Meeting Title: Brainforge AI Workshop Date: 2025-12-04 Meeting participants: Uttam Kumaran, David Cohen, Erkaiym Kozhalieva AmCham, Mirlan M, Daria AmCham Kyrgyzstan, Robert Tseng, Malika Alen, Altynai, Sezim Zhenishbekova, albina, read.ai meeting notes, Ruslan, esamibaev, Esentur Dildebekov, Avitsenna, Нурайым - Бишкек
WEBVTT
1 00:02:30.230 ⇒ 00:02:31.980 Uttam Kumaran: Hello, Sarah, good morning.
2 00:02:36.160 ⇒ 00:02:38.679 David Cohen: Sorry, couldn’t… I was muted. How are you? I’m doing well.
3 00:02:38.680 ⇒ 00:02:41.419 Uttam Kumaran: Good, yeah, just grabbing a coffee.
4 00:02:41.830 ⇒ 00:02:42.430 David Cohen: Nice.
5 00:02:43.340 ⇒ 00:02:44.420 Uttam Kumaran: How’s everything?
6 00:02:45.280 ⇒ 00:02:50.579 David Cohen: It’s good, man, it’s, I got sick in Mexico, so I, oh, no.
7 00:02:53.270 ⇒ 00:02:54.439 Uttam Kumaran: Oh, that’s brutal.
8 00:02:55.860 ⇒ 00:02:56.540 David Cohen: Yup.
9 00:02:58.770 ⇒ 00:02:59.710 Uttam Kumaran: You hear me?
10 00:03:00.080 ⇒ 00:03:00.879 David Cohen: Yeah, yeah, hurry up.
11 00:03:00.880 ⇒ 00:03:02.500 Uttam Kumaran: Okay, yeah, I’m good.
12 00:03:04.880 ⇒ 00:03:06.250 Uttam Kumaran: Yeah, everything’s good.
13 00:03:06.740 ⇒ 00:03:07.720 David Cohen: Busy.
14 00:03:10.260 ⇒ 00:03:13.979 David Cohen: Did, Robert give you a brief rundown of what we’re doing?
15 00:03:14.160 ⇒ 00:03:17.870 David Cohen: or how we’re doing things today, I can do it if you have 5 minutes now.
16 00:03:17.870 ⇒ 00:03:18.910 Uttam Kumaran: Yeah, yeah, please.
17 00:03:19.970 ⇒ 00:03:22.290 David Cohen: Okay, let me, can you see your screen?
18 00:03:22.290 ⇒ 00:03:22.890 Uttam Kumaran: Yes.
19 00:03:23.610 ⇒ 00:03:26.339 David Cohen: Alright, let me share my screen…
20 00:03:34.370 ⇒ 00:03:38.719 David Cohen: Actually, I guess I’ll have to still do this during so the recording gets it, right?
21 00:03:38.900 ⇒ 00:03:39.580 Uttam Kumaran: Yes.
22 00:03:40.030 ⇒ 00:03:40.580 David Cohen: Okay.
23 00:03:40.580 ⇒ 00:03:42.669 Uttam Kumaran: But the recording’s on right now, so…
24 00:03:43.240 ⇒ 00:03:45.760 David Cohen: Yeah, yeah, that’s fine. So, can you, can you see this?
25 00:03:45.760 ⇒ 00:03:46.570 Uttam Kumaran: Yeah.
26 00:03:47.550 ⇒ 00:03:50.410 David Cohen: So when everybody gets here, what we’re gonna do is…
27 00:03:50.490 ⇒ 00:03:59.769 David Cohen: I asked Robert to start by introducing… briefly introducing the team as a whole, so we won’t do individual introductions, because we won’t have time.
28 00:03:59.770 ⇒ 00:04:15.109 David Cohen: But essentially just jump straight into what we’re doing today. And then the first step is I’ll kind of introduce what the activity is, what we’re doing, and then I’m gonna pass it back to you guys to talk about these, like, 5 use cases for AI. That’s why Robert asked you the question.
29 00:04:15.300 ⇒ 00:04:21.570 David Cohen: Sure. I think the group today is primarily education and, banking focused, is what he said?
30 00:04:21.579 ⇒ 00:04:21.989 Uttam Kumaran: Yes.
31 00:04:21.990 ⇒ 00:04:34.949 David Cohen: So, essentially, I’m gonna… these are flip cards, so whenever you guys are ready, we’re gonna do them one by one, and you’re gonna explain what the use case is, so you’re gonna say, like, document searching, and then you’re gonna briefly explain what that means.
32 00:04:35.160 ⇒ 00:04:43.069 David Cohen: And then we’ll go through all 5 of them. So, like, you’re gonna, like, give them ideas for what the use cases are, sort of like this. Does that make sense?
33 00:04:43.070 ⇒ 00:04:44.290 Uttam Kumaran: Okay, perfect.
34 00:04:45.820 ⇒ 00:05:01.819 David Cohen: After that, we will… you’ll pass it back to me, then I’ll start explaining the thing about the challenges. So what this is, is basically talking about the biggest problems that they’re facing. They’re gonna input their ideas into each one of the stickies, so we’ll have, like, 25 people-ish.
35 00:05:02.100 ⇒ 00:05:10.380 Uttam Kumaran: Do you need me to be, like, I guess during this time, while you’re facilitating, do you need me to do anything? Like, I can start sending stuff in the chat, or, like…
36 00:05:10.890 ⇒ 00:05:12.050 David Cohen: Yeah, there’s…
37 00:05:12.050 ⇒ 00:05:16.100 Uttam Kumaran: What’s a good… what’s a good, couple things that I could do to keep engaged?
38 00:05:16.100 ⇒ 00:05:26.480 David Cohen: Two things. There’s people that are gonna have technical issues while we do this. Yeah. If somebody does have technical issues, what I’m going to say is put your answers in the chat.
39 00:05:26.750 ⇒ 00:05:27.660 Uttam Kumaran: Yeah, yeah, yeah, okay.
40 00:05:27.660 ⇒ 00:05:42.200 David Cohen: So if there’s somebody like that, obviously, I need your help to transcribe the… whatever’s in the chat to the stickies. And then the second part is, I need you to start making copies of the stickies that do have answers, and putting them down in the themes section down here.
41 00:05:42.200 ⇒ 00:05:42.910 Uttam Kumaran: Yeah.
42 00:05:42.910 ⇒ 00:05:46.489 David Cohen: start grouping them into themes. That’s why these have theme boxes.
43 00:05:48.330 ⇒ 00:05:54.530 David Cohen: So that by the end of that section, we are already, like, you know, grouping things into sections.
44 00:05:56.240 ⇒ 00:06:13.490 David Cohen: So then what we’ll do is, once everybody’s done after, like, 5 minutes inputting, I’m gonna ask one or two people, I’m gonna, like, pick out one or two people here to explain what their answers are, and then we’ll talk about some themes, like, that we’re seeing. Obviously, these people are, like, all over the place, so we wouldn’t expect
45 00:06:13.670 ⇒ 00:06:24.460 David Cohen: too many themes specifically, but it’s just to give them an idea. Then we’re gonna repeat the same exercise for opportunities, so, like, the flip side of challenges and essentially what they want to do.
46 00:06:24.630 ⇒ 00:06:36.629 David Cohen: And then go through this gridding exercise to help show them that certain things that they want to do are more impactful or more complex, and that they want to focus on the things that are, like, on the top right quadrant.
47 00:06:38.570 ⇒ 00:06:56.119 David Cohen: the main thing that we expect for this exercise is for everything to be above the line. Like, if they want to do it, it’s because it’s impactful, so we want to show them that certain things that they want to do need to be moved below that line in the process of working with us, because they wouldn’t just want to work on one idea, they want to work on many.
48 00:06:56.540 ⇒ 00:06:57.320 Uttam Kumaran: Okay.
49 00:06:58.060 ⇒ 00:07:14.460 David Cohen: Then from there, if we have time, we will do a small breakdown of the ideas into, like, the things that drive those companies to be able to do them, the anchors that they have, the obstacles that they face, and then wrap up with next steps. So this will go pretty quickly overall.
50 00:07:14.920 ⇒ 00:07:18.880 Uttam Kumaran: Okay. Can you send me the… the mirror really quick?
51 00:07:20.090 ⇒ 00:07:26.700 David Cohen: The, the last thing I’ll mention is, at the end, I’m gonna do a plug for us to,
52 00:07:27.850 ⇒ 00:07:29.640 David Cohen: Here, it’s in the… in the Slack.
53 00:07:29.640 ⇒ 00:07:30.710 Uttam Kumaran: Okay.
54 00:07:32.410 ⇒ 00:07:37.070 David Cohen: Actually, let me, put it in the thread that I just created, so I’m not…
55 00:07:37.320 ⇒ 00:07:39.039 David Cohen: Breaking my own rule already.
56 00:07:41.060 ⇒ 00:07:46.970 David Cohen: I’m gonna do a plug at the end to say, basically, when you guys get in, or when you guys…
57 00:07:47.650 ⇒ 00:07:54.439 David Cohen: Finish up today, you’ll get an email from us with the information and the details on how to get in touch with us for next steps.
58 00:07:54.590 ⇒ 00:08:12.679 David Cohen: If anybody wants to talk, what we do next is we do the full-on sprint, and so if you want more information, it’s on… it’s gonna be in that email we send. But we do full-on workshops just like this one for each individual company, and we get an idea of how we can help you more specifically with the context of your needs, or whatever.
59 00:08:13.880 ⇒ 00:08:15.140 David Cohen: And then you wrap it up then.
60 00:08:17.580 ⇒ 00:08:20.359 David Cohen: Obviously, we’ll hand over the floor to you guys.
61 00:08:20.790 ⇒ 00:08:21.430 Uttam Kumaran: Okay.
62 00:08:21.810 ⇒ 00:08:23.989 David Cohen: Let me know if you can access the board.
63 00:08:28.220 ⇒ 00:08:29.049 David Cohen: Hey, Robert.
64 00:08:30.150 ⇒ 00:08:32.339 David Cohen: Oh, we have a few people already.
65 00:08:32.470 ⇒ 00:08:35.110 David Cohen: You’re just talking, I thought I was talking to you only stuff.
66 00:08:35.539 ⇒ 00:08:38.369 Uttam Kumaran: No, no, I just added some folks just now.
67 00:08:45.140 ⇒ 00:08:47.200 Robert Tseng: Hello, hello, hello. Okay, hey guys.
68 00:08:47.930 ⇒ 00:08:48.540 Uttam Kumaran: Boom.
69 00:09:00.020 ⇒ 00:09:01.639 Malika Alen: Hi everyone, how are you?
70 00:09:01.840 ⇒ 00:09:02.399 David Cohen: Hi, good morning.
71 00:09:02.400 ⇒ 00:09:02.990 Uttam Kumaran: Good.
72 00:09:03.750 ⇒ 00:09:04.869 Robert Tseng: Good evening.
73 00:09:05.420 ⇒ 00:09:06.130 Malika Alen: Thank you.
74 00:09:06.130 ⇒ 00:09:06.660 David Cohen: Excellent.
75 00:09:06.960 ⇒ 00:09:08.490 Malika Alen: Good morning to all of you.
76 00:09:10.930 ⇒ 00:09:24.020 David Cohen: Malika, while we’re waiting here, I just posted a… and I guess anybody that’s on the chat right now, I just posted a link on the chat function for the Zoom. If you could start going to that and trying it out, that would be step number one, please.
77 00:09:24.910 ⇒ 00:09:25.570 Malika Alen: Okay.
78 00:09:31.130 ⇒ 00:09:46.670 David Cohen: And for the sake of everybody that’s in the chat as well, if you have any issues while we’re going along, I’ll repeat this while we start the session, but please let us know in the chat if you’re having any issues with either the board or anything, so we can address them, so…
79 00:09:54.000 ⇒ 00:10:01.410 Malika Alen: Just to let you know, in case if some people will be late, we might, give some calls at 7pm.
80 00:10:02.440 ⇒ 00:10:07.419 David Cohen: Yeah, I think what we’ll do, Malika, is we’ll, we’ll do some brief intros at the beginning.
81 00:10:07.520 ⇒ 00:10:12.629 David Cohen: That way, folks have a couple minutes to join us, and then we’ll get started from there.
82 00:10:14.680 ⇒ 00:10:15.340 Malika Alen: Sure.
83 00:10:49.450 ⇒ 00:10:51.950 David Cohen: Okay, so everybody’s able to get into the Miro board, okay?
84 00:10:53.440 ⇒ 00:10:54.240 Malika Alen: Yes.
85 00:10:54.240 ⇒ 00:10:54.890 Uttam Kumaran: Yeah.
86 00:11:25.050 ⇒ 00:11:28.139 Uttam Kumaran: I’m just gonna start letting people in, David. Good with that?
87 00:11:28.140 ⇒ 00:11:34.059 David Cohen: Yeah, no, that’s perfect, so let people in so they can start getting… Set up while we wait.
88 00:11:37.410 ⇒ 00:11:40.839 David Cohen: And, remind me of… what’s the, like, final count of folks that we have?
89 00:11:41.460 ⇒ 00:11:42.079 David Cohen: What’s that?
90 00:11:42.350 ⇒ 00:11:44.250 Uttam Kumaran: I think we have, like, 25 people.
91 00:11:44.750 ⇒ 00:11:45.260 David Cohen: Okay.
92 00:11:45.470 ⇒ 00:11:47.530 David Cohen: It’s a pretty good, pretty good group.
93 00:11:57.360 ⇒ 00:12:01.160 Robert Tseng: Yeah, let’s wait a few minutes, and then Malika and I can kind of kick it off.
94 00:12:01.810 ⇒ 00:12:03.259 David Cohen: Yeah, that works perfect.
95 00:12:03.570 ⇒ 00:12:04.360 Malika Alen: Yeah, sure.
96 00:12:21.550 ⇒ 00:12:31.670 David Cohen: Okay, so I see, several people have joined us now. Thank you, everybody, for joining us. If you are hearing me, my name is David. Robert and Utam from…
97 00:12:31.820 ⇒ 00:12:40.069 David Cohen: We’re all from the Brainforge team, we’re also here. We’re gonna get started here in a couple minutes once, we give everybody a couple more minutes to join us.
98 00:12:40.090 ⇒ 00:12:59.560 David Cohen: And then we will get started. So in the meantime, if you see a link that I just reposted in the chat function, please go ahead and try to open that link so we can start to do the activities in a second. That link will be where we will be doing today’s workshop activities, so please go ahead and try that out.
99 00:12:59.810 ⇒ 00:13:01.800 David Cohen: It’s in the chat, as a reminder.
100 00:13:04.460 ⇒ 00:13:06.009 David Cohen: I’m also sharing my screen.
101 00:13:06.400 ⇒ 00:13:07.749 David Cohen: You can’t access it.
102 00:13:57.040 ⇒ 00:14:02.190 David Cohen: So, Utam, while we’re waiting, do you have to let everybody in manually while we’re… while we’re in here?
103 00:14:02.480 ⇒ 00:14:03.320 Uttam Kumaran: Yeah.
104 00:14:03.640 ⇒ 00:14:06.320 Uttam Kumaran: I just… I can… I can try to turn it off.
105 00:14:07.010 ⇒ 00:14:09.600 David Cohen: Yeah, that’s fine. No sense in messing with it, well.
106 00:14:10.620 ⇒ 00:14:11.190 Uttam Kumaran: Okay.
107 00:14:13.610 ⇒ 00:14:15.240 David Cohen: I guess just keep an eye on it.
108 00:14:24.140 ⇒ 00:14:26.560 Uttam Kumaran: You gotta play, like, waiting room music, David.
109 00:14:26.940 ⇒ 00:14:35.589 David Cohen: I know. I used to… I used to play music here, but people would get distracted by it, and then it, it caused more problems than it was worth.
110 00:14:37.130 ⇒ 00:14:38.490 David Cohen: Maybe we’ll go back to that.
111 00:14:43.680 ⇒ 00:14:49.400 David Cohen: Hi everyone, as you’re joining us, we’re still waiting a couple minutes to get started to let everybody
112 00:14:50.200 ⇒ 00:14:54.790 David Cohen: Trickle into the room, so if you’re just now joining us, there is.
113 00:14:54.790 ⇒ 00:14:58.220 Robert Tseng: I’m gonna keep reposting it, because you guys can’t see it if I’m…
114 00:14:59.510 ⇒ 00:15:07.300 David Cohen: If you’re just joining us, but there’s a chat link in the… or there’s a link in the chat to the Miro board that we will be using for today’s workshop.
115 00:15:07.440 ⇒ 00:15:09.760 David Cohen: Please go to that link, and…
116 00:15:10.050 ⇒ 00:15:16.260 David Cohen: Try to enter the board, so that we will be able to do the activities, and let us know if you have any issues in the chat.
117 00:15:31.650 ⇒ 00:15:38.260 Robert Tseng: Alright, I think we can wait a couple more minutes, like, 17 people. I think, once we hit 20, let’s, I feel like we could start.
118 00:15:38.670 ⇒ 00:15:41.039 Malika Alen: Yeah, let’s do this.
119 00:15:41.040 ⇒ 00:15:41.570 Robert Tseng: Yeah.
120 00:15:43.590 ⇒ 00:15:44.809 David Cohen: That works for me.
121 00:16:00.780 ⇒ 00:16:03.249 David Cohen: It’s nice to see you as well, thank you for joining us.
122 00:16:04.690 ⇒ 00:16:21.210 David Cohen: And as we’re going along, everybody, just for your context, any of you have any technical issues while we’re going along, either with hearing us or in the Miro board that we will be using, please let us know in the chat. We are monitoring the chat, so…
123 00:16:21.210 ⇒ 00:16:26.070 David Cohen: Please do let us know if there’s any concerns or issues as we’re going along, and we will try our best to
124 00:16:26.260 ⇒ 00:16:28.989 David Cohen: To, address them as we’re going along.
125 00:16:52.460 ⇒ 00:16:59.660 David Cohen: Okay. Yeah, I see many of you are either already previous Miro users, or are able to join us, so…
126 00:17:00.840 ⇒ 00:17:02.090 David Cohen: That’s great.
127 00:17:05.260 ⇒ 00:17:08.330 Robert Tseng: Yes, Nirayim, we will have a recording of this event.
128 00:17:10.670 ⇒ 00:17:12.750 Robert Tseng: We can send it out to you, yeah.
129 00:17:13.109 ⇒ 00:17:22.529 David Cohen: We’ll talk about this at the end as well, but we will share all the materials from today’s event, including the recording, so we will be sharing that as well. So…
130 00:17:23.249 ⇒ 00:17:28.259 David Cohen: Robert, do we want to get started with introductions now that we’re past the hour? Yeah, let’s start.
131 00:17:31.210 ⇒ 00:17:36.319 Robert Tseng: Okay, I guess, Monica, do you wanna, do you wanna intro us, and then I can say a few words to you?
132 00:17:36.860 ⇒ 00:17:56.260 Malika Alen: Yes, hello everyone, dear MCM members, thank you so much for joining our webinar today, workshop, and thank you to your team, Robert, David, and Otam, for organizing this workshop for our members. As you know, we have different sectors and companies represented today.
133 00:17:56.260 ⇒ 00:18:15.579 Malika Alen: Including consulting, banking sector, construction companies, retail sectors as well, so I hope this webinar will be interesting. Talking shortly about Brainforge, it’s a consultancy firm based in Austin, Texas, and we are really happy to have you as speakers of this event, and
134 00:18:15.640 ⇒ 00:18:20.080 Malika Alen: I’m looking forward to a very productive call today. Thank you so much.
135 00:18:22.010 ⇒ 00:18:30.560 Robert Tseng: Okay, I’ll say a few words. I’m gonna try to use a little Kyrgyz here. Salaam, everyone.
136 00:18:30.630 ⇒ 00:18:34.120 Robert Tseng: Condices, I guess, I’ve…
137 00:18:34.180 ⇒ 00:18:50.609 Robert Tseng: I love Kyrgyzstan, have been quite a few times, and yeah, I met Malika and the team most recently in September. We were super excited to hear about the interest in AI in Kyrgyzstan, and so…
138 00:18:50.630 ⇒ 00:18:55.350 Robert Tseng: Yeah, after a lot of, kind of ideating on what would be the best
139 00:18:55.410 ⇒ 00:19:09.110 Robert Tseng: way for us to, kind of share some of our experiences and knowledge with a team, we decided to put on this workshop. So, we’re super excited for you to be here, and I’ll turn it over to David and
140 00:19:09.110 ⇒ 00:19:23.229 Robert Tseng: and UTAG who are really going to be kind of driving a lot of the… this activity. It’s very interactive, so I would just encourage you to just participate as much as you can. I know that maybe English isn’t kind of the
141 00:19:23.300 ⇒ 00:19:34.819 Robert Tseng: the first language for most people here, so… but yeah, we’ll get through it, and I’m sure you’ll come away with some really awesome ideas that you can bring back to your teams.
142 00:19:37.130 ⇒ 00:19:39.730 David Cohen: All right, David, I’ll turn it over to you, yeah.
143 00:19:39.940 ⇒ 00:19:54.349 David Cohen: Yeah, thank you so much for the intro. So, everybody, for your context, my name is David. For today’s session, I’ll be facilitating the conversation, so my role in today’s workshop is to essentially guide the activities.
144 00:19:54.350 ⇒ 00:20:06.629 David Cohen: As well as to help you, kind of, answer the questions that we’re going and that we’re going through and answering along the way. So I see a couple more folks joining us now in the chat, so one last time before we begin.
145 00:20:06.630 ⇒ 00:20:12.819 David Cohen: There is a chat link in the chat function for the Zoom meeting that I would like you all to join.
146 00:20:12.820 ⇒ 00:20:27.319 David Cohen: So please click on that Miro board. What a Miro board is, is essentially the equivalent of a whiteboard, as if we were in a physical room together. And what we’re going to be doing is answering a few of these questions, or going through a lot of these activities together.
147 00:20:27.320 ⇒ 00:20:35.130 David Cohen: as Robert said, to ideate or think about some of the possible ways to use AI individually for each of your companies.
148 00:20:36.170 ⇒ 00:20:57.229 David Cohen: Thank you for sending that in the chat as well. So, if you are not able to join the Miro board because you are either on the phone, or for whatever reason you’re having technical issues, I am also sharing my screen, so you’ll be able to see the… what’s happening as we’re going live. My request would be that if you are not able to participate on the board yourself.
149 00:20:57.230 ⇒ 00:21:06.299 David Cohen: that you input any answers you have to the questions in the chat function. We will also monitor that, and then input them into the board that we will send after the fact.
150 00:21:06.330 ⇒ 00:21:14.589 David Cohen: Any other questions or concerns before we begin in terms of logistics, or… Technical issues before we start.
151 00:21:20.070 ⇒ 00:21:35.269 David Cohen: Seems like we’re good. So, to begin with, what I wanted to do was to show you what we’re going to be doing today. So I’m going to bring everybody to what I see, so you guys get a full picture of what’s going to happen in today’s workshop. So, over the course of the next.
152 00:21:35.270 ⇒ 00:21:37.450 Uttam Kumaran: Let’s call it 80 minutes or so.
153 00:21:38.410 ⇒ 00:21:57.629 David Cohen: we are going to have a couple of guided conversations around different parts of the process of ideating how we build things with AI in the first place. So for the first section, here on the top left, what we are going to do is have Utam and Robert walk you guys through a couple AI use cases.
154 00:21:57.630 ⇒ 00:22:06.999 David Cohen: that they’ve already… but that the Brainforge team has already built. One of the areas that we asked you guys to prep with was to, essentially do a…
155 00:22:07.390 ⇒ 00:22:17.510 David Cohen: review or a think on what the biggest business challenges that you’re facing today are, and how you may capture the opportunities that you want in the market, such that
156 00:22:17.770 ⇒ 00:22:36.960 David Cohen: today, as we’re going through these activities, we can essentially talk about the biggest areas where not only can we can help you, but also where you can use AI to bring business value to your particular companies and organizations. I see a couple of you are still joining us on the way, so as we’re going along.
157 00:22:37.410 ⇒ 00:22:48.259 David Cohen: Please think about these three questions as we’re answering the activities, so that you can essentially go through them one by one and give us the answers that will be relevant to you.
158 00:22:48.310 ⇒ 00:23:00.269 David Cohen: So, Robert and Utam, why don’t we start by kind of going through these use cases that Brainforge has built before to start educating or giving the folks some idea of what they… what they can think of as we’re going through today’s things.
159 00:23:00.880 ⇒ 00:23:04.259 Uttam Kumaran: Yeah, it’s great to meet everyone. Thank you so much for taking the time.
160 00:23:04.310 ⇒ 00:23:20.400 Uttam Kumaran: My name’s U-Tam, I lead Brainforge with Robert. I’m super, super excited to have you all here. And we’re hoping today that you get a lot of insight into some practical ways that, you know, in the short term and long term, you can bring AI into your business.
161 00:23:20.400 ⇒ 00:23:43.310 Uttam Kumaran: AI is sort of the hot term right now, but we hope that after today, it goes from this sort of, like, magic term to actually some use cases that you can leverage in your business. And so, please ask any questions in the chat, or, you know, just raise your hand and interrupt. If you have any questions about technical.
162 00:23:43.310 ⇒ 00:24:04.369 Uttam Kumaran: you know, exactly what we’re talking about, use cases, more than happy to answer. So let me just go through a few examples of where we see, you know, both ourselves here at Brainforge and our clients using AI. In particular, I know there’s a lot of folks in the finance and, you know, education industry. So some of these, my examples are going to be a little bit focused
163 00:24:04.370 ⇒ 00:24:17.839 Uttam Kumaran: There, but again, a lot of these are broadly applicable. So the first one I’m just gonna talk about is document searching. So I’m sure all of us here use documents. What is a document? Word doc, PDF?
164 00:24:17.840 ⇒ 00:24:24.349 Uttam Kumaran: Excel. A very common task in business is to find things within documents.
165 00:24:24.350 ⇒ 00:24:43.520 Uttam Kumaran: I’m sorry, I’m very oversimplifying this, but I just want to explain how we sort of think about these tasks and decompose them. So, document searching is a great example of something that AI is now extremely talented at. Whether it’s looking for a value in an Excel sheet, looking for a policy term in a Google Doc.
166 00:24:43.520 ⇒ 00:24:47.769 Uttam Kumaran: Looking for a diagram within a 100-page PDF.
167 00:24:47.770 ⇒ 00:25:04.120 Uttam Kumaran: Those are all great use cases for AI, and what you can really do is now, instead of having a human going through and looking for something, and doing that one by one, you can actually ask a question and search across many documents for answers.
168 00:25:04.120 ⇒ 00:25:08.399 Uttam Kumaran: So really great example of something that’s possible today with AI.
169 00:25:08.560 ⇒ 00:25:27.039 Uttam Kumaran: The next example I want to go through is regulation and policy deep research. So building on our, you know, question, building on our point about document search, one of the things you may do is, hey, I have all these contracts, for my company, or I have all these regulations that my company needs to adhere to.
170 00:25:27.040 ⇒ 00:25:41.599 Uttam Kumaran: Give me a sense if there’s anything new that I should consider, if there’s anything that I’m out of compliance with. These are also questions that you may have to pay people to go and understand legal, compliance, risk,
171 00:25:41.620 ⇒ 00:26:00.949 Uttam Kumaran: when you as a business owner, as an operator, want to just ask AI and tell me, hey, give me some sense of if I’m… if I’m in or out of policy. You now have the ability to actually do this, and not only get an answer back in, like, a chat GPT style, but actually have references back to the documents.
172 00:26:00.950 ⇒ 00:26:06.060 Uttam Kumaran: So again, a great use case for document processing and document AI.
173 00:26:06.110 ⇒ 00:26:26.209 Uttam Kumaran: The next example, is PDF extraction. So, as I mentioned, you know, you may be able to ask questions over documents, but you actually sometimes want to lift some of those out, right? A very common task that many employees do is going through PDFs or documents, taking pieces and moving it elsewhere, right?
174 00:26:26.210 ⇒ 00:26:32.070 Uttam Kumaran: Taking contracts, moving it to a CRM, taking sales information, moving it to an email.
175 00:26:32.070 ⇒ 00:26:42.879 Uttam Kumaran: These are all things, as well, that are all possible with AI today. And so these are great examples of, here’s a PDF of a contract, please extract the key terms.
176 00:26:42.880 ⇒ 00:26:54.689 Uttam Kumaran: and summarize it in an email. Very, very possible today to do. And so the next, kind of going into a little bit of the education, sort of sector, is one is personalized learning.
177 00:26:54.690 ⇒ 00:27:13.920 Uttam Kumaran: So one of the things, if you’re in the education sector, you realize, the importance of what, you know, in the States, we talk about, like, student-teacher ratio. And so, one big problem that’s happening here is there’s a gap between the number of students and the number of teachers, which means every student gets a very generic learning experience.
178 00:27:13.920 ⇒ 00:27:17.700 Uttam Kumaran: One of the things that AI has possibility now is to take
179 00:27:17.700 ⇒ 00:27:37.960 Uttam Kumaran: you know, a learning curriculum, take information about your student, and actually build something tailored to that student. And this is something that you can do at scale. So imagine every student has their own teacher, their own curriculum, it understands their, their superpowers, what they’re struggling with.
180 00:27:37.960 ⇒ 00:27:47.409 Uttam Kumaran: And so these are all, you know, additionally possible, today. And, you know, my girlfriend is actually a teacher, and she does this for her students.
181 00:27:47.410 ⇒ 00:27:50.739 Uttam Kumaran: You know, every day. And so she’s finding that the outcomes…
182 00:27:54.560 ⇒ 00:27:55.509 Uttam Kumaran: explaining that…
183 00:27:55.720 ⇒ 00:28:14.870 Uttam Kumaran: that to David, is onboarding. So one of the things that’s always really difficult, for online or offline schools is onboarding students, making sure you have all the, you know, identification, they fill out all the contracts, they’re filling out all the forms. It’s a big problem for people to go chase down these students.
184 00:28:14.870 ⇒ 00:28:18.589 Uttam Kumaran: And so, one of the things that AI is definitely able to help with
185 00:28:18.590 ⇒ 00:28:38.440 Uttam Kumaran: is taking in form, structured data and moving it to, you know, contracts, moving it to other systems, and so we see a lot of folks starting to use AI to help speed up the student onboarding process and making it more accurate. Validating addresses, validating names, things like that.
186 00:28:38.440 ⇒ 00:28:46.750 Uttam Kumaran: So just a couple examples of, you know, where we’re seeing AI really effective in both these sectors. Yeah, David, you can go ahead.
187 00:28:47.480 ⇒ 00:28:50.480 David Cohen: So, before we begin the next activity, then.
188 00:28:50.640 ⇒ 00:28:58.129 David Cohen: Robert or Utam, do you guys mind sharing the… or re-sharing the link for the folks that were joining us, for the Miro board?
189 00:28:58.380 ⇒ 00:29:00.770 David Cohen: Before we begin the next activity.
190 00:29:01.540 ⇒ 00:29:02.120 Uttam Kumaran: Yep.
191 00:29:03.070 ⇒ 00:29:03.800 David Cohen: Okay.
192 00:29:03.980 ⇒ 00:29:17.789 David Cohen: So, hopefully that was helpful in giving you guys sort of a sense of what we use AI for today. So, obviously, the point of doing that is for the next activity to help you think through the primary areas where you may use AI as well.
193 00:29:17.790 ⇒ 00:29:33.870 David Cohen: So what we’re gonna do now is give you an ability, or an activity to help you think through the biggest challenges that you face in your own businesses. As we think about solving for AI, one of the things that we are big proponents of is AI should solve real business problems for people.
194 00:29:33.870 ⇒ 00:29:49.560 David Cohen: So what we’re going to do is talk about the challenges that each of you individually face today. We know that all of you come from different contexts and have different needs, obviously, from the perspective of day-to-day operations. So what we want to do is get a better sense of what those challenges are more generally.
195 00:29:49.560 ⇒ 00:29:58.839 David Cohen: So, one ask for everybody that’s just joining us, if you go to that link that was just posted in the chat, we are using a mirror board, or a wipe…
196 00:29:59.510 ⇒ 00:30:09.569 David Cohen: whiteboard, workshop board, to be able to capture your answers, and we’re also sharing it on the board. And if you’re having any issues as we’re going along, please post your answers to the upcoming questions in the chat.
197 00:30:10.040 ⇒ 00:30:18.589 David Cohen: So, with that said, let me bring you all to the bottom part, or bottom left part of the board here, where it says, what are the biggest challenges that you face today?
198 00:30:18.640 ⇒ 00:30:26.319 David Cohen: So you’ll notice there’s a series of red squares, or red stickies, similar to the ones that we would use in a physical room.
199 00:30:26.320 ⇒ 00:30:38.669 David Cohen: To talk about the biggest challenges that you’re facing today. What I want you each to do in a second here is to essentially tell us what the largest challenges are that you’re facing in your company. So, for instance.
200 00:30:38.670 ⇒ 00:30:51.190 David Cohen: If you’re running an education company, as Utam was just saying, and you have a problem with the number of students relative to the number of teachers that you’re seeing, and generally being able to manage that, you would input
201 00:30:51.360 ⇒ 00:31:09.099 David Cohen: an answer on each of these stickies here. So what I want you to do is double-click the square, and then type in both your name, so in Utam’s case, it would be UTAM, the name of your company, so it would be Brainforge, and then whatever your issue is. So, issue goes here.
202 00:31:09.490 ⇒ 00:31:17.369 David Cohen: What we’re gonna do is, each person gets one sticky, so talk about your biggest issue. And if you need to, if you have more than one.
203 00:31:17.370 ⇒ 00:31:37.079 David Cohen: go ahead and input any others, but we ask that you, for the most part, try to keep it to one per square. After we finish all the answers, I’m going to give you two to three minutes to input your answers on here, and then I’m gonna call on a couple of you to give us perspectives on what your challenges are today, so that we can provide you some feedback.
204 00:31:37.080 ⇒ 00:31:39.710 David Cohen: Any questions on that before we begin?
205 00:31:45.570 ⇒ 00:31:46.380 David Cohen: Okay.
206 00:31:46.480 ⇒ 00:31:54.539 David Cohen: So, as a reminder, what I’m gonna do here is set a timer for a couple minutes. We’ll do 3 to begin with, since this is the first activity.
207 00:31:54.570 ⇒ 00:32:07.240 David Cohen: Everybody go ahead and start putting in your answers to the… into the board. I see many of you are already typing, so follow the lead of the folks that are typing into the stickies, and myself, Utam, and Robert will
208 00:32:07.340 ⇒ 00:32:10.830 David Cohen: Kind of be watching out. If there’s any issues, please let us know in the chat.
209 00:32:13.490 ⇒ 00:32:16.099 David Cohen: And, Utam, is there any music playing right now?
210 00:32:16.710 ⇒ 00:32:17.370 Uttam Kumaran: -
211 00:32:18.840 ⇒ 00:32:21.249 David Cohen: Okay, maybe for the next one, I’ll play some music.
212 00:32:31.100 ⇒ 00:32:35.250 David Cohen: And as a reminder, the problems that you’re putting down can be as broad as you need.
213 00:33:06.290 ⇒ 00:33:16.880 David Cohen: Okay, I see some of you are still joining us on the board. As a reminder, we are in the section that says, what are your biggest challenges today? What we’re doing is we’re inputting into the red squares
214 00:33:17.030 ⇒ 00:33:22.449 David Cohen: The biggest issues that we each individually have within Our respective companies.
215 00:34:02.920 ⇒ 00:34:19.719 David Cohen: And, for our Amtraum folks, if you guys want to also input any general issues or concerns that you see from your member companies as well, that would be great as well as to get a perspective on you on the different challenges that you see across the areas here.
216 00:34:25.550 ⇒ 00:34:29.800 David Cohen: Okay, I’m gonna give you guys an extra minute. I see that many of you are still working on this.
217 00:34:36.590 ⇒ 00:34:45.209 David Cohen: And feel free to take as many stickies as you need. So if you… if you have more than one, I see several of you are typing, feel free to take as many of these as you need.
218 00:35:15.570 ⇒ 00:35:21.300 David Cohen: Robert, the ones that you’re typing, are they, ones that folks mention, or are there ones that you want to bring up as well?
219 00:35:22.030 ⇒ 00:35:28.700 Robert Tseng: I think it’s… it’s mostly ones that I’ve heard before, for this… for this type of audience.
220 00:35:29.320 ⇒ 00:35:33.489 David Cohen: Yeah, perfect, just, just confirming, because I wasn’t monitoring the chat for a second.
221 00:35:37.210 ⇒ 00:35:47.450 David Cohen: Okay, we have about 15 seconds left, so please go ahead and finish up your answers as you’re coming up with them. If you have any final ones you’d like to add, please let us know, and then we will start discussing them.
222 00:35:55.160 ⇒ 00:36:07.119 David Cohen: So you’ll hear a little noise, go off. That means our timer is off, so please finish up your… your final answers here, and then we’ll get started. I’ll give you all 15 more seconds to finish up your answers before we start.
223 00:36:31.450 ⇒ 00:36:42.419 David Cohen: I see some of you are still inputting any final answers here. So why don’t we start while… while folks are finishing up their final thoughts? Would like to get some…
224 00:36:43.640 ⇒ 00:36:55.569 David Cohen: some general feedback from all of you here, and kind of start to talk through what some of these mean to get a perspective from each of you. So I’m going to summon all of you to see what I’m seeing, first off.
225 00:36:56.710 ⇒ 00:37:11.610 David Cohen: But let’s start with some of the ones that I see here. What I want to do is, I’m gonna, if you’re all comfortable with it, I am going to ask each of you, or some of you, actually, we probably won’t have time for everybody, to give us a context for what you meant with your particular issues that you have on the board.
226 00:37:11.610 ⇒ 00:37:17.950 David Cohen: So the first one that I see says, my key challenges are many operational processes, including international payments.
227 00:37:17.950 ⇒ 00:37:25.330 David Cohen: Can you, can you walk us through what you mean by that, and sort of give us a 30 to 60 second context of what your issue is?
228 00:37:27.970 ⇒ 00:37:32.869 albina: Sure, hello, hello everybody. Thank you, for this workshop.
229 00:37:33.170 ⇒ 00:37:41.680 albina: So, I want to introduce myself, very fastly. My name is Agbina. I work as a project manager in an IT division of a bank.
230 00:37:41.930 ⇒ 00:37:50.130 albina: So, why I’m writing, this topic? Because, today, we are facing several key challenges.
231 00:37:50.190 ⇒ 00:37:58.219 albina: First is fragmentate, partly manual business processes, many operational processes, including international payment.
232 00:37:58.220 ⇒ 00:38:12.809 albina: like SWIFT payment, and are still manual or only partially automated example, this result in high workload of unemployers, risk of human error, slow processing times, limited transparency, and tracking.
233 00:38:13.630 ⇒ 00:38:14.519 albina: Thank you.
234 00:38:16.450 ⇒ 00:38:29.180 David Cohen: And then I… I also saw that you put multiple stickies on the board, so you have one that also says, managing complex multi-domain projects, so relative to automation, digital banking, anything else to add there?
235 00:38:30.440 ⇒ 00:38:31.360 albina: Yes.
236 00:38:31.490 ⇒ 00:38:39.039 albina: For that, of course, I’m responsible for projects across different areas.
237 00:38:39.080 ⇒ 00:38:53.479 albina: RPI. I think everyone knows about the RPI, and digital banking solution. I can… I can say a lot of things, but for RPI, of course, we need to use AI, because we are… when we are,
238 00:38:53.540 ⇒ 00:38:59.020 albina: how you can say, mixing RPI with AI, we can take very power services.
239 00:38:59.200 ⇒ 00:39:04.850 albina: And also in digital banking, like internet banking, mobile banking, yeah.
240 00:39:06.730 ⇒ 00:39:18.150 David Cohen: So that’s, that’s amazing. Thank you for sharing those. So, now that we’re kind of moving away from the… I want to move away from the banking side as well to talk about some of our education folks that we have here as well.
241 00:39:18.190 ⇒ 00:39:31.519 David Cohen: So there’s a sticky here that says, issue is disconnected digital systems. So you talked about the different platforms that you have, Google Workspace, no dashboards that are AI-powered, and having data that is fragmented.
242 00:39:31.800 ⇒ 00:39:37.579 David Cohen: Do you mind explaining what you mean as well, and kind of giving us a breakdown of what the issue is?
243 00:39:41.260 ⇒ 00:39:57.350 Esentur Dildebekov: Yeah, hi there, my name is Ahsan, so I’m from educational sector. So, the biggest challenge for us, this is the AI, of course, we’re all using it, but the thing is, we can integrate AI with one system, let’s say, like.
244 00:39:57.580 ⇒ 00:40:02.000 Esentur Dildebekov: Google Workspace, we store all these documents, but AI cannot…
245 00:40:02.210 ⇒ 00:40:05.090 Esentur Dildebekov: See other part of our data.
246 00:40:05.780 ⇒ 00:40:11.530 Esentur Dildebekov: And… We cannot, we are trying, but we cannot centralize everything, and…
247 00:40:11.980 ⇒ 00:40:14.220 Esentur Dildebekov: You know, connect AI with everything.
248 00:40:15.170 ⇒ 00:40:24.920 Esentur Dildebekov: So, the main issue is no single AI-powered dashboard, maybe… We cannot find like, one AI-powered.
249 00:40:25.110 ⇒ 00:40:26.050 Esentur Dildebekov: tool.
250 00:40:26.650 ⇒ 00:40:34.620 Esentur Dildebekov: that can see all applications that we are using. Instead of, we are just creating a new like, service…
251 00:40:35.110 ⇒ 00:40:40.680 Esentur Dildebekov: And other accounts, and mainly… Transmitting and transferring all data.
252 00:40:41.600 ⇒ 00:40:53.460 David Cohen: Do you typically find that there’s underlying data issues with those as well? Like, the individual systems that you are managing, or is it just primarily being able to connect those systems?
253 00:40:54.860 ⇒ 00:40:56.440 Esentur Dildebekov: Sorry, can you repeat, please?
254 00:40:56.440 ⇒ 00:41:11.249 David Cohen: Do you… do you find that there are individualized data issues in the particular… in each system that… that you’re trying to connect, or is it also… or is it only a connection problem between the different disconnected systems that you have, and platforms?
255 00:41:11.810 ⇒ 00:41:22.039 Esentur Dildebekov: the only connection, I think. We have AI tools, but… We cannot allow them… To connect for all systems.
256 00:41:24.430 ⇒ 00:41:32.559 David Cohen: Okay, and I know, Robert and Utam, this is something that, like, the both this problem and then the one that we heard before from… on the banking side, are…
257 00:41:32.560 ⇒ 00:41:37.829 Esentur Dildebekov: problems that we hear about all the time, and I’m sure things that come up with other Brainforge customers as well.
258 00:41:37.830 ⇒ 00:41:38.630 David Cohen: Correct.
259 00:41:38.880 ⇒ 00:41:53.329 Uttam Kumaran: Yeah, so one of the big areas that we do a lot of work is connecting systems and then combining data. So you have, like, one clear dashboard with your view of your customers, finance, support, all in one place.
260 00:41:53.330 ⇒ 00:42:02.480 Uttam Kumaran: So you can start, you know, understanding your business, and asking questions, and starting to analyze data, and enable your team, to also ask, you know, questions.
261 00:42:03.970 ⇒ 00:42:06.090 Esentur Dildebekov: Yeah, it’s good to hear and know.
262 00:42:08.240 ⇒ 00:42:26.889 David Cohen: Okay, so, I want to call out a couple more here before we move on to the next activity. So, the next one says PDF agreements with clients to database. The reason I call that one specifically is I know PDF is one thing that Utam talked about earlier. So, do you mind walking us through, what you meant by that?
263 00:42:27.320 ⇒ 00:42:33.879 David Cohen: I think it’s Mirilan, that one’s yours, so if you’re on the call, do you mind explaining that one, please?
264 00:42:45.110 ⇒ 00:42:50.309 David Cohen: Okay. I think you might not be here anymore, that’s okay then. So we’ll move on to some of the other ones.
265 00:42:50.840 ⇒ 00:42:55.890 David Cohen: So the one that says prescriptions for labs from external doctors are manual.
266 00:42:56.080 ⇒ 00:42:59.130 David Cohen: That one is from Gulnas, so are you… Gulnas?
267 00:42:59.130 ⇒ 00:43:17.459 Avitsenna: Yeah, I’m here. Hi, thank you very much for your time. So, I’m from the medical sector. I run a medical lab and the clinic. So, with the, lab, we have, questions that external doctors that prescribe the, the tests.
268 00:43:17.490 ⇒ 00:43:20.500 Avitsenna: They do, in… manually in the paper.
269 00:43:20.960 ⇒ 00:43:31.370 Avitsenna: And that’s making really a big challenge, because first we have to identify, first find out there’s handwriting, so this is a big challenge.
270 00:43:31.990 ⇒ 00:43:40.270 Avitsenna: In general, I don’t know how it is done in, other world. Of course, it’s all computerized, I guess, it’s all one system, but…
271 00:43:40.300 ⇒ 00:43:55.410 Avitsenna: Whether AI can, read all the handwritings, I don’t know. So anyway, and, second challenge, so we are working on, automation of contact center.
272 00:43:55.410 ⇒ 00:43:56.840 Avitsenna: With AI.
273 00:43:56.840 ⇒ 00:44:19.309 Avitsenna: But it’s going very slowly, because the data is always changing, the doctor is coming, ending, I don’t know, new doctors is coming, so… and it’s always, the contact center cannot give the correct answers, I don’t know, somehow. But we’re already here in 3 months already working on,
274 00:44:19.640 ⇒ 00:44:23.650 Avitsenna: incorporating AI in the contact center.
275 00:44:25.330 ⇒ 00:44:43.530 David Cohen: Okay, and then in a second, Kulnance, what we’ll also talk about is the ideal state, or what that would look like for you. So when we get to that activity, what I would like you to think about as well is, what does it look like when you say to make it work, right, on the… in the contact center piece, at least, what does that look like for you in an ideal state?
276 00:44:45.220 ⇒ 00:44:48.599 David Cohen: I’ll ask you that question in the next activity, so please keep that in mind.
277 00:44:48.600 ⇒ 00:44:49.989 Avitsenna: Thank you. Yep.
278 00:44:50.250 ⇒ 00:44:56.709 David Cohen: Okay, and Utam, you made a point in the chat, but do you want to bring up the, the item about…
279 00:44:57.020 ⇒ 00:44:59.660 David Cohen: what you said is possible, so in terms of handwriting?
280 00:44:59.810 ⇒ 00:45:18.090 Uttam Kumaran: Yeah, one of the really, like, amazing unlocks in AI recently is the ability to actually do handwriting transcription, in many different languages. And so what you’re seeing is there’s a lot of use cases right now, like, one use case in medical is, like, doctor note transcription.
281 00:45:18.090 ⇒ 00:45:27.429 Uttam Kumaran: Right? Another use case is people filling out manual forms in person, and then, of course, usually someone has to read and write that into the CRM.
282 00:45:28.010 ⇒ 00:45:42.070 Uttam Kumaran: I think one thing that we could totally follow up with is some examples of, basically, AI doing this type of OCR, which is, like, optical. It looks at the image and actually pulls out, structured text from the handwriting.
283 00:45:42.820 ⇒ 00:45:43.490 Uttam Kumaran: Yeah.
284 00:45:45.320 ⇒ 00:45:48.230 Avitsenna: Yeah, that’s really cool. AI is going ahead.
285 00:45:50.820 ⇒ 00:45:56.660 David Cohen: Amazing. Okay, so, I want to do one or two more before we move on to the final, or the next activity.
286 00:45:56.900 ⇒ 00:45:57.830 David Cohen: Rather.
287 00:45:58.200 ⇒ 00:46:12.220 David Cohen: So the next one says, to figure out how to apply AI in bank back office. So, Mira, that one’s from you. Anything to add there? Do you want to explain what you meant by, like, which issues in the back office are you trying to solve for in particular?
288 00:46:23.220 ⇒ 00:46:24.680 David Cohen: Er, are you… are you there?
289 00:46:25.350 ⇒ 00:46:27.980 Malika Alen: I think Albina is raising her hand.
290 00:46:28.240 ⇒ 00:46:28.770 Uttam Kumaran: Oh yeah, go ahead.
291 00:46:28.770 ⇒ 00:46:30.289 Malika Alen: Maybe that’s how I can always clear.
292 00:46:30.290 ⇒ 00:46:32.189 David Cohen: Apologies, sorry. Go ahead.
293 00:46:32.700 ⇒ 00:46:35.840 albina: Oh, thank you. Yeah.
294 00:46:36.180 ⇒ 00:46:42.319 albina: For, first, implementing AI across banking processes,
295 00:46:42.470 ⇒ 00:46:59.380 albina: AI can significantly improve our operation FNCs, customer service, fraud detection, it’s very important, and predictive analytics, cost optimizing, and I think this is one of the strongest opportunities for us.
296 00:46:59.640 ⇒ 00:47:00.300 albina: Alright.
297 00:47:04.430 ⇒ 00:47:05.530 albina: Thank you.
298 00:47:08.840 ⇒ 00:47:10.379 Uttam Kumaran: Did you catch the first part, David?
299 00:47:10.710 ⇒ 00:47:17.229 David Cohen: Now, do you mind… do you mind repeating that one? For… we lost you for a second there, so apologies.
300 00:47:19.510 ⇒ 00:47:32.579 albina: Oh, okay, I guess, no problem. I think first, implementing AI across banking processes, fraud detections, customer service, predictive analytics, cost optimization.
301 00:47:32.720 ⇒ 00:47:43.500 albina: It’s very important. I think this is one of the strongest opportunities for our market, because for our bank system, we need to work very fastly.
302 00:47:43.650 ⇒ 00:47:54.500 albina: And we can’t make a lot of error or mistake, and I think for that issue, AI can be very, very, very, beneficially.
303 00:47:57.380 ⇒ 00:47:59.599 David Cohen: Amazing, now we got it. Thank you for sharing.
304 00:48:01.280 ⇒ 00:48:18.150 David Cohen: Okay, so before we move on to the next, then, I just want to check in with our AmChamp folks. Malika and Daria, you guys both put in a couple answers in here. From either of you, what are the issues that you’re seeing, or challenges that you’re seeing from your member companies that you would like to highlight here?
305 00:48:21.930 ⇒ 00:48:34.659 Malika Alen: So, for us, since we work with a lot of companies, around 100 companies, and representing different sectors, it’s hard for us to catch up on the news on their sectors, on their fields.
306 00:48:34.810 ⇒ 00:48:38.529 Malika Alen: And also keep, updated about their…
307 00:48:38.680 ⇒ 00:48:44.850 Malika Alen: News in the company, within the company, and being in touch with all of them throughout the year.
308 00:48:49.310 ⇒ 00:48:50.210 David Cohen: Crazy.
309 00:48:51.170 ⇒ 00:48:53.010 Daria AmCham Kyrgyzstan: Oh, sorry, you were gonna add something?
310 00:48:53.600 ⇒ 00:49:10.220 Daria AmCham Kyrgyzstan: Yeah, I just want to add to Malika’s answer about that we have a data overload with no time for proper analysis, something like this. So, we have a lot of information, but have no time to, proper analyze it and have a good product for our members.
311 00:49:12.360 ⇒ 00:49:12.950 David Cohen: Okay.
312 00:49:13.230 ⇒ 00:49:25.120 David Cohen: Amazing. So, Utam or Robert, anything else to add in terms of the challenges that you’re hearing, or sort of the themes that you’re seeing across the areas that the different folks are providing answers for?
313 00:49:26.520 ⇒ 00:49:33.929 Uttam Kumaran: Yeah, I mean, I think it is a lot of, like, manual process automation, which is something that we see really often. And then second.
314 00:49:33.930 ⇒ 00:49:53.030 Uttam Kumaran: I think it’s still a lot of, like, data, like, having one consolidated, source of truth for either your customers or your members, you know, so I think those two are, are really… and then the third item I would say is, like, taking action on that data.
315 00:49:53.090 ⇒ 00:50:01.629 Uttam Kumaran: So it’s not only getting that in one place, but leveraging it for emails or for other workflows. So those are probably the three that come to mind.
316 00:50:02.960 ⇒ 00:50:18.589 David Cohen: Yeah, I think, one thing I would add on that, for everybody’s context, is if you think about it, each… many of you provided answers across different industries, and you’ll notice that there’s significant overlap in terms of the opportunities that you have to, or that we have to automate
317 00:50:18.750 ⇒ 00:50:31.309 David Cohen: or to improve the way that things operate today with AI. You’ll notice that despite the fact that many of you are in entirely different sectors, that there are many similar concerns across the different things that you all do.
318 00:50:31.310 ⇒ 00:50:41.939 David Cohen: So one of the things that we… that we Brainforge do as part of this effort is to essentially understand those themes across the different parts of your respective companies.
319 00:50:41.940 ⇒ 00:50:55.220 David Cohen: and understand how we can bring together the disparate parts to achieve these, these challenges that we’re discussing. So, for example, to bring you all to this part, you’ll notice the three themes that Utam was just talking about here at the bottom.
320 00:50:55.230 ⇒ 00:51:08.220 David Cohen: What we may do is, with each of your teams, start talking about these challenges narrowly from the context of your team specifically, and then start to take each of the individual parts of
321 00:51:08.380 ⇒ 00:51:24.640 David Cohen: answers across them and group them into themes like the ones that you’re seeing here. I just did it for representative purposes today, but just to give you a sense of how we generally approach this. But, Robert, anything else to add in terms of themes that you’re seeing before we move on to the next activity?
322 00:51:26.070 ⇒ 00:51:39.309 Robert Tseng: Yeah, I think these are… these cover it. I kind of wrote data consolidation. I think I just want to emphasize that, I think most of us have experience with using AI already, but being able to
323 00:51:39.340 ⇒ 00:51:48.280 Robert Tseng: point AI to the right sources of information, is, is challenging, especially across systems, right? So, I think,
324 00:51:48.280 ⇒ 00:51:59.490 Robert Tseng: you know, we heard that a couple times, being able to pull from different sources, aggregate it, or bring it into a single place, that to us is a data problem, and it is something that
325 00:51:59.490 ⇒ 00:52:17.870 Robert Tseng: you know, when we… when we work with clients is… is not the step that people think about, but it’s… it’s one of the most important steps to make sure that you’re able to access… give AI access to all of your data in a single place. So, I think this is… I mean, we love talking about this problem.
326 00:52:18.850 ⇒ 00:52:28.889 David Cohen: Awesome, okay. So, to move on to the next activity, what I want to do is flip the script a little bit. I want to talk about opportunities rather than challenges.
327 00:52:28.890 ⇒ 00:52:53.859 David Cohen: So I’m gonna bring you to the center of the board, to where the green stickies or squares are, and I’m going to repost the link to the Miro board on the chat. So if any of you are either joining us now or rejoining us, what we’re gonna do now is talk about the opposite side of the challenges. So one of the things with AI is that there’s many things, or many sort of possibilities in terms of what we can achieve
328 00:52:53.860 ⇒ 00:53:02.070 David Cohen: Obviously, many of you individually in your companies may have areas that… or things that you have wanted to do for a long time.
329 00:53:02.210 ⇒ 00:53:09.819 David Cohen: that are not necessarily obstacles, but rather things that you… you wish you could do, right? We call these wish list items, obviously.
330 00:53:09.820 ⇒ 00:53:21.649 David Cohen: And what I want you to do is similar to what you did before in the green stickies, or the green squares, to let us know what those wish list items are. So what are the things that you want to be able to do that you’ve always
331 00:53:21.650 ⇒ 00:53:33.379 David Cohen: thought that would be impactful or positive for your company that you never had the ability to do before, because you did not have the automation capability, or because the technology was just not there in terms of,
332 00:53:33.380 ⇒ 00:53:51.799 David Cohen: how it functions or where it is. So I’m going to set a timer for 3 more minutes, similar to what we did last time. What I want you each to do is to click on each one of the squares and tell us what the things that you wish you could do for your respective jobs are, and please input your name and your company as you’re going along. Any questions?
333 00:53:54.910 ⇒ 00:54:06.340 David Cohen: Okay, so we’ll set a timer for 3 minutes, go ahead, and for our AmChim folks, again, this would be the same thing as before, so what are things that you’re hearing from your members that they wish they could do?
334 00:54:17.580 ⇒ 00:54:22.369 Malika Alen: Sorry, do you mean, like, with AI? Yes, about opportunity?
335 00:54:22.370 ⇒ 00:54:24.410 David Cohen: In general, in your job as well.
336 00:55:26.000 ⇒ 00:55:44.220 David Cohen: Okay, you have about a minute and a half, so please input any answers that you may have. If you’re not on the Miro board, as a reminder, please input any answers you may have to this question on the chat function, and we will copy them over as well. If not, just go ahead and put them in the… in one of the squares on the board.
337 00:56:05.790 ⇒ 00:56:09.960 David Cohen: Okay, I see we have an answer. I’m gonna put your answer on the board, just so we have it recorded.
338 00:56:11.080 ⇒ 00:56:15.299 David Cohen: the safety of the data of which we share while working with AI.
339 00:56:15.570 ⇒ 00:56:19.680 David Cohen: That’s a good topic, so we’ll make sure to talk about it. Thank you for sharing it.
340 00:56:26.810 ⇒ 00:56:27.490 David Cohen: Cope.
341 00:56:30.120 ⇒ 00:56:31.939 David Cohen: Okay, you have about 30 seconds.
342 00:56:58.620 ⇒ 00:57:00.830 David Cohen: Okay, so please finish up your answers.
343 00:57:14.000 ⇒ 00:57:18.649 David Cohen: Alright, so I’m gonna give you guys 15 seconds here just to finish up any final thoughts.
344 00:57:30.690 ⇒ 00:57:33.309 David Cohen: And, Utam, can you guys still see my screen in the Zoom?
345 00:57:33.810 ⇒ 00:57:34.260 Uttam Kumaran: Yeah.
346 00:57:34.550 ⇒ 00:57:39.220 David Cohen: Okay, good, I had a bit of a technical issue there for a second, just making sure.
347 00:57:41.130 ⇒ 00:57:52.429 David Cohen: And, okay. So let’s start going through these one by one, similar to what we did before. So, I’m gonna try and see if there’s any other folks here that are new.
348 00:57:53.640 ⇒ 00:58:04.049 David Cohen: Okay, so we have one that says to have a soft… a software that will automatically calculate all the products needed in shelves at the market. So this is more on the retail side.
349 00:58:04.420 ⇒ 00:58:10.729 David Cohen: So this is, can you walk us through what you meant by this one, or explain, sort of, the idea behind this one, please?
350 00:58:14.060 ⇒ 00:58:14.690 David Cohen: If you’re.
351 00:58:14.690 ⇒ 00:58:15.740 Janazar: Hi, good morning.
352 00:58:16.200 ⇒ 00:58:17.129 David Cohen: Hi, good morning.
353 00:58:17.750 ⇒ 00:58:20.630 Janazar: My name is John Czar, I’m a supply chain manager.
354 00:58:21.160 ⇒ 00:58:23.879 Janazar: For the Chateau Retail, nice to meet you guys.
355 00:58:24.010 ⇒ 00:58:29.840 Janazar: I’d like to know about the, because, the thing is that we are controlling the stock
356 00:58:30.060 ⇒ 00:58:35.369 Janazar: In the markets, and we have a soft that… but mostly, it’s, like, half automated.
357 00:58:35.710 ⇒ 00:58:38.919 Janazar: And I would like to know if maybe there is something that can…
358 00:58:39.080 ⇒ 00:58:47.319 Janazar: See all the errors or something that is missing on the shelves, like, right away, directly, and give us, in the future, in advance, like, how it works.
359 00:58:52.170 ⇒ 00:58:54.180 David Cohen: Lutam, Robert, anything to add there?
360 00:58:55.150 ⇒ 00:59:18.369 Robert Tseng: I guess I have a follow-up question. Yeah, I think we’ve done something similar with helping companies with stock outs. So, basically, like, we were able to use AI to detect when inventory ran out before, the stores knew themselves, and so that helped inform the supplier that they needed to go and push more stock to the shelves.
361 00:59:18.420 ⇒ 00:59:25.419 Robert Tseng: I’m curious for you, when you’re saying it only half… the software only half works, what… what do you think… is it…
362 00:59:25.490 ⇒ 00:59:32.159 Robert Tseng: Is it just delayed inventory count, or kind of what part of it is not working?
363 00:59:32.160 ⇒ 00:59:47.389 Janazar: Like, absolutely, like, the whole process would be really nice to have it, because, everything that is going, like, our products from DC, from distribution center to our shelves, and we have to control everything, and logistics as well. We have, like, at least 10 stores.
364 00:59:47.820 ⇒ 00:59:54.499 Janazar: And sometimes the soft, yeah, sometimes the soft doesn’t work properly, because it’s like, you have to put the targets.
365 00:59:55.290 ⇒ 00:59:55.920 Robert Tseng: Yeah.
366 00:59:56.260 ⇒ 01:00:03.870 Janazar: Yeah, and it would be really nice to have something that calculates the targets by itself with the product’s history, sales.
367 01:00:04.630 ⇒ 01:00:08.670 Robert Tseng: Yeah, yeah, so it’s, like, having accurate forecasting for.
368 01:00:08.670 ⇒ 01:00:09.270 Janazar: Yeah, exactly.
369 01:00:09.270 ⇒ 01:00:13.309 Robert Tseng: With the amount of product that you need, and then it’ll also kind of…
370 01:00:13.440 ⇒ 01:00:22.360 Robert Tseng: trigger the placing the order so that more come… more… more order… more stock comes from the DC and kind of everything else to get products back on.
371 01:00:22.360 ⇒ 01:00:23.239 Janazar: on the shelves.
372 01:00:23.610 ⇒ 01:00:24.240 Robert Tseng: Yeah.
373 01:00:27.480 ⇒ 01:00:29.079 Janazar: Yep, that’s all for me today.
374 01:00:29.540 ⇒ 01:00:36.759 David Cohen: Awesome, thank you so much for sharing. So, I actually want to bring up the one, from the chat. It said, I had a…
375 01:00:36.970 ⇒ 01:00:48.839 David Cohen: it says, I had a change about the safety of the data with which we share while working with NEI. So I think this is something that we hear a lot about, so I want to hear what you meant by this one. So, if you don’t mind explaining.
376 01:00:50.950 ⇒ 01:01:05.590 Нурайым - Бишкек: Hello, everyone. That’s… my name is Nuria Yu. I am working in a marketing and social research company, so we are making a different kind of researchers, and we are working with a lot of data.
377 01:01:05.850 ⇒ 01:01:11.890 Нурайым - Бишкек: This is also 100%, it’s, private information.
378 01:01:11.890 ⇒ 01:01:19.859 Нурайым - Бишкек: So, while I’m researching about anything, this is really hard to every time think about the…
379 01:01:19.860 ⇒ 01:01:33.050 Нурайым - Бишкек: information that you can share or not share with the AI. So, most of the time, we are not sharing anything with AI, we just use AI for the… some kind of really simple, simple tasks.
380 01:01:33.120 ⇒ 01:01:39.100 Нурайым - Бишкек: It’s, like, something right, correctly, or not, these kind of things. And every time the user
381 01:01:39.230 ⇒ 01:01:45.090 Нурайым - Бишкек: user agreement every time it’s uploading, and I don’t know how many
382 01:01:45.540 ⇒ 01:01:48.140 Нурайым - Бишкек: Information they are copying from us.
383 01:01:50.460 ⇒ 01:02:07.279 David Cohen: Yeah, and I know that, obviously in regulated contexts, this is a big topic of discussion, so Utam and Robert, do you guys want to highlight any particular areas, you know, let’s say in regulated industries like banking or healthcare, how we handle this sort of thing?
384 01:02:07.710 ⇒ 01:02:24.920 Robert Tseng: Yeah, I can… I can talk a few things about that. Yeah, so we work with a lot in healthcare right now, actually, and yeah, I think being… keeping patient data, contained within a closed system, in… in the States, there are a lot of regulations around this, and so…
385 01:02:25.010 ⇒ 01:02:32.120 Robert Tseng: Yeah, I think a lot of it is setting up the different systems, to make sure that no data is
386 01:02:32.390 ⇒ 01:02:48.080 Robert Tseng: passed to any third parties. So really, it’s less of an AI problem, I think. It’s more of a contractual problem, making sure that you can get all of your systems to, sign agreements that they won’t share data with a third party.
387 01:02:48.080 ⇒ 01:03:04.449 Robert Tseng: And then, you know, if you’re still worried about whether or not the AI models will be training off of the sensitive data, you can set up, those models as well. So, we’ve done these types of, like, really hyper-local deployments.
388 01:03:04.460 ⇒ 01:03:19.460 Robert Tseng: Which are… oftentimes, you don’t really need, like, a full ChatGPT, like, experience, and you can get by with these really small, mini, mini models that, you’re able to put more, gate…
389 01:03:19.460 ⇒ 01:03:28.209 Robert Tseng: kind of walls around, that you have more control over. So, we do see AI kind of heading in this direction, where
390 01:03:28.230 ⇒ 01:03:30.819 Robert Tseng: You know, there are so many models now, and…
391 01:03:30.970 ⇒ 01:03:42.810 Robert Tseng: You know, it’s not just the big ones that are good, even the small… even the small open source ones, you know, they’re very cheap to use, and you can… you have more control over, over,
392 01:03:43.630 ⇒ 01:03:50.989 Robert Tseng: how data is passed in and out of them. So, I think this will only get better, over time.
393 01:03:54.930 ⇒ 01:03:56.829 David Cohen: Thank you so much for sharing.
394 01:03:57.330 ⇒ 01:04:05.609 David Cohen: So I want to hit some of the… or talk about some of the other ones that we also have on the board. So, Essen, just to call you on… or call on you one more time.
395 01:04:05.690 ⇒ 01:04:20.620 David Cohen: You talked about AI automation of school operations, going back to the educational sector, personalized student learning, which we talked about to some degree, predictive analytics for leadership, AI-powered curriculum creation, and multilingual parent communication.
396 01:04:20.620 ⇒ 01:04:31.619 David Cohen: Maybe you don’t necessarily have to walk us through all of those, but I think there’s a general theme there of, like, automation and, personalization. So, do you mind explaining what you meant by your answer, please?
397 01:04:40.320 ⇒ 01:04:41.610 David Cohen: SN, are you there?
398 01:04:46.760 ⇒ 01:04:48.080 Esentur Dildebekov: Yeah, yeah, I’m here.
399 01:04:48.390 ⇒ 01:04:50.270 David Cohen: So, the thing is…
400 01:04:51.640 ⇒ 01:04:56.560 Esentur Dildebekov: About opportunities, the opportunity for us, just the AI automation of school.
401 01:04:56.950 ⇒ 01:05:04.190 Esentur Dildebekov: operation like everything. We have lots of data, lots of data to analyze and track, and I think it’s a really…
402 01:05:04.550 ⇒ 01:05:06.179 Esentur Dildebekov: good opportunity.
403 01:05:06.360 ⇒ 01:05:12.759 Esentur Dildebekov: since AI… Became the most powerful tool that allows data.
404 01:05:13.270 ⇒ 01:05:15.550 Esentur Dildebekov: And, yeah, I just shared it.
405 01:05:15.820 ⇒ 01:05:17.940 Esentur Dildebekov: This is the best opportunity for us.
406 01:05:19.980 ⇒ 01:05:26.929 David Cohen: Okay, Utam, Robert, anything to add there on the educational context that we haven’t covered yet, in terms of what’s possible?
407 01:05:30.130 ⇒ 01:05:40.039 Uttam Kumaran: No, I feel like anything that’s related to, you know, manual processing, like, of documents or inputs, I think that’s definitely, like, a lot of possibilities.
408 01:05:40.100 ⇒ 01:05:58.179 Uttam Kumaran: You know, we’ve just seen a lot of work happening, you know, around, like, personalized learning, and then also being able to look at, like, predicting outcomes, right, and understanding students that are struggling, students that are performing well, and that’s all, like, actions on top of data, so no, that makes sense.
409 01:06:00.440 ⇒ 01:06:08.729 David Cohen: Okay, and then just to make sure we cover all the ones that were input in here, Gulnas, just to go back to yours in the clinic provider context.
410 01:06:08.810 ⇒ 01:06:20.259 David Cohen: You wrote down wanting to have doctors, or give doctors the ability to identify how to treat patients, follow up with patients, and remind them about regular doctor appointments.
411 01:06:20.280 ⇒ 01:06:29.320 David Cohen: This is something that we hear quite about… quite a lot about here, too, in the… in the healthcare provider context, so would love to hear more from you in terms of
412 01:06:29.380 ⇒ 01:06:37.719 David Cohen: Is there anything beyond that, as well, in terms of the doctor-patient relationship that you see could be improved by AI, or that you want to improve?
413 01:06:40.350 ⇒ 01:06:44.820 Avitsenna: Beyond, doctors, then it would be…
414 01:06:46.270 ⇒ 01:06:50.570 Avitsenna: I don’t know, the AI would know that I need, I don’t know.
415 01:06:51.250 ⇒ 01:07:01.020 Avitsenna: somehow to give some blood tests because of, I don’t know, other factors, and will itself identify whether I should go to the doctor or not, at least.
416 01:07:01.230 ⇒ 01:07:05.660 Avitsenna: I don’t know, it’s something, if… People print.
417 01:07:05.870 ⇒ 01:07:22.849 David Cohen: And so, on the follow-up piece, just to confirm, what you mean is that if I go to the doctor, you’re saying the AI will remind me to go back to the follow-up appointment, correct? And maybe stay with the medicines that I was prescribed, for instance.
418 01:07:23.450 ⇒ 01:07:28.670 Avitsenna: Yeah, maybe AI would follow up, like, whether, I’m better or worse.
419 01:07:28.840 ⇒ 01:07:36.810 Avitsenna: whether I need to modify the, let’s say, the treatment, or I should notify the doctor.
420 01:07:36.810 ⇒ 01:07:48.859 Avitsenna: that the treatment is not going well, or is going well, etc. Because the doctor himself, herself, it would be very timely and very difficult to keep up with everyone.
421 01:07:50.630 ⇒ 01:08:08.489 David Cohen: Okay, awesome. So, in that context, Malika and Daria, what I want to do is, similar to what we did last time, is what do you each hear from the members, or what are other things that you both have heard that you would want to call out here? Let’s talk about the ones that you put in the board as well.
422 01:08:09.150 ⇒ 01:08:11.219 Daria AmCham Kyrgyzstan: Yeah, okay, I can share.
423 01:08:11.350 ⇒ 01:08:13.360 Daria AmCham Kyrgyzstan: Oh, weird.
424 01:08:13.930 ⇒ 01:08:18.639 Daria AmCham Kyrgyzstan: We wrote about stronger networking through smart matches.
425 01:08:18.819 ⇒ 01:08:32.499 Daria AmCham Kyrgyzstan: like, our members see opportunity for introduction to relevant partners and have a good B2B opportunities, yeah? So, maybe AI could help us with this.
426 01:08:32.500 ⇒ 01:08:40.039 Daria AmCham Kyrgyzstan: To have a good matching between members, and maybe, partners with members, something like this.
427 01:08:43.140 ⇒ 01:08:43.760 David Cohen: Okay.
428 01:08:44.040 ⇒ 01:09:04.839 Malika Alen: Another thing that I wanted to add about data analysis is that, since we do a lot of meetings with different companies, I think that would be great for us to use AI to analyze all the information about the different sectors, about the updates, and share it among our members as well.
429 01:09:07.790 ⇒ 01:09:21.860 David Cohen: Okay, and then I’m curious, Robert, Utam, any themes that you’re seeing across these opportunities? We don’t necessarily have to group them like we did last time, I’m just curious what you’re hearing in common across the folks that are… have answered so far.
430 01:09:24.279 ⇒ 01:09:34.139 Uttam Kumaran: Yeah, I still see that a lot of things are around, like, you know, time savings, and, like, basically automating some of the tasks that are, you know.
431 01:09:34.209 ⇒ 01:09:36.229 Uttam Kumaran: Happened very, very frequently.
432 01:09:36.269 ⇒ 01:09:50.669 Uttam Kumaran: You know, and then the second piece is more stuff that’s more proactive, like things that, hey, if I had, you know, an extra 10, 20 hours a week, I could look through all our meetings and understand, you know, relevant summaries.
433 01:09:50.669 ⇒ 01:10:00.029 Uttam Kumaran: But, like, it’s just not possible, you know, right now. And, you know, one thing that is really clear is, like, okay, how can we leverage AI to maybe go after these
434 01:10:00.029 ⇒ 01:10:08.679 Uttam Kumaran: things that are bigger opportunities that maybe we would have to have, like, one full-time person join to do that, but maybe it’s possible to hand off to AI.
435 01:10:10.110 ⇒ 01:10:14.609 Robert Tseng: I’m also seeing, kind of a theme of just
436 01:10:14.690 ⇒ 01:10:29.380 Robert Tseng: coordination, so I think people have really great ideas for how they want to engage with companies, customers, patients, or students, and even parents of students, I guess, and a lot of that is sending
437 01:10:29.380 ⇒ 01:10:35.619 Robert Tseng: Follow-up messages, being able to offer it in multiple languages, you know.
438 01:10:35.640 ⇒ 01:10:44.160 Robert Tseng: You know, whatever, kind of, with business networking matches and business matchmaking, there’s even that being curated on how you, kind of.
439 01:10:44.230 ⇒ 01:10:58.529 Robert Tseng: connect different people, so I think those are all really great problems that AI can solve, and I love that we’re talking about how we can better use AI to coordinate the processes that we think about.
440 01:11:00.140 ⇒ 01:11:14.009 David Cohen: Awesome. Okay. So then, in that sense, what I want to do is move us along to the next activity. So for those of you that, that went outside of the board, I would ask that you come back to the Mira board in the chat function one more time.
441 01:11:14.060 ⇒ 01:11:23.440 David Cohen: And I’m gonna summon all of you, or bring you all to the bottom part, where you’ll see a square, or a grid that says Impact, Simple, and complex.
442 01:11:23.470 ⇒ 01:11:41.469 David Cohen: At the bottom. So the purpose of me bringing you here is that I want to also talk about the realities of enabling and creating the many ideas that you’ve all come up with so far. One of the things that we’ve done is just come up with as many ideas as possible for how to make
443 01:11:41.930 ⇒ 01:11:55.080 David Cohen: AI real for many of you, and to solve some of those challenges that we talked about earlier. One of the processes that Utam, Robert, and myself go through when enabling these things in reality is that
444 01:11:55.110 ⇒ 01:12:03.759 David Cohen: The ideas that each of you gave us are, one, in a group of many ideas that each of your respective companies or organizations
445 01:12:03.760 ⇒ 01:12:28.640 David Cohen: have. So if we brought together not only just each of you, but also you and your teams, we would come up with probably dozens of ideas on how to use AI individually to make your lives better, and your customers’ and stakeholders’ lives better as well. So what we have to do is to take the entire universe of ideas, and then understand them relative to how impactful or complex
446 01:12:28.960 ⇒ 01:12:39.930 David Cohen: those ideas are to make real. Some ideas are more difficult than others, for both technical reasons, for financial reasons, and also for political reasons, or rather,
447 01:12:40.330 ⇒ 01:12:48.899 David Cohen: Internal politics reasons, so the organizational challenges, process challenges, things about the ways that your companies work.
448 01:12:48.900 ⇒ 01:13:08.300 David Cohen: that make it difficult to make these happen. So what I want to do for this exercise is for those of you that input answers, to think about the relationship between the impact of making that idea real. So, like, how meaningful would it be for that idea to become real, and how much better would your life be, or your company’s.
449 01:13:08.300 ⇒ 01:13:19.789 David Cohen: lives be if you made that happen in the y-axis here. So you’ll notice that there’s a word big impact at the top, and then small impact at the bottom, where, for example.
450 01:13:19.800 ⇒ 01:13:22.170 David Cohen: for the idea that Robert brought up.
451 01:13:22.850 ⇒ 01:13:39.950 David Cohen: if it says AI-powered risk analysis on contracts, so let’s say I thought that was extremely important for my company, what I want you to do is to move it towards the top of the graphic, right? So in a second, I’m going to give you 2 minutes to basically move your squares to wherever in the impact
452 01:13:39.960 ⇒ 01:13:48.510 David Cohen: sort of spectrum you think this sits. So if you think it’s very impactful, you would move it here. If you think it’s not that important, you would move it towards the bottom.
453 01:13:48.940 ⇒ 01:13:53.329 David Cohen: There’s also a right axis and left axis as well, which is complexity.
454 01:13:53.420 ⇒ 01:14:09.719 David Cohen: let’s say I thought that this was extremely difficult to make happen, because I… there’s, you know, this is gonna take a lot of time in the context of my industry. I see you’re already working on it, Daria. But essentially, what I want you to do is tell me how easy it is to make this happen from your perspective.
455 01:14:09.870 ⇒ 01:14:10.870 David Cohen: Does that make sense?
456 01:14:11.310 ⇒ 01:14:21.329 David Cohen: I see some of you are still working on it, so I’ll just go ahead and set the timer. Clearly, you understand what I’m saying, so I’ll go ahead and give you time. Robert, you go ahead and do it too, since you put in some answers.
457 01:14:23.520 ⇒ 01:14:24.680 Robert Tseng: Alright, let’s do it.
458 01:14:25.220 ⇒ 01:14:26.210 David Cohen: Yeah, go ahead.
459 01:14:28.490 ⇒ 01:14:31.899 David Cohen: I think I over-explained that one. Clearly, you all understand what I’m saying.
460 01:15:31.880 ⇒ 01:15:38.279 David Cohen: Okay, you have about 45 seconds here, so please finish up moving any of these stickies relative to where you think they should sit.
461 01:15:44.970 ⇒ 01:15:59.680 David Cohen: Okay, I think we’re all done. So what I’m gonna do is, as you guys are moving any final ones here, is to show you something. You’ll notice that all of the sticky, or all the squares that are on the board are, for the most part, above the line.
462 01:15:59.750 ⇒ 01:16:09.649 David Cohen: Right, so that means that, with obvious reason, you guys all think that these ideas are very impactful ideas, that they can make a big difference in the way your organization works.
463 01:16:09.760 ⇒ 01:16:20.579 David Cohen: that’s obvious, given that I asked you earlier what the biggest ideas are that you have to move your organization or company forward with AI.
464 01:16:20.770 ⇒ 01:16:36.650 David Cohen: the reality of when we do these with each of you individually is that not every idea that we come up with is going to be equally impactful. So the point of doing this exercise to begin with is that we want to have that conversation about what is actually going to move us forward.
465 01:16:37.030 ⇒ 01:16:54.489 David Cohen: from a real business perspective, and what is just not, right? What are the things that are going to have the biggest impact and are the easiest to achieve? You’ll also notice that most of the answers are on the top left quadrant here, meaning that many of you think that these ideas that you come up with are
466 01:16:54.490 ⇒ 01:16:57.539 David Cohen: Important, impactful, but also very complex.
467 01:16:57.540 ⇒ 01:17:12.330 David Cohen: And Utam and Robert, I know that this is something that we hear a lot, right, is that enabling some of these AI items or AI ideas take a lot of different parts and different moving kind of components to make real, but does that kind of align with what you would see otherwise?
468 01:17:20.030 ⇒ 01:17:21.050 David Cohen: You guys there?
469 01:17:23.550 ⇒ 01:17:24.820 Uttam Kumaran: Tara, was that for me?
470 01:17:25.050 ⇒ 01:17:34.579 David Cohen: Yeah, sorry, that was for you two. So, does that align to what you see otherwise in terms of, like, complex projects being… like, impactful projects being very complex and having many different parts?
471 01:17:35.660 ⇒ 01:17:45.979 Uttam Kumaran: Yeah, I mean, for the most part, what we find is that, complexity, it’s… that’s sort of, like, what we’re here to sort of help you think through. Meaning.
472 01:17:45.980 ⇒ 01:17:57.879 Uttam Kumaran: Is it complex because there’s, like, you know, a lot of people, issues that need to get resolved? Is there process issues, or is it, like, technology? And so, really, like, what our firm tries to help…
473 01:17:57.880 ⇒ 01:18:10.760 Uttam Kumaran: sort of identify as, like, what is in that top right quadrant? And those are really, like, the number one things to go after, because they can be done fast, and they have the largest impact. And then moving, sort of, like, left.
474 01:18:12.280 ⇒ 01:18:26.400 Uttam Kumaran: You know, typically. But it just depends. And also, even the things on the left side, you need to find ways to understand what portions of that are simple versus difficult, and see, like, if we can break down those issues. That’s typically what we do.
475 01:18:27.340 ⇒ 01:18:43.320 Robert Tseng: Yeah, I would, emphasize that, just being able to break down the complex ones so that you have one… if you can break down one complex into, like, multiple simple ones that they don’t… individually, they will have a big impact, and then also, like.
476 01:18:43.450 ⇒ 01:18:51.529 Robert Tseng: these… these, solutions, they stack on top of each other. So, I think that’s also one of the great, great things, that as you…
477 01:18:51.530 ⇒ 01:19:07.569 Robert Tseng: as you, automate more, then you get to… you get to see, like, you get to experience the full, like, agentic experience when it’s truly hands-off, which, I mean, I think obviously is what everybody is hoping for, but, it takes a few steps to get there.
478 01:19:09.530 ⇒ 01:19:27.449 David Cohen: Okay, so one thing that I did on the board, just so you all will notice, is that I marked the ones above the line in yellow. So, as Utam and Robert were mentioning, one of the things that we want to do is focus specifically on the items that are going to be impactful to you all, obviously, as we’re going through the process of developing these.
479 01:19:27.450 ⇒ 01:19:35.760 David Cohen: But most importantly, we want to focus on the items that are impactful and simple, the orange ones on the top right quadrant, as Utam was saying.
480 01:19:35.780 ⇒ 01:19:53.210 David Cohen: as priorities, and the ones that are more complex, as Robert was saying, can get broken down into parts, where they may, they may sort of have sub-components that we can then enable. But any questions or concerns as far as these go before we move on to the next activity?
481 01:19:59.310 ⇒ 01:20:10.789 David Cohen: Okay, so what I want to do for the final activity here is to talk a little bit about the factors that we think about whenever we enable these tools, or whenever we bring them to reality.
482 01:20:10.790 ⇒ 01:20:20.540 David Cohen: So if, those of you in the Zoom call, if you don’t mind going back to the Miro board, we will input or do one final conversation here before we start.
483 01:20:22.500 ⇒ 01:20:31.689 David Cohen: before we start talking about next steps. So the main thing that we want to do is obviously always achieve some sort of goal or objective through these
484 01:20:31.710 ⇒ 01:20:45.279 David Cohen: these activities. So if you think about the ideas that each of you gave us before, they can be thought of as goals, right? They’re aspirational items that each of you are trying to do for your respective organizations or companies.
485 01:20:45.280 ⇒ 01:20:56.019 David Cohen: And it’s going to be a journey to get there. It’s going to take time and effort to achieve the things that you want. And there’s both organizational factors, as we talked about, also technical ones.
486 01:20:56.020 ⇒ 01:21:10.160 David Cohen: And things that get in the way of you being able to get there. So the way we conceptualize this is that in each of your organizations and companies, there are drivers, meaning things that enable you to achieve the goals that we talked about.
487 01:21:10.160 ⇒ 01:21:20.649 David Cohen: And there are anchors, so, meaning obstacles or challenges that you, that are going to keep you from doing so. So, from the perspective of a driver, for instance, if you’re in the education space.
488 01:21:21.120 ⇒ 01:21:33.870 David Cohen: It may be that your organizational culture enables you to, to achieve the goals that you want, because everybody’s really excited about AI. I’m just, you know, ideating here, that’s not necessarily the truth.
489 01:21:33.980 ⇒ 01:21:50.080 David Cohen: And then on the anchor side, it may be more on the, like, for example, in the regulated industries, it may be that there’s heavy legal implications to these that you need to be thinking about. So what I want you to do for this activity is, in either the blue squares or the red squares.
490 01:21:50.080 ⇒ 01:21:55.359 David Cohen: To input any factors that you think may move your organizations forward.
491 01:21:55.670 ⇒ 01:22:07.840 David Cohen: And then in the red squares, to think about factors that keep you from being able to achieve the problems or the things that you’re wanting to solve for. So, I’m gonna set another 2 minutes, and as a reminder.
492 01:22:08.560 ⇒ 01:22:22.810 David Cohen: In the red squares, what I want to hear is, what keeps you from being able to do the things that you… that you talked about earlier? What are the challenges or the obstacles that keep you from doing that? And then in the blue ones, what are the things that you think will be helpful to you in doing so?
493 01:22:22.810 ⇒ 01:22:29.089 David Cohen: So, I’ll set a timer there, and then Robert and Utam, you guys are welcome to input any ones that you hear elsewhere as well.
494 01:24:02.250 ⇒ 01:24:04.089 David Cohen: Okay, you have about a minute left.
495 01:24:04.250 ⇒ 01:24:18.689 David Cohen: Slightly over a minute, so please input any… any answers you have as a reminder. In the red ones, what we’re hoping to see are the obstacles that you face in enabling AI in the first place, so what are the reasons that keep you from being able to do so?
496 01:24:18.690 ⇒ 01:24:31.140 David Cohen: And in the blue… light blue ones, what we’re hoping to see is what are the factors that enable you to do so. So, like, why… why is there excitement? What is there… what are the things that you can capture in being able to enable AI?
497 01:25:13.550 ⇒ 01:25:15.460 David Cohen: You’ve got about 10 seconds left.
498 01:25:24.260 ⇒ 01:25:28.599 David Cohen: Okay, so I’ll give you guys 10 to 15 seconds to finish up your answers here.
499 01:25:28.890 ⇒ 01:25:33.800 David Cohen: Please go ahead and, input any final items on the… on the board.
500 01:26:08.360 ⇒ 01:26:26.759 David Cohen: Okay, so I see some of you are still finishing up, so we’ll, go ahead and actually get started in talking through some of these. What I want to do is actually start talking about anchors or challenges first. So, I’m going to call, in the interest of time, just one of you, or one or two of you. So, I want to talk…
501 01:26:26.940 ⇒ 01:26:42.159 David Cohen: about lack of expertise, integrator, not clear finances. So, Gulnas, you brought up a sticky here, I think you cover several areas, but do you mind explaining some of the ones that you met there? I think you covered several different topics, but I want to give you a chance to bring it up.
502 01:26:42.570 ⇒ 01:26:43.760 Avitsenna: Hi.
503 01:26:44.220 ⇒ 01:26:54.439 Avitsenna: I think, yeah, that’s, exactly the point that, for example, in our company, we have only one IT specialist.
504 01:26:54.570 ⇒ 01:27:08.760 Avitsenna: And sometimes when I ask something to do, he doesn’t have time, and to take a new team, so I don’t know how much it will be all costs. So even if I knew ahead, so it will cost, like.
505 01:27:09.210 ⇒ 01:27:26.099 Avitsenna: this sum of money, then I will think of, yeah, the… but the thing is, like, when you integrate something new, you really don’t know how much it will end up with. So that’s, I think, the biggest challenge for me, either to hire the new team, so something like this, yeah.
506 01:27:27.000 ⇒ 01:27:28.749 David Cohen: Yeah, and I think that… They’re just pouring you.
507 01:27:29.820 ⇒ 01:27:31.469 David Cohen: Sorry, go ahead, I interrupted you.
508 01:27:31.470 ⇒ 01:27:33.989 Avitsenna: No, no, no, I’m saying very discouraging, yeah.
509 01:27:34.420 ⇒ 01:27:53.640 David Cohen: Oh, okay, yeah, I mean, and Utam and Robert, I think this is something that… it’s pretty clear. Everybody… literally all of the anchors here are the same. It’s budget, no expertise, essentially, like, not clear picture on the finances. This is pretty… pretty obvious and standard, right? Like, all this stuff is stuff we hear elsewhere.
510 01:27:54.090 ⇒ 01:28:07.690 Uttam Kumaran: Yeah, and so part of these is just, like, understanding, one, if it’s a budget, it’s like, okay, what budget is available, or what, you know, are the plans for the future, and where can we free up even some amount of time or money towards tackling this?
511 01:28:07.690 ⇒ 01:28:26.600 Uttam Kumaran: Second is understanding, in terms of resourcing our people, okay, so what are ways that, you know, your folks on your team can take advantage of AI, and implement? But also, where may you have to, you know, use external firms to actually support the development, right? So a lot of this is
512 01:28:26.600 ⇒ 01:28:35.269 Uttam Kumaran: is investment. But, you know, we work with a variety of people in a variety of budgets. So part of it is just, you know, first.
513 01:28:35.620 ⇒ 01:28:55.440 Uttam Kumaran: trying to understand, like, what the opportunity is, right? If implementing AI or data systems allows you to grow revenue 10%, then it may be worth, you know, considering 1% to 2% investment in order to make that happen. You know, so we… these are pretty common things that, you know, we see that everybody is dealing with.
514 01:28:56.190 ⇒ 01:29:09.239 David Cohen: So, sorry to call you on you one more time, but there’s actually a point that you brought up that I think you were the only one to bring this up, so I want to get your perspective on it, which is resistance to change from some staff.
515 01:29:09.270 ⇒ 01:29:23.400 David Cohen: This is something that we see, obviously, in the implementation piece of both AI and data systems, but what is… is your concern there that some folks are resistant to, like, using the new tools, or is there… is it bigger than that? Can you explain, please?
516 01:29:25.160 ⇒ 01:29:32.169 Esentur Dildebekov: Yeah, sure. The thing is, the school community is huge. It includes, like, parents, lots of organization.
517 01:29:32.500 ⇒ 01:29:34.689 Esentur Dildebekov: Students and staff itself.
518 01:29:35.240 ⇒ 01:29:40.929 Esentur Dildebekov: So, some of them just don’t want to change the way of communications, or the way of
519 01:29:41.460 ⇒ 01:29:48.180 Esentur Dildebekov: Tools, and, you know… The people don’t like lots of combo… No.
520 01:29:50.260 ⇒ 01:29:51.000 David Cohen: Yeah.
521 01:29:51.000 ⇒ 01:29:56.450 Esentur Dildebekov: complicating tools, etc. So, the resistance to change is
522 01:29:56.630 ⇒ 01:30:01.699 Esentur Dildebekov: It’s a challenge for us, and it’s really hard to,
523 01:30:02.190 ⇒ 01:30:13.570 Esentur Dildebekov: you know, do everything perfect for all of them. For example, easy tool, or suitable tool for teachers, is not suitable for parents or students.
524 01:30:13.970 ⇒ 01:30:14.960 Esentur Dildebekov: etc.
525 01:30:15.080 ⇒ 01:30:16.290 Esentur Dildebekov: We are just…
526 01:30:16.420 ⇒ 01:30:24.179 Esentur Dildebekov: You know, trying to find, like, a magic button that solves every… like, all complaints from the community.
527 01:30:24.650 ⇒ 01:30:27.010 Esentur Dildebekov: So, this is our end core.
528 01:30:28.270 ⇒ 01:30:43.790 David Cohen: Yeah, and Robert and Utam, I know this is something that we also hear a lot, right? Like, in terms of, let’s call it change management, of being able to enable these tools in a way that gets the most adoption possible. It’s just something we hear significantly often, correct?
529 01:30:45.520 ⇒ 01:30:47.370 Uttam Kumaran: I mean, yeah, go ahead.
530 01:30:47.650 ⇒ 01:31:06.020 Robert Tseng: Yeah, no, I was just gonna say, I think, I think we expect everyone to be as excited as we are, but, you know, if you came here, you’re probably the most excited person from your team, so, I think it… but it usually takes a champion to, really be excited about driving change,
531 01:31:06.020 ⇒ 01:31:11.000 Robert Tseng: For being the first one to kind of put yourself out there to use the new solution.
532 01:31:11.000 ⇒ 01:31:19.299 Robert Tseng: and then kind of teaching other people along the way. So, you know, ironically, we’re an AI engineering company, but
533 01:31:19.300 ⇒ 01:31:35.090 Robert Tseng: You know, it’s not intuitive for people to use these tools. It’s so new. Nobody was using them, you know, 3, 3 plus years ago. So, even we have to kind of find, you know, small ways every day as we’re talking with our colleagues.
534 01:31:35.090 ⇒ 01:31:48.570 Robert Tseng: To show them how to do something, with AI that they maybe didn’t think about before, and that helps us to continue to encourage everyone around us to be… to be using… using it in this… in the way that we hope.
535 01:31:50.770 ⇒ 01:31:53.229 Esentur Dildebekov: Yeah, thank you, I fully understand this.
536 01:31:53.770 ⇒ 01:31:57.719 Esentur Dildebekov: But I… we understand that this is not a short process.
537 01:31:59.650 ⇒ 01:32:00.590 Esentur Dildebekov: Thank you.
538 01:32:00.920 ⇒ 01:32:01.490 Robert Tseng: Yeah.
539 01:32:03.200 ⇒ 01:32:11.150 David Cohen: Awesome. Okay, so just to close out this activity, what I want to do is bring you all to the drivers once more, and then…
540 01:32:11.230 ⇒ 01:32:24.589 David Cohen: to realize that you actually all input sort of similar themes. So, Gulnaz, I’m gonna call on you one more time, because the input, or the sticky that you put down is actually really well summarizing of the rest.
541 01:32:24.620 ⇒ 01:32:33.949 David Cohen: So you talked about motivation and… or the understanding that sooner or later we have to do this, so the earlier we start, the earlier we can become winners in the market.
542 01:32:34.100 ⇒ 01:32:44.560 David Cohen: So I think you pretty much summarized the rest of these, but would love to get your perspective on the motivation as a factor for improving and getting better at using AI tools.
543 01:32:46.370 ⇒ 01:32:48.370 Avitsenna: Yeah, thank you very much for the comment.
544 01:32:48.560 ⇒ 01:32:59.349 Avitsenna: But I think the AI is already coming. It’s like the web page, let’s say, like, many years ago, we all were thinking, like, to-do or not, and now.
545 01:32:59.350 ⇒ 01:33:22.580 Avitsenna: We have to all to do the web page, at least, you know, so… and this is the same with AI tools, like, sooner or later, we have to learn Excel, let’s say the Word, so I think the same idea with AI. Anyway, we have to integrate it to make life easier and simpler. The thing is, where to implement and how to implement is the another question, and
546 01:33:22.580 ⇒ 01:33:28.570 Avitsenna: And that’s a point, like, to have my… actually, the challenge is when… at the beginning, when you asked
547 01:33:28.910 ⇒ 01:33:36.959 Avitsenna: which challenge we face. Like, today, only, I met with the AI, integrator, some guy, and
548 01:33:37.120 ⇒ 01:33:55.259 Avitsenna: We didn’t… we thought that we were meeting about automation of our systems and our work, but he, instead proposed AI, some tools, simple tool, and I didn’t even realize and didn’t think of that… that simple tool could help us.
549 01:33:55.260 ⇒ 01:34:00.319 Avitsenna: That’s a point that we don’t have this thinking that AI could help in
550 01:34:00.320 ⇒ 01:34:19.850 Avitsenna: And plus, in which areas else can help us AI. So, we only know, like, how to correct the text, let’s say, or even the, I don’t know, photos, but not… we are not thinking of how much of AI… what else can it do? So, that’s a point, you know?
551 01:34:19.910 ⇒ 01:34:32.169 Avitsenna: But anyway, we have to integrate sooner or later, and if we will be automated, or, like, as a company, using a lot of AI tools.
552 01:34:32.170 ⇒ 01:34:42.170 Avitsenna: Then, of course, we will be better in the future, and we will, let’s see, yeah, the winner in the market, because the competition is high by every year, every day, etc.
553 01:34:43.960 ⇒ 01:34:45.850 David Cohen: Yeah, no, I think that’s a… that’s a wonderful…
554 01:34:45.850 ⇒ 01:34:52.949 David Cohen: note to end on, and the reason I brought that up specifically is to kind of bring it all full circle
555 01:34:52.950 ⇒ 01:35:07.930 David Cohen: for all of you. So I zoomed us out to see the full board, so that you can get a sense of why we do this session the way that we did it, right? Is that if you think about the capturing the sentiment that was just brought up around
556 01:35:08.200 ⇒ 01:35:30.749 David Cohen: understanding that AI is a reality that we all have to understand, capture, and get proficient at, is that getting there requires understanding, obviously, at the beginning of today, we talked about what we use AI for today. We also have to understand what challenges and problems we’re solving in reality, right? That’s why we asked you the question of, like, what problems do you have today that need to
557 01:35:30.750 ⇒ 01:35:43.639 David Cohen: be solved and can be solved through these things, and most importantly, in understanding what opportunities there are that we can bring into reality. So, as we’re going through that process, the reason why we do these workshops and why we
558 01:35:43.830 ⇒ 01:35:50.469 David Cohen: navigate the conversation around AI in this form is that it enables you and your teams
559 01:35:50.470 ⇒ 01:36:11.300 David Cohen: to participate with us, meaning the Brainforge team, in the process of defining what those opportunities are, how we’re going to get through them, and in creating activities where we can talk about what the impact is relative to the complexity of the activities. So, Utam and Robert, I’m curious, now that you’ve kind of heard the whole story, or heard from everybody here.
560 01:36:11.300 ⇒ 01:36:17.989 David Cohen: Any surprises, any sort of final thoughts in terms of what you heard before we move on to next steps?
561 01:36:19.120 ⇒ 01:36:22.939 Uttam Kumaran: Yeah, maybe the only thing I’ll say is this is something that, like.
562 01:36:22.970 ⇒ 01:36:26.839 Uttam Kumaran: All, sort of, business owners and leaders are facing right now.
563 01:36:26.850 ⇒ 01:36:45.839 Uttam Kumaran: like, running a business and running teams is very expensive. You know, even in our business, part of the reason why we, you know, believe so much in AI is it’s really the reason we were able to grow and keep expenses, you know, stable. And it’s something that I think many folks
564 01:36:45.840 ⇒ 01:36:55.179 Uttam Kumaran: you know, across the world now are finding that AI is a great technology to allow you to not only scale and grow without having to invest.
565 01:36:55.180 ⇒ 01:37:08.100 Uttam Kumaran: at the same rate in people or technology, but additionally allow you to differentiate yourself, right? Like, how is your business giving differentiated outcomes to your clients, to your team.
566 01:37:08.100 ⇒ 01:37:28.030 Uttam Kumaran: And so we find that this is really, like, a competitive advantage as well. How do you stand out, you know, among competition? So, I would say I’m… I’m not surprised, but everybody’s facing this… these types of pain points right now. However, you know, after doing… being in data and technology for a number of years.
567 01:37:28.030 ⇒ 01:37:32.150 Uttam Kumaran: It’s never been cheaper, to take advantage of these.
568 01:37:32.150 ⇒ 01:37:51.000 Uttam Kumaran: And it’s never been, you know, easier for folks to buy software and tools to implement. You know, 5, 10 years ago, you know, it was quite difficult to set up, you know, consolidated data systems, and AI wasn’t available, right? So flipping this on its head, and instead of
569 01:37:51.000 ⇒ 01:37:59.629 Uttam Kumaran: Thinking about how difficult it may be to adopt, you may actually find that there are free or really cheap resources to implement.
570 01:37:59.630 ⇒ 01:38:15.879 Uttam Kumaran: Right? Just enabling your team to use ChatGBT, it could be a real improvement to your day-to-day processes. So, I don’t know, I feel like we think about it as an opportunity, to stand out, you know, as much as it is, you know, definitely, difficult.
571 01:38:17.320 ⇒ 01:38:36.869 David Cohen: And, on that note, right, I’m sure the question on many of your minds is, okay, well, like, this is a great conversation, we’re really excited to capture some of these opportunities, and how do we do that? So, obviously, the next step for us is to kind of share some of how we do that for companies like yours, right? So, Utam, I don’t know if you want to…
572 01:38:36.870 ⇒ 01:38:45.740 David Cohen: I can bring it up to the Growth Sprint demo, but I don’t know if you want to give a quick 2-minute walkthrough of what we do on this front in terms of helping companies like the ones that are here today.
573 01:38:46.080 ⇒ 01:38:54.600 Uttam Kumaran: Yeah, so on, on the growth spread side, so this is an offering that we decided, was important to bring to market because we were…
574 01:38:54.600 ⇒ 01:39:19.499 Uttam Kumaran: coming into many companies that weren’t even clear on where AI can help their business, and what is the potential revenue or cost-saving opportunity. And so, you know, we’re a data and AI consultancy, but of course, we’re here to serve our clients. And so one of the things that we started doing for folks is simple half-day or full-day workshops that gets
575 01:39:19.500 ⇒ 01:39:31.709 Uttam Kumaran: all the right people in your company into one room, goes through a similar exercise that we did today, except, you know, a little bit more expansive, and arrives at, you know, really short-term and long-term
576 01:39:31.710 ⇒ 01:39:37.859 Uttam Kumaran: Things that you can do in your business today to drive outcomes. You know, it’s something that…
577 01:39:37.860 ⇒ 01:39:45.229 Uttam Kumaran: you know, we do a very similar exercise internally at Brainforge, and we realize that this is something that, for a lot of clients.
578 01:39:45.230 ⇒ 01:40:02.489 Uttam Kumaran: it takes a facilitation of a great workshop and a meeting to actually get the ideas from all of your team, you know, out into one board. And then lastly, arrive at, okay, what can I actually do in the next, you know, 6 to 8 weeks? And then what are things I should keep in mind, you know, in the future?
579 01:40:04.550 ⇒ 01:40:15.820 David Cohen: Okay. So, with that, before we talk about next steps, Robert, Malika, anything else to close out with before we talk about tactical next steps for after the workshop?
580 01:40:17.510 ⇒ 01:40:36.269 Malika Alen: I just wanted to add, in the closing remarks, I would like to also invite our Executive Director, and before we finish it, maybe take a photo for all of us, so if the participants can turn their cameras on. But before that, I’d like to give some closing remarks to out and I, to our director.
581 01:40:38.040 ⇒ 01:40:38.590 David Cohen: Peace.
582 01:40:41.150 ⇒ 01:40:41.860 Altynai: So…
583 01:40:42.000 ⇒ 01:41:01.250 Altynai: Good morning, good morning, colleagues. Good morning, good evening, our members. Thank you very much for your time. David, Utam, Robert, that was very interesting, a lot of insights you gave to us, and at the same time, I would like to appreciate for all those information which you shared with us today.
584 01:41:01.300 ⇒ 01:41:14.699 Altynai: AI, it is really, it’s all… it’s… it is already… it’s, not a future. We are living in this, time, in this generation, and one of the drivers is,
585 01:41:14.700 ⇒ 01:41:24.399 Altynai: most of the participants mentioned, and I would like to add to this that the drivers and the anchors, I think it’s a generation as well, the human generation.
586 01:41:24.570 ⇒ 01:41:31.549 Altynai: So the… at the moment, the younger people are more fasted and more,
587 01:41:31.630 ⇒ 01:41:45.240 Altynai: clearly using the old AI instruments in comparison with those generations which are, let’s say, 40 plus or the 50+. And the same, at the same time, they can be entered cars, and they can be drivers.
588 01:41:45.300 ⇒ 01:41:46.360 Altynai: So, thank
589 01:41:46.390 ⇒ 01:42:04.169 Altynai: Thank you very much, and I would like to say, we will continue this kind of the seminars, this kind of the workshops with the Brain Forge. Appreciate for this online training and for these interesting, presentations, which you did, exercises, it’s really interesting.
590 01:42:04.170 ⇒ 01:42:15.779 Altynai: And I would like to say to our members, thank you for being the members of the American Chamber, and a separate thanks to Malika for organizing all these events and all this seminars, so thank you very much once again.
591 01:42:17.810 ⇒ 01:42:24.199 David Cohen: And, just a couple next steps in terms of logistical items before we close out the session for today.
592 01:42:24.200 ⇒ 01:42:49.170 David Cohen: On our end, we will package everything that you’ve seen today, both in terms of the recording, meaning the Brainforge team will package the recording, as well as clean up the board. This will be available for all of you to reference if you would like to come back and get the ideas that are on here, and we will make sure to share it with you all, either via the AmChamp team or directly. You’ll get all the materials, as we talked about earlier, that were covered in the
593 01:42:49.170 ⇒ 01:42:50.150 David Cohen: meeting today.
594 01:42:50.150 ⇒ 01:43:04.499 David Cohen: If you are interested in learning more about the growth sprint that Utam mentioned, or if you want to talk more about how we may enable AI for you, please feel free to reach out after we send out that email, either through the AmChamp team or directly through us.
595 01:43:04.500 ⇒ 01:43:11.170 David Cohen: We’re more than happy to talk about how we can create similar workshops to the ones that we did today to help you enable
596 01:43:11.170 ⇒ 01:43:13.800 David Cohen: AI more specifically for your team.
597 01:43:13.800 ⇒ 01:43:38.069 David Cohen: And then from there, I think the only other thing is let us know if you have any questions, right? If there are any other topics that we can be of support in, or if we can provide any further context for any of the topics that were discussed today, please feel free to reach out to either myself, Robert, or Utam, and then the Amtraum team will, I’m sure, have more details to share with you guys after the fact as well. But regardless, thank you all for your time. We really appreciate
598 01:43:38.070 ⇒ 01:43:45.529 David Cohen: each and every one of you, myself, Robert, and UTAM are always here to help, so please let us know if there’s anything else that we can do for you, okay?
599 01:43:45.580 ⇒ 01:43:46.610 David Cohen: Thank you, off.
600 01:43:48.290 ⇒ 01:43:50.730 Robert Tseng: You wanna take your photo, Malika?
601 01:43:50.970 ⇒ 01:43:51.630 Malika Alen: Yes, please.
602 01:43:51.630 ⇒ 01:43:52.890 David Cohen: I forgot about the photo, sorry.
603 01:43:53.010 ⇒ 01:43:58.339 Malika Alen: If we can turn off the screen, maybe, so we can have, like, a gallery view of all the participants.
604 01:44:00.550 ⇒ 01:44:05.650 Malika Alen: Wow, so happy to see everyone. So, Dadia, let us know if you’re ready.
605 01:44:06.780 ⇒ 01:44:11.539 Daria AmCham Kyrgyzstan: Yeah, guys, I’m ready to turn on your cameras, if possible.
606 01:44:12.180 ⇒ 01:44:16.949 Daria AmCham Kyrgyzstan: If not, I can do them for us. 1, 2, 3, smile, please.
607 01:44:19.520 ⇒ 01:44:21.270 Daria AmCham Kyrgyzstan: Yeah, great, thank you.
608 01:44:22.970 ⇒ 01:44:24.690 Uttam Kumaran: Thank you all, appreciate it.
609 01:44:24.810 ⇒ 01:44:25.600 Uttam Kumaran: Yeah, please read…
610 01:44:25.600 ⇒ 01:44:25.980 Malika Alen: July.
611 01:44:25.980 ⇒ 01:44:35.770 Uttam Kumaran: If anything, this is all we think about every day, so if we can be helpful, or give advice, or recommend tools, or things like that, you know, no problem at all.
612 01:44:36.450 ⇒ 01:44:55.760 Malika Alen: And just the last thing that I wanted to add, if any of your company would like to connect with Robert, Utah, or David, you can connect directly to engage with them directly and do the workshops for your own companies, because this was more of the different and multi-sectorial workshop today.
613 01:44:55.760 ⇒ 01:45:01.380 Malika Alen: So thank you, everyone, and have a great rest of your evening, and have a great day, you guys. Thank you for the workshop.
614 01:45:01.790 ⇒ 01:45:03.499 Robert Tseng: Thank you. Yeah, thank you.
615 01:45:03.720 ⇒ 01:45:04.330 David Cohen: Of course.
616 01:45:04.330 ⇒ 01:45:04.879 Esentur Dildebekov: Thank you.
617 01:45:04.880 ⇒ 01:45:05.890 David Cohen: Bye, everybody.
618 01:45:06.310 ⇒ 01:45:07.030 Robert Tseng: Alright, run.