49: Scaling a Consulting Firm Without Losing Culture with Stuart Packham

1 00:00:00,160 - > 00:00:03,040 SPEAKER_02: You may be late to the game and you'll never catch 2 00:00:03,040 - > 00:00:03,120 up. 3 00:00:03,279 - > 00:00:05,599 SPEAKER_01: And let me ask you, in your opinion, at that level, 4 00:00:05,759 - > 00:00:08,640 what are most companies getting wrong when they're starting to 5 00:00:08,640 - > 00:00:09,679 use AI at work? 6 00:00:09,839 - > 00:00:11,839 SPEAKER_02: Well, I think singular tasks, singular 7 00:00:11,839 - > 00:00:13,839 activity, and that's missing the big impact. 8 00:00:14,080 - > 00:00:15,119 SPEAKER_01: We work with a lot of clients. 9 00:00:15,279 - > 00:00:17,120 We go in, they're excited about using AI. 10 00:00:17,199 - > 00:00:19,679 Maybe some of them have, you know, started experimenting on 11 00:00:19,679 - > 00:00:22,879 their own, but there's usually not some orchestration 12 00:00:22,879 - > 00:00:23,679 occurring, right? 13 00:00:23,839 - > 00:00:28,640 How do they shift their thinking from that task-level integration 14 00:00:28,640 - > 00:00:31,039 to workflow level orchestration? 15 00:00:31,280 - > 00:00:34,880 SPEAKER_02: Yeah, usually it starts with calculating ROI. 16 00:00:35,039 - > 00:00:38,479 What's the difference in having people automate a singular task 17 00:00:38,479 - > 00:00:42,399 versus the orchestration of data to fulfill a business process 18 00:00:42,399 - > 00:00:45,759 that typically crosses not just systems, but the groups that own 19 00:00:45,759 - > 00:00:46,479 those systems? 20 00:00:46,799 - > 00:00:50,560 SPEAKER_01: What is the approach to starting with systemizing, 21 00:00:50,640 - > 00:00:53,280 streamlining, whatever the orchestration is of these 22 00:00:53,280 - > 00:00:54,320 fragmented systems? 23 00:00:54,560 - > 00:00:57,280 SPEAKER_02: The first thing to do is to stop buying another 24 00:00:57,280 - > 00:01:00,399 system to add to your fragment stack because ultimately those 25 00:01:00,399 - > 00:01:05,040 tools, those fragmented systems, are likely to start disappearing 26 00:01:05,040 - > 00:01:05,599 one by one. 27 00:01:05,840 - > 00:01:08,560 SPEAKER_00: Carl Simon is the co-founder of Subatomic, helping 28 00:01:08,560 - > 00:01:12,560 companies move beyond task-based AI into unified end-to-end 29 00:01:12,719 - > 00:01:13,280 workflows. 30 00:01:13,439 - > 00:01:17,120 He shows leaders how clean data, AI coworkers, and secure 31 00:01:17,120 - > 00:01:19,920 orchestration can eliminate business friction and unlock 32 00:01:19,920 - > 00:01:20,959 faster, smarter growth. 33 00:01:21,280 - > 00:01:22,719 SPEAKER_01: Welcome to Using AI at work. 34 00:01:22,959 - > 00:01:24,560 I'm your host, Chris Dagle. 35 00:01:24,719 - > 00:01:26,959 Each week we'll be learning how today's business owners, 36 00:01:27,120 - > 00:01:30,400 entrepreneurs, and ambitious professionals are getting more 37 00:01:30,400 - > 00:01:32,719 done with smart use of tomorrow's tech. 38 00:01:33,040 - > 00:01:34,079 Let's get started. 39 00:01:34,400 - > 00:01:37,040 Right now, every business leader is asking the same question. 40 00:01:37,280 - > 00:01:38,719 What are we going to do about AI? 41 00:01:38,879 - > 00:01:42,000 If this is you, ChiefAIOfficer.com has the 42 00:01:42,000 - > 00:01:42,239 answer. 43 00:01:42,560 - > 00:01:44,879 We give you a simple path forward where we provide 44 00:01:44,879 - > 00:01:48,000 executive and team training so your people know exactly how to 45 00:01:48,000 - > 00:01:50,560 safely use generative AI in their day-to-day. 46 00:01:50,799 - > 00:01:53,840 We also manage the deployment and implementation to make sure 47 00:01:53,840 - > 00:01:56,719 tools actually get adopted and deliver results. 48 00:01:56,879 - > 00:01:59,439 And we'll also guide company-wide transformation so 49 00:01:59,439 - > 00:02:01,840 AI becomes part of your operating system, not just 50 00:02:01,840 - > 00:02:03,120 another shiny object. 51 00:02:03,359 - > 00:02:06,959 The companies that act now will increase productivity, cut 52 00:02:06,959 - > 00:02:09,439 costs, and grow faster than their competitors. 53 00:02:09,680 - > 00:02:11,759 Those that wait will get left behind. 54 00:02:11,919 - > 00:02:14,639 So if you want to make AI work in your business, visit 55 00:02:14,639 - > 00:02:18,639 chiefaiofficer.com and see how we're helping companies of all 56 00:02:18,639 - > 00:02:20,879 sizes finally get results from AI. 57 00:02:21,120 - > 00:02:24,080 Hey everybody, welcome to another episode of Using AI at 58 00:02:24,080 - > 00:02:24,319 Work. 59 00:02:24,400 - > 00:02:27,360 This is my favorite podcast, and I hope it's becoming yours as 60 00:02:27,360 - > 00:02:27,520 well. 61 00:02:27,759 - > 00:02:30,000 Today we, our guest is Carl Simon, and guess what we're 62 00:02:30,000 - > 00:02:30,879 going to be talking about? 63 00:02:31,199 - > 00:02:31,840 AI. 64 00:02:32,560 - > 00:02:36,159 And one of the things that I'm excited to dig in with Carl is 65 00:02:36,159 - > 00:02:40,560 how they are looking at AI holistically rather than 66 00:02:40,560 - > 00:02:42,319 task-based or activity-based. 67 00:02:42,400 - > 00:02:47,840 And they're really looking at ways to introduce AI into the 68 00:02:47,840 - > 00:02:49,039 entire workflow. 69 00:02:49,199 - > 00:02:53,280 Now, some of the uh I guess challenges with that are going 70 00:02:53,280 - > 00:02:54,159 to be pretty universal. 71 00:02:54,240 - > 00:02:55,680 And that's that's the data environment. 72 00:02:55,840 - > 00:03:00,639 So anyway, Carl Simon, he's the co-founder at Subatomic, and 73 00:03:00,639 - > 00:03:01,680 that is what they do. 74 00:03:01,840 - > 00:03:06,159 Carl's belief is that AI should optimize end-to-end workflows 75 00:03:06,159 - > 00:03:10,479 across fragmented systems, not just improve those isolated 76 00:03:10,479 - > 00:03:12,319 tasks that probably a lot of us are working on. 77 00:03:12,479 - > 00:03:15,199 So, Carl, before we get started, anything you want to uh let the 78 00:03:15,199 - > 00:03:17,759 audience know about what you're up to at Subatomic? 79 00:03:18,159 - > 00:03:20,479 SPEAKER_02: Yeah, no, I think you captured it perfectly, 80 00:03:20,560 - > 00:03:20,879 Chris. 81 00:03:20,960 - > 00:03:24,479 I mean, ultimately we're trying to help companies actually 82 00:03:24,479 - > 00:03:28,400 utilize the foundation that they have, which is data, but get it 83 00:03:28,400 - > 00:03:32,000 into a unified state cleaned, standardized, so it becomes 84 00:03:32,000 - > 00:03:32,400 useful. 85 00:03:32,639 - > 00:03:36,240 When you introduce AI on top of poor data, you get poor results. 86 00:03:36,400 - > 00:03:39,520 You want good knowledge because those agents bring their 87 00:03:39,520 - > 00:03:45,280 intellect, and we at Subatomic bring those AI coworkers up to 88 00:03:45,280 - > 00:03:48,479 speed on your processes, your cognitive reasoning. 89 00:03:48,800 - > 00:03:50,400 All of it goes together. 90 00:03:50,639 - > 00:03:54,639 You have a, you know, unless you have strengths across the way 91 00:03:54,639 - > 00:03:57,840 from knowledge to intellect to your way that differentiates 92 00:03:57,840 - > 00:04:00,639 you, you're not going to be positioned for success. 93 00:04:01,120 - > 00:04:03,520 SPEAKER_01: So, Carl, when you and I first did this 94 00:04:03,520 - > 00:04:06,479 pre-interview, I had a good idea of where this conversation was 95 00:04:06,479 - > 00:04:06,719 going. 96 00:04:06,879 - > 00:04:11,439 However, some recent developments have occurred that 97 00:04:11,439 - > 00:04:16,879 um might change the uh the direction of this episode. 98 00:04:16,959 - > 00:04:18,000 So I'm gonna ask you something. 99 00:04:18,079 - > 00:04:20,160 And for those of you who are listening, if you've heard me 100 00:04:20,160 - > 00:04:21,759 talk about this before, please indulge me. 101 00:04:21,839 - > 00:04:23,600 I think this is a very important topic. 102 00:04:23,839 - > 00:04:27,839 Carl, are you familiar with what Jack Dorsey did at Block about a 103 00:04:27,839 - > 00:04:29,279 month and a half ago? 104 00:04:29,759 - > 00:04:33,120 SPEAKER_02: Uh, you might need to summarize that for me. 105 00:04:33,360 - > 00:04:35,199 SPEAKER_01: So, and for the listeners, this is important. 106 00:04:35,360 - > 00:04:38,160 So, um, again, indulge me, it might take a second. 107 00:04:38,319 - > 00:04:42,720 So uh Jack Dorsey, smart guy, started Twitter, uh, started 108 00:04:42,720 - > 00:04:43,199 Block. 109 00:04:43,279 - > 00:04:47,040 Uh, Block is the parent company of Cash App and Square. 110 00:04:47,680 - > 00:04:49,920 And there was an announcement on X. 111 00:04:50,240 - > 00:04:56,079 Block is laying off 4,000 of their 10,000 uh staff. 112 00:04:56,560 - > 00:04:59,360 To me, I thought, okay, AI got them, right? 113 00:04:59,439 - > 00:05:02,000 They they introduced some efficiencies in certain areas 114 00:05:02,000 - > 00:05:04,399 and departments, and they just don't need the people anymore. 115 00:05:04,639 - > 00:05:06,240 I wasn't that surprised. 116 00:05:06,560 - > 00:05:08,879 But that was February 26th. 117 00:05:09,120 - > 00:05:14,959 Um, at the end of March, he released a paper on the website 118 00:05:14,959 - > 00:05:16,959 for Block, but also on the Sequoia website. 119 00:05:17,040 - > 00:05:19,120 Sequoia is a big investor in Blood. 120 00:05:19,360 - > 00:05:24,000 And the title of the article was From Hierarchy to Intelligence. 121 00:05:24,240 - > 00:05:27,519 And in that, basically he said, here's why we were able to fire 122 00:05:27,519 - > 00:05:28,800 those 4,000 people. 123 00:05:28,959 - > 00:05:32,959 He said, we did a deep dive on what does the organization of 124 00:05:32,959 - > 00:05:34,480 tomorrow look like? 125 00:05:34,720 - > 00:05:39,279 And he said, in our analysis, what we realized was that a big 126 00:05:39,279 - > 00:05:42,879 portion of this traditional, like, you know, pyramid-shaped 127 00:05:42,879 - > 00:05:45,439 hierarchy of boss at the top, and then a layer under that, 128 00:05:45,600 - > 00:05:50,079 layer under that, layer under that, was intended to collect 129 00:05:50,079 - > 00:05:53,199 information at a level and then pass that information up to the 130 00:05:53,199 - > 00:05:56,720 next level for evaluation, synthesis, decision making, and 131 00:05:56,720 - > 00:05:59,680 then repeat that up and down the chain, up and down the chain. 132 00:05:59,839 - > 00:06:02,879 And he said, We realized in our organization, it was about 60% 133 00:06:03,360 - > 00:06:07,279 of the humans that we had, their job was moving information 134 00:06:07,279 - > 00:06:07,519 around. 135 00:06:07,600 - > 00:06:10,560 They might not have looked at it that way, but first principles 136 00:06:10,560 - > 00:06:12,720 indicated that that's exactly what they were doing. 137 00:06:12,800 - > 00:06:14,879 And he said, I had an aha moment. 138 00:06:15,360 - > 00:06:17,279 He said, This is what AI does. 139 00:06:17,519 - > 00:06:22,319 AI can ingest this information, uh, evaluate, synthesize new 140 00:06:22,319 - > 00:06:25,680 outcomes or decisions to be made or whatever, and then pass that 141 00:06:25,680 - > 00:06:27,040 to who needs it. 142 00:06:27,360 - > 00:06:30,399 But the challenge is the data. 143 00:06:30,560 - > 00:06:35,279 And he said, Block was uniquely situated to go from this 144 00:06:35,279 - > 00:06:37,839 hierarchy into this, what they're calling the intelligence 145 00:06:37,839 - > 00:06:41,199 layer, because we were a remote company. 146 00:06:41,360 - > 00:06:45,680 Every Slack message, every email, every call transcript, 147 00:06:45,839 - > 00:06:48,720 everything that we were doing all day long was being captured 148 00:06:48,720 - > 00:06:53,279 as an artifact of our business, which is exactly the data that 149 00:06:53,279 - > 00:06:58,160 AI needs to be able to apply its intelligence contextually, 150 00:06:58,480 - > 00:07:00,800 accurately for your business. 151 00:07:01,120 - > 00:07:05,759 So the good news is, Carl, this is like a boon for where 152 00:07:05,759 - > 00:07:08,560 Subatomic is if more companies are going that way. 153 00:07:08,720 - > 00:07:11,040 And I'll tell you personally, for Chief AI officer, as soon as 154 00:07:11,040 - > 00:07:13,040 I heard that, I said, wait a minute, we we're a remote 155 00:07:13,040 - > 00:07:13,439 company. 156 00:07:13,600 - > 00:07:14,399 We're doing that. 157 00:07:14,560 - > 00:07:20,399 And about uh five weeks ago, we started our uh version of 158 00:07:20,399 - > 00:07:23,439 preparing all of these business artifacts that were being 159 00:07:23,439 - > 00:07:26,720 created to create this intelligence layer. 160 00:07:26,959 - > 00:07:30,560 Um, Eric Siou out of Beverly Hills, he's a uh thought leader 161 00:07:30,560 - > 00:07:31,040 in the space. 162 00:07:31,120 - > 00:07:34,079 He calls it a single brain, is the model. 163 00:07:34,319 - > 00:07:38,399 But our company chief AI officer is now doing this, meaning that 164 00:07:38,399 - > 00:07:43,040 our our hierarchy of our, you know, our org chart is about to 165 00:07:43,040 - > 00:07:44,240 flatten considerably. 166 00:07:44,399 - > 00:07:48,879 So if you weren't aware of that, know that that is um, and I 167 00:07:48,879 - > 00:07:51,439 talked to several people since, and I'm like, well, what do you 168 00:07:51,439 - > 00:07:51,600 think? 169 00:07:51,680 - > 00:07:54,079 And they're like, uh, yep, that's spot on. 170 00:07:54,240 - > 00:07:55,680 So, Carl, let's jump into this. 171 00:07:55,839 - > 00:07:57,439 What do you think about that? 172 00:07:58,000 - > 00:08:00,160 SPEAKER_02: Yeah, that's unsurprising to me. 173 00:08:00,319 - > 00:08:03,360 You know, hearing the reasons, I I wasn't up on that particular 174 00:08:03,360 - > 00:08:05,759 story, but I've heard many similar ones. 175 00:08:06,000 - > 00:08:10,480 And when you think about the old way we have done business that 176 00:08:10,480 - > 00:08:15,360 many are still doing today, it's the idea that all that 177 00:08:15,360 - > 00:08:18,959 information passing between people or up and down the 178 00:08:18,959 - > 00:08:22,879 hierarchy, yeah, that's been built in friction all this time 179 00:08:23,040 - > 00:08:28,160 where it's not just a cost, there's just there are mistakes 180 00:08:28,160 - > 00:08:31,920 in transition and there are differences of the information 181 00:08:31,920 - > 00:08:34,879 that's being passed among groups that may not even align on 182 00:08:34,879 - > 00:08:35,600 business terms. 183 00:08:35,759 - > 00:08:39,120 The moment you have that unified data and it's all captured, 184 00:08:39,279 - > 00:08:42,320 everyone has visibility and accessibility to it. 185 00:08:42,480 - > 00:08:46,559 You don't need that, those middlemen, those you know, 186 00:08:46,799 - > 00:08:48,000 intermediaries. 187 00:08:48,240 - > 00:08:50,960 You know, it's you think about system integration. 188 00:08:51,120 - > 00:08:53,360 We talk about that a lot about sharing data. 189 00:08:53,519 - > 00:08:56,559 We have that at the human level, and that never made sense. 190 00:08:56,720 - > 00:09:00,080 But we never had a way to address it with technology 191 00:09:00,080 - > 00:09:01,200 before today. 192 00:09:01,519 - > 00:09:02,159 SPEAKER_01: Yeah. 193 00:09:02,639 - > 00:09:07,679 So this sounds like I mean a perfect environment for what you 194 00:09:07,679 - > 00:09:09,519 guys do at Subatomic, right? 195 00:09:10,159 - > 00:09:10,799 SPEAKER_02: It does. 196 00:09:10,960 - > 00:09:14,240 I mean, we have always believed you got to get the data right in 197 00:09:14,240 - > 00:09:22,480 a unified set of terms, names, all you know Carl with a K with 198 00:09:22,480 - > 00:09:23,840 a C, Carla. 199 00:09:24,159 - > 00:09:27,200 I mean, my middle initial is A, so some people get that messed 200 00:09:27,200 - > 00:09:28,000 up, right? 201 00:09:28,480 - > 00:09:30,159 I mean, who is Carl? 202 00:09:30,320 - > 00:09:31,919 Will the real Carl stand up? 203 00:09:32,240 - > 00:09:38,159 And you know, having a unified language, a unified repository 204 00:09:38,159 - > 00:09:40,799 of information, whether it's structured or unstructured, 205 00:09:40,879 - > 00:09:45,039 where everyone can actually retrieve and know it's saying 206 00:09:45,039 - > 00:09:48,639 the same thing, it's reflecting the same numbers, means you 207 00:09:48,639 - > 00:09:53,840 don't have to present different information and wonder did that 208 00:09:53,840 - > 00:09:57,759 person come to the meeting and give me the high level of how 209 00:09:58,000 - > 00:10:02,480 revenue is being projected, you know, where are key issues that 210 00:10:02,480 - > 00:10:05,679 are preventing us from closing more deals, whatever the domain 211 00:10:05,679 - > 00:10:09,679 is and the use case that needs to be, you know, demonstrated 212 00:10:09,679 - > 00:10:12,559 through clear insights, no longer do you have that issue. 213 00:10:12,720 - > 00:10:15,840 And the workflow of passing information, let alone executing 214 00:10:15,840 - > 00:10:19,200 the processes that make everything like revenue capture 215 00:10:19,200 - > 00:10:24,000 possible or operations fulfilled in the most efficient, 216 00:10:24,080 - > 00:10:27,279 high-velocity way, all those friction points disappear. 217 00:10:27,360 - > 00:10:31,360 If you got the right data, it's accessible, shareable. 218 00:10:32,480 - > 00:10:35,039 SPEAKER_01: So sounds very attractive as a business owner. 219 00:10:35,120 - > 00:10:37,840 And for all of those who are listening to this right now who 220 00:10:37,840 - > 00:10:41,120 are, you know, leading teams or leading entire companies, that 221 00:10:41,120 - > 00:10:42,080 sounds very interesting. 222 00:10:42,240 - > 00:10:44,799 Before, and so at this point, I would say we've we've kind of 223 00:10:44,799 - > 00:10:48,879 covered a macro theme of this, the impact of having this data. 224 00:10:49,039 - > 00:10:52,559 Now, let's bring it all the way back down to maybe the listener 225 00:10:52,559 - > 00:10:57,919 who is um on the AI journey, uh, but they certainly don't have 226 00:10:57,919 - > 00:11:00,320 any kind of like unified approach, or there's still a lot 227 00:11:00,320 - > 00:11:02,720 of open loops or questions that they've got about this. 228 00:11:02,879 - > 00:11:06,320 And let me ask you, in your opinion, at that level, what are 229 00:11:06,320 - > 00:11:09,919 most companies getting wrong when they're starting to use AI 230 00:11:09,919 - > 00:11:10,559 at work? 231 00:11:10,960 - > 00:11:13,440 SPEAKER_02: Well, I think there are probably a few things I can 232 00:11:13,600 - > 00:11:14,480 I can mention. 233 00:11:14,720 - > 00:11:18,080 You mentioned the beginning of the the podcast, Chris, about 234 00:11:18,080 - > 00:11:21,200 singular task, singular activity, and that's missing the 235 00:11:21,200 - > 00:11:21,840 big impact. 236 00:11:22,000 - > 00:11:23,360 So they're getting that wrong. 237 00:11:23,519 - > 00:11:28,399 And so we recommend, you know, operationally, what's the most 238 00:11:28,399 - > 00:11:30,960 challenging thing you do that prevents you from actually 239 00:11:30,960 - > 00:11:35,360 reallocating your team members to more growth-oriented 240 00:11:35,360 - > 00:11:36,480 activities? 241 00:11:36,720 - > 00:11:39,120 And then number two, the rollout itself. 242 00:11:39,279 - > 00:11:42,720 A lot of people think rolling out co-pilot, you know, and 243 00:11:42,720 - > 00:11:45,759 tracking usage is the way to go, but ultimately people don't 244 00:11:45,759 - > 00:11:48,159 really know what to do, what the best practices are. 245 00:11:48,399 - > 00:11:52,399 And again, that fulfills more of the singular task aspect of 246 00:11:52,399 - > 00:11:55,840 create a piece of content for me or tell me about this 247 00:11:55,840 - > 00:12:00,399 spreadsheet that's not really changing, you know, the uh or 248 00:12:00,399 - > 00:12:04,080 moving the needle for your company to be operationally more 249 00:12:04,080 - > 00:12:04,559 efficient. 250 00:12:04,720 - > 00:12:08,639 And then finally, I think people think that just integration is 251 00:12:08,639 - > 00:12:10,639 sufficient instead of orchestration. 252 00:12:10,879 - > 00:12:14,799 I mean, in a unified data layer that brings it together with 253 00:12:14,799 - > 00:12:16,000 that singular view. 254 00:12:16,240 - > 00:12:20,480 When you try to just move data around to different systems in 255 00:12:20,480 - > 00:12:24,879 your existing tool or tech stack, you're not really gaining 256 00:12:24,879 - > 00:12:27,120 the opportunity for that singular view where you can 257 00:12:27,120 - > 00:12:28,000 eliminate the friction. 258 00:12:28,159 - > 00:12:31,200 The friction is still there about people not understanding 259 00:12:31,200 - > 00:12:35,840 really the numbers correctly or in a very, you know, unified 260 00:12:35,840 - > 00:12:36,000 way. 261 00:12:36,480 - > 00:12:38,159 Use that term over and over again. 262 00:12:38,480 - > 00:12:43,039 And so I'll culminate or I'll summarize it with this key 263 00:12:43,039 - > 00:12:43,759 takeaway. 264 00:12:44,080 - > 00:12:48,159 The moment you have a unified view and you can think about AI 265 00:12:48,159 - > 00:12:54,080 coworkers as fulfilling that unified view, let alone doing 266 00:12:54,080 - > 00:12:59,200 the work for you as you remain in control of the final output, 267 00:12:59,519 - > 00:13:04,639 you can finally actualize some material benefits and show that 268 00:13:04,639 - > 00:13:06,159 AI makes a big difference. 269 00:13:06,399 - > 00:13:07,039 SPEAKER_01: Yeah. 270 00:13:07,360 - > 00:13:10,159 You know, this is it, what you said, the difference between 271 00:13:10,159 - > 00:13:12,000 integrating and orchestrating. 272 00:13:12,159 - > 00:13:13,360 We work with a lot of clients. 273 00:13:13,519 - > 00:13:15,679 We go in, they're excited about using AI. 274 00:13:15,759 - > 00:13:18,240 Maybe some of them have, you know, started experimenting on 275 00:13:18,240 - > 00:13:21,360 their own, but there's usually not some orchestration 276 00:13:21,360 - > 00:13:22,399 occurring, right? 277 00:13:22,639 - > 00:13:26,960 They're seeing it at, they're evaluating it through this lens 278 00:13:26,960 - > 00:13:27,919 of the task level. 279 00:13:28,080 - > 00:13:30,080 Oh, there's these things that I could be using AI for. 280 00:13:30,159 - > 00:13:32,559 Oh, there's these things I could be using AI for. 281 00:13:32,879 - > 00:13:37,840 What does what is required for those who are going to be 282 00:13:37,840 - > 00:13:40,320 leading this or kind of driving the message internally about 283 00:13:40,320 - > 00:13:41,679 what AI means? 284 00:13:42,159 - > 00:13:47,600 How do they shift their thinking from that task level integration 285 00:13:47,919 - > 00:13:50,960 to workflow level orchestration? 286 00:13:52,159 - > 00:13:56,639 SPEAKER_02: Yeah, usually it starts with putting an ROI, uh, 287 00:13:56,720 - > 00:13:58,559 you know, calculating ROI. 288 00:13:58,720 - > 00:14:01,840 What's the difference in having people automate a single 289 00:14:02,000 - > 00:14:07,120 singular task versus the uh the orchestration of data to fulfill 290 00:14:07,120 - > 00:14:10,240 a business process that typically crosses not just 291 00:14:10,240 - > 00:14:14,480 systems, but the groups that own those systems or functionally 292 00:14:14,639 - > 00:14:16,000 represent those systems? 293 00:14:16,399 - > 00:14:20,320 When you when you finally get an organization that's crossing 294 00:14:21,200 - > 00:14:25,279 organization, you know, their subgroup barriers, that's when 295 00:14:25,279 - > 00:14:30,720 true you really can unlock the the extended benefits, sales and 296 00:14:30,720 - > 00:14:34,480 marketing across inclusive of service for customer service. 297 00:14:35,039 - > 00:14:38,240 You know, the aspects of manufacturing inventory planning 298 00:14:38,240 - > 00:14:41,440 through manufacturing through the fulfillment process, you 299 00:14:41,440 - > 00:14:43,039 know, in the warehousing space. 300 00:14:43,279 - > 00:14:46,080 There are so many examples where you want to have that through 301 00:14:46,159 - > 00:14:49,919 line where you have visibility up and downstream of you, and 302 00:14:49,919 - > 00:14:54,240 the workflows themselves are the connective tissue if the data is 303 00:14:54,240 - > 00:14:55,600 actually unified. 304 00:14:55,919 - > 00:14:59,519 So, you know, that's all ROI driven. 305 00:14:59,600 - > 00:15:03,600 You can find very quickly the opportunity to reduce the cost 306 00:15:03,600 - > 00:15:06,000 of the actual labor and execution. 307 00:15:06,639 - > 00:15:10,159 But even more importantly, over time, yeah, you realize that if 308 00:15:10,159 - > 00:15:14,320 you build it correctly and AI comes to you where you work, 309 00:15:14,480 - > 00:15:17,200 instead of you having to figure out how to work with AI. 310 00:15:17,360 - > 00:15:21,120 And what I mean by that is single pane of glass, you have 311 00:15:21,120 - > 00:15:25,279 access to all the information across those systems and 312 00:15:25,279 - > 00:15:25,919 perform. 313 00:15:26,399 - > 00:15:32,480 Um actually get uh focus with what you need to focus on for 314 00:15:32,480 - > 00:15:36,000 the week, for today, before noon, or even after. 315 00:15:36,480 - > 00:15:39,600 Like a chief of staff telling you here's the key, here are the 316 00:15:39,600 - > 00:15:42,240 key things you need to work on, say, given tasks within the 317 00:15:42,240 - > 00:15:46,399 workflow, or you know, clients you need to follow up with for 318 00:15:46,399 - > 00:15:51,279 whatever the comp reasons across compliance, service, or revenue 319 00:15:51,279 - > 00:15:56,000 growth, then you can you can enable that great unlock. 320 00:15:56,480 - > 00:16:01,840 SPEAKER_01: So I just listened to um uh uh an episode of a 321 00:16:01,840 - > 00:16:03,679 podcast by a guy named Nate B. 322 00:16:03,840 - > 00:16:04,240 Jones. 323 00:16:04,399 - > 00:16:06,240 If those of you who are listening don't know Nate B. 324 00:16:06,320 - > 00:16:08,639 Jones, go to YouTube, I think you'll love his stuff if you 325 00:16:08,639 - > 00:16:09,519 like our podcast. 326 00:16:09,840 - > 00:16:13,679 But he talks about this concept of this proactive agent, right? 327 00:16:14,159 - > 00:16:18,720 Not um and it sounds like that's what that's what we're we're 328 00:16:18,720 - > 00:16:21,679 going towards uh with this uh concept that you just 329 00:16:21,679 - > 00:16:22,320 introduced. 330 00:16:22,799 - > 00:16:28,240 So for those listening who may not like they maybe they get the 331 00:16:28,240 - > 00:16:31,039 the concept or at least they understand the words 332 00:16:31,360 - > 00:16:35,759 orchestrating fragmented systems, what does that look 333 00:16:35,759 - > 00:16:38,080 like in plain English? 334 00:16:40,399 - > 00:16:43,840 SPEAKER_02: Well, I think what it it means in plain English is 335 00:16:44,320 - > 00:16:47,519 if it's been built correctly in the back end, I get a little 336 00:16:47,519 - > 00:16:51,360 more into that in a moment, then from a front end, from a usage 337 00:16:51,360 - > 00:16:54,960 perspective, you no longer are worried about where information 338 00:16:54,960 - > 00:16:55,919 is sourced from. 339 00:16:56,159 - > 00:16:58,879 You know that it's been built, unified, standardized. 340 00:16:59,039 - > 00:17:02,480 And I feel like in some ways I keep echoing what I've said 341 00:17:02,480 - > 00:17:06,319 earlier, but that's the key aspect of it, where you can just 342 00:17:06,319 - > 00:17:11,119 actually start your day, you get a focused chief of staff, we 343 00:17:11,119 - > 00:17:14,559 call it that, or the concierge, which says this is what you need 344 00:17:14,559 - > 00:17:18,400 to focus on, or they are there to assist on any request that 345 00:17:18,400 - > 00:17:22,720 you give, single pane of glass, or find your you'll find it in 346 00:17:22,720 - > 00:17:29,599 Teams, Slack, email, chat, uh, team or text, however you want, 347 00:17:30,640 - > 00:17:33,279 they're there to serve you the way you like to work. 348 00:17:33,680 - > 00:17:37,920 You want to chase someone down the hall like a human being, you 349 00:17:37,920 - > 00:17:42,000 actually can actually if you have uh subatomic on your phone, 350 00:17:42,240 - > 00:17:44,559 they're with you as you're running down the hall anyway. 351 00:17:44,640 - > 00:17:47,359 So you can interact with your AI coworker. 352 00:17:47,680 - > 00:17:49,279 Again, interesting. 353 00:17:49,519 - > 00:17:53,119 I mean, when people think of it as software, they think about it 354 00:17:53,119 - > 00:17:55,759 as having to go into a different application. 355 00:17:56,079 - > 00:17:59,440 When they think about it as an AI coworker, they realize 356 00:17:59,440 - > 00:18:01,920 they'll talk and communicate through all the existing 357 00:18:01,920 - > 00:18:04,480 communication channels they use today. 358 00:18:04,720 - > 00:18:08,880 That's a big difference in driving adoption and ease of 359 00:18:08,880 - > 00:18:09,200 use. 360 00:18:09,599 - > 00:18:12,319 And again, you hide all that complexity. 361 00:18:12,480 - > 00:18:17,200 Now, the back end, yeah, you want to actually land all your 362 00:18:17,200 - > 00:18:20,720 data raw from all those systems and then start the joining 363 00:18:20,720 - > 00:18:24,400 process, the cleansing process into a nice data lake that is 364 00:18:24,400 - > 00:18:28,880 that standard cleansed, deduped version of your data, and then 365 00:18:29,440 - > 00:18:31,039 create a dimensional model. 366 00:18:31,200 - > 00:18:35,279 Some people refer to it as the star schema data warehouse for 367 00:18:35,279 - > 00:18:39,839 the gold copy, which makes insight retrieval fast and the 368 00:18:40,000 - > 00:18:43,599 operational, even for operational retrieval purposes 369 00:18:43,759 - > 00:18:47,519 for your workflow instantaneous so that you can actually operate 370 00:18:47,519 - > 00:18:48,640 without friction. 371 00:18:48,799 - > 00:18:51,200 Again, you don't see it as a user. 372 00:18:51,359 - > 00:18:54,319 All that's been built for you, so it's ready to go. 373 00:18:56,319 - > 00:19:00,880 SPEAKER_01: So, you know, when I think about um going into a 374 00:19:00,880 - > 00:19:02,960 company, and if they're the first thing they want to talk 375 00:19:02,960 - > 00:19:06,960 about is like like organizing their data, to me, that sounds 376 00:19:06,960 - > 00:19:10,720 like a really boring and heavy lift. 377 00:19:12,160 - > 00:19:16,079 What is required of a company that maybe they're using AI 378 00:19:16,079 - > 00:19:19,279 already, maybe it's fragmented, maybe it's not fragmented, but 379 00:19:19,440 - > 00:19:22,640 the they're still thinking about it at the task level and not 380 00:19:22,640 - > 00:19:23,839 necessarily the workflow level. 381 00:19:23,920 - > 00:19:26,799 They're not thinking about it through this cross-system 382 00:19:27,119 - > 00:19:29,920 context that that you're referencing here. 383 00:19:30,319 - > 00:19:38,480 What is the approach to starting with systemizing, streamlining, 384 00:19:38,720 - > 00:19:42,079 whatever the the orchestration is of these fragmented systems? 385 00:19:42,799 - > 00:19:43,279 SPEAKER_02: Yeah. 386 00:19:43,599 - > 00:19:47,200 The first thing to do is to stop buying another system to add to 387 00:19:47,200 - > 00:19:51,200 your frag stack because ultimately those tools, those 388 00:19:51,200 - > 00:19:55,279 fragmented systems, are likely to start disappearing one by 389 00:19:55,279 - > 00:19:58,559 one, or at least you're going to reduce the licenses needed to go 390 00:19:58,559 - > 00:20:00,480 directly into those tools. 391 00:20:00,799 - > 00:20:06,240 But then the next step is sitting down with Subatomic and 392 00:20:06,240 - > 00:20:07,759 doing a two-week discovery. 393 00:20:08,079 - > 00:20:12,319 And we'll talk about those workflows that are costly today, 394 00:20:12,400 - > 00:20:15,359 and you describe exactly the way you do it today. 395 00:20:15,599 - > 00:20:18,319 Now, if you have great documentation on that already, 396 00:20:18,559 - > 00:20:19,440 fantastic. 397 00:20:19,680 - > 00:20:22,960 But if you need it documented, the great thing about working 398 00:20:22,960 - > 00:20:26,880 with Subatomic is we'll auto-generate your as-is process 399 00:20:26,880 - > 00:20:30,319 today and then compare it to what it can be tomorrow. 400 00:20:30,640 - > 00:20:35,440 And that means that as long as we can get that written down, 401 00:20:35,839 - > 00:20:39,599 and that will be consumed by our AI coworkers who do all the work 402 00:20:39,599 - > 00:20:43,680 behind the scenes to engineer and deliver solutions to you. 403 00:20:44,160 - > 00:20:48,559 And we identified where the data comes to fulfill those workflows 404 00:20:48,559 - > 00:20:52,400 already, and think about that in terms of the back-end unified 405 00:20:52,400 - > 00:20:53,920 data layer engineering. 406 00:20:54,160 - > 00:20:56,559 Nothing is really stopping you from moving forward today. 407 00:20:56,640 - > 00:20:59,759 You just have to identify which workflows to begin first. 408 00:20:59,920 - > 00:21:04,319 And again, stack ranked in terms of the ROI for you. 409 00:21:04,799 - > 00:21:06,559 SPEAKER_01: Yeah, that's a great place to start. 410 00:21:06,799 - > 00:21:12,480 So, you know, one of the I guess pushbacks we get, as interested 411 00:21:12,480 - > 00:21:15,759 as a company is, as excited as they are, one of their questions 412 00:21:15,759 - > 00:21:20,160 is well, how much time is this gonna take from my day? 413 00:21:20,319 - > 00:21:21,359 I'm already busy, right? 414 00:21:21,440 - > 00:21:25,039 And I think you and I, Carl, we know that look, if you do the 415 00:21:25,039 - > 00:21:27,200 thing, you'll open up bandwidth. 416 00:21:27,519 - > 00:21:29,440 Just, but you gotta, you know, there's gonna be a little pain 417 00:21:29,440 - > 00:21:29,759 initially. 418 00:21:29,839 - > 00:21:32,720 Now you mentioned this in your case with Subatomic coming on 419 00:21:32,720 - > 00:21:35,759 site or working with a team, there's a two-week window. 420 00:21:35,839 - > 00:21:37,599 What happens in that two weeks? 421 00:21:38,240 - > 00:21:41,039 SPEAKER_02: It's just uh interviewing everyone to get a 422 00:21:41,039 - > 00:21:44,000 better sense of how the workflows operate today, what 423 00:21:44,000 - > 00:21:48,160 data they rely on to fulfill the data workflows. 424 00:21:48,400 - > 00:21:54,000 And then so uh it's not two weeks where every single person 425 00:21:54,000 - > 00:21:57,440 will be in interview mode for eight hours per day. 426 00:21:57,680 - > 00:22:01,279 It's just a two week period where what Get the right 427 00:22:01,920 - > 00:22:06,480 stakeholders and the right performers, the SMEs, you know, 428 00:22:06,559 - > 00:22:09,839 in those organizations that can represent the process, inform 429 00:22:10,160 - > 00:22:13,359 about what's done, give the tribal knowledge, which has 430 00:22:13,359 - > 00:22:16,880 probably never been documented, so we can capture that into the 431 00:22:16,880 - > 00:22:20,640 cognitive engine that will be built for you and your AI 432 00:22:20,640 - > 00:22:22,880 coworkers that execute for you. 433 00:22:23,680 - > 00:22:30,880 So it's on a total per person or per team or per discovery basis. 434 00:22:30,960 - > 00:22:35,599 It depends on the number of use cases we're tackling within that 435 00:22:35,599 - > 00:22:36,559 two-week period. 436 00:22:36,720 - > 00:22:38,480 But it's not a full-time. 437 00:22:38,559 - > 00:22:42,960 I mean, maybe you'll spend 20% of your week working with us in 438 00:22:42,960 - > 00:22:44,160 that discovery period. 439 00:22:44,480 - > 00:22:49,920 Ongoing post-discovery will be in an iterative process with you 440 00:22:50,000 - > 00:22:50,640 week to week. 441 00:22:50,720 - > 00:22:52,799 And we can meet once or twice per week. 442 00:22:52,880 - > 00:22:58,720 It would probably be an hour max per meeting, maybe 30 minutes, 443 00:22:58,960 - > 00:23:01,279 for the right people who are going to be operationally 444 00:23:01,279 - > 00:23:05,920 keeping track of where we are on delivering, uh, and to provide 445 00:23:05,920 - > 00:23:08,799 the feedback that we need to incorporate through each 446 00:23:08,799 - > 00:23:09,519 iteration. 447 00:23:09,759 - > 00:23:12,400 Because ultimately, we build it fast. 448 00:23:12,640 - > 00:23:18,880 Now, the data loading and unification, um, all that logic 449 00:23:19,039 - > 00:23:22,319 is spun up fast, but the load itself of data could take time, 450 00:23:22,400 - > 00:23:24,799 depending upon the amount of history we're talking about. 451 00:23:25,119 - > 00:23:29,359 But from week one after discovery, you already start 452 00:23:29,359 - > 00:23:31,200 seeing the solution with subatomic. 453 00:23:31,359 - > 00:23:37,119 Our AI coworkers build out the actual pipelines from all your 454 00:23:37,119 - > 00:23:39,759 source systems into that unified data layer. 455 00:23:39,920 - > 00:23:41,519 They build up the workflow. 456 00:23:41,680 - > 00:23:44,799 We capture and incorporate the cognition that you use for 457 00:23:44,799 - > 00:23:48,000 reasoning and decisioning while having captured already your 458 00:23:48,000 - > 00:23:53,200 SOPs for the 2B state that we're building to replace the current 459 00:23:53,200 - > 00:23:56,000 friction-filled existing processes. 460 00:23:56,640 - > 00:23:59,839 SPEAKER_01: So a company that's going to approach this, I think 461 00:23:59,839 - > 00:24:03,519 the takeaway is that it's not, you don't flip a switch. 462 00:24:03,599 - > 00:24:05,039 There is some work required. 463 00:24:05,200 - > 00:24:09,039 There is going to be uh participation from your subject 464 00:24:09,039 - > 00:24:11,599 matter experts, from the domain experts within your 465 00:24:11,599 - > 00:24:13,759 organization, if you want to do this right. 466 00:24:14,000 - > 00:24:17,759 If you don't want to invest that time, the best you can get is 467 00:24:18,000 - > 00:24:20,960 this drive-thru window of getting people Chat GPT and 468 00:24:20,960 - > 00:24:23,200 saying, you can go and ask a question, get an answer, and 469 00:24:23,200 - > 00:24:24,240 then bye-bye. 470 00:24:24,480 - > 00:24:27,039 That is about as far as you're going to be able to get with AI, 471 00:24:27,119 - > 00:24:30,079 unless you do something like what Carl's talking about, which 472 00:24:30,079 - > 00:24:34,880 is this orchestration, this intelligent design being put 473 00:24:34,880 - > 00:24:37,440 into these efforts, not just the next tool. 474 00:24:38,480 - > 00:24:40,400 And you mentioned that Franken stack. 475 00:24:40,640 - > 00:24:40,799 SPEAKER_02: Yeah. 476 00:24:41,119 - > 00:24:43,200 Fundamentally, that's no different than the way it's 477 00:24:43,200 - > 00:24:43,839 always been. 478 00:24:44,240 - > 00:24:49,200 Except now AI auto-generates the delivery of the solution and 479 00:24:49,200 - > 00:24:50,720 helps you iterate fast. 480 00:24:51,599 - > 00:24:55,519 So the opportunity to seize those material benefits actually 481 00:24:55,519 - > 00:24:56,720 compresses in terms of time. 482 00:24:57,519 - > 00:24:57,759 SPEAKER_01: Yeah. 483 00:24:58,000 - > 00:25:00,319 But you know, I think I think that there's like, especially 484 00:25:00,319 - > 00:25:03,119 for people who aren't like in the AI industry, they're running 485 00:25:03,119 - > 00:25:07,599 their business and AI is um there's a little bit of FOMO and 486 00:25:07,599 - > 00:25:09,200 they want, you know, they know they need to do something about 487 00:25:09,200 - > 00:25:09,279 it. 488 00:25:09,359 - > 00:25:11,279 Maybe they're using it and getting some wins. 489 00:25:11,519 - > 00:25:15,839 There's this, you know, uh false narrative of, oh, AI will do it 490 00:25:15,839 - > 00:25:16,079 for me. 491 00:25:16,240 - > 00:25:21,839 Oh, AI is gonna, but the reality is until the the co-pilots, the 492 00:25:21,920 - > 00:25:25,279 the agents that are working with you, the coworkers, take over, 493 00:25:25,359 - > 00:25:27,279 the human still has to drive this stuff. 494 00:25:27,359 - > 00:25:31,039 So I want to make sure that anybody listening, like, that's 495 00:25:31,039 - > 00:25:32,480 just how the sausage gets made. 496 00:25:32,559 - > 00:25:35,519 Like you're gonna, if you want to do it right, you're going to 497 00:25:35,519 - > 00:25:39,279 have to participate with the initial heavy lift. 498 00:25:39,359 - > 00:25:43,279 But once that's done, if you've if you've done it right, you get 499 00:25:43,279 - > 00:25:44,640 benefit immediately. 500 00:25:44,799 - > 00:25:48,000 If you don't do it and you delay it and you want to continue to 501 00:25:48,000 - > 00:25:51,279 get the new tool or you know, try to figure out something and 502 00:25:51,279 - > 00:25:55,039 still have your AI processes primarily still human in the 503 00:25:55,039 - > 00:25:57,119 loop, you're never going to get past that. 504 00:25:57,200 - > 00:26:01,039 You're never gonna so uh uh my advice, and I think Carl would 505 00:26:01,039 - > 00:26:04,799 probably bite the bullet, invest those two weeks, the week, 506 00:26:04,880 - > 00:26:10,240 whatever it is for your company to uh to do the heavy lift so 507 00:26:10,240 - > 00:26:11,359 that you can get the benefit. 508 00:26:11,680 - > 00:26:12,319 SPEAKER_02: 100%. 509 00:26:13,599 - > 00:26:16,720 SPEAKER_01: Um, you know, I I know that you're not pitching 510 00:26:16,720 - > 00:26:17,680 speed at any cost. 511 00:26:17,759 - > 00:26:20,319 That's why there's this kind of two-week environment. 512 00:26:20,400 - > 00:26:23,839 And one of the things that you also reference, which I like a 513 00:26:23,839 - > 00:26:27,039 lot, we tell clients, you know, eat the elephant one bite at a 514 00:26:27,039 - > 00:26:27,599 time, right? 515 00:26:27,839 - > 00:26:31,920 You're not starting initially with doing this across the 516 00:26:31,920 - > 00:26:32,799 entire business. 517 00:26:33,039 - > 00:26:38,319 We're targeting the ROI, the likely ROI environments where if 518 00:26:38,319 - > 00:26:41,440 we can speed this up, if we can compress the sales cycle, if we 519 00:26:41,440 - > 00:26:46,000 can um have more communications with prospects and clients, then 520 00:26:46,000 - > 00:26:47,680 we'll generate more revenue. 521 00:26:47,839 - > 00:26:51,599 But outside of those, where are some of the places where people 522 00:26:51,599 - > 00:26:55,519 should expect to see um the initial efforts be deployed? 523 00:26:55,680 - > 00:26:58,000 What departments, what what activities? 524 00:26:58,799 - > 00:27:03,440 SPEAKER_02: Well, you know, we fulfill for any domain, any 525 00:27:03,440 - > 00:27:09,039 industry, any use case, but a lot of our initial traction has 526 00:27:09,039 - > 00:27:11,359 happened in the wealth management RAA space. 527 00:27:11,680 - > 00:27:16,640 And so what we look to do to help them prepare for their 528 00:27:16,640 - > 00:27:19,440 clients where they have many offerings, for example, 529 00:27:19,680 - > 00:27:22,720 financial management, tax management, estate planning, 530 00:27:23,039 - > 00:27:26,960 retirement planning, and so forth, you know, there's a lot 531 00:27:26,960 - > 00:27:29,680 of work that goes into preparing for quarterly meetings with 532 00:27:29,680 - > 00:27:33,039 their clients or understanding which clients actually should 533 00:27:33,039 - > 00:27:36,880 have that frequency for a direct advisor client relationship, or 534 00:27:36,880 - > 00:27:40,720 otherwise be placed into a service center for more frequent 535 00:27:40,720 - > 00:27:44,160 touches, but not necessarily, you know, in-person meeting to 536 00:27:44,240 - > 00:27:44,720 meeting. 537 00:27:44,880 - > 00:27:48,960 But the collective amount of information that is required to 538 00:27:48,960 - > 00:27:52,640 make sure that it's aligned with what they've done in the past, 539 00:27:52,880 - > 00:27:57,039 what their goals are, you know, what uh the latest actions were 540 00:27:57,039 - > 00:28:00,000 from the prior meetings, and a collect and a better 541 00:28:00,000 - > 00:28:03,680 understanding of the macro and and uh microeconomics and how it 542 00:28:03,680 - > 00:28:07,680 affects their specific portfolios, it takes at least 543 00:28:07,680 - > 00:28:09,440 four hours of prep per client. 544 00:28:09,599 - > 00:28:12,559 You know, that we've, you know, that's been our experience with 545 00:28:12,559 - > 00:28:13,279 our clients. 546 00:28:13,519 - > 00:28:15,920 And so there's an enormous number of hours that could be 547 00:28:15,920 - > 00:28:19,440 saved there, which means you can move towards growing your book 548 00:28:19,440 - > 00:28:22,319 instead of just preparing for the next meeting. 549 00:28:22,640 - > 00:28:26,720 So whether it's that or account opening, you know, which crosses 550 00:28:26,720 - > 00:28:27,839 many systems. 551 00:28:28,000 - > 00:28:31,119 I I think a great way to think about where the opportunities 552 00:28:31,119 - > 00:28:34,720 are, and I'm I know I'm saying this in more of a general state 553 00:28:34,960 - > 00:28:40,480 statement, is when you have five to ten or even more systems out 554 00:28:40,480 - > 00:28:44,240 there where you have to touch these different systems today to 555 00:28:44,240 - > 00:28:48,559 do the work, you know you have an opportunity to unify your 556 00:28:48,559 - > 00:28:53,039 view of the data and get that true orchestrated firm going. 557 00:28:53,279 - > 00:28:55,440 Those are likely going to be the opportunities. 558 00:28:55,519 - > 00:28:59,759 But we see it across, again, the uh the operational prep. 559 00:28:59,839 - > 00:29:04,160 And I gave the example in the RIA space, but there's plenty of 560 00:29:04,160 - > 00:29:06,720 opportunity to streamline sales and marketing. 561 00:29:06,880 - > 00:29:11,200 I think today, even with the best SaaS players, it's still a 562 00:29:11,200 - > 00:29:14,880 disconnected experience between sales, marketing, and service, 563 00:29:15,519 - > 00:29:18,400 and there's an opportunity to get that unified as well. 564 00:29:18,640 - > 00:29:23,440 In fact, at Subatomic, we didn't like any CRM that existed out 565 00:29:23,440 - > 00:29:24,880 there, so we built our own. 566 00:29:25,039 - > 00:29:28,880 And we're likely to commercialize that now for the 567 00:29:28,880 - > 00:29:32,640 market to also purchase because there'll be more of that single 568 00:29:32,880 - > 00:29:36,559 view, and you have your chief of growth officer that guides you 569 00:29:36,559 - > 00:29:40,640 and focuses where you need to focus, tell you who to meet 570 00:29:40,640 - > 00:29:43,839 with, not just from your pro leads, prospects, and client 571 00:29:43,839 - > 00:29:46,880 perspective, independent of whether or not it's with a sales 572 00:29:46,880 - > 00:29:49,519 or marketing focus, but internally with your sales and 573 00:29:49,519 - > 00:29:51,440 marketing and service teammates. 574 00:29:51,759 - > 00:29:54,160 Finally, eliminating that friction. 575 00:29:54,480 - > 00:29:54,960 SPEAKER_01: Yeah. 576 00:29:55,200 - > 00:29:58,559 You know, um uh I guess it was about a year ago we were 577 00:29:58,559 - > 00:30:01,759 sponsoring a vistage event in Chicago. 578 00:30:01,839 - > 00:30:05,119 And I had no idea how much wealth management, you know, 579 00:30:05,279 - > 00:30:07,920 businesses or firms were in Chicago. 580 00:30:08,079 - > 00:30:13,599 And we ended up talking to um uh a multinational bank that was 581 00:30:13,599 - > 00:30:16,799 there uh looking to grow their assets under management. 582 00:30:16,960 - > 00:30:20,400 And one of the things that we do for clients is we say, okay, 583 00:30:20,480 - > 00:30:22,720 we're gonna we call it a McKinsey, a mini McKinsey. 584 00:30:22,799 - > 00:30:24,799 We kind of run in a McKinsey style analysis on their 585 00:30:25,119 - > 00:30:30,240 business, particularly, their industry, to look for some like 586 00:30:30,240 - > 00:30:34,640 low-hanging fruit across primarily sales, general, and 587 00:30:34,640 - > 00:30:35,759 administrative activities. 588 00:30:35,839 - > 00:30:36,960 That's GNA. 589 00:30:37,279 - > 00:30:40,079 And, you know, we had we had recently been doing a lot with 590 00:30:40,079 - > 00:30:41,440 construction companies and that sort of thing. 591 00:30:41,519 - > 00:30:45,039 And we could move the needle uh on primarily looking at like 592 00:30:45,119 - > 00:30:47,200 what's the impact going to be on EBITDA, right? 593 00:30:47,359 - > 00:30:50,079 And for a construction company on the high end, it was like 594 00:30:50,079 - > 00:30:52,640 maybe 18 to 20% lift on EBITDA. 595 00:30:52,880 - > 00:30:53,599 Pretty good. 596 00:30:53,839 - > 00:30:56,640 But the more average was maybe 8 to 12%. 597 00:30:57,519 - > 00:31:00,000 Still better than a sharp stick in the eye. 598 00:31:00,240 - > 00:31:05,839 But when we ran this for wealth management, it was a 35 to like 599 00:31:05,839 - > 00:31:10,960 60% lift on EBITA because, and I had, I mean, it's obvious now, 600 00:31:11,200 - > 00:31:14,480 the bulk of the expenses associated with that industry 601 00:31:14,480 - > 00:31:16,319 are the people and it's their time. 602 00:31:16,400 - > 00:31:18,400 These are highly compensated individuals. 603 00:31:18,559 - > 00:31:21,119 Like 80% of what they spend is the people. 604 00:31:21,279 - > 00:31:24,079 It's not necessarily a technology or you know, uh 605 00:31:24,160 - > 00:31:27,759 equipment or leases, it's the time of these highly compensated 606 00:31:27,759 - > 00:31:28,240 individuals. 607 00:31:28,400 - > 00:31:31,039 And if AI can step in like what you're talking about right 608 00:31:31,039 - > 00:31:35,039 there, like I know for a fact, we saw it, that that the impact 609 00:31:35,039 - > 00:31:38,480 it could have in registered investment advisory or wealth 610 00:31:38,480 - > 00:31:40,880 management, any financial services, huge. 611 00:31:41,039 - > 00:31:44,640 So I think that you guys are are targeting the right thing. 612 00:31:44,799 - > 00:31:50,000 Now, we I don't know, I see a trend really with with what's 613 00:31:50,000 - > 00:31:54,480 preventing companies from being more aggressive about their AI 614 00:31:54,640 - > 00:31:55,759 implementation and deployment. 615 00:31:55,920 - > 00:31:59,200 The first one is love to use it, but I'm not quite sure where. 616 00:31:59,359 - > 00:32:03,440 And it sounds like your two-week uh discovery is the 617 00:32:03,440 - > 00:32:04,720 identification of the where. 618 00:32:05,119 - > 00:32:08,720 The second thing is love to use it, but I don't understand the 619 00:32:08,720 - > 00:32:09,519 risk fully. 620 00:32:09,599 - > 00:32:09,839 Right. 621 00:32:10,000 - > 00:32:12,720 So if I don't understand the risk, especially in a with the 622 00:32:12,799 - > 00:32:16,480 the industries that you're targeting, uh better safe than 623 00:32:16,480 - > 00:32:16,960 sorry, right? 624 00:32:17,119 - > 00:32:20,400 Uh it's easier to say no and not expose ourselves to the risk. 625 00:32:20,640 - > 00:32:24,240 So, what does it mean to actually build AI with 626 00:32:24,559 - > 00:32:28,559 compliance and security as uh, you know, because everybody's 627 00:32:28,720 - > 00:32:32,799 everybody's interested in the the efficiencies and the the ROI 628 00:32:32,799 - > 00:32:35,359 impact and the EBITDA lift, like I talk about. 629 00:32:35,599 - > 00:32:39,119 And it's like almost an afterthought with the compliance 630 00:32:39,119 - > 00:32:40,240 and security side of things. 631 00:32:40,400 - > 00:32:44,240 So what does that look like to build that in from the 632 00:32:44,240 - > 00:32:44,720 beginning? 633 00:32:45,039 - > 00:32:47,440 SPEAKER_02: Yeah, I can tell you what it looks like as subatomic. 634 00:32:47,599 - > 00:32:48,880 I I think you're right, Chris. 635 00:32:49,039 - > 00:32:52,880 A lot of people think about it secondarily, and we treat those 636 00:32:52,880 - > 00:32:56,319 both compliance and security as first-class citizens as 637 00:32:56,319 - > 00:32:57,359 subatomic. 638 00:32:57,680 - > 00:33:03,119 So a lot of companies not named subatomic will say we log or 639 00:33:03,119 - > 00:33:05,440 trace everything, but you don't really have visibility to it. 640 00:33:05,519 - > 00:33:08,559 So, in other words, it's there in the event of discovering 641 00:33:08,559 - > 00:33:12,480 something after the fact, you can actually use that as raw 642 00:33:12,480 - > 00:33:14,480 data and try to pull up some analysis. 643 00:33:14,720 - > 00:33:18,720 They don't give you visibility, they don't give you key 644 00:33:18,720 - > 00:33:24,480 insights, they don't give you proactive and preventative um 645 00:33:24,799 - > 00:33:30,079 correction that would be applied to compliance or full prevention 646 00:33:30,240 - > 00:33:32,160 defensibility when it comes to security. 647 00:33:32,319 - > 00:33:34,559 As subatomic, that's exactly what we do. 648 00:33:34,799 - > 00:33:40,160 Every single request you make is actually traced dynamically and 649 00:33:40,160 - > 00:33:44,480 then auto-generated, give you full visibility to the stepwise 650 00:33:44,799 - > 00:33:49,200 things that happen behind the scenes, including for a given 651 00:33:49,200 - > 00:33:53,200 step the full reasoning trace on what occurred and the decision 652 00:33:53,200 - > 00:33:55,839 he made relative to the different options that could 653 00:33:55,839 - > 00:33:58,480 have been chosen given that scenario. 654 00:33:58,880 - > 00:34:02,400 That level of traceability is not just good for our clients to 655 00:34:02,400 - > 00:34:04,720 give that visibility and get comfortable. 656 00:34:04,880 - > 00:34:08,639 It's important to us at Subatomic and our AI coworkers 657 00:34:08,719 - > 00:34:12,320 that we'll course correct on the fly before it gives you that 658 00:34:12,320 - > 00:34:12,960 final result. 659 00:34:13,679 - > 00:34:18,639 Not to mention at the request level for compliance and 660 00:34:18,639 - > 00:34:22,159 security for that matter, you can see all the security checks 661 00:34:22,159 - > 00:34:24,880 that were performed for that given request. 662 00:34:25,199 - > 00:34:29,760 We track all those individual transactions at an overall 663 00:34:29,760 - > 00:34:34,159 aggregate level, full dashboarding into security and 664 00:34:34,159 - > 00:34:37,599 compliance, dashboards within dashboards, drilling into the 665 00:34:37,599 - > 00:34:41,440 actual data and the executions so that you can actually see 666 00:34:41,599 - > 00:34:44,719 everything that happened within a single pane. 667 00:34:45,039 - > 00:34:49,360 And our insights analyzer, which is another AI subatomic coworker 668 00:34:49,360 - > 00:34:52,880 that comes a part of your foundational core uh core 669 00:34:53,039 - > 00:34:57,119 subscription, will identify opportunities to course correct 670 00:34:57,360 - > 00:35:01,920 and flag, you know, things that require fulfillment in terms of 671 00:35:01,920 - > 00:35:05,280 compliance or you know added security. 672 00:35:06,079 - > 00:35:07,920 SPEAKER_01: I think it's a very sound approach here. 673 00:35:08,079 - > 00:35:10,960 Now, you've you've mentioned the concept of AI coworkers a lot, 674 00:35:11,039 - > 00:35:13,679 and anybody listening to this has heard the term agents and 675 00:35:13,679 - > 00:35:15,840 agentic AI and those sorts of things. 676 00:35:16,079 - > 00:35:18,960 But what do you mean by an AI coworker? 677 00:35:19,119 - > 00:35:21,920 What does that mean in subatomics lexicon? 678 00:35:22,320 - > 00:35:22,800 SPEAKER_02: Yeah. 679 00:35:23,119 - > 00:35:30,639 Why you can think of agents and AI coworkers as synonymous. 680 00:35:30,719 - > 00:35:30,800 Okay. 681 00:35:31,119 - > 00:35:34,400 And a set of AI coworkers making a full agenc team for the 682 00:35:34,400 - > 00:35:36,000 agentic workflow. 683 00:35:36,559 - > 00:35:42,639 But we really distinguish our AI coworkers because they come, you 684 00:35:42,639 - > 00:35:46,000 know, first of all, immersed in your SOPs and your reasoning, 685 00:35:46,159 - > 00:35:50,320 your cognition on how you like to make decisions and perform 686 00:35:50,320 - > 00:35:51,519 and differentiate. 687 00:35:51,760 - > 00:35:55,599 But secondarily, because they've been trained on how to apply and 688 00:35:55,599 - > 00:35:58,400 perform with compliance and security in mind. 689 00:35:58,800 - > 00:36:02,880 They are, you know, subatomic AI coworkers are very different 690 00:36:02,880 - > 00:36:05,760 than your typical agent off the shelf that you can get somewhere 691 00:36:05,760 - > 00:36:06,159 else. 692 00:36:06,480 - > 00:36:10,000 And for those trying to build it themselves, that's awesome. 693 00:36:10,159 - > 00:36:13,360 Uh, but to get it with the compliance and security 694 00:36:13,360 - > 00:36:16,880 foundation ingrained, that's a tough thing to do. 695 00:36:18,000 - > 00:36:19,280 SPEAKER_01: Um, I can concur. 696 00:36:19,440 - > 00:36:22,000 Anyone who's tried to build an open claw and have it break 697 00:36:22,000 - > 00:36:23,920 every five minutes understands this. 698 00:36:24,159 - > 00:36:28,400 Um so I I I like the language of an AI coworker. 699 00:36:28,559 - > 00:36:33,039 An agent sounds very like I picture that that robot, you 700 00:36:33,039 - > 00:36:35,360 know, uh emoji, right? 701 00:36:36,000 - > 00:36:39,679 But an AI coworker, oh, that you know, that sounds a lot less 702 00:36:39,679 - > 00:36:40,480 threatening. 703 00:36:40,719 - > 00:36:45,840 Um, but how should somebody who's managing uh teams that 704 00:36:45,840 - > 00:36:50,239 include AI coworkers, how how should they think differently 705 00:36:50,239 - > 00:36:54,480 about their role when they've got you know AI coworkers 706 00:36:54,639 - > 00:36:57,760 instead of you know 50 butts and seats? 707 00:36:58,159 - > 00:36:58,559 SPEAKER_02: Right. 708 00:36:58,800 - > 00:37:02,239 They should think of themselves as now at a managerial level. 709 00:37:02,480 - > 00:37:06,719 I mean, you let's roll back the tape to the beginning of the 710 00:37:06,719 - > 00:37:10,719 discussion, Chris, where you highlighted uh Dorsey's, you 711 00:37:10,719 - > 00:37:15,039 know, reasoning of uh why AI actually reduced employees, 712 00:37:15,119 - > 00:37:15,360 right? 713 00:37:15,519 - > 00:37:18,559 All those management layers disappear because you didn't 714 00:37:18,559 - > 00:37:20,800 need all that friction happening. 715 00:37:21,039 - > 00:37:24,719 But the thing is, and the reality is, it's not just giving 716 00:37:24,719 - > 00:37:28,800 information to everyone in a unified singular state that 717 00:37:28,800 - > 00:37:30,960 everyone sees that is a benefit. 718 00:37:31,119 - > 00:37:35,760 It's actually getting all the mundane work done for you. 719 00:37:35,840 - > 00:37:38,960 But now you, as an individual contributor of the past, need to 720 00:37:38,960 - > 00:37:39,599 be the manager. 721 00:37:39,760 - > 00:37:42,960 You need to be the domain expert that knows whether or not your 722 00:37:42,960 - > 00:37:47,039 AI coworkers are performing correctly because you are you 723 00:37:47,039 - > 00:37:48,559 must be the human in the loop. 724 00:37:48,719 - > 00:37:52,320 You must provide feedback so your AI coworkers learn along 725 00:37:52,320 - > 00:37:52,960 the way. 726 00:37:53,199 - > 00:37:57,199 And so encoding, I mentioned this earlier, Chris. 727 00:37:57,440 - > 00:38:00,559 We have AI coworkers engineering everything. 728 00:38:00,800 - > 00:38:04,480 Now, we have engineers on our team, but I look to them as the 729 00:38:04,480 - > 00:38:07,840 key architects and the key managers of the AI coworker 730 00:38:07,840 - > 00:38:11,760 personnel that actually generate the solutions for them. 731 00:38:12,000 - > 00:38:15,280 They need to make sure that they understand the design patterns 732 00:38:15,280 - > 00:38:18,800 that should be employed because it's best for this particular 733 00:38:18,800 - > 00:38:22,480 unique uh solution and the architecture that's inherently a 734 00:38:22,480 - > 00:38:23,679 part of the end-to-end. 735 00:38:24,320 - > 00:38:30,559 So you re level up your performance using all the things 736 00:38:30,559 - > 00:38:33,599 you've understood in the past and done yourself, but now 737 00:38:33,599 - > 00:38:37,360 you're managing AI coworkers to do it for you so you can get 738 00:38:37,360 - > 00:38:38,559 everything done faster. 739 00:38:38,960 - > 00:38:39,280 SPEAKER_01: Okay. 740 00:38:39,519 - > 00:38:45,360 Well, then in that case, as the human, what skills are becoming 741 00:38:45,360 - > 00:38:49,840 more valuable when I'm managing AI assisted workflows, AI 742 00:38:49,840 - > 00:38:50,639 co-workers? 743 00:38:51,119 - > 00:38:54,960 SPEAKER_02: So being able to understand and recognize when an 744 00:38:54,960 - > 00:38:58,480 AI delivery or output actually matches your expectations. 745 00:38:58,639 - > 00:39:02,480 What would you have looked for in your output? 746 00:39:02,719 - > 00:39:05,679 And it should be really aligned again at the group level or 747 00:39:05,679 - > 00:39:06,480 corporate vision. 748 00:39:06,559 - > 00:39:10,079 So there are standards within any organization that filter 749 00:39:10,079 - > 00:39:15,440 down of what's expected from quality accuracy, um, you know, 750 00:39:15,519 - > 00:39:18,079 the compliance that's required, everything. 751 00:39:18,400 - > 00:39:23,519 You need to be really thoughtful now of always be the verifier 752 00:39:23,519 - > 00:39:29,920 now of how your AI coworkers are working and you know, actually 753 00:39:30,159 - > 00:39:33,519 the coach, you know, with the feedback you provide, not just 754 00:39:33,519 - > 00:39:37,280 for course correction, but to improve the way you like to have 755 00:39:37,280 - > 00:39:41,920 the outputs done because you know your human manager, your 756 00:39:41,920 - > 00:39:47,599 direct, you know, direct manager actually prefers the information 757 00:39:47,679 - > 00:39:50,400 provided and presented in a very specific way. 758 00:39:50,960 - > 00:39:55,119 SPEAKER_01: How has it been for the individual? 759 00:39:55,199 - > 00:39:57,679 Some atomic comes down, they're introducing this stuff. 760 00:39:57,840 - > 00:40:01,840 How has it, has it been an easy transition for the humans to be 761 00:40:01,840 - > 00:40:07,519 able to move into that role of the the coach for the the AI 762 00:40:07,519 - > 00:40:08,400 coworkers? 763 00:40:09,119 - > 00:40:12,880 SPEAKER_02: I, you know, it never nothing is ever 100% of 764 00:40:12,880 - > 00:40:17,599 the time perfectly easy, but most of them have, again, for a 765 00:40:17,599 - > 00:40:18,320 couple of reasons. 766 00:40:18,400 - > 00:40:22,239 Number one, they're not changing the way they work necessarily in 767 00:40:22,239 - > 00:40:26,800 terms of working in teams, Slack, email, or a single pane 768 00:40:26,800 - > 00:40:28,880 of glass, it simplifies their world. 769 00:40:29,119 - > 00:40:33,599 And when that last piece of that last consideration, where 770 00:40:33,679 - > 00:40:36,960 whether it's in teams or whether it's within the subatomic single 771 00:40:36,960 - > 00:40:42,400 pane of glass, again, their time gets reallocated to actually 772 00:40:42,400 - > 00:40:45,360 just coaching up their AI coworkers, which is not a big 773 00:40:45,360 - > 00:40:46,639 effort, by the way. 774 00:40:46,880 - > 00:40:50,400 I mean, if they weren't already applying best practices in the 775 00:40:50,400 - > 00:40:53,840 role, they were unlikely producing good outputs, which 776 00:40:53,840 - > 00:40:57,280 means they have to be taught what the organization overall 777 00:40:57,280 - > 00:40:57,760 wants. 778 00:40:58,079 - > 00:41:02,639 But as long as they've always applied it, then it should be 779 00:41:02,639 - > 00:41:06,079 automatic to be able to make those same checks you would do 780 00:41:06,079 - > 00:41:11,360 on your own work, but now done on AI coworkers by AI coworkers. 781 00:41:11,760 - > 00:41:13,679 Technically, it's still your work. 782 00:41:13,840 - > 00:41:14,159 Yeah. 783 00:41:14,320 - > 00:41:17,199 You are still accountable and responsible for the output. 784 00:41:17,440 - > 00:41:21,519 Nothing has changed other than you've gotten a lot of help 785 00:41:21,519 - > 00:41:23,039 getting stuff done. 786 00:41:23,280 - > 00:41:26,000 And this is gonna be important not just for today, but 787 00:41:26,000 - > 00:41:30,320 increasingly as we move through the months, quarters, and years, 788 00:41:30,639 - > 00:41:36,079 just the velocity of change is gonna be critical as companies 789 00:41:36,079 - > 00:41:39,360 are actually accelerating timelines to deliver. 790 00:41:39,679 - > 00:41:42,960 Think about the way the early days of mobile phones, you know, 791 00:41:43,199 - > 00:41:45,920 might have had a new phone every two years, then it became every 792 00:41:45,920 - > 00:41:46,400 one year. 793 00:41:46,639 - > 00:41:49,119 I mean, you're gonna have compressed schedules of 794 00:41:49,119 - > 00:41:51,440 delivering, whether you're in product, whether you're in 795 00:41:51,440 - > 00:41:53,840 marketing for messaging and positioning. 796 00:41:54,000 - > 00:41:55,920 It's gonna constantly be evolving fast. 797 00:41:56,239 - > 00:41:59,360 Again, I said this, did I say this in the beginning? 798 00:41:59,679 - > 00:42:02,800 Subatomic helps you adapt, evolve, and scale. 799 00:42:03,119 - > 00:42:06,159 We decided to build ourselves, hire people who can adapt, 800 00:42:06,320 - > 00:42:09,119 evolve, and scale, build solutions to do that because we 801 00:42:09,119 - > 00:42:10,000 want our clients to. 802 00:42:10,639 - > 00:42:11,599 That's the future. 803 00:42:11,760 - > 00:42:16,239 And the future is coming fast because every if your 804 00:42:16,239 - > 00:42:18,800 competitors are already adapting, involving, and 805 00:42:18,800 - > 00:42:20,559 scaling, you've got some cash up to do. 806 00:42:20,719 - > 00:42:22,400 You need to start running at that speed. 807 00:42:22,719 - > 00:42:23,199 You should be able to get it. 808 00:42:23,360 - > 00:42:25,440 SPEAKER_01: Yeah, but let me ask you can you catch up? 809 00:42:25,599 - > 00:42:29,360 If you're if your competitors are already doing this, and I 810 00:42:29,360 - > 00:42:30,639 mean, I've thought about this. 811 00:42:30,880 - > 00:42:31,679 Can you catch up? 812 00:42:31,840 - > 00:42:34,000 If you're like, oh, well, we'll start next quarter, we'll start, 813 00:42:34,079 - > 00:42:36,000 you know, uh Q4, whatever. 814 00:42:36,159 - > 00:42:40,239 But you've got competitors, even if it's messy right now, they're 815 00:42:40,239 - > 00:42:41,039 gonna figure this out. 816 00:42:41,119 - > 00:42:47,679 And when they do, their velocity of production and just uh 817 00:42:48,000 - > 00:42:52,079 increase in bandwidth, like, can you catch up? 818 00:42:54,400 - > 00:42:57,760 SPEAKER_02: That will be a very important thing to watch. 819 00:42:57,920 - > 00:43:00,559 I'm predicting it'll be very hard. 820 00:43:00,960 - > 00:43:05,119 The later you get started, the faster that the sooner that 821 00:43:05,119 - > 00:43:10,719 others have already adopted AI and begin adapting, evolving, 822 00:43:10,960 - > 00:43:15,840 you may be late to the game and you'll never catch up because 823 00:43:15,840 - > 00:43:20,320 they'll their velocity will be iterating you know, multiple 824 00:43:21,199 - > 00:43:23,760 numbers ahead, and you're just trying to catch up. 825 00:43:23,920 - > 00:43:28,719 That's I I talk about it in terms of imagine this is the gap 826 00:43:28,719 - > 00:43:31,920 between your competitor who's ahead of you, and this is where 827 00:43:31,920 - > 00:43:32,559 you are. 828 00:43:32,960 - > 00:43:36,559 It's gonna even when you start trying to catch up, the gap is 829 00:43:36,559 - > 00:43:40,719 gonna continue to widen because they're at a much more advanced, 830 00:43:40,800 - > 00:43:45,360 mature level of uh true efficiency, true growth. 831 00:43:45,760 - > 00:43:48,960 SPEAKER_01: And they're able to absorb these like the new 832 00:43:48,960 - > 00:43:52,639 releases that come out faster because they've already created 833 00:43:52,639 - > 00:43:57,199 the infrastructure and the culture and the training of 834 00:43:57,199 - > 00:43:59,599 their people to where they're used to. 835 00:44:00,159 - > 00:44:01,280 Oh, there's an update. 836 00:44:01,440 - > 00:44:03,760 We're now, we're now going to do, you know, this is faster. 837 00:44:04,079 - > 00:44:05,679 We don't have to do that one step anymore. 838 00:44:05,840 - > 00:44:09,280 So, um, and one thing that I think that is important to note 839 00:44:09,440 - > 00:44:11,679 that if you're listening to this and you're like, well, the good 840 00:44:11,679 - > 00:44:14,400 news is in my industry, people aren't really moving fast. 841 00:44:14,719 - > 00:44:15,519 You don't know. 842 00:44:15,599 - > 00:44:17,360 And I'll tell you what I mean by that. 843 00:44:17,679 - > 00:44:20,480 Your competition has called somebody like me. 844 00:44:20,719 - > 00:44:24,719 And we've been on site for, you know, the kickoff, and we've 845 00:44:24,719 - > 00:44:27,519 been working with their teams weekly for maybe it's been a 846 00:44:27,519 - > 00:44:29,199 month, maybe it's been two or three months. 847 00:44:29,360 - > 00:44:32,320 And they're not out there getting billboards saying, hey, 848 00:44:32,400 - > 00:44:33,440 we're practicing with AI. 849 00:44:33,519 - > 00:44:34,639 Hey, we're using AI. 850 00:44:34,800 - > 00:44:38,320 You, this is all happening unintentionally in stealth mode. 851 00:44:38,480 - > 00:44:39,920 Your competition is doing this stuff. 852 00:44:40,000 - > 00:44:43,199 So if you think that, you know, you've got time, I'm telling 853 00:44:43,199 - > 00:44:44,400 you, brother, you don't. 854 00:44:44,559 - > 00:44:50,880 It's time to like it's time for you to like bang the table and 855 00:44:50,880 - > 00:44:53,920 say, guys, like we, if we're not doing something now, we need to 856 00:44:53,920 - > 00:44:54,400 do something. 857 00:44:54,559 - > 00:44:57,119 Have them listen to this and understand what's possible, 858 00:44:57,280 - > 00:44:57,599 right? 859 00:44:58,000 - > 00:45:03,119 So, you know, uh this strategic agility that's going to be 860 00:45:03,119 - > 00:45:04,239 coming with this stuff. 861 00:45:05,280 - > 00:45:13,519 Um where are the companies struggling to change these, like 862 00:45:13,599 - > 00:45:17,119 what once once there's this opportunity to introduce this 863 00:45:17,360 - > 00:45:21,679 AI-driven workflow, where where is the friction that a that a 864 00:45:21,679 - > 00:45:26,159 business owner should expect while this becomes the new way 865 00:45:26,159 - > 00:45:27,199 of doing things? 866 00:45:28,719 - > 00:45:32,719 SPEAKER_02: I think number one is everyone's fearful for their 867 00:45:32,719 - > 00:45:33,039 job. 868 00:45:33,440 - > 00:45:38,079 And again, this is the opportunity for business leaders 869 00:45:38,079 - > 00:45:44,239 to help create a culture of acceptance that AI is coming and 870 00:45:44,239 - > 00:45:50,400 then support that your le the leaders actually want you to 871 00:45:50,639 - > 00:45:52,880 thrive in the world of AI. 872 00:45:53,199 - > 00:45:56,559 And so starting to think about how do I manage AI coworkers. 873 00:45:57,039 - > 00:45:58,079 That's number one. 874 00:45:58,320 - > 00:46:02,320 Number two, I would say AI leaders need to reduce the 875 00:46:02,480 - > 00:46:05,519 change management aspect of rolling out a new solution. 876 00:46:05,760 - > 00:46:10,719 Don't choose new solutions that require your human workers to 877 00:46:10,719 - > 00:46:12,559 actually learn something new. 878 00:46:12,800 - > 00:46:14,639 Again, AI should work for you. 879 00:46:14,800 - > 00:46:18,719 You should not work for software of any kind like we have in the 880 00:46:18,719 - > 00:46:18,960 past. 881 00:46:19,119 - > 00:46:19,360 SPEAKER_01: Yeah. 882 00:46:19,599 - > 00:46:23,039 SPEAKER_02: Now it's the time for AI to meet you in Microsoft 883 00:46:23,039 - > 00:46:26,079 Teams, Slack, email, right? 884 00:46:26,320 - > 00:46:29,920 Simplify their world, adoption becomes easier. 885 00:46:30,159 - > 00:46:34,320 And then finally, you know, having you know a self-service 886 00:46:34,320 - > 00:46:37,840 source of information to always learn about how AI can help you. 887 00:46:38,079 - > 00:46:42,639 I I think if you put that educational learning system that 888 00:46:42,639 - > 00:46:46,719 is, again, you know, self-service, but also perhaps 889 00:46:46,719 - > 00:46:51,039 mandatory at certain scheduled moments or milestones in your 890 00:46:51,599 - > 00:46:54,480 time at the company, you would minimum need to be up at, you 891 00:46:54,480 - > 00:46:58,400 know, upskilled to a certain level, then I think that will 892 00:46:58,400 - > 00:47:01,519 create the foundation where people are less fearful with the 893 00:47:01,519 - > 00:47:05,039 aspect of, okay, I know I have to do this, but how do I get 894 00:47:05,039 - > 00:47:05,440 started? 895 00:47:05,599 - > 00:47:05,840 Right? 896 00:47:05,920 - > 00:47:10,960 How do I keep up and learn and contribute to the rapid velocity 897 00:47:10,960 - > 00:47:15,360 our organization is likely to undergo because we're starting 898 00:47:15,360 - > 00:47:19,360 to use AI and and uh reap the benefits? 899 00:47:20,320 - > 00:47:22,800 SPEAKER_01: Change is afoot, no doubt. 900 00:47:22,960 - > 00:47:25,840 Well, Carl, thank you so much for sharing what you guys are 901 00:47:25,840 - > 00:47:27,199 doing at Subatomic. 902 00:47:27,440 - > 00:47:32,079 How can individuals who are recognizing that their data 903 00:47:32,079 - > 00:47:36,960 situation is not ready for this and they need some help, how can 904 00:47:36,960 - > 00:47:39,039 they find out more about what you guys are doing at Sub 905 00:47:39,199 - > 00:47:40,320 Subatomic? 906 00:47:40,639 - > 00:47:42,719 SPEAKER_02: Yeah, so there are many ways. 907 00:47:42,880 - > 00:47:47,199 I mean, one of the key ways is go to getsubatomic.ai. 908 00:47:47,519 - > 00:47:48,639 That's our website. 909 00:47:48,880 - > 00:47:53,920 You can go to LinkedIn, find me, or anyone in Subatomic. 910 00:47:54,000 - > 00:47:56,800 If you look for this the company Subatomic, you should find our 911 00:47:56,800 - > 00:47:59,199 company and and the employees there. 912 00:47:59,360 - > 00:48:04,400 But whether it's Carl Simon on LinkedIn, Sam Sova, he's the 913 00:48:04,400 - > 00:48:08,000 other co-founder, will be available to talk to you and 914 00:48:08,159 - > 00:48:11,920 assess your top opportunities for AI to actually materialize 915 00:48:11,920 - > 00:48:14,079 benefits sooner than later for you. 916 00:48:14,320 - > 00:48:17,760 Then finally, you can actually email me at Carl. 917 00:48:18,000 - > 00:48:18,960 Nuts with a K. 918 00:48:19,360 - > 00:48:22,639 Carl with a K at getsubatomic.ai. 919 00:48:23,199 - > 00:48:24,880 SPEAKER_01: So I'm on your website and I love this 920 00:48:24,880 - > 00:48:25,360 headline. 921 00:48:25,519 - > 00:48:27,199 Everyone's buying AI. 922 00:48:27,599 - > 00:48:29,519 Smart firms are hiring it. 923 00:48:29,760 - > 00:48:30,559 I like that a lot. 924 00:48:30,639 - > 00:48:30,880 Yeah. 925 00:48:30,960 - > 00:48:33,519 And so for all the listeners, we're going to have the links to 926 00:48:33,519 - > 00:48:35,440 all this information in the show notes. 927 00:48:35,599 - > 00:48:39,679 And I just want to say thank you again for um, you know, having 928 00:48:39,920 - > 00:48:43,840 this podcast be part of your AI upskilling. 929 00:48:43,920 - > 00:48:46,880 Uh, and if you're getting value out of this and you know 930 00:48:46,880 - > 00:48:50,079 somebody else, a coworker, a peer, a friend, a golf buddy, 931 00:48:50,239 - > 00:48:53,199 whatever, um, please share this with them. 932 00:48:53,360 - > 00:48:57,440 Uh, this is how we get the opportunity to, I don't know, 933 00:48:57,599 - > 00:49:00,400 help other people expand their perspectives on what's possible 934 00:49:00,400 - > 00:49:00,960 with AI. 935 00:49:01,119 - > 00:49:03,760 So um just want to say thank you so much for being a listener. 936 00:49:03,920 - > 00:49:07,840 And if you have a second, leave us a review, uh, forward an 937 00:49:07,840 - > 00:49:10,719 episode along, and make sure that you uh join us for the next 938 00:49:10,719 - > 00:49:10,880 one. 939 00:49:11,039 - > 00:49:12,880 Carl, thank you so much for being here, and everybody will 940 00:49:13,039 - > 00:49:14,239 see you on the next episode. 941 00:49:14,480 - > 00:49:15,280 SPEAKER_02: Thank you, Chris. 942 00:49:15,440 - > 00:49:18,079 SPEAKER_01: Thanks for tuning in to Using AI at Work. 943 00:49:18,239 - > 00:49:21,199 Don't forget to subscribe for more conversations about how to 944 00:49:21,199 - > 00:49:22,239 use AI at work. 945 00:49:22,639 - > 00:49:26,239 And a special thank you to our sponsor, Chief AI Officer, for 946 00:49:26,239 - > 00:49:29,119 empowering businesses with AI education and training. 947 00:49:29,280 - > 00:49:33,280 Visit their website for free AI readiness assessment and AI 948 00:49:33,280 - > 00:49:36,159 strategy guide to help you get started using AI at work. 949 00:49:36,400 - > 00:49:40,800 That's www.chiefaiofficer.com. 950 00:49:40,960 - > 00:49:45,199 Follow us on Twitter at the handle usingAI at work, and 951 00:49:45,199 - > 00:49:50,719 visit www.usingai at work.com for free resources to help you 952 00:49:50,719 - > 00:49:52,880 harness AI in your role.