1 00:00:02,759 --> 00:00:06,760 Speaker 1: Welcome to The Restless Ones. I'm Jonathan Strickland. I've spent 2 00:00:06,800 --> 00:00:10,280 Speaker 1: the last twelve years covering technology and learning how it works, 3 00:00:10,600 --> 00:00:16,079 Speaker 1: demystifying everything from massive parallel processing to advanced robotics and 4 00:00:16,160 --> 00:00:19,319 Speaker 1: everything in between. As we stand at the beginning of 5 00:00:19,360 --> 00:00:23,119 Speaker 1: a new era of unprecedented connectivity with the rollout of 6 00:00:23,200 --> 00:00:27,280 Speaker 1: five G technology, I'm partnering with T Mobile for Business 7 00:00:27,520 --> 00:00:30,400 Speaker 1: to sit down with some of the visionary leaders in 8 00:00:30,520 --> 00:00:34,640 Speaker 1: technology across all industries to get a better understanding of 9 00:00:34,680 --> 00:00:39,560 Speaker 1: how tech and connectivity will change business forever. These leaders 10 00:00:39,600 --> 00:00:43,920 Speaker 1: are the pioneers who don't follow trends. They define them. 11 00:00:43,960 --> 00:00:48,040 Speaker 1: This show is their story. They are the Restless Ones. 12 00:00:56,640 --> 00:01:00,560 Speaker 1: I'm excited for five G to come on mainstream, and 13 00:01:00,560 --> 00:01:04,120 Speaker 1: I'll tell you why. It's because our world is becoming 14 00:01:04,240 --> 00:01:08,200 Speaker 1: a collection of sensors. Um you know, whether we subscribe 15 00:01:08,200 --> 00:01:11,280 Speaker 1: to the monotics in material over time, we're gonna be 16 00:01:11,280 --> 00:01:14,360 Speaker 1: surrounded by censors and will be also part of that 17 00:01:14,440 --> 00:01:19,520 Speaker 1: sensor community. For the first episode in this series, we 18 00:01:19,600 --> 00:01:23,400 Speaker 1: couldn't have picked a better guest than Robbie sim Humbatla, 19 00:01:23,720 --> 00:01:27,640 Speaker 1: chief technology officer at United Airlines. Robbie has a long 20 00:01:27,800 --> 00:01:31,240 Speaker 1: background in technology and leading teams to come up with 21 00:01:31,280 --> 00:01:35,720 Speaker 1: innovative solutions to truly challenging problems. He was employee fifty 22 00:01:35,720 --> 00:01:38,240 Speaker 1: three with Virgin Airlines, where he helped create a new 23 00:01:38,319 --> 00:01:42,080 Speaker 1: airline that could offer services that more entrenched companies couldn't 24 00:01:42,120 --> 00:01:45,000 Speaker 1: at the time. He served for three years as CTO 25 00:01:45,120 --> 00:01:48,800 Speaker 1: for air LINGUS before joining United Airlines, where he continues 26 00:01:48,840 --> 00:01:51,800 Speaker 1: to chart a course that takes advantage of the latest 27 00:01:51,840 --> 00:01:55,520 Speaker 1: technologies in ways that impact all areas of the business, 28 00:01:55,560 --> 00:01:59,760 Speaker 1: from back end operations to customer experiences. I started by 29 00:01:59,800 --> 00:02:03,280 Speaker 1: a king Robbie if there were any particular technological solutions 30 00:02:03,320 --> 00:02:05,560 Speaker 1: he could point to as an example of how his 31 00:02:05,680 --> 00:02:08,640 Speaker 1: vision and leadership came into play in overcoming a challenge. 32 00:02:09,080 --> 00:02:11,320 Speaker 1: Here's what he had to say. My vision is only 33 00:02:11,360 --> 00:02:13,960 Speaker 1: as good as the team that works with me. UM, 34 00:02:14,280 --> 00:02:17,320 Speaker 1: So I think in one of the biggest problems for 35 00:02:17,400 --> 00:02:20,799 Speaker 1: an airline, so I'll talk about my immediate context is 36 00:02:20,840 --> 00:02:25,520 Speaker 1: something called revenue management. And in a nutshell, it is 37 00:02:25,520 --> 00:02:29,280 Speaker 1: a science of when should we fill up our planes 38 00:02:30,200 --> 00:02:33,440 Speaker 1: with people at what price points? UM, And one of 39 00:02:33,440 --> 00:02:36,639 Speaker 1: the big challenges we had about three years ago was 40 00:02:36,800 --> 00:02:42,680 Speaker 1: that our existing revenue management system was producing a questionable forecast, 41 00:02:43,320 --> 00:02:47,400 Speaker 1: which was forcing our teammates to actually really smart women 42 00:02:47,400 --> 00:02:51,400 Speaker 1: and men instead of working on optimizing the forecast the 43 00:02:51,560 --> 00:02:55,160 Speaker 1: work and correcting the forecast UM. And if you do that, 44 00:02:55,280 --> 00:02:56,880 Speaker 1: you spend a lot of you know, a lot of 45 00:02:56,960 --> 00:02:59,520 Speaker 1: your energy and calories and doing things that don't really 46 00:02:59,560 --> 00:03:02,760 Speaker 1: add you UM. So the last three years, my team, 47 00:03:03,360 --> 00:03:07,239 Speaker 1: along with our revenue management experts, we built a brand 48 00:03:07,240 --> 00:03:11,160 Speaker 1: new system called Gemini UM which does what we call 49 00:03:11,200 --> 00:03:15,520 Speaker 1: conditional demand forecasting. It has reduced our incidents of errors 50 00:03:15,600 --> 00:03:20,080 Speaker 1: against forecasts of bias uh dramatically enough that over the 51 00:03:20,160 --> 00:03:23,760 Speaker 1: last two years it has added over six eighty million 52 00:03:23,800 --> 00:03:27,120 Speaker 1: dollars of revenue value back to the company. That is 53 00:03:27,240 --> 00:03:29,880 Speaker 1: huge on the one hand. On the other hand, it 54 00:03:29,960 --> 00:03:34,720 Speaker 1: gives our analysts are really smart people UM, the opportunity 55 00:03:34,760 --> 00:03:38,320 Speaker 1: in the space to actually analyze bigger, deeper problems and 56 00:03:38,360 --> 00:03:40,960 Speaker 1: they can be more future looking. So we're very proud 57 00:03:41,000 --> 00:03:45,240 Speaker 1: of that. Again, it's I would say, it's not my 58 00:03:45,440 --> 00:03:48,080 Speaker 1: vision per se, but it takes it takes a really 59 00:03:48,160 --> 00:03:51,560 Speaker 1: really strong team to bring these things to bear, and 60 00:03:51,640 --> 00:03:54,280 Speaker 1: we did that. That's a that's a great example, and 61 00:03:54,480 --> 00:03:58,080 Speaker 1: I love that you also you make that relevant to 62 00:03:58,120 --> 00:04:01,680 Speaker 1: the idea of freeing up aratunity to tackle bigger problems 63 00:04:01,680 --> 00:04:04,040 Speaker 1: and even using the information that you get through one 64 00:04:04,160 --> 00:04:07,560 Speaker 1: project to inform you for those bigger problems. In the 65 00:04:07,600 --> 00:04:10,400 Speaker 1: world of bigger data we used to say big data, 66 00:04:10,440 --> 00:04:12,720 Speaker 1: I think it's even bigger than it was then. Uh. 67 00:04:12,760 --> 00:04:15,160 Speaker 1: And in the world of things like machine learning, where 68 00:04:15,160 --> 00:04:18,320 Speaker 1: we're able to pair these together in order to understand 69 00:04:18,560 --> 00:04:24,400 Speaker 1: underlying trends and underlying factors that determine different things within 70 00:04:24,480 --> 00:04:28,479 Speaker 1: business that maybe we're not obvious before because we just 71 00:04:28,760 --> 00:04:31,400 Speaker 1: the the information is too big, we can't see the patterns. 72 00:04:31,880 --> 00:04:34,760 Speaker 1: I love what you're saying because it's uh, it's exactly 73 00:04:34,800 --> 00:04:37,200 Speaker 1: how I feel as well. And you know, in fact, um, 74 00:04:37,360 --> 00:04:39,240 Speaker 1: I was talking to some folks about you know and 75 00:04:39,520 --> 00:04:43,400 Speaker 1: uh and AI and you know, and the base benefits 76 00:04:43,400 --> 00:04:46,760 Speaker 1: that I see these uh uh these technologies actually these 77 00:04:46,760 --> 00:04:48,799 Speaker 1: I would say, these thought processes bring to the table. 78 00:04:49,440 --> 00:04:52,120 Speaker 1: And the one thing is this imagine so um uh 79 00:04:52,440 --> 00:04:54,880 Speaker 1: Imagine if I have a process that takes a week 80 00:04:54,920 --> 00:04:59,000 Speaker 1: to complete end to end, um that is seven days, 81 00:04:59,080 --> 00:05:02,279 Speaker 1: seven multiplied, you know, twenty four hours a lot of 82 00:05:02,320 --> 00:05:05,080 Speaker 1: hours going to that. Imagine if I were able to 83 00:05:05,839 --> 00:05:11,279 Speaker 1: achieve that, Uh, in an hour, I've certainly saved, you know, 84 00:05:11,600 --> 00:05:16,039 Speaker 1: almost all of seven days of thinking and work. It 85 00:05:16,120 --> 00:05:17,920 Speaker 1: almost gives me. So if I'm able to do that, 86 00:05:18,080 --> 00:05:20,200 Speaker 1: if I'm able to compress time, that's the one thing 87 00:05:20,200 --> 00:05:22,520 Speaker 1: we do not have any control over, its time. If 88 00:05:22,560 --> 00:05:27,599 Speaker 1: I'm able to even virtually compressed time, I have some 89 00:05:27,800 --> 00:05:30,800 Speaker 1: sort of a crystal ball that can see into the future, um, 90 00:05:31,000 --> 00:05:34,440 Speaker 1: which is I've saved almost seven days of my time. 91 00:05:34,640 --> 00:05:37,640 Speaker 1: I'm able to drive what the future may hold. And 92 00:05:37,680 --> 00:05:40,000 Speaker 1: I think that's that's what gets me really excited about 93 00:05:40,040 --> 00:05:44,000 Speaker 1: these texts. Um, just the ability to get decision ng 94 00:05:44,240 --> 00:05:48,000 Speaker 1: done faster, but testing and learning done faster. So you know, 95 00:05:48,040 --> 00:05:50,599 Speaker 1: if you're gonna make mistakes making faster and learn faster, 96 00:05:51,120 --> 00:05:54,799 Speaker 1: that is going to be incredibly exciting. Robbie, the airline 97 00:05:54,839 --> 00:05:58,479 Speaker 1: industry obviously very tech heavy industry, both back end and 98 00:05:58,600 --> 00:06:02,720 Speaker 1: front end. Can you talk about some of the challenges 99 00:06:02,800 --> 00:06:06,520 Speaker 1: that the industry faces and that United particularly has taken 100 00:06:06,560 --> 00:06:10,280 Speaker 1: great strides towards solving these various challenges. Yeah, I mean 101 00:06:10,320 --> 00:06:13,040 Speaker 1: one of the coolest things we did recently is uh 102 00:06:13,240 --> 00:06:16,560 Speaker 1: something called Connections Saver, So United is a is a 103 00:06:16,640 --> 00:06:20,600 Speaker 1: very connections heavy airline. We have a very deep domestic 104 00:06:20,680 --> 00:06:24,040 Speaker 1: network UM and so folks flyers from cd A to 105 00:06:24,120 --> 00:06:28,039 Speaker 1: CDB to get the CDC and the connections UM. And 106 00:06:28,160 --> 00:06:30,599 Speaker 1: because this is such a large country, many a time, 107 00:06:31,040 --> 00:06:34,919 Speaker 1: a delay anyway up and down the line can result 108 00:06:35,080 --> 00:06:38,520 Speaker 1: in a customer or more customers missing out new connections 109 00:06:38,680 --> 00:06:45,640 Speaker 1: or their time between connections getting hypercompressed UM. And what 110 00:06:45,800 --> 00:06:48,680 Speaker 1: that does is it just creates poor customer experiences, but 111 00:06:48,760 --> 00:06:51,479 Speaker 1: also puts a lot of pressure on our frontline teammates 112 00:06:51,480 --> 00:06:54,320 Speaker 1: at the airports to recover from those situations. So we 113 00:06:54,440 --> 00:06:57,560 Speaker 1: launched this program for Connection Saver. It's a set of 114 00:06:57,600 --> 00:07:01,280 Speaker 1: algorithms that looks at and coming lights UH, looks at 115 00:07:01,320 --> 00:07:04,680 Speaker 1: how much UM, how much time is left for customers 116 00:07:04,680 --> 00:07:07,840 Speaker 1: on let's a flight from Atlanta to Chicago, under to 117 00:07:07,880 --> 00:07:09,920 Speaker 1: San Francisco. How much time do they have do they 118 00:07:09,960 --> 00:07:13,520 Speaker 1: have left to make the connection UM? And in some 119 00:07:13,600 --> 00:07:16,000 Speaker 1: cases what we will do is we will actually hold 120 00:07:16,120 --> 00:07:19,720 Speaker 1: the next flight for a few minutes for the customers 121 00:07:19,720 --> 00:07:23,640 Speaker 1: to actually make that connection UM. This is kind of 122 00:07:23,720 --> 00:07:27,080 Speaker 1: unheard off in the industry because the industry was always 123 00:07:27,160 --> 00:07:29,840 Speaker 1: keyed around. We've got a department Time we have something 124 00:07:29,840 --> 00:07:33,640 Speaker 1: called dzero like departure within zero minutes of your schedule, um. 125 00:07:33,800 --> 00:07:37,520 Speaker 1: And you can imagine, uh, for a customer who is 126 00:07:38,240 --> 00:07:41,640 Speaker 1: trying very hard and she wants to get to her destination. 127 00:07:42,640 --> 00:07:45,600 Speaker 1: If this is how we measure ourselves, then it's kind 128 00:07:45,600 --> 00:07:47,560 Speaker 1: of a wrong way to do it. So the company, 129 00:07:48,160 --> 00:07:52,280 Speaker 1: you know, between airport operations and you know, and technology, 130 00:07:52,920 --> 00:07:57,360 Speaker 1: this this program is designed to help customers make those connections. 131 00:07:57,440 --> 00:08:00,920 Speaker 1: And thus far we've actually saved over fifth the thousands 132 00:08:01,520 --> 00:08:04,200 Speaker 1: connections and this was launched I guess the middle of 133 00:08:04,200 --> 00:08:07,120 Speaker 1: the year, um, and that's a huge, huge difference to 134 00:08:07,160 --> 00:08:09,760 Speaker 1: our customers. It makes you know, we might save on 135 00:08:09,880 --> 00:08:12,800 Speaker 1: the D zero, uh, you know, we might not save 136 00:08:12,840 --> 00:08:14,960 Speaker 1: in the D zero, but we actually make the connection 137 00:08:15,000 --> 00:08:17,800 Speaker 1: for the customer and we complete their journey. Yeah. So 138 00:08:17,920 --> 00:08:20,239 Speaker 1: in this case, you might argue that this is a 139 00:08:20,280 --> 00:08:23,880 Speaker 1: place where the wrong metric was being held up as 140 00:08:23,880 --> 00:08:27,440 Speaker 1: being the most important, at least for the experience of 141 00:08:27,480 --> 00:08:29,880 Speaker 1: the customer who has that connection to make. I don't 142 00:08:29,880 --> 00:08:32,760 Speaker 1: think that they would necessarily feel better if the team 143 00:08:32,800 --> 00:08:35,000 Speaker 1: were to say, well, yeah, you missed your flight, but 144 00:08:35,440 --> 00:08:38,200 Speaker 1: on the flip side, that that departing flight left on time. 145 00:08:38,720 --> 00:08:41,280 Speaker 1: It's not not a big comfort, but yeah, yeah, it's 146 00:08:41,440 --> 00:08:44,120 Speaker 1: to me, it's an outstanding example. And I'm going to 147 00:08:44,200 --> 00:08:47,640 Speaker 1: brag a little bit about United where uh, you know, 148 00:08:48,640 --> 00:08:53,120 Speaker 1: folks throughout a company are thinking and very innovative and 149 00:08:53,320 --> 00:08:56,120 Speaker 1: you know, I wouldn't say it radical, but really really 150 00:08:56,160 --> 00:08:59,000 Speaker 1: innovative and you know, deep thinking on how we can 151 00:08:59,040 --> 00:09:02,160 Speaker 1: improve cause experience and what are the Because we have 152 00:09:02,160 --> 00:09:04,320 Speaker 1: such a big airline, we carry hunh sixteen million people 153 00:09:04,360 --> 00:09:08,040 Speaker 1: every year, um, so these small things make a big, 154 00:09:08,080 --> 00:09:11,040 Speaker 1: big difference, you know. So it's not just a technology, 155 00:09:11,080 --> 00:09:15,960 Speaker 1: it's how our teammates have started thinking about how we 156 00:09:16,000 --> 00:09:19,080 Speaker 1: can do better. So it's it's it's a very prideful 157 00:09:19,120 --> 00:09:23,000 Speaker 1: thing for us. And as I expect, as airline keeps growing, 158 00:09:23,360 --> 00:09:26,040 Speaker 1: connection sables going to become more and more and more 159 00:09:26,120 --> 00:09:29,959 Speaker 1: important to United and to our customers well. And and 160 00:09:30,240 --> 00:09:34,720 Speaker 1: another challenge I think that we're seeing throughout all industries 161 00:09:34,800 --> 00:09:37,280 Speaker 1: right now when it comes to technology is how do 162 00:09:37,320 --> 00:09:41,040 Speaker 1: you decide which technologies to adopt and win to implement 163 00:09:41,200 --> 00:09:44,640 Speaker 1: and how do you how do you evaluate the technologies 164 00:09:44,679 --> 00:09:48,360 Speaker 1: that represent true value versus those that might be more 165 00:09:49,000 --> 00:09:53,480 Speaker 1: buzzword ish? How do you go about evaluating these different 166 00:09:53,520 --> 00:09:58,120 Speaker 1: technologies and deciding sort of which ones are worth the 167 00:09:58,240 --> 00:10:01,040 Speaker 1: risk of investing in versus which ones you might want 168 00:10:01,080 --> 00:10:06,560 Speaker 1: to wait and see if there's actually a true implementation, right. UM, 169 00:10:06,640 --> 00:10:11,320 Speaker 1: So I think my north star has always been enhancing 170 00:10:11,360 --> 00:10:17,120 Speaker 1: customer experience and enhancing employee experience and returning to the shareholders. So, UM, 171 00:10:17,160 --> 00:10:18,720 Speaker 1: there is a lot of technology, there's a lot of 172 00:10:18,800 --> 00:10:22,760 Speaker 1: I would say, noise in the system. UM. But even 173 00:10:22,760 --> 00:10:26,240 Speaker 1: in that noise, there these you know, nuances that we 174 00:10:26,240 --> 00:10:28,760 Speaker 1: can pick up on, and you know, we can find 175 00:10:28,960 --> 00:10:32,559 Speaker 1: value and tech that light. Uh eight in one of 176 00:10:32,800 --> 00:10:34,560 Speaker 1: one or all of these three areas which are just 177 00:10:34,600 --> 00:10:37,400 Speaker 1: mentioned is customer experience, employee experience, and uh you know, 178 00:10:37,440 --> 00:10:42,960 Speaker 1: shareholder value. And for us, it is creating those those vehicles, 179 00:10:43,400 --> 00:10:48,200 Speaker 1: you know, enabling business, enabling experiences that matter the most 180 00:10:48,200 --> 00:10:49,800 Speaker 1: for us. UM. At the end of the day, it 181 00:10:49,840 --> 00:10:52,720 Speaker 1: has to it has to provide value back, right And 182 00:10:53,240 --> 00:10:56,319 Speaker 1: in some cases we see monetary value. But for an airline, 183 00:10:56,400 --> 00:10:58,960 Speaker 1: one of the biggest value driver's customer satisfaction and of 184 00:10:59,000 --> 00:11:01,800 Speaker 1: course employees hades action. So is what we're doing going 185 00:11:01,840 --> 00:11:05,719 Speaker 1: to provide value for that? Um? Certainly you know economic 186 00:11:05,840 --> 00:11:08,320 Speaker 1: value is very very important to us as well. All 187 00:11:08,360 --> 00:11:10,120 Speaker 1: that being said, you know, so I just talked about 188 00:11:10,200 --> 00:11:13,640 Speaker 1: the traditional business case model. All that being said, we 189 00:11:13,679 --> 00:11:17,600 Speaker 1: do need to leave room for innovation where in it 190 00:11:17,760 --> 00:11:19,960 Speaker 1: is okay to play around with a little bit of 191 00:11:19,960 --> 00:11:23,600 Speaker 1: the futuristic tech and bring it back into the folds 192 00:11:23,640 --> 00:11:26,520 Speaker 1: of like i'd say, corporate Americans, see how that might 193 00:11:26,640 --> 00:11:28,760 Speaker 1: fit in. Yeah, And and one of the things I 194 00:11:28,800 --> 00:11:31,480 Speaker 1: wanted to talk about is, you know, the technology that 195 00:11:31,600 --> 00:11:34,160 Speaker 1: enables these things to happen. I think of connectivity as 196 00:11:34,200 --> 00:11:36,040 Speaker 1: being sort of the backbone for a lot of these 197 00:11:36,040 --> 00:11:39,120 Speaker 1: individual pieces of technology, because it's how they communicate with 198 00:11:39,160 --> 00:11:42,040 Speaker 1: each other, how they communicate potentially with an end user, 199 00:11:42,080 --> 00:11:46,640 Speaker 1: whether that's a customer or an employee. In this world, 200 00:11:46,760 --> 00:11:50,319 Speaker 1: as we're seeing of evolving connectivity, with faster connectivity and 201 00:11:50,760 --> 00:11:55,079 Speaker 1: more options along those lines, do you see that as 202 00:11:55,080 --> 00:12:00,120 Speaker 1: a huge opportunity as well. Absolutely. Uh. You know, I'm 203 00:12:00,160 --> 00:12:03,679 Speaker 1: excited for five G to come on mainstream, and I'll 204 00:12:03,679 --> 00:12:07,320 Speaker 1: tell you why. It's because our world is becoming a 205 00:12:07,360 --> 00:12:11,240 Speaker 1: collection of sensors um you know, whether we subscribe to 206 00:12:11,280 --> 00:12:14,160 Speaker 1: them or not. As in material over time, we're gonna 207 00:12:14,160 --> 00:12:17,240 Speaker 1: be surrounded by sensors and will be also part of 208 00:12:17,240 --> 00:12:22,880 Speaker 1: that sensor community. UM. As this the world becomes bigger 209 00:12:22,920 --> 00:12:27,600 Speaker 1: and bigger, the traditional h I would say capabilities of 210 00:12:27,720 --> 00:12:31,559 Speaker 1: networks simply will not be able to sustain the kind 211 00:12:31,600 --> 00:12:34,959 Speaker 1: of workload that they're being expected to write. So pushing 212 00:12:35,000 --> 00:12:38,080 Speaker 1: out compute mode of the edge, pushing out connectivity mode 213 00:12:38,080 --> 00:12:40,640 Speaker 1: to the edge. UM, I think it's going to be. 214 00:12:41,280 --> 00:12:42,920 Speaker 1: In my opinion, it is going to be a very 215 00:12:43,040 --> 00:12:49,199 Speaker 1: very big UH innovation created an animation driver and I'm 216 00:12:49,320 --> 00:12:52,040 Speaker 1: off the belief right now. You know, unless stuff changes, 217 00:12:52,120 --> 00:12:54,880 Speaker 1: that five G is going to enable that on the edges. 218 00:12:55,600 --> 00:12:57,760 Speaker 1: And that's the one place where today we you know, 219 00:12:57,920 --> 00:13:01,959 Speaker 1: whether you're an airport, or you're stadium, or you're walking 220 00:13:01,960 --> 00:13:05,600 Speaker 1: in them all or wherever you might be. UM being 221 00:13:05,640 --> 00:13:09,520 Speaker 1: able to connect at hyper fast speeds, having you know, 222 00:13:10,160 --> 00:13:13,840 Speaker 1: gods and gods of bandwidth is going to completely change, 223 00:13:14,360 --> 00:13:16,240 Speaker 1: you know, how we think about the solutions you put 224 00:13:16,240 --> 00:13:20,680 Speaker 1: in front of people. Airlines expanding, United expanding, which means 225 00:13:20,880 --> 00:13:23,800 Speaker 1: we have more capacity, we have more customers flying the systems. 226 00:13:23,800 --> 00:13:25,840 Speaker 1: You know, you can see the airports, they're more and 227 00:13:25,880 --> 00:13:29,800 Speaker 1: more people flying. UM, it's it's it's such an enablor 228 00:13:29,960 --> 00:13:35,079 Speaker 1: for commerce UM and and I think it's it's enabling 229 00:13:35,080 --> 00:13:40,439 Speaker 1: customer experiences is great. But moving computing the edge and 230 00:13:40,480 --> 00:13:44,120 Speaker 1: the speed of computing, the speed of data UM also 231 00:13:44,200 --> 00:13:47,480 Speaker 1: greatly helps our frontline teammates who actually are in the 232 00:13:47,559 --> 00:13:51,520 Speaker 1: business of helping customers have great experiences. So it has 233 00:13:52,160 --> 00:13:55,000 Speaker 1: it has benefits on both sides of the coin, which 234 00:13:55,040 --> 00:13:58,160 Speaker 1: is what makes it such a such a a nice 235 00:13:58,200 --> 00:14:00,600 Speaker 1: thing to look forward to. Another should have a hand 236 00:14:00,600 --> 00:14:03,120 Speaker 1: for you though. Was we had mentioned earlier about machine 237 00:14:03,200 --> 00:14:06,120 Speaker 1: learning and artificial intelligence. Do you have any sort of 238 00:14:06,640 --> 00:14:09,600 Speaker 1: dream visions of what that kind of implementation might be 239 00:14:09,600 --> 00:14:12,200 Speaker 1: in the future. Yeah. Absolutely, Today if we already do 240 00:14:12,320 --> 00:14:17,000 Speaker 1: a fair bit of MLU to create personalization, certainly it 241 00:14:17,000 --> 00:14:19,960 Speaker 1: could be a lot deeper. But yeah, my vision is 242 00:14:20,000 --> 00:14:25,880 Speaker 1: to give incredibly deep I would say, personal experiences to 243 00:14:25,960 --> 00:14:28,840 Speaker 1: consumers because you know, um, you know, in a few 244 00:14:28,920 --> 00:14:32,720 Speaker 1: years ago, the industry thought that, you know, customers have 245 00:14:32,840 --> 00:14:35,720 Speaker 1: maybe eight or nine personas any you know, that they 246 00:14:35,800 --> 00:14:40,360 Speaker 1: might carry when they're traveling. UM. My belief is, and 247 00:14:40,440 --> 00:14:42,560 Speaker 1: I might be wrong, but I think I might be 248 00:14:42,600 --> 00:14:45,400 Speaker 1: more on the right side of this, is that consumers 249 00:14:45,560 --> 00:14:49,080 Speaker 1: can have multitudes of personas depending upon where they are 250 00:14:49,120 --> 00:14:52,680 Speaker 1: in their travel experience, UM, what they're going through in 251 00:14:52,680 --> 00:14:55,360 Speaker 1: that particular travel experience, what they're expecting at the end 252 00:14:55,360 --> 00:14:59,640 Speaker 1: of it. Um. So to be able to grab all 253 00:14:59,760 --> 00:15:04,600 Speaker 1: the that interaction data from from our customers and in 254 00:15:04,640 --> 00:15:09,000 Speaker 1: every subsequent interaction back with them using what we already 255 00:15:09,080 --> 00:15:12,560 Speaker 1: know about them to create better experiences is to me 256 00:15:13,280 --> 00:15:17,160 Speaker 1: the holy grail. Because you make the experience great, they 257 00:15:17,200 --> 00:15:19,400 Speaker 1: want to come back, uh, and they want to fly 258 00:15:19,520 --> 00:15:22,600 Speaker 1: you because they feel that you know them and they 259 00:15:22,600 --> 00:15:25,760 Speaker 1: come back. Well, that's that creates commerce, right, that's great 260 00:15:25,760 --> 00:15:28,680 Speaker 1: for business. Um So, I think the deep personalization is 261 00:15:28,720 --> 00:15:31,600 Speaker 1: definitely you know, the aviation industry has talked a lot 262 00:15:31,640 --> 00:15:35,600 Speaker 1: about it. United is I would say pretty forward. Uh. 263 00:15:35,720 --> 00:15:38,000 Speaker 1: We we do a pretty good job and we have 264 00:15:38,120 --> 00:15:40,560 Speaker 1: some very solid plans going into the next three or 265 00:15:40,560 --> 00:15:44,680 Speaker 1: four years to create deep personalization, you know, enable experiences 266 00:15:44,680 --> 00:15:47,800 Speaker 1: for our customers. We started that with on a mobile app. 267 00:15:48,680 --> 00:15:52,120 Speaker 1: We have you know, gamification that kind of uh you know, 268 00:15:52,720 --> 00:15:56,040 Speaker 1: nudges are members to interact with us more so they 269 00:15:56,040 --> 00:15:58,960 Speaker 1: get rewards, but they also also get great benefits back 270 00:15:58,960 --> 00:16:01,880 Speaker 1: from United and US all based on the machine learning. 271 00:16:01,960 --> 00:16:05,360 Speaker 1: So that's incredible. We're gonna take a quick break with 272 00:16:05,400 --> 00:16:13,920 Speaker 1: our conversation with Robb, but we'll be right back. You 273 00:16:13,960 --> 00:16:17,360 Speaker 1: know who you are, A boundary pusher, a big thinker 274 00:16:17,400 --> 00:16:20,280 Speaker 1: in the relentless pursuit of the next big innovation for 275 00:16:20,320 --> 00:16:23,680 Speaker 1: your business. T Mobile for Business knows that the future 276 00:16:23,720 --> 00:16:26,960 Speaker 1: demands true workforce mobility, and in the new era of 277 00:16:27,000 --> 00:16:29,360 Speaker 1: five G being able to assess the needs of your 278 00:16:29,360 --> 00:16:33,080 Speaker 1: company in real time could transform everyday functions. The five 279 00:16:33,120 --> 00:16:36,200 Speaker 1: G revolution has begun and the future of businesses like 280 00:16:36,240 --> 00:16:39,440 Speaker 1: yours will be powered by advancements in five gene networks 281 00:16:39,680 --> 00:16:43,040 Speaker 1: built to reach more people in more places without slowing 282 00:16:43,080 --> 00:16:45,920 Speaker 1: you down. T Mobile for Business can help you realize 283 00:16:45,960 --> 00:16:49,479 Speaker 1: the full potential of your business as five G unfolds, 284 00:16:49,880 --> 00:16:53,240 Speaker 1: business is changing. Learn more at t mobile for Business 285 00:16:53,320 --> 00:16:56,960 Speaker 1: dot com. The future is closer than you think, and 286 00:16:57,040 --> 00:16:59,800 Speaker 1: it all starts in the palm of your hand. You've 287 00:16:59,800 --> 00:17:03,160 Speaker 1: heard the news five G is here, but what does 288 00:17:03,240 --> 00:17:06,760 Speaker 1: that really mean? How will it impact you? In this 289 00:17:06,840 --> 00:17:10,040 Speaker 1: I Heart series This Time Tomorrow, presented by T Mobile 290 00:17:10,080 --> 00:17:13,879 Speaker 1: for Business, join hosts os Volition and Cara Price as 291 00:17:13,960 --> 00:17:16,880 Speaker 1: they walk us through a mobile revolution that will start 292 00:17:16,920 --> 00:17:19,600 Speaker 1: to change the future of business and the way we 293 00:17:19,640 --> 00:17:23,000 Speaker 1: interact with the world around us, from environmental science to 294 00:17:23,080 --> 00:17:29,160 Speaker 1: law enforcement, entertainment, healthcare, and travel innovation is coming. Join 295 00:17:29,280 --> 00:17:31,840 Speaker 1: them as they explore how this revolution could impact your 296 00:17:31,920 --> 00:17:34,920 Speaker 1: life and give you new ways to connect and engage. 297 00:17:35,440 --> 00:17:37,920 Speaker 1: This Time Tomorrow is now available on the I Heart 298 00:17:38,000 --> 00:17:51,960 Speaker 1: Radio app or wherever you listen to podcasts. One other 299 00:17:52,000 --> 00:17:55,919 Speaker 1: thing I wanted to mention or get into is, uh, 300 00:17:56,359 --> 00:17:58,840 Speaker 1: the airline industry in particular is one of those where 301 00:17:58,840 --> 00:18:01,760 Speaker 1: you have so many different systems that all have to 302 00:18:01,800 --> 00:18:07,800 Speaker 1: work together in order for things to to actually work overall. Right, 303 00:18:07,880 --> 00:18:09,880 Speaker 1: So can you talk a bit about that challenge. I'm 304 00:18:09,880 --> 00:18:13,280 Speaker 1: assuming you have like a really deep test environment where 305 00:18:13,320 --> 00:18:15,600 Speaker 1: you go through lots of testing in order to make 306 00:18:15,640 --> 00:18:19,000 Speaker 1: sure that all these changes you're putting in that they 307 00:18:19,000 --> 00:18:22,200 Speaker 1: work on their own and that they're not breaking anything else. Yeah, 308 00:18:22,200 --> 00:18:25,879 Speaker 1: I mean are so our rigor is very deep and 309 00:18:26,000 --> 00:18:30,840 Speaker 1: very wide. So you know, we have excellent engineering um 310 00:18:31,080 --> 00:18:35,639 Speaker 1: skills here. We also have excellent testing skills here. But 311 00:18:35,680 --> 00:18:39,640 Speaker 1: what's most important, we have a very strong governance around 312 00:18:39,760 --> 00:18:43,240 Speaker 1: change management and recoverability, you know. So for us, we 313 00:18:43,480 --> 00:18:47,240 Speaker 1: know because these are computing systems, they will break. So 314 00:18:48,080 --> 00:18:51,199 Speaker 1: preventing the breakage is very very important, but meantime to 315 00:18:51,320 --> 00:18:54,440 Speaker 1: recovery is far more important. Right. So when you're when 316 00:18:54,440 --> 00:18:57,800 Speaker 1: you're in a live environment and a system goes down 317 00:18:57,960 --> 00:19:00,600 Speaker 1: or a component of a system goes down, it is 318 00:19:00,640 --> 00:19:05,560 Speaker 1: affecting customers and our employees somewhere, and so for us, 319 00:19:05,600 --> 00:19:09,639 Speaker 1: recovering that is very very key for us. But respect 320 00:19:09,680 --> 00:19:12,080 Speaker 1: to testing, you know, most of our digital production now 321 00:19:12,119 --> 00:19:16,280 Speaker 1: we we have adopted whole higdedly the agile methodology, and 322 00:19:16,400 --> 00:19:20,520 Speaker 1: our quality engineers are actually embedded in our paths. So 323 00:19:20,600 --> 00:19:23,239 Speaker 1: as we move forward in time, what is happening is 324 00:19:23,320 --> 00:19:25,879 Speaker 1: the code that we built out is getting better and 325 00:19:25,920 --> 00:19:27,760 Speaker 1: better in terms of quality, not just in terms of 326 00:19:27,800 --> 00:19:30,960 Speaker 1: code quality, but also in terms of security. UM. So yeah, 327 00:19:30,960 --> 00:19:32,400 Speaker 1: it's firm me, I mean it. We take it very 328 00:19:32,400 --> 00:19:37,280 Speaker 1: seriously because you know, I uh, Sunday after Thanksgiving we 329 00:19:37,400 --> 00:19:41,400 Speaker 1: carried about five eight thousand customers across the system and 330 00:19:41,480 --> 00:19:44,320 Speaker 1: can you imagine if you did not have reliable systems 331 00:19:44,800 --> 00:19:47,320 Speaker 1: what they would go through? Um, And so for us, 332 00:19:47,400 --> 00:19:49,959 Speaker 1: we we it's very near and dear to us. In 333 00:19:50,000 --> 00:19:52,720 Speaker 1: the last three years, UM, I think we've done a 334 00:19:52,720 --> 00:19:56,800 Speaker 1: fantastic job here to kind of greatly reduced um, you know, 335 00:19:57,320 --> 00:20:01,080 Speaker 1: giving examples, We've reduced our you know, tech related flight 336 00:20:01,080 --> 00:20:05,720 Speaker 1: delays by which is huge. And so we we actually 337 00:20:05,840 --> 00:20:10,840 Speaker 1: record every impact every flight because of technology, and those 338 00:20:10,840 --> 00:20:12,960 Speaker 1: are our KPI s that we we we drive back 339 00:20:12,960 --> 00:20:15,440 Speaker 1: into the team um to ensure that we're getting better 340 00:20:15,480 --> 00:20:19,280 Speaker 1: every day. That's incredible and and I love that. We 341 00:20:19,920 --> 00:20:24,000 Speaker 1: also got into another big challenge that leaders in tech face, 342 00:20:24,040 --> 00:20:28,080 Speaker 1: which is that that concept of change management versus the 343 00:20:28,320 --> 00:20:30,960 Speaker 1: that desire that I think is deep within any techie 344 00:20:31,359 --> 00:20:34,679 Speaker 1: to really explore the new stuff. I think at the 345 00:20:34,720 --> 00:20:40,400 Speaker 1: heart of every real technologically oriented person is that that 346 00:20:40,920 --> 00:20:43,679 Speaker 1: hacker mentality of I want to learn how this works 347 00:20:44,160 --> 00:20:45,600 Speaker 1: and I want to learn what I can do with 348 00:20:45,640 --> 00:20:48,679 Speaker 1: it beyond what maybe it was intended to do. But 349 00:20:48,880 --> 00:20:54,080 Speaker 1: being able to to balance that with a change management 350 00:20:54,400 --> 00:20:59,600 Speaker 1: strategy where you have the space to play in that world. 351 00:21:00,000 --> 00:21:04,280 Speaker 1: I mean, my heavy opinion is technologists, especially programmers, are 352 00:21:04,280 --> 00:21:08,880 Speaker 1: like artists, and you know, they're very creative beings. And yes, 353 00:21:09,560 --> 00:21:12,920 Speaker 1: given half a chance, they will create, and they'll find 354 00:21:12,960 --> 00:21:16,159 Speaker 1: many different ways of creating. Uh, But we do you know, 355 00:21:16,240 --> 00:21:20,120 Speaker 1: so we we have room for that creativity. We want 356 00:21:20,119 --> 00:21:23,159 Speaker 1: to make sure we leave room for that because that's 357 00:21:23,200 --> 00:21:25,840 Speaker 1: that's the energy that drives them. But at the same 358 00:21:25,880 --> 00:21:28,600 Speaker 1: time they clearly understand, you know, the balance, you know 359 00:21:29,000 --> 00:21:32,200 Speaker 1: why we are here, why what we does may impact 360 00:21:32,359 --> 00:21:36,840 Speaker 1: you know, uh, customers, employees positively and negatively, and so 361 00:21:37,000 --> 00:21:40,320 Speaker 1: we maintain those balances. But it works out pretty well. 362 00:21:40,560 --> 00:21:43,119 Speaker 1: Big companies obviously they have a lot of infrastructure, they 363 00:21:43,200 --> 00:21:46,400 Speaker 1: got a lot of processes, there's a lot of momentum 364 00:21:46,560 --> 00:21:50,880 Speaker 1: built up behind what the things that go on within 365 00:21:50,920 --> 00:21:53,320 Speaker 1: a big company. But that also means that it presents 366 00:21:53,320 --> 00:21:56,760 Speaker 1: a challenge when you want to implement really sort of 367 00:21:56,800 --> 00:22:03,160 Speaker 1: revolutionary changes or or new uh products or new abilities 368 00:22:03,200 --> 00:22:06,720 Speaker 1: within existing products. So how do you tackle that particular 369 00:22:06,800 --> 00:22:11,159 Speaker 1: challenge when you want to implement something new inside a 370 00:22:11,160 --> 00:22:15,880 Speaker 1: a an established company the size of United UH. So 371 00:22:15,920 --> 00:22:19,320 Speaker 1: we have very simply puard, we have adopted user centric design. 372 00:22:19,920 --> 00:22:24,199 Speaker 1: So you know, before I say all technology teams in 373 00:22:24,240 --> 00:22:27,000 Speaker 1: the past, sometimes in the past would have jumped into 374 00:22:27,040 --> 00:22:31,760 Speaker 1: engineering first, designs later, but we whole heartedly adopt user 375 00:22:31,800 --> 00:22:34,840 Speaker 1: centric design. So what we do is we bring our 376 00:22:34,880 --> 00:22:37,520 Speaker 1: stakeholders into the room. So if you're going to roll 377 00:22:37,600 --> 00:22:42,240 Speaker 1: something out to our customers. You know, we'll bring a 378 00:22:42,320 --> 00:22:44,640 Speaker 1: customer panel from the outside if you're going to roll 379 00:22:44,720 --> 00:22:47,520 Speaker 1: something out to a flight attendance, but we bring them 380 00:22:47,960 --> 00:22:49,639 Speaker 1: uh into the room. If you're going to roll it 381 00:22:49,640 --> 00:22:52,240 Speaker 1: out to the frontline agents, we bring them into the 382 00:22:52,320 --> 00:22:56,480 Speaker 1: room and we get them to help us design the 383 00:22:56,560 --> 00:22:59,679 Speaker 1: products before we get into engineering. So there's a lot 384 00:22:59,720 --> 00:23:02,359 Speaker 1: of design book that goes on there. There's also a 385 00:23:02,400 --> 00:23:05,080 Speaker 1: lot of stakeholder management that needs to be done. I'm 386 00:23:05,080 --> 00:23:08,639 Speaker 1: gonna put the prognosticator hat on you. Are there any 387 00:23:08,760 --> 00:23:14,040 Speaker 1: general trends or any emerging things in technology that you 388 00:23:14,119 --> 00:23:20,120 Speaker 1: feel are really poised to make dramatic impacts on industry 389 00:23:20,160 --> 00:23:23,920 Speaker 1: in general, the airline industry in particular, Things that that 390 00:23:24,880 --> 00:23:28,440 Speaker 1: you cannot wait to get your your hands into that 391 00:23:28,480 --> 00:23:31,880 Speaker 1: you really see as being transformative. The new tech out 392 00:23:31,880 --> 00:23:36,120 Speaker 1: there is really going to help us solve some deeply 393 00:23:36,320 --> 00:23:42,560 Speaker 1: embedded problems in our industry. The cloud providers are really 394 00:23:42,600 --> 00:23:46,639 Speaker 1: taking on some massive challenges because they have the I 395 00:23:46,680 --> 00:23:48,960 Speaker 1: would say they have the ability to do so, and 396 00:23:49,000 --> 00:23:52,919 Speaker 1: they're solving some some very deep challenges that you know, 397 00:23:53,040 --> 00:23:54,760 Speaker 1: at the end of the day, what they allow you 398 00:23:54,840 --> 00:23:59,000 Speaker 1: to do is they allow your technology teams to operate 399 00:23:59,040 --> 00:24:01,320 Speaker 1: at a very very high speed, which they have never 400 00:24:01,359 --> 00:24:05,280 Speaker 1: seen before. UM. At the same time, they're providing tool 401 00:24:05,320 --> 00:24:09,959 Speaker 1: sets out there that enable uh, you know, very iterative, 402 00:24:10,119 --> 00:24:14,200 Speaker 1: very fast paced UH test and learn UH scenarios. So 403 00:24:14,640 --> 00:24:17,199 Speaker 1: I think, you know, it's more of a concept, but 404 00:24:17,240 --> 00:24:19,359 Speaker 1: that concept, I think is going to be very deeply 405 00:24:19,400 --> 00:24:22,159 Speaker 1: embedded and very deeply rooted. And I really think that 406 00:24:22,240 --> 00:24:26,480 Speaker 1: you So, when I speak to folks coming out of university, UM, hey, 407 00:24:26,760 --> 00:24:29,000 Speaker 1: what do you want to work on? Okay, I want 408 00:24:29,000 --> 00:24:31,000 Speaker 1: to work on AI. I want to work on mL 409 00:24:31,040 --> 00:24:32,920 Speaker 1: I want to work on robotics. Robotics is going to 410 00:24:33,000 --> 00:24:37,560 Speaker 1: be huge. I want to work on autonomous vehicles. UM. Okay. 411 00:24:37,840 --> 00:24:42,280 Speaker 1: All of these things are enabled at scale. UM. And 412 00:24:42,440 --> 00:24:43,960 Speaker 1: you have to enable these at scale because it doesn't 413 00:24:44,000 --> 00:24:46,960 Speaker 1: otherwise it doesn't really matter. UM. And that's where I 414 00:24:47,000 --> 00:24:48,639 Speaker 1: think the cloud providers are going to be are going 415 00:24:48,680 --> 00:24:51,840 Speaker 1: to be very key you know, in this in this, 416 00:24:52,080 --> 00:24:56,000 Speaker 1: in this journey, we'll just take a stab at autonomous vehicles. 417 00:24:56,040 --> 00:24:59,160 Speaker 1: I really think there's a huge future for it. I'm 418 00:24:59,200 --> 00:25:02,920 Speaker 1: not so sure ab the Roads of Chicago. I think 419 00:25:02,920 --> 00:25:06,040 Speaker 1: autonomous vehicles will be very successful as far as long 420 00:25:06,080 --> 00:25:08,080 Speaker 1: as there are few and few human beings driving in 421 00:25:08,119 --> 00:25:12,600 Speaker 1: the same roads, UM, which is why uh A vs 422 00:25:12,640 --> 00:25:18,639 Speaker 1: will be I think really effective in highly controlled environments. UM. 423 00:25:18,680 --> 00:25:22,760 Speaker 1: And you know highly regulated environments UM. And somewhere sometime 424 00:25:22,800 --> 00:25:25,720 Speaker 1: in the future, I would say, you know, the freeway, 425 00:25:25,760 --> 00:25:28,000 Speaker 1: the logistics and the freeways, you know, the big trucks 426 00:25:28,280 --> 00:25:33,560 Speaker 1: autonomous or the ramps inside airports right, so again fully autonomous. 427 00:25:34,160 --> 00:25:36,280 Speaker 1: But that's I think they out in the future. I mean, 428 00:25:36,280 --> 00:25:39,040 Speaker 1: I tell you, I gave an example as in an 429 00:25:39,119 --> 00:25:42,600 Speaker 1: Uber yesterday and my driver was from Nigeria, from veals 430 00:25:42,640 --> 00:25:44,359 Speaker 1: and you know he's been the US for you know, 431 00:25:44,480 --> 00:25:47,800 Speaker 1: many many years. We were just talking about hyper loop UM. 432 00:25:48,040 --> 00:25:52,600 Speaker 1: And so this is an Uber driver who immigrated, immigrated 433 00:25:52,640 --> 00:25:56,720 Speaker 1: away from his home country thirty years ago and he 434 00:25:56,800 --> 00:26:00,080 Speaker 1: sees the future of hyper loop in Africa, connecting the 435 00:26:00,080 --> 00:26:02,359 Speaker 1: east coast and the west coast and all the cities 436 00:26:02,359 --> 00:26:07,760 Speaker 1: in the middle and that continent, you know, has great potential. UH. 437 00:26:07,760 --> 00:26:10,119 Speaker 1: And you know technologies like this, you know, it's instant 438 00:26:10,160 --> 00:26:13,440 Speaker 1: emerging tech now that that's hyper loop or bullet trains 439 00:26:13,520 --> 00:26:16,119 Speaker 1: or ultra high speed chrains, whatever that might be, UH, 440 00:26:16,320 --> 00:26:20,840 Speaker 1: connecting people that comes down to people. Connecting people creates 441 00:26:20,840 --> 00:26:25,040 Speaker 1: commerce and creates relationships and you know, social structures, and 442 00:26:24,880 --> 00:26:27,800 Speaker 1: and that just has very good connotations to it. So 443 00:26:27,840 --> 00:26:29,640 Speaker 1: I think it's it's gonna be very important to do 444 00:26:29,720 --> 00:26:34,160 Speaker 1: that and not to let technology go so far ahead 445 00:26:34,280 --> 00:26:37,679 Speaker 1: without really connecting the human being back to it. I 446 00:26:37,680 --> 00:26:40,879 Speaker 1: couldn't agree with you more. I think that that human 447 00:26:40,880 --> 00:26:42,720 Speaker 1: element is one of those things we have to keep 448 00:26:42,760 --> 00:26:46,520 Speaker 1: first and foremost in our minds. As exciting as the 449 00:26:46,520 --> 00:26:50,359 Speaker 1: prospect is of raw technology to to the tech is 450 00:26:50,400 --> 00:26:53,040 Speaker 1: among us, we do occasionally remember, oh, wait, this is 451 00:26:53,080 --> 00:26:55,639 Speaker 1: for people. We have to remember that. Uh. And I 452 00:26:56,560 --> 00:26:59,720 Speaker 1: fully agree as well that connectivity is in fact key 453 00:26:59,760 --> 00:27:04,119 Speaker 1: to at whether it's actually physically connecting communities together through 454 00:27:04,480 --> 00:27:10,480 Speaker 1: infrastructure like rail or hyperloop, whether we're talking about wireless 455 00:27:10,480 --> 00:27:16,520 Speaker 1: communications connectivity, all these things are enabling that next revolution 456 00:27:17,119 --> 00:27:22,120 Speaker 1: in both technological development and the industrial implementation of that technology. 457 00:27:22,280 --> 00:27:27,000 Speaker 1: So this has been a phenomenal conversation. So I suppose 458 00:27:27,000 --> 00:27:32,399 Speaker 1: i'll conclude with you personally, what is your ideal vision 459 00:27:32,480 --> 00:27:36,400 Speaker 1: of a perfect day fifteen years from now, in fifteen 460 00:27:36,440 --> 00:27:40,399 Speaker 1: to twenty years, UM, I would like to see men 461 00:27:40,800 --> 00:27:44,720 Speaker 1: and women on Mars UM. And I really want that 462 00:27:44,800 --> 00:27:49,680 Speaker 1: because you know, the last time the space program UH 463 00:27:50,000 --> 00:27:54,399 Speaker 1: kicked in high gear, it created an incredible slew of 464 00:27:54,400 --> 00:27:58,760 Speaker 1: offshooting alternate technologies that we hadn't even considered UM. For 465 00:27:58,880 --> 00:28:01,480 Speaker 1: us to be able to send human beings all that 466 00:28:01,600 --> 00:28:05,840 Speaker 1: distance UM and sustain them and create a sustainable environment 467 00:28:05,840 --> 00:28:10,080 Speaker 1: there would, in my opinion, also create a lot of 468 00:28:10,320 --> 00:28:14,359 Speaker 1: technologies as a result that would benefit humans who were 469 00:28:14,400 --> 00:28:17,399 Speaker 1: still here, you know, whether it's the environment or medicine 470 00:28:18,240 --> 00:28:22,800 Speaker 1: or compute or education. I think, Uh, I'm very excited 471 00:28:22,840 --> 00:28:25,200 Speaker 1: for that future. And you know, and so when I'm 472 00:28:25,280 --> 00:28:26,680 Speaker 1: retired and I can look up in the sky, I 473 00:28:26,680 --> 00:28:30,239 Speaker 1: can see, well, we've been there. Yeah. I love that 474 00:28:30,320 --> 00:28:34,480 Speaker 1: thought too, and I agree. I think that human ingenuity 475 00:28:34,600 --> 00:28:39,320 Speaker 1: is a remarkable resource that we have. And once we 476 00:28:39,360 --> 00:28:42,280 Speaker 1: have identified our goal, especially if it's a really hard one, 477 00:28:42,680 --> 00:28:44,760 Speaker 1: whether it's getting to the Moon or getting to Mars, 478 00:28:45,400 --> 00:28:48,520 Speaker 1: then then we start looking at all, right, well, if 479 00:28:48,640 --> 00:28:50,560 Speaker 1: that's our goal, what are the steps we have to 480 00:28:50,600 --> 00:28:52,080 Speaker 1: take in order to get there? And we start to 481 00:28:52,080 --> 00:28:54,080 Speaker 1: identify what those challenges are, and then we start to 482 00:28:54,080 --> 00:28:57,640 Speaker 1: solve for them, and we realize in retrospect we had 483 00:28:57,640 --> 00:29:00,920 Speaker 1: that capability to make all that stuff from the beginning. 484 00:29:01,360 --> 00:29:07,200 Speaker 1: Finding that that goal, finding that challenge is what drives 485 00:29:07,280 --> 00:29:11,720 Speaker 1: people and gives them the motivation and the passion to 486 00:29:12,600 --> 00:29:15,840 Speaker 1: tap into that innovative spirit that I think is just 487 00:29:16,440 --> 00:29:18,880 Speaker 1: part and parcel with being a human. It's what being 488 00:29:18,960 --> 00:29:23,960 Speaker 1: human is, and being able to do that is really exciting. Well, 489 00:29:23,960 --> 00:29:26,840 Speaker 1: thank you so much. This has been an incredible conversation, 490 00:29:27,320 --> 00:29:29,640 Speaker 1: and uh, I very much look forward to your future. 491 00:29:30,120 --> 00:29:33,160 Speaker 1: And if you ever, you know, have a motorcycle you're 492 00:29:33,200 --> 00:29:36,920 Speaker 1: you're you're looking to sell, just give me a call. Johnson. 493 00:29:37,000 --> 00:29:39,880 Speaker 1: Thank you. It's been a privilege followed you for a 494 00:29:39,960 --> 00:29:43,080 Speaker 1: number of years, and I think today was a highlight, 495 00:29:43,320 --> 00:29:45,080 Speaker 1: uh that I've had in the last few years. So 496 00:29:45,120 --> 00:29:51,840 Speaker 1: thank you very much for the time. Robbie really helped 497 00:29:51,880 --> 00:29:55,760 Speaker 1: me better understand how the airline industry can best implement 498 00:29:55,840 --> 00:30:01,680 Speaker 1: solutions by finding that delicate balance between innovation implementation and 499 00:30:01,760 --> 00:30:04,920 Speaker 1: change management. The real trick is knowing when to put 500 00:30:04,960 --> 00:30:08,440 Speaker 1: your thumb down on the scale to tip that balance 501 00:30:08,440 --> 00:30:11,440 Speaker 1: when the time is right. His thoughts on how technology 502 00:30:11,480 --> 00:30:17,080 Speaker 1: is poised to really revolutionize business operations are inspiring, and 503 00:30:17,200 --> 00:30:21,000 Speaker 1: like Robbie, I agree that machine learning, artificial intelligence, and 504 00:30:21,040 --> 00:30:25,000 Speaker 1: connectivity will all play vital roles in this future. As 505 00:30:25,000 --> 00:30:28,040 Speaker 1: we see five G technologies roll out across the world 506 00:30:28,480 --> 00:30:35,000 Speaker 1: will enable unprecedented implementations that will change literally everything about 507 00:30:35,080 --> 00:30:38,680 Speaker 1: how we conduct business, from the infrastructure that underlies it 508 00:30:38,760 --> 00:30:42,560 Speaker 1: all to the end user experience. In our next episode, 509 00:30:42,760 --> 00:30:46,120 Speaker 1: we'll be looking at how Carrie, North Carolina is incorporating 510 00:30:46,160 --> 00:30:50,920 Speaker 1: technology to transform how cities work, creating a truly smart 511 00:30:51,080 --> 00:30:55,360 Speaker 1: city that uses data to maximize efficiency and provide incredible 512 00:30:55,440 --> 00:30:59,080 Speaker 1: value to citizens and visitors alike. I can't wait for 513 00:30:59,160 --> 00:31:02,400 Speaker 1: you to hear it. That's on the next The Restless Ones. 514 00:31:05,880 --> 00:31:08,400 Speaker 1: This has been The Restless Ones, a production of T 515 00:31:08,560 --> 00:31:14,240 Speaker 1: Mobile for Business and I Heart Radio. No matter what 516 00:31:14,360 --> 00:31:17,080 Speaker 1: you're after, T Mobile for Business is here with a 517 00:31:17,160 --> 00:31:20,840 Speaker 1: network born mobile and built from the ground up for 518 00:31:20,880 --> 00:31:24,760 Speaker 1: the next wave of innovation, from mobile broadband to IoT 519 00:31:25,000 --> 00:31:28,920 Speaker 1: to workforce mobility, and everything in between. 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