1 00:00:00,153 --> 00:00:02,923 Alex: Imagine being in a Formula One race where the track is 2 00:00:02,923 --> 00:00:04,073 being billed as your drive. 3 00:00:04,363 --> 00:00:08,343 There are no pit stops and the slowest speed is yesterday's maximum. 4 00:00:08,823 --> 00:00:12,383 That's how fast artificial intelligence is changing the business world. 5 00:00:12,713 --> 00:00:16,723 Some leaders are accelerating with it, others are falling behind. 6 00:00:17,093 --> 00:00:19,133 So what does it take to stay in the race? 7 00:00:19,693 --> 00:00:22,963 My name is Alexandre Nevski and this is Innovation Tales. 8 00:00:38,662 --> 00:00:42,912 My guest today insists that AI is not just a tool, it's an intelligence, and 9 00:00:42,912 --> 00:00:44,542 it's rewriting the rules of business. 10 00:00:44,542 --> 00:00:48,982 Fahed Bizzari is the founder of a Dubai based AI consultancy, where 11 00:00:48,982 --> 00:00:53,392 he helps chief digital officers move beyond experimentation into 12 00:00:53,392 --> 00:00:55,192 real, scalable transformation. 13 00:00:55,882 --> 00:00:59,552 He has developed frameworks that bridge AI's technical power with the 14 00:00:59,552 --> 00:01:03,762 human experience, an often overlooked factor in digital transformation. 15 00:01:04,262 --> 00:01:07,502 For Fahed, success with AI isn't about chasing hype. 16 00:01:07,632 --> 00:01:10,322 It's about knowing how to integrate it into strategy, 17 00:01:10,582 --> 00:01:12,472 operations, and the workforce. 18 00:01:13,082 --> 00:01:16,092 In this episode, we cut through the noise and get straight 19 00:01:16,112 --> 00:01:17,502 into the practical insights. 20 00:01:17,862 --> 00:01:21,392 How do you turn skeptical executives into AI advocates? 21 00:01:21,802 --> 00:01:24,772 How do you identify use cases with the highest ROI? 22 00:01:25,182 --> 00:01:28,282 And most importantly, how do you ensure AI enhances your 23 00:01:28,282 --> 00:01:30,752 workforce rather than replacing it? 24 00:01:31,362 --> 00:01:34,562 His approach is pragmatic, battle tested, and built for 25 00:01:34,562 --> 00:01:36,242 decision makers who need results. 26 00:01:36,877 --> 00:01:39,977 Without further ado, here's my conversation with Fahed Bizzari. 27 00:01:40,542 --> 00:01:41,788 Fahed, welcome to the show. 28 00:01:42,028 --> 00:01:42,848 Fahed: Thank you for having me. 29 00:01:43,160 --> 00:01:43,940 Alex: Glad you could join. 30 00:01:44,340 --> 00:01:46,880 You've built a strong reputation in the field of AI literacy 31 00:01:46,880 --> 00:01:47,760 in the corporate world. 32 00:01:48,150 --> 00:01:51,160 Before we dive in, how did you first get into that? 33 00:01:51,586 --> 00:01:54,056 Fahed: There's the long story and then there's the final part. 34 00:01:54,056 --> 00:01:57,836 So the long story is my whole career, the 20 years since my first 35 00:01:57,836 --> 00:02:03,036 business, which I set up in 2002, it was a purely digital business. 36 00:02:03,036 --> 00:02:06,146 So if you can imagine 2002, ahead of everybody else. 37 00:02:06,566 --> 00:02:10,076 it was a fully automated business as early as 2005. 38 00:02:10,506 --> 00:02:14,806 The whole idea of the online world and digital, it just seemed 39 00:02:14,806 --> 00:02:16,246 so strange for most people. 40 00:02:16,366 --> 00:02:20,346 So because I had a fully automated business that was running in my sleep. 41 00:02:20,821 --> 00:02:24,391 I saw everybody else just the way that they were doing things and the 42 00:02:24,391 --> 00:02:28,691 long queues that would take three hours and the paper forms and so on. 43 00:02:28,961 --> 00:02:32,661 One of the, one of the blessings about having your own business that runs itself 44 00:02:33,011 --> 00:02:34,781 is the amount of free time that you have. 45 00:02:34,851 --> 00:02:40,751 And if you are addicted to work like I am, it means that you can have years spent on 46 00:02:40,751 --> 00:02:43,581 self development and developing others. 47 00:02:43,841 --> 00:02:46,411 The culmination, of course, was in 2022. 48 00:02:47,664 --> 00:02:53,851 When we started using GPT, not ChatGPT, but GPT, the underlying tech, about six 49 00:02:53,851 --> 00:02:56,131 months before ChatGPT became a thing. 50 00:02:56,681 --> 00:03:00,561 And at that time we started using for very small use cases 51 00:03:00,561 --> 00:03:01,951 inside one of my businesses. 52 00:03:02,631 --> 00:03:07,301 And of course, when ChatGPT came out, I had the very good fortune of 53 00:03:07,311 --> 00:03:09,331 being on a sabbatical with my father. 54 00:03:09,861 --> 00:03:14,631 One of the things that keeps people from learning AI is that they're 55 00:03:14,631 --> 00:03:18,391 so busy in their day to day lives, they can't make time for it. 56 00:03:18,961 --> 00:03:25,031 And when ChatGPT was released in November, 2022, I had all the time in the world. 57 00:03:25,051 --> 00:03:26,631 I was working on a nonprofit project. 58 00:03:27,251 --> 00:03:28,821 But I had all the time in the world. 59 00:03:28,971 --> 00:03:32,781 And every day I was pushing it and pushing it and pushing it because 60 00:03:32,781 --> 00:03:35,641 I have a technical background, because I have business expertise. 61 00:03:36,371 --> 00:03:40,801 And then we finally hit one use case in December, 2022. 62 00:03:40,868 --> 00:03:45,628 I was on a transit back from London to Sri Lanka, where my development team is. 63 00:03:46,258 --> 00:03:49,978 And, when I reached Dubai for a 10 day transit, I was asking 64 00:03:49,978 --> 00:03:53,808 people, you know, you guys tell me about your use of ChatGPT. 65 00:03:54,178 --> 00:03:56,208 This is February 2023 now. 66 00:03:56,258 --> 00:04:00,428 ChatGPT's been out for three months, and they all said the same thing. 67 00:04:01,958 --> 00:04:03,758 But we haven't started using it. 68 00:04:04,758 --> 00:04:09,838 So, during that 10 days, I told my old network, listen, round up your friends and 69 00:04:09,838 --> 00:04:14,248 I'll give you guys a free seminar to help you understand what this is all about and 70 00:04:14,248 --> 00:04:16,198 help you break the ice and start using it. 71 00:04:17,538 --> 00:04:22,878 And I did about 5 seminars and webinars completely free and then they all 72 00:04:22,968 --> 00:04:24,068 said, listen, this is not enough. 73 00:04:24,278 --> 00:04:25,308 You have to show us more. 74 00:04:25,348 --> 00:04:26,918 You can't just tease us like this. 75 00:04:27,483 --> 00:04:31,323 You have to give us step by step walkthroughs so that we can get 76 00:04:31,623 --> 00:04:34,213 to the types of productivity that you're talking about. 77 00:04:34,623 --> 00:04:38,093 So I extended my 10 days into 20, turned into 30, turned into 78 00:04:38,093 --> 00:04:41,633 two months, three months, started getting corporate requests and so on. 79 00:04:42,188 --> 00:04:45,368 And then, ultimately, this has now been my full time work for the 80 00:04:45,368 --> 00:04:47,448 last two, for the last two years. 81 00:04:48,033 --> 00:04:48,323 Alex: Wow. 82 00:04:48,353 --> 00:04:50,073 What's an amazing story. 83 00:04:50,278 --> 00:04:54,948 And so what was that first use case when you were using GPT before ChatGPT? 84 00:04:56,024 --> 00:04:58,444 Fahed: My main business was involved in communication. 85 00:04:58,884 --> 00:05:02,514 So we were using GPT to evaluate the messages that were going 86 00:05:02,514 --> 00:05:05,794 through, to see whether they were spam or phishing messages. 87 00:05:06,144 --> 00:05:09,884 And also to see if there was any swearing and things like that going through. 88 00:05:09,884 --> 00:05:15,514 So by the time we got to ChatGPT, I was already over these small use cases. 89 00:05:15,554 --> 00:05:21,264 I was already over content creation and by December, 2022, I was already 90 00:05:21,264 --> 00:05:22,764 using it as a thinking partner. 91 00:05:23,064 --> 00:05:28,104 Alex: And so as you described in your story in the last two and a 92 00:05:28,104 --> 00:05:33,164 half years, you've been accompanying large and, smaller enterprises, 93 00:05:33,444 --> 00:05:38,504 through that new transformation, can you tell us more about this? 94 00:05:38,864 --> 00:05:40,924 is there maybe one story that stands out? 95 00:05:42,209 --> 00:05:45,039 Fahed: I think it's useful to know about the evolution of the journey 96 00:05:45,039 --> 00:05:49,319 because when it started, my entire focus was actually on individuals. 97 00:05:49,659 --> 00:05:53,729 Myself as an individual, I wasn't thinking about it as a business at the start. 98 00:05:53,769 --> 00:05:59,019 I was just thinking about, AI is coming along and it's transforming society. 99 00:05:59,029 --> 00:06:00,789 It's going to transform humanity. 100 00:06:00,789 --> 00:06:03,239 It's going to transform the way that we think. 101 00:06:03,499 --> 00:06:05,619 It's going to transform the way that we communicate. 102 00:06:05,639 --> 00:06:06,699 It's going to transform. 103 00:06:07,049 --> 00:06:10,079 The extent to which we live our life and achieve our goals. 104 00:06:10,419 --> 00:06:13,779 And that was the only thing on my mind at the time when I started 105 00:06:14,089 --> 00:06:19,979 was how can we facilitate as many people embracing this as possible? 106 00:06:20,599 --> 00:06:24,734 And even when the corporates were reaching out for training, I still 107 00:06:24,734 --> 00:06:28,704 had it in my mind that I was training individuals and that the individuals are 108 00:06:28,704 --> 00:06:33,284 part of a company, of course, but it was still, focused on training individuals. 109 00:06:33,924 --> 00:06:37,464 Eventually, it came to realize that, look, the needs of corporates are very 110 00:06:37,464 --> 00:06:39,144 different to the needs of individuals. 111 00:06:39,534 --> 00:06:42,709 So the individual focus, you could say, was for the first year. 112 00:06:43,029 --> 00:06:45,359 I'd say the first year and four months. 113 00:06:45,739 --> 00:06:48,899 And then the focus on corporates was the last eight months. 114 00:06:49,449 --> 00:06:51,699 so in terms of stories, it happens all the time. 115 00:06:51,809 --> 00:06:56,039 the most common outreach that we receive is people looking for training. 116 00:06:56,799 --> 00:07:01,859 But they don't realize really the extent to which AI is 117 00:07:01,859 --> 00:07:02,979 going to change their world. 118 00:07:03,009 --> 00:07:06,159 They just think of it as being a tool, like it's Microsoft Word 119 00:07:06,159 --> 00:07:09,749 or, like a Google Doc, or they just need a bit of tool training. 120 00:07:10,469 --> 00:07:14,979 And then when they actually get the training, now their whole world changes. 121 00:07:15,369 --> 00:07:18,229 I remember when we did with one corporate, it was an investment bank. 122 00:07:18,931 --> 00:07:22,829 And the most senior person we had in the room was their chief operating officer. 123 00:07:24,369 --> 00:07:26,249 And he got it from the word Go. 124 00:07:26,309 --> 00:07:29,639 once we actually, he'd never, he had at that time with the training was being 125 00:07:29,639 --> 00:07:35,959 done through ChatGPT and he created his ChatGPT account in the session. 126 00:07:35,959 --> 00:07:39,279 In fact, I think he created the night before a session, his executive 127 00:07:39,279 --> 00:07:40,619 assistant created it for him. 128 00:07:40,989 --> 00:07:46,849 His first time opening it was during the session and he was just blown away. 129 00:07:47,304 --> 00:07:53,374 And anytime any of the people in the class were like distracted by anything, he was 130 00:07:53,384 --> 00:07:57,114 getting genuinely angry with them saying, guys, this is going to change your world. 131 00:07:57,124 --> 00:07:58,094 Please stay focused. 132 00:07:58,134 --> 00:08:01,224 This is, so he got it and he came back the following day 133 00:08:01,224 --> 00:08:02,294 cause it was a two day program. 134 00:08:02,684 --> 00:08:05,454 He came back the following day and he said, I couldn't sleep last night. 135 00:08:06,014 --> 00:08:10,244 so people don't really understand what it is until they start to go deep. 136 00:08:10,324 --> 00:08:15,499 When they go deep, Then really the whole world of AI and the potential 137 00:08:15,499 --> 00:08:19,569 of AI to augment us as humans and to transform us as humans. 138 00:08:20,059 --> 00:08:21,339 Uh, starts to open up. 139 00:08:21,370 --> 00:08:26,160 Alex: So if I understand correctly, this investment bank approaches you and 140 00:08:26,170 --> 00:08:30,420 asks you for training on the tool, which is, if I understand correctly, ChatGPT. 141 00:08:31,390 --> 00:08:37,150 And then through, the exposure, the experience of the workshop, they, and 142 00:08:37,150 --> 00:08:43,600 this, the COO discovers that this is more than just a tool, but in finance 143 00:08:43,600 --> 00:08:49,320 and investment banking is one of those industries where it's a bit hard, 144 00:08:49,900 --> 00:08:56,090 let's say, to apply, generative AI indiscriminately because of the, risk 145 00:08:56,680 --> 00:08:58,530 tolerance, let's say, of these businesses. 146 00:08:59,160 --> 00:09:03,730 So can you share your insights about, maybe not with this specific, 147 00:09:03,780 --> 00:09:10,030 person and organization, but how in a context like this, then, these tools 148 00:09:10,060 --> 00:09:13,740 change the way that, these businesses and these people then operate. 149 00:09:15,100 --> 00:09:17,345 Fahed: Well of course, if you take investment management as an 150 00:09:17,345 --> 00:09:20,845 example, and you look at it as a core function, you'll see that there are 151 00:09:20,845 --> 00:09:22,495 many different roles within that. 152 00:09:22,495 --> 00:09:24,985 So one of the things that they're always trying to do is find 153 00:09:24,985 --> 00:09:29,205 new deal opportunities and then they are looking to market those 154 00:09:29,205 --> 00:09:30,805 opportunities to their clients. 155 00:09:31,055 --> 00:09:34,515 that's a very classical, generative AI use case. 156 00:09:34,900 --> 00:09:39,730 Where through AI they're now able to monitor far more sources than they could 157 00:09:39,730 --> 00:09:44,240 before and then they're able to package it All those new deals they're able to 158 00:09:44,240 --> 00:09:49,010 package them much quicker and get them out to their client base much quicker 159 00:09:49,300 --> 00:09:52,550 So the fact that we're looking at the number side, if you think about it, 160 00:09:53,110 --> 00:09:56,570 when we're looking at AI transformation, we're looking at a couple of levels. 161 00:09:56,580 --> 00:10:00,950 So the first level are basically the quick wins within the company. 162 00:10:00,950 --> 00:10:02,660 What are those things that are bottlenecks? 163 00:10:02,680 --> 00:10:05,660 So we're not necessarily talking about, level five, which is 164 00:10:05,660 --> 00:10:07,280 actually working with the numbers. 165 00:10:07,640 --> 00:10:10,430 We're looking at the lowest level, which is what are these bottlenecks? 166 00:10:10,820 --> 00:10:14,270 you've got a hundred deals on the, on, in the queue and you've got 167 00:10:14,280 --> 00:10:17,640 two research analysts that are looking at these hundred deals. 168 00:10:18,030 --> 00:10:20,850 For these two analysts to reach deal number 50, it's 169 00:10:20,850 --> 00:10:22,140 going to take them a few weeks. 170 00:10:22,590 --> 00:10:25,480 Whereas now when you bring and I like to call it more general AI. 171 00:10:25,530 --> 00:10:27,340 I don't like using the phrase generative AI. 172 00:10:27,340 --> 00:10:32,210 I know that everybody does, but I prefer to call it general AI because general AI 173 00:10:32,210 --> 00:10:35,380 is adaptable into all sorts of scenarios. 174 00:10:35,630 --> 00:10:39,825 Whereas generative AI is just suggests the word generate, that 175 00:10:39,825 --> 00:10:43,815 it can generate content, but when it generates intelligence, is 176 00:10:43,815 --> 00:10:45,365 it really just generating now? 177 00:10:45,385 --> 00:10:48,625 No, now it's actually acting as an intelligent being, right? 178 00:10:49,065 --> 00:10:52,875 so those two taking those research analysts as an example, we now got 179 00:10:52,905 --> 00:10:57,015 ourselves in a situation where those guys can actually complete that 180 00:10:57,015 --> 00:10:59,575 list of a hundred in no time at all. 181 00:10:59,695 --> 00:11:01,555 something that would have taken them months. 182 00:11:01,835 --> 00:11:04,755 It's not that each individual task took them months. 183 00:11:05,115 --> 00:11:07,405 And now it's being brought down to hours. 184 00:11:07,785 --> 00:11:10,985 It's the whole backlog that would have taken the month. 185 00:11:10,995 --> 00:11:14,185 And of course, new items are being added to that backlog. 186 00:11:14,655 --> 00:11:18,895 The velocity, the operational velocity speeds up dramatically. 187 00:11:19,235 --> 00:11:22,615 Now eventually, so this specific company that I have in mind as I'm talking 188 00:11:22,615 --> 00:11:29,225 to you, the numbers element to it, if you think about, if you think about 189 00:11:29,255 --> 00:11:31,705 traditional business process automation. 190 00:11:33,350 --> 00:11:38,730 The challenge in traditional business process automation is programming 191 00:11:38,870 --> 00:11:41,820 complicated communication between systems. 192 00:11:42,470 --> 00:11:47,050 So when it comes to general AI, you could have a calculator, you could 193 00:11:47,060 --> 00:11:49,500 have an AI that specializes in numbers. 194 00:11:49,910 --> 00:11:53,420 Now the challenge is, how do we get information from one 195 00:11:53,420 --> 00:11:55,170 system to the other accurately? 196 00:11:56,020 --> 00:11:59,070 And then all you have to do is put evaluations in place to make 197 00:11:59,070 --> 00:12:00,780 sure that checks are taking place. 198 00:12:01,280 --> 00:12:05,400 So it's not like you're going to ChatGPT or some, even GPT and saying, do this 199 00:12:05,410 --> 00:12:07,010 for me and then just accepting it. 200 00:12:07,460 --> 00:12:11,290 No, you're saying do this for me and then you have checks and measures to 201 00:12:11,290 --> 00:12:16,150 make sure that it was done correctly as it's, as the information and the, the 202 00:12:16,150 --> 00:12:17,870 processes are going through that workflow. 203 00:12:18,334 --> 00:12:22,644 Alex: I think the way I'm thinking about this is, there is some good 204 00:12:23,254 --> 00:12:29,434 patterns that you can follow in order to identify the use cases with 205 00:12:29,444 --> 00:12:31,294 the highest return on investment. 206 00:12:32,164 --> 00:12:37,514 Looking more traditionally, thinking of your core process and doing like a 207 00:12:37,544 --> 00:12:41,909 little bit of a six sigma or something, and just focusing on what would have 208 00:12:41,909 --> 00:12:45,829 been more the territory of traditional machine learning algorithm, predictive 209 00:12:45,829 --> 00:12:49,839 analytics, in investment banking, of course, focused on the numbers. 210 00:12:50,249 --> 00:12:53,529 there is a certain number use cases for that as well. 211 00:12:53,919 --> 00:12:58,739 But I think what you were referring to is different pattern, as I would name it. 212 00:12:58,749 --> 00:13:05,079 Because now it's something that is more on the research side, on the 213 00:13:05,579 --> 00:13:08,269 scaling outreach or communications. 214 00:13:08,279 --> 00:13:13,189 It's not directly an actor of the process. 215 00:13:13,209 --> 00:13:19,119 It's more like something used to augment the capability of 216 00:13:19,149 --> 00:13:20,519 the human actors of the process. 217 00:13:20,539 --> 00:13:21,609 Would you agree with that? 218 00:13:22,414 --> 00:13:23,414 Fahed: Yeah, absolutely. 219 00:13:23,414 --> 00:13:27,284 with all businesses, the number one expense in every business without fail is 220 00:13:27,284 --> 00:13:29,284 always going to be human resources, right? 221 00:13:29,724 --> 00:13:33,714 And it's not about eliminating the human resources. 222 00:13:33,714 --> 00:13:38,244 It's about using the human resources more efficiently and more effectively, right? 223 00:13:38,244 --> 00:13:41,014 Because if you take a company, let's say, for example, these 224 00:13:41,014 --> 00:13:42,194 guys have two research analysts. 225 00:13:43,019 --> 00:13:44,904 And now they want to add a third. 226 00:13:45,384 --> 00:13:47,104 Why do they want to add a third? 227 00:13:47,364 --> 00:13:51,584 they want to add a third because the first two are feeling they can't handle 228 00:13:51,594 --> 00:13:53,444 the capacity that's being asked of them. 229 00:13:54,064 --> 00:13:58,324 Okay, how about if we reduce the demand on those two, right? 230 00:13:58,324 --> 00:14:00,324 So we don't need to hire the third anymore. 231 00:14:00,334 --> 00:14:04,864 Now let's imagine we reduce, they were thinking about going from two to three. 232 00:14:05,239 --> 00:14:07,959 And now we've reduced the burden from two to one. 233 00:14:08,839 --> 00:14:12,759 If it's a small company, they don't need to let go of that second person. 234 00:14:13,139 --> 00:14:16,089 Now what they can do is they say, okay, what value do these two 235 00:14:16,089 --> 00:14:22,239 bring that the AI can't bring, which is presenting it to clients. 236 00:14:22,579 --> 00:14:26,069 So let's improve, let's upskill them in their presentation skills. 237 00:14:26,279 --> 00:14:29,919 Let's upskill them in their communication skills so that now they 238 00:14:29,929 --> 00:14:32,513 can be presenting these to clients, whether it's through presentations. 239 00:14:32,743 --> 00:14:35,103 or through, online presentations and so on. 240 00:14:35,543 --> 00:14:38,973 that's the biggest thing is always going to be the cost of human resources. 241 00:14:39,403 --> 00:14:43,673 And the goal is not to reduce the cost so that you get rid of the human resources. 242 00:14:43,983 --> 00:14:47,883 Although truth be told in very large corporates, that is a thing, but in 243 00:14:47,883 --> 00:14:52,153 small corporates, it's about becoming more efficient, small, more efficient 244 00:14:52,283 --> 00:14:54,583 and more effective with the workforce. 245 00:14:54,783 --> 00:14:57,803 Alex: That's a very interesting observation. 246 00:14:57,813 --> 00:15:03,733 How it creates a slightly more level playing field between the 247 00:15:03,743 --> 00:15:05,443 larger and the smaller corporates. 248 00:15:06,493 --> 00:15:11,283 And that's maybe one of the myths that concerns a lot of people. 249 00:15:11,333 --> 00:15:14,713 Are companies is going to be letting go of people en mass? 250 00:15:15,653 --> 00:15:20,063 But there is something that's related to that, that I think 251 00:15:20,063 --> 00:15:21,543 is also a valid concern. 252 00:15:22,253 --> 00:15:24,563 And I wanted to get your take on it. 253 00:15:24,573 --> 00:15:29,473 So in, in the scenario you were giving with, team doing something 254 00:15:29,473 --> 00:15:33,033 for a small organization that might have gone from two to three. 255 00:15:33,863 --> 00:15:38,083 Very often, this is also down to the natural progression that 256 00:15:38,083 --> 00:15:41,123 we all aspire to in our jobs. 257 00:15:41,173 --> 00:15:45,163 We join companies, maybe as juniors at least for the specific 258 00:15:45,163 --> 00:15:47,293 role, we develop in that role. 259 00:15:47,663 --> 00:15:54,533 And then eventually we are asked to take responsibility for onboarding more junior 260 00:15:54,548 --> 00:15:59,728 members of the team who, in order to bring themselves up to speed initially 261 00:16:00,058 --> 00:16:02,368 deal with some of the more mundane tasks. 262 00:16:02,368 --> 00:16:07,073 And that's a bit of a generalization, of course, but I do think that this 263 00:16:07,073 --> 00:16:14,933 is a typical pattern for how human teams organize, but how does, from your 264 00:16:14,933 --> 00:16:19,283 perspective, does the introduction of AI tools who are taking care of the 265 00:16:19,283 --> 00:16:21,803 more mundane tasks affect this dynamic? 266 00:16:22,103 --> 00:16:26,433 Fahed: The first effect, unfortunately, and I have children that are in university 267 00:16:26,433 --> 00:16:32,058 now, and the traditional career paths that we thought we would see them taking, where 268 00:16:32,058 --> 00:16:36,408 they go and get experience as juniors and then they graduate from there and so on. 269 00:16:36,838 --> 00:16:40,318 the first thing AI is doing is even eliminating the need for juniors. 270 00:16:40,358 --> 00:16:45,158 And, that is, overall, I think that's a devastating impact on the world. 271 00:16:45,628 --> 00:16:51,268 Because that kind of mundane work was the buffer between a life of studies and 272 00:16:51,268 --> 00:16:53,488 the life of more advanced work, right? 273 00:16:53,738 --> 00:16:54,828 So that's the first thing. 274 00:16:55,258 --> 00:17:00,398 The second thing, from a future of work point of view, is that where is 275 00:17:00,398 --> 00:17:03,378 the value of humans in the AI era? 276 00:17:03,468 --> 00:17:08,358 The value of humans is When AI can do so many of the tasks that so many of us 277 00:17:08,358 --> 00:17:11,488 used to be able to do is in two places. 278 00:17:11,618 --> 00:17:16,288 Number one in being able to direct the AI. 279 00:17:17,003 --> 00:17:20,003 Being able to work with the AI to produce the results. 280 00:17:20,343 --> 00:17:24,433 Now, I don't know if you remember many years ago, there was a film called Aliens. 281 00:17:24,983 --> 00:17:29,473 And Sigourney Weaver, the female actress, she has to beat these aliens. 282 00:17:29,943 --> 00:17:34,193 And the only way she can do that was to step into this exoskeleton, 283 00:17:34,443 --> 00:17:36,223 like this huge metal suit. 284 00:17:36,683 --> 00:17:40,823 And despite her being quite a weak human being, when she stepped into 285 00:17:40,823 --> 00:17:44,353 this big machine, now she could command all sorts of superpower. 286 00:17:45,223 --> 00:17:49,863 So that's the first value that we will have, in the future of work is our 287 00:17:49,883 --> 00:17:55,073 ability to work with the AI, because the AI has proven itself to be so 288 00:17:55,073 --> 00:18:00,203 competent, to be so effective, to be so efficient, but it's not running by itself. 289 00:18:00,223 --> 00:18:01,643 It still needs direction. 290 00:18:02,003 --> 00:18:04,353 It still needs people to work with it and make sure that it's 291 00:18:04,353 --> 00:18:06,233 doing what it's supposed to do. 292 00:18:06,903 --> 00:18:12,013 And the second area where we will be valuable is contributing 293 00:18:12,233 --> 00:18:13,673 towards the AI engine. 294 00:18:14,493 --> 00:18:18,163 When we think about the current workforce, everybody in the current 295 00:18:18,163 --> 00:18:22,603 workforce who still wants to be valuable in the AI era, not only do they need 296 00:18:22,603 --> 00:18:26,363 to understand how to work with the AI, but they also need to understand how to 297 00:18:26,413 --> 00:18:29,853 contribute to their company's AI engine. 298 00:18:30,223 --> 00:18:34,973 If these research analysts, for example, they have to be thinking themselves, 299 00:18:35,338 --> 00:18:37,198 Oh my god, we're doing this process. 300 00:18:37,238 --> 00:18:38,628 It's taking us this much time. 301 00:18:39,028 --> 00:18:41,408 With AI, couldn't we do this much quicker? 302 00:18:41,748 --> 00:18:43,508 Hey, boss, I have an idea. 303 00:18:43,558 --> 00:18:47,158 If we work with AI, we could do this and this and this and this, and 304 00:18:47,158 --> 00:18:48,978 this will speed things up and so on. 305 00:18:49,558 --> 00:18:53,548 He or she now becomes very valuable to the company because he's a 306 00:18:53,568 --> 00:18:56,708 contributor to their AI engine. 307 00:18:57,238 --> 00:19:00,618 And then the third area where of course will be valuable is actually 308 00:19:00,618 --> 00:19:02,918 the human interaction element, right? 309 00:19:02,988 --> 00:19:06,678 Especially if you're in a high value business like investment management 310 00:19:06,958 --> 00:19:10,288 where you are dealing with ultra high net worth individuals, you are 311 00:19:10,288 --> 00:19:14,248 dealing with banks, you're dealing with a whole lot of high profile people 312 00:19:14,248 --> 00:19:16,108 who are demanding human connection. 313 00:19:16,108 --> 00:19:20,253 So anywhere that human connection is needed, will all still be relevant. 314 00:19:20,283 --> 00:19:25,153 So AI is going to affect, it has already started to affect all of these dynamics. 315 00:19:25,153 --> 00:19:28,863 It's moving at different speeds in different places, different 316 00:19:28,873 --> 00:19:32,663 cities, different regions, different industries, and so on. 317 00:19:33,133 --> 00:19:39,003 And the question that we all have to be asking is how we can stay relevant 318 00:19:40,193 --> 00:19:43,033 in the face of AI's rising relevance? 319 00:19:43,490 --> 00:19:44,060 Alex: Very good. 320 00:19:44,060 --> 00:19:50,230 So adaptability was your third point, and I guess that relates to what each of 321 00:19:50,230 --> 00:19:53,900 us, when we come to work have to embrace. 322 00:19:54,460 --> 00:19:58,180 And then the first two you've mentioned is using the exoskeleton from Alien. 323 00:19:58,180 --> 00:20:04,090 So being, ready at work to, take on these, very powerful tools. 324 00:20:04,150 --> 00:20:07,280 And the second you said was to contribute to them. 325 00:20:07,830 --> 00:20:12,220 But now I'd like to also look at it from the perspective of leadership, right? 326 00:20:12,220 --> 00:20:14,970 And I know you've been working a lot with, corporates. 327 00:20:14,990 --> 00:20:19,885 So what kind of definition do you give them when you start working with them? 328 00:20:19,935 --> 00:20:21,215 They say, we want to learn a tool. 329 00:20:21,215 --> 00:20:21,835 You say, hold on. 330 00:20:21,835 --> 00:20:22,665 it's AI literacy. 331 00:20:22,665 --> 00:20:23,605 It's not just a tool. 332 00:20:23,875 --> 00:20:25,915 And so what do you say to them? 333 00:20:25,935 --> 00:20:30,865 What is that thing that they now need to embrace from a leadership perspective? 334 00:20:31,795 --> 00:20:33,445 Fahed: Actually, I try not to say anything. 335 00:20:33,495 --> 00:20:35,815 I try to give them a firsthand experience. 336 00:20:36,145 --> 00:20:41,265 So when working with leaders of large enterprises, what we do in advance 337 00:20:41,275 --> 00:20:46,285 of any training that we do is that we do background research to understand 338 00:20:46,285 --> 00:20:51,285 the company in full, as well as understanding the leadership themselves. 339 00:20:51,305 --> 00:20:56,305 So we will go in and make profiles for each of the participants 340 00:20:56,305 --> 00:20:57,565 that we know will be there. 341 00:20:58,325 --> 00:21:04,555 We then use the company information and the profiles of each of the individuals 342 00:21:04,625 --> 00:21:07,045 and we create micro AI models. 343 00:21:07,625 --> 00:21:12,305 Where effectively, the AI knows exactly who is using the AI at that 344 00:21:12,305 --> 00:21:16,925 moment we'll have one AI that is trained as the CFO of that company. 345 00:21:17,195 --> 00:21:20,625 We'll have one AI that is trained as the CEO of that company. 346 00:21:20,955 --> 00:21:24,295 when these leaders sit down to have their conversations, we give them a 347 00:21:24,295 --> 00:21:28,205 bunch of conversations to have, and we give them some guided conversations. 348 00:21:28,645 --> 00:21:37,285 So they themselves experience the level where the AI has reached in understanding 349 00:21:37,285 --> 00:21:42,245 the nuances of their individual roles and their responsibilities with the company. 350 00:21:42,685 --> 00:21:47,715 So we could have something like, having the CMO asking a question, literally 351 00:21:47,725 --> 00:21:51,655 opening up the model and saying something like, where do you think we might 352 00:21:51,655 --> 00:21:53,055 be going wrong with our marketing? 353 00:21:53,915 --> 00:21:58,595 And instantly the AI is talking in the context of their specific company. 354 00:21:58,900 --> 00:22:01,560 Knowing that it's speaking to the CMO, which is the most 355 00:22:01,560 --> 00:22:03,470 senior marketer in the company. 356 00:22:04,080 --> 00:22:08,900 And so the CMO, the CFO, the CEO, all of them are having uh, the 357 00:22:08,940 --> 00:22:14,830 AI epiphany moments, where now without being told anything about 358 00:22:14,870 --> 00:22:20,855 AI, they're having a taste of how advanced the intelligence has become. 359 00:22:21,525 --> 00:22:25,505 And that's the most important point, Alex, is that people need to understand 360 00:22:25,815 --> 00:22:28,945 that we're talking about an intelligence. 361 00:22:29,265 --> 00:22:30,575 We're not talking about a tool. 362 00:22:30,585 --> 00:22:32,005 We're not talking about chatbots. 363 00:22:32,015 --> 00:22:35,345 We're not talking about, any of these surface level things. 364 00:22:36,030 --> 00:22:40,320 What we're talking about is a new type of intelligence. 365 00:22:40,440 --> 00:22:45,140 It's a synthetic intelligence that can emulate the intelligence of human beings. 366 00:22:45,850 --> 00:22:50,890 And so when they experience it, emulate their own intelligence, that's when 367 00:22:50,900 --> 00:22:54,120 they have the moment that they realize, oh my god, this thing is serious. 368 00:22:54,915 --> 00:22:56,345 Okay, I'm focused now. 369 00:22:56,505 --> 00:22:56,935 Let's talk. 370 00:22:57,585 --> 00:22:58,355 Help me understand. 371 00:22:58,695 --> 00:23:02,025 How do we deploy this intelligence in the service of our company? 372 00:23:02,595 --> 00:23:05,385 How do we deploy this intelligence in the service of our staff? 373 00:23:05,645 --> 00:23:09,275 How do we deploy this intelligence in the pursuit of our short term 374 00:23:09,275 --> 00:23:10,685 goals and our long term goals? 375 00:23:10,965 --> 00:23:14,690 What are the opportunities that this intelligence can provide us? 376 00:23:14,940 --> 00:23:19,020 What are the challenges, the threats, the risks, the mitigations? 377 00:23:19,320 --> 00:23:22,640 How are we going to now bring this into our company? 378 00:23:22,660 --> 00:23:23,470 What's involved? 379 00:23:24,030 --> 00:23:26,150 So it all starts without telling them anything. 380 00:23:26,690 --> 00:23:29,370 It starts with letting them experience it. 381 00:23:30,070 --> 00:23:33,990 Once they've experienced it, now we can start moving forward. 382 00:23:34,911 --> 00:23:35,531 Alex: Amazing. 383 00:23:35,871 --> 00:23:38,651 Thank you very much for sharing this unique insight. 384 00:23:38,741 --> 00:23:42,122 And as we're about to wrap up, is there one thing that you would expect 385 00:23:42,152 --> 00:23:44,112 to remain constant 10 years from now? 386 00:23:44,248 --> 00:23:46,398 Fahed: the only constant that we have is change now. 387 00:23:46,598 --> 00:23:50,098 The situation we're in, as I explained to corporate leaders, I said, look, for the 388 00:23:50,098 --> 00:23:55,178 last 10, 20, 30 years, it's like we've been, it's like you've been racing in a 389 00:23:55,178 --> 00:24:00,778 race, if you go to the F1, whether it's in Qatar or Azerbaijan or Abu Dhabi or 390 00:24:00,788 --> 00:24:04,828 straight, like anywhere you go for an F1 race, you're going to find the same teams. 391 00:24:05,793 --> 00:24:10,818 Every team knows who's racing, every team knows the tracks, they know the 392 00:24:10,818 --> 00:24:14,213 turns, they know everything about everything, and now all they've been 393 00:24:14,213 --> 00:24:16,183 doing is making these small changes. 394 00:24:16,893 --> 00:24:19,833 In the last two years, and that's basically what the corporate 395 00:24:19,833 --> 00:24:23,423 world has been like for the last 10, 20, 30, 50, 100 years. 396 00:24:23,963 --> 00:24:29,263 And two years ago, the race owners came along and said, okay, 397 00:24:29,733 --> 00:24:31,083 we're changing the race now. 398 00:24:31,543 --> 00:24:35,603 Rule number one, the previous maximum speed is now the minimum speed. 399 00:24:36,863 --> 00:24:39,693 Rule number two, there's no pit stops anymore. 400 00:24:39,693 --> 00:24:40,833 You have to just keep going. 401 00:24:42,323 --> 00:24:48,243 Number three, we're changing the track and we're building it as you go along. 402 00:24:48,253 --> 00:24:49,353 You can't prepare for it. 403 00:24:49,393 --> 00:24:50,823 There's no preparation anymore. 404 00:24:50,823 --> 00:24:55,183 We're building the track as we go along and good luck. 405 00:24:55,973 --> 00:25:00,833 So we're in, it's going to keep on changing and changing and changing. 406 00:25:01,243 --> 00:25:04,893 And that's where that whole adaptability that you highlighted earlier on 407 00:25:05,253 --> 00:25:07,423 is the importance of adaptability. 408 00:25:07,643 --> 00:25:10,903 And adaptability, it's not just about mindset. 409 00:25:10,933 --> 00:25:12,393 It's also about resources. 410 00:25:12,873 --> 00:25:16,623 You can have the most adaptable mindset on earth, but you also have to put 411 00:25:16,623 --> 00:25:19,413 resources to support that adaptability. 412 00:25:19,413 --> 00:25:23,523 And I think that's going to be, maybe they even need to make an adaptability budget 413 00:25:23,533 --> 00:25:26,513 from now on, 5% adaptability budget. 414 00:25:27,493 --> 00:25:27,983 Alex: All right. 415 00:25:28,043 --> 00:25:28,313 Yeah. 416 00:25:28,333 --> 00:25:28,753 Wow. 417 00:25:28,913 --> 00:25:31,373 That's a very nice metaphor. 418 00:25:32,163 --> 00:25:33,523 I'm sure I'll be using that. 419 00:25:34,473 --> 00:25:37,663 And I think with that, Fahed, it's been a real pleasure. 420 00:25:37,993 --> 00:25:38,733 Thank you very much. 421 00:25:39,533 --> 00:25:40,373 Fahed: Thank you for having me. 422 00:25:41,228 --> 00:25:45,928 Alex: Fahed argues that adaptability and AI literacy are no longer optional. 423 00:25:46,318 --> 00:25:50,088 Companies that use AI wisely see major efficiency gains. 424 00:25:50,338 --> 00:25:53,598 But success isn't just about technology, it's about people. 425 00:25:54,088 --> 00:25:58,868 The best leaders ensure AI helps their teams work smarter, not replaces them. 426 00:25:59,718 --> 00:26:01,898 Fahed's journey leaves us with a key question though. 427 00:26:02,468 --> 00:26:06,908 As AI transforms business, how will you keep your organization ahead? 428 00:26:07,848 --> 00:26:10,038 To continue the conversation, connect with him on LinkedIn. 429 00:26:10,038 --> 00:26:12,458 The link is in the description. 430 00:26:13,098 --> 00:26:17,578 As always, we have more exciting topics and guest appearances lined up, so 431 00:26:17,578 --> 00:26:22,388 stay tuned for more tales of innovation that inspire, challenge, and transform. 432 00:26:22,958 --> 00:26:24,818 Until next time, peace.