Meeting Title: Brainforge x BCTG: Collaboration Discussion! Date: 2026-03-17 Meeting participants: Uttam Kumaran, Kyle Montgomery, Luke Scorziell
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1 00:07:21.230 ⇒ 00:07:22.430 Kyle Montgomery: Hello!
2 00:07:22.870 ⇒ 00:07:24.060 Uttam Kumaran: Hey, how are ya?
3 00:07:24.060 ⇒ 00:07:27.000 Kyle Montgomery: Thank you for your patience, I’m so sorry.
4 00:07:27.000 ⇒ 00:07:27.860 Uttam Kumaran: All good.
5 00:07:28.490 ⇒ 00:07:29.600 Uttam Kumaran: How’s the day going?
6 00:07:30.160 ⇒ 00:07:34.000 Kyle Montgomery: It says… It’s going. We had…
7 00:07:34.750 ⇒ 00:07:42.400 Kyle Montgomery: A weird day yesterday where everybody thought the world was coming to an end, with the band of storms coming across, that…
8 00:07:42.910 ⇒ 00:07:47.220 Kyle Montgomery: like, all of North Carolina canceled school, and .
9 00:07:47.220 ⇒ 00:07:49.530 Uttam Kumaran: Yeah, and I saw in DC it was very similar.
10 00:07:49.730 ⇒ 00:07:56.310 Kyle Montgomery: Yeah, and nothing happened. I mean, I know forecasters can’t always get it right, but sometimes the reactions of.
11 00:07:56.580 ⇒ 00:08:03.989 Kyle Montgomery: it’s gonna be a windy day with some potential for tornadoes, like, so? Crawl under your desk! I think it’s been a particular.
12 00:08:03.990 ⇒ 00:08:10.249 Uttam Kumaran: particularly, like, bad season, so maybe they’re trying to just, like, act like…
13 00:08:10.990 ⇒ 00:08:14.130 Uttam Kumaran: Oh, we’re gonna get ahead of every single one that comes.
14 00:08:14.130 ⇒ 00:08:15.860 Kyle Montgomery: I guess. I guess.
15 00:08:17.330 ⇒ 00:08:18.490 Uttam Kumaran: Cool. Lucas…
16 00:08:18.490 ⇒ 00:08:20.640 Kyle Montgomery: How’s your week? How’s your week going?
17 00:08:21.240 ⇒ 00:08:25.289 Uttam Kumaran: Things are good, I feel like we’re just planning out our next quarter,
18 00:08:25.450 ⇒ 00:08:31.809 Uttam Kumaran: We are hiring, so we’re just, like, kind of going through interview process with some people.
19 00:08:33.020 ⇒ 00:08:41.000 Uttam Kumaran: It’s good, I think it’s just, like, towards the end of the quarter, it’ll just, like, I just try to reflect on, like, okay, what do we think about going into this quarter, and…
20 00:08:41.309 ⇒ 00:08:45.779 Uttam Kumaran: how are we thinking about some of those assumptions and changes? So it’s good.
21 00:08:45.780 ⇒ 00:08:47.270 Kyle Montgomery: Yeah, good.
22 00:08:47.270 ⇒ 00:08:47.610 Uttam Kumaran: Yeah.
23 00:08:47.610 ⇒ 00:08:53.309 Kyle Montgomery: Do you guys talk… I don’t know if you personally did, but somebody on your team talked to that guy, Leo, from Disney.
24 00:08:53.790 ⇒ 00:09:02.469 Uttam Kumaran: Yeah, I need to, I need to check on our notes from him. I feel like… let me see if I have any slack about it.
25 00:09:03.560 ⇒ 00:09:10.159 Uttam Kumaran: I feel like he may have just been too senior for some of the stuff that we’re actively doing.
26 00:09:10.160 ⇒ 00:09:10.630 Kyle Montgomery: Well.
27 00:09:10.630 ⇒ 00:09:14.350 Uttam Kumaran: Like, but I actually need to check, I don’t remember what…
28 00:09:15.220 ⇒ 00:09:19.400 Uttam Kumaran: Kayla, told me. So yeah, Kayla, who leads, sort of our recruiting, I think.
29 00:09:19.520 ⇒ 00:09:21.470 Uttam Kumaran: Gave him a… gave him a ring.
30 00:09:22.180 ⇒ 00:09:23.029 Kyle Montgomery: Oh, yeah, good.
31 00:09:28.750 ⇒ 00:09:33.459 Uttam Kumaran: Yeah, I can… It’s somewhere in my notes, I can go check.
32 00:09:36.710 ⇒ 00:09:38.700 Kyle Montgomery: Yeah, that was very.
33 00:09:38.700 ⇒ 00:09:44.010 Uttam Kumaran: I think she used to work for Disney, so she actually was… she stopped that role before, I think.
34 00:09:44.010 ⇒ 00:09:48.510 Kyle Montgomery: Yeah, that’s… that, was a remarkably small world.
35 00:09:49.540 ⇒ 00:09:57.869 Kyle Montgomery: And… and it was also just timing that here you and I were talking about something unrelated, and… and then out of nowhere, this…
36 00:09:58.040 ⇒ 00:10:01.079 Kyle Montgomery: We had a consultant who had to roll off of,
37 00:10:02.080 ⇒ 00:10:05.490 Kyle Montgomery: Monster Energy. And she felt bad.
38 00:10:05.580 ⇒ 00:10:22.159 Kyle Montgomery: leaving this hole, she was a project manager, and so she just put that guy forward as like, hey, maybe, you know, we could slot him in there to take over, and the ship had already sailed, but when I read his profile, I was like, that Disney stuff sounds pretty darn cool, I don’t…
39 00:10:22.160 ⇒ 00:10:22.820 Uttam Kumaran: Yeah.
40 00:10:22.820 ⇒ 00:10:28.170 Kyle Montgomery: he’s not a data engineer by any stretch, but… I don’t know, I just thought that that sounded like some interesting work.
41 00:10:28.170 ⇒ 00:10:33.279 Uttam Kumaran: I think he’s somewhere… I’ll have to double check. He’s somewhere in our pipeline, so I need to check again.
42 00:10:33.460 ⇒ 00:10:39.230 Uttam Kumaran: But yeah, I think Luke is on his way. Let me message him again.
43 00:10:47.520 ⇒ 00:10:53.819 Uttam Kumaran: And then any… how did… how is it going with, anything on the J&J stuff? Did they end up coming back to you on anything?
44 00:10:53.820 ⇒ 00:11:02.449 Kyle Montgomery: I have not… this guy’s been hard to, track down, but, my account manager that has the relationship, she just was…
45 00:11:02.590 ⇒ 00:11:09.459 Kyle Montgomery: Reaching out to him again today to try to get some time on his calendar to walk through next steps and…
46 00:11:10.510 ⇒ 00:11:15.239 Kyle Montgomery: I think as I described to you, when I came at him.
47 00:11:15.490 ⇒ 00:11:20.279 Kyle Montgomery: Originally, after our first conversation.
48 00:11:20.430 ⇒ 00:11:28.839 Kyle Montgomery: I was putting somebody in front of him that was much more of, like, an AI… I guess…
49 00:11:30.280 ⇒ 00:11:37.410 Kyle Montgomery: I don’t even know what to call it, AI developer. He wasn’t a real data engineer guy. He was more in,
50 00:11:37.630 ⇒ 00:11:47.899 Kyle Montgomery: laying out the processes and leveraging some of the tools, and he came right back, said, no, no, no, no, I need more data engineers, so I called you. Yeah. And so I’m…
51 00:11:48.070 ⇒ 00:11:49.679 Kyle Montgomery: I, I feel like…
52 00:11:50.710 ⇒ 00:11:55.260 Kyle Montgomery: that it’s a little… I wouldn’t say it’s a moving target, but I just don’t have the target in.
53 00:11:55.260 ⇒ 00:11:55.620 Uttam Kumaran: Yeah.
54 00:11:55.620 ⇒ 00:12:00.719 Kyle Montgomery: perfect site yet. Yeah. And because he’s very difficult to…
55 00:12:00.720 ⇒ 00:12:01.350 Uttam Kumaran: Yeah.
56 00:12:01.570 ⇒ 00:12:06.789 Kyle Montgomery: track down, I’m like, like, real-time feedback would be wonderful, because I can adjust quickly.
57 00:12:06.790 ⇒ 00:12:07.629 Uttam Kumaran: Yeah, yeah, yeah.
58 00:12:07.660 ⇒ 00:12:10.069 Kyle Montgomery: can’t adjust in the dark. I’d like a little bit of feedback.
59 00:12:10.070 ⇒ 00:12:14.999 Uttam Kumaran: And if it’s, like, if you’re getting something every, like, a week or two weeks, then it’s like…
60 00:12:15.120 ⇒ 00:12:19.289 Uttam Kumaran: Yeah. Okay, well, at least you have some ammo, I feel like, for…
61 00:12:19.290 ⇒ 00:12:33.969 Kyle Montgomery: Yeah, I thought the way you laid it out, besides just your background and how you spelled out what would be important to them was great. I was basically able to take that and just pass along and say, here’s a really smart guy, he’s got a great team, let’s figure out how this might…
62 00:12:34.080 ⇒ 00:12:35.210 Kyle Montgomery: Might work out.
63 00:12:35.410 ⇒ 00:12:35.980 Uttam Kumaran: Cool.
64 00:12:36.180 ⇒ 00:12:40.290 Luke Scorziell: Okay. Sorry, I just hopped on, I’m… yeah, so sorry.
65 00:12:40.290 ⇒ 00:12:40.779 Kyle Montgomery: I was late.
66 00:12:40.780 ⇒ 00:12:41.240 Luke Scorziell: delight.
67 00:12:41.240 ⇒ 00:12:43.230 Kyle Montgomery: I was late, so you’re good.
68 00:12:43.230 ⇒ 00:12:46.160 Uttam Kumaran: I’m the only one on time!
69 00:12:46.580 ⇒ 00:12:50.679 Uttam Kumaran: I just so happen to be today. That’s not common.
70 00:12:50.680 ⇒ 00:12:52.369 Luke Scorziell: It’s fine, it’s like,
71 00:12:52.660 ⇒ 00:13:01.439 Luke Scorziell: It’s one of those days where just, first thing, it’s a couple fires, and we’re like, yeah. So, but anyway, super glad to be here, and excited to get to talk, I know.
72 00:13:01.440 ⇒ 00:13:04.890 Uttam Kumaran: Yeah, I think I told Luke that I feel like
73 00:13:05.060 ⇒ 00:13:10.270 Uttam Kumaran: Kyle, you were someone who reached out because you were like, hey, I’ve been seeing some of your content, and it’s sort of…
74 00:13:10.450 ⇒ 00:13:24.970 Uttam Kumaran: like, reminded you of, like, okay, there’s potentially some opportunities. So, I think two things. One, I’m sure Luke is certainly interested in, like, what in particular you were following, and, like, what caught your eye, but also, I think we talked about, like, how do we collaborate
75 00:13:25.020 ⇒ 00:13:37.019 Uttam Kumaran: And either on the content side or whatever. And so I just wanted to connect the three of us, because Luke sort of leads a lot of… a lot of that on our, like, brand go-to-market side.
76 00:13:37.490 ⇒ 00:13:38.070 Kyle Montgomery: Sure.
77 00:13:39.090 ⇒ 00:13:40.919 Kyle Montgomery: Sure, yeah, I… I mean, the…
78 00:13:41.110 ⇒ 00:13:44.210 Kyle Montgomery: to cut to it, the LinkedIn posts
79 00:13:44.580 ⇒ 00:13:52.910 Kyle Montgomery: That you’ve been putting out there on multiple levels. So, one, just because I know you, it’s… I… I find it…
80 00:13:53.110 ⇒ 00:14:03.700 Kyle Montgomery: interesting and fun to watch you post about your company, and just the growth, and you… I don’t know, your most recent one, something about, like, wow, this quarter’s flying by, and we’re setting up
81 00:14:03.950 ⇒ 00:14:09.500 Kyle Montgomery: I mean, as somebody’s running a business, too, that catches my eye. But then…
82 00:14:10.020 ⇒ 00:14:14.229 Kyle Montgomery: Even previously, over the last few months, just some of your…
83 00:14:14.840 ⇒ 00:14:22.549 Kyle Montgomery: insights and perspectives on AI, and everything. I think if I had to come up with one
84 00:14:24.000 ⇒ 00:14:27.199 Kyle Montgomery: Concept or theme is that it just…
85 00:14:27.340 ⇒ 00:14:30.189 Kyle Montgomery: Part of your personality, whether it’s yours.
86 00:14:30.370 ⇒ 00:14:34.359 Kyle Montgomery: Odom, or just the brand that you put together, it’s very pragmatic.
87 00:14:34.860 ⇒ 00:14:35.350 Kyle Montgomery: like.
88 00:14:35.350 ⇒ 00:14:35.990 Uttam Kumaran: Yeah.
89 00:14:36.290 ⇒ 00:14:40.420 Kyle Montgomery: One, it’s pragmatic, and two, it’s in a language that dummies like me can understand.
90 00:14:40.420 ⇒ 00:14:53.039 Uttam Kumaran: No, very by design. I wouldn’t say dummies like you, but I vary by design, meaning, like, and you can tell Luke that I constantly hit our team over the head with, like.
91 00:14:53.200 ⇒ 00:15:05.309 Uttam Kumaran: the con… the other… some of… some stuff that I consume, I’m like, this is so esoteric. Like, I want us to carve out the, like, literally how this affects the thing you just did, like, before reading.
92 00:15:05.590 ⇒ 00:15:13.749 Uttam Kumaran: this note, like, really try to do that, and I feel like we practically very… we use it practically in a lot of ways, and I think trying to convey that
93 00:15:13.860 ⇒ 00:15:16.299 Uttam Kumaran: Is definitely, like, an angle where…
94 00:15:16.590 ⇒ 00:15:27.510 Uttam Kumaran: We’re trying to… to go after, but it can be very daunting, so, like, talking about, like, a workflow, or, like, a way of doing things, or how it affected a team member is definitely…
95 00:15:27.680 ⇒ 00:15:30.719 Uttam Kumaran: The way we’re trying to talk about that, you know, for sure.
96 00:15:30.720 ⇒ 00:15:31.430 Kyle Montgomery: Yep.
97 00:15:32.000 ⇒ 00:15:37.410 Kyle Montgomery: Yeah, it’s been very appealing and easily digestible, as I just kind of…
98 00:15:37.680 ⇒ 00:15:44.660 Kyle Montgomery: I’m a flyby observer. I’m just out on LinkedIn for our work all the time, and so I’m seeing stuff that catches my eye.
99 00:15:45.700 ⇒ 00:15:46.070 Uttam Kumaran: Yeah.
100 00:15:46.070 ⇒ 00:15:52.460 Luke Scorziell: Yeah, and I think, yeah, that’s… I mean, we’re trying to speak to the… more of the business outcomes, too, of AI, so it’s not just, like.
101 00:15:53.030 ⇒ 00:16:01.100 Luke Scorziell: oh, here’s all these cool… like, our engineers are very up to speed on all the cool tools that you can use and the different processes and types of things, but I think
102 00:16:01.450 ⇒ 00:16:09.810 Luke Scorziell: we’re trying to more so make it clear that, like, one, even non-technical users are able to start using AI in ways that drive business outcomes.
103 00:16:10.060 ⇒ 00:16:12.830 Luke Scorziell: And then two, like,
104 00:16:13.060 ⇒ 00:16:22.969 Luke Scorziell: Yeah, we’re… I mean, like, I’m… I’m a marketer, and came from, like, a journalism background, was running a, like, brand agency before this for small businesses, and then…
105 00:16:23.550 ⇒ 00:16:28.069 Luke Scorziell: was, like, AI interested, and probably, like, hobbyist in some ways.
106 00:16:28.850 ⇒ 00:16:36.919 Luke Scorziell: But now it’s, like, I’m using, like, Cursor and some internal, like, coding tools to streamline my workflows and, like, seeing how effective it is, and that’s where…
107 00:16:37.230 ⇒ 00:16:41.559 Luke Scorziell: What the agency campaign that we kind of started was,
108 00:16:42.320 ⇒ 00:16:44.660 Luke Scorziell: Just seeing, like, hey, this is a bottleneck that, like.
109 00:16:44.790 ⇒ 00:16:50.919 Luke Scorziell: a lot of people feel intimidated by AI and by data, but the reality is, like.
110 00:16:51.260 ⇒ 00:16:54.439 Luke Scorziell: It’s… yeah, we just don’t want to be a company that…
111 00:16:55.160 ⇒ 00:16:58.660 Luke Scorziell: Puts that hurdle up, where it’s like, you have to be the expert, because, like.
112 00:16:58.660 ⇒ 00:16:59.050 Kyle Montgomery: Right.
113 00:16:59.050 ⇒ 00:17:04.160 Luke Scorziell: Yeah, the experts already are probably doing their stuff, but we’re kind of wanting to help out with,
114 00:17:04.420 ⇒ 00:17:09.989 Luke Scorziell: Yeah, wherever we can, too. So… so that’s kind of the lay of the land, I guess, on the content that we’ve been…
115 00:17:10.240 ⇒ 00:17:12.309 Luke Scorziell: Trying to do.
116 00:17:12.660 ⇒ 00:17:15.490 Kyle Montgomery: One of the things that I can’t remember…
117 00:17:16.130 ⇒ 00:17:24.960 Kyle Montgomery: Udam, if we talked through this, but… I have a growing… Perspective or theory that…
118 00:17:25.230 ⇒ 00:17:33.810 Kyle Montgomery: And even some words that you just said helped to crystallize it further, that… There… there’s a…
119 00:17:34.390 ⇒ 00:17:37.880 Kyle Montgomery: macro enterprise view of AI.
120 00:17:38.560 ⇒ 00:17:51.030 Kyle Montgomery: that is coming from the C-suite, and coming from the… just the executive stakeholders organizations who are going, we’re supposed to be investing in this stuff. Give me an enterprise AI platform.
121 00:17:51.690 ⇒ 00:17:57.880 Kyle Montgomery: And it’s a… for these large companies like J&J, it’s this global effort.
122 00:17:59.750 ⇒ 00:18:07.290 Kyle Montgomery: And then… there’s the grassroots AI, which is your, call it, average, even consumer, but
123 00:18:07.430 ⇒ 00:18:21.069 Kyle Montgomery: professional who is feeling left behind because they know that people are doing some cool stuff with AI, and they know that they’re probably, missing the boat on some efficiencies and workflows and things.
124 00:18:22.290 ⇒ 00:18:29.150 Kyle Montgomery: And where I see the opportunity for companies like us, and where you guys, I think, are attacking this, is right in the middle.
125 00:18:29.460 ⇒ 00:18:36.699 Kyle Montgomery: That it’s gonna meet in the middle, that the grassroots efforts are coming up, they’re bubbling up, organizations are starting to see that
126 00:18:36.820 ⇒ 00:18:39.740 Kyle Montgomery: Happen from the individual user.
127 00:18:40.330 ⇒ 00:18:40.900 Luke Scorziell: Yeah.
128 00:18:40.900 ⇒ 00:18:46.939 Kyle Montgomery: And they don’t have time, To continue down this multi-year strategy.
129 00:18:47.160 ⇒ 00:18:52.530 Kyle Montgomery: to shove AI from… in this beautiful, pristine enterprise strategy.
130 00:18:53.010 ⇒ 00:19:07.109 Kyle Montgomery: So how can they take, and you guys, again, pragmatic is what comes to mind, how can you take a very pragmatic approach to say, like, hey, it can be more than just Susie in accounting futzing around with ChatGPT or Claude?
131 00:19:07.720 ⇒ 00:19:11.149 Kyle Montgomery: It doesn’t necessarily need to be a full enterprise.
132 00:19:11.880 ⇒ 00:19:12.690 Luke Scorziell: For sure.
133 00:19:12.690 ⇒ 00:19:21.480 Kyle Montgomery: And so how do we meet in the middle? How do we put it in a box and say, like, oh, well, you want at a department level? Not an enterprise level, forget that. Too, too big.
134 00:19:21.620 ⇒ 00:19:24.719 Kyle Montgomery: What about a department level? Can we take finance?
135 00:19:25.440 ⇒ 00:19:25.820 Luke Scorziell: to…
136 00:19:25.820 ⇒ 00:19:34.840 Kyle Montgomery: deploy some department-level capabilities That are built on a sophisticated platform, but they’re not over…
137 00:19:35.080 ⇒ 00:19:40.389 Kyle Montgomery: engineered that you could take it, go run it in supply chain and HR, like, let them do their thing.
138 00:19:40.800 ⇒ 00:19:53.199 Luke Scorziell: Yeah, and that’s… no, I 100% agree, and it’s kind of… what you’re getting at is a bigger pain point that we’ve identified, too, is, like, when most people, especially enterprise companies at this point, think AI strategy, they’re… it’s like.
139 00:19:53.600 ⇒ 00:20:07.740 Luke Scorziell: I don’t know what the strategy is other than just buying, like, an enterprise plan to ChatGBT for everyone. And, like, what we’re seeing is that that’s just a waste of time, honestly, and, like, it probably costs the organization more than it gets them back, and there’s been a lot of articles, too, out about, like.
140 00:20:08.050 ⇒ 00:20:14.499 Luke Scorziell: you know, we’re… whatever, 2, 3 years into this AI thing, and it’s like… Where’s the ROI? .
141 00:20:15.310 ⇒ 00:20:21.580 Luke Scorziell: And that’s where I think, for us, the strategic angle has been not…
142 00:20:21.680 ⇒ 00:20:27.319 Luke Scorziell: how do we get people to use, like, ChatGPT better through, like, better prompting or whatever? It’s more…
143 00:20:27.520 ⇒ 00:20:32.230 Luke Scorziell: There are some really boring and mundane tasks that people have to do.
144 00:20:32.460 ⇒ 00:20:37.820 Luke Scorziell: Like, all the time, and an example in the agency world is reporting, so it’s like.
145 00:20:38.020 ⇒ 00:20:45.150 Luke Scorziell: Any… any account manager or someone who’s kind of running analytics for an agency, is gonna have to spend, like.
146 00:20:45.260 ⇒ 00:20:48.380 Luke Scorziell: 15 to 20 hours grabbing, like, reports from
147 00:20:48.770 ⇒ 00:20:57.839 Luke Scorziell: Facebook ads, LinkedIn ads, Google ads, like, whatever different platforms they’re on, just to put those into one platform to then
148 00:20:58.090 ⇒ 00:21:01.750 Luke Scorziell: be able to analyze it. And if that’s per client, then it’s like…
149 00:21:02.310 ⇒ 00:21:13.149 Luke Scorziell: like, oh my gosh. And then you have… and then it’s, like, other people want to query and ask questions of, like, well, what does this data mean? And then it’s… if you have an analyst, then they get bottlenecked, because
150 00:21:13.210 ⇒ 00:21:22.929 Luke Scorziell: they’re trying to answer all these different questions. But the nice thing that AI can do is it fits in, like… or when it’s… when it’s at a very, like, return on investment, especially at an agency.
151 00:21:23.060 ⇒ 00:21:34.559 Luke Scorziell: model. It’s… it’s plugged in where the users are at, so, like, in Slack or in Teams. It’s… all of the data is unified behind the scenes into a warehouse so that,
152 00:21:34.870 ⇒ 00:21:37.060 Luke Scorziell: And then as updated, you know.
153 00:21:37.250 ⇒ 00:21:45.670 Luke Scorziell: Not typically, probably in real time, probably once. I mean, you could set, like, once a day, once an hour, just to save on processing costs.
154 00:21:46.030 ⇒ 00:21:47.640 Luke Scorziell: But yeah, and then it’s like…
155 00:21:47.920 ⇒ 00:21:54.779 Luke Scorziell: Then that’s where we see that companies are, like, moving tasks down by, like, 90% of time, and then you can…
156 00:21:54.780 ⇒ 00:22:00.560 Uttam Kumaran: Kyle, you described it sort of as, like, the plumbing, right? That’s kind of what we talked about, which is, like, that was actually more of the advantage.
157 00:22:00.720 ⇒ 00:22:09.760 Uttam Kumaran: And so that’s also how we’re trying to market it as most of this is, like, a context engineering and, like, making sure you have the right data at the right time.
158 00:22:09.960 ⇒ 00:22:15.909 Uttam Kumaran: And as the tools develop, whatever it is, that context is actually, like, most…
159 00:22:16.140 ⇒ 00:22:28.549 Uttam Kumaran: the most crucial part, whether you’re, like, using something like Cursor, or, like, Claude Code, or whatever it ends up being, all of those tools in order to succeed need, like, the right… the right context.
160 00:22:30.160 ⇒ 00:22:47.170 Uttam Kumaran: and ChatGPT is adding, like, small connectors here and there, but again, at the enterprise level, it’s not… they’re… they’re having… a lot of them are nervous to give their data to these platforms. And then second, you’re sort of beholden to, like, whatever their product roadmap is, versus…
161 00:22:47.220 ⇒ 00:22:50.550 Uttam Kumaran: Actually being able to develop your own pieces, you know?
162 00:22:51.660 ⇒ 00:22:58.750 Kyle Montgomery: Sure. Well, that, the… the rapid innovation
163 00:22:59.150 ⇒ 00:23:14.939 Kyle Montgomery: I don’t know how anybody’s keeping up with it, and that… the, also is just kind of a specter of trying to do this at the enterprise levels. By the time you go through 3 different steering committee meetings to decide what it… well, everything you wanted to do changed.
164 00:23:15.920 ⇒ 00:23:20.340 Kyle Montgomery: And so… Again, kind of keeping it more bite-sized.
165 00:23:20.520 ⇒ 00:23:25.319 Kyle Montgomery: to realize some of those benefits and the ROI.
166 00:23:25.620 ⇒ 00:23:28.810 Kyle Montgomery: In maybe smaller chunks, but faster.
167 00:23:29.370 ⇒ 00:23:29.970 Luke Scorziell: Yeah.
168 00:23:30.630 ⇒ 00:23:40.039 Luke Scorziell: Well, I’d love to hear, too, like, what are some of the ways that you’re seeing, maybe, that we can collaborate, and I know you guys have already had some… some conversations, so apologies if I’m…
169 00:23:40.340 ⇒ 00:23:46.669 Luke Scorziell: rehashing stuff, but yeah, I would love to just think about how we can best support… support you.
170 00:23:47.400 ⇒ 00:23:48.640 Kyle Montgomery: Well, the,
171 00:23:48.930 ⇒ 00:23:58.970 Kyle Montgomery: Our business model as a, staff… staffing, staff augmentation firm is, we’re pretty agnostic to the
172 00:23:59.090 ⇒ 00:24:01.930 Kyle Montgomery: Technology, or the…
173 00:24:02.290 ⇒ 00:24:13.649 Kyle Montgomery: the technology problem at hand, though we really, thrive in the enterprise application market. Like, we grew up implementing SAP,
174 00:24:14.340 ⇒ 00:24:23.120 Kyle Montgomery: companies, whether or not it’s related to AI, are running massive projects in the SAP space, and that’s our bread and butter.
175 00:24:23.930 ⇒ 00:24:30.760 Kyle Montgomery: but where I’m looking… Ahead is, okay, for all of this client base we have.
176 00:24:31.050 ⇒ 00:24:39.839 Kyle Montgomery: So for the… excuse me, the number of active clients today, and the hundred and some clients that we’ve worked with in the last 9 years.
177 00:24:40.960 ⇒ 00:24:47.099 Kyle Montgomery: if they’re not doing work actively with SAP, if that’s not on their roadmap, or where
178 00:24:47.260 ⇒ 00:24:51.259 Kyle Montgomery: we could help them traditionally. What… where are they headed next?
179 00:24:52.100 ⇒ 00:24:55.589 Kyle Montgomery: And how do we show up on their radar?
180 00:24:56.390 ⇒ 00:25:00.559 Kyle Montgomery: And if I go back even 3 years, and certainly more than that.
181 00:25:00.680 ⇒ 00:25:13.610 Kyle Montgomery: They would just look at us as, like, this SAP staffing shop. They’d say, oh, you know, get me a SAP FICO consultant, or a warehouse management consultant. It never would put us in the conversation.
182 00:25:13.810 ⇒ 00:25:18.330 Kyle Montgomery: with… more advanced data analytics, data engineering, and AI.
183 00:25:19.130 ⇒ 00:25:21.169 Kyle Montgomery: And so that’s part of my…
184 00:25:21.620 ⇒ 00:25:27.680 Kyle Montgomery: effort, and why I like talking with… with you guys, is… How do we…
185 00:25:28.600 ⇒ 00:25:38.859 Kyle Montgomery: in tandem, like, so we’re in all these places. We have relationships, we have MSAs, like, we’re doing work there. How do we put forth
186 00:25:40.790 ⇒ 00:25:46.430 Kyle Montgomery: The conversation, start the conversation, that would be more in your sweet spot.
187 00:25:46.610 ⇒ 00:25:47.010 Uttam Kumaran: Yeah.
188 00:25:47.190 ⇒ 00:25:50.070 Kyle Montgomery: Right? And so then we kind of play off of each other.
189 00:25:50.180 ⇒ 00:25:52.309 Kyle Montgomery: To, like, if… if you…
190 00:25:52.460 ⇒ 00:26:00.509 Kyle Montgomery: realize, tremendous growth, and are going out there doing things, and you need resources, oh, well, we can provide resources. That’s what we do.
191 00:26:00.950 ⇒ 00:26:08.549 Kyle Montgomery: Right? And how do you get that growth? Well, potentially, we’re able to say, like I’m doing with J&J, here’s Brainforge.
192 00:26:08.660 ⇒ 00:26:13.160 Kyle Montgomery: Yeah. This is a group that, for such and such opportunity.
193 00:26:13.400 ⇒ 00:26:15.339 Kyle Montgomery: Could help lead the charge.
194 00:26:17.070 ⇒ 00:26:27.409 Uttam Kumaran: I’m… I’m wondering, like… Yeah, I’m wondering how we can, one, like, our set of capabilities.
195 00:26:27.530 ⇒ 00:26:46.979 Uttam Kumaran: both on the data side, which I think you kind of are familiar with, some of the work that I did through Bull City for Snowflake, like, we’re still doing a lot of that type of work. So, Snowflake, dbt, BI tooling, stuff like that, as well as, like, some of the AI work, how can we best
196 00:26:47.320 ⇒ 00:27:00.369 Uttam Kumaran: collect that information and make it part of, like, the skill set that you can start to bring up in conversation, whether it is, hey, if an inbound request comes in, or it’s like, hey, we can now do these things, and here are, like.
197 00:27:00.480 ⇒ 00:27:11.659 Uttam Kumaran: Like, here are one-pagers or more information on, like, what those are, so that when you can go back to your contacts and say, hey, we actually have inroads into these capabilities now,
198 00:27:12.640 ⇒ 00:27:27.669 Uttam Kumaran: Is there any need there? And then, I think second is, I think, how can we use our platforms on LinkedIn or otherwise to also continue, like, to raise the noise on the fact that we’re collaborating and that we have these
199 00:27:27.780 ⇒ 00:27:34.480 Uttam Kumaran: various offerings, across, like, the pie of things that we cover, you know? Totally.
200 00:27:35.720 ⇒ 00:27:36.300 Luke Scorziell: Hmm.
201 00:27:36.600 ⇒ 00:27:42.399 Kyle Montgomery: Yeah, a little bit of it for my sales team, which is, you know, where the rubber reached the road.
202 00:27:42.540 ⇒ 00:27:47.410 Kyle Montgomery: In their client conversations is arming them with
203 00:27:48.810 ⇒ 00:27:55.700 Kyle Montgomery: The confidence and the credibility to go into the right buying community
204 00:27:56.000 ⇒ 00:28:04.109 Kyle Montgomery: for your array of services, and I’ll start just kind of with the data engineering side, and the work that I am familiar with, and that we’ve placed
205 00:28:04.210 ⇒ 00:28:11.629 Kyle Montgomery: resources before. Yeah. But I can’t say that’s at the top of our list. Like, my team, if they’re out there.
206 00:28:11.760 ⇒ 00:28:17.519 Kyle Montgomery: banging the phones and email and trying to get meetings. It’s not on the premise of…
207 00:28:17.640 ⇒ 00:28:24.269 Kyle Montgomery: hey, what are you doing in the way of data architecture and engineering, and oh, Snowflake, and whatever tools?
208 00:28:24.490 ⇒ 00:28:27.609 Kyle Montgomery: It’s… it’s not up there. It could be.
209 00:28:27.980 ⇒ 00:28:31.749 Kyle Montgomery: Or it could be at least in the top 3 things that they’re…
210 00:28:32.290 ⇒ 00:28:35.319 Kyle Montgomery: Trying to, target with their clients.
211 00:28:35.440 ⇒ 00:28:41.140 Kyle Montgomery: And that, to me, is the fastest way to then be able to tell some stories.
212 00:28:41.550 ⇒ 00:28:46.139 Kyle Montgomery: back to your LinkedIn comments, that if we’re able to say, like, hey, in…
213 00:28:46.380 ⇒ 00:28:52.970 Kyle Montgomery: Understanding our clients’ data transformation efforts.
214 00:28:53.390 ⇒ 00:28:56.869 Kyle Montgomery: we’ve deployed resources with BrainForge.
215 00:28:57.020 ⇒ 00:29:02.719 Kyle Montgomery: Like, these are our go-to resources anytime our clients speak of
216 00:29:03.020 ⇒ 00:29:05.440 Kyle Montgomery: You know, this laundry list of needs.
217 00:29:07.520 ⇒ 00:29:12.749 Kyle Montgomery: But a lot of that is just me building it into the psyche of my sales team, that they know to be…
218 00:29:13.140 ⇒ 00:29:14.890 Kyle Montgomery: Looking at it and talking, I like…
219 00:29:15.020 ⇒ 00:29:22.260 Kyle Montgomery: Fastest way for me to do that is to put together a case study on what we did at Athletic Greens, or AG1.
220 00:29:22.260 ⇒ 00:29:22.800 Uttam Kumaran: Yeah.
221 00:29:23.370 ⇒ 00:29:25.070 Kyle Montgomery: They’ll say, like, hey guys, this is…
222 00:29:25.230 ⇒ 00:29:30.270 Kyle Montgomery: This is the realm of the work that… That you were doing.
223 00:29:30.380 ⇒ 00:29:35.729 Kyle Montgomery: And how does that translate to something that another of our clients is doing today?
224 00:29:37.280 ⇒ 00:29:39.330 Uttam Kumaran: But I think… Yeah, go ahead, Luke.
225 00:29:39.470 ⇒ 00:29:44.179 Luke Scorziell: I was just gonna say, maybe there’s a roadmap, too, where we can collaborate on, like.
226 00:29:44.580 ⇒ 00:29:49.990 Luke Scorziell: Yeah, either, like, a case study on, kind of, what we’ve done, or… and then…
227 00:29:50.780 ⇒ 00:30:01.129 Luke Scorziell: like, it… I don’t know if it’d be helpful then after that, if there’s, like, engagement from, like, maybe your client base, too, to just say, like, hey, you know, we can host, like, a…
228 00:30:01.930 ⇒ 00:30:07.139 Luke Scorziell: a webinar, or we’ve been doing, like, these office hours things where,
229 00:30:07.370 ⇒ 00:30:18.400 Luke Scorziell: Basically, like, people just come in and get to ask questions, and we kind of present, like, a demo. And then if you provided, like, the list to that, and we provided, like, the expertise and the…
230 00:30:20.190 ⇒ 00:30:25.920 Luke Scorziell: And the… people there, I think that could be interesting, but maybe, yeah, maybe doing, like, a…
231 00:30:26.180 ⇒ 00:30:28.399 Luke Scorziell: Case study or a blog post first would…
232 00:30:28.880 ⇒ 00:30:31.690 Luke Scorziell: Would be a good, like, just… start.
233 00:30:32.450 ⇒ 00:30:43.140 Uttam Kumaran: Yeah, and I’m wondering, Kyle, also, is the SAP stuff still, like, you mentioned, like, okay, what is the number one thing that folks are going after? Is it still everything around SAP, or is AI, like, starting to…
234 00:30:43.270 ⇒ 00:30:46.120 Uttam Kumaran: Is that something that your team is pitching?
235 00:30:46.240 ⇒ 00:30:49.579 Uttam Kumaran: Or, like, yeah, I guess compare and contrast that.
236 00:30:50.160 ⇒ 00:30:54.149 Kyle Montgomery: It’s still very much SAP, like, if you were to look at…
237 00:30:54.810 ⇒ 00:31:07.590 Kyle Montgomery: where most of our opportunities come from. They’re associated with organizations going from the previous SAP realm called ECC into the next SAP realm called S4HANA.
238 00:31:07.800 ⇒ 00:31:11.959 Kyle Montgomery: Right? Believe it or not, there are a ton of companies still.
239 00:31:13.030 ⇒ 00:31:17.100 Kyle Montgomery: Contemplating and moving towards that. That creates a bunch of work for us.
240 00:31:17.100 ⇒ 00:31:17.650 Uttam Kumaran: Yeah.
241 00:31:17.890 ⇒ 00:31:18.660 Kyle Montgomery: Right.
242 00:31:18.810 ⇒ 00:31:23.050 Kyle Montgomery: So that, again, comes to the bulk of the types of projects we get involved in.
243 00:31:23.870 ⇒ 00:31:27.880 Kyle Montgomery: But those projects then get us a seat at the table.
244 00:31:28.180 ⇒ 00:31:28.520 Uttam Kumaran: Yeah.
245 00:31:28.520 ⇒ 00:31:31.429 Kyle Montgomery: Where we then can poke around and ask about.
246 00:31:31.650 ⇒ 00:31:38.730 Kyle Montgomery: what’s going on with AI. Or we might get pulled into a project that’s running in parallel
247 00:31:38.940 ⇒ 00:31:43.999 Kyle Montgomery: to that SAP transformation that is around,
248 00:31:44.410 ⇒ 00:31:49.179 Kyle Montgomery: Data engineering, and it could be something…
249 00:31:49.430 ⇒ 00:31:53.789 Kyle Montgomery: that’s tied directly to the SAP project, or it just happens to be going on at the same time?
250 00:31:55.070 ⇒ 00:31:55.570 Kyle Montgomery: So that’s.
251 00:31:55.570 ⇒ 00:32:03.609 Uttam Kumaran: But I think, like, I would say the AI piece is certainly, out of both of the stuff that we do, more experimental, but it is
252 00:32:03.760 ⇒ 00:32:04.799 Uttam Kumaran: Kind of, like.
253 00:32:05.320 ⇒ 00:32:15.999 Uttam Kumaran: it’s just a hotter ticket right now. So, it could be that both are interesting, but I would be surprised if, while folks are having conversations about SAP resourcing.
254 00:32:16.030 ⇒ 00:32:27.600 Uttam Kumaran: that they mention that, hey, we also are able to place resources on projects related to Agentic workflow automation, Snowflake Cortex, AI,
255 00:32:27.640 ⇒ 00:32:41.439 Uttam Kumaran: like, I feel like that’s worth having in the arsenal, so maybe that’s what we do. We sort of, one, try to just put together some resources, and maybe we could do a joint blog post or something around just a traditional set of
256 00:32:41.820 ⇒ 00:32:50.719 Uttam Kumaran: data capabilities. Similarly, though, I would love for us to put together a similar set of resources around what we’re able to do on the AI side.
257 00:32:51.000 ⇒ 00:32:55.850 Uttam Kumaran: And, again, I think hopefully it’s just more fodder for the sales team.
258 00:32:56.270 ⇒ 00:32:58.870 Uttam Kumaran: If they’re poking around to say, like.
259 00:32:59.060 ⇒ 00:33:02.610 Uttam Kumaran: okay, we… we have MSA in place. Are there any…
260 00:33:03.160 ⇒ 00:33:08.940 Uttam Kumaran: Everywhere now has all the same… The data modernization plays.
261 00:33:09.050 ⇒ 00:33:15.340 Uttam Kumaran: But again, if AI is a bigger ticket way, oftentimes they’re kind of all tied in.
262 00:33:15.450 ⇒ 00:33:18.390 Uttam Kumaran: Right? So the AI might actually drag the data work
263 00:33:19.390 ⇒ 00:33:22.689 Uttam Kumaran: It’s sort of, like, what I’m trying to say.
264 00:33:22.800 ⇒ 00:33:27.700 Uttam Kumaran: Or they may not have been approached by a, Like, a resourcing partner yet.
265 00:33:28.160 ⇒ 00:33:29.490 Uttam Kumaran: For that, you know?
266 00:33:29.490 ⇒ 00:33:29.940 Kyle Montgomery: Right.
267 00:33:29.940 ⇒ 00:33:32.270 Uttam Kumaran: Cause it’s just so, so new.
268 00:33:33.350 ⇒ 00:33:41.790 Kyle Montgomery: I could see an interesting… Blog post or, like, roundtable discussion, or, like, your office hours model.
269 00:33:41.900 ⇒ 00:33:56.030 Kyle Montgomery: on… the nut, I don’t want to say it, like, the… the… the intersection between SAP and AI.
270 00:33:56.480 ⇒ 00:33:57.460 Uttam Kumaran: Yeah.
271 00:33:57.460 ⇒ 00:34:13.339 Kyle Montgomery: I guess what I’m trying… like, okay, let’s first say we want to talk to companies who are relatively mature SAP shops. Yeah. It doesn’t mean that they have to be on the latest S4 HANA, because they could be a mature shop who is still moving to that.
272 00:34:13.690 ⇒ 00:34:17.370 Kyle Montgomery: Okay, so these guys are established in that.
273 00:34:17.710 ⇒ 00:34:22.839 Kyle Montgomery: And what comes with that? Well, they have certain characteristics to how their data is governed.
274 00:34:23.139 ⇒ 00:34:27.779 Kyle Montgomery: that in the enterprise realm, and SAP is all about…
275 00:34:27.780 ⇒ 00:34:29.209 Uttam Kumaran: W, or something.
276 00:34:29.210 ⇒ 00:34:33.170 Kyle Montgomery: Yeah, for sure. And their big push towards the clean core.
277 00:34:33.179 ⇒ 00:34:33.999 Uttam Kumaran: Yeah, yeah.
278 00:34:34.000 ⇒ 00:34:35.569 Kyle Montgomery: Okay, well…
279 00:34:36.199 ⇒ 00:34:46.090 Kyle Montgomery: with all of that, or in the context of that, or if that is, like, a sub-theme that everybody would have in common, then where does AI sit?
280 00:34:46.090 ⇒ 00:34:46.620 Uttam Kumaran: Okay.
281 00:34:46.850 ⇒ 00:34:58.879 Kyle Montgomery: On top of that, right? Because then, one, we know our audience, we know that SAP, we know the terminology and the technologies around data in an SAP shop.
282 00:34:59.090 ⇒ 00:35:08.899 Kyle Montgomery: But then, what’s the AI story to go with that? There’s, of course, the SAP AI story with Juul. Yes. And what they’re trying to market with that, which is all smoke and mirrors.
283 00:35:10.030 ⇒ 00:35:14.269 Uttam Kumaran: I was gonna let you let me know. Yeah.
284 00:35:14.270 ⇒ 00:35:30.409 Kyle Montgomery: So, it’s really cool, but, it’s not pervasive, and nobody that’s focused on SAP investments has time to really think about that. It’s too cumbersome and too much. And it kind of comes at the enterprise level.
285 00:35:30.410 ⇒ 00:35:36.759 Uttam Kumaran: Yeah. So do you think it’s on… do you think the AI… because when I think about these types of, of, of…
286 00:35:36.980 ⇒ 00:35:49.520 Uttam Kumaran: folks that you described are moving, or planning on moving, like, one key area is on impacting the speed of that migration. The speed, the organization, like, the auditing, right? So there’s certainly a piece there.
287 00:35:49.520 ⇒ 00:35:57.989 Uttam Kumaran: Certainly, there’s also a piece on using AI to query and ask data… ask questions about the data that’s in SAP.
288 00:35:58.290 ⇒ 00:36:06.490 Uttam Kumaran: The second thing is something that we do across a ton of systems already. We enable, like, ask questions on top of your CRM.
289 00:36:06.600 ⇒ 00:36:10.629 Uttam Kumaran: We do a lot of, like, agentic data analysis work, where…
290 00:36:10.820 ⇒ 00:36:23.980 Uttam Kumaran: people are no longer… like, a lot… people are no longer having to… for example, I have a… we have a client, that we enabled Cortex for. They’re gonna drop their Power BI, license in order to
291 00:36:24.120 ⇒ 00:36:30.670 Uttam Kumaran: like, they have, like, 80 dashboards in Power BI, they’re transitioning it, every single thing over so people can use natural language and Cortex.
292 00:36:30.780 ⇒ 00:36:33.780 Uttam Kumaran: Because they found that most of their… half those dashboards are stale.
293 00:36:33.980 ⇒ 00:36:49.880 Uttam Kumaran: Most of those people are just ask… want to ask one question or a couple questions a week, and just get to the answer, versus, like, tracking another right dashboard, finding a way to export it. They want to use natural language, because it’s… it hasn’t gotten easier to teach people how to use dashboards or build dashboards.
294 00:36:50.010 ⇒ 00:36:50.710 Uttam Kumaran: But…
295 00:36:51.030 ⇒ 00:36:59.170 Uttam Kumaran: the language is now getting more English, where people can say, like, tell me how many customers we had, and then, of course, like, the Tier 2, Tier 3 questions, so…
296 00:36:59.820 ⇒ 00:37:10.309 Uttam Kumaran: in addition to, like, helping speed up a potential migration, the second piece is, like, are you having trouble building dashboards on your SAP data, or your business teams are getting value out of that data?
297 00:37:10.500 ⇒ 00:37:14.459 Uttam Kumaran: You should totally put a natural language query engine on top of that.
298 00:37:14.860 ⇒ 00:37:15.740 Uttam Kumaran: Right.
299 00:37:17.110 ⇒ 00:37:21.100 Kyle Montgomery: I have this metaphor cooking in my brain now for that, that…
300 00:37:21.940 ⇒ 00:37:32.459 Kyle Montgomery: what you describe as AI having a role or a hand in the migration or the transformation. To me, that’s, like, heavy lifting.
301 00:37:32.670 ⇒ 00:37:36.210 Kyle Montgomery: And so I’m referring it to as the blue-collar AI.
302 00:37:36.210 ⇒ 00:37:36.900 Uttam Kumaran: Yeah.
303 00:37:36.900 ⇒ 00:37:38.879 Kyle Montgomery: This is, like, the dirty work.
304 00:37:38.880 ⇒ 00:37:39.609 Uttam Kumaran: Yes, yeah.
305 00:37:39.610 ⇒ 00:37:43.100 Kyle Montgomery: AI has a significant, or can…
306 00:37:43.100 ⇒ 00:37:43.530 Uttam Kumaran: You know, it’s.
307 00:37:43.530 ⇒ 00:37:44.000 Kyle Montgomery: second roll.
308 00:37:44.000 ⇒ 00:37:50.149 Uttam Kumaran: huge two-year Gantt chart, all of the, like, all of that stuff, right? That’s what I… yeah, I feel like I’m.
309 00:37:50.150 ⇒ 00:37:52.080 Kyle Montgomery: So that’s your working man’s AI.
310 00:37:52.080 ⇒ 00:37:52.760 Uttam Kumaran: Yeah.
311 00:37:52.910 ⇒ 00:37:54.719 Kyle Montgomery: Then, you’ve got the…
312 00:37:55.040 ⇒ 00:38:05.710 Kyle Montgomery: and metaphor isn’t all that sexy, but you’ve got the white-collar AI, which is, hey, what’s all this cool, sexy marketing stuff we’re getting out of it? Or what.
313 00:38:06.220 ⇒ 00:38:13.320 Kyle Montgomery: Data optimization and process optimization that can be delivered by some elegant querying…
314 00:38:13.450 ⇒ 00:38:16.719 Kyle Montgomery: Powered by AI. That’s the big brain…
315 00:38:16.870 ⇒ 00:38:28.679 Kyle Montgomery: stuff. So you’ve got the beauty and the brawn. The brawn is, hey, help me get this migration going faster, and cut out time in my project plan by making these steps go
316 00:38:29.150 ⇒ 00:38:45.199 Kyle Montgomery: faster and more efficient. And then there’s, oh, well, once you have all that done, now the big brain folks can come in and really look into the data and learn and optimize on… on our go-to-market and our operations.
317 00:38:46.790 ⇒ 00:39:01.979 Uttam Kumaran: Yeah, I mean, like, again, painting… painting the SAP AI in a different light, too, could be like, hey, SAP released this, but finding… are you having difficulty getting adoption of it, or you want it to go further, or you want to own your data, right? So…
318 00:39:02.280 ⇒ 00:39:06.039 Uttam Kumaran: Partly, you could… Paint that as, like, a way to say, like.
319 00:39:06.180 ⇒ 00:39:08.199 Uttam Kumaran: You might be familiar with that, but…
320 00:39:08.400 ⇒ 00:39:12.879 Uttam Kumaran: But you’re also probably familiar with the fact that it doesn’t work really great, and, like, you want to build your own.
321 00:39:13.210 ⇒ 00:39:16.999 Uttam Kumaran: So, I, I, I think, like…
322 00:39:17.150 ⇒ 00:39:20.750 Uttam Kumaran: there’s both of those pieces. We use it for both.
323 00:39:21.130 ⇒ 00:39:40.910 Uttam Kumaran: a lot of the work that we do for clients, we project manage, and we build using AI. So, managing tickets, looking at burndowns, Gantt charts, things like that. So, again, whether that’s, hey, your existing project management teams just need to get empowered to use AI, to talk to your JIRA board, to update things.
324 00:39:41.130 ⇒ 00:39:47.019 Uttam Kumaran: So there’s one piece of that. So you know how far things can slide if just things aren’t aligned there. So there’s something around…
325 00:39:47.610 ⇒ 00:39:53.480 Uttam Kumaran: project management, migration, execution. There’s also totally this white-collar piece of, like.
326 00:39:53.740 ⇒ 00:40:05.180 Uttam Kumaran: your data is in SAP, and getting it out has always been through the form of a dashboard or a report summarized by somebody. You have to bring on all these data analysts to do so. Well, you want to, like.
327 00:40:05.670 ⇒ 00:40:15.090 Uttam Kumaran: you want to actually fulfill the point of, like, democratizing that, so maybe a natural language engine where you can query, maybe you can create or edit SAP assets.
328 00:40:15.390 ⇒ 00:40:21.690 Uttam Kumaran: like, talking about that, and I think those two are… are, like, fairly good angles.
329 00:40:21.800 ⇒ 00:40:26.630 Uttam Kumaran: we can do some research on our side on, like, what exists within SAP now on the AI side, but…
330 00:40:26.950 ⇒ 00:40:40.499 Uttam Kumaran: Again, we turn on AI on top of a lot of these types of platforms, and it’s able to query, it’s able to, like, answer questions, produce charts, and take a lot of what would have to traditionally go through, I think, a data analyst
331 00:40:40.670 ⇒ 00:40:41.950 Uttam Kumaran: to… to do.
332 00:40:42.110 ⇒ 00:40:45.369 Uttam Kumaran: it’s now able to handle. And so…
333 00:40:45.810 ⇒ 00:40:51.329 Uttam Kumaran: Maybe there’s an angle in both of those, or to kind of do something just around…
334 00:40:51.660 ⇒ 00:40:54.060 Uttam Kumaran: This… this… these two topics.
335 00:40:59.740 ⇒ 00:41:04.230 Kyle Montgomery: Yeah, I… Those, to me, make it a lot more tangible.
336 00:41:04.540 ⇒ 00:41:08.280 Kyle Montgomery: Yeah. Right? That, what?
337 00:41:09.630 ⇒ 00:41:15.530 Kyle Montgomery: Yeah, we, we, we could… I think tell some really interesting stories about… that…
338 00:41:15.640 ⇒ 00:41:20.349 Kyle Montgomery: now I’m thinking selfishly to the types of work we typically see.
339 00:41:20.560 ⇒ 00:41:23.929 Kyle Montgomery: So, what if… But my sales team.
340 00:41:24.160 ⇒ 00:41:26.179 Kyle Montgomery: Is talking to…
341 00:41:26.280 ⇒ 00:41:38.860 Kyle Montgomery: the Director of Program Management, or the Director of Digital Transformation, Director of Enterprise Applications, these people who are sitting on top of these big, messy projects that are either underway or
342 00:41:39.080 ⇒ 00:41:40.440 Kyle Montgomery: Kicking off soon.
343 00:41:40.710 ⇒ 00:41:48.540 Kyle Montgomery: Yeah. And they’re able to just pose the question, okay, we know that you’re talking to us because you’re gonna need this or that consultant.
344 00:41:49.190 ⇒ 00:41:52.019 Kyle Montgomery: On the typical functional realm.
345 00:41:52.590 ⇒ 00:41:57.390 Kyle Montgomery: But where does AI fit in the migration itself? Not in the outcome.
346 00:41:57.540 ⇒ 00:42:06.699 Kyle Montgomery: We know you’re building some things that are going to deliver some outcomes that’ll be empowered by AI on the project itself, where does AI… how are you leveraging AI?
347 00:42:07.200 ⇒ 00:42:11.189 Kyle Montgomery: to drive that project plan, to drive the ROI itself.
348 00:42:11.650 ⇒ 00:42:14.149 Kyle Montgomery: In ways you haven’t done before.
349 00:42:14.150 ⇒ 00:42:17.470 Uttam Kumaran: Yeah, or give more visibility that you weren’t expecting to give.
350 00:42:17.610 ⇒ 00:42:19.810 Uttam Kumaran: Sure. de-risk certain things.
351 00:42:19.980 ⇒ 00:42:21.790 Uttam Kumaran: Right. Yeah, totally.
352 00:42:21.790 ⇒ 00:42:22.630 Kyle Montgomery: By the way to…
353 00:42:22.630 ⇒ 00:42:23.050 Uttam Kumaran: Yeah.
354 00:42:23.050 ⇒ 00:42:30.649 Kyle Montgomery: Common project tasks and… Project analysis, things that you know you’re gonna have to do anyway.
355 00:42:30.910 ⇒ 00:42:37.200 Kyle Montgomery: how are you leveraging AI to do it? And I think that most…
356 00:42:37.570 ⇒ 00:42:39.759 Kyle Montgomery: Most times, we ask that question.
357 00:42:40.050 ⇒ 00:42:42.489 Kyle Montgomery: We’ll be met with a blank stare.
358 00:42:42.750 ⇒ 00:42:43.870 Luke Scorziell: Like… Hmm.
359 00:42:43.910 ⇒ 00:42:45.190 Kyle Montgomery: Fuck have I know.
360 00:42:45.240 ⇒ 00:42:46.440 Uttam Kumaran: Like, I’m…
361 00:42:46.510 ⇒ 00:42:56.499 Kyle Montgomery: I’m running this project according to the Activate methodology that SAP said, and Deloitte came in here, and they’re gonna run it. Maybe Deloitte’s probably using some sexy tools. Are they?
362 00:42:56.970 ⇒ 00:43:10.060 Kyle Montgomery: Right? Do you know… you own this project? Maybe… maybe that’s an opportunity for you to hold a workshop on AI in a massive SAP transformation project. Is it your friend, or isn’t it?
363 00:43:10.500 ⇒ 00:43:10.940 Uttam Kumaran: Yes.
364 00:43:10.940 ⇒ 00:43:13.730 Luke Scorziell: Yeah. It’s all… yeah, it’s interesting, too, because it’s like…
365 00:43:14.170 ⇒ 00:43:21.889 Luke Scorziell: I think, like, the imagination part is so key of kind of setting the vision for what people can and can’t do, so…
366 00:43:22.020 ⇒ 00:43:26.669 Luke Scorziell: I think so many people are… or so many of us are, like, face down of looking, like.
367 00:43:26.790 ⇒ 00:43:31.020 Luke Scorziell: What’s beneath us, and like, in the fear, like, oh, what does this mean for…
368 00:43:31.140 ⇒ 00:43:34.990 Luke Scorziell: work and all this stuff, but as you, like, start to look forward and see, like, oh, these are the.
369 00:43:34.990 ⇒ 00:43:35.510 Uttam Kumaran: Powering.
370 00:43:35.510 ⇒ 00:43:39.709 Luke Scorziell: Yeah, these are the possibilities of things that I can do, like.
371 00:43:40.040 ⇒ 00:43:45.649 Luke Scorziell: all of that type of stuff, that… that, I think, then gets people’s minds churning of, like.
372 00:43:46.140 ⇒ 00:43:49.720 Luke Scorziell: Oh, and that’s how we want to position Brainforge, is like, we’re not just…
373 00:43:50.010 ⇒ 00:43:59.670 Luke Scorziell: Here, to, like… like, don’t just come to us with a specific problem that you want solved, but come to us with whatever problem you have, we’ll help you solve it, but then we’ll also help you identify so many other areas.
374 00:43:59.810 ⇒ 00:44:03.830 Luke Scorziell: where we can add value. And that’s what’s exciting, too, I think.
375 00:44:04.090 ⇒ 00:44:09.480 Luke Scorziell: Like, just getting to work on these projects, too, and do the messaging is helping people kind of imagine what’s possible.
376 00:44:12.420 ⇒ 00:44:22.200 Uttam Kumaran: Yeah, so maybe there is… I think I like the program management, project management area, and then maybe it’s on us to go explore what’s possible on the analytics side, like natural language-driven.
377 00:44:22.350 ⇒ 00:44:28.010 Uttam Kumaran: analysis on top of SAP data. I mean, the first is sort of super, super well known.
378 00:44:28.510 ⇒ 00:44:34.299 Uttam Kumaran: You know, and I think we can identify. I think there’s probably a demo there. I think the second piece is more…
379 00:44:36.100 ⇒ 00:44:43.479 Uttam Kumaran: implementation… You know, or something like that.
380 00:44:47.420 ⇒ 00:44:50.410 Uttam Kumaran: It feels like two separate audiences, right, for both.
381 00:44:50.410 ⇒ 00:44:51.680 Kyle Montgomery: Yeah, I think so.
382 00:44:52.470 ⇒ 00:45:00.929 Kyle Montgomery: Yes, there’s a, a pretty distinct group who are currently, or will be soon, heads down in some of these big programs.
383 00:45:01.290 ⇒ 00:45:01.680 Uttam Kumaran: Yeah.
384 00:45:01.680 ⇒ 00:45:03.920 Kyle Montgomery: Projects, transformations, whatever I want to call them.
385 00:45:04.030 ⇒ 00:45:08.800 Kyle Montgomery: But then it’s a different group that is looking out on,
386 00:45:09.380 ⇒ 00:45:12.200 Kyle Montgomery: In the generic realm of data analytics or data marketing.
387 00:45:12.200 ⇒ 00:45:14.679 Uttam Kumaran: Reporting, yeah, on top of all that.
388 00:45:14.930 ⇒ 00:45:15.550 Kyle Montgomery: Yep.
389 00:45:19.240 ⇒ 00:45:19.840 Uttam Kumaran: Okay.
390 00:45:20.250 ⇒ 00:45:22.220 Kyle Montgomery: Yeah, okay, where do we start?
391 00:45:22.370 ⇒ 00:45:23.370 Kyle Montgomery: I’m just like…
392 00:45:23.900 ⇒ 00:45:24.710 Luke Scorziell: Well…
393 00:45:25.680 ⇒ 00:45:34.550 Luke Scorziell: Yeah, we could take a look at the notes, and maybe just putting together, like, either a joint LinkedIn post or a blog post to start off, and maybe we can try to get that up,
394 00:45:35.250 ⇒ 00:45:38.020 Luke Scorziell: We could send over, like, some topic ideas.
395 00:45:38.240 ⇒ 00:45:40.990 Luke Scorziell: Tom and I can debrief, and then…
396 00:45:41.280 ⇒ 00:45:50.260 Luke Scorziell: Yeah, maybe we could just get that up, and then if that catches some interest, and you want to just send it around to your network of people, and just say, like, hey, check this out, like.
397 00:45:50.630 ⇒ 00:45:55.390 Luke Scorziell: You know, then… then we can…
398 00:45:56.040 ⇒ 00:46:00.430 Luke Scorziell: I always forget that word, but just come on, like, leverage that into doing,
399 00:46:00.940 ⇒ 00:46:07.610 Luke Scorziell: maybe a more involved, work stream. So that could give us some signal in the next, probably, like.
400 00:46:07.820 ⇒ 00:46:09.380 Luke Scorziell: Week and a half, two weeks.
401 00:46:09.590 ⇒ 00:46:28.580 Kyle Montgomery: Okay, yeah, I’ll… from these notes to try to flesh out some of the ideas in the… I’m more familiar with it in the realm of the program and project management side, and have a few people who are sitting in program roles to just ask them, kind of, informally.
402 00:46:29.020 ⇒ 00:46:30.390 Kyle Montgomery: So… Yeah.
403 00:46:30.390 ⇒ 00:46:36.850 Uttam Kumaran: I’m gonna go call my SAP’s data people and ask them about the other side, you know,
404 00:46:37.290 ⇒ 00:46:42.010 Uttam Kumaran: And maybe we see, like, what sticks. Maybe we do two posts around both of those.
405 00:46:42.190 ⇒ 00:46:44.200 Uttam Kumaran: Circulate it, and then…
406 00:46:44.560 ⇒ 00:46:51.309 Uttam Kumaran: some people are interested, like, we will hit our network too, and then we can drive towards, like, yeah, I think a webinar is a good place to just get
407 00:46:51.480 ⇒ 00:47:02.089 Uttam Kumaran: a tight group of people discussing about it, we can show something, and it’s… it tries to build this type of relationship, which is, like, you’re talking to somebody, right, versus…
408 00:47:02.230 ⇒ 00:47:08.050 Uttam Kumaran: software tool, it’s always, like, it’s one tool for all. For us, it’s like, we have to hear the solution.
409 00:47:08.390 ⇒ 00:47:16.469 Uttam Kumaran: you know, and maybe it’s a good discussion between people, and they’re like, hey, I liked what you guys were talking about, would love to see how you can help us, and it just sort of rolls from there.
410 00:47:16.600 ⇒ 00:47:21.860 Uttam Kumaran: Yeah. And then, out of both of these, I think we’ll get a lot of talking points for…
411 00:47:22.210 ⇒ 00:47:32.519 Uttam Kumaran: both sales teams, like, you know, for us, whenever we… I mean, that’s something good, Luke, too, like, we should go look at anybody who’s mentioned SAP on our stuff, too, over the last, like, year or two.
412 00:47:32.690 ⇒ 00:47:37.939 Uttam Kumaran: And it just leaves talking points for both of us to, like, kind of co-market stuff, you know?
413 00:47:38.460 ⇒ 00:47:51.370 Luke Scorziell: Yeah, I mean, the beautiful thing about the webinar, too, is you get an hour to just talk with people who are frustrated. So, they basically just tell you all the things that they’re frustrated about, then you take that and turn it into content and say, we had a.
414 00:47:51.370 ⇒ 00:47:51.820 Kyle Montgomery: Very good.
415 00:47:51.820 ⇒ 00:47:52.450 Luke Scorziell: bandwidth.
416 00:47:52.560 ⇒ 00:47:58.999 Luke Scorziell: you know, X amount of SAP, whatever, and then, yeah, so that’s… that’s been the fun.
417 00:47:59.440 ⇒ 00:48:04.210 Luke Scorziell: Part of some of what we’ve done so far, too, so… Yeah, sounds exciting.
418 00:48:04.210 ⇒ 00:48:05.109 Kyle Montgomery: the,
419 00:48:05.660 ⇒ 00:48:12.840 Kyle Montgomery: We’ll table this, but for another day, we have a new team member joining us out of,
420 00:48:12.960 ⇒ 00:48:19.420 Kyle Montgomery: he lives in the Dallas-Fort Worth area, but he is a… the volunteer chair for the SA…
421 00:48:19.650 ⇒ 00:48:23.940 Kyle Montgomery: America’s SAP user group, Texas…
422 00:48:24.820 ⇒ 00:48:31.500 Kyle Montgomery: Texas chapter. I think it’s Houston-based. This would be a tremendous topic to host.
423 00:48:31.870 ⇒ 00:48:32.630 Uttam Kumaran: Oh, wow, okay.
424 00:48:32.630 ⇒ 00:48:38.300 Kyle Montgomery: an SAP user group event, and they usually do two a year at the state or chapter level.
425 00:48:38.510 ⇒ 00:48:48.090 Kyle Montgomery: That he’ll have some influence there to say, like, hey, would this be an interesting topic to put on the agenda to be able to… you’ll have… usually those things draw, like.
426 00:48:48.430 ⇒ 00:48:53.389 Kyle Montgomery: 50 to 75 people. They all are SAP…
427 00:48:53.940 ⇒ 00:49:00.539 Kyle Montgomery: Project owners and, application directors and stuff are getting together.
428 00:49:00.690 ⇒ 00:49:02.080 Kyle Montgomery: captive audience.
429 00:49:02.190 ⇒ 00:49:07.349 Kyle Montgomery: Hold a 20-minute presentation on it and see what conversation that starts.
430 00:49:09.560 ⇒ 00:49:15.689 Uttam Kumaran: Yeah, maybe, Luke, also, we can go check what their past agenda items have been, if that’s public, to go see if there’s been anything on AI.
431 00:49:16.260 ⇒ 00:49:16.820 Luke Scorziell: Yeah.
432 00:49:17.060 ⇒ 00:49:20.350 Luke Scorziell: So that’s sweet. Do you know when those are? You said twice per year.
433 00:49:21.030 ⇒ 00:49:25.600 Kyle Montgomery: I know where they are in North Carolina, I don’t really know the, Texas schedule.
434 00:49:25.870 ⇒ 00:49:27.330 Luke Scorziell: Okay. But.
435 00:49:27.330 ⇒ 00:49:31.209 Kyle Montgomery: This guy starts with us at the end of the month, and so,
436 00:49:31.400 ⇒ 00:49:34.450 Kyle Montgomery: It’ll be… he’ll be on the inside track there.
437 00:49:35.420 ⇒ 00:49:35.850 Uttam Kumaran: Nice.
438 00:49:36.310 ⇒ 00:49:39.260 Luke Scorziell: Okay, well, sounds like we’ve got some good stuff to…
439 00:49:40.780 ⇒ 00:49:44.959 Kyle Montgomery: Yeah, yeah, I’ll pull together some thoughts in, in this, and
440 00:49:45.070 ⇒ 00:49:52.260 Kyle Montgomery: just pass some emails back and forth to then see what, what might come to life, at least as a blog post, and that would… might get the ball rolling. Perfect.
441 00:49:53.820 ⇒ 00:49:54.420 Uttam Kumaran: Okay.
442 00:49:54.740 ⇒ 00:49:55.530 Uttam Kumaran: Right.
443 00:49:55.680 ⇒ 00:49:58.269 Uttam Kumaran: Well, thank you both, I appreciate it. Hopefully.
444 00:49:58.270 ⇒ 00:49:59.180 Kyle Montgomery: Thank you guys, good to meet you.
445 00:49:59.180 ⇒ 00:50:01.870 Uttam Kumaran: Productive week ahead, I know it’s just Tuesday, so…
446 00:50:01.870 ⇒ 00:50:06.480 Luke Scorziell: Yes, I know, I’m like, it feels like a Thursday or Friday already.
447 00:50:07.680 ⇒ 00:50:14.939 Kyle Montgomery: For sure. All right, fellas, well, thanks very much, and Utam, I’ll keep you posted if we get that meeting with J&J, and next steps there.
448 00:50:15.150 ⇒ 00:50:16.519 Uttam Kumaran: Okay, perfect. Alright.
449 00:50:16.780 ⇒ 00:50:18.530 Kyle Montgomery: Thanks, guys. Byeers.