1 00:00:00,000 --> 00:00:01,920 Hey, everyone. Welcome back. 2 00:00:01,920 --> 00:00:03,760 Today, we're going to be taking a deep dive 3 00:00:03,760 --> 00:00:05,960 into the world of tech leadership. 4 00:00:05,960 --> 00:00:09,600 Specifically, how it's being totally transformed by AI, right? 5 00:00:09,600 --> 00:00:12,000 Exactly. We've got this fascinating transcript 6 00:00:12,000 --> 00:00:14,080 from a McKinsey Live event called 7 00:00:14,080 --> 00:00:16,720 Redefining the Role of the Tech Officer. 8 00:00:16,720 --> 00:00:19,720 And it's all about the impact of generative AI and all that. 9 00:00:19,720 --> 00:00:22,360 Think of this deep dive as your cheat sheet 10 00:00:22,360 --> 00:00:26,600 to understanding how AI is changing the game for tech leaders. 11 00:00:26,600 --> 00:00:29,680 But also, and this is key, what it means for you. 12 00:00:29,680 --> 00:00:31,400 Like, no matter what your job is. 13 00:00:31,400 --> 00:00:33,640 Yeah, because this isn't just a tech thing anymore. 14 00:00:33,640 --> 00:00:35,280 This is like reshaping everything. 15 00:00:35,280 --> 00:00:37,640 It really is, and the numbers are pretty mind-blowing. 16 00:00:37,640 --> 00:00:40,040 McKinsey's research suggests that generative AI 17 00:00:40,040 --> 00:00:43,560 could add a whopping $4.4 trillion. 18 00:00:43,560 --> 00:00:44,440 Wait, trillion? 19 00:00:44,440 --> 00:00:46,920 Trillion with a T to the global economy. 20 00:00:46,920 --> 00:00:49,920 Wow, that's like, what, the entire GDP of Germany or something? 21 00:00:49,920 --> 00:00:50,640 Pretty much. 22 00:00:50,640 --> 00:00:54,160 Crazy. It's no wonder companies are jumping on this AI bandwagon. 23 00:00:54,160 --> 00:00:58,520 Yeah, I mean, 65% of enterprises are already using generative AI in some way. 24 00:00:58,520 --> 00:00:59,480 65%. 25 00:00:59,480 --> 00:01:02,280 That's more than double the rate from just a year ago. 26 00:01:02,280 --> 00:01:03,720 That's a huge jump. 27 00:01:03,720 --> 00:01:07,040 So with all this AI craziness going on, 28 00:01:07,040 --> 00:01:09,840 what's happening to the people leading the tech charge? 29 00:01:09,840 --> 00:01:13,000 It's definitely forcing tech leaders to evolve, right? 30 00:01:13,000 --> 00:01:15,120 Like, their roles are changing big time. 31 00:01:15,120 --> 00:01:16,280 So how are they adapting? 32 00:01:16,280 --> 00:01:19,400 McKinsey outlines four major shifts they're seeing. 33 00:01:19,400 --> 00:01:22,960 Protector, operator, orchestrator, and builder. 34 00:01:22,960 --> 00:01:24,840 OK, I'm intrigued. 35 00:01:24,840 --> 00:01:26,000 Break those down for us. 36 00:01:26,000 --> 00:01:30,040 What does it mean to be a protector in this new world of AI? 37 00:01:30,040 --> 00:01:33,440 It's all about cybersecurity, but the stakes are so much higher now, you know? 38 00:01:33,440 --> 00:01:35,520 Yeah, cyber attacks are getting scarier by the day. 39 00:01:35,520 --> 00:01:39,760 Cybercrime could cost the world something like $10.5 trillion a year. 40 00:01:39,760 --> 00:01:40,560 That's insane. 41 00:01:40,560 --> 00:01:42,200 And it's not just some future threat either. 42 00:01:42,200 --> 00:01:46,400 Downtime from these attacks is already costing global 2000 companies. 43 00:01:46,400 --> 00:01:47,440 Yeah, those big companies. 44 00:01:47,440 --> 00:01:49,760 Yeah, like $400 billion every year. 45 00:01:49,760 --> 00:01:51,080 Ouch. 46 00:01:51,080 --> 00:01:52,520 That's a lot of money down the drain. 47 00:01:52,520 --> 00:01:53,640 And here's the kicker. 48 00:01:53,640 --> 00:01:56,680 Only about 30% of enterprises feel like they're actually 49 00:01:56,680 --> 00:01:58,520 well protected from these threats. 50 00:01:58,520 --> 00:02:00,440 That's a surprisingly low number. 51 00:02:00,440 --> 00:02:02,720 You'd think with all the technology at our fingertips, 52 00:02:02,720 --> 00:02:03,880 we'd be better prepared. 53 00:02:03,880 --> 00:02:05,040 But here's the thing. 54 00:02:05,040 --> 00:02:07,920 It's not just about having the latest fancy tech. 55 00:02:07,920 --> 00:02:08,880 So what else is there? 56 00:02:08,880 --> 00:02:12,640 McKinsey found that a lot of these outages, like up to 60% of them, 57 00:02:12,640 --> 00:02:14,520 aren't even caused by tech failures. 58 00:02:14,520 --> 00:02:15,120 Really? 59 00:02:15,120 --> 00:02:16,160 What are they caused by that? 60 00:02:16,160 --> 00:02:20,000 Outdated processes, like clunky, inefficient processes. 61 00:02:20,000 --> 00:02:20,520 Huh. 62 00:02:20,520 --> 00:02:21,480 So it's not the machines. 63 00:02:21,480 --> 00:02:22,600 It's the humans. 64 00:02:22,600 --> 00:02:23,800 In a way, yeah. 65 00:02:23,800 --> 00:02:27,080 So even if you're not a tech leader, this applies to you. 66 00:02:27,080 --> 00:02:29,680 Think about those processes you use every day at work, 67 00:02:29,680 --> 00:02:31,120 or even in your personal life. 68 00:02:31,120 --> 00:02:33,960 Yeah, like how I still use sticky notes to remember my passwords. 69 00:02:33,960 --> 00:02:35,040 Exactly. 70 00:02:35,040 --> 00:02:39,000 Are those processes as efficient and as secure as they could be? 71 00:02:39,000 --> 00:02:40,960 That's something we all need to be thinking about. 72 00:02:40,960 --> 00:02:41,440 Got it. 73 00:02:41,440 --> 00:02:43,720 So back to those tech leaders for a second. 74 00:02:43,720 --> 00:02:46,480 How can they up their game in this protector role? 75 00:02:46,480 --> 00:02:49,000 McKinsey has some really solid advice. 76 00:02:49,000 --> 00:02:51,880 First, don't try to protect everything at once. 77 00:02:51,880 --> 00:02:53,160 OK, so prioritize. 78 00:02:53,160 --> 00:02:54,200 Exactly. 79 00:02:54,200 --> 00:02:57,720 Figure out your most critical assets, like the 20% that drive 80 00:02:57,720 --> 00:02:59,640 80% of your business value. 81 00:02:59,640 --> 00:03:01,680 The MVPs of your company's data. 82 00:03:01,680 --> 00:03:02,840 Exactly. 83 00:03:02,840 --> 00:03:04,920 Focus on protecting those first. 84 00:03:04,920 --> 00:03:07,400 Second, shift security considerations 85 00:03:07,400 --> 00:03:09,360 earlier in the development process. 86 00:03:09,360 --> 00:03:12,880 So bake it in from the start rather than trying to fix it later. 87 00:03:12,880 --> 00:03:13,960 Exactly. 88 00:03:13,960 --> 00:03:15,640 Like if you're building a house, it's 89 00:03:15,640 --> 00:03:18,680 easier to lay a strong foundation from the beginning, right? 90 00:03:18,680 --> 00:03:19,760 Makes sense. 91 00:03:19,760 --> 00:03:22,800 Retrofitting security always feels like a Band-Aid solution. 92 00:03:22,800 --> 00:03:27,240 And finally, tech leaders need to be champions for digital trust. 93 00:03:27,240 --> 00:03:28,720 What does that look like practically? 94 00:03:28,720 --> 00:03:32,800 Clear policies, transparency about how you're using customer data, 95 00:03:32,800 --> 00:03:35,480 effectively managing those third party risks. 96 00:03:35,480 --> 00:03:38,320 So basically, walk the walk and don't just talk the talk 97 00:03:38,320 --> 00:03:39,720 when it comes to data security. 98 00:03:39,720 --> 00:03:40,440 Exactly. 99 00:03:40,440 --> 00:03:43,320 Because in this world where data is everything, 100 00:03:43,320 --> 00:03:45,080 trust is non-negotiable. 101 00:03:45,080 --> 00:03:46,160 All right, makes sense. 102 00:03:46,160 --> 00:03:48,120 So we've talked about protecting the organization, 103 00:03:48,120 --> 00:03:51,480 but what about that second shift you mentioned, the operator role? 104 00:03:51,480 --> 00:03:52,440 What's that all about? 105 00:03:52,440 --> 00:03:54,240 This is where things get really cool. 106 00:03:54,240 --> 00:03:57,120 This role is all about harnessing the power of tech 107 00:03:57,120 --> 00:03:59,400 to optimize how a business actually runs. 108 00:03:59,400 --> 00:04:01,840 So we're talking about using AI to streamline everything. 109 00:04:01,840 --> 00:04:02,320 Totally. 110 00:04:02,320 --> 00:04:05,800 Think generative AI for boosting administrative tasks, 111 00:04:05,800 --> 00:04:08,120 automation to make supply chains smoother, 112 00:04:08,120 --> 00:04:10,760 AI powered tools for speeding up R&D. 113 00:04:10,760 --> 00:04:13,640 So AI is like the ultimate efficiency guru. 114 00:04:13,640 --> 00:04:14,320 Right. 115 00:04:14,320 --> 00:04:16,200 And the potential gains are huge. 116 00:04:16,200 --> 00:04:18,840 It's interesting because McKinsey highlights 117 00:04:18,840 --> 00:04:23,320 how this shift is changing the very structure of some companies. 118 00:04:23,320 --> 00:04:26,480 Like one global life sciences company actually 119 00:04:26,480 --> 00:04:30,200 merged the roles of their CIO and chief strategy officer. 120 00:04:30,200 --> 00:04:30,720 Oh, wow. 121 00:04:30,720 --> 00:04:31,280 That's interesting. 122 00:04:31,280 --> 00:04:32,120 I hadn't heard that. 123 00:04:32,120 --> 00:04:34,760 Yeah, they recognize that, especially with AI in the mix, 124 00:04:34,760 --> 00:04:37,720 technology and business strategy are basically inseparable now. 125 00:04:37,720 --> 00:04:39,880 Yeah, they're two sides of the same coin. 126 00:04:39,880 --> 00:04:41,800 So it's not just about changing job titles. 127 00:04:41,800 --> 00:04:44,320 It's about a whole mindset shift. 128 00:04:44,320 --> 00:04:47,000 So tech leaders need to think less like techies 129 00:04:47,000 --> 00:04:48,960 and more like business leaders. 130 00:04:48,960 --> 00:04:49,520 Yep. 131 00:04:49,520 --> 00:04:53,040 They need to understand the company's big picture goals. 132 00:04:53,040 --> 00:04:55,720 And figure out how to use tech to achieve those goals. 133 00:04:55,720 --> 00:04:56,400 Exactly. 134 00:04:56,400 --> 00:04:58,600 It's like, how can we leverage technology 135 00:04:58,600 --> 00:05:01,640 to make this company more successful overall? 136 00:05:01,640 --> 00:05:03,960 So do you have any real world examples 137 00:05:03,960 --> 00:05:05,040 of how this is playing out? 138 00:05:05,040 --> 00:05:08,280 How are tech leaders actually operating in this new way? 139 00:05:08,280 --> 00:05:09,320 Oh, for sure. 140 00:05:09,320 --> 00:05:11,320 Let's take the banking sector, for example. 141 00:05:11,320 --> 00:05:14,200 One bank decided to give their CIO responsibility 142 00:05:14,200 --> 00:05:15,840 for customer experience. 143 00:05:15,840 --> 00:05:18,720 By connecting technology directly to customer needs, 144 00:05:18,720 --> 00:05:22,440 they saw awesome improvements in both their digital offerings, 145 00:05:22,440 --> 00:05:24,640 A&D customer satisfaction. 146 00:05:24,640 --> 00:05:27,080 It's like they're breaking down the silos between the tech 147 00:05:27,080 --> 00:05:29,160 department and the rest of the company. 148 00:05:29,160 --> 00:05:30,280 Exactly. 149 00:05:30,280 --> 00:05:32,400 And that's a big part of the shift. 150 00:05:32,400 --> 00:05:34,920 Tech leaders are becoming key players 151 00:05:34,920 --> 00:05:37,800 in shaping the whole customer experience. 152 00:05:37,800 --> 00:05:40,680 And driving strategic initiatives. 153 00:05:40,680 --> 00:05:43,800 It's a far cry from the traditional view of the CIO, 154 00:05:43,800 --> 00:05:46,840 as the person who just keeps the servers running. 155 00:05:46,840 --> 00:05:47,340 Right. 156 00:05:47,340 --> 00:05:49,240 It's a much more dynamic and strategic role. 157 00:05:49,240 --> 00:05:51,680 And this is happening across all kinds of industries, too. 158 00:05:51,680 --> 00:05:53,400 Tech leaders are really stepping up. 159 00:05:53,400 --> 00:05:54,800 They really are. 160 00:05:54,800 --> 00:05:57,920 So we've gone from protector to operator. 161 00:05:57,920 --> 00:06:00,280 What's next in this tech leader evolution? 162 00:06:00,280 --> 00:06:02,200 That brings us to the orchestrator, 163 00:06:02,200 --> 00:06:04,120 which is about leading big changes 164 00:06:04,120 --> 00:06:06,160 across the entire organization. 165 00:06:06,160 --> 00:06:09,120 So we're talking about big picture strategic leadership. 166 00:06:09,120 --> 00:06:10,040 Totally. 167 00:06:10,040 --> 00:06:12,160 It's about driving the strategy and making 168 00:06:12,160 --> 00:06:15,240 sure everyone's ready to embrace things like AI and all 169 00:06:15,240 --> 00:06:16,520 these emerging technologies. 170 00:06:16,520 --> 00:06:18,440 But wouldn't that be kind of overwhelming? 171 00:06:18,440 --> 00:06:19,240 Oh, for sure. 172 00:06:19,240 --> 00:06:20,360 It's a lot to handle. 173 00:06:20,360 --> 00:06:22,000 Yeah, like where do you even start? 174 00:06:22,000 --> 00:06:23,360 What are some of the challenges? 175 00:06:23,360 --> 00:06:26,480 Well, it's about managing people, processes, 176 00:06:26,480 --> 00:06:29,640 and technology all together in a way that actually works. 177 00:06:29,640 --> 00:06:32,080 It's not just, hey, here's this new software. 178 00:06:32,080 --> 00:06:32,680 Use it. 179 00:06:32,680 --> 00:06:34,800 It's about more than just new tools, then. 180 00:06:34,800 --> 00:06:35,300 Right. 181 00:06:35,300 --> 00:06:38,680 It's about creating a culture of innovation and collaboration. 182 00:06:38,680 --> 00:06:39,400 Got it. 183 00:06:39,400 --> 00:06:43,000 McKinsey actually uses their own in-house AI platform 184 00:06:43,000 --> 00:06:46,080 called Lily as a case study for this orchestrator role. 185 00:06:46,080 --> 00:06:46,880 Lily. 186 00:06:46,880 --> 00:06:47,920 Tell me more about that. 187 00:06:47,920 --> 00:06:49,560 What makes it such a good example? 188 00:06:49,560 --> 00:06:51,600 So about a year and a half ago, McKinsey 189 00:06:51,600 --> 00:06:54,560 realized that they had all this valuable knowledge 190 00:06:54,560 --> 00:06:56,240 trapped inside the company, hidden 191 00:06:56,240 --> 00:06:57,760 in all these different departments. 192 00:06:57,760 --> 00:06:59,320 So all their expertise was siloed. 193 00:06:59,320 --> 00:07:00,320 Totally. 194 00:07:00,320 --> 00:07:01,840 And it was really hard for people 195 00:07:01,840 --> 00:07:04,040 to find the information they needed. 196 00:07:04,040 --> 00:07:06,600 So their tech team took charge and created 197 00:07:06,600 --> 00:07:09,400 this generative AI platform called Lily. 198 00:07:09,400 --> 00:07:10,320 Sounds impressive. 199 00:07:10,320 --> 00:07:13,080 It basically makes all that knowledge accessible 200 00:07:13,080 --> 00:07:14,680 to everyone in the company. 201 00:07:14,680 --> 00:07:18,680 So Lily was like this big company-wide transformation 202 00:07:18,680 --> 00:07:19,360 project. 203 00:07:19,360 --> 00:07:20,400 Exactly. 204 00:07:20,400 --> 00:07:21,880 And here's a cool detail. 205 00:07:21,880 --> 00:07:23,800 50% of the people working on Lily 206 00:07:23,800 --> 00:07:25,560 weren't even from tech backgrounds. 207 00:07:25,560 --> 00:07:26,400 That's awesome. 208 00:07:26,400 --> 00:07:29,240 Yeah, it really highlights how important collaboration 209 00:07:29,240 --> 00:07:31,120 is in this orchestrator role. 210 00:07:31,120 --> 00:07:33,520 So it's about bringing together all these different people 211 00:07:33,520 --> 00:07:35,200 with different skills and perspectives. 212 00:07:35,200 --> 00:07:35,680 Exactly. 213 00:07:35,680 --> 00:07:36,840 OK, that makes sense. 214 00:07:36,840 --> 00:07:38,360 But how can someone actually make 215 00:07:38,360 --> 00:07:42,200 this shift from being an individual contributor 216 00:07:42,200 --> 00:07:43,360 to becoming an orchestrator? 217 00:07:43,360 --> 00:07:45,040 What are some steps they can take? 218 00:07:45,040 --> 00:07:48,640 McKinsey actually outlines six key shifts 219 00:07:48,640 --> 00:07:50,640 that are super important. 220 00:07:50,640 --> 00:07:51,360 I'm listening. 221 00:07:51,360 --> 00:07:55,360 One is building teams with both tech and business people, 222 00:07:55,360 --> 00:07:56,720 not just one or the other. 223 00:07:56,720 --> 00:07:58,280 Yeah, breaking down those silos again. 224 00:07:58,280 --> 00:07:58,920 Right. 225 00:07:58,920 --> 00:08:01,080 Then there's things like continuous upskilling 226 00:08:01,080 --> 00:08:04,120 programs for everyone, not just the tech folks. 227 00:08:04,120 --> 00:08:06,840 So everyone needs to be on board with this AI stuff. 228 00:08:06,840 --> 00:08:07,600 Absolutely. 229 00:08:07,600 --> 00:08:10,120 And then there's treating data as a product, something 230 00:08:10,120 --> 00:08:11,880 that can be used across the whole company. 231 00:08:11,880 --> 00:08:13,600 Instead of just hoarding in one department. 232 00:08:13,600 --> 00:08:14,280 Exactly. 233 00:08:14,280 --> 00:08:17,800 But that also means you need good data governance, 234 00:08:17,800 --> 00:08:20,640 solid infrastructure, all that good stuff. 235 00:08:20,640 --> 00:08:23,680 It's like laying the groundwork for a data-driven culture. 236 00:08:23,680 --> 00:08:27,520 And of course, you need vision and leadership. 237 00:08:27,520 --> 00:08:30,960 Tech leaders need to be proactive, set the agenda, 238 00:08:30,960 --> 00:08:33,000 and make sure everyone's on the same page. 239 00:08:33,000 --> 00:08:35,760 Working closely with the CEO and all the other bigwigs. 240 00:08:35,760 --> 00:08:36,360 Right. 241 00:08:36,360 --> 00:08:38,840 So it's a lot to juggle, but it's super important. 242 00:08:38,840 --> 00:08:41,920 Sounds like it takes a pretty unique mix of skills. 243 00:08:41,920 --> 00:08:43,080 Oh, yeah, for sure. 244 00:08:43,080 --> 00:08:45,880 Technical expertise, business smarts, leadership abilities. 245 00:08:45,880 --> 00:08:47,640 It's not just about understanding the tech. 246 00:08:47,640 --> 00:08:49,300 It's about understanding how to use it 247 00:08:49,300 --> 00:08:50,400 to make the business better. 248 00:08:50,400 --> 00:08:52,040 Absolutely. 249 00:08:52,040 --> 00:08:55,120 All right, so we've gone from protector to operator 250 00:08:55,120 --> 00:08:56,600 to orchestrator. 251 00:08:56,600 --> 00:08:58,040 That brings us to the final shift 252 00:08:58,040 --> 00:09:00,120 you mentioned, the builder role. 253 00:09:00,120 --> 00:09:01,320 What's that all about? 254 00:09:01,320 --> 00:09:03,400 This is where things get really interesting. 255 00:09:03,400 --> 00:09:05,040 Think of it like this. 256 00:09:05,040 --> 00:09:07,760 Tech leaders are now taking full responsibility 257 00:09:07,760 --> 00:09:11,200 for the profit and loss of external products. 258 00:09:11,200 --> 00:09:14,080 So not just internal tools anymore. 259 00:09:14,080 --> 00:09:16,800 They're creating things that make money and compete 260 00:09:16,800 --> 00:09:17,600 in the marketplace. 261 00:09:17,600 --> 00:09:18,160 Exactly. 262 00:09:18,160 --> 00:09:20,880 They're becoming entrepreneurs within their own companies. 263 00:09:20,880 --> 00:09:22,760 That's a huge shift in responsibility. 264 00:09:22,760 --> 00:09:23,840 Do you have an example of what that 265 00:09:23,840 --> 00:09:25,200 looks like in the real world? 266 00:09:25,200 --> 00:09:25,640 Sure. 267 00:09:25,640 --> 00:09:28,280 McKinsey has a great one from the e-commerce world. 268 00:09:28,280 --> 00:09:30,240 There is this life sciences company, 269 00:09:30,240 --> 00:09:33,000 and they asked their tech team to build an e-commerce 270 00:09:33,000 --> 00:09:34,080 platform. 271 00:09:34,080 --> 00:09:34,720 For what? 272 00:09:34,720 --> 00:09:36,440 It was for renewing subscriptions, 273 00:09:36,440 --> 00:09:38,400 like on those products that aren't super popular. 274 00:09:38,400 --> 00:09:40,200 Seemed like a pretty basic project, right? 275 00:09:40,200 --> 00:09:41,720 Yeah, not exactly groundbreaking. 276 00:09:41,720 --> 00:09:43,120 So what happened? 277 00:09:43,120 --> 00:09:46,440 Well, the tech team saw an opportunity to do more. 278 00:09:46,440 --> 00:09:48,760 They didn't just build the platform and call it a day. 279 00:09:48,760 --> 00:09:49,800 They got ambitious. 280 00:09:49,800 --> 00:09:53,240 They dug into the data, analyzed customer behavior, 281 00:09:53,240 --> 00:09:56,400 and realized there was a huge unmet need. 282 00:09:56,400 --> 00:09:56,960 For what? 283 00:09:56,960 --> 00:09:59,200 For a more personalized, easier way 284 00:09:59,200 --> 00:10:01,640 to buy those less popular products. 285 00:10:01,640 --> 00:10:04,240 They used data to completely change the game. 286 00:10:04,240 --> 00:10:05,080 That's pretty awesome. 287 00:10:05,080 --> 00:10:05,640 Yeah. 288 00:10:05,640 --> 00:10:07,840 They ended up creating a whole new business model, 289 00:10:07,840 --> 00:10:10,480 expanding beyond just subscription renewals. 290 00:10:10,480 --> 00:10:12,160 And it was all driven by the tech team. 291 00:10:12,160 --> 00:10:12,840 Yep. 292 00:10:12,840 --> 00:10:14,600 They used their expertise to build 293 00:10:14,600 --> 00:10:18,080 a platform that let customers buy a wider range of products 294 00:10:18,080 --> 00:10:20,440 tailored to their specific needs. 295 00:10:20,440 --> 00:10:23,680 It led to a big boost in revenue and happy customers. 296 00:10:23,680 --> 00:10:26,000 So the tech team wasn't just building a product. 297 00:10:26,000 --> 00:10:28,400 They were shaping the company's strategy. 298 00:10:28,400 --> 00:10:29,440 Exactly. 299 00:10:29,440 --> 00:10:31,040 That's the power of the builder role. 300 00:10:31,040 --> 00:10:32,720 They're not just techies anymore. 301 00:10:32,720 --> 00:10:33,680 They're innovators. 302 00:10:33,680 --> 00:10:35,080 They're strategists. 303 00:10:35,080 --> 00:10:37,600 They're driving real business value. 304 00:10:37,600 --> 00:10:40,360 It's making me think about my own role 305 00:10:40,360 --> 00:10:43,440 and how it might be changing with all this AI stuff 306 00:10:43,440 --> 00:10:44,040 happening. 307 00:10:44,040 --> 00:10:46,480 I think it's a question we should all be asking ourselves. 308 00:10:46,480 --> 00:10:49,720 Whether you're a CEO or a product manager or a marketer, 309 00:10:49,720 --> 00:10:53,240 this AI revolution is going to impact how we work. 310 00:10:53,240 --> 00:10:55,800 It's not just about keeping up with the latest gadgets 311 00:10:55,800 --> 00:10:57,040 and gizmos. 312 00:10:57,040 --> 00:11:01,080 It's about understanding how to use technology to achieve goals 313 00:11:01,080 --> 00:11:02,640 and create new opportunities. 314 00:11:02,640 --> 00:11:04,120 Couldn't have said it better myself. 315 00:11:04,120 --> 00:11:04,640 OK. 316 00:11:04,640 --> 00:11:08,040 So we've covered a lot of ground here, from protector to operator 317 00:11:08,040 --> 00:11:10,040 to orchestrator to builder. 318 00:11:10,040 --> 00:11:13,440 It's clear that tech leadership is evolving at warp speed. 319 00:11:13,440 --> 00:11:14,600 But let's be real. 320 00:11:14,600 --> 00:11:16,640 Most tech leaders are already super busy. 321 00:11:16,640 --> 00:11:18,040 Yeah, they wear a lot of hats. 322 00:11:18,040 --> 00:11:21,240 So where do they even begin to implement all these changes? 323 00:11:21,240 --> 00:11:22,200 That's a great question. 324 00:11:22,200 --> 00:11:24,680 It can definitely feel overwhelming. 325 00:11:24,680 --> 00:11:27,000 But luckily, McKinsey did a bunch of interviews 326 00:11:27,000 --> 00:11:29,320 with CEOs, tech leaders, and board members 327 00:11:29,320 --> 00:11:31,640 who have been through this and come out the other side. 328 00:11:31,640 --> 00:11:33,760 So they've got some real world wisdom to share. 329 00:11:33,760 --> 00:11:34,840 Oh, yeah, they do. 330 00:11:34,840 --> 00:11:37,040 And it boils down to a few key things. 331 00:11:37,040 --> 00:11:38,040 All right, lay it on us. 332 00:11:38,040 --> 00:11:40,040 First, shift your mindset. 333 00:11:40,040 --> 00:11:42,120 You're not just a functional leader anymore. 334 00:11:42,120 --> 00:11:45,920 You're a business leader, a people leader, a change agent. 335 00:11:45,920 --> 00:11:47,360 So think bigger picture. 336 00:11:47,360 --> 00:11:48,400 Exactly. 337 00:11:48,400 --> 00:11:50,680 Second, set aspirational goals. 338 00:11:50,680 --> 00:11:54,480 Figure out which of these four roles, protector, operator, 339 00:11:54,480 --> 00:11:57,160 orchestrator, builder, really resonates with you 340 00:11:57,160 --> 00:11:59,400 and where you want to focus your energy. 341 00:11:59,400 --> 00:12:03,280 Third, you've got to get the CEO and other execs on board. 342 00:12:03,280 --> 00:12:04,680 Their support is crucial. 343 00:12:04,680 --> 00:12:07,000 So build those alliances early on. 344 00:12:07,000 --> 00:12:08,720 Yeah, and don't be afraid to take initiative. 345 00:12:08,720 --> 00:12:11,280 Identify opportunities where tech can solve problems 346 00:12:11,280 --> 00:12:12,720 and just go for it. 347 00:12:12,720 --> 00:12:13,800 So be proactive. 348 00:12:13,800 --> 00:12:16,320 But also, don't forget about your existing commitments, 349 00:12:16,320 --> 00:12:16,800 right? 350 00:12:16,800 --> 00:12:18,160 You've got to live around those, too. 351 00:12:18,160 --> 00:12:18,800 Oh, absolutely. 352 00:12:18,800 --> 00:12:20,360 Flawless execution is key. 353 00:12:20,360 --> 00:12:22,040 You've got to build that trust and prove 354 00:12:22,040 --> 00:12:23,000 you can get things done. 355 00:12:23,000 --> 00:12:24,600 OK, so you're a reliable leader. 356 00:12:24,600 --> 00:12:26,480 You're delivering on your promises. 357 00:12:26,480 --> 00:12:29,440 But what about those hidden obstacles that can trip you up? 358 00:12:29,440 --> 00:12:31,600 Like McKinsey talks about this thing called tech debt. 359 00:12:31,600 --> 00:12:33,360 Oh, yeah, tech debt. 360 00:12:33,360 --> 00:12:34,400 That's a big one. 361 00:12:34,400 --> 00:12:37,240 Can you explain what that is for our listeners who might not 362 00:12:37,240 --> 00:12:38,960 be familiar with the term? 363 00:12:38,960 --> 00:12:40,240 And why should they care? 364 00:12:40,240 --> 00:12:42,800 Tech debt is kind of like the hidden cost 365 00:12:42,800 --> 00:12:45,720 of using outdated tech and inefficient processes. 366 00:12:45,720 --> 00:12:47,960 It's all those shortcuts and quick fixes 367 00:12:47,960 --> 00:12:49,400 that pile up over time. 368 00:12:49,400 --> 00:12:51,880 So it's like building a house on a shaky foundation. 369 00:12:51,880 --> 00:12:53,320 Eventually, it's going to collapse. 370 00:12:53,320 --> 00:12:53,920 Exactly. 371 00:12:53,920 --> 00:12:55,840 And a lot of companies don't even realize 372 00:12:55,840 --> 00:12:57,680 how much tech debt they have. 373 00:12:57,680 --> 00:13:01,000 McKinsey estimates that like 20% to 40% 374 00:13:01,000 --> 00:13:02,840 of many companies' balance sheets 375 00:13:02,840 --> 00:13:04,520 are tied up in tech debt. 376 00:13:04,520 --> 00:13:06,280 Wow, that's a huge amount of resources 377 00:13:06,280 --> 00:13:07,760 that could be used for something better. 378 00:13:07,760 --> 00:13:08,240 Right. 379 00:13:08,240 --> 00:13:10,480 Imagine if all that money was invested in innovation 380 00:13:10,480 --> 00:13:11,880 and growth instead. 381 00:13:11,880 --> 00:13:15,640 So how does tech debt actually impact a business? 382 00:13:15,640 --> 00:13:17,520 What are some real world consequences 383 00:13:17,520 --> 00:13:18,960 we should be worried about? 384 00:13:18,960 --> 00:13:21,120 Well, it can slow you down big time. 385 00:13:21,120 --> 00:13:24,480 It can stifle innovation, make your operations more expensive, 386 00:13:24,480 --> 00:13:26,680 and even hurt your relationships with customers. 387 00:13:26,680 --> 00:13:28,400 Yeah, if your systems are always crashing 388 00:13:28,400 --> 00:13:31,000 and your data is messed up, no one's going to be happy. 389 00:13:31,000 --> 00:13:33,360 And it's easy to let tech debt build up 390 00:13:33,360 --> 00:13:34,960 because it happens gradually. 391 00:13:34,960 --> 00:13:37,640 You take one shortcut here, another quick fix there, 392 00:13:37,640 --> 00:13:39,520 and before you know it, you're drowning in it. 393 00:13:39,520 --> 00:13:42,080 So for anyone listening who's thinking, oh, crap, 394 00:13:42,080 --> 00:13:44,480 I think we might have some tech debt, 395 00:13:44,480 --> 00:13:45,640 what can they do about it? 396 00:13:45,640 --> 00:13:47,040 Where do you even start? 397 00:13:47,040 --> 00:13:49,920 The first step is admitting you have a problem, right? 398 00:13:49,920 --> 00:13:52,880 Do a thorough assessment of your tech and your processes 399 00:13:52,880 --> 00:13:55,160 to figure out how bad this situation is. 400 00:13:55,160 --> 00:13:57,440 So it's like taking inventory of your technology. 401 00:13:57,440 --> 00:13:58,080 Exactly. 402 00:13:58,080 --> 00:13:59,720 Once you know what you're dealing with, 403 00:13:59,720 --> 00:14:02,200 you can start making a plan to tackle it. 404 00:14:02,200 --> 00:14:04,680 OK, so you've identified the tech debt. 405 00:14:04,680 --> 00:14:05,760 What's next? 406 00:14:05,760 --> 00:14:07,640 Do you just rip and replace everything? 407 00:14:07,640 --> 00:14:08,240 Not always. 408 00:14:08,240 --> 00:14:10,320 It's more about being strategic and focusing 409 00:14:10,320 --> 00:14:12,560 on the areas that will make the biggest difference. 410 00:14:12,560 --> 00:14:14,920 You might need to modernize some legacy systems, 411 00:14:14,920 --> 00:14:18,320 clean up some code, maybe even get rid of some applications 412 00:14:18,320 --> 00:14:18,960 altogether. 413 00:14:18,960 --> 00:14:21,040 So it's about making smart investments that 414 00:14:21,040 --> 00:14:22,360 will pay off in the long run. 415 00:14:22,360 --> 00:14:23,280 Exactly. 416 00:14:23,280 --> 00:14:26,280 It's a long-term commitment, but it's worth it. 417 00:14:26,280 --> 00:14:29,400 By reducing tech debt, you can free up resources, 418 00:14:29,400 --> 00:14:32,800 become more agile, and create a more sustainable tech 419 00:14:32,800 --> 00:14:33,520 environment. 420 00:14:33,520 --> 00:14:35,280 It's like getting rid of all that dead weight 421 00:14:35,280 --> 00:14:36,440 that's been holding you back. 422 00:14:36,440 --> 00:14:37,920 Exactly. 423 00:14:37,920 --> 00:14:40,120 And speaking of being prepared, McKinsey 424 00:14:40,120 --> 00:14:42,880 also talks a lot about business resiliency, 425 00:14:42,880 --> 00:14:45,520 especially when it comes to those unavoidable system 426 00:14:45,520 --> 00:14:46,360 outages. 427 00:14:46,360 --> 00:14:48,600 Yeah, because no matter how much you prepare, 428 00:14:48,600 --> 00:14:51,080 sometimes things just go wrong. 429 00:14:51,080 --> 00:14:53,680 Right, so it's not just about preventing outages. 430 00:14:53,680 --> 00:14:56,560 It's about being ready for when they happen. 431 00:14:56,560 --> 00:14:58,760 So how can tech leaders make sure their companies are 432 00:14:58,760 --> 00:15:01,880 prepared for those moments when the tech goes kaput? 433 00:15:01,880 --> 00:15:05,400 Well, one key thing is to test your systems regularly 434 00:15:05,400 --> 00:15:07,520 and plan for different scenarios. 435 00:15:07,520 --> 00:15:09,560 Work with your business partners and vendors 436 00:15:09,560 --> 00:15:11,920 to simulate different kinds of outages. 437 00:15:11,920 --> 00:15:14,400 So like a fire drill, but for your IT systems. 438 00:15:14,400 --> 00:15:15,160 Exactly. 439 00:15:15,160 --> 00:15:18,040 By practicing your response, you can minimize the downtime, 440 00:15:18,040 --> 00:15:20,400 reduce the disruption, and protect your company's 441 00:15:20,400 --> 00:15:21,200 reputation. 442 00:15:21,200 --> 00:15:23,120 Because when things go wrong, it's 443 00:15:23,120 --> 00:15:25,040 how you handle it that really matters. 444 00:15:25,040 --> 00:15:25,680 Absolutely. 445 00:15:25,680 --> 00:15:28,560 And don't forget to include your vendors in these drills, 446 00:15:28,560 --> 00:15:30,320 because a lot of outages actually 447 00:15:30,320 --> 00:15:32,240 come from third party systems. 448 00:15:32,240 --> 00:15:34,040 Communication and collaboration are key. 449 00:15:34,040 --> 00:15:34,640 Always. 450 00:15:34,640 --> 00:15:36,800 You know, throughout this whole conversation, 451 00:15:36,800 --> 00:15:39,520 it's really striking how much technology and business 452 00:15:39,520 --> 00:15:40,840 are becoming intertwined. 453 00:15:40,840 --> 00:15:42,440 Oh, yeah, absolutely. 454 00:15:42,440 --> 00:15:45,000 It's not just about coding and servers anymore. 455 00:15:45,000 --> 00:15:47,440 It's about understanding how technology 456 00:15:47,440 --> 00:15:49,480 can drive business outcomes. 457 00:15:49,480 --> 00:15:52,440 And that's especially true with the rise of generative AI. 458 00:15:52,440 --> 00:15:54,680 It feels like a whole new way of thinking about the role 459 00:15:54,680 --> 00:15:56,280 of technology in business. 460 00:15:56,280 --> 00:15:57,000 It is. 461 00:15:57,000 --> 00:16:00,360 And as AI gets more embedded in how businesses run, 462 00:16:00,360 --> 00:16:02,520 tech leaders really need to get fluent 463 00:16:02,520 --> 00:16:04,200 in the language of business. 464 00:16:04,200 --> 00:16:06,400 So it's like they need to become bilingual, 465 00:16:06,400 --> 00:16:08,000 speaking both tech and business. 466 00:16:08,000 --> 00:16:08,640 Exactly. 467 00:16:08,640 --> 00:16:10,260 They need to be able to translate 468 00:16:10,260 --> 00:16:12,840 those complex technical ideas into something 469 00:16:12,840 --> 00:16:15,880 that business people understand and vice versa. 470 00:16:15,880 --> 00:16:18,760 So those soft skills are becoming just as important 471 00:16:18,760 --> 00:16:19,840 as technical chops. 472 00:16:19,840 --> 00:16:20,680 Oh, absolutely. 473 00:16:20,680 --> 00:16:23,240 Communication, collaboration, strategic thinking, 474 00:16:23,240 --> 00:16:24,720 those are all essential now. 475 00:16:24,720 --> 00:16:27,360 And McKinsey also talked a lot about the need 476 00:16:27,360 --> 00:16:29,360 for continuous learning and development, 477 00:16:29,360 --> 00:16:31,600 not just for the tech teams, but for everyone. 478 00:16:31,600 --> 00:16:35,480 Yeah, in this world of crazy fast technological change, 479 00:16:35,480 --> 00:16:37,640 everyone needs to be constantly upskilling. 480 00:16:37,640 --> 00:16:39,320 It's like investing in your people. 481 00:16:39,320 --> 00:16:40,200 Exactly. 482 00:16:40,200 --> 00:16:42,440 And it's not just about technical skills either. 483 00:16:42,440 --> 00:16:45,280 It's about those soft skills we were talking about. 484 00:16:45,280 --> 00:16:49,600 Communication, collaboration, problem solving, adaptability. 485 00:16:49,600 --> 00:16:51,120 Because the only constant is change. 486 00:16:51,120 --> 00:16:51,720 Right. 487 00:16:51,720 --> 00:16:53,920 And McKinsey also had some really interesting insights 488 00:16:53,920 --> 00:16:56,360 about how data needs to be managed differently 489 00:16:56,360 --> 00:16:58,480 in this new AI era. 490 00:16:58,480 --> 00:16:59,720 Yeah, tell me more about that. 491 00:16:59,720 --> 00:17:04,240 Well, for a long time, data has been kind of siloed 492 00:17:04,240 --> 00:17:05,520 within different departments. 493 00:17:05,520 --> 00:17:07,240 Each department hoarding its own data. 494 00:17:07,240 --> 00:17:08,080 Pretty much. 495 00:17:08,080 --> 00:17:10,800 But to really unlock the power of AI, 496 00:17:10,800 --> 00:17:13,200 companies need to start treating data as a product 497 00:17:13,200 --> 00:17:14,320 that everyone can use. 498 00:17:14,320 --> 00:17:16,200 So breaking down those data silos 499 00:17:16,200 --> 00:17:18,560 and creating a more data-driven culture. 500 00:17:18,560 --> 00:17:19,440 Exactly. 501 00:17:19,440 --> 00:17:22,200 And that means having good data governance policies, 502 00:17:22,200 --> 00:17:25,880 strong data infrastructure, and a commitment to data quality. 503 00:17:25,880 --> 00:17:28,920 So it's a big shift in how we think about and manage data. 504 00:17:28,920 --> 00:17:29,560 It is. 505 00:17:29,560 --> 00:17:31,640 But it's essential if we want to fully realize 506 00:17:31,640 --> 00:17:33,080 the potential of AI. 507 00:17:33,080 --> 00:17:35,240 OK, so we've covered a lot of ground here, 508 00:17:35,240 --> 00:17:37,600 from tech debt to business resiliency 509 00:17:37,600 --> 00:17:40,640 to the importance of soft skills and data management. 510 00:17:40,640 --> 00:17:42,360 I'm sure some of our listeners are feeling 511 00:17:42,360 --> 00:17:43,960 a bit overwhelmed by all this. 512 00:17:43,960 --> 00:17:45,520 Yeah, it's a lot to digest. 513 00:17:45,520 --> 00:17:47,880 But luckily, McKinsey has some great advice 514 00:17:47,880 --> 00:17:49,080 on how to get started. 515 00:17:49,080 --> 00:17:50,400 Start small. 516 00:17:50,400 --> 00:17:53,680 Pick one or two areas where you can make a real difference. 517 00:17:53,680 --> 00:17:55,800 Don't try to do everything at once. 518 00:17:55,800 --> 00:17:59,720 Build a strong team around you, people who share your vision. 519 00:17:59,720 --> 00:18:03,360 And don't be afraid to experiment and learn as you go. 520 00:18:03,360 --> 00:18:05,040 So it's about embracing the journey 521 00:18:05,040 --> 00:18:06,920 and being open to new possibilities. 522 00:18:06,920 --> 00:18:07,560 Exactly. 523 00:18:07,560 --> 00:18:10,800 And remember, it's OK to not have all the answers. 524 00:18:10,800 --> 00:18:13,280 Yeah, asking questions and learning from your mistakes 525 00:18:13,280 --> 00:18:14,720 is all part of the process. 526 00:18:14,720 --> 00:18:15,640 Totally. 527 00:18:15,640 --> 00:18:18,000 The most important thing is to be proactive, 528 00:18:18,000 --> 00:18:20,760 be willing to adapt, and never stop learning. 529 00:18:20,760 --> 00:18:22,480 It's a marathon, not a sprint. 530 00:18:22,480 --> 00:18:24,040 Well said. 531 00:18:24,040 --> 00:18:26,240 You know, one of the themes that has really stood out to me 532 00:18:26,240 --> 00:18:30,200 today is this idea of the tech leader as a change agent. 533 00:18:30,200 --> 00:18:32,440 Yeah, that's a powerful concept. 534 00:18:32,440 --> 00:18:34,560 It's not just about keeping the lights on anymore. 535 00:18:34,560 --> 00:18:36,480 It's about leading the charge into the future. 536 00:18:36,480 --> 00:18:37,400 Exactly. 537 00:18:37,400 --> 00:18:39,360 And that takes a different kind of leadership. 538 00:18:39,360 --> 00:18:42,360 It takes vision, courage, resilience, 539 00:18:42,360 --> 00:18:43,760 and a passion for innovation. 540 00:18:43,760 --> 00:18:45,960 They need to be able to paint a picture of the future 541 00:18:45,960 --> 00:18:47,680 and get everyone excited about it. 542 00:18:47,680 --> 00:18:48,720 They do. 543 00:18:48,720 --> 00:18:50,680 And they need to be comfortable with uncertainty 544 00:18:50,680 --> 00:18:53,200 because the tech world is constantly changing. 545 00:18:53,200 --> 00:18:56,000 So being able to adapt and learn quickly is crucial. 546 00:18:56,000 --> 00:18:57,200 Absolutely. 547 00:18:57,200 --> 00:18:59,560 And building strong relationships is key, too, 548 00:18:59,560 --> 00:19:02,760 both within the company and with outside partners. 549 00:19:02,760 --> 00:19:03,680 You can't do it alone. 550 00:19:03,680 --> 00:19:04,480 Nope. 551 00:19:04,480 --> 00:19:07,640 You need a strong network of support and expertise. 552 00:19:07,640 --> 00:19:09,880 And McKinsey also emphasized the importance 553 00:19:09,880 --> 00:19:11,240 of being proactive. 554 00:19:11,240 --> 00:19:13,240 Oh, yeah, that's huge. 555 00:19:13,240 --> 00:19:16,200 Tech leaders can't just sit back and wait for things to happen. 556 00:19:16,200 --> 00:19:17,640 They need to be driving the change. 557 00:19:17,640 --> 00:19:18,320 They do. 558 00:19:18,320 --> 00:19:20,080 They have a unique opportunity to shape 559 00:19:20,080 --> 00:19:21,880 the future of their organizations, 560 00:19:21,880 --> 00:19:24,360 but they need to seize that opportunity. 561 00:19:24,360 --> 00:19:25,160 Be bold. 562 00:19:25,160 --> 00:19:26,080 Be visionary. 563 00:19:26,080 --> 00:19:27,000 Be relentless. 564 00:19:27,000 --> 00:19:27,520 I love it. 565 00:19:27,520 --> 00:19:29,920 So for our listeners out there, if you're a tech leader 566 00:19:29,920 --> 00:19:31,720 or you're hoping to become one, what's 567 00:19:31,720 --> 00:19:34,520 the one key takeaway you want them to leave with today? 568 00:19:34,520 --> 00:19:37,120 The future of tech leadership is about being bold, 569 00:19:37,120 --> 00:19:39,120 being visionary, and being relentless 570 00:19:39,120 --> 00:19:40,920 in your pursuit of innovation. 571 00:19:40,920 --> 00:19:43,640 Don't be afraid to challenge the status quo, 572 00:19:43,640 --> 00:19:46,080 push the boundaries, and use technology 573 00:19:46,080 --> 00:19:47,720 to create a better future. 574 00:19:47,720 --> 00:19:49,800 So it's not just about the technology itself. 575 00:19:49,800 --> 00:19:51,840 It's about the impact you can have with it. 576 00:19:51,840 --> 00:19:52,800 Exactly. 577 00:19:52,800 --> 00:19:54,920 And that's what makes tech leadership so exciting. 578 00:19:54,920 --> 00:19:57,880 You have the power to make a real difference in the world. 579 00:19:57,880 --> 00:19:58,640 I love that. 580 00:19:58,640 --> 00:20:00,520 So it's not just about coding and algorithms. 581 00:20:00,520 --> 00:20:02,400 It's about making a positive impact. 582 00:20:02,400 --> 00:20:03,480 Absolutely. 583 00:20:03,480 --> 00:20:05,480 Well, I think we've given our listeners a lot 584 00:20:05,480 --> 00:20:06,440 to think about today. 585 00:20:06,440 --> 00:20:07,280 We have. 586 00:20:07,280 --> 00:20:09,800 What's one final thought you want to leave them with 587 00:20:09,800 --> 00:20:11,440 as we wrap up this deep dive? 588 00:20:11,440 --> 00:20:13,800 The world is changing faster than ever. 589 00:20:13,800 --> 00:20:17,080 And technology is at the heart of that change. 590 00:20:17,080 --> 00:20:20,280 It's an incredibly exciting time to be a tech leader. 591 00:20:20,280 --> 00:20:23,280 But it's also a time of great responsibility. 592 00:20:23,280 --> 00:20:25,800 The decisions you make today will shape the future. 593 00:20:25,800 --> 00:20:29,560 So be bold, be thoughtful, and never stop learning. 594 00:20:29,560 --> 00:20:30,040 It really is. 595 00:20:30,040 --> 00:20:32,960 It's a call to action for all of us, no matter what our job is. 596 00:20:32,960 --> 00:20:34,080 Definitely. 597 00:20:34,080 --> 00:20:35,600 This AI thing, it's going to touch 598 00:20:35,600 --> 00:20:36,760 every part of our lives. 599 00:20:36,760 --> 00:20:38,720 So we've got to figure out how to use it for good, right? 600 00:20:38,720 --> 00:20:39,440 Absolutely. 601 00:20:39,440 --> 00:20:42,680 It's all about being proactive, being curious, and just 602 00:20:42,680 --> 00:20:44,640 being open to change, I think. 603 00:20:44,640 --> 00:20:45,160 Yeah. 604 00:20:45,160 --> 00:20:48,680 The future belongs to those who are willing to learn and adapt. 605 00:20:48,680 --> 00:20:50,320 To kind of evolve with it all. 606 00:20:50,320 --> 00:20:51,440 Well said. 607 00:20:51,440 --> 00:20:53,400 So for everyone listening out there, 608 00:20:53,400 --> 00:20:55,480 I want to leave you with this one thought. 609 00:20:55,480 --> 00:20:57,280 We spent this whole episode talking 610 00:20:57,280 --> 00:21:00,960 about how tech leadership is changing with AI, right? 611 00:21:00,960 --> 00:21:03,160 We've talked about those four big shifts. 612 00:21:03,160 --> 00:21:06,280 Protector, operator, orchestrator, builder. 613 00:21:06,280 --> 00:21:08,600 We talked about all the challenges like tech debt, 614 00:21:08,600 --> 00:21:10,160 making sure businesses are resilient, 615 00:21:10,160 --> 00:21:12,960 the importance of soft skills, all that stuff. 616 00:21:12,960 --> 00:21:17,400 But now, it's time to take all this and apply to yourself. 617 00:21:17,400 --> 00:21:18,880 OK, yeah. 618 00:21:18,880 --> 00:21:22,040 Think about your own job, your industry, what you 619 00:21:22,040 --> 00:21:24,400 want to achieve in your career. 620 00:21:24,400 --> 00:21:27,520 How is this AI revolution impacting you? 621 00:21:27,520 --> 00:21:29,960 What opportunities are out there for you? 622 00:21:29,960 --> 00:21:32,960 What can you do today to get ready for the future? 623 00:21:32,960 --> 00:21:35,000 Yeah, that's the question, isn't it? 624 00:21:35,000 --> 00:21:36,440 It's about taking control. 625 00:21:36,440 --> 00:21:39,000 You know, don't wait for someone to tell you what the future's 626 00:21:39,000 --> 00:21:39,440 going to be. 627 00:21:39,440 --> 00:21:40,560 Go out there and create it. 628 00:21:40,560 --> 00:21:41,120 I like that. 629 00:21:41,120 --> 00:21:41,640 Make it happen. 630 00:21:41,640 --> 00:21:43,480 Yeah, exactly. 631 00:21:43,480 --> 00:21:45,200 Thanks for joining us on this deep dive 632 00:21:45,200 --> 00:21:46,600 into the world of tech leadership. 633 00:21:46,600 --> 00:21:47,280 It's been fun. 634 00:21:47,280 --> 00:21:48,560 We hope you learned something new 635 00:21:48,560 --> 00:21:50,000 and we hope you're feeling inspired. 636 00:21:50,000 --> 00:21:52,680 Yeah, go out there and make the future awesome. 637 00:21:52,680 --> 00:21:55,880 Until next time, keep learning, keep growing, 638 00:21:55,880 --> 00:21:58,200 and keep pushing the boundaries. 639 00:21:58,200 --> 00:22:06,960 See you.