Revolutionize your marketing: Join Kelley's cutting-edge AI marketing course

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WEBVTT 1 00:00:03.065 --> 00:00:04.675 [Garfield Bowen] What role does AI play 2 00:00:05.675 --> 00:00:07.695 in moving us into the future of innovation? 3 00:00:08.715 --> 00:00:12.655 And I think that is why, um, the things 4 00:00:12.655 --> 00:00:15.335 that we are doing at 3M are so very important, 5 00:00:15.915 --> 00:00:17.935 and we don't have all the answers, I'll tell you, 6 00:00:18.435 --> 00:00:23.265 but we've gotta make sure that we're not being too far 7 00:00:23.265 --> 00:00:26.385 behind where things are going. 8 00:00:26.805 --> 00:00:29.865 So how do we capitalize on the value 9 00:00:30.495 --> 00:00:34.305 that AI provides while at the same time being mindful 10 00:00:34.405 --> 00:00:35.985 of the risks that can be created? 11 00:00:42.275 --> 00:00:43.725 [Dr. Alain Barker] I've been pondering that, obviously for, 12 00:00:43.985 --> 00:00:45.085 uh, a few years now. 13 00:00:45.265 --> 00:00:48.605 And, um, I wanna throw out an idea. 14 00:00:49.585 --> 00:00:54.245 Um, I think that when we, when we create things and, 15 00:00:54.305 --> 00:00:56.765 and I, I see this happening all the time in, 16 00:00:56.825 --> 00:00:57.885 in, in the music world. 17 00:00:58.705 --> 00:01:02.405 We spend a lot of time, uh, emulating and perfecting 18 00:01:02.705 --> 00:01:05.845 and objective that we have identified before ourselves. 19 00:01:07.065 --> 00:01:11.485 And we think that by doing that, we've created something 20 00:01:13.235 --> 00:01:17.615 and with this surge of AI as a new technology, 21 00:01:17.915 --> 00:01:20.095 and we open our eyes and we look in front of ourselves, 22 00:01:20.095 --> 00:01:22.775 and we cannot believe that this technology is basically 23 00:01:23.775 --> 00:01:27.735 reproducing what we consider to be creative work. 24 00:01:29.535 --> 00:01:33.655 I wonder whether we are, um, at a point where we have 25 00:01:33.655 --> 00:01:36.215 to start distinguishing between things 26 00:01:36.215 --> 00:01:38.095 that are being emulated, things 27 00:01:38.095 --> 00:01:39.815 that are being repeated at a very, very, 28 00:01:39.815 --> 00:01:41.055 very high quality level, 29 00:01:41.835 --> 00:01:46.275 and things that are meaningful in a creative sense, 30 00:01:46.735 --> 00:01:48.875 uh, in a sense of it being new, in a sense 31 00:01:48.875 --> 00:01:51.955 of it being impactful to the world that we live in, 32 00:01:52.015 --> 00:01:54.795 in a sense of it accelerating our ability 33 00:01:54.855 --> 00:01:57.115 to impact the world that we, that we live in. 34 00:01:59.545 --> 00:02:01.625 [Dr. Caroline Ylitalo] I am tasked with creating new technologies 35 00:02:01.685 --> 00:02:03.105 and developing new products. 36 00:02:03.785 --> 00:02:08.665 I have over 300 documented inventions for 3M that led 37 00:02:08.725 --> 00:02:11.025 to over a hundred patent applications 38 00:02:11.565 --> 00:02:13.665 and, uh, hundreds of millions of dollars 39 00:02:13.725 --> 00:02:14.985 of new product sales. 40 00:02:16.125 --> 00:02:18.145 So I'm at the forefront of invention. 41 00:02:18.505 --> 00:02:20.745 I used it to free up my time 42 00:02:20.885 --> 00:02:24.225 so I can focus on doing things I am good at, 43 00:02:24.235 --> 00:02:25.905 which is coming up with new ideas. 44 00:02:30.355 --> 00:02:34.175 [Sarah Bellamy] So if we're relying on instruments without this perceptual 45 00:02:34.495 --> 00:02:37.495 function, which like can't engage in discernment 46 00:02:37.495 --> 00:02:41.815 and meaning making, to expedite expedite these processes, 47 00:02:41.885 --> 00:02:43.855 this governance of our society in different ways, 48 00:02:44.835 --> 00:02:46.695 it really does feel like we're opting out 49 00:02:46.695 --> 00:02:47.895 from a moral standpoint. 50 00:02:47.955 --> 00:02:48.975 And that scares me. 51 00:02:49.635 --> 00:02:51.895 Um, I really, I can't abide by that 52 00:02:51.895 --> 00:02:54.335 because for me, like from my perspective, 53 00:02:54.445 --> 00:02:56.695 that is the quintessential facet of being human. 54 00:02:56.805 --> 00:02:59.055 It's our reasoning, our sentience, our capacity to, 55 00:02:59.085 --> 00:03:00.365 to empathize and relate. 56 00:03:01.025 --> 00:03:03.805 And these are the things that are really at the foundation 57 00:03:03.805 --> 00:03:04.965 of our ethical positions. 58 00:03:04.965 --> 00:03:06.365 We have opinions, you know, 59 00:03:06.555 --> 00:03:08.165 they might sometimes be incorrect, 60 00:03:08.865 --> 00:03:10.965 but they're informed by our life experiences. 61 00:03:11.545 --> 00:03:13.365 And that's a composite process, right? 62 00:03:13.705 --> 00:03:14.805 And I think one of the things 63 00:03:14.805 --> 00:03:18.565 that's tricky about this is we're replicating only one 64 00:03:18.665 --> 00:03:21.325 aspect of this, our cognitive reasoning as a proxy 65 00:03:21.425 --> 00:03:24.285 for human engagement, and it that feels dangerous on a, 66 00:03:24.285 --> 00:03:26.325 on a number of of levels for me. 67 00:03:30.625 --> 00:03:32.615 [Paul Acito] Hello everyone, and thank you 68 00:03:32.675 --> 00:03:35.375 for joining this webinar on AI Applications 69 00:03:35.375 --> 00:03:37.415 in Business sponsored by the Kelley School 70 00:03:38.155 --> 00:03:39.655 at Indiana University. 71 00:03:40.415 --> 00:03:42.895 I wanted to share those four vignettes from our guest 72 00:03:42.925 --> 00:03:45.655 instructors that we, instructors that we use in the courses, 73 00:03:45.795 --> 00:03:48.815 but they're also, obviously many of my colleagues 74 00:03:48.835 --> 00:03:49.895 and former colleagues. 75 00:03:50.705 --> 00:03:54.575 These video shorts also provide a window into the thoughtful 76 00:03:54.775 --> 00:03:56.055 approach that we're taking at Kelley 77 00:03:56.075 --> 00:03:57.975 around artificial intelligence. 78 00:03:58.955 --> 00:04:00.175 It was a warmup, uh, 79 00:04:00.195 --> 00:04:03.015 but it's kind of important to have a warmup 80 00:04:03.015 --> 00:04:05.375 because we're coming to you live from Minneapolis, 81 00:04:05.375 --> 00:04:09.095 where it's negative one degrees Fahrenheit for our European, 82 00:04:09.235 --> 00:04:12.655 uh, and other country and international, uh, visitors. 83 00:04:12.715 --> 00:04:17.615 Uh, that's negative 18 C uh, in January in Minnesota. 84 00:04:17.795 --> 00:04:19.535 We have to think warm thoughts, 85 00:04:19.535 --> 00:04:21.815 so please send your warm thoughts to us. 86 00:04:22.035 --> 00:04:23.095 Uh, it's cold. 87 00:04:23.595 --> 00:04:27.455 Uh, we're all wearing sweaters indoors and coats indoors. 88 00:04:28.835 --> 00:04:31.375 To continue with our warming trend, uh, I'd like 89 00:04:31.375 --> 00:04:34.455 to extend a special welcome to our Indiana University 90 00:04:35.075 --> 00:04:36.415 alumni, Kelley alumni, 91 00:04:36.995 --> 00:04:39.615 and attendees from the Chief Marketing, uh, 92 00:04:39.885 --> 00:04:42.295 Officer Collaborative who are 93 00:04:42.295 --> 00:04:45.255 among our 1,700 registrants for this course. 94 00:04:46.495 --> 00:04:48.755 You're gonna hear the word a lot today from me: 95 00:04:48.765 --> 00:04:51.915 leadership. Leadership and big leadership. 96 00:04:52.735 --> 00:04:56.515 Uh, it's important to have a strong leadership context, 97 00:04:56.745 --> 00:04:57.835 context right now. 98 00:04:58.495 --> 00:05:03.475 Uh, AI is the most important technical transformation 99 00:05:03.575 --> 00:05:07.515 of our lifetime, and we need big leaders to get it right. 100 00:05:09.435 --> 00:05:11.735 The purpose of this webinar really are twofold. 101 00:05:12.075 --> 00:05:14.255 One, to provide a glimpse into 102 00:05:14.255 --> 00:05:18.055 how business can leverage starting in 2025, starting today. 103 00:05:18.485 --> 00:05:21.135 It's already started to accelerate growth, 104 00:05:21.235 --> 00:05:23.255 reduce cycle times, reduce costs, 105 00:05:23.795 --> 00:05:27.255 and just overall change the landscape for business 106 00:05:27.315 --> 00:05:28.655 and frankly for society. 107 00:05:29.635 --> 00:05:33.095 And two, to give you a quick introduction to the portfolio 108 00:05:33.115 --> 00:05:36.655 of courses that we have, coming from Kelley Exec Ed, starting 109 00:05:36.725 --> 00:05:39.775 with our third, uh, implementation 110 00:05:39.955 --> 00:05:43.695 of AI Applications in Marketing starting February 4th. 111 00:05:45.255 --> 00:05:47.875 You know, we're all mesmerized with ChatGPT, 112 00:05:47.875 --> 00:05:49.755 and we're astonished with the amount of capital 113 00:05:50.415 --> 00:05:53.955 and human capital that's been deployed against AI. 114 00:05:54.895 --> 00:05:56.435 But we're just getting started. 115 00:05:57.565 --> 00:05:59.805 I had a, uh, CEO who used to say, we're closer 116 00:05:59.865 --> 00:06:02.765 to the beginning than the end of this transformation. 117 00:06:04.415 --> 00:06:09.395 Compute, data, frontier models from OpenAI, Google, 118 00:06:09.505 --> 00:06:12.755 Anthropic, and others are on a three- to four-month cycle time. 119 00:06:13.255 --> 00:06:18.155 So you go on holiday for the holiday break, you come back, 120 00:06:18.895 --> 00:06:20.155 the landscape has changed. 121 00:06:21.355 --> 00:06:25.355 Companies and countries are vying for competitive advantage. 122 00:06:26.095 --> 00:06:28.795 Uh, leaders are repositioning their organizations 123 00:06:29.495 --> 00:06:30.595 for the new frontier. 124 00:06:30.595 --> 00:06:33.395 And know that was not a coincidence that we started out with 125 00:06:33.465 --> 00:06:36.355 that song by, some of you recognized, which, 126 00:06:36.645 --> 00:06:38.955 which tells me something about you that that was, uh, 127 00:06:38.955 --> 00:06:39.995 called The New Frontier. 128 00:06:40.455 --> 00:06:44.835 So why take the risk to try to learn something now about AI, 129 00:06:45.255 --> 00:06:47.595 or worse yet, why take the risk to try 130 00:06:47.595 --> 00:06:49.955 to teach something about AI when things 131 00:06:49.955 --> 00:06:50.995 are changing so fast? 132 00:06:51.185 --> 00:06:52.595 Well, my colleagues 133 00:06:52.615 --> 00:06:56.155 and I hope to share what we know, not just about AI, 134 00:06:56.295 --> 00:06:58.275 but about the diffusion of innovations. 135 00:06:58.295 --> 00:06:59.915 We know a thing or two about innovation, 136 00:07:00.865 --> 00:07:04.115 even though this is an unprecedented invention. 137 00:07:04.745 --> 00:07:09.195 It's the invention of, uh, super intelligence. 138 00:07:09.775 --> 00:07:12.315 We will see AGI, 139 00:07:12.875 --> 00:07:16.595 artificial general intelligence this year, 2025. 140 00:07:17.955 --> 00:07:19.255 And let's not get confused. 141 00:07:19.845 --> 00:07:23.015 This is going to be a turbulent transformation. 142 00:07:23.965 --> 00:07:26.335 Just got some data from the World Economic Forum 143 00:07:26.335 --> 00:07:29.535 that reports that half of employers plan 144 00:07:29.555 --> 00:07:31.855 to reorient their businesses around AI. 145 00:07:32.635 --> 00:07:36.375 Two thirds plan to hire employees with AI skills. 146 00:07:37.225 --> 00:07:40.935 40% anticipate reducing their workforce, 147 00:07:41.225 --> 00:07:44.935 where AI can automate tasks and processes. 148 00:07:46.135 --> 00:07:48.555 So mitigate, mitigating these risks will require 149 00:07:48.835 --> 00:07:52.235 compassionate leadership, harnessing its potential, 150 00:07:52.385 --> 00:07:55.155 will require inspired leadership and governance. 151 00:07:55.815 --> 00:07:56.955 But we're here 152 00:07:57.145 --> 00:07:59.355 because we believe in business as a force 153 00:07:59.455 --> 00:08:01.635 for positive change in the world. 154 00:08:01.735 --> 00:08:05.395 So let's dig into some of these details. 155 00:08:08.685 --> 00:08:09.685 I like to quote 156 00:08:09.865 --> 00:08:13.685 and follow the leaders, uh, in AI, particularly Sam Altman, 157 00:08:14.225 --> 00:08:16.885 Jensen Huang, and we'll hear from both of them, uh, during, 158 00:08:17.025 --> 00:08:19.685 uh, this and, and Dario Amodei, since they have 159 00:08:19.685 --> 00:08:20.845 access to AI models. 160 00:08:21.585 --> 00:08:23.405 And we won't see 161 00:08:23.405 --> 00:08:24.845 what they know for another one to two years. 162 00:08:24.865 --> 00:08:25.965 So imagine what they've got. 163 00:08:25.965 --> 00:08:28.965 They've got these very powerful models. 164 00:08:29.945 --> 00:08:33.025 And I don't know, I'm, I'm imagining 165 00:08:33.025 --> 00:08:34.185 that they're modeling the economy. 166 00:08:34.575 --> 00:08:36.705 They may be making stock picks, who knows? 167 00:08:37.165 --> 00:08:39.105 But they see what we can't. 168 00:08:39.485 --> 00:08:42.585 So anytime they signal something, I like 169 00:08:42.585 --> 00:08:44.745 to listen carefully, and I encourage 170 00:08:44.745 --> 00:08:45.825 you to do the same thing too. 171 00:08:45.845 --> 00:08:49.345 In our courses, we actually have a leaderboard of, of 172 00:08:49.365 --> 00:08:52.545 who these, these leaders are and who should listen to. 173 00:08:52.545 --> 00:08:54.745 But let's just take a listen to Sam Altman. 174 00:08:54.745 --> 00:08:58.005 This was a recent, uh, interview he had with, uh, 175 00:08:58.165 --> 00:09:01.245 Y Combinator in San Francisco, which he used to run. 176 00:09:02.395 --> 00:09:05.895 [Sam Altman] It is easy when there's a new technology platform to say, 177 00:09:05.895 --> 00:09:09.335 well, because I'm doing some of AI, the, the rule, the laws 178 00:09:09.335 --> 00:09:10.735 of business don't apply to me. 179 00:09:10.735 --> 00:09:11.935 That's obviously not true. 180 00:09:13.785 --> 00:09:16.215 And, and remembering not to fall for that, 181 00:09:16.215 --> 00:09:17.135 and that you still have to build 182 00:09:17.135 --> 00:09:18.135 something of enduring value. 183 00:09:21.315 --> 00:09:23.255 [Paul] So this is where we're gonna spend the rest of our time. 184 00:09:24.145 --> 00:09:27.645 Uh, it's going to seem like too much, too fast. 185 00:09:28.585 --> 00:09:32.165 Uh, that's because it's too much, too fast. 186 00:09:32.505 --> 00:09:34.805 And this is, this is the pace of AI. 187 00:09:35.115 --> 00:09:36.565 This is the pace of change, 188 00:09:36.865 --> 00:09:38.485 and this is what your teams are dealing with. 189 00:09:39.185 --> 00:09:41.045 Um, in fact, this list is most 190 00:09:41.045 --> 00:09:42.445 notable in what it doesn't cover. 191 00:09:42.505 --> 00:09:45.525 It doesn't cover Copilot, doesn't cover xAI or Meta 192 00:09:45.985 --> 00:09:47.005 or Perplexity. 193 00:09:47.305 --> 00:09:49.765 It doesn't cover any of the tens of thousands 194 00:09:49.765 --> 00:09:51.365 of wraparound AI apps. 195 00:09:51.945 --> 00:09:55.765 Um, this first eight that we're going to cover, the, 196 00:09:55.765 --> 00:09:57.565 the ones in white are designed 197 00:09:57.565 --> 00:09:58.765 to give you something you can take 198 00:09:58.765 --> 00:10:00.085 and start applying in your business. 199 00:10:00.085 --> 00:10:02.325 Yeah, some of 'em you're gonna have to pay money to, to use, 200 00:10:02.745 --> 00:10:03.765 uh, most are free. 201 00:10:04.265 --> 00:10:07.725 Uh, the last two are a tip off 202 00:10:07.725 --> 00:10:10.205 of what's coming next, proof of concept. 203 00:10:10.985 --> 00:10:13.885 Uh, this is about agentic workflows. 204 00:10:13.885 --> 00:10:16.045 You're hearing a lot about AI agents. I know that. 205 00:10:16.225 --> 00:10:18.645 And they can't really even define 206 00:10:18.645 --> 00:10:20.085 what an AI agent is clearly. 207 00:10:20.505 --> 00:10:24.165 But we'll take a stab at looking at how this might impact, 208 00:10:24.625 --> 00:10:26.965 uh, what goes on at your work desk, uh, today. 209 00:10:27.025 --> 00:10:30.725 So, um, to illustrate these models that, 210 00:10:30.725 --> 00:10:32.085 that we're gonna go through the first eight. 211 00:10:34.215 --> 00:10:38.765 Again, use your warm imagination, uh, for those 212 00:10:38.765 --> 00:10:40.245 of us freezing up here in Minnesota, 213 00:10:40.635 --> 00:10:42.165 imagine you just got a new job. 214 00:10:43.315 --> 00:10:45.405 Imagine that you got asked to run a P&L, 215 00:10:45.405 --> 00:10:48.005 new product program, on bicycling accessories. 216 00:10:48.005 --> 00:10:49.125 This case, it's reflectors. 217 00:10:49.945 --> 00:10:54.005 And we're not only new to the company, you're not only new 218 00:10:54.005 --> 00:10:55.445 to the company, but, uh, 219 00:10:55.625 --> 00:10:57.845 you don't really know anything about the bicycle 220 00:10:57.915 --> 00:10:59.605 accessories, but it got a good offer. 221 00:11:00.385 --> 00:11:04.045 And, um, congratulations. Good news is you got this offer. 222 00:11:04.045 --> 00:11:06.605 You got your big, big comp plan. 223 00:11:07.185 --> 00:11:09.605 Uh, you got a great new boss, new company, new product. 224 00:11:10.025 --> 00:11:12.725 Uh, the bad news is they want a full review 225 00:11:12.725 --> 00:11:14.085 of the business next week. 226 00:11:14.965 --> 00:11:18.465 So we're hoping that we can leverage AI 227 00:11:19.365 --> 00:11:20.745 and get us off to a good start. 228 00:11:20.745 --> 00:11:22.665 Start. And the reason I'm going through this more 229 00:11:22.665 --> 00:11:24.425 or less academic case study, actually, this is kind 230 00:11:24.425 --> 00:11:26.305 of a real company, but we'll get to that later. 231 00:11:27.125 --> 00:11:31.745 The, the idea is that this will illustrate 232 00:11:32.335 --> 00:11:36.185 that AI goes so far beyond cute TikTok videos 233 00:11:36.765 --> 00:11:39.865 and, um, you know, writing 234 00:11:41.665 --> 00:11:42.985 teenagers' essays for, for high school. 235 00:11:43.045 --> 00:11:47.505 It, it, if you're not pushing beyond the free version 236 00:11:47.505 --> 00:11:51.185 of ChatGPT, I think you are going 237 00:11:51.185 --> 00:11:53.165 to be amazed at some of this. 238 00:11:53.265 --> 00:11:56.045 Now, we had 1,700 people sign up for this. 239 00:11:56.545 --> 00:12:00.555 So when you get 1,700 people, you get a normal distribution. 240 00:12:00.585 --> 00:12:02.995 That means there's a hundred of you on the phone 241 00:12:03.025 --> 00:12:05.195 that probably know more than I do, uh, 242 00:12:05.195 --> 00:12:08.435 and have probably forgotten, uh, more than I know about AI. 243 00:12:09.055 --> 00:12:10.795 And there's probably a hundred who 244 00:12:11.675 --> 00:12:13.085 haven't hit the keyboard yet. 245 00:12:13.425 --> 00:12:15.485 And then the rest of us are in the middle somewhere. 246 00:12:15.625 --> 00:12:19.805 So my hope for this time that we have together is 247 00:12:19.805 --> 00:12:23.725 that you get one or two new applications that you can, 248 00:12:23.905 --> 00:12:26.485 you can use some of this may frankly astonish you. 249 00:12:26.715 --> 00:12:28.245 It's, it's, it's pretty incredible. 250 00:12:28.665 --> 00:12:33.565 So let's get started, started. Google has been busy. 251 00:12:34.325 --> 00:12:37.045 I think if you rewind about four to six months, 252 00:12:37.045 --> 00:12:38.125 people were concerned 253 00:12:38.435 --> 00:12:40.685 that Google was falling behind somehow. 254 00:12:40.985 --> 00:12:42.685 I'm, I'm not very worried about Google. 255 00:12:42.885 --> 00:12:45.725 I don't worry about Google or Microsoft or the rest. 256 00:12:46.325 --> 00:12:50.005 I like to say, who's gonna win in AI? Meet the new boss, 257 00:12:51.025 --> 00:12:52.075 same as the old boss. 258 00:12:52.095 --> 00:12:54.515 You're, you're starting to pick up on a classic rock, 259 00:12:55.095 --> 00:12:56.355 uh, theme here as well. 260 00:12:56.935 --> 00:12:59.555 But the point is that 261 00:13:00.575 --> 00:13:03.515 the Google is just been on a tear lately. 262 00:13:03.935 --> 00:13:08.815 And let's take a look at one way Google's implementation 263 00:13:08.855 --> 00:13:10.455 of AI can help us. 264 00:13:10.955 --> 00:13:12.135 We need to do some research. 265 00:13:12.275 --> 00:13:14.135 We don't know anything about this bicycle market, 266 00:13:14.755 --> 00:13:17.215 so let's do some research. 267 00:13:17.315 --> 00:13:20.455 But let's use Google Deep Research. 268 00:13:21.165 --> 00:13:24.255 This is currently only available, so this is a pay -for one. 269 00:13:24.595 --> 00:13:26.415 Uh, but I expect this will be available soon. 270 00:13:26.415 --> 00:13:27.855 It's, it's kind of in beta. 271 00:13:28.515 --> 00:13:32.455 So the features that differentiate deep research are 272 00:13:32.455 --> 00:13:34.415 that you get citations, which is really great. 273 00:13:34.955 --> 00:13:36.415 Um, it's deep research. 274 00:13:36.635 --> 00:13:40.095 Um, what I'm gonna show you took six minutes to do. 275 00:13:40.925 --> 00:13:42.295 It's a summary 276 00:13:43.115 --> 00:13:46.015 and a competent generation of a report. 277 00:13:46.175 --> 00:13:50.175 I hope you'll agree. And importantly, no ads. 278 00:13:50.715 --> 00:13:55.355 So let's take a look. So you can see here I'm 279 00:13:55.355 --> 00:13:59.875 picking, um, Google 1.5 Pro with deep research. 280 00:14:03.155 --> 00:14:06.355 I give it a kind of a quick prompt, said, 281 00:14:07.585 --> 00:14:11.215 write me a marketing analysis on the US market 282 00:14:11.395 --> 00:14:12.655 for bicycle accessories. 283 00:14:13.515 --> 00:14:15.175 So it starts the research process. 284 00:14:20.345 --> 00:14:24.245 At first, it picks up 27 websites. So again, this is key. 285 00:14:24.825 --> 00:14:26.565 Uh, I think it gives you some confidence 286 00:14:26.565 --> 00:14:28.565 that it's not hallucinating when you can actually go back, 287 00:14:28.615 --> 00:14:31.005 click on and find the source document, 288 00:14:37.965 --> 00:14:39.985 and then it says, hey, here's my research plan. 289 00:14:40.375 --> 00:14:43.425 What do you think? I'll analyze results, create a report. 290 00:14:43.815 --> 00:14:47.065 Look good, sounds good. Now again, this took six minutes. 291 00:14:47.125 --> 00:14:48.225 So I've accelerated this. 292 00:14:48.575 --> 00:14:51.525 Then I went back and found 42 resources, 293 00:14:52.585 --> 00:14:56.365 and it generates a Word document, 294 00:14:56.365 --> 00:15:00.935 or in this case a Google Docs that is a pretty comprehensive 295 00:15:00.995 --> 00:15:03.935 and well-structured report on the 296 00:15:03.935 --> 00:15:05.055 bicycle accessories market. 297 00:15:05.155 --> 00:15:07.895 So that's Google Deep research. 298 00:15:13.285 --> 00:15:17.125 Next, we kind of are under some pressure 299 00:15:17.125 --> 00:15:18.845 because we've never worked in this industry. 300 00:15:20.265 --> 00:15:23.765 We have to do a financial statement 301 00:15:24.625 --> 00:15:26.805 and we don't even know where to start. 302 00:15:27.745 --> 00:15:30.125 So even though we have little domain expertise, 303 00:15:30.275 --> 00:15:33.605 this doesn't stop our division president from putting us on 304 00:15:33.605 --> 00:15:34.965 the agenda next week 305 00:15:35.505 --> 00:15:38.525 and insisting on a full set of financial forecasts. 306 00:15:39.495 --> 00:15:40.695 I don't know about you, but I've worked 307 00:15:40.715 --> 00:15:41.775 for these folks before. 308 00:15:42.235 --> 00:15:45.055 That's exactly how it happens. So where do we start? 309 00:15:45.685 --> 00:15:47.575 Well, you know, the way I would start is 310 00:15:48.145 --> 00:15:49.895 let's take a look at somebody else's P&L, 311 00:15:49.895 --> 00:15:51.855 maybe a publicly traded company. 312 00:15:53.105 --> 00:15:54.195 Shimano comes to mind. 313 00:15:54.335 --> 00:15:55.395 If you've ever ridden a bike, 314 00:15:55.395 --> 00:15:57.275 you've probably ridden on Shimano gears. 315 00:15:58.335 --> 00:16:01.435 So wouldn't it be great though, as we're looking at these, 316 00:16:01.435 --> 00:16:04.235 because, you know, I, I can read a P&L 317 00:16:04.235 --> 00:16:05.995 with the best of 'em and a balance sheet, 318 00:16:05.995 --> 00:16:07.955 but you know, I might miss something and, 319 00:16:08.055 --> 00:16:09.315 and time is of the essence. 320 00:16:09.415 --> 00:16:10.555 So wouldn't it be great 321 00:16:10.555 --> 00:16:13.355 to have an expert looking over our shoulder to help 322 00:16:13.355 --> 00:16:14.435 with a P&L assessment? 323 00:16:15.095 --> 00:16:17.115 That's where Google Stream Realtime, 324 00:16:17.335 --> 00:16:20.395 an experimental app you can use today, comes in, 325 00:16:22.635 --> 00:16:26.375 Oh, there we go. 326 00:16:26.795 --> 00:16:29.405 Oh, excuse me. 327 00:16:32.135 --> 00:16:36.335 There we go. So the first thing you do is you give, 328 00:16:36.595 --> 00:16:38.455 and this is a little bit scary, you give Google 329 00:16:39.055 --> 00:16:40.295 complete control of your screen, 330 00:16:40.295 --> 00:16:43.015 or at least visibility on your screen and it can hear you. 331 00:16:43.555 --> 00:16:45.775 So you'll hear a recorded version of me talking 332 00:16:46.355 --> 00:16:47.535 to Google Gemini. 333 00:16:47.835 --> 00:16:50.255 [Recorded Paul] Hey Gemini, can you take a look at a p and l for me? 334 00:16:52.055 --> 00:16:55.225 [Gemini] Sure. I can take a look at the P&L. 335 00:16:55.635 --> 00:16:56.825 Could you please show it to me? 336 00:16:58.105 --> 00:17:00.975 [Recorded Paul] Yeah, can you see that? [Gemini] Yes, 337 00:17:01.295 --> 00:17:04.255 I can see the P&L, also known 338 00:17:04.275 --> 00:17:05.895 as the profit and loss statement. 339 00:17:06.275 --> 00:17:07.855 It appears to be for Shimano 340 00:17:07.875 --> 00:17:10.375 and it shows the financial results for the first nine months 341 00:17:10.375 --> 00:17:13.255 of fiscal years 2023 and 2024. 342 00:17:14.495 --> 00:17:16.195 Do you have any specific questions about it? 343 00:17:17.225 --> 00:17:19.035 [Recorded Paul] Yeah, just, uh, since we're through, 344 00:17:19.035 --> 00:17:22.155 through the nine month period on both of these, uh, both 345 00:17:22.155 --> 00:17:26.995 of these columns here, gimme some observations from, uh, 346 00:17:26.995 --> 00:17:31.315 professional, uh, cost accounting standpoint, 347 00:17:31.815 --> 00:17:36.795 uh, particularly focused on growth cost and, uh, income. 348 00:17:37.535 --> 00:17:40.115 And, uh, just take a look, for example, at net sales 349 00:17:40.495 --> 00:17:42.875 and anything you can tell me about what's going on 350 00:17:42.945 --> 00:17:44.275 between these two periods. 351 00:17:46.095 --> 00:17:47.145 [Gemini] Okay, I can do that. 352 00:17:47.615 --> 00:17:48.945 Looking at the profit and 353 00:17:48.985 --> 00:17:51.565 and loss statement, net sales decreased from 354 00:17:51.565 --> 00:17:56.365 375,264 million yen in the first nine 355 00:17:56.365 --> 00:17:58.125 months of fiscal year 2023 356 00:17:58.665 --> 00:18:03.645 to 334,879 million yen in the first nine months 357 00:18:03.705 --> 00:18:05.205 of fiscal year 2024. 358 00:18:06.285 --> 00:18:08.575 This indicates a significant drop in sales. 359 00:18:09.455 --> 00:18:11.455 Additionally, the cost of sales also decreased, 360 00:18:11.715 --> 00:18:14.375 but it appears to be by a lower percentage than the net 361 00:18:14.375 --> 00:18:16.655 sales resulting in a lower gross profit. 362 00:18:17.905 --> 00:18:20.165 [Recorded Paul] Hey, let's, uh, let's take a look at the balance sheet 363 00:18:20.165 --> 00:18:22.245 and see if there's any surprises there for you. 364 00:18:22.545 --> 00:18:26.445 Um, start off by, with just looking at current assets, 365 00:18:28.555 --> 00:18:30.325 [Gemini] Alright, I can look at the balance sheet 366 00:18:30.325 --> 00:18:32.045 with you focusing on current assets. 367 00:18:32.845 --> 00:18:35.965 I can see that total current assets increased slightly from 368 00:18:35.965 --> 00:18:40.925 652,753 million yen in fiscal year 2023 369 00:18:41.265 --> 00:18:45.245 to 658,909 million yen in the first nine months 370 00:18:45.245 --> 00:18:46.765 of fiscal year 2024. 371 00:18:47.065 --> 00:18:48.285 Do you wanna go into more detail? 372 00:18:49.625 --> 00:18:51.555 [Paul Acito] Okay, before I leave here, if you do try this, I, 373 00:18:51.555 --> 00:18:54.075 I'll tell you, um, one thing I learned is that, um, 374 00:18:54.975 --> 00:18:57.835 the bigger the font, the better accuracy you get. 375 00:18:57.855 --> 00:18:59.235 So I'm, I'm just learning how to use this. 376 00:18:59.235 --> 00:19:00.555 These are brand new tools by the way. 377 00:19:01.575 --> 00:19:05.315 So we've got a market research report, we've got a model, 378 00:19:05.855 --> 00:19:08.555 uh, P&L, we know some things about the market. 379 00:19:09.415 --> 00:19:13.675 Um, let's flip over to OpenAI and custom GPTs. 380 00:19:14.415 --> 00:19:18.715 Um, if you're not using custom GPTs, um, couple of things. 381 00:19:18.815 --> 00:19:23.075 One is you can only develop custom GPTs if you've got a 382 00:19:23.695 --> 00:19:27.475 $20, um, a month subscription to, uh, 383 00:19:27.985 --> 00:19:31.035 ChatGPT, which is about what it costs to get a, 384 00:19:31.155 --> 00:19:32.635 a good Netflix subscription. 385 00:19:33.295 --> 00:19:35.355 Um, you can use custom GPTs 386 00:19:35.355 --> 00:19:37.275 that someone else develops if you've got a free account. 387 00:19:37.855 --> 00:19:41.835 But for me, this is kind of the gateway application for AI. 388 00:19:41.935 --> 00:19:44.515 If you can develop custom GPT, you are going 389 00:19:44.515 --> 00:19:49.075 to get significantly more leverage out of your use of the 390 00:19:49.605 --> 00:19:52.715 model than, than you can without, um, developing that. 391 00:19:52.735 --> 00:19:54.755 And I think you'll see that in the next example. 392 00:19:55.495 --> 00:19:58.275 So we've got, um, this market study. 393 00:19:58.645 --> 00:20:01.395 We've got some ideas around a P&L, uh, 394 00:20:01.615 --> 00:20:03.795 but there's a good deal of uncertainty 395 00:20:04.055 --> 00:20:05.835 and, you know, you've talked with the team 396 00:20:06.375 --> 00:20:08.515 and they've told you a little bit about your new boss 397 00:20:09.335 --> 00:20:12.315 and they're, uh, they know that they like 398 00:20:12.315 --> 00:20:13.555 to beat up scenarios. 399 00:20:13.555 --> 00:20:17.415 So you developed, along with the help of Kelley School, 400 00:20:18.055 --> 00:20:22.375 a scenario-planning custom GPT that cranks out scenarios 401 00:20:22.565 --> 00:20:25.855 because of course, the only thing you know, with a, 402 00:20:25.855 --> 00:20:27.575 about a forecast with a hundred percent 403 00:20:27.575 --> 00:20:29.615 certainty is that it's wrong. 404 00:20:30.195 --> 00:20:31.975 So it's always great to do scenario planning. 405 00:20:32.115 --> 00:20:34.775 So, um, I love this art of the long view approach 406 00:20:34.835 --> 00:20:35.935 to strategic planning. 407 00:20:35.955 --> 00:20:39.575 So let's take a look at a custom GPT implementation. 408 00:20:39.575 --> 00:20:41.015 I'll walk you through what's happening here. 409 00:20:41.035 --> 00:20:44.495 So you can see this is a 10-dimension scenario planning. 410 00:20:44.515 --> 00:20:47.725 It looks at social economic political trends. 411 00:20:48.255 --> 00:20:52.125 We've given it this bicycle market, uh, study that, uh, 412 00:20:52.305 --> 00:20:54.685 we did with deep research from Google. 413 00:20:55.585 --> 00:20:57.445 The other thing you'll note here is the 414 00:20:57.445 --> 00:20:58.725 interaction of these tools. 415 00:20:59.015 --> 00:21:00.965 These tools are all having 416 00:21:01.925 --> 00:21:03.005 relative strengths to one another. 417 00:21:03.305 --> 00:21:06.045 And the combination of these tools is 418 00:21:06.045 --> 00:21:08.845 what I feel is extremely important for, 419 00:21:09.065 --> 00:21:10.925 uh, business applications. 420 00:21:15.655 --> 00:21:17.715 So again, it makes quick work of this 421 00:21:22.935 --> 00:21:24.195 and funny how impatient we get. 422 00:21:24.195 --> 00:21:25.395 We didn't have this three months ago 423 00:21:25.395 --> 00:21:26.475 and now it's like taking too long. 424 00:21:30.175 --> 00:21:33.235 And look at this all the way to a PowerPoint slide. 425 00:21:33.375 --> 00:21:34.635 That's a pretty good Venn diagram. 426 00:21:35.455 --> 00:21:37.475 And we've got three scenarios. 427 00:21:38.055 --> 00:21:42.835 The smart safety-first scenario, the eco-style revolution, 428 00:21:43.415 --> 00:21:45.995 and the value-driven, uh, basics. 429 00:21:46.375 --> 00:21:48.115 So these are three scenarios 430 00:21:48.545 --> 00:21:51.395 that gives you a good scaffolding for your planning. 431 00:21:55.635 --> 00:21:57.605 Alright, another one from OpenAI. 432 00:22:01.575 --> 00:22:03.885 Again, now we've got three scenarios, 433 00:22:04.385 --> 00:22:06.245 but you know, you've got tough management. 434 00:22:06.325 --> 00:22:08.405 I always liken upper management to my dog. 435 00:22:08.485 --> 00:22:10.405 I give my dog a biscuit, she eats it. 436 00:22:10.985 --> 00:22:12.445 And then she immediately forgets 437 00:22:12.445 --> 00:22:14.285 I gave her a biscuit, and she wants another biscuit. 438 00:22:15.425 --> 00:22:17.325 Has anybody ever worked for a manager 439 00:22:17.325 --> 00:22:18.725 that's like my golden retriever? 440 00:22:19.585 --> 00:22:22.205 Our financial-driven leadership team 441 00:22:22.835 --> 00:22:25.045 will wanna see those financials, 442 00:22:25.045 --> 00:22:26.205 will wanna see the scenarios, 443 00:22:26.705 --> 00:22:29.365 but I anticipate the next thing they're going to ask is 444 00:22:30.485 --> 00:22:34.495 what about a P&L structure for each of the scenarios? 445 00:22:34.605 --> 00:22:39.055 This is a demanding group, so we're lucky we have ChatGPT 446 00:22:39.055 --> 00:22:42.735 with code interpreter, and we have some capabilities there. 447 00:22:42.735 --> 00:22:45.015 We can do a new product forecast for each scenario, 448 00:22:45.685 --> 00:22:47.015 then a P&L. 449 00:22:47.715 --> 00:22:51.845 But wait, new product forecasting, that's a tough deal 450 00:22:52.115 --> 00:22:53.485 because you have no history. 451 00:22:54.385 --> 00:22:59.205 So I remember a long, long time ago, longer than for you 452 00:22:59.715 --> 00:23:02.725 that I learned something called the Bass model when I was 453 00:23:02.785 --> 00:23:06.165 in, um, school, my undergraduate and graduate courses. 454 00:23:07.025 --> 00:23:08.805 But it's always been a little clunky to use. 455 00:23:09.905 --> 00:23:11.885 But I bet pat ChatGPT 456 00:23:11.885 --> 00:23:13.205 with its code interpreter can use that. 457 00:23:13.345 --> 00:23:16.965 So let's ask ChatGPT to develop 458 00:23:17.615 --> 00:23:18.685 three forecasts, 459 00:23:18.785 --> 00:23:21.445 one for each scenario; three P&Ls, 460 00:23:21.445 --> 00:23:23.885 based on the Shimano model; 461 00:23:24.785 --> 00:23:28.625 um, and three complete scenario, 462 00:23:29.085 --> 00:23:30.385 uh, representations. 463 00:23:30.435 --> 00:23:35.385 Let's see how it does. So we feed it the 464 00:23:35.385 --> 00:23:36.985 P&Ls, we feed it the market study, 465 00:23:37.205 --> 00:23:39.385 and we feed it the Venn diagram so it knows the, 466 00:23:40.045 --> 00:23:41.865 the three scenarios that it's planning towards. 467 00:23:43.825 --> 00:23:46.045 So I'm, I'm showing this, I know, know it's going fast, 468 00:23:46.585 --> 00:23:49.405 but here's an estimated market size. 469 00:23:50.585 --> 00:23:52.805 So it predicted that based on a prompt, 470 00:23:54.865 --> 00:23:56.245 you give it some assumptions about 471 00:23:56.265 --> 00:23:58.645 how fast you think you can penetrate, um, 472 00:23:58.855 --> 00:24:00.405 based on this Bass model, 473 00:24:00.405 --> 00:24:01.965 which actually works pretty slick here 474 00:24:02.545 --> 00:24:05.345 and there you can see the Greek notation. 475 00:24:05.695 --> 00:24:08.545 Well, I, I've not used it in a few years [pause] 476 00:24:26.515 --> 00:24:28.015 and I'm getting a little greedy here 477 00:24:28.415 --> 00:24:29.655 'cause time is of the essence. 478 00:24:31.095 --> 00:24:33.595 I'm gonna ask chat GP to GPT to, uh, 479 00:24:34.095 --> 00:24:36.235 to develop my whole PowerPoint presentation for me. 480 00:24:36.245 --> 00:24:37.275 Let's see how it does with that. [pause] 481 00:24:48.295 --> 00:24:51.875 Pretty good goes through the market. 482 00:25:04.575 --> 00:25:05.665 There's our Venn diagram 483 00:25:08.085 --> 00:25:09.625 and here's each P&L 484 00:25:09.625 --> 00:25:13.145 scenario for each of the three scenarios 485 00:25:18.635 --> 00:25:20.255 and some recommendations for this three 486 00:25:20.255 --> 00:25:21.415 to five year strategic plan. 487 00:25:21.515 --> 00:25:26.215 So again, ChatGPT, 488 00:25:26.745 --> 00:25:29.935 these models can do a ton of stuff to help you. 489 00:25:30.045 --> 00:25:32.815 They're not always right, you still have to supervise them, 490 00:25:33.075 --> 00:25:34.895 but you still have to supervise your interns 491 00:25:35.155 --> 00:25:36.455 and your marketing assistants 492 00:25:36.455 --> 00:25:37.735 and your product managers as well. 493 00:25:40.255 --> 00:25:42.835 So, another dilemma. 494 00:25:43.215 --> 00:25:47.115 We know that our upper management is hungry for information. 495 00:25:47.115 --> 00:25:49.635 They're very enthusiastic about this project 496 00:25:49.635 --> 00:25:50.715 that you've been assigned to. 497 00:25:51.135 --> 00:25:53.635 You're gonna present them with these three scenarios. 498 00:25:54.055 --> 00:25:58.155 But again, anticipating what leadership and you need to do. 499 00:25:58.715 --> 00:26:03.075 Wouldn't it be great to be able to pressure test the 500 00:26:03.835 --> 00:26:06.395 P&Ls once they decide which one they 501 00:26:06.395 --> 00:26:07.475 like and which one they'll fund. 502 00:26:08.095 --> 00:26:10.195 So next up is Anthropics' Claude, 503 00:26:10.935 --> 00:26:13.915 and an application, uh, or a feature called Artifacts. 504 00:26:14.815 --> 00:26:18.385 And if you don't code 505 00:26:18.565 --> 00:26:22.065 and write Python, congratulations this afternoon, you can. 506 00:26:22.965 --> 00:26:26.425 Um, here I'm gonna ask Claude to take, 507 00:26:27.365 --> 00:26:28.905 uh, one of the P&L models. 508 00:26:28.985 --> 00:26:32.825 I think I, I gave it the, um, most likely 509 00:26:32.925 --> 00:26:34.025 to start out in the middle. 510 00:26:35.045 --> 00:26:36.625 And I thought, wouldn't it be interesting 511 00:26:36.625 --> 00:26:40.425 to have an interactive app to share with my team to go back 512 00:26:40.425 --> 00:26:44.535 and forth and really be able 513 00:26:44.535 --> 00:26:47.415 to pressure-test this live, maybe even put it up in front of 514 00:26:47.415 --> 00:26:48.935 that, that management team 515 00:26:49.635 --> 00:26:52.415 and make a good impression on my first two weeks on the job. 516 00:26:53.155 --> 00:26:56.855 And you can see here in a second, this makes quick work. 517 00:26:56.875 --> 00:26:57.935 Writes the Python code. 518 00:26:57.935 --> 00:27:00.175 That's what you saw scrolling by was the Python code. 519 00:27:00.515 --> 00:27:03.215 And here is a fully functional widget 520 00:27:03.235 --> 00:27:04.695 or app, um, 521 00:27:05.585 --> 00:27:08.335 which doesn't necessitate you going into a clunky Excel, 522 00:27:08.995 --> 00:27:12.695 um, a clunky Excel application in front 523 00:27:12.695 --> 00:27:13.735 of the leadership team. 524 00:27:13.955 --> 00:27:15.575 The only thing worse than, uh, 525 00:27:15.795 --> 00:27:17.175 making a presentation in front 526 00:27:17.175 --> 00:27:18.895 of management is making a presentation in management 527 00:27:18.895 --> 00:27:21.415 where you have to pull up Excel and, 528 00:27:21.475 --> 00:27:23.495 and, um, do PowerPoint live. 529 00:27:26.895 --> 00:27:28.995 So now we've got a research study, 530 00:27:28.995 --> 00:27:30.515 we've got strategic planning scenarios. 531 00:27:30.515 --> 00:27:32.835 We've got a full deck for presentation. 532 00:27:32.845 --> 00:27:34.395 We've even got an interactive app. 533 00:27:35.055 --> 00:27:38.685 Um, but we got a green light. 534 00:27:39.795 --> 00:27:41.815 The good news is we got our green light. Go. 535 00:27:41.815 --> 00:27:44.095 They bought out, I don't remember which, uh, 536 00:27:44.195 --> 00:27:45.495 uh, P&L they they did. 537 00:27:45.495 --> 00:27:48.495 But now it's time to communicate, communicate, communicate. 538 00:27:48.495 --> 00:27:50.215 You've gotta get going 'cause you've got a forecast 539 00:27:50.235 --> 00:27:51.535 to make that you just sold. 540 00:27:52.195 --> 00:27:54.415 So let's turn a little bit more now to, 541 00:27:54.715 --> 00:27:55.815 to content development. 542 00:27:57.035 --> 00:28:00.465 Um, they didn't give us the budget. 543 00:28:02.505 --> 00:28:03.675 They gave us the forecast, 544 00:28:04.335 --> 00:28:05.435 but they didn't give us the budget. 545 00:28:05.785 --> 00:28:07.395 They didn't give us the headcount. 546 00:28:07.825 --> 00:28:10.235 They didn't uh okay, any international travel 547 00:28:10.235 --> 00:28:12.595 that I mentioned, that we do business in 60 countries. 548 00:28:13.815 --> 00:28:15.715 And we can barely afford social media. 549 00:28:15.815 --> 00:28:19.275 So one way to get the program quickly into the hands 550 00:28:19.275 --> 00:28:23.115 of our global team, what if we create, uh, 551 00:28:23.415 --> 00:28:25.355 an AI version of a program guide? 552 00:28:26.095 --> 00:28:28.635 And I know many of you have probably already discovered 553 00:28:28.955 --> 00:28:32.675 NotebookLM, a very powerful app from Google that is, uh, um, 554 00:28:32.725 --> 00:28:36.115 again, free to use, uh, but with incredible power. 555 00:28:36.215 --> 00:28:39.955 So let's, uh, let's take a listen and a note here. 556 00:28:40.015 --> 00:28:42.955 So you start out by, um, going in 557 00:28:43.015 --> 00:28:44.715 and giving it some resources. 558 00:28:44.715 --> 00:28:48.795 These can be YouTube videos, transcripts, um, websites. 559 00:28:49.055 --> 00:28:50.955 In this case I gave it our market study. 560 00:28:52.815 --> 00:28:54.555 And it can produce an outline, 561 00:28:54.615 --> 00:28:58.555 it can produce an FAQ, and imagine sharing with your team 562 00:28:59.055 --> 00:29:01.995 all this immediately generated on your business 563 00:29:02.005 --> 00:29:03.715 plans and your market studies. 564 00:29:04.535 --> 00:29:08.235 But the real clicker is this feature called 565 00:29:08.565 --> 00:29:09.915 audio summaries. Listen, 566 00:29:10.775 --> 00:29:12.835 [AI voice 1] Hey everyone, and welcome to another deep dive. 567 00:29:13.165 --> 00:29:15.795 Today, we'll be exploring the world of bicycle accessories. 568 00:29:16.715 --> 00:29:18.875 Specifically, we're gonna zero in on reflectors. 569 00:29:18.935 --> 00:29:20.915 [AI voice 2] Sounds good. [AI voice 1] You know, especially since you've been looking 570 00:29:20.915 --> 00:29:22.075 into cycling gear lately, 571 00:29:22.795 --> 00:29:26.115 I thought why not shine some light on this often overlooked 572 00:29:26.295 --> 00:29:27.835 but vital piece of equipment. 573 00:29:27.865 --> 00:29:28.865 [AI voice 2] Yeah, good idea. 574 00:29:30.115 --> 00:29:32.055 [Paul] What's interesting about that is it is good 575 00:29:32.055 --> 00:29:34.295 that those podcasters are talking about my work, but 576 00:29:34.355 --> 00:29:36.135 [AI voice 1] Hey everyone, and welcome to another deep dive. 577 00:29:36.145 --> 00:29:38.895 Today we'll be exploring the world of bicycle accessories. 578 00:29:39.495 --> 00:29:41.570 Specifically, we're gonna zero in on reflectors. 579 00:29:41.570 --> 00:29:43.405 [AI voice 2] Sounds good. [AI voice 1] You know, especially since you've been looking 580 00:29:43.405 --> 00:29:44.885 at a cycling gear lately, I thought, 581 00:29:45.305 --> 00:29:48.085 why not shine some light on this often overlooked 582 00:29:48.225 --> 00:29:49.885 but vital piece of equipment. [AI voice 2] Yeah, 583 00:29:49.885 --> 00:29:50.885 Good idea. [AI voice 1] So 584 00:29:50.885 --> 00:29:52.445 the big question we're gonna tackle today is, 585 00:29:52.785 --> 00:29:55.485 is there a real opportunity in the bicycle reflector market? 586 00:29:55.485 --> 00:29:57.845 We're gonna sift through the data, look at some trends 587 00:29:58.065 --> 00:30:00.205 and try to get a clear picture of this niche, 588 00:30:00.625 --> 00:30:02.045 but potentially lucrative market. 589 00:30:02.585 --> 00:30:04.725 [Recorded Paul] Hey, so I was wondering if you're gonna take a deep dive 590 00:30:04.725 --> 00:30:07.285 in the market, if you're gonna be looking at any particular 591 00:30:07.375 --> 00:30:10.125 years and which segments you're gonna be looking at? 592 00:30:11.265 --> 00:30:12.685 [AI voice 1] That's a great question, and yes, 593 00:30:12.705 --> 00:30:14.445 we absolutely are gonna dig into the market 594 00:30:14.445 --> 00:30:15.645 details. [AI voice 2] We definitely 595 00:30:15.675 --> 00:30:17.725 plan to look closely at specific years 596 00:30:17.865 --> 00:30:18.885 and market segments. 597 00:30:18.985 --> 00:30:21.005 [AI voice 2] In fact, we've got some data from 2023 598 00:30:21.005 --> 00:30:23.045 and projections all the way out to 2030. 599 00:30:23.705 --> 00:30:27.325 [Paul Acito] So outside of these two synthetic podcasters 600 00:30:28.155 --> 00:30:29.565 talking about your study 601 00:30:29.625 --> 00:30:33.965 and the data that you gave to them, you can interject, 602 00:30:34.055 --> 00:30:35.205 steer the conversation, 603 00:30:35.825 --> 00:30:38.205 and all of it's captured in your notebook. 604 00:30:39.345 --> 00:30:43.565 Um, I couldn't resist to say so that's great. 605 00:30:43.905 --> 00:30:46.765 Um, but if we're sending this around the world 606 00:30:46.785 --> 00:30:48.965 and around to our teams, uh, 607 00:30:48.965 --> 00:30:51.005 what if we gave it a little more personality? [AI voice 1] Hey 608 00:30:51.205 --> 00:30:52.845 everyone, and welcome to another deep dive. 609 00:30:53.175 --> 00:30:55.965 Today we'll be exploring the world of bicycle accessories. 610 00:30:56.685 --> 00:30:58.885 Specifically, we're gonna zero in on reflectors. 611 00:30:58.985 --> 00:31:00.965 [AI voice 2] Sounds good. [AI voice 1] You know, especially since you've been looking 612 00:31:00.965 --> 00:31:02.125 into cycling gear lately, 613 00:31:02.805 --> 00:31:06.165 I thought why not shine some light on this often overlooked 614 00:31:06.345 --> 00:31:08.405 but vital piece of equipment. [AI voice 2] Yeah, good 615 00:31:08.405 --> 00:31:09.405 idea. [AI voice 1] So the 616 00:31:09.405 --> 00:31:10.725 big question we're gonna tackle today is, 617 00:31:10.725 --> 00:31:14.365 is there a real opportunity in the bicycle reflector market? 618 00:31:14.575 --> 00:31:16.845 We're gonna sift through the data, look at some trends, 619 00:31:17.025 --> 00:31:20.245 and try to get a clear picture of this niche, 620 00:31:20.665 --> 00:31:22.685 but potentially lucrative market. 621 00:31:22.915 --> 00:31:25.165 [AI voice 2] Okay. [AI voice 1] So let's jump right in. 622 00:31:25.415 --> 00:31:28.045 First up, let's talk market size. [AI voice 2] Okay. 623 00:31:28.305 --> 00:31:30.725 [AI voice 1] Now, the US bicycle accessories market is already 624 00:31:30.725 --> 00:31:32.245 pretty substantial, right? [AI voice 2] Oh yeah. 625 00:31:32.305 --> 00:31:36.565 It was valued at a cool $2.1 billion in 2023 626 00:31:36.985 --> 00:31:38.845 and projections for 2024, 627 00:31:39.025 --> 00:31:40.485 [AI voice 1] Let me guess, even bigger. [AI voice 2] You 628 00:31:40.545 --> 00:31:41.925 bet. It's estimated to hit [trails off] 629 00:31:42.735 --> 00:31:47.475 [Paul Acito] Pretty cool. Did I mention we do business in 60 countries? 630 00:32:08.935 --> 00:32:09.935 Thirty Languages. [Overlapping Spanish and English AI voices, unintelligible] One 631 00:32:09.935 --> 00:32:10.855 idea. 632 00:32:12.545 --> 00:32:13.875 [Paul Acito] Even without a communication, 633 00:32:16.275 --> 00:32:18.855 you can really scale your operation. 634 00:32:18.915 --> 00:32:21.255 And again, this is early days. 635 00:32:21.765 --> 00:32:24.815 This is the worst AI you are ever going to work with. 636 00:32:24.885 --> 00:32:28.695 It's only gonna get better, smoother, and easier to use. 637 00:32:30.785 --> 00:32:35.165 One thing that the management team was insistent upon is 638 00:32:35.165 --> 00:32:39.085 they really liked the proposal for your brand voice, 639 00:32:40.005 --> 00:32:43.865 but they wanna make sure that you stay on brand voice. 640 00:32:43.865 --> 00:32:46.705 They've had issues before with as they go international, 641 00:32:46.705 --> 00:32:48.105 as they go across divisions, 642 00:32:48.375 --> 00:32:51.465 that the brand voice gets, gets off track. 643 00:32:52.485 --> 00:32:55.585 And this is where Claude style guides comes in. 644 00:32:55.685 --> 00:33:00.025 Claude actually has a style guide feature where 645 00:33:00.595 --> 00:33:03.505 it'll either walk you through how to create a brand voice 646 00:33:03.565 --> 00:33:05.145 or style guide if you don't have one, 647 00:33:05.485 --> 00:33:08.865 or you can upload yours and then ask it to write. 648 00:33:09.525 --> 00:33:11.105 In this case, I'll ask it to write a 649 00:33:11.955 --> 00:33:16.625 email welcoming a customer or an email welcoming or, 650 00:33:16.685 --> 00:33:19.305 or responding to a customer who didn't get the right order, 651 00:33:19.565 --> 00:33:24.265 or whatever communication you need to make using your voice 652 00:33:24.885 --> 00:33:27.225 and making sure that you are on brand 653 00:33:27.525 --> 00:33:28.705 for all your communications. 654 00:33:29.285 --> 00:33:33.425 And you can see here, um, just crank me out 655 00:33:33.425 --> 00:33:35.655 an email. Does it instantly, 656 00:33:36.565 --> 00:33:40.225 and it uses the terminology, the vocabulary, the style, 657 00:33:40.565 --> 00:33:42.905 the dos, and the don'ts of your brand voice. 658 00:33:43.695 --> 00:33:47.865 This is available for free on Claude this afternoon. 659 00:33:52.785 --> 00:33:57.255 But again, did I mention we do business in 60 countries? 660 00:33:57.675 --> 00:34:01.035 Um, so this 661 00:34:02.005 --> 00:34:04.195 style guide can also translate your brand voice 662 00:34:04.225 --> 00:34:05.435 into 30 languages. 663 00:34:07.695 --> 00:34:09.995 So again, why show off all this? 664 00:34:10.335 --> 00:34:14.275 Why talk about this if you're not leveraging 665 00:34:15.205 --> 00:34:19.835 ChatGPT, Claude, Gemini, xAI, 666 00:34:20.105 --> 00:34:24.755 Meta, Perplexity, the rest, and I suspect that you are, 667 00:34:25.895 --> 00:34:28.715 but many organizations still have a policy 668 00:34:29.015 --> 00:34:32.715 of prohibiting the use within the company that you work for, 669 00:34:33.215 --> 00:34:34.715 and it's important that you get governance. 670 00:34:39.225 --> 00:34:41.175 Let's round out our business planning. 671 00:34:41.455 --> 00:34:43.935 I remember I kind of talked about we're not gonna do an 672 00:34:43.935 --> 00:34:45.215 awful lot with content 673 00:34:45.325 --> 00:34:47.975 because that's been well covered in the media, 674 00:34:48.555 --> 00:34:50.975 but I thought I'd show you one example of Sora, 675 00:34:50.975 --> 00:34:52.735 which is brand new from OpenAI 676 00:34:53.275 --> 00:34:54.295 and how you can use that 677 00:34:54.295 --> 00:34:57.895 to develop either social media guides, excuse me, 678 00:34:57.895 --> 00:35:00.175 social media ads or even online ads. 679 00:35:00.715 --> 00:35:02.695 And this is, uh, this is Sora, 680 00:35:03.075 --> 00:35:07.935 and this is, uh, how one use case for taking a, a, 681 00:35:07.955 --> 00:35:10.415 uh, stock photo from a Adobe stock 682 00:35:10.795 --> 00:35:12.495 and asking it to animate it. 683 00:35:12.495 --> 00:35:14.895 So it started out with a picture, not a, not a video, 684 00:35:15.675 --> 00:35:18.455 and, uh, introduced motion into this. 685 00:35:19.195 --> 00:35:23.815 And with a common video editor like, uh, Adobe Premiere, 686 00:35:24.555 --> 00:35:27.615 or you know, others, you can, 687 00:35:27.675 --> 00:35:31.335 you can pretty much drop your logo in, do 688 00:35:31.985 --> 00:35:33.025 some experimentation 689 00:35:33.445 --> 00:35:37.255 and get your product, uh, in motion literally. 690 00:35:37.595 --> 00:35:40.055 And by the way, traffic is one of the harder things 691 00:35:40.075 --> 00:35:41.455 for these models to do right now. 692 00:35:41.955 --> 00:35:43.415 And again, it's early days. 693 00:35:43.415 --> 00:35:46.695 This is less than I think 30 days released, is Sora. 694 00:35:46.755 --> 00:35:48.615 So that's it. 695 00:35:49.075 --> 00:35:51.015 Um, if I've done my job right, 696 00:35:51.015 --> 00:35:52.175 you should have seen at least one 697 00:35:52.175 --> 00:35:55.015 or two new AI applications for business. 698 00:35:55.565 --> 00:35:59.295 This is a sliver of what can be done with AI for business. 699 00:35:59.915 --> 00:36:02.295 But if you stick around, wait, there's more. 700 00:36:02.835 --> 00:36:05.375 Uh, there is two more quick examples 701 00:36:06.035 --> 00:36:10.495 and you can see what our agentic future holds for us, uh, 702 00:36:10.635 --> 00:36:14.135 for the, uh, um, 2025 year. 703 00:36:15.355 --> 00:36:20.255 So, um, we'll start out by listening to the CEO 704 00:36:20.395 --> 00:36:22.255 of the world's most valuable company, 705 00:36:22.275 --> 00:36:25.575 and you can see the quote from Jensen here on agents. 706 00:36:26.315 --> 00:36:28.615 [Jensen Huang] AI has been advancing at an incredible pace. 707 00:36:29.245 --> 00:36:31.335 Started with perception AI. 708 00:36:32.155 --> 00:36:34.295 We now can understand images and words 709 00:36:34.315 --> 00:36:37.255 and sounds to generative AI. 710 00:36:37.795 --> 00:36:40.055 We can generate images and texts and sounds 711 00:36:41.275 --> 00:36:45.635 and now agentic AI, AIs that can 712 00:36:46.155 --> 00:36:48.915 perceive, reason, plan, and act. 713 00:36:52.045 --> 00:36:56.225 [Paul Acito] So while we're still defining agentic AI, 714 00:36:57.045 --> 00:36:59.505 we can be off to the races in terms of our use. 715 00:37:00.525 --> 00:37:03.865 Uh, you've probably heard about Anthropics' 716 00:37:03.885 --> 00:37:06.865 Claude computer control. If you haven't, 717 00:37:07.295 --> 00:37:11.215 what this is is a program whereby 718 00:37:11.955 --> 00:37:15.375 you can offer control of your keyboard, mouse, 719 00:37:15.875 --> 00:37:18.045 and apps to Claude. 720 00:37:19.425 --> 00:37:23.455 Lemme repeat that: entire control of your computer to the AI. 721 00:37:24.285 --> 00:37:28.015 What could possibly go wrong? So here's an example. 722 00:37:28.035 --> 00:37:29.855 You'll note that I'm using a container. 723 00:37:29.855 --> 00:37:32.175 I didn't let it have access to my whole computer. 724 00:37:32.875 --> 00:37:35.135 Of all of the apps that I'm showing you 725 00:37:35.155 --> 00:37:36.775 and applications that I'm showing you today, 726 00:37:36.775 --> 00:37:40.335 this is one I would not recommend, uh, that you use it. 727 00:37:40.335 --> 00:37:44.745 If you do, be sure to containerize it with, um, one of the, 728 00:37:45.245 --> 00:37:46.745 um, one of the containers 729 00:37:46.745 --> 00:37:48.465 that's available online, usually for free. 730 00:37:49.045 --> 00:37:50.945 But here I've given it a one line quote 731 00:37:50.945 --> 00:37:54.225 and I said, go download some pictures or images of cyclists. 732 00:37:54.365 --> 00:37:56.705 That's all I asked it to do. I didn't tell where to look. 733 00:37:57.225 --> 00:37:59.905 I didn't tell, uh, it, uh, how to look. 734 00:38:00.165 --> 00:38:03.505 It went in, figured out which search engines to look to, uh, 735 00:38:03.525 --> 00:38:05.505 to use and goes through 736 00:38:06.005 --> 00:38:09.865 and downloads, as you can see to my, um, downloads, 737 00:38:10.005 --> 00:38:11.475 uh, folder, 738 00:38:11.655 --> 00:38:15.195 the, the, the pictures. And there you are. 739 00:38:15.575 --> 00:38:17.235 And that was just one simple example. 740 00:38:18.215 --> 00:38:22.875 Um, but where I think the big payout is going to be 741 00:38:23.655 --> 00:38:27.475 in 2025 is with AI automations and agents. 742 00:38:28.015 --> 00:38:29.675 So we've got, um, 743 00:38:30.185 --> 00:38:32.235 this built into some of our courses already. 744 00:38:32.305 --> 00:38:35.965 This is, uh, the logo for make.com, which if you're familiar 745 00:38:35.965 --> 00:38:38.365 with Zapier or Make or Plum 746 00:38:38.825 --> 00:38:41.205 or uh, uh, n8n, 747 00:38:41.305 --> 00:38:44.725 or the rest, this is one of these, uh, AI automation 748 00:38:45.255 --> 00:38:46.685 agent, uh, environments. 749 00:38:47.665 --> 00:38:52.015 And let's imagine, once again, back to our business 750 00:38:52.015 --> 00:38:53.015 with your budget cuts, 751 00:38:53.035 --> 00:38:56.415 you didn't get the requisition signed that you wanted 752 00:38:56.435 --> 00:38:58.495 for a market research analyst. 753 00:38:58.675 --> 00:39:02.975 So you designed one, and his name is Paul. 754 00:39:03.435 --> 00:39:06.295 And all you have to do is send Paul an email. 755 00:39:06.295 --> 00:39:07.375 Here we have an email that says, 756 00:39:07.795 --> 00:39:09.695 please design the following survey. 757 00:39:10.595 --> 00:39:12.335 And, uh, you'll probably get this survey. 758 00:39:12.835 --> 00:39:16.215 Uh, what, uh, what kind of use do you make of, of the, uh, 759 00:39:16.215 --> 00:39:18.135 different, the different tools? 760 00:39:18.355 --> 00:39:19.895 And, uh, ask an open-ended question 761 00:39:19.895 --> 00:39:21.175 and see if you can collect their job title. 762 00:39:22.555 --> 00:39:25.655 So all you do, just like you would with Paul if Paul 763 00:39:25.655 --> 00:39:27.335 were your market research analyst, 764 00:39:27.955 --> 00:39:29.375 is you send Paul an email. 765 00:39:30.885 --> 00:39:35.115 Um, our agent gets this email, 766 00:39:35.305 --> 00:39:37.635 it's automatically filed in a specific folder. 767 00:39:37.695 --> 00:39:40.635 And this triggers an automation, an AI automation, 768 00:39:41.045 --> 00:39:43.195 which goes through and designs the survey. 769 00:39:44.795 --> 00:39:47.175 So it designs the survey in Google Forms in this instance. 770 00:39:48.015 --> 00:39:50.315 And you can see there's the form that goes out. 771 00:39:50.935 --> 00:39:55.755 So this is, uh, um, a pretty competent survey, online survey, 772 00:39:56.505 --> 00:40:00.915 that is sent out to a prescribed mailing list, email list. 773 00:40:04.055 --> 00:40:06.395 And once the due date has passed, 774 00:40:06.785 --> 00:40:10.815 that triggers a second AI automation, which goes through 775 00:40:11.675 --> 00:40:15.695 and analyzes the data, the sentiment, 776 00:40:16.035 --> 00:40:18.255 and produces a market research report. 777 00:40:18.435 --> 00:40:20.735 So this is where this is headed. 778 00:40:21.395 --> 00:40:24.855 You will hopefully be in 2025 creating your own team. 779 00:40:24.955 --> 00:40:29.215 So leave space on your work chart for AI agents. 780 00:40:32.345 --> 00:40:34.365 So that's, um, that's what I had. 781 00:40:34.385 --> 00:40:35.685 I'm gonna give one more plug here 782 00:40:35.685 --> 00:40:37.245 and then hand things over quickly to Kim. 783 00:40:37.365 --> 00:40:39.565 I think we're right on the money in terms of time. 784 00:40:40.345 --> 00:40:43.405 Um, this is the course that we're doing for the third time. 785 00:40:43.585 --> 00:40:45.405 Uh, it's frankly all new. 786 00:40:45.985 --> 00:40:48.605 Uh, but, um, if you take the course, you'll have access 787 00:40:48.625 --> 00:40:53.125 to the, um, changed content as, as things progress. 788 00:40:55.085 --> 00:40:57.575 It's, uh, called AI Applications in Marketing. 789 00:40:57.645 --> 00:40:58.735 It's spring, obviously 790 00:40:58.735 --> 00:41:01.815 marketing is gonna be a early mover in the application. 791 00:41:02.045 --> 00:41:04.095 There's a link to the, uh, signup. 792 00:41:04.595 --> 00:41:07.135 Um, we'll show off a little bit of the, uh, 793 00:41:07.165 --> 00:41:08.455 faculty that we've got. 794 00:41:08.645 --> 00:41:11.615 This is all, uh, not all of these are confirmed 795 00:41:11.615 --> 00:41:15.015 for February, but these are all speakers that we have, uh, 796 00:41:15.365 --> 00:41:19.495 used in the past, uh, including, uh, people from Nvidia, 797 00:41:20.065 --> 00:41:24.455 Intel, three CEOs of ad agencies, 3M, uh, 798 00:41:24.635 --> 00:41:27.695 and of course Kelley's, uh, uh, um, 799 00:41:29.325 --> 00:41:31.835 staff of, uh, of, uh, professors. 800 00:41:32.375 --> 00:41:34.485 So with that, um, I'll turn things over to Kim. 801 00:41:34.495 --> 00:41:36.165 Thank you very much for your kind attention 802 00:41:36.465 --> 00:41:38.725 and, uh, uh, send warm thoughts. 803 00:41:40.115 --> 00:41:41.185 [Kim Allison] Thank you so much, Paul. 804 00:41:41.485 --> 00:41:43.025 Hi everyone, my name's Kim Allison 805 00:41:43.215 --> 00:41:44.745 with Kelley Executive Education. 806 00:41:44.745 --> 00:41:46.225 Thank you all for joining us today. 807 00:41:46.845 --> 00:41:48.385 So I hope you guys have enjoyed this session. 808 00:41:48.605 --> 00:41:50.905 If you wanna learn more about AI applications 809 00:41:51.205 --> 00:41:53.705 and dive a little deeper into some of these topics, 810 00:41:53.845 --> 00:41:55.665 we have a great course coming up called 811 00:41:56.325 --> 00:41:57.905 AI Applications for Marketing. 812 00:41:58.125 --> 00:41:59.425 Um, Paul Acito 813 00:41:59.565 --> 00:42:01.465 and some outstanding faculty are 814 00:42:01.465 --> 00:42:02.505 gonna be teaching this program. 815 00:42:03.325 --> 00:42:04.985 The course will be offered online 816 00:42:05.055 --> 00:42:07.505 with some live meetings Tuesdays and Thursdays. 817 00:42:07.805 --> 00:42:11.265 We have a QR code here on the slide, so feel free to scan 818 00:42:11.265 --> 00:42:14.505 that and take a look at, um, more details about the course. 819 00:42:15.365 --> 00:42:16.985 If you have any questions, feel free 820 00:42:16.985 --> 00:42:18.065 to reach out to me anytime. 821 00:42:18.245 --> 00:42:20.145 I'm gonna put my email address in the chat. 822 00:42:20.845 --> 00:42:22.505 Um, and again, if you're not familiar 823 00:42:22.505 --> 00:42:24.945 with Kelley Executive Education, we provide a range 824 00:42:24.945 --> 00:42:27.185 of different courses and certificate programs 825 00:42:27.285 --> 00:42:29.305 to meet the needs of working professionals. 826 00:42:29.885 --> 00:42:31.625 Um, we cover a number of different topics 827 00:42:31.685 --> 00:42:35.505 around AI leadership, operational excellence, finance, 828 00:42:35.655 --> 00:42:37.265 project management, and more. 829 00:42:37.765 --> 00:42:40.145 So, um, anyway, I'll add my email to the chat, 830 00:42:40.145 --> 00:42:41.505 so if you have any questions, feel free 831 00:42:41.505 --> 00:42:42.585 to reach out to me anytime. 832 00:42:43.085 --> 00:42:44.305 And Paul, I'll hand it back to you. 833 00:42:48.085 --> 00:42:50.195 [Paul] Great. I'm, you know, we're at time. 834 00:42:50.615 --> 00:42:52.355 Um, that, that's fine. 835 00:42:52.355 --> 00:42:54.475 So you can get back to get back to work 836 00:42:55.055 --> 00:42:57.875 or if you're using AI, um, you can be more productive 837 00:42:57.875 --> 00:42:59.115 and stick around for some Q&A. 838 00:42:59.575 --> 00:43:02.475 Uh, but I'd be happy to stick around for 5, 10, 15 minutes. 839 00:43:02.655 --> 00:43:04.675 Uh, um, I don't have another, uh, appointment 840 00:43:04.675 --> 00:43:06.715 until 12, uh, our time. 841 00:43:07.575 --> 00:43:10.835 But, um, if you wanna put a question in the chat, um, 842 00:43:10.835 --> 00:43:12.195 especially about the course or, 843 00:43:12.255 --> 00:43:15.155 or, you know, any other thoughts, um, 844 00:43:15.415 --> 00:43:18.235 or any other, uh, things that you'd like me to share, uh, 845 00:43:18.235 --> 00:43:21.035 because I can't really put you on on speaker, um, 846 00:43:21.615 --> 00:43:23.955 you can say that I'll, I'm gonna just brag a little bit. 847 00:43:23.955 --> 00:43:26.635 These are some of the, uh, comments that, uh, 848 00:43:26.975 --> 00:43:28.875 our students have made about, uh, 849 00:43:29.005 --> 00:43:30.675 about the course that in marketing. 850 00:43:30.815 --> 00:43:33.155 And we're, we're going to have an entire portfolio 851 00:43:33.895 --> 00:43:36.155 of courses, AI applications in business 852 00:43:36.175 --> 00:43:38.915 and strategy, uh, Python for marketers. 853 00:43:39.295 --> 00:43:42.155 Uh, it's, it's gonna be a fun year. [pause] [unknown voice in the background] Could 854 00:43:43.765 --> 00:43:46.405 you ask Kim to drop the, to register 855 00:43:46.405 --> 00:43:47.605 for the course in the chat? 856 00:43:48.465 --> 00:43:51.005 [Paul] Hey, Kim, if you're still on, can you drop the link 857 00:43:51.025 --> 00:43:53.205 to register for the course in the chat? 858 00:43:54.645 --> 00:43:56.285 [Kim] Absolutely. [Paul] Oh, oh, okay. You're still there. 859 00:43:56.365 --> 00:43:57.805 I was gonna say, I could probably figure that out. 860 00:43:58.235 --> 00:44:00.165 [Kim] Yeah, absolutely. I'll drop it in the chat for anyone 861 00:44:00.165 --> 00:44:01.205 that needs some more information. 862 00:44:02.025 --> 00:44:03.765 Um, and Paul, we do have, we have a couple 863 00:44:03.765 --> 00:44:05.605 of questions about privacy. 864 00:44:06.015 --> 00:44:07.405 Could you touch a little bit on that? 865 00:44:07.585 --> 00:44:11.405 Um, Candace was wondering, you know, are there any privacy, 866 00:44:11.865 --> 00:44:15.245 what's the privacy around information you're feeding to AI? 867 00:44:15.425 --> 00:44:17.125 For example, is ChatGPT 868 00:44:17.585 --> 00:44:19.525 or Google capturing what you're asking for 869 00:44:19.525 --> 00:44:21.485 and connecting it back to your company? 870 00:44:22.225 --> 00:44:23.325 Can you touch on that a little bit? 871 00:44:23.915 --> 00:44:28.125 [Paul] Yeah, we've got, in of course, of course the course is, 872 00:44:28.125 --> 00:44:31.645 of course, we, we cover privacy, bias, uh, and 873 00:44:31.825 --> 00:44:33.325 and security quite a bit. 874 00:44:33.555 --> 00:44:37.965 Yeah, this is, this is a, I'm, I'm gonna answer it depends. 875 00:44:38.395 --> 00:44:40.565 This is a, is a big deal. Privacy is a big deal. 876 00:44:40.595 --> 00:44:41.885 Data privacy is a big deal, 877 00:44:42.625 --> 00:44:44.245 so you should always proceed with caution. 878 00:44:44.585 --> 00:44:47.165 But there are things you can do if you have a paid version. 879 00:44:48.345 --> 00:44:50.165 I'm not working for ChatGPT, 880 00:44:50.165 --> 00:44:52.165 but if you have a paid version of ChatGPT, 881 00:44:52.165 --> 00:44:55.365 you can toggle off the, uh, sharing. 882 00:44:55.465 --> 00:44:57.365 In other words, they won't train on your data. 883 00:44:58.165 --> 00:44:59.845 I feel pretty good about that. 884 00:45:00.065 --> 00:45:01.365 Now, if you're working 885 00:45:01.465 --> 00:45:04.005 for a multibillion dollar organization, you're, you, 886 00:45:04.005 --> 00:45:07.685 you gotta go talk to your IT and, and marketing and, 887 00:45:07.685 --> 00:45:10.365 and financial governance teams to do that. 888 00:45:10.865 --> 00:45:13.645 So the answer is, it depends. You can protect it. 889 00:45:13.715 --> 00:45:15.965 Some of these experimental apps like, um, 890 00:45:17.875 --> 00:45:18.995 NotebookLM is okay, 891 00:45:19.055 --> 00:45:22.475 but Google Deep Research is probably training on that data. 892 00:45:22.655 --> 00:45:25.395 So my advice to you is if you're using something that's 893 00:45:26.315 --> 00:45:29.635 a free app, do not put any, first of all, 894 00:45:29.635 --> 00:45:33.795 never put any sensitive data anywhere in a, uh, 895 00:45:34.155 --> 00:45:35.235 a nonsecure system. 896 00:45:35.895 --> 00:45:38.595 Uh, but if you must, if you wanna do P&L 897 00:45:38.595 --> 00:45:43.475 for example, uh, things, don't put that into that, uh, 898 00:45:43.535 --> 00:45:45.235 uh, Stream Live at this point 899 00:45:45.505 --> 00:45:47.555 because they could be training on it. 900 00:45:47.555 --> 00:45:50.115 You're gonna have to read the fine print on some 901 00:45:50.115 --> 00:45:51.355 of these more experimental ones. 902 00:45:51.895 --> 00:45:53.315 But these companies are smart. 903 00:45:53.345 --> 00:45:56.515 They know you're not gonna use their apps if they're, 904 00:45:56.535 --> 00:45:58.435 if they're stealing your data. 905 00:45:58.975 --> 00:46:00.315 So they'll, they'll make that available. 906 00:46:00.375 --> 00:46:02.435 But that usually is behind a paywall. 907 00:46:02.535 --> 00:46:04.875 So I think you have to do a deep dive. 908 00:46:04.975 --> 00:46:07.635 We cover, again, all this in the, in the coursework where 909 00:46:08.015 --> 00:46:09.035 and how to set that up. 910 00:46:09.035 --> 00:46:12.835 But you can set up your preferences in ChatGPT paid version. 911 00:46:12.845 --> 00:46:16.235 Again, it's just ChatGPT, also in Gemini, the paid versions, 912 00:46:16.425 --> 00:46:18.555 that it will not train on your data. 913 00:46:18.855 --> 00:46:20.555 That's pretty, pretty good. 914 00:46:21.295 --> 00:46:22.675 And in the, in the course, again, 915 00:46:22.935 --> 00:46:26.555 we give everybody a ChatGPT team license, uh, when, 916 00:46:26.615 --> 00:46:28.995 and then we walk everyone through not to share the data. 917 00:46:29.135 --> 00:46:31.915 So, so, uh, it's a long answer, but it's, 918 00:46:31.915 --> 00:46:32.955 and it's a great question. 919 00:46:33.255 --> 00:46:37.675 And what I would say and cap off with that, don't let you 920 00:46:37.775 --> 00:46:41.265 or your organizations use 921 00:46:42.005 --> 00:46:45.615 the privacy risk to slow the adoption. 922 00:46:46.485 --> 00:46:49.615 There's a phenomenon called B-Y-O-A-I. 923 00:46:50.195 --> 00:46:53.375 So if the organization thinks it's stopping their employees 924 00:46:53.595 --> 00:46:56.175 or their students or their faculty from using AI 925 00:46:56.175 --> 00:46:57.615 because of the security risk, 926 00:46:57.765 --> 00:46:59.815 they bring their own AI to work. 927 00:47:00.785 --> 00:47:02.565 So there's this whole movement, 928 00:47:03.225 --> 00:47:08.125 and the entire reason that I'm doing these courses is 929 00:47:08.145 --> 00:47:10.685 to make sure you don't have to wait for the enterprise. 930 00:47:10.705 --> 00:47:14.845 If, if your enterprise is a year behind, that's like 10, 931 00:47:15.225 --> 00:47:19.045 10 years in, in dog years is, is, uh, in AI years. 932 00:47:19.945 --> 00:47:22.285 So you have to continue to hone your skillset. 933 00:47:22.475 --> 00:47:25.125 This stuff is not waiting for you to figure it out. 934 00:47:25.625 --> 00:47:28.165 And the skills that you developed were prompting the skills 935 00:47:28.165 --> 00:47:30.845 that you developed for understanding the capabilities. 936 00:47:31.515 --> 00:47:34.895 This is only gonna get better. Anything else? 937 00:47:34.955 --> 00:47:35.955 Any other questions, Kim? 938 00:47:38.215 --> 00:47:40.395 [Kim] Um, no, I just wanted to quickly mention, 939 00:47:40.555 --> 00:47:41.755 I forgot to mention this before. 940 00:47:42.215 --> 00:47:44.555 We are, for everyone that's attended the webinar, 941 00:47:44.805 --> 00:47:48.155 we're offering a $500 discount if you sign up for one 942 00:47:48.155 --> 00:47:50.435 of our, uh, Kelley Executive Education courses, 943 00:47:50.745 --> 00:47:52.755 including the AI course that we're talking about. 944 00:47:53.095 --> 00:47:56.715 So if you use Code PD500, which I added to the chat, 945 00:47:56.735 --> 00:47:58.155 you'll receive $500 off. 946 00:47:58.155 --> 00:47:59.395 So I just wanted to mention 947 00:47:59.395 --> 00:48:01.155 that quickly in case anyone is interested. 948 00:48:01.855 --> 00:48:03.635 [Paul] That's a, that's a serious discount. 949 00:48:04.265 --> 00:48:05.595 [Kim] Yeah. Um, 950 00:48:05.595 --> 00:48:08.635 and then we had just a couple questions asking about, 951 00:48:08.685 --> 00:48:11.995 maybe talking a little bit more about the AI marketing 952 00:48:11.995 --> 00:48:14.475 course that you're gonna be offering, um, in February. 953 00:48:14.895 --> 00:48:16.555 If you could just, uh, maybe go into a 954 00:48:16.555 --> 00:48:17.635 little bit more detail on that. 955 00:48:17.755 --> 00:48:18.995 I had a few questions around that. 956 00:48:19.795 --> 00:48:21.165 [Paul] Yeah, sure. So it's, 957 00:48:22.005 --> 00:48:24.055 it's gonna be a little bit different this time around. 958 00:48:24.055 --> 00:48:26.175 We're gonna have more emphasis on 959 00:48:26.245 --> 00:48:28.655 what we call playground activity, which is 960 00:48:29.265 --> 00:48:31.295 using these apps, learning how to develop the apps. 961 00:48:32.035 --> 00:48:33.815 Um, and that's, 962 00:48:33.815 --> 00:48:36.375 that's from feedback we've gotten from previous, uh, 963 00:48:36.375 --> 00:48:40.815 sessions and the, and we got great reviews as you can see, 964 00:48:41.075 --> 00:48:43.905 but we also cover a great deal. 965 00:48:44.005 --> 00:48:46.945 We start out with looking at leadership in AI 966 00:48:46.945 --> 00:48:48.185 because you have to watch this space. 967 00:48:48.235 --> 00:48:50.465 We're only about one half of 1%. 968 00:48:50.485 --> 00:48:51.785 The fact that you're on the phone 969 00:48:51.925 --> 00:48:55.865 or on the Zoom call today puts you in the top one half of 1% 970 00:48:56.165 --> 00:48:58.425 of people thinking about this in business right now. 971 00:48:58.925 --> 00:49:02.145 So I, you're not late, 972 00:49:03.675 --> 00:49:05.495 but you're behind, so to speak. 973 00:49:05.915 --> 00:49:08.535 Uh, if that makes any sense, uh, because we're all behind. 974 00:49:09.275 --> 00:49:12.895 And, and, um, so the course, it proceeds in four modules, 975 00:49:12.915 --> 00:49:16.975 uh, eight sessions, so twice a week for 90 minutes. 976 00:49:17.515 --> 00:49:19.695 And, uh, we, we, I think, 977 00:49:19.695 --> 00:49:21.895 have done a very thoughtful job about how we arrange this. 978 00:49:21.895 --> 00:49:26.455 So we get into and start out with discussions around, uh, 979 00:49:26.535 --> 00:49:30.055 a little bit of history, but, um, it's, it's all about 980 00:49:30.755 --> 00:49:32.535 AI applications for business 981 00:49:32.555 --> 00:49:36.215 and privacy concerns, bias, security, what some 982 00:49:36.215 --> 00:49:38.015 of the history is, who some of the leaders are. 983 00:49:38.435 --> 00:49:40.175 You know, things like, did you know 984 00:49:40.175 --> 00:49:44.535 that if you add up the top AI companies, the, uh, 985 00:49:44.535 --> 00:49:48.575 market cap is 18 trillion, which is the GDP of China, 986 00:49:49.595 --> 00:49:51.895 as an example of the kind of investment that's going on. 987 00:49:52.715 --> 00:49:54.735 And, and we, we walk through all that. 988 00:49:54.955 --> 00:49:57.975 And the reason is that this course is geared for leaders. 989 00:49:58.195 --> 00:50:01.915 You don't need to be a CMO, but if you've been asked 990 00:50:02.095 --> 00:50:05.115 or you would like to lead the AI deployment, 991 00:50:05.215 --> 00:50:08.195 or at least help foster the AI deployment in your 992 00:50:08.195 --> 00:50:10.355 organization, that's what this is doing. 993 00:50:10.455 --> 00:50:12.715 I'm trying to train leaders and we have something 994 00:50:12.715 --> 00:50:14.635 that nobody else does, which is our 995 00:50:15.295 --> 00:50:16.995 AI, GenAI code of ethics. 996 00:50:17.655 --> 00:50:19.475 Uh, and you'll have an opportunity to sign, 997 00:50:19.495 --> 00:50:21.515 and it's a, it's a commitment that says, look, 998 00:50:21.535 --> 00:50:23.875 I'm gonna use, basically use AI for good. 999 00:50:24.105 --> 00:50:25.355 Because as you can imagine, 1000 00:50:25.355 --> 00:50:27.075 these are some very powerful tools 1001 00:50:27.505 --> 00:50:29.965 and the powers of persuasion that they have, 1002 00:50:30.305 --> 00:50:32.565 and the ability to scrape data from the internet 1003 00:50:32.865 --> 00:50:35.485 or violate privacy is incredible. 1004 00:50:35.705 --> 00:50:37.605 And, and we'd like Kelley trained, 1005 00:50:38.025 --> 00:50:40.645 and certainly people who are, uh, trained on, 1006 00:50:40.665 --> 00:50:43.685 on my philosophy on business, uh, to, to use it for good. 1007 00:50:44.585 --> 00:50:48.405 Um, we have, um, Tim Lemper from our, um, uh, 1008 00:50:48.585 --> 00:50:49.925 law school coming in and, 1009 00:50:49.945 --> 00:50:53.405 and, uh, he's a Harvard, uh, law graduate who talks about, 1010 00:50:53.825 --> 00:50:55.885 uh, fair use and copyright. 1011 00:50:56.305 --> 00:50:58.525 So you'll get some clarification on, on that. 1012 00:50:58.535 --> 00:51:00.085 Again, not always a satisfying answer 1013 00:51:00.085 --> 00:51:01.245 because you still have to pay attention. 1014 00:51:01.705 --> 00:51:04.165 We have Sarah Bellamy coming in from, uh, 1015 00:51:04.185 --> 00:51:05.805 the Penumbra Center for Racial Healing 1016 00:51:05.825 --> 00:51:07.485 to talk about coded bias 1017 00:51:07.905 --> 00:51:10.725 and how we're training these systems to be biased, 1018 00:51:10.795 --> 00:51:13.045 just like the internet, just like frankly, 1019 00:51:13.115 --> 00:51:14.125 society in some cases. 1020 00:51:15.025 --> 00:51:19.445 Um, we have a whole session on AI analytics, the ability, 1021 00:51:19.685 --> 00:51:22.365 I kind of, I kind of tipped a hat on the Bass model. 1022 00:51:22.865 --> 00:51:24.205 So all those formulas 1023 00:51:24.205 --> 00:51:26.565 and all the very complex multivariate statistics 1024 00:51:26.565 --> 00:51:29.245 that you were trained on in business analytics, uh, 1025 00:51:29.275 --> 00:51:31.245 that you have to walk down the hall 1026 00:51:31.305 --> 00:51:34.325 and deal with a grumpy PhD in statistics 1027 00:51:34.345 --> 00:51:37.405 to get anything done are all three clicks away now. 1028 00:51:38.425 --> 00:51:39.685 And, and it's just incredible. 1029 00:51:40.665 --> 00:51:43.805 And then we have Kelly King coming in who teaches, uh, 1030 00:51:43.805 --> 00:51:45.605 innovation and communications at IU, 1031 00:51:45.985 --> 00:51:47.205 at Kelley School of Business. 1032 00:51:47.385 --> 00:51:49.725 And we'll walk you through all of this stuff. 1033 00:51:49.745 --> 00:51:52.205 So you can create your own ads, your own Facebook posts, 1034 00:51:52.205 --> 00:51:57.005 your own, um, Insta posts, uh, TikTok videos, uh, 1035 00:51:57.065 --> 00:51:58.725 in a matter of seconds with quality. 1036 00:51:58.725 --> 00:52:01.205 That's, uh, it's approaching Hollywood quality. 1037 00:52:01.625 --> 00:52:04.365 And then we round out with what I call the five A's. 1038 00:52:04.985 --> 00:52:07.525 And this is, uh, I always forget one A, 1039 00:52:07.525 --> 00:52:08.565 so let's see if I can do this. 1040 00:52:08.715 --> 00:52:11.445 It's, it's automations, assistance, agents, 1041 00:52:11.635 --> 00:52:12.765 avatars, and APIs. 1042 00:52:12.805 --> 00:52:17.485 I got 'em all. So it's, it's, it's kind one step 1043 00:52:17.585 --> 00:52:21.605 beyond, um, custom GPT, um, 1044 00:52:22.265 --> 00:52:25.585 not still no code or potentially low code, 1045 00:52:25.965 --> 00:52:29.825 but things that you can do that will blow the socks off 1046 00:52:29.825 --> 00:52:33.265 of anything else, including a very big emphasis on those 1047 00:52:33.265 --> 00:52:35.185 automations that we just showed, so 1048 00:52:35.185 --> 00:52:38.665 that you can create your own team of agents 1049 00:52:38.845 --> 00:52:40.465 to go do stuff for you. 1050 00:52:40.765 --> 00:52:42.385 And this is marketing at this point. 1051 00:52:44.465 --> 00:52:47.255 Again, sorry, long answers. I did I mention I'm a lecturer. 1052 00:52:54.225 --> 00:52:55.225 Anything else? Kim? 1053 00:52:57.695 --> 00:52:59.075 [Kim] Um, we have one question asking, 1054 00:52:59.255 --> 00:53:02.515 can you speak a little bit more to AI prompt engineering 1055 00:53:02.695 --> 00:53:05.555 as a skillset beyond the regular research 1056 00:53:05.555 --> 00:53:07.075 that people may use in AI? 1057 00:53:09.435 --> 00:53:12.935 [Paul] Yeah, prompt 1058 00:53:13.375 --> 00:53:14.655 engineering is a skill. 1059 00:53:16.195 --> 00:53:20.935 I would not recommend investing in learning 1060 00:53:20.935 --> 00:53:25.625 that skill because you're gonna need 1061 00:53:25.625 --> 00:53:26.665 to get good at prompt engineering 1062 00:53:26.745 --> 00:53:28.345 'cause especially in video generation 1063 00:53:28.345 --> 00:53:30.065 and image generation, there's some tricks there. 1064 00:53:31.445 --> 00:53:34.875 But the AI's doing the prompt engineering for you. 1065 00:53:34.895 --> 00:53:36.865 Now, both OpenAI 1066 00:53:37.525 --> 00:53:42.165 and Anthropic have prompt creators. 1067 00:53:43.065 --> 00:53:46.965 So, and my experience is that you come up with a prompt 1068 00:53:46.965 --> 00:53:49.205 that's kind of, for lack of a better term, 1069 00:53:49.205 --> 00:53:51.285 and like when I do it, it's an amateur prompt. 1070 00:53:51.965 --> 00:53:56.045 I put it into the, um, uh, Claude prompt generator 1071 00:53:56.045 --> 00:53:57.725 and it comes out like code. 1072 00:53:58.275 --> 00:54:00.085 It's unbelievable. I mean, you still have to go through 1073 00:54:00.465 --> 00:54:01.605 and you should still know what the 1074 00:54:01.605 --> 00:54:02.925 principles of a good prompt are. 1075 00:54:02.925 --> 00:54:04.365 You need to be specific, you need 1076 00:54:04.365 --> 00:54:06.005 to define a role and other things. 1077 00:54:06.105 --> 00:54:09.005 But you know, that, that, um, this idea 1078 00:54:09.075 --> 00:54:10.725 that there's a career path 1079 00:54:10.745 --> 00:54:15.045 or something in prompt engineering, um, I, I don't buy. 1080 00:54:15.305 --> 00:54:17.365 Um, I think you can, I mean, 1081 00:54:17.365 --> 00:54:20.285 if you're planning on writing apps by using English prompts, 1082 00:54:20.585 --> 00:54:23.605 you probably need to be an expert at prompt engineering. 1083 00:54:24.105 --> 00:54:26.485 But, uh, for what we're doing with business, I think 1084 00:54:26.485 --> 00:54:29.965 for the most part, uh, prompt engineering is, uh, something 1085 00:54:29.965 --> 00:54:33.005 that's better left to learning, you know, 1086 00:54:33.225 --> 00:54:34.725 and you know, what we teach in the course 1087 00:54:35.065 --> 00:54:37.885 and then using the tools, uh, that are available in, in some 1088 00:54:37.885 --> 00:54:39.885 of the frontier models to develop prompts. 1089 00:54:39.885 --> 00:54:40.885 Does that make sense? 1090 00:54:42.745 --> 00:54:44.875 [Kim] Yeah, that's great. Paul, one last question. 1091 00:54:44.935 --> 00:54:47.755 How familiar, um, someone asked, how familiar do I have 1092 00:54:47.755 --> 00:54:51.275 to be with AI, uh, to prepare for the, 1093 00:54:51.295 --> 00:54:52.635 the AI in Marketing course? 1094 00:54:52.895 --> 00:54:54.395 Do you need to have any background 1095 00:54:54.695 --> 00:54:56.195 or, you know, what should they prepare? 1096 00:54:56.615 --> 00:55:00.905 [Paul] Uh, the, the answer is, uh, you know, come, come along 1097 00:55:01.045 --> 00:55:03.265 and, and I'll tell you why that I, I wouldn't let 1098 00:55:03.265 --> 00:55:04.625 that were you for a second, 1099 00:55:04.685 --> 00:55:08.165 and here's why we have created, 1100 00:55:09.475 --> 00:55:12.415 go figure an AI agent. 1101 00:55:12.515 --> 00:55:16.095 So you will take a survey that's generated by AI 1102 00:55:16.755 --> 00:55:20.525 that goes in and asks you with your basic level 1103 00:55:20.525 --> 00:55:21.605 of familiarity with you. 1104 00:55:21.665 --> 00:55:23.365 In other words, it'll ask questions like, 1105 00:55:23.365 --> 00:55:25.285 do you have a paid subscription to ChatGPT? 1106 00:55:25.445 --> 00:55:26.605 'cause that tells us something about 1107 00:55:26.785 --> 00:55:28.125 how, how you're using it. 1108 00:55:28.665 --> 00:55:30.925 Um, um, how familiar are you 1109 00:55:30.925 --> 00:55:32.965 with prompt engineering as an example? 1110 00:55:33.635 --> 00:55:36.605 Then we will generate a personalized report. 1111 00:55:36.705 --> 00:55:38.525 So before module one, you'll have, 1112 00:55:38.945 --> 00:55:40.165 you'll have to watch a movie. 1113 00:55:40.705 --> 00:55:42.445 Uh, I mean, it's pretty light homework, 1114 00:55:42.945 --> 00:55:44.605 but maybe read this article. 1115 00:55:45.065 --> 00:55:46.565 Uh, what do you know about the history? 1116 00:55:46.865 --> 00:55:48.805 Uh, in fact, the only homework we're giving out, 1117 00:55:48.885 --> 00:55:49.885 I think at this point is a couple 1118 00:55:49.885 --> 00:55:53.285 of really good movies on AI, not not sci-fi movies, but, 1119 00:55:53.285 --> 00:55:54.925 but, uh, kind of documentaries. 1120 00:55:55.385 --> 00:55:58.485 So, um, so the answer is, I, there, 1121 00:55:58.485 --> 00:56:00.405 there are no prerequisites at this point. 1122 00:56:00.465 --> 00:56:02.685 You don't need to know anything about Python, you don't need 1123 00:56:02.685 --> 00:56:03.765 to know about any of that. 1124 00:56:04.385 --> 00:56:08.205 And, you know, it's, it's a class there again, we'll, 1125 00:56:08.205 --> 00:56:10.405 we'll have 30 to 50 students, um, 1126 00:56:10.785 --> 00:56:15.165 and it will be a couple people will be brand new, 1127 00:56:15.325 --> 00:56:17.165 a couple people will know more than I do, and 1128 00:56:17.185 --> 00:56:18.845 and they'll make that, then they'll share that 1129 00:56:18.845 --> 00:56:21.685 with the class and everyone else is in the middle. 1130 00:56:21.825 --> 00:56:23.525 And it's a really nice cohort. 1131 00:56:23.745 --> 00:56:25.965 The cohorts pulled together in an 1132 00:56:25.965 --> 00:56:27.325 incredible fashion, by the way. 1133 00:56:27.325 --> 00:56:29.045 They wanna continue to, and, 1134 00:56:29.045 --> 00:56:30.845 and we, we we're gonna offer those opportunities 1135 00:56:30.905 --> 00:56:31.925 to connect after the course. 1136 00:56:32.065 --> 00:56:34.445 So, anything else, Kim? Is that a good answer? 1137 00:56:35.385 --> 00:56:36.815 [Kim] Great. Yeah, I think that's it. 1138 00:56:36.995 --> 00:56:38.975 Um, if you guys have any other questions, 1139 00:56:39.195 --> 00:56:40.655 you can go ahead and type them in the chat. 1140 00:56:40.675 --> 00:56:42.895 If not, we hope you have a wonderful day 1141 00:56:42.995 --> 00:56:44.415 and thank you so much for joining us. 1142 00:56:44.575 --> 00:56:46.535 I hope this was beneficial. [Paul] Yeah, 1143 00:56:47.185 --> 00:56:48.185 Thank you. Thanks everybody. 1144 00:56:48.185 --> 00:56:49.415 Everybody send warm 1145 00:56:49.415 --> 00:56:50.455 thoughts up to Minneapolis. 1146 00:56:52.505 --> 00:56:54.295 Thank you all. [Paul] Uh, hang on, 1147 00:56:54.295 --> 00:56:55.415 Kim, did we get another question? 1148 00:56:55.475 --> 00:56:56.895 Uh, is the course offered at other times? 1149 00:56:57.315 --> 00:56:59.015 It, it depends if we fill this up 1150 00:56:59.355 --> 00:57:01.215 or if, if, uh, if many of you say, hey, 1151 00:57:01.295 --> 00:57:03.855 I can't make those dates, we'll schedule another one. 1152 00:57:05.295 --> 00:57:07.095 [Kim] Absolutely. [Paul] And the only the I'll, 1153 00:57:07.095 --> 00:57:08.735 I'll revise my comment about what, 1154 00:57:08.735 --> 00:57:10.095 what prerequisites are there. 1155 00:57:10.235 --> 00:57:11.815 If you don't like the sound of my voice, 1156 00:57:12.235 --> 00:57:13.415 you probably shouldn't sign 1157 00:57:13.415 --> 00:57:14.695 up 'cause you're gonna get a lot of me. 1158 00:57:18.025 --> 00:57:22.035 [overlapping voices] Thank you all. Salude. Thanks. 1159 00:57:22.815 --> 00:57:23.795 Bye. Take care. Thank you.