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WEBVTT 1 00:00:00.000 --> 00:00:02.935 [audio logo] 2 00:00:02.935 --> 00:00:09.920 3 00:00:09.920 --> 00:00:10.920 (Kim Goad) Hi, everyone. 4 00:00:10.920 --> 00:00:13.160 Thank you for joining us today for another discussion 5 00:00:13.160 --> 00:00:15.520 of the Kelley executive education leading 6 00:00:15.520 --> 00:00:16.763 with AI series. 7 00:00:16.763 --> 00:00:19.180 In these sessions, if you've been following along with us, 8 00:00:19.180 --> 00:00:22.680 we've been talking about how AI is transforming everything 9 00:00:22.680 --> 00:00:24.760 really, from strategy to critical thinking 10 00:00:24.760 --> 00:00:27.800 to leadership, every discipline you can think of. 11 00:00:27.800 --> 00:00:29.560 Today, we're going to shift gears and talk 12 00:00:29.560 --> 00:00:33.160 about yet another discipline that is greatly enabled by AI, 13 00:00:33.160 --> 00:00:34.680 and that is finance. 14 00:00:34.680 --> 00:00:36.980 I'm Kim Goad, and over the next few minutes, 15 00:00:36.980 --> 00:00:39.520 I'm joined by someone who is uniquely qualified 16 00:00:39.520 --> 00:00:41.760 to talk about how you can leverage AI 17 00:00:41.760 --> 00:00:43.500 in your financial operations. 18 00:00:43.500 --> 00:00:45.920 My colleague, Majid Darvishan. 19 00:00:45.920 --> 00:00:47.800 Majid is a clinical assistant professor 20 00:00:47.800 --> 00:00:49.720 of finance at the Kelley School of Business, 21 00:00:49.720 --> 00:00:52.040 and beginning August 1, will serve as the chair 22 00:00:52.040 --> 00:00:54.560 of finance graduate programs. 23 00:00:54.560 --> 00:00:56.980 His research focuses on corporate finance, 24 00:00:56.980 --> 00:00:59.420 corporate governance, institutional investors, 25 00:00:59.420 --> 00:01:01.420 and industrial organization. 26 00:01:01.420 --> 00:01:03.980 Majid teaches courses in corporate financial risk 27 00:01:03.980 --> 00:01:06.660 management, corporate financial strategy, bank 28 00:01:06.660 --> 00:01:10.420 and financial intermediation, and intermediate investments, 29 00:01:10.420 --> 00:01:13.500 and that teaching has earned him multiple awards, including 30 00:01:13.500 --> 00:01:16.820 the MBA Teaching Excellence Award, the MS Best Faculty 31 00:01:16.820 --> 00:01:20.080 Award, the Indiana University Trustees' Teaching Award, 32 00:01:20.080 --> 00:01:21.740 and many more. 33 00:01:21.740 --> 00:01:25.020 In addition to having had an extensive corporate career prior 34 00:01:25.020 --> 00:01:28.460 to joining academia, Majid co-founded the Corporate Finance 35 00:01:28.460 --> 00:01:31.380 Workshop at Kelley, and he serves on the Supervisory 36 00:01:31.380 --> 00:01:35.580 Committee of IU Credit Union and the FP&A Advisory Council 37 00:01:35.580 --> 00:01:38.380 for the Association for Financial Professionals. 38 00:01:38.380 --> 00:01:40.573 Majid, thank you so much for taking the time today. 39 00:01:40.573 --> 00:01:41.740 (Majid Darvishan) Thank you. 40 00:01:41.740 --> 00:01:42.720 It's great to be here. 41 00:01:42.720 --> 00:01:43.595 (Kim Goad) Thank you. 42 00:01:43.595 --> 00:01:45.300 Let's just get started at what seems 43 00:01:45.300 --> 00:01:47.240 like an obvious first question to me. 44 00:01:47.240 --> 00:01:49.000 And that is, there is, of course, 45 00:01:49.000 --> 00:01:51.100 a lot of hype-- and finance leaders 46 00:01:51.100 --> 00:01:53.660 are hearing a lot about how AI may 47 00:01:53.660 --> 00:01:57.460 be greatly changing their jobs or even replacing their jobs. 48 00:01:57.460 --> 00:02:02.260 How much of it is exaggeration and how much of it is real? 49 00:02:02.260 --> 00:02:04.792 How much still relies on human judgment? 50 00:02:04.792 --> 00:02:05.750 (Majid Darvishan) Sure. 51 00:02:05.750 --> 00:02:10.070 So what AI is not changing is the role of finance. 52 00:02:10.070 --> 00:02:12.170 The finance has the same role. 53 00:02:12.170 --> 00:02:15.470 It plays the same role and same purpose. 54 00:02:15.470 --> 00:02:18.510 So finance role is to help organizations 55 00:02:18.510 --> 00:02:20.350 make better decisions. 56 00:02:20.350 --> 00:02:25.070 What AI is changing is where financial professionals 57 00:02:25.070 --> 00:02:26.110 spend their time. 58 00:02:26.110 --> 00:02:29.590 So in most finance role, the work 59 00:02:29.590 --> 00:02:32.090 can be divided into two different buckets. 60 00:02:32.090 --> 00:02:34.510 The preparation bucket is collecting 61 00:02:34.510 --> 00:02:40.030 data like reconciling numbers, preparing reports, forecasting. 62 00:02:40.030 --> 00:02:45.390 And then we have another bucket, high-value work bucket. 63 00:02:45.390 --> 00:02:49.770 So here is more like interpreting the result. 64 00:02:49.770 --> 00:02:52.170 Like you're telling the story behind the numbers, 65 00:02:52.170 --> 00:02:55.750 making sure that the assumptions are correct and technically 66 00:02:55.750 --> 00:02:57.630 like making decisions. 67 00:02:57.630 --> 00:03:01.910 So depending where you stand in firm organization, 68 00:03:01.910 --> 00:03:04.930 you spend more time in one of these buckets. 69 00:03:04.930 --> 00:03:06.910 So for example, if you're an analyst, 70 00:03:06.910 --> 00:03:10.950 you spend a lot of time in a preparation bucket. 71 00:03:10.950 --> 00:03:14.210 So you're collecting data, preparing report, 72 00:03:14.210 --> 00:03:16.610 forecasting, building models. 73 00:03:16.610 --> 00:03:19.790 Whereas if you're a CEO or financial leader, 74 00:03:19.790 --> 00:03:21.950 you rarely touch a spreadsheet. 75 00:03:21.950 --> 00:03:25.250 You're asking a bigger question, whether we 76 00:03:25.250 --> 00:03:27.990 should allocate resource to this project or not. 77 00:03:27.990 --> 00:03:29.690 What decision should we make? 78 00:03:29.690 --> 00:03:32.690 What's the story behind these numbers? 79 00:03:32.690 --> 00:03:37.170 So what AI has done is, it compressed the preparation 80 00:03:37.170 --> 00:03:38.930 work considerably. 81 00:03:38.930 --> 00:03:44.410 It can help you prepare a model really fast, like cleaning up 82 00:03:44.410 --> 00:03:47.610 the data really fast, helping you forecast cash flow really 83 00:03:47.610 --> 00:03:53.350 fast, but it hasn't changed the financial judgment. 84 00:03:53.350 --> 00:03:57.630 It's still, like you need to make that judgment. 85 00:03:57.630 --> 00:04:02.430 And what AI actually did was shorten the preparation work. 86 00:04:02.430 --> 00:04:06.990 That means finance now is much closer to financial judgment. 87 00:04:06.990 --> 00:04:10.930 So the value now is not to building the model, 88 00:04:10.930 --> 00:04:12.710 because the tool can do that. 89 00:04:12.710 --> 00:04:17.630 So the value that you bring in is asking the right question: 90 00:04:17.630 --> 00:04:22.190 whether the assumption makes sense or not, 91 00:04:22.190 --> 00:04:24.990 whether the interpretation is correct. 92 00:04:24.990 --> 00:04:28.550 And more importantly, helping organizations 93 00:04:28.550 --> 00:04:29.990 make better decisions. 94 00:04:29.990 --> 00:04:33.910 So in that regard, AI makes a financial judgment 95 00:04:33.910 --> 00:04:37.310 more important now. 96 00:04:37.310 --> 00:04:40.710 And that's how AI changed the landscape. 97 00:04:40.710 --> 00:04:43.310 It's actually, I want to say one thing its, 98 00:04:43.310 --> 00:04:49.390 AI makes finance leader a strategic partner right now. 99 00:04:49.390 --> 00:04:52.850 Because now you don't have to deal with all the preparation, 100 00:04:52.850 --> 00:04:54.910 collecting data, making sure-- now, 101 00:04:54.910 --> 00:04:57.790 your role is to helping organizations make 102 00:04:57.790 --> 00:04:59.022 better strategic decisions. 103 00:04:59.022 --> 00:05:01.230 (Kim Goad) It sounds like it's like other disciplines 104 00:05:01.230 --> 00:05:04.710 that we hear about where it's elevating their role, 105 00:05:04.710 --> 00:05:07.280 taking out some of that more menial routine work 106 00:05:07.280 --> 00:05:08.200 and elevating-- 107 00:05:08.200 --> 00:05:10.380 (Majid Darvishan) The time-consuming part is gone. 108 00:05:10.380 --> 00:05:13.600 Now, you actually have to understand the business 109 00:05:13.600 --> 00:05:18.440 side of it better, and you have to understand the intuition 110 00:05:18.440 --> 00:05:20.100 behind the number and more importantly 111 00:05:20.100 --> 00:05:23.820 how you can communicate that with stakeholders. 112 00:05:23.820 --> 00:05:26.160 (Kim Goad) Oh, very good, very good. 113 00:05:26.160 --> 00:05:28.420 Well, I think you may have partly answered this, 114 00:05:28.420 --> 00:05:30.960 but many people first think of it 115 00:05:30.960 --> 00:05:34.460 and we've talked about this as an efficiency thing. 116 00:05:34.460 --> 00:05:36.640 It's a way to save time. 117 00:05:36.640 --> 00:05:39.700 Is that the biggest part of the story, efficiency, 118 00:05:39.700 --> 00:05:41.400 or are there bigger opportunities 119 00:05:41.400 --> 00:05:46.440 here for finance leaders than just that minimizing 120 00:05:46.440 --> 00:05:48.600 the time required to set up the analysis? 121 00:05:48.600 --> 00:05:51.060 (Majid Darvishan) So efficiency is real. 122 00:05:51.060 --> 00:05:54.060 AI save time, but that's just one side of it. 123 00:05:54.060 --> 00:05:56.680 It's easy highlight. 124 00:05:56.680 --> 00:06:01.360 So AI can-- as I said, help you prepare your analysis, 125 00:06:01.360 --> 00:06:06.760 help you forecast, or help you run multiple scenarios. 126 00:06:06.760 --> 00:06:10.680 So if, for example, if you want to make a big decision, 127 00:06:10.680 --> 00:06:12.540 usually we have a best case, and then we 128 00:06:12.540 --> 00:06:15.920 have the worst case and best case scenario. 129 00:06:15.920 --> 00:06:18.740 So you look at all these three, but AI helps you 130 00:06:18.740 --> 00:06:21.300 instead of just three, look at the wide array 131 00:06:21.300 --> 00:06:24.420 of scenarios like 40, 50. 132 00:06:24.420 --> 00:06:27.420 So that changed the quality of conversation, 133 00:06:27.420 --> 00:06:30.700 because now you can actually use more analysis 134 00:06:30.700 --> 00:06:32.360 to make better decision. 135 00:06:32.360 --> 00:06:35.520 So instead of-- now you can ask bigger questions. 136 00:06:35.520 --> 00:06:39.460 So in a sense, you can ask what happens to my cash flows 137 00:06:39.460 --> 00:06:42.140 if tariff increase by 10%? 138 00:06:42.140 --> 00:06:46.200 What would happen to my margin if I change my pricing? 139 00:06:46.200 --> 00:06:49.200 These are hard and complicated question. 140 00:06:49.200 --> 00:06:51.740 Now, with AI you would be able to actually answer 141 00:06:51.740 --> 00:06:53.300 these complicated questions. 142 00:06:53.300 --> 00:06:58.440 So I would say, yes, AI save time. 143 00:06:58.440 --> 00:07:01.500 But the bigger question is, what would you do with the time 144 00:07:01.500 --> 00:07:02.780 that it gives you back? 145 00:07:02.780 --> 00:07:05.100 So it shouldn't be preparing more reports 146 00:07:05.100 --> 00:07:06.520 or doing more analysis. 147 00:07:06.520 --> 00:07:09.390 It should be asking better question, 148 00:07:09.390 --> 00:07:11.990 like testing the assumption, making 149 00:07:11.990 --> 00:07:13.930 sure the interpretation is correct, 150 00:07:13.930 --> 00:07:18.270 and finding a way to communicate that result with your manager 151 00:07:18.270 --> 00:07:19.290 and shareholders. 152 00:07:19.290 --> 00:07:20.332 (Kim Goad) Oh, very good. 153 00:07:20.332 --> 00:07:21.710 Very good. 154 00:07:21.710 --> 00:07:23.510 Well, speaking of that, and you've 155 00:07:23.510 --> 00:07:25.830 touched on this a little bit, but where-- 156 00:07:25.830 --> 00:07:28.550 either in with your work with grad students 157 00:07:28.550 --> 00:07:31.190 or with the corporate work that you've done, 158 00:07:31.190 --> 00:07:35.150 where do you see the most practical AI use cases 159 00:07:35.150 --> 00:07:36.270 right now? 160 00:07:36.270 --> 00:07:39.070 (Majid Darvishan) So most AI use cases 161 00:07:39.070 --> 00:07:40.570 have been recognized already. 162 00:07:40.570 --> 00:07:44.230 Forecasting, it's really like a helpful tool 163 00:07:44.230 --> 00:07:47.410 to actually forecast cash flow collecting data, 164 00:07:47.410 --> 00:07:48.630 finding pattern. 165 00:07:48.630 --> 00:07:49.790 AI is really good at it. 166 00:07:49.790 --> 00:07:51.950 Scenario analysis. 167 00:07:51.950 --> 00:07:54.990 But again, you have to make sure that the assumptions 168 00:07:54.990 --> 00:07:58.110 are correct, because analysis is not perfect 169 00:07:58.110 --> 00:07:59.370 and AI is not perfect. 170 00:07:59.370 --> 00:08:01.190 So you have to make sure that everything is 171 00:08:01.190 --> 00:08:02.650 correct-- assumption correct. 172 00:08:02.650 --> 00:08:05.190 Your data is clean enough for AI. 173 00:08:05.190 --> 00:08:09.650 So forecasting, like scenario analysis. 174 00:08:09.650 --> 00:08:12.250 Finding anomaly, anomaly detection, 175 00:08:12.250 --> 00:08:14.010 AI is really good at it. 176 00:08:14.010 --> 00:08:17.850 If there is an unusual transaction 177 00:08:17.850 --> 00:08:23.870 or there is a big margin movement or a KPI movement, 178 00:08:23.870 --> 00:08:26.850 like a change, then it would help you actually-- 179 00:08:26.850 --> 00:08:31.110 it identify those areas that needs attention. 180 00:08:31.110 --> 00:08:33.990 So it doesn't give you the whole answer and solution, 181 00:08:33.990 --> 00:08:37.090 but it directs you to the right direction, 182 00:08:37.090 --> 00:08:39.794 so that you can pay attention to changes that you see. 183 00:08:39.794 --> 00:08:40.669 (Kim Goad) Very good. 184 00:08:40.669 --> 00:08:41.169 Yeah. 185 00:08:41.169 --> 00:08:43.450 186 00:08:43.450 --> 00:08:46.090 When thinking about the organizations that 187 00:08:46.090 --> 00:08:49.970 are figuring this out very well, and those who maybe are stuck 188 00:08:49.970 --> 00:08:51.670 still, what's separating those? 189 00:08:51.670 --> 00:08:53.003 How are-- what's the difference? 190 00:08:53.003 --> 00:08:55.670 How are the ones that are stuck, why are they not getting there? 191 00:08:55.670 --> 00:08:57.930 What do you see as the obstacles? 192 00:08:57.930 --> 00:09:00.170 (Majid Darvishan) Based on what I have seen 193 00:09:00.170 --> 00:09:05.110 is, organization that actually utilizing AI efficiently, 194 00:09:05.110 --> 00:09:08.850 they do like a few simple things like better than others. 195 00:09:08.850 --> 00:09:12.750 The first one is that they start with the problem, 196 00:09:12.750 --> 00:09:15.650 so they don't say, oh, I bought this AI tool, 197 00:09:15.650 --> 00:09:17.270 where can I use it? 198 00:09:17.270 --> 00:09:19.650 They start with the problem: what decision 199 00:09:19.650 --> 00:09:23.530 I'm trying to improve, what process I'm trying to improve. 200 00:09:23.530 --> 00:09:26.050 So they start with the real problem. 201 00:09:26.050 --> 00:09:29.270 They know that the true quality of their data. 202 00:09:29.270 --> 00:09:31.870 Because if you don't have a high-quality data 203 00:09:31.870 --> 00:09:36.110 or clean enough data, you won't be able to utilize AI. 204 00:09:36.110 --> 00:09:41.670 AI can make your wrong data or bad data 205 00:09:41.670 --> 00:09:45.110 look polish, which is very dangerous in finance. 206 00:09:45.110 --> 00:09:47.390 So they start with the real problem. 207 00:09:47.390 --> 00:09:51.710 They have a clean enough data and they have discipline. 208 00:09:51.710 --> 00:09:55.110 They have discipline because they 209 00:09:55.110 --> 00:09:58.990 should know that what question they're trying to answer: 210 00:09:58.990 --> 00:10:01.710 what-- how they can improve it, where 211 00:10:01.710 --> 00:10:05.930 they can rely on AI, who owns the data, who should review it. 212 00:10:05.930 --> 00:10:12.320 And then they try to build an AI fluency among managers, 213 00:10:12.320 --> 00:10:16.140 because managers, they don't need to be data scientists, 214 00:10:16.140 --> 00:10:18.420 they don't need to be AI specialists, 215 00:10:18.420 --> 00:10:21.720 but they need to understand what AI is capable of 216 00:10:21.720 --> 00:10:24.800 and what AI is not capable of. 217 00:10:24.800 --> 00:10:26.180 What are the shortcomings? 218 00:10:26.180 --> 00:10:28.680 What are the issues they're going to face? 219 00:10:28.680 --> 00:10:32.920 So they know the limitation of AI, 220 00:10:32.920 --> 00:10:37.240 and they start with the real problem, clean enough data, 221 00:10:37.240 --> 00:10:42.080 and at the center of everything is just the communication 222 00:10:42.080 --> 00:10:46.600 of the solution that they come up with the AI, 223 00:10:46.600 --> 00:10:51.000 with their manager, leaders, or stockholders. 224 00:10:51.000 --> 00:10:52.520 (Kim Goad) Yeah, so it sounds like-- 225 00:10:52.520 --> 00:10:55.180 and we've been talking about this throughout this series, 226 00:10:55.180 --> 00:10:57.080 is that so much of this is intertwined, 227 00:10:57.080 --> 00:11:01.080 in that a lot of those tried and true disciplines 228 00:11:01.080 --> 00:11:04.220 are still there in terms of starting with the end in mind, 229 00:11:04.220 --> 00:11:06.460 as you said, having the good data to begin with, 230 00:11:06.460 --> 00:11:10.480 understanding your corporate flow, workflows, and policies 231 00:11:10.480 --> 00:11:14.820 and how you-- and just using that critical thinking piece is 232 00:11:14.820 --> 00:11:16.440 all still very important. 233 00:11:16.440 --> 00:11:17.523 (Majid Darvishan) Exactly. 234 00:11:17.523 --> 00:11:20.920 Because AI helps you analyze data faster, 235 00:11:20.920 --> 00:11:23.120 like you do your forecasting analysis. 236 00:11:23.120 --> 00:11:25.360 But it doesn't change financial judgment. 237 00:11:25.360 --> 00:11:27.540 It doesn't change accountability. 238 00:11:27.540 --> 00:11:30.820 So preparation work can be done by AI, 239 00:11:30.820 --> 00:11:35.340 but the thinking, decision-making, accountability 240 00:11:35.340 --> 00:11:37.683 still belongs to financial professionals. 241 00:11:37.683 --> 00:11:39.100 (Kim Goad) For that finance leader 242 00:11:39.100 --> 00:11:42.180 who feels like they're a little behind right now, maybe stuck, 243 00:11:42.180 --> 00:11:45.340 what's a logical place for them to get started? 244 00:11:45.340 --> 00:11:50.060 (Majid Darvishan) So start small and real. 245 00:11:50.060 --> 00:11:52.460 Again, start with the question, they 246 00:11:52.460 --> 00:11:55.660 shouldn't ask what AI tools I should use or where 247 00:11:55.660 --> 00:11:57.060 should I use AI? 248 00:11:57.060 --> 00:11:59.780 Start with the problem that you're facing, something 249 00:11:59.780 --> 00:12:04.220 simple and real, something that you are familiar and low risk. 250 00:12:04.220 --> 00:12:09.620 So, for example, find a recurring decision or process 251 00:12:09.620 --> 00:12:11.380 that needs to be improved. 252 00:12:11.380 --> 00:12:15.480 Maybe a variance analysis or forecasting, monthly 253 00:12:15.480 --> 00:12:16.560 forecasting. 254 00:12:16.560 --> 00:12:20.660 So you're going to use AI and then for that real problem. 255 00:12:20.660 --> 00:12:23.160 And then you need to compare the output 256 00:12:23.160 --> 00:12:24.860 that you get with something. 257 00:12:24.860 --> 00:12:25.900 You need a benchmark. 258 00:12:25.900 --> 00:12:29.860 So it could be historical like a number that you have. 259 00:12:29.860 --> 00:12:33.120 Or you can compare it against the work 260 00:12:33.120 --> 00:12:35.760 that your team actually has put together. 261 00:12:35.760 --> 00:12:40.140 So then you would be able to identify where AI did help, 262 00:12:40.140 --> 00:12:45.440 where AI did a poor job, when AI can be utilized, 263 00:12:45.440 --> 00:12:48.000 when someone needs to review the output. 264 00:12:48.000 --> 00:12:51.560 So you see all the ups and downs of using 265 00:12:51.560 --> 00:12:53.360 AI, downside of using AI. 266 00:12:53.360 --> 00:12:54.920 So all the shortcomings. 267 00:12:54.920 --> 00:12:59.900 So this way it's actually help finance leaders in two ways. 268 00:12:59.900 --> 00:13:03.760 One is that it gives them a visible learning opportunity 269 00:13:03.760 --> 00:13:06.360 to build confidence in using AI. 270 00:13:06.360 --> 00:13:09.680 And also the second one is that they understand 271 00:13:09.680 --> 00:13:12.240 the nature of the work, the texture of the work, 272 00:13:12.240 --> 00:13:14.570 because it takes time. 273 00:13:14.570 --> 00:13:16.990 So you start with the real question. 274 00:13:16.990 --> 00:13:20.350 You apply AI, you see whether it was helpful, 275 00:13:20.350 --> 00:13:23.330 whether it wasn't helpful, when you need to actually have 276 00:13:23.330 --> 00:13:26.850 a human to review the output. 277 00:13:26.850 --> 00:13:29.730 The assumption that you need, the quality of data, 278 00:13:29.730 --> 00:13:32.990 so you understand the texture and nature of the work. 279 00:13:32.990 --> 00:13:35.470 And then once you real understand it, 280 00:13:35.470 --> 00:13:38.250 then you can apply it to a bigger and more complicated 281 00:13:38.250 --> 00:13:38.990 question. 282 00:13:38.990 --> 00:13:41.970 So they should not wait until they 283 00:13:41.970 --> 00:13:46.750 find the perfect platform or perfect tools or perfect data. 284 00:13:46.750 --> 00:13:52.190 Just start small and then build that AI fluency. 285 00:13:52.190 --> 00:13:55.890 Because by reading articles, watching videos, 286 00:13:55.890 --> 00:13:57.470 you cannot build fluency. 287 00:13:57.470 --> 00:14:01.330 You can-- you only learn it by applying the tools 288 00:14:01.330 --> 00:14:02.970 to their real problems. 289 00:14:02.970 --> 00:14:05.273 (Kim Goad) That's a great lead into my next question. 290 00:14:05.273 --> 00:14:06.690 And as we think about-- we're here 291 00:14:06.690 --> 00:14:09.355 obviously because of this Leading with AI series. 292 00:14:09.355 --> 00:14:10.730 And you're going to be delivering 293 00:14:10.730 --> 00:14:13.210 a course on October 29. 294 00:14:13.210 --> 00:14:15.570 It's-- for the listening audience, 295 00:14:15.570 --> 00:14:18.370 it's the same format as the other Leading with AI courses 296 00:14:18.370 --> 00:14:22.250 in that Majid will spend a day with the participants, 297 00:14:22.250 --> 00:14:24.990 a full day in Indianapolis in-person, and then a 298 00:14:24.990 --> 00:14:27.790 follow up for about 2, 2.5 hours live 299 00:14:27.790 --> 00:14:29.910 virtually the following week. 300 00:14:29.910 --> 00:14:31.410 Boy, there's a lot to digest. 301 00:14:31.410 --> 00:14:32.950 So as you think about-- as you're 302 00:14:32.950 --> 00:14:37.070 preparing to deliver this course or program, 303 00:14:37.070 --> 00:14:40.590 who is it designed for and what can they realistically expect 304 00:14:40.590 --> 00:14:45.310 to walk away with after just a day and a half worth of work? 305 00:14:45.310 --> 00:14:47.990 (Majid Darvishan) This course is for financial professionals 306 00:14:47.990 --> 00:14:51.830 or business leaders who use financial information, 307 00:14:51.830 --> 00:14:57.350 FP&A people, business leaders, analysts, controller, treasurer. 308 00:14:57.350 --> 00:15:01.550 They don't need to have finance in their title, 309 00:15:01.550 --> 00:15:04.670 as long as they're working with financial data 310 00:15:04.670 --> 00:15:08.190 or making a strategic decision, this course would 311 00:15:08.190 --> 00:15:10.070 be helpful for them. 312 00:15:10.070 --> 00:15:11.730 It's not a technical course. 313 00:15:11.730 --> 00:15:15.200 So if someone wants to build an AI system 314 00:15:15.200 --> 00:15:17.632 or become a data scientist. 315 00:15:17.632 --> 00:15:18.840 That's a different path for-- 316 00:15:18.840 --> 00:15:18.920 (Kim Goad) Not for them. 317 00:15:18.920 --> 00:15:19.300 (Majid Darvishan) --them. 318 00:15:19.300 --> 00:15:20.160 [chuckles] 319 00:15:20.160 --> 00:15:22.020 I know I cannot teach that as well. 320 00:15:22.020 --> 00:15:24.600 I don't have the knowledge to be-- 321 00:15:24.600 --> 00:15:26.140 to teach data science course. 322 00:15:26.140 --> 00:15:31.800 So what we do-- what they need to bring financial judgment 323 00:15:31.800 --> 00:15:33.280 and business judgment. 324 00:15:33.280 --> 00:15:37.920 So they need to understand cash flow, capital allocation, 325 00:15:37.920 --> 00:15:39.040 and margin. 326 00:15:39.040 --> 00:15:40.720 And then in this class, we're going 327 00:15:40.720 --> 00:15:44.160 to focus on the application side of AI. 328 00:15:44.160 --> 00:15:46.800 So once they're done with the program, 329 00:15:46.800 --> 00:15:49.480 they would be able to confidently apply 330 00:15:49.480 --> 00:15:51.300 AI in their own work. 331 00:15:51.300 --> 00:15:55.080 So instead of just taking, living with notes 332 00:15:55.080 --> 00:15:57.160 and just AI intuition, we're going 333 00:15:57.160 --> 00:16:01.040 to focus on the problem and the process and decision 334 00:16:01.040 --> 00:16:04.040 that they are facing at their own organization. 335 00:16:04.040 --> 00:16:06.920 And we're going to work through the different tools they 336 00:16:06.920 --> 00:16:15.480 can utilize to help them make a better decision or perform-- 337 00:16:15.480 --> 00:16:18.020 or complete the process faster. 338 00:16:18.020 --> 00:16:21.960 So it's not-- we talk about the intuition, 339 00:16:21.960 --> 00:16:23.740 but the more important part of the class 340 00:16:23.740 --> 00:16:25.760 is the application side of it. 341 00:16:25.760 --> 00:16:28.800 When we talk about all these different tools, 342 00:16:28.800 --> 00:16:33.112 it's specifically focusing on the problem that they're facing. 343 00:16:33.112 --> 00:16:33.820 (Kim Goad) Great. 344 00:16:33.820 --> 00:16:35.237 (Majid Darvishan) So they're going 345 00:16:35.237 --> 00:16:39.380 to be able to actually leave with actionable items, something 346 00:16:39.380 --> 00:16:43.308 that they can use the next day, the next week in at their work. 347 00:16:43.308 --> 00:16:44.100 (Kim Goad) Perfect. 348 00:16:44.100 --> 00:16:46.683 That's a big philosophy that we weave throughout everything we 349 00:16:46.683 --> 00:16:50.100 do in exec ed is that it's got to be contextualized to the work 350 00:16:50.100 --> 00:16:51.400 that they're really doing. 351 00:16:51.400 --> 00:16:53.700 Good, good. 352 00:16:53.700 --> 00:16:57.923 I'll end with one of my questions I tend to end with. 353 00:16:57.923 --> 00:17:00.340 But if you'd prefer to answer something else that maybe we 354 00:17:00.340 --> 00:17:02.700 haven't touched on, you can choose this one or something 355 00:17:02.700 --> 00:17:03.900 else that you'd rather. 356 00:17:03.900 --> 00:17:06.260 And that is that just-- 357 00:17:06.260 --> 00:17:08.339 what is one practical piece of advice 358 00:17:08.339 --> 00:17:11.140 that you would give a financial leader who's looking to just get 359 00:17:11.140 --> 00:17:11.640 started? 360 00:17:11.640 --> 00:17:13.240 And we've talked about that a bit, 361 00:17:13.240 --> 00:17:15.500 but is there anything else you would say 362 00:17:15.500 --> 00:17:18.224 to close out for the audience? 363 00:17:18.224 --> 00:17:20.599 (Majid Darvishan) They should start with the problem, not 364 00:17:20.599 --> 00:17:21.400 the tool. 365 00:17:21.400 --> 00:17:24.200 I think I touched on that before, 366 00:17:24.200 --> 00:17:29.600 so they should not ask what AI tool I should buy right now. 367 00:17:29.600 --> 00:17:32.040 They should start with the problem 368 00:17:32.040 --> 00:17:35.580 that they're facing, a process that needs to be improved. 369 00:17:35.580 --> 00:17:39.040 And then think about what would they 370 00:17:39.040 --> 00:17:41.620 need to make a better decision. 371 00:17:41.620 --> 00:17:45.200 What would they need to complete that process faster? 372 00:17:45.200 --> 00:17:47.940 If AI can help, they should apply it. 373 00:17:47.940 --> 00:17:50.240 If not, they should not force it. 374 00:17:50.240 --> 00:17:55.160 And then once they find a way to actually implement AI. 375 00:17:55.160 --> 00:17:57.040 Because as I said, there are some steps 376 00:17:57.040 --> 00:18:01.920 that needs to be done by human, like the judgment side. 377 00:18:01.920 --> 00:18:04.720 And they need to actually review the output as well, 378 00:18:04.720 --> 00:18:06.840 because AI can make a mistake. 379 00:18:06.840 --> 00:18:09.840 So start with something real. 380 00:18:09.840 --> 00:18:14.280 Try to find a way to improve that process. 381 00:18:14.280 --> 00:18:16.540 See whether AI would help you or not. 382 00:18:16.540 --> 00:18:20.490 And then check the output, verify your assumption, 383 00:18:20.490 --> 00:18:23.690 the quality of data, and build on the simple case 384 00:18:23.690 --> 00:18:27.030 to be able to work on a bigger problem. 385 00:18:27.030 --> 00:18:27.930 (Kim Goad) Perfect. 386 00:18:27.930 --> 00:18:28.870 Thank you so much. 387 00:18:28.870 --> 00:18:28.953 (Majid Darvishan) Thank you. 388 00:18:28.953 --> 00:18:31.453 (Kim Goad) I know we're just touching the surface because we 389 00:18:31.453 --> 00:18:35.090 can't-- as I said, even the day and a half will be a lot to get 390 00:18:35.090 --> 00:18:35.590 through. 391 00:18:35.590 --> 00:18:38.130 And you can teach a whole semester long course 392 00:18:38.130 --> 00:18:39.530 on all of this, and then some-- 393 00:18:39.530 --> 00:18:42.430 I'm sure, so that we're just scratching the surface. 394 00:18:42.430 --> 00:18:44.090 But if folks have any questions, they 395 00:18:44.090 --> 00:18:46.570 can reach out to us on the website. 396 00:18:46.570 --> 00:18:48.090 I know you're a busy guy, especially 397 00:18:48.090 --> 00:18:49.677 with this new appointment coming up, 398 00:18:49.677 --> 00:18:51.510 and I appreciate you taking the time, Majid. 399 00:18:51.510 --> 00:18:51.950 (Majid Darvishan) Sure. 400 00:18:51.950 --> 00:18:52.450 Thank you. 401 00:18:52.450 --> 00:18:54.908 (Kim Goad) Thank you to everyone who's been watching today. 402 00:18:54.908 --> 00:18:56.458 If the conversation has resonated 403 00:18:56.458 --> 00:18:58.250 and you're a finance leader out there who's 404 00:18:58.250 --> 00:19:01.530 interested in learning more about how you can apply AI 405 00:19:01.530 --> 00:19:04.930 to your finance role, please join 406 00:19:04.930 --> 00:19:08.690 Majid and the rest of the cohort on October 29, 407 00:19:08.690 --> 00:19:10.450 and we look forward to seeing you 408 00:19:10.450 --> 00:19:14.170 at the next installment of this leader Leading with AI series. 409 00:19:14.170 --> 00:19:14.670 Thank you. 410 00:19:14.670 --> 00:19:17.720 [audio logo] 411 00:19:17.720 --> 00:19:20.000
