WEBVTT 1 00:00:00.080 --> 00:00:02.520 Welcome back to another episode of the Restaurant Report. Today 2 00:00:02.520 --> 00:00:04.960 we're going to be talking about AI and what it 3 00:00:05.080 --> 00:00:07.000 means for the restaurant space. So it's going to be 4 00:00:07.000 --> 00:00:08.199 a good one. I think you guys are going to 5 00:00:08.320 --> 00:00:12.160 like it. I've got the CEO and founder of Biki, 6 00:00:12.759 --> 00:00:15.759 this is a customer data platform and CDP out there, 7 00:00:15.759 --> 00:00:18.399 and his name is Abanov Kapor, and we're going to 8 00:00:18.440 --> 00:00:23.120 be talking about the potential of where the restaurant industry 9 00:00:23.800 --> 00:00:26.199 could go in terms of AI. You guys don't want 10 00:00:26.239 --> 00:00:28.199 to miss this one. Stick around and. 11 00:00:28.120 --> 00:00:29.320 We will be right back. 12 00:00:38.439 --> 00:00:40.560 This episode is brought to you in part by Gusto, 13 00:00:40.679 --> 00:00:44.719 the number one rated HR platform for payroll, benefits and more. 14 00:00:45.320 --> 00:00:48.799 With Gusto's easy to use platform, you can empower your 15 00:00:48.840 --> 00:00:52.960 people and push your business forward. Over four hundred thousand 16 00:00:53.039 --> 00:00:56.880 businesses choose Gusto every day. And let's get into it, guys. 17 00:00:56.960 --> 00:00:59.079 There's a couple of things you can do with Gusto 18 00:00:59.159 --> 00:01:01.920 that you should check out. Some of the solutions that 19 00:01:01.960 --> 00:01:05.319 you're just absolutely going to want to know about is 20 00:01:05.359 --> 00:01:08.840 of course, their business type. New businesses and startups, You 21 00:01:08.879 --> 00:01:11.920 guys are welcome coming in small businesses. 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The thing about 31 00:01:40.359 --> 00:01:45.040 Gusto is flexible plans and features honest pricing, no hidden fees. 32 00:01:45.640 --> 00:01:47.760 This is the plan that we use, which is the 33 00:01:47.760 --> 00:01:51.480 Plus plan sixty bucks a month. Guys, you cannot go wrong. 34 00:01:51.560 --> 00:01:53.719 It's about nine bucks a person, so you guys can 35 00:01:53.760 --> 00:01:57.840 definitely afford it. Get in there and choose Gusto with 36 00:01:57.920 --> 00:02:04.560 a full suite of tax services, HR services, time tracking, scheduling, expenses, reimbursements. 37 00:02:05.159 --> 00:02:07.480 You get the picture. Gusto is the place for you. 38 00:02:07.760 --> 00:02:10.240 Check it all out. Just go over to Gusto dot 39 00:02:10.280 --> 00:02:14.400 com use our link down below to get started. See 40 00:02:14.439 --> 00:02:17.039 you there. All right, we're back here and we're going 41 00:02:17.080 --> 00:02:20.240 to dive into AI and the use case. As I said, 42 00:02:20.360 --> 00:02:22.919 we're gonna have Abanov come on today and talk a 43 00:02:22.960 --> 00:02:25.240 little bit more about it. Biki is one of those 44 00:02:25.280 --> 00:02:30.400 companies that powers database decisions for brands like Bojangles, Mod Pizza, 45 00:02:30.479 --> 00:02:33.680 Dave's Hot Chicken, Plia Bowls, and also Eggs Upgrill. So 46 00:02:34.199 --> 00:02:37.879 he's already in a lot of brands, and the question 47 00:02:38.080 --> 00:02:40.800 is is how does Ai truly fit. So let's just 48 00:02:40.840 --> 00:02:43.199 get him into the show and we'll kind of bring 49 00:02:43.280 --> 00:02:45.400 him in and see what Abanava is doing. Abanov, how 50 00:02:45.400 --> 00:02:45.680 are you? 51 00:02:46.080 --> 00:02:48.240 I said, great for you? Yeah, you got it? Yeah? 52 00:02:48.280 --> 00:02:49.599 How are you give me. 53 00:02:49.680 --> 00:02:51.840 A quick rundown on Beki? What do you guys? 54 00:02:51.840 --> 00:02:52.000 Do? 55 00:02:52.240 --> 00:02:55.000 You talked about customer data platform, but what does that 56 00:02:55.080 --> 00:02:55.960 mean to a brand? 57 00:02:56.400 --> 00:02:56.599 Yeah? 58 00:02:56.680 --> 00:02:57.039 Totally. 59 00:02:57.120 --> 00:02:59.199 So I started the business seven and a half years ago. 60 00:02:59.280 --> 00:03:02.240 My mother also restaurant operator, and she was literally writing 61 00:03:02.280 --> 00:03:05.840 down phone numbers off of delivery tablets and emailing customers 62 00:03:05.879 --> 00:03:08.639 who made reservations on open table and just got obsessed 63 00:03:08.680 --> 00:03:10.280 with this idea that you know, she's in the business 64 00:03:10.280 --> 00:03:13.039 of hospitality and really doesn't know anything about the person 65 00:03:13.080 --> 00:03:15.400 behind the transaction, you know, in New York City or 66 00:03:15.400 --> 00:03:17.639 something like Restaurant Week, and she would do a restaurant 67 00:03:17.680 --> 00:03:20.680 week menu, and she would say, are new people actually 68 00:03:20.719 --> 00:03:22.560 coming in on the restaurant week menu? Am I taking 69 00:03:22.599 --> 00:03:24.639 an existing guest and just pulling their visit forward and 70 00:03:24.680 --> 00:03:27.639 giving them, you know, a cheaper prefix option. Am I 71 00:03:27.800 --> 00:03:31.159 driving more frequency as a result of this restaurant week menu? 72 00:03:31.199 --> 00:03:33.560 And again she had all these sort of like breadcrumbs 73 00:03:33.599 --> 00:03:35.719 of data around her business, but no real way to 74 00:03:35.759 --> 00:03:38.520 answer some of these critical questions. And so I got 75 00:03:38.520 --> 00:03:40.560 obsessed with this idea of you know, how do we 76 00:03:40.599 --> 00:03:43.680 basically pull together all of a restaurant's data across point 77 00:03:43.680 --> 00:03:48.080 of sale payments, online ordering, reservations, loyalty Wi Fi and 78 00:03:48.159 --> 00:03:51.120 really map the entire customer journey for them. And then 79 00:03:51.159 --> 00:03:53.280 when they pull a lever in their business, whether it's 80 00:03:53.319 --> 00:03:56.639 a marketing promotion or a new menu item, instead of 81 00:03:56.680 --> 00:03:59.560 looking at you know, top line sales, which is really 82 00:03:59.639 --> 00:04:02.599 the only real indicator most brands have to understand the 83 00:04:02.599 --> 00:04:05.080 success of what's happening in how do we give them 84 00:04:05.080 --> 00:04:08.599 the customer level data, the impact on traffic, on frequency, 85 00:04:08.680 --> 00:04:12.599 on churn to really know what decisions are successful and 86 00:04:12.639 --> 00:04:15.879 how successful are they. And so today we work with 87 00:04:15.960 --> 00:04:16.959 as you mentioned. 88 00:04:16.639 --> 00:04:20.839 Around Yeah, quite a few brands across the US now, yeah, yeah. 89 00:04:20.439 --> 00:04:23.639 So thousands of locations. Dave sat Chicken. How what's the 90 00:04:23.639 --> 00:04:25.600 oldest brand that you guys have deployed on. 91 00:04:26.160 --> 00:04:29.920 EXCEP Grill is definitely our our you know, the og 92 00:04:30.240 --> 00:04:32.959 as I would say they took a leap of faith 93 00:04:33.000 --> 00:04:36.120 on us in the early days, and uh, you know 94 00:04:36.160 --> 00:04:39.759 it was for them, it was a very simple initial 95 00:04:39.839 --> 00:04:42.839 use case, which was like, we just want to personalize 96 00:04:42.839 --> 00:04:45.439 our local store marketing. Yeah, and we need a way 97 00:04:45.439 --> 00:04:47.120 to use data to use that, and then we need 98 00:04:47.160 --> 00:04:50.199 a way to measure how successful those efforts are. And 99 00:04:50.480 --> 00:04:54.040 it's really it's expanded from there to incorporate the menu operations, 100 00:04:54.040 --> 00:04:54.879 all kinds of stuff. 101 00:04:55.040 --> 00:04:58.360 Let's talk about the the idea of AI usage. I mean, 102 00:04:58.399 --> 00:05:00.680 it's been more and more of a topic here on 103 00:05:00.720 --> 00:05:02.680 our network as we cover it on across a lot 104 00:05:02.720 --> 00:05:05.360 of the podcasts. But when you look at the real 105 00:05:05.560 --> 00:05:09.000 use case for the restaurant industry, many people will point 106 00:05:09.000 --> 00:05:11.879 to back a house. Possibly we could see it in 107 00:05:12.079 --> 00:05:16.480 the operational side in terms of equipment AI integration to 108 00:05:16.480 --> 00:05:20.360 where you you know, there's just less of the everyday 109 00:05:20.439 --> 00:05:23.519 functionality of someone running an additional machine or having another 110 00:05:23.560 --> 00:05:27.240 body back there that maybe AI could be the potential 111 00:05:27.240 --> 00:05:30.360 holy grail of back of house. What is your opinion. 112 00:05:30.360 --> 00:05:31.600 Do you think we're going to see it more on 113 00:05:31.639 --> 00:05:32.959 back of house or do you think we're going to 114 00:05:33.000 --> 00:05:34.439 see more customer facing stuff? 115 00:05:34.959 --> 00:05:37.800 I think, honestly, I think the short answer is we'll 116 00:05:37.839 --> 00:05:39.800 probably see it everywhere over time. 117 00:05:41.319 --> 00:05:44.680 I would caution anybody from thinking. 118 00:05:44.360 --> 00:05:49.639 That there are really good, you know, game changing use 119 00:05:49.680 --> 00:05:51.920 cases for AI in the restaurant industry, and I would 120 00:05:51.920 --> 00:05:54.560 apply that actually to I mean, I think about it 121 00:05:54.560 --> 00:05:56.879 this way right like I don't. I'm thinking about how 122 00:05:56.920 --> 00:05:59.560 I use AI in my life as a founder and CEO, 123 00:06:00.240 --> 00:06:02.680 and I'm only just still feel like I'm scratching the 124 00:06:02.720 --> 00:06:05.480 surface of what it's capability for me in my work, 125 00:06:05.800 --> 00:06:08.920 let alone someone who is, you know, trying to execute 126 00:06:09.160 --> 00:06:13.360 perfectly across order prep, across hiring and labor management, across 127 00:06:13.399 --> 00:06:16.759 inventory management, across P and L management. And so I 128 00:06:16.800 --> 00:06:20.279 think again, this is the arc of AI is long, 129 00:06:20.480 --> 00:06:24.160 and adoption and value will take time to create. That 130 00:06:24.240 --> 00:06:27.879 being said, you know, there are some options to take 131 00:06:27.920 --> 00:06:30.279 some of the low hanging fruit, the more manual work 132 00:06:30.279 --> 00:06:34.240 that operators are doing, and automate some of those through AI. Right, 133 00:06:34.240 --> 00:06:37.199 exactly what I think of is, you know, there's obviously 134 00:06:37.199 --> 00:06:41.000 a very high correlation from responding to reviews on Yelp 135 00:06:41.040 --> 00:06:44.079 and Google with having a higher profile, and then you 136 00:06:44.079 --> 00:06:46.360 get surface more in the search results, and that leads 137 00:06:46.360 --> 00:06:48.800 to higher sales and traffic, and it can be very 138 00:06:49.120 --> 00:06:51.600 you know, when my mother in law came home from 139 00:06:51.639 --> 00:06:52.959 at the end of the day, the last thing she 140 00:06:53.000 --> 00:06:55.959 wants to do is flip open her computer at eleven 141 00:06:56.000 --> 00:06:57.639 thirty at night and say, let me respond to all 142 00:06:57.680 --> 00:07:00.800 my Google reviews. But that being said, yeah, you could 143 00:07:00.879 --> 00:07:04.160 use AI, and maybe it doesn't remove the personal touch, 144 00:07:04.160 --> 00:07:06.160 but you could start to use AI to at least 145 00:07:06.680 --> 00:07:10.319 engage guests who leave good reviews yeah, or flag the 146 00:07:10.360 --> 00:07:14.040 ones that are potentially negative reviews that need your time 147 00:07:14.360 --> 00:07:16.560 as an operator, need some of your valuable time to 148 00:07:16.639 --> 00:07:17.959 investigate and understand. 149 00:07:17.639 --> 00:07:18.439 What I've talked to. 150 00:07:18.560 --> 00:07:21.439 I've talked to a lot of operators that have kind 151 00:07:21.480 --> 00:07:23.439 of said, hey, listen, I'm starting to use it in 152 00:07:23.480 --> 00:07:27.560 workflow management, whether it's hiring, much like to your point 153 00:07:27.600 --> 00:07:30.560 customer service to a certain level, being able to do 154 00:07:31.279 --> 00:07:36.240 more and easier, I should say, functional workflow projects that 155 00:07:36.279 --> 00:07:38.279 they might be like a training manual, or maybe they're 156 00:07:38.360 --> 00:07:41.240 doing something, you know, in a market research around real 157 00:07:41.360 --> 00:07:43.600 estate all that kind of those seem to be much 158 00:07:43.639 --> 00:07:47.519 more clerical and more functional tools and tasks for a 159 00:07:47.560 --> 00:07:51.480 restaurant operator. But once you get inside the operations, then 160 00:07:51.480 --> 00:07:54.160 that's where it gets to be a question for me 161 00:07:54.360 --> 00:07:56.680 is just how quickly is it going to come and 162 00:07:56.759 --> 00:07:59.480 become a thing I hear? And I've talked to a 163 00:07:59.519 --> 00:08:03.120 lot of different technology companies that are starting to move 164 00:08:03.120 --> 00:08:05.319 in this space. I had one company on not too 165 00:08:05.360 --> 00:08:08.639 long ago that was talking about agentic AI and what 166 00:08:08.680 --> 00:08:12.000 they had done is creating AI agents to kind of 167 00:08:12.560 --> 00:08:17.800 create these mundane task scenarios that might play out in 168 00:08:18.000 --> 00:08:19.720 just through a food order. This is one of the 169 00:08:19.759 --> 00:08:23.519 early stage companies that we've talked with, and this could 170 00:08:23.519 --> 00:08:26.399 be a factor going forward, especially if we see a 171 00:08:26.439 --> 00:08:29.920 new interface. Do you think there is a possibility that 172 00:08:30.040 --> 00:08:33.559 because AI is moving so quickly on the consumer front, 173 00:08:34.399 --> 00:08:38.080 that maybe we see some AI front ends coming at 174 00:08:38.120 --> 00:08:40.919 restaurants in unique ways through like ordering, et cetera. 175 00:08:41.080 --> 00:08:43.120 Yeah, I think voice is the big thing that obviously 176 00:08:43.120 --> 00:08:45.919 a lot of people are exploring, you know, and I 177 00:08:45.960 --> 00:08:52.120 won't it's still amazing to me that even within our 178 00:08:52.200 --> 00:08:55.240 guest base that you have high single digit percentages of 179 00:08:55.399 --> 00:08:59.120 orders for some brands right right being calling being phone orders, 180 00:08:59.200 --> 00:09:01.879 And I think there's a very there are some really 181 00:09:01.879 --> 00:09:04.720 good founders out there that are experimenting with ways to 182 00:09:04.799 --> 00:09:06.519 basically automate. 183 00:09:06.159 --> 00:09:07.200 That entire process. 184 00:09:07.240 --> 00:09:10.559 I mean using a voice, a natural human sounding voice, 185 00:09:10.799 --> 00:09:16.639 to answer the phone, take the order, send place the order, PULD, 186 00:09:16.679 --> 00:09:18.440 send a payment link, like I have a friend of 187 00:09:18.480 --> 00:09:21.720 mine who's working on this right now, and it is 188 00:09:21.799 --> 00:09:25.120 pretty minimally like as a user, you don't necessarily know 189 00:09:25.159 --> 00:09:27.960 you're talking to an AI. And I think that'll get 190 00:09:27.960 --> 00:09:31.399 even better over time, meaning it'll be harder to kind 191 00:09:31.399 --> 00:09:32.600 of perceive are you talking to a. 192 00:09:32.559 --> 00:09:33.320 Human our emissions? 193 00:09:33.399 --> 00:09:37.200 Yeah, from an experience standpoint, Like my whole philosophy with 194 00:09:37.440 --> 00:09:40.399 restaurants is, you know, if you look at sort of 195 00:09:40.440 --> 00:09:43.360 what happened with retail a decade ago, you had this 196 00:09:43.799 --> 00:09:46.080 hollowing out of the middle where you had, you know, 197 00:09:46.159 --> 00:09:49.559 the department stores like Macy's, JC Penny, the things that 198 00:09:49.600 --> 00:09:52.840 tried to be everyone for everything, for everyone, they sort 199 00:09:52.879 --> 00:09:56.159 of kind of lost away. But if you look at 200 00:09:56.200 --> 00:09:59.039 sort of where the spectrum went, people either anchored on 201 00:09:59.399 --> 00:10:02.639 convenience or experience, and I think the same thing is 202 00:10:02.679 --> 00:10:05.120 going to happen to the restaurant industry, for better or worse. 203 00:10:05.480 --> 00:10:09.559 And so things like voice ordering and AI and voice 204 00:10:09.559 --> 00:10:15.240 AI for ordering leans into a convenience aspect where you 205 00:10:15.360 --> 00:10:17.120 are someone who is trying to serve I think in 206 00:10:17.159 --> 00:10:18.639 my mother in law again, you are someone who's trying 207 00:10:18.639 --> 00:10:21.399 to serve the guest in store, someone who's actually sitting 208 00:10:21.440 --> 00:10:24.360 down and spending their hard earned dollars and wants that 209 00:10:24.480 --> 00:10:27.279 more experiential environment. The last thing you want to do 210 00:10:27.360 --> 00:10:29.919 is rip away from the guest experience and answer phone 211 00:10:29.919 --> 00:10:32.080 and take a phone order, which is what again my 212 00:10:32.120 --> 00:10:35.240 mother in law would do. And so, you know, taking 213 00:10:35.240 --> 00:10:38.399 some of this low hanging fruit and enabling operators to 214 00:10:38.480 --> 00:10:43.240 serve more guests more frictionlessly through technology, I think is 215 00:10:43.360 --> 00:10:48.919 certainly a powerful trend that will continue and enhances the 216 00:10:48.919 --> 00:10:49.960 overall guest experience. 217 00:10:50.200 --> 00:10:53.919 Yeah, I wonder if we're going to see that point 218 00:10:53.960 --> 00:10:59.960 where the customer's leverage of AI tools themselves becomes a superpower. 219 00:11:00.279 --> 00:11:03.120 You know, if you think about find me the best reservation, 220 00:11:03.639 --> 00:11:06.120 find me the restaurants that do this, this or that, 221 00:11:07.120 --> 00:11:09.679 especially on travel planning. I know we've started to do 222 00:11:09.720 --> 00:11:12.240 that within our own family. I've been surprised at how 223 00:11:12.799 --> 00:11:15.919 well it does going into a city. I'm traveling to 224 00:11:16.039 --> 00:11:19.000 DC here soon, and I gave it a whole litany 225 00:11:19.039 --> 00:11:21.399 of things to do. And this was perplexity, I think, 226 00:11:21.440 --> 00:11:26.240 and it came back with some fairly competent answers that 227 00:11:26.559 --> 00:11:29.440 I was like, that's not bad. I mean, I might 228 00:11:29.480 --> 00:11:31.919 do a little bit better on this topic. But do 229 00:11:32.000 --> 00:11:34.840 you think that consumers are now going to kind of 230 00:11:34.879 --> 00:11:38.919 have that one up on where the industry is going? Well? 231 00:11:38.960 --> 00:11:41.559 I think so, But you know, I think consumers are 232 00:11:41.600 --> 00:11:46.279 for better or worse, you know, because of how I 233 00:11:46.960 --> 00:11:49.639 never like when people say restaurant operators are slow to 234 00:11:49.679 --> 00:11:52.279 adopt technology because they don't think technology can work for them. 235 00:11:52.320 --> 00:11:54.639 I don't think that's true. I think the business is 236 00:11:54.679 --> 00:11:58.720 so demanding that is genuinely hard to take time out 237 00:11:58.720 --> 00:12:00.519 of your day to be like, what's the latest tech 238 00:12:00.559 --> 00:12:02.679 trend and how can I adopt it for my business? Right, 239 00:12:02.679 --> 00:12:04.679 you're just working in the business so much that it's 240 00:12:04.679 --> 00:12:07.320 hard to work on the business, and so a lot 241 00:12:07.360 --> 00:12:10.679 of the tech trends that happen in this industry for 242 00:12:10.759 --> 00:12:13.919 better or worse are consumer are changes on the consumer 243 00:12:13.960 --> 00:12:17.720 side that drag the restaurant industry along with them. Marketplaces 244 00:12:17.799 --> 00:12:20.679 are a great example of this. Now to your question 245 00:12:20.840 --> 00:12:23.279 specifically around will consumers sort of be on the front 246 00:12:23.320 --> 00:12:24.960 end of AI and will that bring restaurants along. 247 00:12:25.799 --> 00:12:26.639 I saw a post. 248 00:12:26.799 --> 00:12:28.639 I saw the CEO of par which is a big 249 00:12:28.679 --> 00:12:31.879 publicly traded company in our industry. He tweeted or he 250 00:12:31.960 --> 00:12:35.480 wrote something on LinkedIn interest recently which was what happens 251 00:12:35.519 --> 00:12:37.919 to the third party delivery platforms in a role in 252 00:12:37.960 --> 00:12:41.559 the world of agentic and voice AI where you can 253 00:12:41.639 --> 00:12:45.559 say to an AI, go do this, or order me 254 00:12:45.559 --> 00:12:48.080 a burger from shakeshak ye, you know, and it's all 255 00:12:48.080 --> 00:12:50.240 through your voice, and it's like, okay, what do you want? Okay, 256 00:12:50.240 --> 00:12:52.039 do you want? What are the modifiers that you want? 257 00:12:52.279 --> 00:12:54.600 Do you want to and it up sells you through 258 00:12:54.639 --> 00:12:57.240 the power of your voice. Where does that order actually 259 00:12:57.279 --> 00:12:59.759 get placed? Does it through a phone call to that restaurant, 260 00:13:00.000 --> 00:13:02.559 does it happen on the restaurant's direct online ordering platform, 261 00:13:02.879 --> 00:13:05.919 or does it happen through the third party delivery platform? 262 00:13:06.000 --> 00:13:09.480 And I think that is a very interesting, like a 263 00:13:09.559 --> 00:13:13.080 nuanced case where we aren't even grappling with the with 264 00:13:13.120 --> 00:13:16.480 the ramifications of that type of behavior. I think we're still, 265 00:13:16.799 --> 00:13:19.000 you know the world of ais progressing quickly, so we're 266 00:13:19.000 --> 00:13:22.360 probably at least twelve months away from something like that happening. 267 00:13:22.840 --> 00:13:25.639 It's actually happening now. We just we saw one of 268 00:13:25.679 --> 00:13:29.679 the tests that was done at a project build not 269 00:13:29.759 --> 00:13:33.279 too long ago on blockchain, and this was a company 270 00:13:33.320 --> 00:13:35.200 that was doing a gentic ai and they were using 271 00:13:35.399 --> 00:13:40.480 basically using blockchain to track the payment mechanism that converted 272 00:13:40.519 --> 00:13:43.279 it into US dollars, but they were using some webbooks 273 00:13:43.279 --> 00:13:46.919 directly with Postmates and uber eats to be able to 274 00:13:46.960 --> 00:13:50.799 execute on it. Sure, on the restaurant side, it goes 275 00:13:50.840 --> 00:13:54.240 back to that kind of that tablet farm that used 276 00:13:54.240 --> 00:13:56.320 to exist in third party delivery where you had a 277 00:13:56.320 --> 00:13:58.759 tablet from everyone. I wonder if that will be the 278 00:13:58.799 --> 00:14:01.679 factor going forward, But that has been done with agentic ai. 279 00:14:01.840 --> 00:14:02.279 Yeah. 280 00:14:02.320 --> 00:14:05.440 The question will be is when does a restaurant build 281 00:14:05.519 --> 00:14:10.000 their own agent and that is able to handle it 282 00:14:10.080 --> 00:14:14.080 and handle whether it's up selling, you know, dynamic pricing, 283 00:14:14.200 --> 00:14:16.120 all sorts of things that could play into this. Do 284 00:14:16.200 --> 00:14:17.840 you think that will be coming first? 285 00:14:18.720 --> 00:14:20.639 I think that's tough for you know, and like our 286 00:14:20.679 --> 00:14:22.919 sort of philosophy at BICKI here from a from a 287 00:14:22.919 --> 00:14:26.240 customer data and analytics standpoint is, you know, and we 288 00:14:26.320 --> 00:14:28.679 work with brands that are up to one thousand units. 289 00:14:28.879 --> 00:14:31.320 And the way we like to frame it is always 290 00:14:31.320 --> 00:14:34.480 like you're in the business of ordering, of making food, and. 291 00:14:34.480 --> 00:14:36.759 Executing on the guest experience. 292 00:14:36.440 --> 00:14:38.639 Like that is your at the end of the day 293 00:14:38.679 --> 00:14:41.360 when you think about, like source what drives retention in 294 00:14:41.399 --> 00:14:43.639 a restaurant, Like, I'll just give you some high level stats, 295 00:14:43.679 --> 00:14:46.799 like typically we have about three hundred and fifty guest profiles, 296 00:14:46.799 --> 00:14:49.120 three hundred and fifty million guest profiles in our system. 297 00:14:49.399 --> 00:14:51.320 And the thing that we found is that on average, 298 00:14:51.360 --> 00:14:53.879 eighty percent of guests never come back after the first visit, 299 00:14:54.360 --> 00:14:56.279 and the twenty percent that do come back can drive 300 00:14:56.320 --> 00:14:59.759 anywhere from fifty to seventy five percent of a brand's revenue. Yeah, 301 00:15:00.200 --> 00:15:03.480 and so making the food, executing on the food, and 302 00:15:03.559 --> 00:15:07.240 executing on that experience is the highest probability thing that 303 00:15:07.279 --> 00:15:11.600 you can do as a restaurant to increase frequency, drive retention, 304 00:15:11.720 --> 00:15:16.159 and drive long term repeatable sales. Now Nowhere in that 305 00:15:16.200 --> 00:15:19.639 equation does building technology factor in for better or worse. 306 00:15:19.679 --> 00:15:22.960 And so I think to your question, maybe the biggest, 307 00:15:22.960 --> 00:15:26.440 the top five, the top ten brands, McDonald's, Domino's, you know, 308 00:15:27.279 --> 00:15:30.840 Chick fil A. They have engineering teams, they are thinking 309 00:15:30.840 --> 00:15:33.519 about the cutting edge of leveraging AI and data and 310 00:15:33.559 --> 00:15:37.200 machine learning. Maybe they will invest in building out some 311 00:15:37.240 --> 00:15:39.559 of these capabilities. But I would say the other ninety 312 00:15:39.639 --> 00:15:41.919 nine point nine percent of brands, it's hard enough doing 313 00:15:41.960 --> 00:15:45.960 that number one priority well consistently enough over time that 314 00:15:46.360 --> 00:15:49.600 again I think, you know, entrepreneurs, people who innovate in 315 00:15:49.600 --> 00:15:52.440 the space will come in and provide some of that 316 00:15:52.559 --> 00:15:55.320 value and make it easier for restaurants to use those 317 00:15:55.399 --> 00:15:58.080 tools and use that technology to grow their businesses. 318 00:15:58.120 --> 00:16:01.120 What do you Okay, So let's jump over to adoption barriers, 319 00:16:01.159 --> 00:16:05.000 because this could happen on two fronts. One front, of course, 320 00:16:05.159 --> 00:16:08.240 is adoption in the restaurant space, which you, as a 321 00:16:08.279 --> 00:16:10.480 tech company, has got to You've got a chance to 322 00:16:10.519 --> 00:16:14.200 experience that very slow moving in terms of the restaurant 323 00:16:14.240 --> 00:16:18.039 industry attaching to technology and being able to leverage it. 324 00:16:18.320 --> 00:16:21.279 That's number one. Then you have the adoption cycle of 325 00:16:21.360 --> 00:16:24.879 the consumer, which seems to be much faster. I mean, 326 00:16:24.919 --> 00:16:27.240 we saw with social media. We've seen it with mobile 327 00:16:27.960 --> 00:16:31.360 that all of that was kind of the restaurant industry 328 00:16:31.399 --> 00:16:34.240 was kind of pulled through by the consumer. Do you 329 00:16:34.240 --> 00:16:36.559 feel like that's going to be the case with a 330 00:16:36.600 --> 00:16:37.480 lot of the AI tools. 331 00:16:37.759 --> 00:16:39.960 So yeah, I don't see a reason that could change. 332 00:16:39.960 --> 00:16:43.639 I mean, the thing that the big exception, the big 333 00:16:43.639 --> 00:16:46.080 caveat here and this will probably make me eat my words, 334 00:16:46.120 --> 00:16:49.039 but you never know when someone's gonna when someone is 335 00:16:49.080 --> 00:16:54.519 going to solve the operational toil for lack of our term, 336 00:16:54.679 --> 00:16:57.759 involved in running the business in a day to day basis. 337 00:16:57.840 --> 00:17:01.240 So the example that comes to mind is is if 338 00:17:01.240 --> 00:17:05.279 you have an operator that can just say, hey, like 339 00:17:05.880 --> 00:17:08.720 we're out of we're out of onions, like eighty six 340 00:17:08.720 --> 00:17:11.359 everything with onions from my instore menu, my and my 341 00:17:11.440 --> 00:17:14.960 delivery menu. Right Like, of course we will get to that. 342 00:17:15.039 --> 00:17:16.599 I don't know what the timeline is, but we will 343 00:17:16.680 --> 00:17:19.160 get to that point. And that's when you sort of 344 00:17:19.200 --> 00:17:24.039 have the restaurant industry themselves trying to adopt some of 345 00:17:24.039 --> 00:17:27.720 these technologies. But that being said, that always happened. Second, 346 00:17:27.960 --> 00:17:32.079 the consumer. The consumer is the primary driver of what 347 00:17:32.200 --> 00:17:35.079 sort of like brings the restaurant the restaurant business along 348 00:17:35.119 --> 00:17:37.640 from a technology standpoint, like, even think about it with 349 00:17:37.680 --> 00:17:40.319 my business, right, Like I started the business seven and 350 00:17:40.319 --> 00:17:43.279 a half years ago, back in twenty seventeen, no one 351 00:17:43.319 --> 00:17:46.240 cared about data for restaurants like I was out there. 352 00:17:46.279 --> 00:17:47.799 Even my mother in law was like, this seems like 353 00:17:47.839 --> 00:17:50.279 a great platform. I can't use it. Right. But then 354 00:17:50.279 --> 00:17:53.680 when the pandemic happened and every consumer was like, I 355 00:17:53.720 --> 00:17:55.960 still want to order food out, I don't want to 356 00:17:55.960 --> 00:17:58.720 cook all the time. Then my phone started blowing up 357 00:17:58.759 --> 00:18:00.839 and people were saying, hey, you talked to us about 358 00:18:00.920 --> 00:18:02.440 data for the last two and a half years. 359 00:18:03.160 --> 00:18:03.559 I don't know. 360 00:18:03.640 --> 00:18:05.680 I never see my guess anymore. Please help me figure 361 00:18:05.680 --> 00:18:08.000 out who these guests are and how I can understand 362 00:18:08.000 --> 00:18:08.960 them and serve them better. 363 00:18:09.119 --> 00:18:09.319 Right. 364 00:18:09.359 --> 00:18:12.680 And so it was consumer switching their behavior and the 365 00:18:12.720 --> 00:18:16.920 adoption of digital tools that force the restaurants to sort 366 00:18:16.960 --> 00:18:19.759 of modernize their tech stacks for lack of a better term. 367 00:18:19.960 --> 00:18:21.799 And I think that is the wave that we are 368 00:18:21.839 --> 00:18:24.440 still riding today. And so I think there will be 369 00:18:24.559 --> 00:18:29.240 breakthrough moment in AI from an ordering standpoint, probably that again, 370 00:18:30.279 --> 00:18:33.200 you know, kicks off this next cycle for AI for operations, 371 00:18:33.200 --> 00:18:36.519 AI for supply chain, AI for finance, AI for analysis, 372 00:18:36.519 --> 00:18:37.720 which is some of the stuff that work. 373 00:18:38.319 --> 00:18:43.440 So there has been some discussion out there around some 374 00:18:43.519 --> 00:18:45.759 of the vcs that are saying, hey, you know, with 375 00:18:45.880 --> 00:18:49.920 AI tools, it's very possible that we could see you know, 376 00:18:51.480 --> 00:18:56.559 the first handful of billion dollar businesses that are solo entrepreneurs, 377 00:18:56.720 --> 00:18:59.960 you know. And if you translate that into a restaurant 378 00:19:00.359 --> 00:19:04.400 and say, hmmm, what about a restaurant that could operate 379 00:19:05.119 --> 00:19:07.720 at you know, maybe a five million dollar clip at 380 00:19:07.839 --> 00:19:12.880 UV with half or one third of the staff. Do 381 00:19:12.920 --> 00:19:15.880 you think that is a potential in the future of 382 00:19:15.960 --> 00:19:16.599 where this goes. 383 00:19:17.200 --> 00:19:17.759 It's tough. 384 00:19:17.839 --> 00:19:20.920 I mean, I think it's I think it's it's good 385 00:19:21.000 --> 00:19:25.400 to sort of speculate on a software side, But like restaurants, 386 00:19:25.400 --> 00:19:29.240 are these are still still businesses you know manual Yeah, yeah, 387 00:19:29.319 --> 00:19:31.880 these are still real businesses now. I think if the 388 00:19:31.960 --> 00:19:34.880 question is will there be efficiencies, absolutely I think so. 389 00:19:35.200 --> 00:19:37.240 But at the end of the day, like if you 390 00:19:37.279 --> 00:19:40.359 look at five million dollar AUV at a twenty dollars 391 00:19:40.440 --> 00:19:43.119 average check, my mental math is not good, but that's 392 00:19:43.160 --> 00:19:45.920 a lot of transactions on a per score basis. That's 393 00:19:45.960 --> 00:19:48.319 a lot of meals that you are serving to the 394 00:19:48.400 --> 00:19:50.759 got someone's got to make them, Someone's still going to 395 00:19:50.759 --> 00:19:52.839 make them, someone's still got a problem, someone still well. 396 00:19:52.839 --> 00:19:55.279 It depends on automation. I mean, Chipotle's done a fairly 397 00:19:55.279 --> 00:19:58.000 good job on on creating some of the robot lines, 398 00:19:58.160 --> 00:20:00.960 and it's very early stage. The couple of companies that 399 00:20:01.000 --> 00:20:03.240 have already kind of gone down that direction where we 400 00:20:03.319 --> 00:20:07.319 could see more, you know, assembly line style restaurants. Now, 401 00:20:07.359 --> 00:20:11.359