1 00:00:00,240 --> 00:00:03,880 Welcome back to Data Driven, where we dive headfirst into the bubbling 2 00:00:03,880 --> 00:00:07,720 cauldron of AI, data science and the occasional existential 3 00:00:07,720 --> 00:00:11,280 crisis about digital transformation. In this episode, 4 00:00:11,360 --> 00:00:15,200 Frank chats with Itai Habber, CEO of Data Noetic, 5 00:00:15,200 --> 00:00:18,720 a company daring to bring order to the chaos of supply chain data. 6 00:00:19,200 --> 00:00:22,960 Forget dashboards and spreadsheets. Data Noetic is building an 7 00:00:22,960 --> 00:00:25,760 autonomous digital brain for supply chain operations. 8 00:00:26,590 --> 00:00:30,350 No, not Skynet. Though the temptation must be overwhelming. 9 00:00:30,670 --> 00:00:34,030 From AI agents that flag delivery issues before they become 10 00:00:34,030 --> 00:00:37,830 disasters, to why your 3 month wait for bathroom tiles could have 11 00:00:37,830 --> 00:00:41,270 been avoided with better data orchestration, this episode is a 12 00:00:41,270 --> 00:00:44,910 masterclass in how agentic AI is moving from hype to hard results. 13 00:00:45,630 --> 00:00:49,230 So grab your headphones and your favorite supply chain KPI. 14 00:00:49,230 --> 00:00:52,510 It's time to get Data Driven with a dose of dry wit and digital 15 00:00:52,590 --> 00:00:56,350 wisdom. Hello and welcome back 16 00:00:56,350 --> 00:00:58,970 to Data Driven, the podcast we explore the emerging 17 00:00:59,770 --> 00:01:03,490 ecosystem of AI machine learning, data 18 00:01:03,490 --> 00:01:06,770 science and data engineering. Now, my favorite is data 19 00:01:06,770 --> 00:01:10,490 engineering. Data engineer in the world is not here 20 00:01:10,490 --> 00:01:12,970 today because he is presenting at SQL Pass 21 00:01:14,090 --> 00:01:17,810 in Seattle this week. And I'm actually going to be at Microsoft 22 00:01:17,810 --> 00:01:21,410 Ignite this week. So Andy and I will be in the same time 23 00:01:21,410 --> 00:01:24,890 zone, but not the same city. But we must march on. 24 00:01:25,130 --> 00:01:28,920 So today I have with me the an excellent 25 00:01:28,920 --> 00:01:32,600 guest. He is the CEO of Data Noetic 26 00:01:32,600 --> 00:01:36,120 and it's Itai Haber. How's it going, 27 00:01:36,120 --> 00:01:39,080 sir? Very good, thanks. Thank you very much, Frank. 28 00:01:39,640 --> 00:01:43,400 Awesome. Great to meet you as well. Thanks for scheduling this. And 29 00:01:43,640 --> 00:01:46,840 you know, we love, we love talking data. We love talking AI. 30 00:01:48,040 --> 00:01:51,480 When I saw the name of your company, I had to go back and 31 00:01:51,960 --> 00:01:54,830 relive some freshman philosophy, 32 00:01:55,950 --> 00:01:59,550 Data Noetic. And I actually, not gonna lie, how to pull up 33 00:01:59,870 --> 00:02:03,710 ChatGPT because I'm like, I remember that means 34 00:02:03,710 --> 00:02:07,470 something around like not gnosis because that's more spiritual 35 00:02:07,470 --> 00:02:11,150 knowledge, but more intellectual kind of understanding. And it turns out 36 00:02:11,150 --> 00:02:14,990 it does. It's a, it's an ancient Greek word and it 37 00:02:16,670 --> 00:02:19,150 in noetic refers to 38 00:02:20,530 --> 00:02:23,730 wisdom, intellectual insight, and so on. So how do we get, 39 00:02:24,930 --> 00:02:27,490 what does Data Noetic do? 40 00:02:28,930 --> 00:02:32,050 How does it live up to its name? Right, okay, 41 00:02:32,530 --> 00:02:35,890 so the origins, I can't take credit for 42 00:02:36,450 --> 00:02:40,210 the naming. That goes to our founder, 43 00:02:40,610 --> 00:02:44,450 Sandeep, who's been in 44 00:02:44,450 --> 00:02:47,970 supply chain industry for a couple of decades and has 45 00:02:48,610 --> 00:02:52,270 had the original idea of the 46 00:02:52,270 --> 00:02:55,390 company. But I can definitely talk about what we are, 47 00:02:56,750 --> 00:03:00,430 what we're trying, what we're trying to do is basically Data 48 00:03:00,430 --> 00:03:04,230 Analytic was founded to become the autonomous digital brain 49 00:03:04,230 --> 00:03:06,910 for supply chain process optimization, automation 50 00:03:08,510 --> 00:03:12,150 and to jump to the kind of where data analytics comes 51 00:03:12,150 --> 00:03:15,950 from, it is in a way taking advantage of 52 00:03:16,190 --> 00:03:19,870 new developments in AI, machine learning, data science, etc. Which we might 53 00:03:19,870 --> 00:03:23,380 come to a bit later, in order to tackle a gap 54 00:03:23,770 --> 00:03:27,290 that exists in a lot of organization at the moment. And the gap is currently 55 00:03:27,290 --> 00:03:30,810 between on the one side and the 56 00:03:32,890 --> 00:03:36,610 lots of transactional analytical data that exist in various places, 57 00:03:36,610 --> 00:03:40,370 data warehouses, data lakes, etc. And on the other hand, the 58 00:03:40,370 --> 00:03:43,130 same organizations have the process improvement initiatives, 59 00:03:44,010 --> 00:03:47,690 lean robotic process automation, et cetera. And those two things, 60 00:03:47,930 --> 00:03:51,370 the data that they have and the process improvement initiatives don't always 61 00:03:52,140 --> 00:03:55,900 sync. There's no sync between them. And so data analytic 62 00:03:57,820 --> 00:04:01,260 aims to be, as I said, the autonomous digital brain for optimization 63 00:04:01,340 --> 00:04:05,020 and automation. By tapping into the data 64 00:04:05,020 --> 00:04:08,780 that exists in various systems in the organization, applying 65 00:04:08,780 --> 00:04:12,500 AI, agentic AI more specifically, or slightly 66 00:04:12,500 --> 00:04:15,980 more specifically, and trying to 67 00:04:17,260 --> 00:04:20,599 predict and suggest actions that can be taken, 68 00:04:21,319 --> 00:04:24,919 can be taken, sorry to, to improve things, 69 00:04:24,919 --> 00:04:28,519 and by so doing, orchestrating the data 70 00:04:29,479 --> 00:04:32,519 and making it actionable. 71 00:04:33,079 --> 00:04:36,919 That's interesting. So, so getting to the brush tacks 72 00:04:36,919 --> 00:04:40,199 like how do you make it actionable? Like what, what happens? Do you, do you 73 00:04:40,199 --> 00:04:43,799 have like a UI where business user would use it, or do you have. 74 00:04:43,799 --> 00:04:46,999 Or do you enabled kind of data engineers to kind of 75 00:04:47,640 --> 00:04:51,280 work stuff and then surface it in a tool like tableau, power BI, 76 00:04:51,280 --> 00:04:55,080 etc. Great question. The intention is actually 77 00:04:55,240 --> 00:04:59,040 to allow people who wouldn't necessarily be able to 78 00:04:59,040 --> 00:05:02,880 do all the data analysis on their own, so to 79 00:05:02,880 --> 00:05:06,440 kind of rather 80 00:05:07,000 --> 00:05:10,520 augment the ability of a business manager to 81 00:05:11,560 --> 00:05:15,360 take actions without necessarily having to rely as heavily as they 82 00:05:15,360 --> 00:05:19,070 might otherwise have to on the. On the business analyst that can 83 00:05:19,300 --> 00:05:23,060 go and query the Power BI or various other 84 00:05:23,300 --> 00:05:26,660 analytical tools that exist at the moment. And so 85 00:05:28,420 --> 00:05:31,460 to give an example of a prospect that we've 86 00:05:32,020 --> 00:05:35,860 spoken to recently, this 87 00:05:35,860 --> 00:05:39,380 is a company without naming names. They are in the business of 88 00:05:39,540 --> 00:05:43,140 providing household. It's not appliances because it's 89 00:05:43,140 --> 00:05:44,260 like taps 90 00:05:46,820 --> 00:05:50,180 and syncs and things like that. And 91 00:05:50,740 --> 00:05:53,700 they had an order from a customer. 92 00:05:54,660 --> 00:05:58,100 Now one of the things that they care deeply about is delivering on time and 93 00:05:58,100 --> 00:06:01,939 in full for customer orders, also known as OTIF or otfd. On time, in 94 00:06:01,939 --> 00:06:05,620 full delivery. And it was almost by coincidence 95 00:06:05,780 --> 00:06:09,220 that some process analyst has actually looked at the data and figured out that that 96 00:06:09,220 --> 00:06:11,460 particular custom order couldn't actually be 97 00:06:13,250 --> 00:06:16,930 delivered in full and on time because the particular item or 98 00:06:16,930 --> 00:06:20,370 items that they had in the order didn't exist, it would take too long to 99 00:06:20,610 --> 00:06:24,290 manufacture, to deliver, etc. Etc. Now that's great 100 00:06:24,290 --> 00:06:28,130 that they kind of figured it out ahead of it 101 00:06:28,130 --> 00:06:31,450 actually happening. But that was the kind of 102 00:06:31,450 --> 00:06:35,170 exception that proved the rule that normally that information 103 00:06:35,570 --> 00:06:39,330 comes to light at the point at which the customer delivery order has already 104 00:06:39,330 --> 00:06:43,090 been kind of missed and on time, in full delivery was not 105 00:06:43,090 --> 00:06:45,090 met. Now 106 00:06:47,410 --> 00:06:50,610 what can be done and what data ethics 107 00:06:51,090 --> 00:06:54,890 helps to do is apply for example, what we call like the 108 00:06:54,890 --> 00:06:58,730 KPI guard, a KPI guard agent, which is 109 00:06:58,730 --> 00:06:59,330 basically 110 00:07:02,530 --> 00:07:05,930 an agent, think about it like a virtual assistant, a 111 00:07:05,930 --> 00:07:08,960 copilot as an example 112 00:07:09,840 --> 00:07:13,520 that looks at the information that already exists. The information 113 00:07:13,600 --> 00:07:16,960 that the customer order has just been placed exists, the 114 00:07:16,960 --> 00:07:19,840 SKUs, the particular products that have been 115 00:07:20,160 --> 00:07:23,680 requested, that data exists not necessarily on the same 116 00:07:23,680 --> 00:07:26,760 systems. And here I go back to what I said about the lack of autonomous 117 00:07:26,760 --> 00:07:30,000 thing. The information about what exists in the warehouses 118 00:07:30,240 --> 00:07:33,840 exists in some potentially warehouse management system, etc. Etc. 119 00:07:34,160 --> 00:07:37,670 And so by being a little bit more proactive on an 120 00:07:37,670 --> 00:07:41,470 ongoing sort of automated basis, it can flag 121 00:07:41,470 --> 00:07:44,990 the point that okay, this customer over here has just made an order for this 122 00:07:44,990 --> 00:07:48,830 particular items that you don't have enough of in the system. And 123 00:07:48,830 --> 00:07:51,590 given the knowledge I have about what is happening in the, 124 00:07:52,790 --> 00:07:56,430 in your, in your business up to now, you will not be 125 00:07:56,430 --> 00:08:00,230 able to meet the delivery timelines that you have just told 126 00:08:00,230 --> 00:08:03,430 me are your, your effective delivery timelines. 127 00:08:04,390 --> 00:08:08,070 And, and therefore I'm alerting you that hey, this is an 128 00:08:08,070 --> 00:08:11,830 issue. So now you can either try and if maybe there's an 129 00:08:11,830 --> 00:08:15,550 option to move some stock from warehouse A to warehouse B that would allow 130 00:08:15,550 --> 00:08:19,350 you to deliver that, or if maybe, 131 00:08:19,350 --> 00:08:22,750 maybe that's not an option. Another option might be, hey, why don't you reach out 132 00:08:22,750 --> 00:08:26,190 to the customer proactively and say I need to change the delivery date because of 133 00:08:26,190 --> 00:08:26,830 so and so. 134 00:08:30,190 --> 00:08:33,550 That's another example. Well, it's easier 135 00:08:33,790 --> 00:08:36,910 to have that conversation early in the process. 136 00:08:37,880 --> 00:08:40,040 As someone who's done a lot of home renovations and 137 00:08:41,640 --> 00:08:45,480 more than I care to. I remember it was from a major 138 00:08:45,640 --> 00:08:48,120 big box hardware store. I won't name them, 139 00:08:49,320 --> 00:08:52,440 but we had these really nice like tile 140 00:08:53,240 --> 00:08:57,080 set up. But it took them three months to get this tile. And the 141 00:08:57,080 --> 00:09:00,760 frustrating thing I understood, it was stuck in customs, right? Or 142 00:09:00,760 --> 00:09:03,920 there was an issue with the supplier that I can relate to. But the fact 143 00:09:03,920 --> 00:09:07,660 that I wasn't told, I had to basically go through 144 00:09:08,860 --> 00:09:12,380 I don't know how many hours on hold, how many people to talk to, 145 00:09:12,780 --> 00:09:16,380 right? That to me makes me like whenever 146 00:09:16,380 --> 00:09:20,100 they, you know, we do another project, I'm like, if it's not in the 147 00:09:20,100 --> 00:09:23,860 store And I have to order it. I don't want to do it right because 148 00:09:23,860 --> 00:09:27,140 I, you know, I had to, I had to hold up contractors and stuff like 149 00:09:27,140 --> 00:09:29,700 that. It was, it was, it was very painful. Now if they had told me 150 00:09:29,700 --> 00:09:33,440 straight up that it's going to take three months to get this, you know, instead 151 00:09:33,440 --> 00:09:37,200 of the normal 14 days, I would have chose 152 00:09:37,200 --> 00:09:40,760 a different tile or found a different 153 00:09:40,760 --> 00:09:44,520 supplier. Like, you know, and then like to this day, every time I 154 00:09:44,520 --> 00:09:47,880 walk into that store, it kind of taints my like 155 00:09:48,200 --> 00:09:51,400 Absolutely, absolutely. You know and I think 156 00:09:53,000 --> 00:09:56,760 the amazing thing is that the, it's not like the information didn't exist. 157 00:09:57,270 --> 00:10:00,710 If somebody cared enough to connect the dots, it would have 158 00:10:00,790 --> 00:10:04,630 absolutely been possible. Now the actions that could have been taken once 159 00:10:04,630 --> 00:10:08,070 those dots were connected, there are probably different things that they could do. They could 160 00:10:08,070 --> 00:10:11,910 have actively decided, hey, we don't want to tell Frank that 161 00:10:11,910 --> 00:10:14,870 it's late because we're worried that he's going to cancel the order. 162 00:10:15,270 --> 00:10:18,470 Fine, you can do that. But you have to take 163 00:10:19,910 --> 00:10:23,710 the risk associated risk as that particular operator that you're going to end 164 00:10:23,710 --> 00:10:27,030 up having a very unsatisfied customer. You might get the order not 165 00:10:27,030 --> 00:10:30,570 cancelled. Small gain. Shorter, but might have 166 00:10:30,570 --> 00:10:34,410 very meaningful potential. Oh, any, 167 00:10:34,410 --> 00:10:38,090 any other tile job at. Since like unless they have it in the building, I 168 00:10:38,090 --> 00:10:41,850 don't order it. Like I go with somewhere else. Right. So yeah, I mean 169 00:10:41,850 --> 00:10:45,570 granted I'm not a big contractor, although I think, I think what my wife's second 170 00:10:45,570 --> 00:10:49,210 career one might be becoming a contractor. I don't know. 171 00:10:49,450 --> 00:10:52,250 But no, like it totally. You know 172 00:10:53,740 --> 00:10:57,260 actually just as we're recording this this weekend we had a, 173 00:10:57,820 --> 00:11:01,100 our hot water heater like basically flooded our basement. Right. 174 00:11:01,500 --> 00:11:04,780 So I have to go back. It's very relevant, right? Because I have to go 175 00:11:04,780 --> 00:11:07,300 back and I have to figure out what you know, tile I want to put 176 00:11:07,300 --> 00:11:11,140 down or flooring. And I'm like, my wife was like what if 177 00:11:11,140 --> 00:11:13,100 we go to this store? I'm like, no. 178 00:11:16,060 --> 00:11:19,660 Exactly. But you're right though. Like it is a short term gain. But 179 00:11:19,850 --> 00:11:23,250 even if they, I would have been okay if they told me honestly because I, 180 00:11:23,250 --> 00:11:26,010 they would be in the running for any kind of future work. But I guess 181 00:11:26,010 --> 00:11:28,970 people don't think like that. And, and, and the fact by the way, even telling 182 00:11:28,970 --> 00:11:32,770 you in, in advance might actually you might really want that tile. And you 183 00:11:32,770 --> 00:11:36,330 would say you know what, fine, I'll, I'll wait those three months but I will 184 00:11:36,410 --> 00:11:40,170 reschedule my plumber or my Tyler or whatever. Right. So that I don't up 185 00:11:40,170 --> 00:11:44,010 being annoying or, or kind of frustrating. 186 00:11:44,010 --> 00:11:47,810 Another call it process that you have a few, which is 187 00:11:47,810 --> 00:11:51,530 for you is renovating your bathroom or whatever, you can 188 00:11:51,530 --> 00:11:54,850 adapt accordingly. Maybe you say okay, I'll do, I'll check, whatever. 189 00:11:55,730 --> 00:11:58,130 But I imagine that also would impact, you know, 190 00:11:59,250 --> 00:12:02,850 larger projects. Right. If I was a real estate developer or 191 00:12:03,170 --> 00:12:06,890 whatever. Right. We actually had a previous guest that, 192 00:12:06,890 --> 00:12:09,330 that does, you know, basically optimization for 193 00:12:10,610 --> 00:12:14,300 construction jobs. Right. Because if there's a delay, the project 194 00:12:14,300 --> 00:12:17,780 manager would rather know that. And we're talking like massive 195 00:12:17,780 --> 00:12:21,580 skyscrapers, like sort of thing type GC and like the UAE 196 00:12:21,580 --> 00:12:25,260 and stuff like that apparently. So some of the work that that company had 197 00:12:25,260 --> 00:12:28,980 assisted on. But like you know, a delay of a day is like 198 00:12:28,980 --> 00:12:32,020 millions of dollars, tens of millions of dollars in some cases. Right. So 199 00:12:32,660 --> 00:12:36,300 if I am presented, if I'm a project manager, I'm presented. Well, you 200 00:12:36,300 --> 00:12:39,700 know, if going, going dark on the customer 201 00:12:39,940 --> 00:12:43,780 right now, obviously me, Frank as an individual is probably a way 202 00:12:43,780 --> 00:12:47,100 less influence over a supplier than like somebody who's building 203 00:12:47,100 --> 00:12:50,900 skyscrapers. But you know, I would at least have, I would be, 204 00:12:50,900 --> 00:12:54,300 I would as a customer be able to make an informed choice. Right. I could 205 00:12:54,300 --> 00:12:57,940 be delayed by one day. I could be delayed by four weeks if I can't 206 00:12:57,940 --> 00:13:01,220 avoid the delay. I think I know which one I would pick. Right. 207 00:13:02,100 --> 00:13:05,740 I mean, I think, I think everybody appreciates stuff happens. Yes. 208 00:13:06,780 --> 00:13:10,620 And it's just about the ability to be more informed about it. So 209 00:13:10,620 --> 00:13:13,340 you can actually take the appropriate actions about it. 210 00:13:14,300 --> 00:13:18,020 And look, to use another example, spoke to another customer, this 211 00:13:18,020 --> 00:13:21,820 time not in household goods, more in pharmaceutical. And there 212 00:13:22,060 --> 00:13:25,180 again actually OTFD was on time in full delivery was 213 00:13:25,980 --> 00:13:29,740 a key factor for them. And there was an instance where one of 214 00:13:29,740 --> 00:13:33,500 the key executives went to their customers and proudly presented how their 215 00:13:33,660 --> 00:13:37,500 on time, in full delivery of the pharmaceutical goods to the particular 216 00:13:37,580 --> 00:13:41,420 healthcare provider was 90 plus percent whatever they, they thought it was 217 00:13:41,580 --> 00:13:45,220 only to be then told by the customer. Well actually no it 218 00:13:45,220 --> 00:13:48,740 isn't. When we are, according to what we know, it's like 219 00:13:48,740 --> 00:13:51,420 whatever 70, 80%, whatever it is, 220 00:13:52,379 --> 00:13:56,220 whatever it actually was that the numbers don't make, don't 221 00:13:56,380 --> 00:14:00,100 aren't relevant for, for the purpose of the point that there was a 222 00:14:00,100 --> 00:14:03,500 difference between what the pharmaceutical executive thought 223 00:14:04,000 --> 00:14:07,800 that their performance was and what their actual performance was as reported by 224 00:14:07,800 --> 00:14:11,640 the customer. Obviously very embarrassing for the, for the executive coming 225 00:14:11,640 --> 00:14:15,280 back into the organization saying what the hell is going on? What's going on here? 226 00:14:15,360 --> 00:14:19,160 That must have been an uncomfortable meeting or two. Absolutely set meetings 227 00:14:19,160 --> 00:14:22,320 actually in the Organization. And 228 00:14:24,400 --> 00:14:28,040 what they figure out actually is that as they were kind of 229 00:14:28,040 --> 00:14:31,680 summing up, the amount of time that it takes to provide the full delivery was 230 00:14:31,680 --> 00:14:35,440 being done by different departments. Now, for all sorts 231 00:14:35,440 --> 00:14:39,060 of semi valid internal reasons, various 232 00:14:39,060 --> 00:14:42,780 departments chose what components to include and what to exclude 233 00:14:42,860 --> 00:14:46,140 from what they reported as for the time that it takes to deliver. 234 00:14:46,860 --> 00:14:48,940 So for example, this one department that 235 00:14:50,300 --> 00:14:53,420 counted the amount of days that it took them to 236 00:14:55,020 --> 00:14:58,780 get the thing from point A to point B, they excluded 237 00:14:58,780 --> 00:15:02,220 credit checks because credit checks is not part of what the department did. 238 00:15:03,420 --> 00:15:07,230 So very kind of, which is, which. Is my point of view, look, not a 239 00:15:07,230 --> 00:15:10,790 customer pov exactly. Which is fair enough for the department which maybe is 240 00:15:10,790 --> 00:15:14,510 doing, actually shipping the thing from, from the warehouse to the 241 00:15:14,510 --> 00:15:18,270 distribution center, but they can't distribute it, they can't do it before the 242 00:15:18,270 --> 00:15:22,070 credit check is done. Okay, so for the purposes of their work, yeah, 243 00:15:22,070 --> 00:15:25,870 it's true that the credit check is irrelevant and they shouldn't be quote unquote punished 244 00:15:25,870 --> 00:15:29,670 or, or, or in somehow in some way kind of 245 00:15:29,830 --> 00:15:33,510 made to look worse than underperformance than they actually were. 246 00:15:34,010 --> 00:15:37,810 But for the purposes of the customer, the fact that however many one or 247 00:15:37,810 --> 00:15:41,370 three or seven days have taken an additional days for 248 00:15:41,370 --> 00:15:44,530 somebody in the finance team or the procurement team or whatever to do a credit 249 00:15:44,530 --> 00:15:48,330 check on, the customer still added those exact same days, which would then 250 00:15:48,330 --> 00:15:51,650 manifest themselves into the amount of time that it takes from the point at which 251 00:15:51,650 --> 00:15:55,330 the customer in their view made, not in their view, in reality made the 252 00:15:55,330 --> 00:15:58,810 order until the point is delivered. And 253 00:15:59,130 --> 00:16:02,900 that is just one example of the sorts 254 00:16:02,900 --> 00:16:06,500 of discrepancies that can 255 00:16:07,620 --> 00:16:11,420 create problems and where 256 00:16:11,420 --> 00:16:14,860 what I'm talking about, the orchestration that we're talking about, the data 257 00:16:14,860 --> 00:16:18,100 noetic system, the data knowing system 258 00:16:18,420 --> 00:16:22,180 that looks at the various components kind of dispassionately 259 00:16:22,340 --> 00:16:26,140 and practically and kind of is able to give the 260 00:16:26,140 --> 00:16:29,180 suggestions, in this case, it would be able to connect to 261 00:16:29,820 --> 00:16:33,500 the CRM that maybe 262 00:16:33,660 --> 00:16:35,820 captures the date at which the order is made, 263 00:16:38,860 --> 00:16:42,420 the financial system that does the credit 264 00:16:42,420 --> 00:16:45,900 check and then the warehouse management system and the 265 00:16:45,900 --> 00:16:49,700 ERP etc that track the various other steps that 266 00:16:49,700 --> 00:16:53,220 go along the way and give you a complete and hopefully more 267 00:16:53,220 --> 00:16:55,750 accurate picture of everything that's going on. 268 00:16:56,790 --> 00:17:00,470 Right. So, so what do you think is blocking organizations from doing this? 269 00:17:00,470 --> 00:17:03,110 Right. Is it data silos? Is it the fact that 270 00:17:04,150 --> 00:17:07,510 when these data systems, particularly the larger, the enterprise, 271 00:17:08,070 --> 00:17:10,950 when these were built, supply chains were not as complicated 272 00:17:11,750 --> 00:17:15,310 as they are today? Do you think, you think it's a combination of those data 273 00:17:15,310 --> 00:17:19,070 silos organizational politics. You did say, you did kind of allude 274 00:17:19,070 --> 00:17:22,890 that, you know, the problem was some of the problems are valid. 275 00:17:23,290 --> 00:17:26,930 I can assume the ones that are not valid are kind of ridiculous internal 276 00:17:26,930 --> 00:17:30,730 politics within that organization. Or is there something else I'm missing? 277 00:17:32,090 --> 00:17:34,890 First of all, just in terms, I think the answer is that it's probably a 278 00:17:34,890 --> 00:17:38,530 combination. And just to correct any 279 00:17:38,530 --> 00:17:42,090 misconception, I'm not blaming the organization for doing something 280 00:17:42,570 --> 00:17:46,210 that's outright ridiculous. I think that when you check each individual 281 00:17:46,210 --> 00:17:49,770 action or decision on its own, it kind of makes sense. But when you edit 282 00:17:49,770 --> 00:17:53,600 and aggregate, it creates a situation where you have an executive going 283 00:17:53,600 --> 00:17:57,240 saying, our delivery performance is X, 284 00:17:57,240 --> 00:18:00,320 where actually the delivery performance is worse than. Worse than X. 285 00:18:00,960 --> 00:18:03,920 Going back to why that's happening, I think it's a combination of 286 00:18:04,720 --> 00:18:07,760 probably most, if not all the things you said. It's a combination of 287 00:18:08,640 --> 00:18:12,480 silos. It's a combination of kind 288 00:18:12,480 --> 00:18:16,120 of people looking a little bit different, people for 289 00:18:16,120 --> 00:18:19,840 valid reasons having a bit of tunnel vision. Exactly. And also 290 00:18:20,160 --> 00:18:23,540 there has been, up until relatively recently, 291 00:18:24,180 --> 00:18:27,980 it's been very hard to be able to orchestrate all 292 00:18:27,980 --> 00:18:31,580 those things, which is something that the 293 00:18:31,580 --> 00:18:35,180 advent of various forms of artificial intelligence and machine 294 00:18:35,180 --> 00:18:38,660 learning is manifested by large language models and the 295 00:18:39,220 --> 00:18:42,820 increasingly amazing capabilities that AI agent building 296 00:18:43,220 --> 00:18:46,660 brings on board. Those things haven't been around. And so 297 00:18:47,140 --> 00:18:50,780 being able to connect all those dots that once you tell a 298 00:18:50,780 --> 00:18:54,260 story after the fact sound obvious. Like, why didn't your 299 00:18:54,340 --> 00:18:58,100 tiling company know that. That they're going to be delayed? And 300 00:18:58,100 --> 00:19:00,540 why didn't they tell you that it's going to take three months? And why did 301 00:19:00,540 --> 00:19:04,220 it take you 15 calls to. To 302 00:19:04,220 --> 00:19:08,020 figure that out? It's all, yeah, it sounds 303 00:19:08,020 --> 00:19:11,660 kind of obvious, but the reality is I don't think that anybody in this 304 00:19:11,660 --> 00:19:15,300 company, in the company that the retailer that or the company 305 00:19:15,300 --> 00:19:19,140 you're working with kind of set out, okay, how do we deceive 306 00:19:19,140 --> 00:19:22,800 Frank? That's not. No, no. Absolutely no. No. And if I phrase the 307 00:19:22,800 --> 00:19:26,520 question that way, I apologize. That's not what I meant. I mean, I 308 00:19:26,520 --> 00:19:30,160 think that what you described with like each little, each 309 00:19:30,160 --> 00:19:33,520 little error added to one big massive compound error. 310 00:19:33,680 --> 00:19:37,480 There's a fancy word, there's like a fancy word to 311 00:19:37,480 --> 00:19:41,120 describe that in engineering of complex systems, right? And the classic example 312 00:19:41,120 --> 00:19:44,560 is like the space shuttle, right? The issues that they had, like, 313 00:19:44,720 --> 00:19:47,960 some people knew that, that, you know, whether it was the, the what, the heat 314 00:19:47,960 --> 00:19:51,600 tiles, whether it was the O ring, Some people knew, some people didn't know they. 315 00:19:51,820 --> 00:19:55,420 How to communicate. It was there Maybe some other things going 316 00:19:55,420 --> 00:19:59,180 on. Maybe. But you know, but, but you know, 317 00:19:59,180 --> 00:20:02,820 honest mistakes can happen and honest little mistakes add up to 318 00:20:02,820 --> 00:20:06,660 one big. One big honest, you know, mistake. I, I 319 00:20:06,660 --> 00:20:10,380 really doubt that this company was, you know, you know, had a picture of me 320 00:20:10,380 --> 00:20:14,140 on their wall and it's like if this guy calls, like. But exactly. 321 00:20:14,300 --> 00:20:18,000 But I mean, you know, just the same, like, it's still frustrating. Right. And 322 00:20:18,880 --> 00:20:22,480 that's a great point that you brought up like up until now with Agentic. 323 00:20:22,800 --> 00:20:25,960 You bring up a great point about Agentic. AI really would make this much easier 324 00:20:25,960 --> 00:20:29,680 because the alternative historically would have been doubling or tripling 325 00:20:29,680 --> 00:20:33,280 the size of your data analytics team. Right. And even then 326 00:20:33,920 --> 00:20:37,680 that's not a guarantee. But I suppose you could say the same about agents. 327 00:20:37,680 --> 00:20:40,880 Right. Like an agent that is operating on bad data. 328 00:20:42,080 --> 00:20:45,480 Right. Could also do some serious 329 00:20:45,480 --> 00:20:48,720 damage. Absolutely, absolutely. I think that is why, 330 00:20:49,400 --> 00:20:53,240 look, when we talk about, to use data analytics, just an example, and 331 00:20:53,320 --> 00:20:57,080 you can extrapolate from that afterwards what we are trying to do, 332 00:20:57,240 --> 00:21:00,200 we'll kind of try and think about it a little bit like a brain. There's 333 00:21:00,200 --> 00:21:03,920 a left side, right side. The left side for us is what 334 00:21:03,920 --> 00:21:07,640 we call Data Pro V. Looking at the data processes and 335 00:21:07,640 --> 00:21:11,240 actually process value. So we use principles of value stream 336 00:21:11,240 --> 00:21:13,640 mapping and we are, 337 00:21:15,090 --> 00:21:18,490 and we're relying, we're not trying to replace the systems that you already have. So 338 00:21:18,490 --> 00:21:22,250 you probably already have an ERP system in place and a CRM 339 00:21:22,250 --> 00:21:25,730 and various other warehouse transport, various other management 340 00:21:25,730 --> 00:21:29,410 systems. So it's not about ripping and replacing everything. No, you've probably made 341 00:21:29,410 --> 00:21:32,930 a decent choice and they're probably doing a good job of 342 00:21:33,010 --> 00:21:36,770 managing the particular part of the process that they were meant to 343 00:21:37,010 --> 00:21:40,610 deal with. But the problem is that they were all provided as point solutions 344 00:21:41,570 --> 00:21:44,610 and they don't necessarily talk to each other. And so up to now, 345 00:21:45,170 --> 00:21:48,770 what you needed to do is to somehow connect the data points 346 00:21:48,770 --> 00:21:52,530 yourself. But going back to what we're doing. So dataprov is about 347 00:21:53,330 --> 00:21:56,530 first of all capturing the value stream map as it matters to you, to your 348 00:21:56,530 --> 00:22:00,290 process, to your supply chain, capturing the KPIs 349 00:22:00,290 --> 00:22:03,890 as they matter to you. Because for you, maybe cost is the most important 350 00:22:03,890 --> 00:22:07,650 thing, maybe on time, in full delivery, various other things. And, 351 00:22:08,530 --> 00:22:12,250 and irrespective of what that thing is, you probably also have 352 00:22:12,250 --> 00:22:16,010 a quantitative measure for what is good versus bad. One company's own time 353 00:22:16,010 --> 00:22:19,730 in full delivery should be over 90, another might be 75. Doesn't matter, but 354 00:22:19,730 --> 00:22:23,370 it's kind of your stuff. So that's kind of the Data proofy side of what 355 00:22:23,370 --> 00:22:26,530 we're talking about. And this is where it's crucial that the data 356 00:22:27,090 --> 00:22:30,810 that we are able to connect to the, to the data and that the data 357 00:22:30,810 --> 00:22:33,890 is valid because. Absolutely right. If you're saying, 358 00:22:34,710 --> 00:22:38,430 you are absolutely right in saying that if the data is incorrect, all the 359 00:22:38,430 --> 00:22:42,230 conclusions you're going to draw on top of it are going to be problematic. 360 00:22:43,110 --> 00:22:46,270 So that's on the one side. On the other side we've got what we call 361 00:22:46,270 --> 00:22:49,990 dnai. So the data analytic AI part, which is where at the most basic level 362 00:22:49,990 --> 00:22:53,750 we provide you with some sort of copilot, let's call it, which 363 00:22:54,470 --> 00:22:58,270 allows you to interact with it a bit like a 364 00:22:58,270 --> 00:23:01,710 consumer would interact with ChatGPT or Claude or whatever the favorite 365 00:23:01,710 --> 00:23:05,560 LLM model is, which is basically ask a question in 366 00:23:05,560 --> 00:23:09,400 plain language and it should be able to give you a 367 00:23:09,400 --> 00:23:13,160 contextually correct answer. And in our case, in the context 368 00:23:13,160 --> 00:23:16,920 of your supply chain, your supply chain data. So it's not about 369 00:23:17,080 --> 00:23:19,960 data analytics, isn't about asking it a question like, okay, 370 00:23:21,480 --> 00:23:25,080 what does the word noetic mean for that you have Gemini and whatever other 371 00:23:25,480 --> 00:23:29,320 tools. But if you want to ask, okay, how much of SKU 372 00:23:29,400 --> 00:23:32,680 1 to 3 have I sold from the distribution center 373 00:23:33,160 --> 00:23:36,870 in Baltimore over the last six months? It should give 374 00:23:36,870 --> 00:23:40,590 you the right answer that would otherwise have taken you 375 00:23:41,150 --> 00:23:44,710 and put you on the queue for the business analyst to interrogate the 376 00:23:44,710 --> 00:23:48,550 various SQL or other databases and give you an answer maybe in 377 00:23:48,550 --> 00:23:52,350 a week. Or if you get to 378 00:23:52,350 --> 00:23:56,190 the queue. Get access to and dig through 30, 40 379 00:23:56,190 --> 00:24:00,030 different dashboards or spreadsheets. Right, that's the thing. I see, 380 00:24:00,670 --> 00:24:04,480 absolutely, yeah, absolutely. And 381 00:24:04,480 --> 00:24:08,240 so that's at kind of a. Call it a basic level, but then 382 00:24:08,240 --> 00:24:11,560 you can take it and not chop, because that basic level of a 383 00:24:11,560 --> 00:24:14,360 copilot requires you to proactively ask a question. 384 00:24:16,280 --> 00:24:19,640 Whereas what an agent can do is, 385 00:24:19,880 --> 00:24:23,560 and you can have actually a set of agents that do 386 00:24:23,560 --> 00:24:26,880 a particular job for you, like what I mentioned as an example, you can have 387 00:24:26,880 --> 00:24:30,280 a KPI guard you might want. So let's take the case of 388 00:24:30,280 --> 00:24:34,060 dashboards that you rightly said. Lots of organizations have various dashboards and 389 00:24:34,060 --> 00:24:37,580 various systems. And then those dashboards get complemented by those 390 00:24:37,580 --> 00:24:41,220 spreadsheet dashboards which collect information for all sorts of data points and some 391 00:24:41,220 --> 00:24:44,900 manual intervention, etc. They tend to be, 392 00:24:44,900 --> 00:24:48,620 okay, a weekly or monthly report that somebody sees and kind 393 00:24:48,620 --> 00:24:52,180 of, it could be that a week or month after the fact that you have 394 00:24:52,180 --> 00:24:55,700 breached whatever key performance indicator you wanted to meet, 395 00:24:55,860 --> 00:24:59,140 you get to know, oh, My costs have just gone 396 00:25:00,500 --> 00:25:03,540 20% higher than what I need them to be or something like that. 397 00:25:04,580 --> 00:25:08,340 At the base, at one level you can say okay, let me have 398 00:25:08,340 --> 00:25:11,540 a KPI guard that tells me as soon as 399 00:25:13,540 --> 00:25:17,340 a part of my process has breached a particular KPI 400 00:25:17,340 --> 00:25:21,140 against a particular guardrail or a boundary that I 401 00:25:21,140 --> 00:25:24,980 set, I want a notification immediately. And you can choose whether the notification 402 00:25:24,980 --> 00:25:27,890 is a slack message or an email or whatever else. 403 00:25:28,690 --> 00:25:31,410 You can go a level beyond that and say, 404 00:25:32,210 --> 00:25:36,010 okay, I want you to actually, on a particular part of the 405 00:25:36,010 --> 00:25:39,810 process or a particular KPI, I want you to 406 00:25:40,610 --> 00:25:44,450 try and kind of simulate or predict basically 407 00:25:44,450 --> 00:25:48,170 what's going to happen and tell me if you think it's likely that I'm going 408 00:25:48,170 --> 00:25:51,650 to breach a particular KPI. 409 00:25:52,610 --> 00:25:56,330 Those things are. There's a lot of kind of 410 00:25:56,330 --> 00:25:59,530 work that needs to go behind the scenes and lots of ifs and 411 00:26:00,170 --> 00:26:03,850 ends and buts etc that kind of need to take into 412 00:26:03,850 --> 00:26:07,530 account. But in principle you can see, I think 413 00:26:07,850 --> 00:26:11,210 it's kind of exciting that the 414 00:26:11,530 --> 00:26:14,890 emerging and constantly evolving capabilities of 415 00:26:15,050 --> 00:26:15,930 Gen AI 416 00:26:18,650 --> 00:26:22,010 and either various types of models, be it LLMs or 417 00:26:22,010 --> 00:26:25,530 SLMs or VLMs, whatever is 418 00:26:25,530 --> 00:26:28,950 relevant to your. In our, 419 00:26:29,110 --> 00:26:32,070 in our data analytics example in enterprise context, 420 00:26:33,030 --> 00:26:36,870 allow you to do things that have up to now been either 421 00:26:36,870 --> 00:26:40,070 impossible or very, very hard. Interesting. 422 00:26:44,390 --> 00:26:47,390 So does it help, Is it fair to say this helps with governance? Right. There 423 00:26:47,390 --> 00:26:51,190 were discovery, not necessarily governance, but kind of the discovery like what does 424 00:26:51,190 --> 00:26:54,990 the agent do? In particular, does it. How do you discover all these 425 00:26:54,990 --> 00:26:57,750 different disparate sources? Is it. 426 00:26:59,190 --> 00:27:02,470 How much of a degree, to a degree is it automated? So 427 00:27:03,430 --> 00:27:07,070 this is if I understand the question correctly and I may not have. 428 00:27:07,070 --> 00:27:10,790 Phrased it right, so. I'll have a go. 429 00:27:10,950 --> 00:27:12,390 I think you write those like 430 00:27:15,110 --> 00:27:18,830 let me try and raise the question a little bit differently and you tell me 431 00:27:18,830 --> 00:27:21,960 if it was kind of. If I got the general gist and 432 00:27:25,150 --> 00:27:28,590 can I rely on the agent to kind of. I'll exaggerate a little bit. And 433 00:27:28,590 --> 00:27:32,350 can you rely on the agent or agents to 434 00:27:33,550 --> 00:27:36,990 kind of save me for any. From any possible 435 00:27:37,150 --> 00:27:40,830 kind of fire drill or problem that I might face 436 00:27:42,430 --> 00:27:45,710 is one way of asking the question or another way of asking the question is 437 00:27:45,870 --> 00:27:49,350 how specific do I need to be in what I'm 438 00:27:49,350 --> 00:27:52,670 asking the agent to do? Am I kind of roughly on the right track? 439 00:27:53,080 --> 00:27:56,320 Yeah, I would say so. Like that. That's one of, the, one of the, one 440 00:27:56,320 --> 00:28:00,120 of the aspects of it. But the first one I was going for is I 441 00:28:00,440 --> 00:28:04,240 get your product, I sign up. What happened what's the first thing 442 00:28:04,240 --> 00:28:07,480 that happens? Do I talk. You do get together with the business. Like who orders 443 00:28:07,480 --> 00:28:10,440 the product? Is the cto, is it the CEO, is it the. 444 00:28:11,800 --> 00:28:15,000 I don't know how many companies have chief logistics officers. Like who, who, 445 00:28:16,120 --> 00:28:19,760 who you sell to. Is basically, it could be. There's a number of 446 00:28:19,760 --> 00:28:22,840 kind of levels of, of buyers, and it could be any one of the, 447 00:28:24,170 --> 00:28:27,450 of the following from the Chief Digital 448 00:28:27,450 --> 00:28:31,250 officer. Different companies have different names for it, but could 449 00:28:31,250 --> 00:28:33,770 be chief Digital Officer, chief Information Officer, 450 00:28:34,890 --> 00:28:37,050 kind of somebody who's responsible for the. 451 00:28:38,490 --> 00:28:42,090 What historically has been called the IT side of things 452 00:28:42,090 --> 00:28:44,810 to the systems management. 453 00:28:46,490 --> 00:28:50,170 Or it could be the chief supply chain officer. Which companies have. 454 00:28:51,040 --> 00:28:54,720 It could be the layer below that, but 455 00:28:54,800 --> 00:28:58,520 doesn't matter about the titles. It's still the same functions all the way 456 00:28:58,520 --> 00:29:01,280 through to. It could be the 457 00:29:02,160 --> 00:29:05,880 Personas, the managers. It could be the 458 00:29:05,880 --> 00:29:09,720 product manager of a particular product in the 459 00:29:09,720 --> 00:29:12,400 pharmaceutical organization or written organization 460 00:29:13,520 --> 00:29:16,400 that can use the capabilities that we're talking about. 461 00:29:17,720 --> 00:29:21,360 So that's a little bit in terms of the types of users and buyers that 462 00:29:21,360 --> 00:29:24,840 we, that we're looking into, that we're, that we're working with 463 00:29:25,320 --> 00:29:25,720 The. 464 00:29:30,680 --> 00:29:33,560 Sorry, that was the question about who we're dealing with. I think there was another 465 00:29:33,560 --> 00:29:36,840 problem. Yeah, no, no, that was really it. And then like, what's the first step? 466 00:29:36,840 --> 00:29:40,520 Right? Like, you know, say, like, you know, you or your sales rep have come 467 00:29:40,520 --> 00:29:44,340 to me and you explain it and I'm a, I'm a company. Whether 468 00:29:45,300 --> 00:29:48,700 I was like, oh, pretend I'm the executive that got kind of 469 00:29:48,700 --> 00:29:52,060 embarrassed by that thing, we need this today, we need this 470 00:29:52,060 --> 00:29:55,860 yesterday. What happens next? Does the 471 00:29:55,860 --> 00:29:58,820 agent go out and search around for 472 00:29:59,460 --> 00:30:03,260 SQL Server instances and spreadsheets, or do you tell the 473 00:30:03,260 --> 00:30:05,780 agent, hey, I got my data here, I got my data here, I got my 474 00:30:05,780 --> 00:30:09,470 data here, and have at it. So there 475 00:30:09,470 --> 00:30:13,110 is a. I think it's probably before going 476 00:30:13,110 --> 00:30:16,550 into the specific process that we go through and kind of the 477 00:30:16,550 --> 00:30:20,110 steps. Yeah, sorry, I was just excited because this sounds. No, no, it's fine. It's 478 00:30:20,110 --> 00:30:23,150 fine. It's great. I think it's worth maybe 479 00:30:23,870 --> 00:30:27,150 pausing for a second and doing a slight detour 480 00:30:27,390 --> 00:30:31,230 to talking about the evolving business models that 481 00:30:31,390 --> 00:30:34,910 are happening, I think, in the industry overall. And then I'll tie it back to 482 00:30:35,470 --> 00:30:39,120 how we're dealing with things. The, the 483 00:30:39,120 --> 00:30:42,960 fact that AI is making the rapid progress that it is. I think, I 484 00:30:42,960 --> 00:30:46,560 think it's kind of fairly evident to, to everybody that we're talking about 485 00:30:46,560 --> 00:30:50,280 a, a fundamental technology evolution, 486 00:30:50,280 --> 00:30:54,120 if not revolution that we're, that we're seeing similar, if 487 00:30:54,120 --> 00:30:57,480 not at least as impactful as the Internet and cloud revolution 488 00:30:58,440 --> 00:31:02,000 etc and, and the same way that the 489 00:31:02,000 --> 00:31:05,440 advent of, of the Internet revolution or the cloud 490 00:31:05,440 --> 00:31:08,280 revolution has, has given birth to a new 491 00:31:09,200 --> 00:31:12,800 a paradigm of delivery which we all know is software as a service, 492 00:31:13,840 --> 00:31:16,640 which replaced kind of client server software, 493 00:31:18,080 --> 00:31:21,680 the advent of AI is very likely to also usher 494 00:31:21,680 --> 00:31:25,480 in a different delivery model which is not so much going to 495 00:31:25,480 --> 00:31:29,160 be the software serve model, software as a service 496 00:31:29,160 --> 00:31:32,840 model, whereby there are those monolithic kind of systems that 497 00:31:32,840 --> 00:31:36,170 you more or less need to adapt to. Because the whole purpose of software as 498 00:31:36,170 --> 00:31:39,930 a service or the whole. One of the basic tenets of it was that 499 00:31:39,930 --> 00:31:43,490 you kind of build it once for everyone and which means that everybody needs to 500 00:31:43,490 --> 00:31:47,290 adapt to you. These new models, there are different names 501 00:31:47,290 --> 00:31:50,530 being given to them, they're not all the same. But you might have heard of 502 00:31:50,530 --> 00:31:54,090 things like bespoke at scale or service as a software to kind of revert 503 00:31:54,090 --> 00:31:57,330 the SAS acronym or 504 00:31:57,730 --> 00:32:01,250 outcomes as a service. Those are all kind of different 505 00:32:01,970 --> 00:32:04,370 models that try and 506 00:32:05,090 --> 00:32:08,730 verbalize a changing a paradigm in, 507 00:32:08,730 --> 00:32:12,450 in delivery of software in that context. Now, going back to 508 00:32:12,450 --> 00:32:16,290 how we're, how we're doing things, we are, we're 509 00:32:16,290 --> 00:32:20,050 not seeing ourselves as kind of charging on, on a, on a perceived 510 00:32:20,050 --> 00:32:23,490 basis. Not, not that I'm talking about pricing now, but the 511 00:32:23,490 --> 00:32:27,330 delivery is, is intended to be tailored 512 00:32:27,330 --> 00:32:31,170 per customer in the sense that when we get to you say you're excited 513 00:32:31,170 --> 00:32:33,970 and you just bought the product, we will come. And one of the first things 514 00:32:33,970 --> 00:32:37,670 we you is a process discovery and data maturity 515 00:32:37,670 --> 00:32:41,510 assessment because exactly as you said earlier, if the data that 516 00:32:41,510 --> 00:32:44,270 you have is actually not going to give us 517 00:32:45,070 --> 00:32:48,670 sufficient information in order to make any decisions, 518 00:32:50,110 --> 00:32:53,870 we're going to fail. However brilliant the agents that we have are 519 00:32:54,030 --> 00:32:57,430 going to be later because the data is not going to be there. So we 520 00:32:57,430 --> 00:33:01,270 have to do this process discovery and data maturity. Then we have to 521 00:33:01,270 --> 00:33:04,990 kind of connect to various systems that you have. We need to understand your 522 00:33:06,010 --> 00:33:09,050 value Stream, map your KPIs, your, your targets, 523 00:33:09,690 --> 00:33:12,730 ensure that all that is kind of 524 00:33:15,850 --> 00:33:19,610 adapted for your, for your circumstances. And then 525 00:33:19,690 --> 00:33:23,170 we can start saying okay, here's maybe a library of a few agents that you 526 00:33:23,170 --> 00:33:26,850 can choose to use as is, or here's a sort 527 00:33:26,850 --> 00:33:30,650 of call it a canvas in an agent builder that you can take 528 00:33:30,650 --> 00:33:34,490 a few capabilities and build again an agent that's specifically 529 00:33:34,490 --> 00:33:38,280 tasked with addressing challenges that, that you have. 530 00:33:38,520 --> 00:33:42,200 So that that's kind of a slightly longer answer 531 00:33:42,200 --> 00:33:44,920 to your question about what happens next? 532 00:33:45,720 --> 00:33:49,440 Interesting, interesting. So it's almost like software 533 00:33:49,440 --> 00:33:53,200 as an agent, right? You know, saw. I 534 00:33:53,200 --> 00:33:56,960 never heard that acronym before. But. So agent is 535 00:33:56,960 --> 00:34:00,520 a service. I don't know, there's different ways. No, that acronym has. 536 00:34:00,920 --> 00:34:03,750 Would be pronounced very awkwardly. Asian. 537 00:34:08,870 --> 00:34:12,630 So your website says you 538 00:34:12,630 --> 00:34:16,430 focus on, you know, the 539 00:34:16,430 --> 00:34:19,750 four main industries are pharmaceuticals and life science, Omni channel retail, 540 00:34:21,110 --> 00:34:24,790 consumer goods and what is fmcg? 541 00:34:25,030 --> 00:34:28,830 Fast moving consumer goods. Gotcha, gotcha. Okay. And logistics and supply 542 00:34:28,830 --> 00:34:32,390 chain. But I guess any industry really has to 543 00:34:32,470 --> 00:34:35,339 rely on some kind of supply chain, right? 544 00:34:35,979 --> 00:34:39,459 Correct, Correct. The reason why you're seeing the particular 545 00:34:39,459 --> 00:34:43,139 industries you just called out is that we think that 546 00:34:43,139 --> 00:34:46,899 the. If you have a relatively 547 00:34:46,899 --> 00:34:50,699 large number of products that you have to deal with in a relatively complicated 548 00:34:50,699 --> 00:34:54,219 supply chain, this is where the potential added 549 00:34:54,219 --> 00:34:57,419 benefits of having like proper orchestration 550 00:34:58,779 --> 00:35:02,290 and the assistance of AI agents and is going to be more 551 00:35:02,290 --> 00:35:06,010 pronounced if, if you have just one product in a super 552 00:35:06,010 --> 00:35:09,690 simple process. Yes, you can benefit, but it's probably 553 00:35:09,690 --> 00:35:13,090 something you might be able to do kind of intuitive. 554 00:35:13,170 --> 00:35:16,690 Intuitively on your own or relatively LinkedIn system. That, 555 00:35:16,690 --> 00:35:20,410 that's why you're seeing the industries there which have 556 00:35:20,410 --> 00:35:24,050 the characteristics I said. I mean that makes sense. Right? Pharmaceuticals, life 557 00:35:24,050 --> 00:35:27,860 sciences, those are very highly regulated. People's lives are literally online 558 00:35:28,500 --> 00:35:32,300 on the trend of retail. I mean to compete in a world with 559 00:35:32,300 --> 00:35:36,060 Amazons and Walmarts, etc, you have to 560 00:35:36,060 --> 00:35:39,580 be. You have to bring your A game. Right. And 561 00:35:39,580 --> 00:35:43,060 consumer goods, probably the same thing. Right. Because any consumer good or 562 00:35:43,460 --> 00:35:47,140 what is a fast moving consumer good? I. I've not heard that term yet, 563 00:35:48,500 --> 00:35:52,020 to be honest. I'm not sure where is the definition of what's fast 564 00:35:52,020 --> 00:35:55,650 versus not fast. It's just a. It's one of the definitions. 565 00:35:55,650 --> 00:35:58,770 I. Another term that I've heard for this is 566 00:35:59,170 --> 00:36:02,970 cpg, Consumer packaged goods. Okay. That, that. I know what that is. 567 00:36:02,970 --> 00:36:06,290 Yeah. I mean I would imagine something like food, right? 568 00:36:07,650 --> 00:36:11,090 Yeah. There's a time component to a lot of 569 00:36:11,090 --> 00:36:14,930 foodstuffs possibly. Although I think, I think 570 00:36:15,090 --> 00:36:18,810 I am not sure of the look, not being 571 00:36:18,810 --> 00:36:22,570 an fmcg, I have not been in the FMCG industry 572 00:36:22,570 --> 00:36:25,290 myself, but I would imagine that they would 573 00:36:26,170 --> 00:36:29,810 refer to things like anything that you can kind of 574 00:36:29,810 --> 00:36:33,210 take and use quickly. A bar of soap. 575 00:36:33,450 --> 00:36:37,090 Oh, okay. That makes sense. Perish necessarily quickly. But it's going to use 576 00:36:37,090 --> 00:36:40,410 it. Within a week it's gone. It's. I think it also falls under. 577 00:36:41,210 --> 00:36:44,610 That makes sense. McG as an example. That makes sense. 578 00:36:44,610 --> 00:36:48,150 Okay. Wow. It's 579 00:36:48,150 --> 00:36:51,830 fascinating stuff. And like you Know, I think one of the big concerns 580 00:36:51,910 --> 00:36:55,750 is about AI of late. Right. It's always fascinating 581 00:36:55,750 --> 00:36:59,470 me how the, the tech news cycle works. 582 00:36:59,470 --> 00:37:02,830 Right? It works and it finds something to grab 583 00:37:02,830 --> 00:37:06,510 onto. It's like a little like, it's like a toddler 584 00:37:06,510 --> 00:37:10,190 basically. I have a three year old and you know, when he gets 585 00:37:10,190 --> 00:37:13,950 his mind on one thing, nothing else in the universe 586 00:37:13,950 --> 00:37:17,510 exists, you know what I mean? And I think the 587 00:37:17,510 --> 00:37:21,190 tech news industry like. Right, like so, you 588 00:37:21,190 --> 00:37:24,950 know, earlier this year it was agentic this, 589 00:37:24,950 --> 00:37:28,670 agentic that. Right now the last week or two it's all been 590 00:37:28,670 --> 00:37:31,350 about oh my God, we're in AI bubble. Are we in an AI bubble? Are 591 00:37:31,350 --> 00:37:35,190 we like, it's almost like so. But I think that, you 592 00:37:35,190 --> 00:37:37,830 know, one of the things you kind of pull back with the concerns about AI 593 00:37:37,830 --> 00:37:41,130 bubble is the concern of how do you add value. 594 00:37:41,930 --> 00:37:45,450 How does AI realistically add value to organization? 595 00:37:46,170 --> 00:37:49,930 I would imagine that when you get your product installed and everything's working 596 00:37:50,650 --> 00:37:54,449 amazingly, you probably have pretty quick ideas in terms of 597 00:37:54,449 --> 00:37:57,610 how much time is saved in terms of analysts, how much more 598 00:37:57,770 --> 00:38:01,570 effective people can be. I mean, is 599 00:38:01,570 --> 00:38:05,210 that something you see? Yeah, I think, look, 600 00:38:05,370 --> 00:38:08,530 there's a lot to unpack. What you said we can go back to the bubble 601 00:38:08,530 --> 00:38:11,190 and tech news maybe later. But 602 00:38:12,550 --> 00:38:16,310 in terms of the tangible results that you can get, 603 00:38:16,790 --> 00:38:20,350 it's. Yeah, I mean it depends on again, going back to the value 604 00:38:20,350 --> 00:38:24,150 stream map and the KPIs that that matter to you. If you care 605 00:38:24,150 --> 00:38:27,830 for example about cost, you might find that transportation cost per unit 606 00:38:27,830 --> 00:38:31,630 is particularly relevant for you and for various reasons because it's 607 00:38:31,630 --> 00:38:34,790 been, actually because it's been hard to analyze the data, to 608 00:38:35,110 --> 00:38:38,880 collate, collate and synthesize the data from different sources to orchestrate 609 00:38:38,880 --> 00:38:42,480 it. Basically you haven't been able to achieve 610 00:38:42,480 --> 00:38:46,280 the reductions that could have been achieved in transportation. So you can 611 00:38:46,280 --> 00:38:50,000 end up finding you got 5 or 10% improvement there. 612 00:38:50,000 --> 00:38:53,640 If you care about asset utilization, the same thing can be 613 00:38:53,640 --> 00:38:57,200 said for inventory turns and, or days inventory outstanding. 614 00:38:57,200 --> 00:39:01,040 Again, you can buy better orchestration of the data and looking into it, 615 00:39:01,200 --> 00:39:04,634 you might find improvements that are 10 to 20%, 616 00:39:04,746 --> 00:39:08,330 etc. Etc. And so almost every KPI that you, 617 00:39:08,330 --> 00:39:10,970 that you look at, there are bound to be 618 00:39:12,250 --> 00:39:15,610 improvements that you can make. Some of them can translate immediately 619 00:39:15,850 --> 00:39:19,370 into capex savings or cost 620 00:39:19,370 --> 00:39:22,730 reduction or revenue enhancement capabilities, 621 00:39:22,730 --> 00:39:26,410 etc. Some of them are going to be a little bit more, 622 00:39:28,650 --> 00:39:32,010 I was going to say qualitative, but let's go back to the example of what 623 00:39:32,010 --> 00:39:35,710 I said earlier about the OT3 for the pharmaceutical companies, the fact that 624 00:39:35,710 --> 00:39:38,710 the executive came with the wrong number to the customer, 625 00:39:40,070 --> 00:39:43,830 I'm sure there is a value to it. So they would like to have the 626 00:39:43,830 --> 00:39:47,510 right number if they have the right number, as opposed to the wrong number. 627 00:39:47,590 --> 00:39:50,630 How, how much exactly does that quantifiably? 628 00:39:51,590 --> 00:39:55,350 Well, there's trust. It's a trust issue, right? Exactly, exactly. It's a trust issue. Like 629 00:39:55,350 --> 00:39:58,390 if they're wrong, you start wondering if they're wrong about that. 630 00:39:58,790 --> 00:40:02,510 Yeah, I agree. What else are they wrong about? Right, exactly. And 631 00:40:02,510 --> 00:40:05,970 so all I'm saying is that some things will be very easy to 632 00:40:05,970 --> 00:40:09,650 translate immediately to cash, to dollars. Some things definitely 633 00:40:09,650 --> 00:40:13,210 have value in dollars, but are not as easy or obvious to make the 634 00:40:13,210 --> 00:40:16,490 connection. But on the whole, there are, 635 00:40:16,970 --> 00:40:20,570 there are so many different places in which you can, you can see 636 00:40:20,570 --> 00:40:24,090 additional value here that it's just, I mean, the 637 00:40:24,090 --> 00:40:27,410 opportunities I think are, are endless. We can go back if you want. We can 638 00:40:27,410 --> 00:40:30,320 discuss. Oh, no, I think it's great because, like, you know, I, I've been, I've 639 00:40:30,320 --> 00:40:33,440 been poking around agentic AI. I've been fascinated by it. But 640 00:40:34,000 --> 00:40:37,800 when it comes down to breast hacks, as people would 641 00:40:37,800 --> 00:40:41,560 say, it's hard to figure out what exactly 642 00:40:41,560 --> 00:40:45,240 would be a good objective source of value. I guess what 643 00:40:45,240 --> 00:40:49,040 we're saying is there's objective value, like hard cash numbers, 644 00:40:50,240 --> 00:40:52,560 hard dollar or pound numbers, because you're in the UK 645 00:40:54,880 --> 00:40:58,490 as well as, you know, kind of that 646 00:40:58,490 --> 00:41:02,290 soft kind of subjective stuff, whether that's trust, whether that's, 647 00:41:02,290 --> 00:41:05,810 you know, et cetera, et cetera, both are important. 648 00:41:06,050 --> 00:41:09,130 But I think that, you know, if we do get into a situation where people 649 00:41:09,130 --> 00:41:12,450 are going to tighten their belts or the hype wave is going to crash, 650 00:41:12,850 --> 00:41:15,730 having hard numbers, yep. Is always, 651 00:41:17,010 --> 00:41:20,370 always good to have the hard numbers. Right. 652 00:41:21,090 --> 00:41:24,130 But I mean, I would imagine that, you know, and again, you're right. Like, you 653 00:41:24,130 --> 00:41:27,880 know, I would. Even within the same organization, I would imagine, like there are 654 00:41:27,880 --> 00:41:31,120 different metrics to track, right. Like, you know, whether it's time, whether 655 00:41:31,440 --> 00:41:35,280 time to fulfillment, cost 656 00:41:35,280 --> 00:41:38,960 of transportation. Right. There's probably some kind of ecological 657 00:41:38,960 --> 00:41:42,280 things too, right. Like, you know, you know, we use this much fuel versus that 658 00:41:42,280 --> 00:41:44,800 much fuel, which again does tie to cost. But 659 00:41:46,320 --> 00:41:50,000 I think that there is a number of different. It's. It's. 660 00:41:51,690 --> 00:41:54,490 I think that over the last, say, 20 years, 661 00:41:55,450 --> 00:41:58,970 companies, supply chains have gotten orders of magnitude more complicated 662 00:41:59,610 --> 00:42:03,210 and the demands of a business environment have gotten orders of magnitude more 663 00:42:03,210 --> 00:42:05,850 complicated. And the 664 00:42:06,730 --> 00:42:10,450 people, the headcount for the departments that would figure stuff like this 665 00:42:10,450 --> 00:42:14,090 out have not grown by orders the same orders of magnitude. 666 00:42:14,250 --> 00:42:18,050 And I think that AI, far from being this job taker, 667 00:42:18,050 --> 00:42:21,850 could actually solve a lot of these problems that people don't have the 668 00:42:21,850 --> 00:42:25,530 job anyway. Right? No, they're, you know, they're not gonna hide, they're not 669 00:42:25,530 --> 00:42:28,410 gonna go out on a hiring spree and hire like a thousand people to kind 670 00:42:28,410 --> 00:42:32,130 of sort out the stuff. Right. They're gonna, they're gonna demand it of the 671 00:42:32,130 --> 00:42:35,810 existing or even less people. Right. So this is, 672 00:42:36,130 --> 00:42:39,970 yeah, I, I, I. Think that this is, look, we can, we can talk, let's 673 00:42:39,970 --> 00:42:43,690 talk a little bit about the, the, the potential hypo bubble and, and 674 00:42:43,690 --> 00:42:47,410 let's talk about the, the jobs. So, so the bubble 675 00:42:47,410 --> 00:42:50,290 talk of the last how many days or weeks. 676 00:42:51,490 --> 00:42:54,970 In a way, irrespective of whatever my personal 677 00:42:54,970 --> 00:42:58,610 opinion is, it almost doesn't matter if 678 00:42:58,610 --> 00:43:02,370 there is a bubble or not. Because first of 679 00:43:02,370 --> 00:43:06,090 all, let's be clear, I think my understanding, at least when people talk about 680 00:43:06,090 --> 00:43:09,890 the bubble, they talk about the financial valuation bubble. So people 681 00:43:09,890 --> 00:43:13,130 will ask, okay, is Nvidia really worth 5, should it be worth 682 00:43:13,130 --> 00:43:16,760 $5 trillion? Yes or no. And even if you 683 00:43:16,760 --> 00:43:20,560 subscribe to the notion that no, it isn't, and it's actually how much 684 00:43:20,560 --> 00:43:24,400 overpriced and so instead of 5 trillion, it should be worth 40 less, 685 00:43:24,400 --> 00:43:28,240 first of all, still worth 3, 3, 3 trillion and still a lot 686 00:43:28,240 --> 00:43:31,920 of money. And second, again, irrespective of how much 687 00:43:31,920 --> 00:43:35,600 Nvidia in particular is worth or open AI or any other company, it doesn't 688 00:43:35,600 --> 00:43:39,360 matter. There's a completely separate question as to whether 689 00:43:39,360 --> 00:43:42,700 or not the underlying technology that it is part of the, of 690 00:43:42,940 --> 00:43:46,460 the industry that enables AI. Is that 691 00:43:46,700 --> 00:43:50,460 hype? Is it hype that AI is actually never 692 00:43:50,460 --> 00:43:54,100 going to achieve anything meaningful? I think that is a completely 693 00:43:54,100 --> 00:43:57,940 separate question and I don't hear anybody, and I would disagree 694 00:43:57,940 --> 00:44:01,580 with anybody who would say no, no, AI in itself, the actual technology, 695 00:44:01,660 --> 00:44:05,340 the actual capabilities are a bubble. It's actually meaningless. 696 00:44:05,980 --> 00:44:09,100 As I said earlier, I think it's going to be at least as meaningful as 697 00:44:09,260 --> 00:44:13,020 the advent of the Internet or mobile telephony and the combination of 698 00:44:13,020 --> 00:44:16,650 which have enabled things like, like Uber and Airbnb 699 00:44:16,650 --> 00:44:20,210 and a lot of, I mean to name just two of like many, 700 00:44:20,210 --> 00:44:23,050 many applications that have made our lives different. 701 00:44:24,170 --> 00:44:27,050 No, that's true. Right. You know, when you look back and I'm old enough to 702 00:44:27,050 --> 00:44:30,690 remember the.com boom, right, the.com and the. Com 703 00:44:30,690 --> 00:44:34,450 bust, right? And a lot of the 704 00:44:34,450 --> 00:44:38,290 things that these.com startups in the late 90s promised have come 705 00:44:38,290 --> 00:44:42,140 true. Right. I can. Pets.com 706 00:44:42,940 --> 00:44:46,540 didn't optimize their supply chain. Right. The cost of getting you dog 707 00:44:46,540 --> 00:44:50,140 food, they 708 00:44:50,140 --> 00:44:53,900 hadn't figured that out. But obviously Amazon does. I get my dog 709 00:44:53,900 --> 00:44:57,740 food 90% of the time as an auto delivery. Right. 710 00:44:58,140 --> 00:45:01,980 Because they can use. And it's not so much 711 00:45:01,980 --> 00:45:04,540 the technology aspect of it. Right. Because HTML 712 00:45:06,220 --> 00:45:09,740 hasn't really changed radically in that 713 00:45:09,740 --> 00:45:13,400 intervening time. The backend systems have changed in a lot of 714 00:45:13,400 --> 00:45:15,920 ways. But you know, for the end of the day, I mean it was really 715 00:45:15,920 --> 00:45:19,680 the process. You know, Amazon built out a whole delivery network and 716 00:45:19,680 --> 00:45:22,440 worked out deals with other delivery companies. Right. 717 00:45:23,160 --> 00:45:26,880 So it is now, you're right, like it is now possible to do that. Right. 718 00:45:26,880 --> 00:45:30,560 It is now possible to call an Uber and you 719 00:45:30,560 --> 00:45:34,320 know, get, you know, 720 00:45:34,320 --> 00:45:37,040 get a car. But like the whole notion of, I think a lot of that 721 00:45:37,040 --> 00:45:40,600 relied on, you know, having smartphones. Right. Because now it's easy to order 722 00:45:40,600 --> 00:45:44,320 stuff versus in the olden days you had to sit down at a 723 00:45:44,320 --> 00:45:47,840 computer, you had to wait for it to boot up, see the loading 724 00:45:47,840 --> 00:45:50,240 screen and then you had to dial, 725 00:45:51,360 --> 00:45:54,960 click to Internet, connect to Internet. Then you had to hear the screeching of the 726 00:45:54,960 --> 00:45:58,160 modem. It was a five minute process to get online, 727 00:45:58,960 --> 00:46:01,680 plus the page had a load. Now it's just 728 00:46:03,280 --> 00:46:07,040 a lot of people have broadband or certainly faster than dial up 729 00:46:07,040 --> 00:46:10,750 today. You know, it seems much 730 00:46:10,750 --> 00:46:14,350 more feasible to do that. Like if I need dog food I can just, you 731 00:46:14,350 --> 00:46:16,950 know, even when I'm talking to you, even though I shouldn't because it's kind of 732 00:46:16,950 --> 00:46:20,590 rude, I could click open another window and say click order now. 733 00:46:20,590 --> 00:46:24,310 Right. But I think that's a 734 00:46:24,310 --> 00:46:27,590 long winded way of saying I agree with you because the, the promise of E 735 00:46:27,590 --> 00:46:31,230 commerce, the promise of the Internet has been fulfilled 736 00:46:31,390 --> 00:46:34,830 right now though how we got there 737 00:46:35,480 --> 00:46:39,120 was not the way that the startups in the 90s kind of 738 00:46:39,120 --> 00:46:42,520 thought. Right. But yeah, sorry, go ahead. 739 00:46:43,000 --> 00:46:46,760 Yeah, so, so we're very much agreeing on the fact that 740 00:46:46,760 --> 00:46:50,600 the underlying capabilities and new capabilities 741 00:46:50,839 --> 00:46:54,560 that AI in its broader term will enable, I 742 00:46:54,560 --> 00:46:58,320 don't think anybody's questioning that. I think very few people know exactly how 743 00:46:58,320 --> 00:47:01,920 it's going to work out, but I think there's wide consensus that it's going to 744 00:47:01,920 --> 00:47:05,540 be fundamentally, it's going to have 745 00:47:05,540 --> 00:47:09,020 fundamental, a fundamental impact. Now part of the 746 00:47:09,020 --> 00:47:12,700 fundamental impact that people worry about is about jobs, which is what you said earlier 747 00:47:12,700 --> 00:47:16,420 about oh my God, is AI going to take everybody's jobs? Etc. And 748 00:47:16,900 --> 00:47:20,500 look, again, I don't I don't know, I'm not, I'm not a prophet. There 749 00:47:20,500 --> 00:47:24,260 are valid arguments as to why AI might 750 00:47:24,420 --> 00:47:28,060 be, might be risky for some jobs. My 751 00:47:28,060 --> 00:47:31,860 white might be disruptive 752 00:47:32,180 --> 00:47:35,980 to all sorts of jobs. But if, if we want to take the optimistic 753 00:47:35,980 --> 00:47:39,780 view, which also is, has a lot of valid arguments for 754 00:47:40,180 --> 00:47:43,780 one of which being every technological evolution or 755 00:47:43,780 --> 00:47:47,220 revolution has impacted certain jobs, 756 00:47:47,380 --> 00:47:51,020 but by and large created more opportunity, more jobs, more 757 00:47:51,020 --> 00:47:54,620 advancement than it, than it created, to use my favorite 758 00:47:54,620 --> 00:47:58,460 example is just because I can't remember where I came across it, but 759 00:47:58,460 --> 00:48:02,250 the word computer used to mean a person that did 760 00:48:02,250 --> 00:48:05,850 computations. Yes, that's right. And, and lo and behold, 761 00:48:06,090 --> 00:48:09,810 computers. Now when anybody says computer nowadays, they don't mean 762 00:48:09,810 --> 00:48:13,650 a person doing computational because we've got machines that do that. So if 763 00:48:13,650 --> 00:48:17,490 you want to find a job as a computer, good luck. Right, right, 764 00:48:17,490 --> 00:48:21,330 right. Gonna be quite hard. But does that mean that kind of nobody can find 765 00:48:21,330 --> 00:48:25,170 a job anymore? Absolutely not. Is that, is that 766 00:48:25,170 --> 00:48:28,290 a promise that, that, that's exactly what's going to happen with AI? 767 00:48:28,770 --> 00:48:32,610 No, but at least the trajectory up to now has been 768 00:48:33,490 --> 00:48:36,770 one of, I don't know to call it like 769 00:48:37,010 --> 00:48:40,530 a positive, positive trend going forward. 770 00:48:43,090 --> 00:48:46,850 And I think, I personally think that the, exactly as you said, at 771 00:48:46,850 --> 00:48:50,690 least some of the capabilities that AI builds and the examples that we talked 772 00:48:50,690 --> 00:48:54,180 about about what data analytics does and being able to give you this 773 00:48:54,180 --> 00:48:57,460 KPI guard or various other agentic capabilities, 774 00:48:58,100 --> 00:49:01,860 I'm not necessarily seeing it, in fact, I'm not at all seeing it 775 00:49:01,940 --> 00:49:05,540 as kind of taking away anybody's job. I don't think 776 00:49:05,540 --> 00:49:08,820 that the business analyst that currently 777 00:49:08,980 --> 00:49:12,500 can't deal with all the tasks that they're being given 778 00:49:13,700 --> 00:49:16,820 is going to be replaced. I think that they are going to be helped 779 00:49:17,380 --> 00:49:21,180 by both them. So they're going to be helped by it. And more 780 00:49:21,180 --> 00:49:24,920 importantly all the business managers who up to now just wouldn't deliver 781 00:49:25,320 --> 00:49:28,960 what they needed to deliver because they didn't have access to the business analyst. Now 782 00:49:28,960 --> 00:49:32,800 they have access to an agent that is able to. Yeah, I wish Andy 783 00:49:32,800 --> 00:49:36,640 was here because Andy has a really good anecdote about how 784 00:49:36,640 --> 00:49:40,000 DBAs used to be. Like you would have a database 785 00:49:40,000 --> 00:49:43,400 administrator and typically you had, it was a one to one 786 00:49:43,400 --> 00:49:47,040 relationship. Every database had one DBA and 787 00:49:47,040 --> 00:49:49,960 then sometimes a backup if it was important enough. Right. 788 00:49:51,390 --> 00:49:55,150 But now the job of a DBA is they realistically manage 789 00:49:55,470 --> 00:49:59,270 dozens if not hundreds of databases. Right. And that's because of the 790 00:49:59,270 --> 00:50:02,350 cloud and automation and things like that, even before AI. 791 00:50:03,070 --> 00:50:05,870 But the job of a DBA still exists. 792 00:50:06,670 --> 00:50:09,710 Right. It just looks really different. And I agree with you. 793 00:50:10,830 --> 00:50:14,510 I have faith in the trend line. Right. Historically, every 794 00:50:15,230 --> 00:50:18,990 aspect of automation has created more 795 00:50:18,990 --> 00:50:22,630 jobs over the longer haul. And my only 796 00:50:22,630 --> 00:50:26,110 concern is irrational. There's irrational exuberance. Right. 797 00:50:26,830 --> 00:50:30,430 But there's also what people don't talk about as much as irrational 798 00:50:30,430 --> 00:50:34,150 pessimism. Right. And that was, I 799 00:50:34,150 --> 00:50:37,550 lived through that in the dot com bubble. That's the part of the dot com 800 00:50:37,550 --> 00:50:40,670 bubble I remember the most because it was the most difficult 801 00:50:41,710 --> 00:50:45,260 where it was, oh, you know, the Internet's just a fad and like it's over 802 00:50:45,260 --> 00:50:48,940 and it's like, you know, we, we don't 803 00:50:49,740 --> 00:50:52,980 laugh enough at the, the people who said that. Right. You know what I mean? 804 00:50:52,980 --> 00:50:56,300 Like, you know, so to your point, right, like, you know, is, is 805 00:50:56,300 --> 00:51:00,140 Nvidia worth really worth $5 trillion? Is it worth. 806 00:51:00,140 --> 00:51:03,820 Who knows? Today it could be like worth seven. Right. I haven't 807 00:51:03,820 --> 00:51:07,660 checked the markets, but. But it's certainly not worth zero. 808 00:51:08,300 --> 00:51:12,150 Right. It's certainly not worth like I can easily see kind 809 00:51:12,150 --> 00:51:14,030 of the way the 810 00:51:15,630 --> 00:51:19,150 clickbait machine works is, you know, we go from 811 00:51:19,950 --> 00:51:23,550 they make the money on the roller coaster right up and they make the money 812 00:51:23,550 --> 00:51:27,350 on the roller coaster right down. Right. Irrational exuberance, 813 00:51:27,350 --> 00:51:30,350 irrational pessimism. And that's kind of the, 814 00:51:31,630 --> 00:51:35,430 there are dangers to both. But the part that at least traumatized 815 00:51:35,430 --> 00:51:39,140 me more was the, the way down and how far 816 00:51:39,140 --> 00:51:42,860 that kind of went in the other direction. That's the only thing that would 817 00:51:42,860 --> 00:51:46,180 keep me up at night. Yeah, look, there's no, 818 00:51:46,980 --> 00:51:49,060 there's no guarantee, I think that we are, 819 00:51:51,540 --> 00:51:55,260 it's possible, let's phrase it not in double negative. It's possible that we 820 00:51:55,260 --> 00:51:58,740 are in a financial bubble and it's possible as a result that at some point 821 00:51:58,740 --> 00:52:02,540 there's going to be a correction and that correction might be a short and sharp 822 00:52:02,540 --> 00:52:06,220 kind of decline or it could be a, a long and steady 823 00:52:06,220 --> 00:52:09,830 decline. It could be all sorts of things. Things. And whichever, if it, if it 824 00:52:09,830 --> 00:52:13,430 does happen, whichever form it takes, it's going to carry with it 825 00:52:13,430 --> 00:52:17,190 something. All of that is under rival if it goes down 826 00:52:17,910 --> 00:52:21,550 that path, irrespective of whether it does that or 827 00:52:21,550 --> 00:52:24,310 not. In the same way that happened with the dot com 828 00:52:25,110 --> 00:52:28,790 boom and bust of 2000, the late 90s and 829 00:52:28,870 --> 00:52:31,350 what then happened in 2000 and a little bit afterwards, 830 00:52:32,710 --> 00:52:36,360 the boom and bust, the financial boom and bust and the absolute 831 00:52:36,360 --> 00:52:39,960 roller coaster that the NASDAQ had and the implication, the financial, very real 832 00:52:39,960 --> 00:52:43,800 financial implications that it had for people didn't change the fact, as we've 833 00:52:43,800 --> 00:52:46,760 said and agreed, that the Internet, or the 834 00:52:47,560 --> 00:52:50,920 Internet, which is what created the to begin with, has 835 00:52:51,720 --> 00:52:55,160 fundamentally changed the way a lot of things 836 00:52:55,320 --> 00:52:59,080 operate the same, absolutely the same will be true of 837 00:52:59,640 --> 00:53:01,560 AI. I have no doubt about that. 838 00:53:03,340 --> 00:53:06,940 I think that in the same way that the Internet has had a lot of 839 00:53:06,940 --> 00:53:10,620 positive impacts and some impacts that people would argue are actually 840 00:53:10,620 --> 00:53:14,380 not that positive, I'm sure the same will be over. And 841 00:53:14,620 --> 00:53:18,460 hopefully again, as with the trend up to now, 842 00:53:18,940 --> 00:53:21,260 hopefully we can all, 843 00:53:23,500 --> 00:53:27,340 if everybody does everything they can in order to maximize the positive 844 00:53:27,340 --> 00:53:29,900 and minimize the negative, we have a very, very, 845 00:53:31,090 --> 00:53:33,090 we can be very, very optimistic about the future. 846 00:53:35,170 --> 00:53:39,010 And, and, and to give a few examples, I mean, AI in theory holds 847 00:53:39,010 --> 00:53:42,650 the promise of helping us solve really, really hard problems like climate 848 00:53:42,650 --> 00:53:45,810 change, like the world hunger, 849 00:53:46,450 --> 00:53:50,250 how do we feed the population, how do, how do we manage resources in a, 850 00:53:50,250 --> 00:53:54,050 in a, a, an earth that is limited in size, with 851 00:53:54,050 --> 00:53:55,410 a population that keeps growing, 852 00:53:57,840 --> 00:54:01,280 how do we fight cancer, et cetera, et cetera. Those are all things that 853 00:54:01,520 --> 00:54:05,160 theoretically AI should help us kind of increase 854 00:54:05,160 --> 00:54:08,880 manifold. Yeah, so there's, you also have to think too. 855 00:54:08,880 --> 00:54:11,680 Like the cognitive load that we have today 856 00:54:12,560 --> 00:54:15,280 could be reduced. Like if you're a business analyst and 857 00:54:16,960 --> 00:54:20,520 you have a book next, you used to have a book next to you. How 858 00:54:20,520 --> 00:54:22,880 do I do this in SQL? Because they don't want to wait for the data 859 00:54:22,880 --> 00:54:25,800 people, right? How do I do that? How do I do I go look and 860 00:54:25,800 --> 00:54:29,280 I do, I go, I do a Google search, right? And you know, Stack 861 00:54:29,280 --> 00:54:32,840 Overflow would have a billion different answers. 862 00:54:33,080 --> 00:54:36,840 Well, two or three different answers and then 100 people, anytime 863 00:54:36,840 --> 00:54:40,480 you post a question would chomp on you. Like check through the existing answers rather 864 00:54:40,480 --> 00:54:44,160 than. Whereas now you go to ChatGPT. How do I do this? And it gives 865 00:54:44,160 --> 00:54:47,960 it to you right now. Is it always accurate? You know, obviously there's some 866 00:54:47,960 --> 00:54:51,690 rough edges there, but for the most part, you know, if you 867 00:54:51,690 --> 00:54:55,490 run into a problem in an unfamiliar space, it's a 868 00:54:55,490 --> 00:54:59,330 lot easier to get an answer now than it was before. It's a lot 869 00:54:59,330 --> 00:55:02,610 more time efficient. So if you think about the cognitive load that now can be 870 00:55:02,610 --> 00:55:05,690 shifted to actual other more 871 00:55:06,010 --> 00:55:09,570 pertinent problems. I don't know. I see that as a net 872 00:55:09,570 --> 00:55:12,410 positive. I think we both agree, which is cool. That's always nice. 873 00:55:13,050 --> 00:55:16,650 Brilliant minds do think alike. I know we're coming at the 874 00:55:17,290 --> 00:55:21,050 top of the hour, so where can folks find out 875 00:55:21,050 --> 00:55:23,430 more about data noet and about you? 876 00:55:25,190 --> 00:55:28,990 The obvious places. So for data analytics, best place to start is our 877 00:55:28,990 --> 00:55:31,830 website which is datanoetic AI. So data 878 00:55:33,190 --> 00:55:36,710 D A T A N O E T I C 879 00:55:36,950 --> 00:55:40,550 A I data analytic AI. The website about myself. 880 00:55:40,550 --> 00:55:44,190 I'm on LinkedIn so I spell my name it A 881 00:55:44,190 --> 00:55:47,910 Y. Like Italy without the L and last name Haber H A 882 00:55:47,910 --> 00:55:51,650 B for Bravo E R. You can find me on LinkedIn, you 883 00:55:51,650 --> 00:55:53,370 can find our website and 884 00:55:55,530 --> 00:55:59,250 happy exploring from there on. Awesome. I think this is great. I think 885 00:55:59,250 --> 00:56:02,810 it's the reason why I bring up the bubbles and is 886 00:56:02,890 --> 00:56:06,450 I. Think that. What your company 887 00:56:06,450 --> 00:56:10,250 optimizes will give people the hard numbers to kind of 888 00:56:10,250 --> 00:56:12,810 splash cold water into the irrational pessimism. 889 00:56:14,810 --> 00:56:18,300 That's what I think. Because I think if you're an AI company 890 00:56:18,380 --> 00:56:22,220 today, start thinking about gathering those hard numbers. Right? Because those 891 00:56:22,220 --> 00:56:25,700 hard numbers are going to be. I mean that's how Amazon survived. That's how any 892 00:56:25,700 --> 00:56:29,060 of the survivors of the dot com crash, they had the hard numbers to prove 893 00:56:29,060 --> 00:56:32,860 it. Right. And most 894 00:56:32,860 --> 00:56:36,300 of the major Internet companies today, 895 00:56:36,860 --> 00:56:40,140 not all of them, but a lot of them had 896 00:56:40,460 --> 00:56:44,220 the survivors. There were survivors in the dot com bust, right? Absolutely. 897 00:56:44,810 --> 00:56:48,450 And they came out stronger for it. I think Amazon being the most 898 00:56:48,450 --> 00:56:52,250 notable. Amazon being the most obvious. Google was started kind 899 00:56:52,250 --> 00:56:55,850 of in that era and they're a major player. And so 900 00:56:56,330 --> 00:57:00,010 I think that, yeah, just remember, you know, the 901 00:57:00,010 --> 00:57:03,810 sun always rises, right? No, I 902 00:57:03,810 --> 00:57:07,650 think again, we're probably violently agreeing. If you're 903 00:57:07,650 --> 00:57:09,770 able to deliver real tangible outcomes, 904 00:57:11,540 --> 00:57:13,700 you'll weather the storm. You'll, you'll be able to, 905 00:57:16,740 --> 00:57:20,460 you'll become indispensable as people find Amazon at the moment at a consumer 906 00:57:20,460 --> 00:57:24,220 level and actually AWS as an example at 907 00:57:24,220 --> 00:57:27,900 the business level. So yeah, absolutely. Well, I think on 908 00:57:27,900 --> 00:57:31,460 that thought, thanks for having, thanks for coming on the show 909 00:57:31,460 --> 00:57:35,300 and what a great conversation. We'd love to have you back sometime and 910 00:57:35,700 --> 00:57:39,230 we'll let our AI finish the show. And that's a wrap on 911 00:57:39,230 --> 00:57:42,870 another illuminating journey into the dataverse. Huge 912 00:57:42,870 --> 00:57:46,510 thanks to Itai Haba for joining us and proving that AI isn't just 913 00:57:46,510 --> 00:57:50,270 about generating cat poems or pretending to write your emails. It can 914 00:57:50,270 --> 00:57:53,910 actually prevent multimillion dollar supply chain nightmares and possibly, 915 00:57:54,070 --> 00:57:57,670 just possibly stop Frank from reliving tile related trauma. 916 00:57:57,910 --> 00:58:01,430 If you enjoyed this episode, be sure to subscribe, rate 917 00:58:01,510 --> 00:58:05,320 and leave a review because somewhere an AI agent is judging you 918 00:58:05,320 --> 00:58:09,040 based on your podcast engagement. Until next time, keep 919 00:58:09,040 --> 00:58:12,840 your data clean, your models lean, and remember, in a world 920 00:58:12,840 --> 00:58:16,400 full of dashboards, be the agent of change. Bailey 921 00:58:16,480 --> 00:58:20,080 signing off with perfect on time in full delivery.