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WEBVTT 1 00:00:00.405 --> 00:00:01.575 [Kim Goad] Alex, Happy New Year. 2 00:00:02.055 --> 00:00:04.775 [Alex Barsi Lopes] [Brazillian accent] Happy New Year. [Kim] Nice to see you. 3 00:00:04.775 --> 00:00:06.695 Thank you so much for coming in and doing this with us. 4 00:00:06.795 --> 00:00:11.015 [Alex] Of course. [Kim] Yeah. I, um, I wonder if you to, 5 00:00:11.015 --> 00:00:13.335 to just set the stage before we really get into the meat 6 00:00:13.355 --> 00:00:16.815 of the topic, if you wouldn't mind sharing for the audience 7 00:00:16.885 --> 00:00:19.335 what your background here is at the Kelley School 8 00:00:19.755 --> 00:00:20.895 and, and your current role. 9 00:00:21.405 --> 00:00:25.455 [Alex] Sure. Of course. I have been at Kelley School since 10 00:00:25.475 --> 00:00:28.335 2011 now, so it has been quite some time. 11 00:00:29.035 --> 00:00:31.095 Um, I'm a clinical professor here 12 00:00:31.315 --> 00:00:33.735 and, um, also, right now I'm in charge 13 00:00:33.755 --> 00:00:36.615 of all our technology programs. 14 00:00:37.435 --> 00:00:40.535 I'm associate chair, uh, for the MSIS, 15 00:00:41.315 --> 00:00:43.895 and I'm associate, I'm associate chair mostly 16 00:00:44.535 --> 00:00:47.335 managing our Online MS in IT Management 17 00:00:47.435 --> 00:00:50.095 and Online MS in Business Analytics. 18 00:00:50.835 --> 00:00:53.975 And before I was in the part of our KEEP, right, 19 00:00:54.905 --> 00:00:56.695 Kelley's executive education program, and 20 00:00:56.695 --> 00:00:59.175 before there I was part of Kelley Direct as well. 21 00:00:59.315 --> 00:01:01.975 So multiple different roles, all them. 22 00:01:01.985 --> 00:01:04.055 Super exciting and happy to be doing 23 00:01:04.055 --> 00:01:05.095 what I'm doing here at Kelley. 24 00:01:05.325 --> 00:01:07.535 [Kim] Yeah. Well, thank you. You, you alluded to the work 25 00:01:07.535 --> 00:01:09.735 that you've done with KEEP or Kelley Executive Education 26 00:01:09.735 --> 00:01:10.935 Programs and, um, 27 00:01:11.235 --> 00:01:14.095 and as a, as a, um, an associate faculty chair, 28 00:01:14.515 --> 00:01:17.655 and then also just from my experience working with you 29 00:01:17.765 --> 00:01:19.575 with some of our corporate clients 30 00:01:19.635 --> 00:01:22.375 and open enrollment programs, I honestly can't think 31 00:01:22.375 --> 00:01:24.415 of anybody better to have this conversation with. 32 00:01:24.995 --> 00:01:26.295 So I really appreciate that, 33 00:01:26.295 --> 00:01:28.175 and I appreciate that you are the faculty lead 34 00:01:28.195 --> 00:01:31.095 for this particular course in the Leading with AI series. 35 00:01:31.715 --> 00:01:34.135 Um, and we, we should probably say what the title of 36 00:01:34.135 --> 00:01:37.895 that is, AI, uh, for strategy for executives. 37 00:01:38.205 --> 00:01:41.375 [Alex] Yeah. So we actually have a variety of different titles, 38 00:01:41.635 --> 00:01:43.215 uh, for this course. 39 00:01:43.305 --> 00:01:46.935 Right. And I think we are essentially settling in Leading 40 00:01:47.045 --> 00:01:50.775 with AI or some variation of that. [Kim] Uhhuh. 41 00:01:51.515 --> 00:01:54.415 [Alex] Um, but it is really a course 42 00:01:54.525 --> 00:01:57.975 that is focusing on helping people, right, 43 00:01:58.195 --> 00:02:01.615 to be transformative in terms of 44 00:02:01.955 --> 00:02:05.895 how AI is impacting their organizations, their careers. 45 00:02:06.235 --> 00:02:07.695 So that's the main focus, right? 46 00:02:07.695 --> 00:02:11.735 Is how to really elevate yourself to a position 47 00:02:11.745 --> 00:02:14.615 where you can lead with AI, you can create strategies 48 00:02:14.615 --> 00:02:18.335 with AI, you can really make AI, you know, uh, something 49 00:02:18.335 --> 00:02:21.255 that adds value to you, your stakeholders. [Kim] Mm-hmm. 50 00:02:21.405 --> 00:02:23.535 Good. You, you started to talk about that too, 51 00:02:23.535 --> 00:02:25.175 about the participants for this program. 52 00:02:25.395 --> 00:02:27.175 Who is the ideal, when you think about 53 00:02:27.175 --> 00:02:30.055 where someone is in their leadership or, um, journey 54 00:02:30.075 --> 00:02:32.495 or in their career, who is the ideal person 55 00:02:32.595 --> 00:02:33.975 to take this particular course? 56 00:02:34.815 --> 00:02:37.255 [Alex] I would say that it varies a little bit, 57 00:02:37.475 --> 00:02:41.295 and in part of the deal is that, uh, I think people 58 00:02:41.295 --> 00:02:43.975 that are starting to be in managerial roles, 59 00:02:44.195 --> 00:02:46.495 but also even higher level executives, 60 00:02:46.765 --> 00:02:51.245 because the course is, it starts 61 00:02:51.245 --> 00:02:52.405 to be very comprehensive. 62 00:02:52.665 --> 00:02:55.885 So we're attacking this from three different, uh, angles. 63 00:02:55.885 --> 00:02:58.365 [Kim] Mm-hmm. [Alex] Um, we have the technology angle, 64 00:02:58.545 --> 00:03:02.325 and this is a course that is co-taught by me, uh, 65 00:03:02.745 --> 00:03:06.725 my professor Sagar Samtani, and by Professor Alan Denis. 66 00:03:07.145 --> 00:03:09.765 And all of us have extensive experience working 67 00:03:09.765 --> 00:03:11.725 with clients, with executives. 68 00:03:11.795 --> 00:03:14.525 Professor Denis has been, uh, uh, entrepreneur 69 00:03:14.585 --> 00:03:16.285 has [UNINTELLIGIBLE] companies. 70 00:03:16.985 --> 00:03:21.245 Uh, Professor Samtani, he has been in the key role of director 71 00:03:21.245 --> 00:03:24.685 of AI for TSMC, which is the top 10 companies in the world. 72 00:03:24.705 --> 00:03:25.845 And, and 73 00:03:25.845 --> 00:03:28.685 so the idea is really attack these from different angles, 74 00:03:28.905 --> 00:03:33.085 uh, from the technology sides, from the individual sides, 75 00:03:33.425 --> 00:03:36.125 and from the managerial kind of organization side. 76 00:03:36.465 --> 00:03:39.605 So that's the reason that you can come a little bit earlier 77 00:03:39.705 --> 00:03:40.845 in your career and come 78 00:03:40.845 --> 00:03:43.365 and say, hey, what is happening with AI right now? 79 00:03:44.035 --> 00:03:47.765 What are the practical implications for me as a, as a, 80 00:03:47.765 --> 00:03:49.365 as a worker, as a knowledge worker, 81 00:03:49.385 --> 00:03:50.805 as a contributor to my organization? 82 00:03:51.385 --> 00:03:53.805 But you can also think about this as someone 83 00:03:54.085 --> 00:03:58.765 that is looking at understanding what the effects of 84 00:03:59.305 --> 00:04:02.285 AI for an organizational level. 85 00:04:02.425 --> 00:04:04.645 How do I manage my employees 86 00:04:04.785 --> 00:04:07.485 in a situation where, you know, AI is becoming more 87 00:04:07.485 --> 00:04:08.565 and more prominent, right? 88 00:04:08.565 --> 00:04:12.725 And how do I leverage AI to really get value? Right. 89 00:04:12.985 --> 00:04:14.365 That's one of the biggest challenges 90 00:04:14.365 --> 00:04:15.485 we have with AI right now. 91 00:04:15.595 --> 00:04:17.365 Lots of technology and important, not 92 00:04:17.365 --> 00:04:19.645 as much value being delivered in this far. [Kim] Mm-hmm. 93 00:04:20.075 --> 00:04:22.285 Good. Yeah, there is so much out there. 94 00:04:22.425 --> 00:04:24.965 And when I think about the, the format of this course 95 00:04:24.965 --> 00:04:28.285 that I don't know that, um, that, that this has been clear 96 00:04:28.285 --> 00:04:30.485 yet for the audience, it's a little unique with our Leading 97 00:04:30.485 --> 00:04:34.285 with AI series and that this one is live virtual, um, 98 00:04:34.515 --> 00:04:36.005 once a week for six weeks. 99 00:04:36.265 --> 00:04:40.045 [Alex] Yes. [Kim] And each session is what, 60 to 90 minutes long? 100 00:04:40.185 --> 00:04:42.525 [Alex] About that, about 90 minutes long. [Kim] Okay. 101 00:04:42.585 --> 00:04:47.565 [Alex] Um, and, uh, so we start with two sessions about technology. 102 00:04:47.675 --> 00:04:51.725 [Kim] Okay. [Alex] I, so, you know, we talk about, you know, GenAI, 103 00:04:52.845 --> 00:04:54.925 right, agentic AI, and is, what is that? 104 00:04:54.925 --> 00:04:58.285 What exactly does it entail, right? 105 00:04:58.345 --> 00:04:59.965 And, and sometimes we neglect 106 00:05:00.025 --> 00:05:03.725 to really understand there's a lot of AI that has been 107 00:05:03.725 --> 00:05:04.725 around for quite some time 108 00:05:04.725 --> 00:05:06.325 that actually delivers a lot of value, right. 109 00:05:06.325 --> 00:05:09.005 Things like machine learning, things 110 00:05:09.005 --> 00:05:10.445 more traditional deep learning. 111 00:05:10.935 --> 00:05:12.845 Those are things that are having around 112 00:05:13.035 --> 00:05:14.485 that are very effective. 113 00:05:15.065 --> 00:05:17.085 It generate a lot of value for organizations. 114 00:05:17.105 --> 00:05:18.365 And sometimes we neglect 115 00:05:18.365 --> 00:05:20.965 because everybody's excited with the shiny new, [Kim] Right. [Alex] right? 116 00:05:21.055 --> 00:05:24.285 So this is something that Sagar covers early. 117 00:05:24.985 --> 00:05:28.325 Um, just, you know, let's situate where the technology is, 118 00:05:28.595 --> 00:05:29.965 what are the things that you can do, 119 00:05:29.995 --> 00:05:32.885 what the things you cannot do or the things you should not do. 120 00:05:32.885 --> 00:05:37.405 Uh, and then we have, uh, uh, Alan, um, teaching 121 00:05:37.405 --> 00:05:39.685 for two sessions about individual effects. 122 00:05:39.935 --> 00:05:42.845 Right? So what are in the individual uses, 123 00:05:42.865 --> 00:05:45.165 if you're thinking about agents, right? 124 00:05:45.305 --> 00:05:46.525 How do you create agents? 125 00:05:46.625 --> 00:05:48.005 How do you create AI agents 126 00:05:48.005 --> 00:05:50.205 that can help you in your workflows? 127 00:05:50.265 --> 00:05:53.245 How do you think about the next evolutions 128 00:05:53.245 --> 00:05:54.365 or like digital humans? 129 00:05:54.365 --> 00:05:57.525 Right, in which you can replace, uh, you know, 130 00:05:57.525 --> 00:06:00.845 certain services with AI constructs 131 00:06:00.845 --> 00:06:03.525 that can perhaps interact, uh, in a more, 132 00:06:03.545 --> 00:06:06.365 in a deeper way than you have with a regular chatbot. 133 00:06:06.365 --> 00:06:09.085 Right? And then I wrap up the last, 134 00:06:09.185 --> 00:06:11.085 the two sessions really talking about 135 00:06:11.855 --> 00:06:15.005 value with stakeholders, implementation with AI, right? 136 00:06:15.005 --> 00:06:17.205 What other things that you have to consider. 137 00:06:17.275 --> 00:06:18.805 What is AI governance? 138 00:06:19.675 --> 00:06:23.165 What are, you know, the data concerns that we have with AI 139 00:06:23.165 --> 00:06:24.245 and things like privacy. 140 00:06:24.625 --> 00:06:29.165 How do you create a plan for organizations to absorb AI 141 00:06:29.665 --> 00:06:33.885 and not go into that, you know, experimental threat, right? 142 00:06:34.425 --> 00:06:36.605 Try new fields. It's proof of concepts, 143 00:06:36.605 --> 00:06:37.685 it never goes anywhere. 144 00:06:38.345 --> 00:06:41.125 How do you go beyond that to actually have something 145 00:06:41.125 --> 00:06:43.245 that can be moved into production, 146 00:06:43.745 --> 00:06:45.365 so really starts delivering value? 147 00:06:45.635 --> 00:06:48.085 [Kim] Yeah. You know, it's, it's, um, interesting 148 00:06:48.085 --> 00:06:50.605 that you talk about that and this whole idea of strategy 149 00:06:50.605 --> 00:06:51.605 around AI 150 00:06:51.865 --> 00:06:54.805 and how do we decide which projects to go with so 151 00:06:54.805 --> 00:06:57.405 that it doesn't just end in the, on the cutting room floor. 152 00:06:57.905 --> 00:07:00.085 Um, I, you may not remember this, 153 00:07:00.085 --> 00:07:01.845 but it was just a handful of years ago 154 00:07:01.845 --> 00:07:04.725 that I first met you at a Kelley holiday party, 155 00:07:05.145 --> 00:07:07.805 and we were all talking about, I mean, uh, um, 156 00:07:07.925 --> 00:07:10.205 ChatGPT was new for students using that, 157 00:07:10.345 --> 00:07:12.645 and we were concerned about what that was going to, 158 00:07:12.785 --> 00:07:14.485 the impact that was gonna have in the classroom. 159 00:07:14.585 --> 00:07:16.605 And I mean, our consensus was, it's here, 160 00:07:16.605 --> 00:07:19.365 obviously at universities, were all across the globe 161 00:07:19.875 --> 00:07:22.005 grappling with, we've gotta embrace it, 162 00:07:22.065 --> 00:07:23.245 but what does that look like? 163 00:07:23.865 --> 00:07:28.205 So speaking to the executives, I mean, it seems like, um, 164 00:07:28.225 --> 00:07:31.365 for a lot of people, there are a lot of emotions 165 00:07:31.665 --> 00:07:35.045 or beliefs, preconceived notions around AI. 166 00:07:35.055 --> 00:07:38.165 Maybe they are, maybe there's some paralysis or fear, 167 00:07:38.425 --> 00:07:40.165 or, I just wanna avoid it 168 00:07:40.165 --> 00:07:41.645 because it's, there's too much to do. 169 00:07:41.805 --> 00:07:43.805 I mean, how do you, can, can you, well, 170 00:07:43.845 --> 00:07:45.485 I guess on a personal level, can you speak, 171 00:07:46.265 --> 00:07:47.365 and this might not be fair 172 00:07:47.365 --> 00:07:50.405 because you've been an a, a tech guy your whole life, 173 00:07:50.625 --> 00:07:54.245 but when did think the thinking for you start to change 174 00:07:54.245 --> 00:07:56.725 around how do we embrace this in our work? 175 00:07:57.385 --> 00:08:01.165 And maybe, maybe give some examples for you, um, and, 176 00:08:01.185 --> 00:08:04.605 and then addressing, um, the, those emotions 177 00:08:04.625 --> 00:08:05.885 for the, the executive. 178 00:08:06.545 --> 00:08:10.705 [Alex] Of course. Of course. So I have been teaching about AI in 179 00:08:11.065 --> 00:08:13.625 business before AI was applied, right? 180 00:08:13.845 --> 00:08:16.785 So, uh, I have been teaching, uh, course related 181 00:08:16.785 --> 00:08:18.945 to AI business for like seven, eight years 182 00:08:19.045 --> 00:08:22.905 before ChatGPT. I actually used to demo, um, you know, 183 00:08:22.905 --> 00:08:24.745 before ChatGPT was public, I used 184 00:08:24.745 --> 00:08:26.945 to demo large language models in my class. 185 00:08:26.945 --> 00:08:30.345 Right? And I think that was the first kind 186 00:08:30.345 --> 00:08:31.385 of light bulb. 187 00:08:31.475 --> 00:08:33.425 Right? When I start talking to students 188 00:08:33.685 --> 00:08:35.825 and then like, okay, let's try this, right? 189 00:08:35.825 --> 00:08:39.665 Like early versions, early pilot versions for OpenAi, 190 00:08:40.245 --> 00:08:43.065 and, uh, and then, okay, let's, let's try this. 191 00:08:43.135 --> 00:08:44.665 What do, what would you like to know? Right? 192 00:08:44.725 --> 00:08:47.625 And, and the students together creating these queries 193 00:08:48.205 --> 00:08:49.505 and everybody's looking 194 00:08:49.525 --> 00:08:53.145 at the outcome like, wow, right, this is something 195 00:08:53.145 --> 00:08:56.925 that is big to help to get prepared, goes well 196 00:08:56.985 --> 00:08:58.005 beyond the [UNINTELLIGIBLE]. 197 00:08:58.005 --> 00:09:00.685 So that to me was my first, you know, 198 00:09:00.905 --> 00:09:03.725 beyond teaching the traditional neural networks, 199 00:09:03.725 --> 00:09:05.965 machine learning stuff, that was when I say, okay, 200 00:09:05.965 --> 00:09:07.605 this is something that really have to account, 201 00:09:07.605 --> 00:09:09.285 that really can transform organizations. 202 00:09:09.865 --> 00:09:13.005 And over the years, uh, we have been in contact with, 203 00:09:13.005 --> 00:09:15.645 you know, many different companies, organizations, right? 204 00:09:16.005 --> 00:09:18.805 I, I do a lot of interviews with tech leaders 205 00:09:19.025 --> 00:09:21.845 and just to see what the post, uh, is right now. 206 00:09:22.385 --> 00:09:24.805 So AI is here, uh, for this, 207 00:09:25.065 --> 00:09:28.165 and that's something that, uh, we are seeing right now is 208 00:09:28.975 --> 00:09:31.805 first of all, right, is there, is, is, you know, 209 00:09:32.005 --> 00:09:34.485 sometimes in, in the academic, you know, uh, uh, 210 00:09:34.665 --> 00:09:37.685 in the academic where you are, like, 211 00:09:38.035 --> 00:09:39.605 everybody's using AI, right? 212 00:09:39.605 --> 00:09:42.165 And they realize that that's not really true. 213 00:09:43.155 --> 00:09:44.885 Many organizations, we have 214 00:09:44.885 --> 00:09:46.045 people that have been avoiding it. 215 00:09:46.045 --> 00:09:48.205 We have people that are saying, this is not for me, 216 00:09:48.665 --> 00:09:50.565 or a few unprepared, right? 217 00:09:50.665 --> 00:09:53.325 And I, I don't feel that they have the, the right guidance. 218 00:09:53.675 --> 00:09:55.885 What is allowed, what is not allowed. 219 00:09:56.385 --> 00:09:59.525 So I think this is what you're trying to do in this, 220 00:09:59.545 --> 00:10:01.845 in this course, is really figure out, okay, 221 00:10:02.745 --> 00:10:06.205 to feel comfortable using AI 222 00:10:06.205 --> 00:10:08.845 right? You need to have, you don't need to know, 223 00:10:09.145 --> 00:10:11.965 you know, that, uh, that transformers 224 00:10:12.345 --> 00:10:15.445 or convolutional neural networks here and there, right? 225 00:10:15.825 --> 00:10:17.445 We need to know what's the power 226 00:10:17.795 --> 00:10:19.205 that this technology brings, 227 00:10:19.305 --> 00:10:21.645 and what is the proper utilization of that. 228 00:10:21.645 --> 00:10:24.245 So that's something that you try to address, uh, 229 00:10:24.555 --> 00:10:28.565 very much, um, from the very early stages, especially 230 00:10:28.565 --> 00:10:31.485 with Sagar and with Alan, like, you know, they teach you 231 00:10:31.505 --> 00:10:32.605 how to create an agent. 232 00:10:32.665 --> 00:10:35.045 Right? How familiar should you be in terms 233 00:10:35.065 --> 00:10:39.165 of the capabilities, in terms of tackling workflows 234 00:10:39.355 --> 00:10:42.685 that you can delegate to this AI intern. 235 00:10:42.885 --> 00:10:47.285 Right, you know this, this unpaid very, you know, energetic, 236 00:10:48.505 --> 00:10:51.625 somewhat naive intern, which is our AI, and, 237 00:10:53.005 --> 00:10:56.025 but you also have to figure it out on a higher level, um, 238 00:10:56.055 --> 00:10:59.785 from organizations is, okay, so what happens, it is 239 00:10:59.805 --> 00:11:01.225 how do we redefine the work? 240 00:11:01.225 --> 00:11:04.905 Right? And I think that the more people understands 241 00:11:04.905 --> 00:11:07.185 that, you know, hey, uh, this is changing a lot. 242 00:11:07.315 --> 00:11:08.905 There is no question about that. 243 00:11:09.765 --> 00:11:13.505 How do you position yourself in your organization in a way 244 00:11:13.505 --> 00:11:14.745 that you can take advantage 245 00:11:14.965 --> 00:11:17.065 and can be more proactive. 246 00:11:17.065 --> 00:11:19.425 Instead of having things like advance to the point like, oh, 247 00:11:19.535 --> 00:11:21.225 this is happening now have to react. 248 00:11:21.855 --> 00:11:23.185 Like, how do you make sure 249 00:11:23.345 --> 00:11:25.505 that I will be designing processes, 250 00:11:25.685 --> 00:11:29.385 I'm redesigning workflows, I'm creating types 251 00:11:29.485 --> 00:11:31.985 of behaviors in which, you know, both AI 252 00:11:32.245 --> 00:11:36.185 and humans are collaborating in terms of what they do best. 253 00:11:36.195 --> 00:11:39.065 Right? AI cannot do empathy. 254 00:11:39.245 --> 00:11:43.305 [UNINTELLIGBILE] AI cannot put itself in the place 255 00:11:43.325 --> 00:11:44.825 of someone who's facing a problem. 256 00:11:44.925 --> 00:11:47.545 Right? Humans can do that. 257 00:11:47.565 --> 00:11:50.145 Humans can actually leverage the knowledge 258 00:11:50.145 --> 00:11:52.105 and the patterns that AI can bring 259 00:11:52.525 --> 00:11:54.925 to actually solve the problems with that empathy 260 00:11:55.165 --> 00:11:59.205 right, that connection, that AI just isn't able to do. 261 00:11:59.335 --> 00:12:00.335 [Kim] Right? Yeah. Good. 262 00:12:00.335 --> 00:12:00.685 Good. 263 00:12:00.985 --> 00:12:05.325 Um, I, I happen to know that with, with all of our classes, 264 00:12:05.745 --> 00:12:08.485 we bring in participants from varied industries. 265 00:12:08.665 --> 00:12:11.365 So, uh, someone may come to this class 266 00:12:11.545 --> 00:12:14.445 and they may be in the banking industry, 267 00:12:14.445 --> 00:12:17.685 and they may be in a cohort with somebody from healthcare 268 00:12:17.745 --> 00:12:20.645 or manufacturing or utilities or you name it. 269 00:12:20.645 --> 00:12:23.205 Right? And I also know that you and Sagar 270 00:12:23.345 --> 00:12:27.205 and Alan are very equipped to, um, pivot 271 00:12:27.275 --> 00:12:29.125 with whoever's in the room with you 272 00:12:29.385 --> 00:12:34.045 and to make that very, um, contextualized to their, the, 273 00:12:34.045 --> 00:12:36.365 the participant's real world experiences. 274 00:12:36.545 --> 00:12:40.365 So, um, I'm, I'm thinking about, uh, they're going 275 00:12:40.365 --> 00:12:42.725 to be coming with lots of different experiences and problems 276 00:12:42.825 --> 00:12:45.245 and some notions about what they might be able 277 00:12:45.245 --> 00:12:46.365 to use AI for. 278 00:12:47.185 --> 00:12:50.965 You've, um, you, you spoke to this a little bit ago, 279 00:12:50.985 --> 00:12:52.405 and you've worked with students a lot 280 00:12:52.405 --> 00:12:54.205 with the Technology Consulting Workshop 281 00:12:54.225 --> 00:12:55.765 or with companies with exec ed. 282 00:12:56.315 --> 00:12:57.405 What are some cool 283 00:12:57.505 --> 00:13:00.685 and varied things that you've seen companies just 284 00:13:00.785 --> 00:13:03.045 as they started to get their toes in the water with AI? 285 00:13:03.045 --> 00:13:04.205 What are they doing or what, 286 00:13:04.305 --> 00:13:06.765 how are you using it in your own work 287 00:13:06.845 --> 00:13:07.885 that might surprise people? 288 00:13:09.185 --> 00:13:11.685 [Alex] So it's definitely two things here. 289 00:13:11.685 --> 00:13:14.125 First, I have, one of my favorite quotes, is actually my 290 00:13:14.325 --> 00:13:17.725 signature in my emails, which is "The future is already here. 291 00:13:18.065 --> 00:13:20.005 It's just not evenly distributed." right? 292 00:13:20.005 --> 00:13:24.045 This is by-- a tribute to William Gibson who is my favorite sci-fi authors. 293 00:13:24.585 --> 00:13:28.885 But that's the thing I think is, is admirable about our classes is 294 00:13:29.075 --> 00:13:32.125 because we have people with different backgrounds, right? 295 00:13:32.635 --> 00:13:35.205 What some organizations are doing, 296 00:13:36.025 --> 00:13:38.725 um, it might be more advanced in certain 297 00:13:38.725 --> 00:13:39.965 areas than other organizations. 298 00:13:39.965 --> 00:13:41.045 We're not, you know, 299 00:13:41.045 --> 00:13:42.565 thinking about like revealing trade 300 00:13:42.565 --> 00:13:43.685 secrets or not. 301 00:13:43.945 --> 00:13:46.125 But most of our classes are going 302 00:13:46.125 --> 00:13:47.525 to involve sharing, right? 303 00:13:47.585 --> 00:13:50.445 Say, hey, this is what I'm facing in my industry. 304 00:13:51.065 --> 00:13:54.365 You know, might be something that other, someone representing 305 00:13:54.365 --> 00:13:55.965 another industry might have already faced 306 00:13:55.985 --> 00:13:57.125 and might have a solution, right? 307 00:13:57.125 --> 00:14:00.085 And we believe, I mean, we truly believe, right, 308 00:14:00.115 --> 00:14:03.925 that is the learning is, is all around us. 309 00:14:04.325 --> 00:14:05.485 Right, it's not like, you know, 310 00:14:05.865 --> 00:14:08.805 and the, you know, the sage on the mountain saying, like 311 00:14:09.435 --> 00:14:10.805 this is the truth, right? 312 00:14:10.805 --> 00:14:13.205 It's really about the students collaborating, 313 00:14:13.305 --> 00:14:14.965 the students sharing the experiences 314 00:14:15.585 --> 00:14:16.765 in learning from each other, 315 00:14:16.815 --> 00:14:18.205 which I believe is very important 316 00:14:18.205 --> 00:14:20.325 because there are so many interesting cases. 317 00:14:20.665 --> 00:14:25.485 We have many cases right now with AI that are, you know, 318 00:14:25.485 --> 00:14:27.925 helping, for example, in financial advising. 319 00:14:28.665 --> 00:14:29.685 It is a great area 320 00:14:29.685 --> 00:14:33.005 because a lot of the prospectors, a lot of the data 321 00:14:33.115 --> 00:14:34.485 that can be summarized 322 00:14:34.545 --> 00:14:37.165 and can be helpful, uh, to people that, 323 00:14:37.165 --> 00:14:38.845 of reading tons of these, right? 324 00:14:38.845 --> 00:14:42.045 I was, uh, in the University 325 00:14:42.145 --> 00:14:45.005 of Pittsburgh some time ago attending a presentation 326 00:14:45.145 --> 00:14:48.845 and they were talking about, um, uh, Pitt has like, uh, 327 00:14:49.085 --> 00:14:52.085 a dean for AI in medicine, right? 328 00:14:52.225 --> 00:14:53.445 And they're looking about, hey, 329 00:14:53.465 --> 00:14:57.285 how do you create those systems that would help new doctors 330 00:14:57.995 --> 00:15:01.245 that do not have a lot of that own experience 331 00:15:01.585 --> 00:15:05.285 to eventually use this knowledge that is available, not 332 00:15:05.705 --> 00:15:08.365 as a crutch, not as a replacement, 333 00:15:09.025 --> 00:15:10.565 but to as, as a coach, 334 00:15:10.785 --> 00:15:14.325 as a support mechanism in which you can validate you know, 335 00:15:14.325 --> 00:15:16.605 your ideas and say, hey, this is likely, 336 00:15:16.775 --> 00:15:17.885 maybe this is not likely. 337 00:15:17.985 --> 00:15:21.005 Gimme some alternative diagnosis here, right? 338 00:15:21.275 --> 00:15:22.485 This kind of conversation, 339 00:15:22.495 --> 00:15:25.565 which an AI can be your assistant, your support. 340 00:15:26.225 --> 00:15:28.165 Um, that is really, really helpful. 341 00:15:28.305 --> 00:15:30.685 And we can apply it to variety of 342 00:15:30.685 --> 00:15:31.765 different industries, right. 343 00:15:31.905 --> 00:15:34.845 Uh, personally I do a lot of AI 344 00:15:34.865 --> 00:15:36.685 for like storytelling, right? 345 00:15:36.745 --> 00:15:39.485 So, one the things sometimes I'm looking, um, um, 346 00:15:39.625 --> 00:15:43.325 so I just did one class recently about the, a, a day in the, 347 00:15:44.665 --> 00:15:47.865 a day in the future of anticipatory. 348 00:15:48.605 --> 00:15:51.745 And the idea here is really thinking about, hey, how 349 00:15:52.405 --> 00:15:54.905 for a young professional, how is the world going 350 00:15:54.905 --> 00:15:56.145 to be different, right? 351 00:15:56.205 --> 00:15:59.305 If you really do not have as many constraints. 352 00:15:59.565 --> 00:16:01.425 Uh, it was how can have AI 353 00:16:02.085 --> 00:16:04.745 and, uh, it's kind of a, so I use a lot of AI 354 00:16:05.025 --> 00:16:07.745 to create illustrations to tell a story. 355 00:16:07.765 --> 00:16:10.785 Like how someone is waking up in the morning 356 00:16:11.005 --> 00:16:13.745 and being adjusted in terms of the alarm clock 357 00:16:13.745 --> 00:16:15.625 because there was perhaps an accident 358 00:16:16.095 --> 00:16:17.265 that is blocking traffic. 359 00:16:17.265 --> 00:16:21.345 Right? How is someone using AI to, uh, 360 00:16:21.415 --> 00:16:23.305 interview a potential candidate 361 00:16:23.485 --> 00:16:26.185 and having, uh, systems that are helping 362 00:16:26.885 --> 00:16:30.905 to dig some additional questions to help to react in terms 363 00:16:30.965 --> 00:16:33.625 of the, of the, of the responses for the candidates. 364 00:16:33.625 --> 00:16:37.025 So, you know, so to me, like I use a lot to just say, 365 00:16:37.085 --> 00:16:39.545 hey, you know, if I talk about this, it's very abstract. 366 00:16:39.765 --> 00:16:42.025 [Kim] Yes. [Alex] So I actually use a lot of, uh, you know, uh, 367 00:16:42.105 --> 00:16:43.425 generation of, uh, images 368 00:16:43.455 --> 00:16:45.225 to say, hey, this is the stage. 369 00:16:45.715 --> 00:16:47.985 Let's go through this, kind of do a lot of storytelling. 370 00:16:48.005 --> 00:16:49.705 That's, [Kim] Yeah [Alex] that's one my favorite things to do. 371 00:16:49.765 --> 00:16:51.505 [Kim] Oh, yeah. I love that. I love that. 372 00:16:51.865 --> 00:16:54.145 I was talking with a client the other day in the 373 00:16:54.145 --> 00:16:55.265 pharmaceutical industry, 374 00:16:55.285 --> 00:16:58.385 and they're using AI as a way 375 00:16:58.385 --> 00:17:02.505 to train new reps at, the AI agent acts as a physician, 376 00:17:02.565 --> 00:17:04.305 and they are detailing that physician 377 00:17:04.605 --> 00:17:06.265 and practicing with AI. 378 00:17:06.525 --> 00:17:08.025 Um, and I, yes-- 379 00:17:08.365 --> 00:17:09.365 [Alex] As well. Things like, 380 00:17:09.365 --> 00:17:11.545 you know, uh, technical sales 381 00:17:11.765 --> 00:17:14.425 was working with, uh, with, uh, with a client as well 382 00:17:14.925 --> 00:17:17.545 and the ideas of the clients like, well, sometimes you have 383 00:17:17.545 --> 00:17:20.865 to reply to these, um, RFP [Kim] Yeah. 384 00:17:20.865 --> 00:17:25.345 [Alex] Right. RF, you know, RFQs, and, uh, the information is there. 385 00:17:25.885 --> 00:17:29.945 We have some previous experience serving similar customers. 386 00:17:29.965 --> 00:17:33.105 And the more we can create that like a playbook, 387 00:17:33.745 --> 00:17:38.025 right, understand, what is the main criteria for those RFPs or, 388 00:17:38.165 --> 00:17:40.745 or those requests for proposals of, um, 389 00:17:40.965 --> 00:17:42.945 and build from their knowledge 390 00:17:42.945 --> 00:17:45.225 and have accumulate over time, like a slide decks 391 00:17:45.325 --> 00:17:46.745 and proposals, right. 392 00:17:46.845 --> 00:17:51.205 We can quickly craft with human supervision. [Kim] Yeah. [Alex] Right. 393 00:17:51.395 --> 00:17:53.325 We can quickly craft something 394 00:17:53.425 --> 00:17:57.365 and then can respond quicker, which increases the likelihood 395 00:17:57.385 --> 00:17:58.605 of the getting the contract. 396 00:17:58.735 --> 00:17:59.885 [Kim] Right. Right. Yeah. 397 00:17:59.965 --> 00:18:02.845 I mean the, I've can think of so many success stories 398 00:18:02.845 --> 00:18:04.205 that clients have shared with me lately. 399 00:18:04.205 --> 00:18:08.725 Another client who is, um, consulting with, um, uh, uh, 400 00:18:08.725 --> 00:18:12.485 police force and they are using AI now to figure out how 401 00:18:12.485 --> 00:18:14.125 to do patrol scheduling, so 402 00:18:14.155 --> 00:18:17.085 what areas should we send patrol cars to in our area? 403 00:18:17.465 --> 00:18:19.645 And he said it was a project that took him six months, 404 00:18:19.745 --> 00:18:20.885 uh, uh, 20 years ago. 405 00:18:20.905 --> 00:18:22.685 He did the same project for the same client, 406 00:18:22.945 --> 00:18:24.765 and it took, you know, months to do it. 407 00:18:24.825 --> 00:18:26.965 And now he said, it took me more time 408 00:18:26.965 --> 00:18:29.085 to clean the data than it did to actually do the project. [Alex] Yes. 409 00:18:29.345 --> 00:18:32.165 [Kim] But yeah. So lots of success stories, 410 00:18:32.305 --> 00:18:36.125 but can you think of where maybe you've experienced, 411 00:18:37.335 --> 00:18:39.765 maybe where AI didn't live up to your expectations 412 00:18:39.765 --> 00:18:41.805 or it failed, or, you know, you mentioned 413 00:18:41.805 --> 00:18:43.645 that there are some things that just can't do, 414 00:18:43.985 --> 00:18:46.125 and you had like empathy for example [laughs] 415 00:18:47.305 --> 00:18:49.725 [Alex] Not there yet. I think that a lot 416 00:18:49.725 --> 00:18:51.005 of the things at issue-- 417 00:18:51.025 --> 00:18:54.045 So I was just, uh, actually prepping some, some new, 418 00:18:54.145 --> 00:18:57.955 new content in classes about, um, um, 419 00:18:58.135 --> 00:18:59.355 uh, AI governance, right. 420 00:18:59.355 --> 00:19:01.395 How do you minimize the risk 421 00:19:01.655 --> 00:19:04.475 or how to anticipate the risk so we can have mitigation. 422 00:19:04.985 --> 00:19:06.715 [Kim] Yeah, yeah. [Alex] One of the biggest issues 423 00:19:06.715 --> 00:19:10.315 that we're facing right now, especially with the need 424 00:19:10.315 --> 00:19:13.475 to keep the humans in the loop, is accuracy. 425 00:19:13.535 --> 00:19:16.635 Right? So there are certain things, I, I think part 426 00:19:16.635 --> 00:19:19.995 of the deal here is really to instruct people about 427 00:19:20.465 --> 00:19:22.315 what is your risk appetite. 428 00:19:22.315 --> 00:19:25.115 What is your risk tolerance, [Kim] Right. [Alex] right? [Kim] Yeah. 429 00:19:25.115 --> 00:19:28.235 [Alex] Because for certain things, you know, being wrong, 430 00:19:28.815 --> 00:19:31.315 not a big deal. You know, occasionally going 431 00:19:31.315 --> 00:19:32.435 to have an inaccurate thing, 432 00:19:32.535 --> 00:19:33.555 but in general, 433 00:19:33.655 --> 00:19:35.835 you've got some good results so we can move on, right? 434 00:19:35.835 --> 00:19:37.955 Like for certain things like, you know, 435 00:19:37.955 --> 00:19:39.515 medical diagnosis for example. 436 00:19:40.055 --> 00:19:44.955 See, you can not be wrong [Kim laughs] Accuracy, again, not 437 00:19:44.955 --> 00:19:46.675 to say that humans are never wrong, 438 00:19:46.935 --> 00:19:49.755 but, you know, transferring that responsibility to a machine, 439 00:19:49.975 --> 00:19:51.115 to the AI system, right. 440 00:19:51.375 --> 00:19:53.315 It definitely cannot get wrong 441 00:19:53.315 --> 00:19:54.435 because, you know, sometimes 442 00:19:54.435 --> 00:19:55.595 people could trust a little bit too much. 443 00:19:56.295 --> 00:19:59.955 Uh, so I think this is really what we're seeing is like, 444 00:20:00.145 --> 00:20:02.155 what is the level of comfortable people 445 00:20:02.255 --> 00:20:05.835 and what's the appropriate right level of certainty 446 00:20:05.865 --> 00:20:08.795 that you need to have to the task at hand. 447 00:20:08.855 --> 00:20:12.955 There's some stories of, uh, you know, uh, chatbot selling, 448 00:20:13.455 --> 00:20:16.835 you know, air fare for like one third of the price. [Kim] Uhhuh, 449 00:20:17.045 --> 00:20:18.045 right! [Kim laughs] 450 00:20:18.095 --> 00:20:20.755 [Alex] That's, [Kim laughing] I was not so lucky to get in on that 451 00:20:21.625 --> 00:20:23.235 [Alex] I wish, but so, you know, 452 00:20:23.295 --> 00:20:24.835 but again, this is tolerable, right? 453 00:20:25.055 --> 00:20:26.315 So we, can fix this, right? 454 00:20:26.575 --> 00:20:29.715 If you get the wrong diagnosis of someone, cannot be fixed. 455 00:20:29.895 --> 00:20:32.955 So it's that level of comfort, I think that's the next barrier 456 00:20:32.955 --> 00:20:36.955 that we're trying to kind of really, uh, look at as a way 457 00:20:36.975 --> 00:20:39.115 to keep increasing the reach of AI. 458 00:20:39.225 --> 00:20:40.235 [Kim] Yeah. Good. Good. 459 00:20:41.055 --> 00:20:43.435 Wow, there's so much, and I think about last six weeks, 460 00:20:43.695 --> 00:20:46.595 six weeks in this course has got to fly by, if you had 461 00:20:46.595 --> 00:20:49.725 to boil it down to like one main thing you would want people 462 00:20:49.725 --> 00:20:51.605 to walk away with, like, if you think, okay, 463 00:20:51.605 --> 00:20:53.005 you've finished up in six weeks 464 00:20:53.065 --> 00:20:54.525 and we've had a great time together, 465 00:20:54.985 --> 00:20:57.005 and you think of those learners 466 00:20:57.145 --> 00:21:00.445 or leaders out one week out in their real world, 467 00:21:00.635 --> 00:21:03.405 what do you hope that they've gained from the six weeks 468 00:21:03.515 --> 00:21:05.525 with you and Alan and Sagar? 469 00:21:06.105 --> 00:21:08.805 [Alex] So actually something that we have know, 470 00:21:08.805 --> 00:21:10.005 the three of us have debated 471 00:21:10.035 --> 00:21:12.405 [Kim] Yeah. [Alex] quite a bit, and, um, 472 00:21:12.665 --> 00:21:16.105 and to us it's like, okay, you finish this, this week, 473 00:21:16.615 --> 00:21:20.065 next week, we want you to be able in two things. 474 00:21:20.175 --> 00:21:22.705 Once you be involved in conversations 475 00:21:22.815 --> 00:21:25.385 that involve AI in organization, right. 476 00:21:25.505 --> 00:21:28.785 Is really take that leadership and, 477 00:21:29.005 --> 00:21:31.145 and say like, well, I can talk with people, 478 00:21:31.505 --> 00:21:32.865 I can converse with people. 479 00:21:33.165 --> 00:21:35.945 I'm knowledgeable, right? I can give opinions. 480 00:21:36.105 --> 00:21:37.585 I have a basis for my opinion. 481 00:21:37.685 --> 00:21:41.065 So get that level of involvement in the AI in organization. 482 00:21:42.085 --> 00:21:44.305 But the other part is very practical is like, you know, 483 00:21:44.555 --> 00:21:46.345 think about something you can do. 484 00:21:46.345 --> 00:21:47.465 [Kim] Mm-hmm. [Alex] Right? [Kim] Mm-hmm. 485 00:21:47.775 --> 00:21:51.545 What is, look around, I mean, I always finish my, my many 486 00:21:51.545 --> 00:21:54.805 of my classes say tomorrow you going back to 487 00:21:54.805 --> 00:21:56.125 work, look around. 488 00:21:56.545 --> 00:21:59.725 I'm sure that you are going to be able to find a couple 489 00:21:59.725 --> 00:22:03.125 of opportunities in which you can apply AI. 490 00:22:03.125 --> 00:22:05.045 You can deliver value, right? 491 00:22:05.585 --> 00:22:07.445 And not only looking around, 492 00:22:07.745 --> 00:22:11.765 but now you should feel prepared, right, to go 493 00:22:11.865 --> 00:22:14.685 and start tackling, like, those objectives, 494 00:22:14.925 --> 00:22:15.925 tackling those projects and 495 00:22:15.925 --> 00:22:18.405 before they feel like, oh, you know, interesting. 496 00:22:18.625 --> 00:22:22.285 Be great to have that, we want to move you from, 497 00:22:22.305 --> 00:22:24.845 it would be great to have that to go ahead 498 00:22:24.845 --> 00:22:27.205 and say, right, you are able to do it. 499 00:22:27.205 --> 00:22:29.045 You have that. Do you have key knowledge? 500 00:22:29.385 --> 00:22:31.245 You have the capability, go 501 00:22:31.245 --> 00:22:32.925 [Kim] Right. [Alex] and start working on this right away. 502 00:22:32.925 --> 00:22:33.965 [Kim] Wonderful. Good. Good. 503 00:22:34.515 --> 00:22:36.845 Well, what else would you want everyone to know 504 00:22:36.905 --> 00:22:38.085 before we wrap up? 505 00:22:38.225 --> 00:22:39.805 Is there anything else about the class we, 506 00:22:39.905 --> 00:22:41.565 we haven't talked about that you would hope they know? 507 00:22:42.485 --> 00:22:45.925 I think that is, it's a lot of fun. [Kim] Yeah. Yeah. 508 00:22:46.025 --> 00:22:49.205 [Alex] So I think that's something that, you know, I, I, you know, 509 00:22:49.485 --> 00:22:52.045 teaching this class and just interacting and just talking 510 00:22:52.145 --> 00:22:54.765 and, and believe me, there's some detours 511 00:22:54.765 --> 00:22:56.485 that you take in class, right, 512 00:22:56.485 --> 00:22:59.325 because you start to talk about some of the content 513 00:22:59.625 --> 00:23:01.525 and then you starts, you know, all of a sudden like, oh, 514 00:23:01.525 --> 00:23:03.805 you know what, if this was possible, what if this? 515 00:23:03.805 --> 00:23:08.405 Right? So I think there's a lot of, uh, this nice guided, 516 00:23:08.585 --> 00:23:09.885 you know, speculation 517 00:23:09.995 --> 00:23:12.445 that you do in the large session, right? 518 00:23:12.445 --> 00:23:15.805 When someone comes and say, wow, I have, you know, 519 00:23:15.835 --> 00:23:17.485 this issue I'm facing right now. 520 00:23:17.795 --> 00:23:20.285 What, what, what, what does everybody think? [Kim] Mm-hmm. 521 00:23:20.465 --> 00:23:21.765 [Alex] And I might offer an opinion 522 00:23:21.865 --> 00:23:24.085 and some other students offer their opinions 523 00:23:24.185 --> 00:23:26.325 and all of a sudden, we're kind of problem-solving. [Kim] Mm-hmm. 524 00:23:26.965 --> 00:23:30.645 [Alex] I get kind of, uh, brainstorming stuff, a bit of the class. 525 00:23:30.785 --> 00:23:33.645 So it is a lot of fun. I mean, the students are great. 526 00:23:33.955 --> 00:23:37.045 They're very engaged. We have really awesome conversations. 527 00:23:37.105 --> 00:23:39.045 And, uh, I, I, 528 00:23:39.245 --> 00:23:41.925 I should remember like the last time I taught this class was 529 00:23:41.925 --> 00:23:44.645 like, this was just full of, just so exciting, 530 00:23:44.865 --> 00:23:46.765 So big smile, right? 531 00:23:46.765 --> 00:23:48.085 And make everybody super excited. 532 00:23:48.185 --> 00:23:50.005 [Kim] You know, it's interesting because when we do those 533 00:23:50.395 --> 00:23:53.245 post-course surveys, that is one of the things 534 00:23:53.245 --> 00:23:55.325 that always ends up being a positive, is that 535 00:23:55.325 --> 00:23:57.485 what we think are detours, that that's 536 00:23:57.485 --> 00:24:00.645 what mo- people love sometimes the most, is what came out, 537 00:24:00.865 --> 00:24:02.485 um, in unexpected ways. 538 00:24:02.485 --> 00:24:04.605 That, yes, you come in with a problem, 539 00:24:04.605 --> 00:24:05.885 what does the class think about this? 540 00:24:06.105 --> 00:24:08.925 And, uh, you, I dunno if you've read the book "Range," 541 00:24:09.065 --> 00:24:10.845 but it's very much that idea 542 00:24:11.105 --> 00:24:13.925 or if, you've taught design thinking, uh, you, you come up 543 00:24:13.925 --> 00:24:15.885 with ideas for your own application 544 00:24:16.395 --> 00:24:18.885 from some completely unrelated conversation. 545 00:24:18.905 --> 00:24:20.285 No, what you thought was a completely 546 00:24:20.285 --> 00:24:21.725 unrelated conversation. 547 00:24:21.745 --> 00:24:24.525 So tho I, those are the most fun. [Alex] Absolutely. [Kim] Yeah. 548 00:24:24.875 --> 00:24:26.405 Well, it's been fun talking with you too. 549 00:24:26.685 --> 00:24:27.885 [Alex] Always. [Kim] I always love seeing you. 550 00:24:28.045 --> 00:24:29.965 I miss seeing you up in our office suite. 551 00:24:30.345 --> 00:24:32.205 Um, I, we should probably say 552 00:24:32.205 --> 00:24:35.405 that your class starts on March 24th? [Alex] I believe. 553 00:24:35.845 --> 00:24:38.325 [Kim] Yeah. And, and if there are questions about that, they can 554 00:24:38.345 --> 00:24:42.445 of course email, uh, kelleypd@iu.edu. 555 00:24:42.445 --> 00:24:46.245 [Alex] Mm-hmm. [Kim] Um, and uh, I just again appreciate you being here 556 00:24:46.305 --> 00:24:49.165 and, um, uh, Happy New Year again, Alex. 557 00:24:49.165 --> 00:24:50.165 [Alex] Happy New Year. I thank you for having 558 00:24:50.165 --> 00:24:51.405 me. That was great. [Kim] Thanks.
