for better search engine. 722 00:42:58,080 --> 00:43:01,719 Speaker 3: If I'm using AI for or making a summary of 723 00:43:01,840 --> 00:43:04,120 Speaker 3: art talk, which somebody can do, and that's a very 724 00:43:04,120 --> 00:43:06,359 Speaker 3: different use case, and one can argue is it going 725 00:43:06,360 --> 00:43:08,400 Speaker 3: to add productivity or not? Or is it going to 726 00:43:08,400 --> 00:43:11,759 Speaker 3: take away jobs. That's a different scenario from simply not 727 00:43:11,880 --> 00:43:15,719 Speaker 3: having people to do work. Way different scenario, and I 728 00:43:15,719 --> 00:43:19,239 Speaker 3: think that makes our case more compelling. The adoption rates 729 00:43:19,239 --> 00:43:22,520 Speaker 3: are driven by a near real need versus we are 730 00:43:22,520 --> 00:43:24,680 Speaker 3: trying to create a need which is unknown, and that's 731 00:43:24,760 --> 00:43:28,280 Speaker 3: not being talked a lot more. A lot more dialogue 732 00:43:28,320 --> 00:43:31,319 Speaker 3: is around job displacements, but those are more in the 733 00:43:31,840 --> 00:43:35,640 Speaker 3: jobs which could be automated like finance function or HR function. 734 00:43:36,320 --> 00:43:39,160 Speaker 3: Maybe to a certain degree it's true, but not to 735 00:43:39,239 --> 00:43:41,560 Speaker 3: the point. My personal view is that it's going to 736 00:43:41,600 --> 00:43:43,680 Speaker 3: have the amount of impact which is being talked about. 737 00:43:43,960 --> 00:43:47,040 Speaker 2: So let's talk about some of the challenges of this 738 00:43:47,239 --> 00:43:50,720 Speaker 2: technology layer and some of the black hats out there. 739 00:43:51,719 --> 00:43:54,319 Speaker 2: When Mythos came out, I would imagine a company like 740 00:43:54,360 --> 00:44:00,200 Speaker 2: Honeywell set up and took notice the idea of of 741 00:44:00,320 --> 00:44:05,799 Speaker 2: AI taking over industrial controllers, power, water, air conditioning. All 742 00:44:05,800 --> 00:44:09,360 Speaker 2: that stuff has to be thought of as a genuine threat. 743 00:44:09,800 --> 00:44:13,920 Speaker 2: Nobody wants rogue thermostats or what have you. How do 744 00:44:14,000 --> 00:44:19,840 Speaker 2: you look at the threat from a powerful entity like 745 00:44:19,920 --> 00:44:23,040 Speaker 2: Mythos and how much of an arms race are we 746 00:44:23,120 --> 00:44:27,080 Speaker 2: in to harden all of our soft underbelly. 747 00:44:27,520 --> 00:44:30,200 Speaker 3: So I think we have to appreciate the fact that 748 00:44:31,760 --> 00:44:35,920 Speaker 3: where we are deploying AI, it is substantially different from 749 00:44:36,600 --> 00:44:40,000 Speaker 3: what we are ginerally talking about in broader public domain. 750 00:44:40,760 --> 00:44:43,439 Speaker 3: If you think of applying AI in an industrials Let's 751 00:44:43,480 --> 00:44:45,279 Speaker 3: take a case of a hospital and I want to 752 00:44:45,320 --> 00:44:48,200 Speaker 3: apply AI into automation system to make it more efficient. 753 00:44:48,840 --> 00:44:51,680 Speaker 3: The data of that is not in public domain. The 754 00:44:51,760 --> 00:44:54,399 Speaker 3: data is in Honeymoon system or it's one of our 755 00:44:54,400 --> 00:44:57,160 Speaker 3: competitor system, So you cannot go to internet and train 756 00:44:57,239 --> 00:45:01,600 Speaker 3: anything because there's nothing to train on. So that makes 757 00:45:01,680 --> 00:45:04,439 Speaker 3: data friction as a big problem in industrial sector, which 758 00:45:04,440 --> 00:45:07,319 Speaker 3: in a way becomes a protection layer for us. But 759 00:45:07,400 --> 00:45:09,920 Speaker 3: that doesn't mean friction becomes a protection layer. But it 760 00:45:09,920 --> 00:45:11,880 Speaker 3: doesn't mean we should not do anything about it. So 761 00:45:11,920 --> 00:45:14,040 Speaker 3: you say, oh, unprotected, it means we should take it 762 00:45:14,080 --> 00:45:17,520 Speaker 3: seriously to think of potential threats coming in because if 763 00:45:17,520 --> 00:45:20,279 Speaker 3: the data friction is removed, which is hard to do, 764 00:45:20,360 --> 00:45:24,920 Speaker 3: but humans are very intelligent, so we have worked very 765 00:45:24,960 --> 00:45:27,799 Speaker 3: hard to remove the data friction and also use our 766 00:45:27,800 --> 00:45:33,839 Speaker 3: domain knowledge because interestingly, you cannot solve a horizontal problem 767 00:45:34,440 --> 00:45:37,680 Speaker 3: in industrial domain. What I mean by that is you 768 00:45:37,760 --> 00:45:40,560 Speaker 3: do not have a software application like a CRM system 769 00:45:40,800 --> 00:45:44,640 Speaker 3: or an HR system. The problems of each sectors are 770 00:45:44,719 --> 00:45:47,680 Speaker 3: very different. If you're a refinery, you're trying to produce 771 00:45:47,719 --> 00:45:51,480 Speaker 3: more jet fuel and more diesel. If you are a 772 00:45:51,520 --> 00:45:54,279 Speaker 3: life census manufacturing facility, you're trying to produce drug with 773 00:45:54,320 --> 00:45:57,759 Speaker 3: minimal quality giveaway. But if you're a data center you 774 00:45:57,800 --> 00:46:00,719 Speaker 3: want more uptime. Your problems are so different. So we 775 00:46:00,760 --> 00:46:03,640 Speaker 3: can't create a magic AI application and sell to everybody. 776 00:46:04,160 --> 00:46:06,359 Speaker 3: We have to be purposeful that where do we use 777 00:46:06,360 --> 00:46:09,000 Speaker 3: our data and what problem we solve, which only come 778 00:46:09,000 --> 00:46:12,560 Speaker 3: from years of experience. So those two really become in 779 00:46:12,600 --> 00:46:15,879 Speaker 3: a way a constraint for a genuary company to come 780 00:46:15,920 --> 00:46:19,920 Speaker 3: in because of data friction and lack of understanding of domain, 781 00:46:20,840 --> 00:46:23,319 Speaker 3: which means companies like us which possess both have to 782 00:46:23,320 --> 00:46:25,800 Speaker 3: solve this problem. And that's why we are very bullish 783 00:46:25,800 --> 00:46:28,040 Speaker 3: about it to say we're going to do it. We're 784 00:46:28,040 --> 00:46:30,160 Speaker 3: going to take all the capabilities from tech companies and 785 00:46:30,160 --> 00:46:33,359 Speaker 3: build new set of capabilities to take our industry from 786 00:46:33,520 --> 00:46:36,800 Speaker 3: a pure play automation to more towards autonomy. And autonomy 787 00:46:36,880 --> 00:46:40,799 Speaker 3: doesn't mean humans will disappear. Humans will become more empowered, 788 00:46:40,920 --> 00:46:44,880 Speaker 3: human will become more capable, and to the extent is 789 00:46:44,920 --> 00:46:47,800 Speaker 3: some skilled shortages, it'll address that point. 790 00:46:48,160 --> 00:46:51,440 Speaker 2: So let's talk a little bit about how tumultuous the 791 00:46:51,520 --> 00:46:56,160 Speaker 2: past twelve months have been in terms of geopolitics. We 792 00:46:56,680 --> 00:46:59,920 Speaker 2: not only have the war in the Ukraine, but now 793 00:47:00,320 --> 00:47:03,880 Speaker 2: in Iran we had the on again, off again, on 794 00:47:04,000 --> 00:47:08,239 Speaker 2: again and most recently off again tariffs. How does this 795 00:47:08,280 --> 00:47:12,799 Speaker 2: affect how a company like Honeywell thinks about resuring and 796 00:47:12,840 --> 00:47:16,400 Speaker 2: bringing manufacturing back to the United States, things about supply 797 00:47:16,520 --> 00:47:21,360 Speaker 2: chain issues. How do you plan in such a tumultuous environment. 798 00:47:21,560 --> 00:47:24,000 Speaker 3: But it's definitely a challenge for companies. To have more 799 00:47:24,000 --> 00:47:27,719 Speaker 3: stability is what companies want. So I would say that 800 00:47:27,840 --> 00:47:32,400 Speaker 3: companies like US have very mature processes to deal with it. 801 00:47:32,520 --> 00:47:35,759 Speaker 3: So every time this issue occurs, we have some sort 802 00:47:35,760 --> 00:47:41,320 Speaker 3: of disturbance for depends four weeks, eight weeks, twenty weeks, 803 00:47:41,320 --> 00:47:44,240 Speaker 3: who knows, depending on the situation. So we have learned 804 00:47:44,239 --> 00:47:46,080 Speaker 3: how to deal with it. But it doesn't come without 805 00:47:46,080 --> 00:47:48,600 Speaker 3: a cost. You lose some growth in that window. You 806 00:47:48,640 --> 00:47:50,640 Speaker 3: may have to incorrect stra cost like it happened in 807 00:47:50,680 --> 00:47:53,120 Speaker 3: case of tariff, because when tariff got announced, we have 808 00:47:53,160 --> 00:47:55,759 Speaker 3: no choice but to paid right now whether we can 809 00:47:55,800 --> 00:47:57,680 Speaker 3: recover it or not as a subsequent decision. 810 00:47:57,760 --> 00:47:59,880 Speaker 2: Are you one of the many companies that have filed 811 00:48:00,080 --> 00:48:01,720 Speaker 2: litigation to get. 812 00:48:01,520 --> 00:48:03,279 Speaker 3: Redated not find any litigation? 813 00:48:03,760 --> 00:48:05,040 Speaker 2: How big of a hit was? 814 00:48:05,160 --> 00:48:07,480 Speaker 3: It was not big for us. We were mostly down 815 00:48:07,520 --> 00:48:10,239 Speaker 3: to the second part of your queshion. We have been 816 00:48:10,239 --> 00:48:14,080 Speaker 3: doing manufacturing local for local for multiple years, so we 817 00:48:14,200 --> 00:48:18,000 Speaker 3: made for us in US made for Europe in Europe, 818 00:48:18,040 --> 00:48:20,560 Speaker 3: made for China in China, so we don't move a 819 00:48:20,600 --> 00:48:23,600 Speaker 3: lot of stuff around. However, what we cannot control is 820 00:48:23,640 --> 00:48:26,600 Speaker 3: the global nature of the components we buy. Right if 821 00:48:26,640 --> 00:48:27,240 Speaker 3: I have to buy. 822 00:48:27,120 --> 00:48:29,960 Speaker 2: Everything in the supply chain and raw materials. 823 00:48:29,400 --> 00:48:33,120 Speaker 3: Correct, because we can't make everything. So if a component 824 00:48:33,239 --> 00:48:35,799 Speaker 3: is made in Korea, like batteries, we have to buy 825 00:48:35,840 --> 00:48:38,480 Speaker 3: it from there, and if a component is made in 826 00:48:38,600 --> 00:48:41,000 Speaker 3: China or somewhere else, we have to buy it from there. 827 00:48:41,480 --> 00:48:46,640 Speaker 3: So that impact is certainly not under our coverage because 828 00:48:46,840 --> 00:48:49,600 Speaker 3: we don't have an endless capacity to invest in everything 829 00:48:49,880 --> 00:48:54,080 Speaker 3: but our core manufacturing. We have one hundred and fifty factories, 830 00:48:54,920 --> 00:48:57,600 Speaker 3: you know, and they're well disabled around the world, and 831 00:48:57,640 --> 00:48:59,680 Speaker 3: they are well distributed across the world. I mean, so 832 00:48:59,719 --> 00:49:02,920 Speaker 3: we are, so we don't have this foundational challenge of resharing, 833 00:49:04,239 --> 00:49:07,759 Speaker 3: but we certainly have to deal with changing environment in 834 00:49:07,800 --> 00:49:10,520 Speaker 3: which we have to think about more local supply based 835 00:49:10,560 --> 00:49:14,200 Speaker 3: development aligned to what the expectations are at this point 836 00:49:14,200 --> 00:49:14,600 Speaker 3: of time. 837 00:49:15,360 --> 00:49:19,320 Speaker 2: So we've noticed that defense budgets really around the world, 838 00:49:19,400 --> 00:49:22,320 Speaker 2: not just here in the United States, have been rising, 839 00:49:23,080 --> 00:49:27,800 Speaker 2: and there certainly has been fairly robust demand for aerospace 840 00:49:27,920 --> 00:49:30,560 Speaker 2: there's a big upgrade cycle just kind of starting. A 841 00:49:30,600 --> 00:49:33,520 Speaker 2: lot of the fleets are pretty old. How do you 842 00:49:33,560 --> 00:49:35,880 Speaker 2: look at this in terms of risk and opportunity? How 843 00:49:35,920 --> 00:49:38,200 Speaker 2: are you thinking about defense and aerospace? 844 00:49:38,480 --> 00:49:41,040 Speaker 3: Defense is a big opportunity for our aerospace business. That's 845 00:49:41,080 --> 00:49:45,400 Speaker 3: about forty percent of aerospace business. So it's certainly the 846 00:49:45,480 --> 00:49:49,480 Speaker 3: current changes in geobolgical environment and government spending more money 847 00:49:49,560 --> 00:49:52,800 Speaker 3: is only positive. So it's going to become an even 848 00:49:52,880 --> 00:49:56,080 Speaker 3: more growth driver for the business compared to what it had. 849 00:49:56,120 --> 00:49:58,680 Speaker 3: So when we started this thesis two two and a 850 00:49:58,719 --> 00:50:01,520 Speaker 3: half years back, we did not predict this level of 851 00:50:01,520 --> 00:50:05,520 Speaker 3: demand in the defense, but now that's really a reality, 852 00:50:06,120 --> 00:50:08,920 Speaker 3: whether it's in US, whether it's some of our US allies. 853 00:50:09,680 --> 00:50:12,040 Speaker 3: There's a lot more growth opportunity across the board for 854 00:50:12,160 --> 00:50:14,120 Speaker 3: different products and services we provide. 855 00:50:14,640 --> 00:50:18,759 Speaker 2: And then there's been some debate about the future of 856 00:50:18,800 --> 00:50:22,319 Speaker 2: technology and industry. China seems to be running away in 857 00:50:22,360 --> 00:50:27,319 Speaker 2: a couple of areas like energy transition and robotics. From 858 00:50:27,320 --> 00:50:31,680 Speaker 2: where you sit, is the lead going to pass back 859 00:50:31,680 --> 00:50:34,520 Speaker 2: and forth or is there a clear winner? And that's 860 00:50:34,520 --> 00:50:39,719 Speaker 2: a potential problem for the United States, both strategically and economically. 861 00:50:39,960 --> 00:50:41,840 Speaker 3: I think we have to look at what's look ahead. 862 00:50:42,000 --> 00:50:44,239 Speaker 3: Was just trying to look back and be you know, 863 00:50:45,480 --> 00:50:49,520 Speaker 3: skeptical about it. We will look ahead the problems which 864 00:50:49,560 --> 00:50:52,759 Speaker 3: the world is in head of us. We clearly know 865 00:50:52,840 --> 00:50:55,160 Speaker 3: the US lead in AI, So how do we protect 866 00:50:55,160 --> 00:50:57,879 Speaker 3: the lead? We clearly have a lead in quantum. It's 867 00:50:57,960 --> 00:50:59,840 Speaker 3: one of the businesses we own that. How do we 868 00:51:00,280 --> 00:51:01,160 Speaker 3: keep that scale? 869 00:51:01,239 --> 00:51:04,000 Speaker 2: You do? I didn't realize what does Honeywell do in 870 00:51:04,000 --> 00:51:04,960 Speaker 2: the quantum space. 871 00:51:05,040 --> 00:51:07,520 Speaker 3: So we own a business called Quantinum in which Honeywell 872 00:51:07,520 --> 00:51:09,920 Speaker 3: has a majority stake. We spun it off as separate 873 00:51:09,920 --> 00:51:12,400 Speaker 3: company in twenty twenty one. Oh okay, all right, so 874 00:51:12,440 --> 00:51:16,879 Speaker 3: it's not it's Honeywell investments in that company versus it's 875 00:51:16,880 --> 00:51:17,760 Speaker 3: not part of Honeywell. 876 00:51:17,880 --> 00:51:21,640 Speaker 2: I recall I call that way back when that's right, really. 877 00:51:21,520 --> 00:51:25,040 Speaker 3: Twenty fist correct. So there are technologies in which US 878 00:51:25,080 --> 00:51:28,359 Speaker 3: have an advantage. US have to rebuild its supply base 879 00:51:28,400 --> 00:51:32,040 Speaker 3: for some of the critical sectors like semiconductor, like pharmaceutical 880 00:51:32,120 --> 00:51:36,840 Speaker 3: which are mission critical, and I think that's underway, but 881 00:51:36,960 --> 00:51:39,320 Speaker 3: we need to have patients that those things take years 882 00:51:39,360 --> 00:51:42,480 Speaker 3: to happen. There's not a switch to say we want 883 00:51:42,520 --> 00:51:44,200 Speaker 3: to do it, and those things show up. They can 884 00:51:44,280 --> 00:51:46,919 Speaker 3: take five, seven, ten years, so I think it's heading 885 00:51:46,920 --> 00:51:49,960 Speaker 3: in a right direction. We as a country has all 886 00:51:49,960 --> 00:51:53,520 Speaker 3: the capabilities, we have the capital. We haven't know how, 887 00:51:53,920 --> 00:51:56,080 Speaker 3: but we have to refurbase some of our skill which 888 00:51:56,080 --> 00:51:58,200 Speaker 3: we last over a couple of years in few portions 889 00:51:58,239 --> 00:52:01,239 Speaker 3: of industrial sector. But let's not forget we have very, 890 00:52:01,280 --> 00:52:04,200 Speaker 3: very capable companies which created the same sector all over 891 00:52:04,239 --> 00:52:06,719 Speaker 3: the world, so those have not gone away. 892 00:52:06,880 --> 00:52:09,560 Speaker 2: So resuring is not as challenging as a lot of 893 00:52:09,560 --> 00:52:10,120 Speaker 2: people make it. 894 00:52:10,040 --> 00:52:13,040 Speaker 3: It is more thoughtful in terms of which how do 895 00:52:13,080 --> 00:52:16,400 Speaker 3: you prioritize? All things being equal, reshoring is the right 896 00:52:16,480 --> 00:52:19,040 Speaker 3: thing to do. But my personal view is we should 897 00:52:19,040 --> 00:52:20,920 Speaker 3: pick up the top five and say, okay, here are 898 00:52:20,920 --> 00:52:22,520 Speaker 3: the five we want to go really go after I 899 00:52:22,600 --> 00:52:25,000 Speaker 3: make it successful, because try to do everything is going 900 00:52:25,040 --> 00:52:28,200 Speaker 3: to be just extremely difficult in auto a prioritization. 901 00:52:28,480 --> 00:52:31,560 Speaker 2: So final question before I get to our speed rounds, 902 00:52:32,040 --> 00:52:34,560 Speaker 2: what do you think when it comes to automation and 903 00:52:34,680 --> 00:52:39,640 Speaker 2: artificial intelligence? What do you think business people and investors 904 00:52:39,640 --> 00:52:44,799 Speaker 2: for that matter, really are misunderstanding what little nugget that 905 00:52:44,840 --> 00:52:47,960 Speaker 2: you've experienced would give them a little more insight and 906 00:52:48,000 --> 00:52:49,360 Speaker 2: so what the future looks like. 907 00:52:49,560 --> 00:52:53,920 Speaker 3: I think the point we discussed earlier that the automation 908 00:52:54,000 --> 00:52:57,120 Speaker 3: gets heavily enabled by AI and really create the intelligence layer, 909 00:52:57,640 --> 00:53:00,520 Speaker 3: and that opportunity to create it as being under a estimated. 910 00:53:01,120 --> 00:53:03,880 Speaker 3: I think this opportunity is real because of the skill shortage, 911 00:53:03,920 --> 00:53:06,920 Speaker 3: because of the knowledge capatures that got created over a 912 00:53:07,040 --> 00:53:11,120 Speaker 3: period of time. So I truly believe that's something which 913 00:53:11,200 --> 00:53:15,160 Speaker 3: needs more, more conversation and more emphasis. 914 00:53:15,440 --> 00:53:17,880 Speaker 2: So I only have you for another three minutes, so 915 00:53:17,960 --> 00:53:21,600 Speaker 2: let me click through these questions really quickly, starting with 916 00:53:22,719 --> 00:53:26,520 Speaker 2: tell us about your mentors who helped shape your career. 917 00:53:26,840 --> 00:53:29,080 Speaker 3: My early managers. I mean, I was lucky to have 918 00:53:29,120 --> 00:53:32,920 Speaker 3: some very good managers who taught me different things. You know, 919 00:53:33,239 --> 00:53:35,680 Speaker 3: not to be fearful about whom you are talking to, 920 00:53:36,280 --> 00:53:39,000 Speaker 3: how do you think about value propositions, how to think 921 00:53:39,040 --> 00:53:42,120 Speaker 3: global scale. So I think in honeywell, you're blessed to 922 00:53:42,160 --> 00:53:44,400 Speaker 3: have some very strong leaders in different part of my 923 00:53:44,560 --> 00:53:47,239 Speaker 3: career in the first fifteen twenty years, which really shape you. 924 00:53:47,760 --> 00:53:51,520 Speaker 3: Because what shapes you as the first fifteenish years of 925 00:53:51,600 --> 00:53:54,480 Speaker 3: your life, because once those value system is built in 926 00:53:54,560 --> 00:53:57,279 Speaker 3: your brain, you kind of live with that and I 927 00:53:57,320 --> 00:54:00,520 Speaker 3: was benefiting from some very powerful winters intoferent parts of 928 00:54:00,520 --> 00:54:01,040 Speaker 3: the company. 929 00:54:01,480 --> 00:54:03,680 Speaker 2: Let's talk about books. What are some of your favorites. 930 00:54:03,719 --> 00:54:04,840 Speaker 2: What are you reading recently? 931 00:54:05,520 --> 00:54:09,240 Speaker 3: So books I read variety, both from leadership to sectors 932 00:54:09,280 --> 00:54:13,120 Speaker 3: specific The recent one of the book I'm reading is 933 00:54:13,120 --> 00:54:16,160 Speaker 3: The Price from Dan Jurgen. If anybody is interested about 934 00:54:16,200 --> 00:54:19,759 Speaker 3: oil economy, please do read it. Six months back I 935 00:54:19,800 --> 00:54:23,919 Speaker 3: started reading Chip War so some of the sectors specific things, 936 00:54:23,920 --> 00:54:25,959 Speaker 3: but also read about leadership of some of the people 937 00:54:26,000 --> 00:54:28,880 Speaker 3: I admire. Dave Cody, who was Chairsea of Honeywell for 938 00:54:28,920 --> 00:54:32,160 Speaker 3: a long time. He has some very fascinating book Winning 939 00:54:32,200 --> 00:54:37,080 Speaker 3: Now and Winning Later inter and joined our board recently. 940 00:54:37,160 --> 00:54:39,399 Speaker 3: She has some fascinating leadership books, so I read some 941 00:54:39,440 --> 00:54:41,720 Speaker 3: of them. I read a lot of books on China. 942 00:54:42,760 --> 00:54:46,319 Speaker 3: I think it's underestimated the scale of that economy, so 943 00:54:46,360 --> 00:54:49,439 Speaker 3: I think we just need to There's a book called 944 00:54:49,520 --> 00:54:52,440 Speaker 3: Words View China's view of the word. Very interesting book. 945 00:54:52,800 --> 00:54:54,879 Speaker 3: It's like, we have a view about China, what about 946 00:54:54,920 --> 00:54:57,759 Speaker 3: their view? Have you ever asked them the question why 947 00:54:57,760 --> 00:54:59,879 Speaker 3: do you do? What do you do? So? I kind 948 00:54:59,880 --> 00:55:05,720 Speaker 3: of you have very diverse The reading habits of waiting 949 00:55:05,760 --> 00:55:08,840 Speaker 3: from my business specific to leadership to some of the 950 00:55:08,880 --> 00:55:11,000 Speaker 3: countries specifics. You have tuggled around a lot. 951 00:55:11,040 --> 00:55:13,840 Speaker 2: On that and our final two questions, what sort of 952 00:55:13,880 --> 00:55:17,520 Speaker 2: advice would you give to a recent college graduate interest 953 00:55:17,560 --> 00:55:21,680 Speaker 2: in the career in either engineering or management. 954 00:55:22,239 --> 00:55:25,000 Speaker 3: I mean both are fascinating carrier. I would say engineering 955 00:55:25,200 --> 00:55:27,839 Speaker 3: is a carrier which gives you a lot of options, 956 00:55:28,880 --> 00:55:31,160 Speaker 3: So do pursue that because it gives you a wide 957 00:55:31,200 --> 00:55:35,960 Speaker 3: variety of choices. Management is something that people should do 958 00:55:36,080 --> 00:55:38,440 Speaker 3: who have more willingness to take a risk and have 959 00:55:38,480 --> 00:55:41,200 Speaker 3: courage to make decisions, because in the end, at some 960 00:55:41,280 --> 00:55:43,440 Speaker 3: point in your career you will have to do both. 961 00:55:44,120 --> 00:55:47,920 Speaker 3: And if you think that's not your sphere, that's something 962 00:55:47,960 --> 00:55:50,440 Speaker 3: you're not good at it, I would rather argue than 963 00:55:50,520 --> 00:55:53,520 Speaker 3: you choose something really good at versus otherwise you're going 964 00:55:53,560 --> 00:55:56,840 Speaker 3: to get saturated at some point. But management is an 965 00:55:56,880 --> 00:56:00,000 Speaker 3: excellent carrier by itself, so both are both are excellent. 966 00:56:00,040 --> 00:56:03,120 Speaker 2: And our final question, what do you know about the 967 00:56:03,160 --> 00:56:10,359 Speaker 2: world of automation, engineering and artificial technology today that might 968 00:56:10,360 --> 00:56:13,000 Speaker 2: have been useful thirty seven years ago when you first 969 00:56:13,000 --> 00:56:14,560 Speaker 2: started at Honeywa. 970 00:56:14,760 --> 00:56:17,480 Speaker 3: I don't know. I think I'm always excited about learning 971 00:56:17,480 --> 00:56:21,040 Speaker 3: new technology all the time. You know, I'm still very 972 00:56:21,120 --> 00:56:25,640 Speaker 3: curious to read things how they work. I think I 973 00:56:25,680 --> 00:56:29,360 Speaker 3: will say that staying curious is very important for us 974 00:56:29,400 --> 00:56:31,839 Speaker 3: as a human being. We should never be satisfied on 975 00:56:31,920 --> 00:56:34,799 Speaker 3: what we know. We should always ask the question what 976 00:56:34,880 --> 00:56:37,200 Speaker 3: we do not know, whether it is about a technology 977 00:56:37,280 --> 00:56:39,640 Speaker 3: or a business process, or for that matter, any fact 978 00:56:39,680 --> 00:56:43,799 Speaker 3: of life and more. You are curious. More successful you 979 00:56:43,840 --> 00:56:46,879 Speaker 3: are because you're open minded and you're always willing to learn. 980 00:56:46,880 --> 00:56:49,040 Speaker 3: And that has been my principle all my life. Always 981 00:56:49,120 --> 00:56:52,680 Speaker 3: learned something new about anything, and you feel very fulfilled. 982 00:56:52,960 --> 00:56:56,279 Speaker 2: Huh really really terrific. The mouse thank you so much 983 00:56:56,280 --> 00:56:59,640 Speaker 2: for thanking generous with your time. We have been speaking 984 00:56:59,680 --> 00:57:04,120 Speaker 2: with them. Kapor, CEO and chairman of Honeywell. If you 985 00:57:04,320 --> 00:57:07,359 Speaker 2: enjoy this conversation, well be sure and check out any 986 00:57:07,400 --> 00:57:11,279 Speaker 2: of the previous six hundred and thirty seven we've done 987 00:57:11,880 --> 00:57:15,120 Speaker 2: over the past twelve and a half years. You can 988 00:57:15,120 --> 00:57:21,160 Speaker 2: find those at Bloomberg, iTunes, Spotify, YouTube, or wherever you 989 00:57:21,280 --> 00:57:24,720 Speaker 2: find your favorite podcasts. I would be remiss if I 990 00:57:24,760 --> 00:57:27,880 Speaker 2: didn't thank the correct team that helps put these conversations together. 991 00:57:28,480 --> 00:57:32,120 Speaker 2: Amongst the many people who help me Alexis Noriega is 992 00:57:32,200 --> 00:57:37,080 Speaker 2: my video producer. Sean Russo is my researcher. Anna Luke 993 00:57:37,200 --> 00:57:41,400 Speaker 2: is my producer. I'm Barry Results. You've been listening to 994 00:57:41,560 --> 00:57:44,960 Speaker 2: Masters in Business on Bloomberg Radio.