Meeting Title: Uttam <> Josh: Brainforge Opportunities! Date: 2025-12-05 Meeting participants: Uttam Kumaran, Clarence Stone, Joshua Wiley
WEBVTT
1 00:00:32.659 ⇒ 00:00:33.710 Joshua Wiley: Can you hear me?
2 00:00:34.290 ⇒ 00:00:35.740 Clarence Stone: Yeah. Hey, Joshua.
3 00:00:36.220 ⇒ 00:00:37.030 Joshua Wiley: Hi.
4 00:00:39.960 ⇒ 00:00:40.859 Joshua Wiley: How’s it going.
5 00:00:42.120 ⇒ 00:00:44.479 Clarence Stone: Good! How about you? How’s everything going for you?
6 00:00:44.750 ⇒ 00:00:47.920 Clarence Stone: We’re gonna chat a little bit.
7 00:00:48.590 ⇒ 00:00:49.370 Joshua Wiley: What’s that?
8 00:00:49.540 ⇒ 00:00:55.449 Clarence Stone: Utom’s working on… I think he’s got some audio problems, so we’ll chat a little bit while he figures it out.
9 00:00:55.450 ⇒ 00:00:56.619 Joshua Wiley: Oh, okay, no worries.
10 00:00:56.910 ⇒ 00:00:59.910 Clarence Stone: So Josh, where are you based out of?
11 00:01:00.600 ⇒ 00:01:10.339 Joshua Wiley: I’m based out of, I guess best described as a suburb of Minneapolis, Minnesota. Cool. I’m out of Monticello, Minnesota, about an hour… 45 minutes north of Minneapolis.
12 00:01:10.640 ⇒ 00:01:11.450 Clarence Stone: Nice.
13 00:01:11.450 ⇒ 00:01:12.000 Joshua Wiley: How about you?
14 00:01:12.000 ⇒ 00:01:13.989 Clarence Stone: That’s covered in snow by now?
15 00:01:14.510 ⇒ 00:01:16.470 Joshua Wiley: Yeah, we got some this morning, so…
16 00:01:16.470 ⇒ 00:01:18.510 Clarence Stone: Well, interesting, you know.
17 00:01:18.510 ⇒ 00:01:21.279 Joshua Wiley: Drop off at school for my little one, but…
18 00:01:22.420 ⇒ 00:01:24.889 Joshua Wiley: I kind of like driving in the snow, so it works for me.
19 00:01:25.300 ⇒ 00:01:28.539 Clarence Stone: Nice. Yeah, I like talking to snug, too. I don’t think, like, when I was…
20 00:01:29.010 ⇒ 00:01:32.709 Uttam Kumaran: when I was in, in New York, I feel like…
21 00:01:33.080 ⇒ 00:01:36.940 Uttam Kumaran: I was, like, just sliding around a bunch. It’s fun, in the truck.
22 00:01:37.530 ⇒ 00:01:39.080 Joshua Wiley: Absolutely.
23 00:01:39.080 ⇒ 00:01:39.860 Uttam Kumaran: Fair.
24 00:01:40.460 ⇒ 00:01:46.799 Uttam Kumaran: Nice to meet you, Joshua. Thanks for taking the time. Sorry, just getting my AirPods fixed up. Yeah.
25 00:01:46.850 ⇒ 00:01:58.120 Uttam Kumaran: Yeah, maybe I just, I know I sent you a DM on… on LinkedIn, and yeah, I’d love to tell you a little bit about, like, what we do here at Brave Forge, and just kind of get your…
26 00:01:58.150 ⇒ 00:02:13.350 Uttam Kumaran: feedback and thoughts on, you know, some things we’re thinking about on how we’re growing. Yeah, and just, like, kind of casual. Clarence is on my team and, sort of, you know, helping to think about a little bit of a new operating model for our business, but,
27 00:02:13.350 ⇒ 00:02:24.279 Uttam Kumaran: Yeah, it’s just, like, all kind of interesting stuff. So, yeah, again, my name is Utam. Brainforge is a business I started about two and a half years ago. We’re a data and AI consultancy.
28 00:02:24.310 ⇒ 00:02:28.040 Uttam Kumaran: mostly US-based clients, probably around, like.
29 00:02:28.110 ⇒ 00:02:37.600 Uttam Kumaran: 13 or 14 clients right now. We have about, like, 16 people in the company, sort of mix of folks U.S. and abroad.
30 00:02:39.200 ⇒ 00:02:54.809 Uttam Kumaran: Yeah, so our main goal for clients is implementing data infrastructure, data modeling, and then basically layering on AI strategy and analytics on top. So, we typically work with companies that are, you know, north of $20 million in revenue, but
31 00:02:54.930 ⇒ 00:03:09.730 Uttam Kumaran: you know, growing into a couple hundred million, you know, a few clients that are in that world now. You know, our work spans everything from setting up data warehouses, data models, BI, and then also into strategy and analytics.
32 00:03:11.320 ⇒ 00:03:22.409 Uttam Kumaran: And yeah, so we’re kind of, like, at the point where, my business partner and I sort of are leading, you know, kind of like a… basically all the clients, sort of split half and half.
33 00:03:22.580 ⇒ 00:03:27.619 Uttam Kumaran: And that’s getting tough. And so we’re…
34 00:03:27.660 ⇒ 00:03:39.039 Uttam Kumaran: we’re basically thinking about a new model where, you know, we can bring in, you know, leaders in the company that have experience in leading projects and clients, you know, on data and AI projects.
35 00:03:39.040 ⇒ 00:03:51.559 Uttam Kumaran: But also, like, may have an interest in, you know, a new… a way of being incentivized, or, you know, more ownership and responsibility over, you know, functional areas or, you know, industry areas.
36 00:03:51.560 ⇒ 00:04:02.399 Uttam Kumaran: And so that’s… that’s sort of what, you know, Clarence and I are… are sort of figuring out and having a lot of conversations with… with folks, you know, that have a lot of background in this industry.
37 00:04:02.400 ⇒ 00:04:11.610 Uttam Kumaran: On, like, what to learn from, you know, the big companies that have sort of built these structures before, but also maybe how to, like, innovate and do it in an interesting way to attract
38 00:04:11.790 ⇒ 00:04:20.840 Uttam Kumaran: you know, awesome people, and sort of scale the company up, so… Yeah, just a little bit about us. I don’t know, maybe, Clarence, do you want to give a little bit of an intro, and then… yeah.
39 00:04:21.750 ⇒ 00:04:33.680 Clarence Stone: Sure, thanks, Utom. So, I’ve got a long history in Big Four consulting, and I’m so glad Utom brought me on board. You know, prior to this, I was working on,
40 00:04:33.680 ⇒ 00:04:44.609 Clarence Stone: a AI startup that focused on, you know, building products instead, so this is an interesting shift, but also back to home for me into consulting, so…
41 00:04:44.610 ⇒ 00:04:49.310 Clarence Stone: And really an interesting mix of the two things.
42 00:04:49.310 ⇒ 00:05:13.500 Clarence Stone: You know, you definitely have some consulting experience, you know that structures of the organizations are right now being challenged, right? The operational processes are being challenged, the red tape that exists, and even progression is being questioned, right? What’s really interesting is Utam already had started building this amazing model that solves a lot of those problems, and what we want to do next is
43 00:05:13.500 ⇒ 00:05:19.460 Clarence Stone: scale it, right? And bring more team members in and continue to grow the business and improve the model.
44 00:05:19.780 ⇒ 00:05:20.650 Joshua Wiley: It’s amazing.
45 00:05:23.070 ⇒ 00:05:24.820 Uttam Kumaran: Yeah, I, good luck.
46 00:05:25.130 ⇒ 00:05:25.909 Joshua Wiley: Go ahead.
47 00:05:25.910 ⇒ 00:05:27.209 Uttam Kumaran: I got it. Oh, you got it.
48 00:05:28.010 ⇒ 00:05:34.449 Joshua Wiley: I thought it was a natural segue to an intro for myself, so I figured I’d let you know a little bit about, kind of where I come from.
49 00:05:34.460 ⇒ 00:05:50.479 Joshua Wiley: And I guess what I do on a daily. So, I’m obviously with Deloitte right now, you know that from my LinkedIn. I’ve been there since April of 2023, and I work, for, basically on a project in government public services, so…
50 00:05:50.480 ⇒ 00:05:58.910 Joshua Wiley: We work with large government and state, you know, state and federal entities, throughout the U.S, and I just happen to be on one of the,
51 00:05:59.550 ⇒ 00:06:14.070 Joshua Wiley: working with one of the largest healthcare organizations in the country, helping manage their data and analytics. So, a lot of what I do is what we call deep dives, affectionately, and it’s essentially
52 00:06:14.250 ⇒ 00:06:26.660 Joshua Wiley: Pulling data, raw data from SQL, transforming it, creating charts and visualizations, getting them into PowerPoint, creating a story to support
53 00:06:26.860 ⇒ 00:06:32.530 Joshua Wiley: Operational improvements for… The, the organization that, you know, that we, that we support.
54 00:06:32.970 ⇒ 00:06:42.199 Joshua Wiley: As for my background, how I got here is… I did a variety of things before I got into the contact center world, which is, you know, really where this
55 00:06:42.380 ⇒ 00:06:45.780 Joshua Wiley: My current role is surrounded, within…
56 00:06:46.250 ⇒ 00:06:58.180 Joshua Wiley: But I started as, like, a customer service representative for Wells Fargo, and then I worked my way into workforce management, which is just the kind of process of
57 00:06:58.210 ⇒ 00:07:09.929 Joshua Wiley: Looking at real-time scheduling and then planning activities for the contact center to determine if we need to, you know, make adjustments to when people are scheduled, how many folks we have.
58 00:07:10.280 ⇒ 00:07:14.130 Joshua Wiley: Et cetera, and budgeting and things along with that, so…
59 00:07:14.260 ⇒ 00:07:27.470 Joshua Wiley: I worked for Wells Fargo doing that for a while. Wells Fargo, the portion that I work for, was acquired by Aquinity PLC, which is, they do a similar thing that that portion of Wells Fargo did, but out of the UK.
60 00:07:27.900 ⇒ 00:07:46.130 Joshua Wiley: And, then I was recruited to Willis Towers Watson, who handles pension and health benefits administration. And I worked on the real-time analytics team for a while in workforce management, and then I transitioned into a senior planning role, where I… it was more focused on, instead of
61 00:07:46.550 ⇒ 00:07:55.869 Joshua Wiley: Figuring out if we had the right butts in seats, and if they were doing the right things, how many we needed, and when, and how much it cost.
62 00:07:56.980 ⇒ 00:08:06.970 Joshua Wiley: After which I was recruited to Deloitte, placed on this project, in workforce management, transitioned to reporting and analytics, where I got a lot more involved in, in,
63 00:08:07.460 ⇒ 00:08:12.249 Joshua Wiley: SQL, and a little bit of Python, but I…
64 00:08:12.410 ⇒ 00:08:21.079 Joshua Wiley: A lot of what I do is… is pulling and conditioning data, creating visualizations, building spread, PowerPoints, presenting, delivering.
65 00:08:21.240 ⇒ 00:08:23.750 Joshua Wiley: You know, impactful analyses for the client.
66 00:08:26.580 ⇒ 00:08:37.689 Uttam Kumaran: Well, yeah, I guess my sort of question was, sort of, what stood out about, you know, my message over to you, and, like, wondering just, like, kind of how you’re thinking about your career, and what’s next.
67 00:08:38.760 ⇒ 00:08:43.240 Joshua Wiley: Well, what really kind of stands out to me is that your company is…
68 00:08:43.679 ⇒ 00:08:53.540 Joshua Wiley: AI-focused, right? Or it has an AI focus. And I know that a large portion of the industry, or I guess many industries, are transitioning.
69 00:08:53.770 ⇒ 00:09:03.150 Joshua Wiley: to new ways of working and new org… you know, new organizational structures because of AI. And there’s been a lot of, obviously.
70 00:09:03.560 ⇒ 00:09:22.320 Joshua Wiley: the noise floating through my inbox about, you know, AI trainings and things, and leveraging it in your work, and… and so we’ve really begun to kind of dive into, just at least basics of prompt engineering and things, and leveraging AI in our work, mostly through… we have a…
71 00:09:22.720 ⇒ 00:09:23.730 Joshua Wiley: like,
72 00:09:24.330 ⇒ 00:09:39.389 Joshua Wiley: built-in-house GPT sort of setup, with a variety of features, including document upload and analysis and things of that nature, on both sides. So both the government client has their own, and then Deloitte has their own.
73 00:09:39.630 ⇒ 00:09:51.919 Joshua Wiley: And so, I’ve been leveraging that a lot, and AI is something that’s really interesting to me. I’d like… I’d love to learn more about it, and, you know, hopefully one day work in the industry. And…
74 00:09:52.270 ⇒ 00:10:10.079 Joshua Wiley: I think that that’s something that a lot of data professionals are going to have to learn to leverage and learn about if they want to continue to be… to grow and be successful, because a lot of positions are going to become relatively obsolete, though there needs to be that human component much of the time.
75 00:10:10.480 ⇒ 00:10:16.289 Joshua Wiley: It’s less and less the case as the technology becomes better.
76 00:10:16.730 ⇒ 00:10:18.110 Joshua Wiley: Is my understanding.
77 00:10:20.100 ⇒ 00:10:29.269 Uttam Kumaran: Yeah, I mean, I think that’s sort of spot on. I mean, we’ve taken advantage of it in a few ways. One is, you know, when I started the business.
78 00:10:29.380 ⇒ 00:10:30.330 Uttam Kumaran: We’ve sort of…
79 00:10:30.420 ⇒ 00:10:49.839 Uttam Kumaran: built everything, you know, with AI, and so it’s sort of in the DNA of the company, and everything in terms of delivering for a client, you know, delivering faster and better outcomes is powered by that, you know? And when I think about how we compete, I think a lot about why that’s our advantage.
80 00:10:49.920 ⇒ 00:10:57.769 Uttam Kumaran: I think secondarily, also, as we did that, we learned, like, how to actually go to market and deliver those same outcomes for clients.
81 00:10:57.930 ⇒ 00:11:12.710 Uttam Kumaran: And so it’s like a vicious cycle of using AI internally, understanding, like, how and when it works, and then going and deploying it within data, you know, and various different, you know, service lines. So that’s been, you know, really, really awesome.
82 00:11:12.730 ⇒ 00:11:28.370 Uttam Kumaran: I think where we’re sort of at now is, like, also trying to find folks that have that same mindset or drive, and maybe have a chip on their shoulder of, like, hey, we’re not adopting it fast enough, or I would like to go deeper, and taking that, and not only bringing it to
83 00:11:28.370 ⇒ 00:11:37.100 Uttam Kumaran: clients, but also taking ownership of, you know, delivering a certain service. And, you know, that’s, like, a lot of, like, what we’re
84 00:11:37.240 ⇒ 00:11:41.789 Uttam Kumaran: we’re basically thinking about, you know, here at the company right now. I think we… we’ve…
85 00:11:41.960 ⇒ 00:11:49.800 Uttam Kumaran: we’ve taken a lot, you know, in learning about how, you know, the big companies like Deloitte and, you know, other, like, North Stars are operating.
86 00:11:49.840 ⇒ 00:12:06.369 Uttam Kumaran: But also, considering the fact that, like, we’re much more nimble, and we’re much more AI-driven, how does that allow us to compete, you know, with them, and win business from them, and have… and create an outsized income… outsized outcome for our clients, you know?
87 00:12:06.420 ⇒ 00:12:18.519 Uttam Kumaran: And not… also starting to move past a lot of the idiosyncrasies and the tough parts of those companies, which is, like, the optimization towards, like, the billable hour, maybe a
88 00:12:18.520 ⇒ 00:12:36.399 Uttam Kumaran: lack of care of, like, what exactly we’re delivering. And for us, like, we… our incentive is to do the best for clients as fast as possible, and I can go get us the… the pricing and the cash to do that, right? And so that’s always ultimately why we’ve been in business and we’ve grown, is, like, we…
89 00:12:36.530 ⇒ 00:12:44.250 Uttam Kumaran: We’ve consistently over-exceeded expectations, and we’ve leveraged all technology in order to sort of make that happen.
90 00:12:45.700 ⇒ 00:13:02.949 Joshua Wiley: Yeah, and something that really stands out to me about what you just said, too, is, you know, how do we compete with large companies like Deloitte? And I think you nailed it on the head with the fact that your company is nimble, right? It’s early, it’s in early stages. I mean, I believe you… this was founded in 2023, yeah?
91 00:13:03.550 ⇒ 00:13:05.040 Uttam Kumaran: Correct. Yeah, just a few years ago.
92 00:13:05.040 ⇒ 00:13:05.700 Joshua Wiley: Perfectly.
93 00:13:05.860 ⇒ 00:13:12.229 Joshua Wiley: Yeah, that’s what I thought. So, I mean, you’re in a phase of your company where you can really, kind of.
94 00:13:12.440 ⇒ 00:13:27.540 Joshua Wiley: name the game when it comes to your talent model, your comp model, you know, allowing for folks to really be partners and have that ownership in the process and care along the way, right? And I think that
95 00:13:27.850 ⇒ 00:13:33.720 Joshua Wiley: companies like Deloitte, since they are so large, and they’ve been around for so long, they can have some…
96 00:13:33.990 ⇒ 00:13:36.460 Joshua Wiley: Interesting habits that… that…
97 00:13:36.740 ⇒ 00:13:43.170 Joshua Wiley: aren’t always beneficial to the overall process, and so some of that red tape that you were talking about doesn’t…
98 00:13:43.340 ⇒ 00:13:50.250 Joshua Wiley: in theory, potentially exist within your company, or it’s more controlled by the… It doesn’t make it in.
99 00:13:50.250 ⇒ 00:13:50.860 Uttam Kumaran: No.
100 00:13:50.960 ⇒ 00:13:52.440 Uttam Kumaran: Yeah, it just never.
101 00:13:52.510 ⇒ 00:13:53.080 Joshua Wiley: unemployment.
102 00:13:53.080 ⇒ 00:14:12.639 Uttam Kumaran: gets in… it never gets in the door, and that’s, like, that’s one thing, but it’s also, you know, part of… we’ve arrived, I think, at a lot of our structures, just in being naive, somewhat. We’ve also arrived, to many of them, just in being very, very thoughtful. And so, for me, when I wake up every day, I think about our clients, and then I think about our team.
103 00:14:12.780 ⇒ 00:14:17.449 Uttam Kumaran: And I’m, like, somewhere way at the bottom. And…
104 00:14:17.550 ⇒ 00:14:23.400 Uttam Kumaran: I think, like, that has been our number one priority. Without clients, we don’t have a team.
105 00:14:23.430 ⇒ 00:14:38.540 Uttam Kumaran: And then with that team, we don’t have… we don’t have a product or service. And so, for me, those are my two stakeholders, and how do I go to a company like Deloitte, like, talk to someone like yourself, and think about an opportunity
106 00:14:38.670 ⇒ 00:14:55.610 Uttam Kumaran: you know, that’s not only, like, more, you know, advantageous in terms of, like, potential upside, in terms of, like, cash and opportunity responsibility, but doesn’t come with, like, okay, yeah, but then that, yeah, I have to work 100 hours, and it’s, like, psychotic, right? Like, how… how are we thinking about it sort of an…
107 00:14:57.460 ⇒ 00:15:11.490 Uttam Kumaran: a layer of leadership that is, can still own client outcomes, that is still interested in the commercial side of the business, but also, like, we can nurture long-term people that want to come in here and take us
108 00:15:11.580 ⇒ 00:15:21.569 Uttam Kumaran: From, you know, where we are to our revenue goals. And, like, that’s a lot of, like, what, you know, Clarence and I are thinking about and trying to arrive at right now.
109 00:15:22.400 ⇒ 00:15:23.030 Joshua Wiley: Sure.
110 00:15:25.960 ⇒ 00:15:27.409 Uttam Kumaran: I don’t know, Clarence, yeah.
111 00:15:27.440 ⇒ 00:15:31.290 Clarence Stone: Yeah, so I want to expand on that a little bit.
112 00:15:31.720 ⇒ 00:15:45.379 Clarence Stone: I think it’s really cool on one side that, you know, there’s this opportunity to rethink how we deliver, you know, excellent products and services to the clients, right? But on the flip side, Josh.
113 00:15:45.880 ⇒ 00:16:01.499 Clarence Stone: This job comes with a new challenge, because we are now enabled by technology that never existed before, facing a question of how do we operate efficiently in that model, right, to deliver the best products at the best rates that we can possibly do.
114 00:16:01.500 ⇒ 00:16:26.400 Clarence Stone: Well, it solves a lot of, you know, the problems that we couldn’t solve in the past, where we can have smaller teams, right? Because we can use AI-enabled functions to kind of take away bits and pieces of the repetitive tasks that used to be an entire role. But then it brings into question, how do we grow, how do we scale? There’s no template out there, right? So I think a really important characteristic of anybody that would join this team
115 00:16:26.400 ⇒ 00:16:40.539 Clarence Stone: Someone who’s excited about the future, has this curiosity to experiment with reasonable, you know, analytics and insights, and come up with ways to, you know, build this new model.
116 00:16:40.680 ⇒ 00:17:05.530 Clarence Stone: And, you know, that is going to come along with practically every role that comes into this organization, because, you know, everyone is, like, contributing some sort of their DNA insights and thoughts to forming what this future is going to be. So, what is your take on that, right? Because we told you all the good stuff, no red tape, right? But, you know, I think that’s an interesting thing to highlight, you know, this
117 00:17:05.530 ⇒ 00:17:09.620 Clarence Stone: to the right person, this could be a really great opportunity. Someone else, it’s like, hey, there’s…
118 00:17:09.619 ⇒ 00:17:11.829 Clarence Stone: You know, some ambiguity, right?
119 00:17:12.619 ⇒ 00:17:20.769 Joshua Wiley: Yeah, and, I mean, I think I… I operate in incredibly ambiguous situations every day, so, you know, from…
120 00:17:20.809 ⇒ 00:17:30.269 Joshua Wiley: that perspective, it would be nothing new, with the exception of the fact that within Deloitte, obviously, we have many layers of that red tape, right? We have
121 00:17:30.269 ⇒ 00:17:43.399 Joshua Wiley: extensive review and approval processes, which is not always a bad thing. It can be a positive in many regards, because, you know, with each iteration of a product.
122 00:17:43.719 ⇒ 00:17:45.279 Joshua Wiley: whether it’s…
123 00:17:45.909 ⇒ 00:17:52.159 Joshua Wiley: You know, whether it takes a long time or a short period of time with each iteration, it’s bound to get a little bit better, because you have
124 00:17:52.279 ⇒ 00:17:58.529 Joshua Wiley: You’ve reviewed, you know, all the details of the situation. In terms of curiosity.
125 00:17:58.679 ⇒ 00:18:03.309 Joshua Wiley: That’s something that we’ve been… that’s been…
126 00:18:04.449 ⇒ 00:18:09.989 Joshua Wiley: An incredibly large part of what we do, because we work with such a large amount of data.
127 00:18:10.229 ⇒ 00:18:16.919 Joshua Wiley: that… It’s… it’s kind of… and a lot of recurring reporting, right? So it’s easy to get.
128 00:18:17.029 ⇒ 00:18:34.199 Joshua Wiley: caught up in, well, this is how it’s always been done, as opposed to thinking it through, why should it be done differently, and what are the advantages of doing it differently? And just kind of having that natural curiosity for extracting things from the data that maybe we hadn’t thought of before.
129 00:18:34.379 ⇒ 00:18:40.509 Joshua Wiley: And so… You know, that being said, it’s…
130 00:18:41.129 ⇒ 00:18:48.419 Joshua Wiley: this has been a great environment to grow, and I’m really looking for the next place where I can continue to with, I guess, less
131 00:18:48.719 ⇒ 00:18:55.189 Joshua Wiley: Less leash, if that makes sense. You know, a little bit more partnership ownership, having the ability to,
132 00:18:55.409 ⇒ 00:18:59.969 Joshua Wiley: Really kind of dive in with a smaller group of folks that have
133 00:19:00.119 ⇒ 00:19:05.179 Joshua Wiley: more push within the organization, you know, that I’d be working for to…
134 00:19:05.619 ⇒ 00:19:09.289 Joshua Wiley: To really make impactful change in society.
135 00:19:09.939 ⇒ 00:19:11.969 Joshua Wiley: So, hope that answers your question.
136 00:19:15.840 ⇒ 00:19:26.210 Clarence Stone: Yeah, it did. And, you know, it brings to mind, you mentioned that you had a pretty extensive, ops background as well. And Josh, funny thing is, I worked
137 00:19:26.210 ⇒ 00:19:49.989 Clarence Stone: on the EY Future of Work, which is the same COE workflow solution, but for tax technology services. So, I know that the best laid plans, when you come up with ops plans and workflows, don’t always, you know, work out the way you intended to, right? So, Josh, I want to hear about some of the trouble that you might have ran into, and how you or your team kind of overcame that, right? Those are the real stories about
138 00:19:49.990 ⇒ 00:19:53.439 Clarence Stone: How you handle, you know, things that don’t go to plan.
139 00:19:54.320 ⇒ 00:19:58.709 Joshua Wiley: Yeah, I think that one of the things that we use to
140 00:19:58.860 ⇒ 00:20:02.139 Joshua Wiley: Work against, you know, kind of those forces that… that…
141 00:20:02.400 ⇒ 00:20:13.190 Joshua Wiley: destroy even some of the best planning is keeping in sync as a team, and keeping, you know, morale in a place, and communication in a place where everyone is.
142 00:20:13.600 ⇒ 00:20:20.390 Joshua Wiley: happy, fully functional, and… and at their… at their best, if that makes sense. And…
143 00:20:20.520 ⇒ 00:20:26.190 Joshua Wiley: Another part of it is… Making sure to strike the right balance between
144 00:20:27.120 ⇒ 00:20:39.739 Joshua Wiley: high-level and really detail-oriented work. And so, making sure not to go down rabbit holes when it’s not going to improve on the final product.
145 00:20:40.060 ⇒ 00:20:49.140 Joshua Wiley: But being willing to do so when it’s going to be impactful, right? And so I think that that’s been a challenge that we’ve overcome
146 00:20:49.140 ⇒ 00:21:02.289 Joshua Wiley: through… partially through our review process, right? Partially a lot… a lot of it through collaboration, utilizing different people’s brains that have been through different experiences, and maybe have a different perspective on how to solve that problem.
147 00:21:02.290 ⇒ 00:21:09.649 Joshua Wiley: So a lot of that really comes through leveraging your resources, and the resources in this case are not only are, you know.
148 00:21:09.860 ⇒ 00:21:22.129 Joshua Wiley: our AI solutions that we use internally, and, you know, existing documents that we’ve created in the past, but the people that have just such a broad spectrum of experience within so many different fields that we work with every day.
149 00:21:26.200 ⇒ 00:21:34.789 Clarence Stone: Yeah, I mean, those are excellent points, and and some lessons that you learn the hard way, right? So, yeah, I love those insights.
150 00:21:34.790 ⇒ 00:21:41.840 Joshua Wiley: It can be challenging, but maintaining a positive attitude and, you know, working through The times when
151 00:21:42.000 ⇒ 00:21:48.609 Joshua Wiley: When you are feeling negative, through just, you know, staying open, communicating, and, fixing
152 00:21:49.050 ⇒ 00:21:52.500 Joshua Wiley: Or not fixing, but improving upon
153 00:21:52.670 ⇒ 00:21:59.880 Joshua Wiley: relationships, so that, you know, everybody meshes, and we move forward as one big team, I think is really important.
154 00:22:01.360 ⇒ 00:22:06.869 Uttam Kumaran: Yeah, I guess, Josh, I’m interested in, like, give me a… give me a sense of, like, an average engagement, like.
155 00:22:07.080 ⇒ 00:22:16.840 Uttam Kumaran: I mean, we do a lot of data analytics work, and as I mentioned, like, a lot of our work on the data side is standing up a data warehouse, setting up dbt for data models.
156 00:22:16.880 ⇒ 00:22:29.180 Uttam Kumaran: you know, establishing BI, you know, reporting, and then we also move into insights and analytics. You know, each one of those has a lot of complexities, but I don’t think we’re…
157 00:22:29.260 ⇒ 00:22:34.120 Uttam Kumaran: You know, more of our challenge these days is sort of someone to
158 00:22:34.220 ⇒ 00:22:51.239 Uttam Kumaran: guide the customer into understanding those pieces, why they matter, but still delivering, you know, value as we go. Sort of wondering if you could walk through, you know, an average engagement in, like, your role, you know, what the kind of esteem structure looks like. I think that’d be really, really helpful context.
159 00:22:53.040 ⇒ 00:22:58.830 Joshua Wiley: Yeah, so… The average engagement for, at least for my role.
160 00:22:59.100 ⇒ 00:23:07.979 Joshua Wiley: works… I mean, a lot of my work is relatively independent. I do… I do some collaboration within our data and analytics team, on…
161 00:23:08.610 ⇒ 00:23:20.629 Joshua Wiley: Mostly review cycle, but also in storyboarding, you know, building out what it is that we’re trying to either sell to the client or present options for, because much of the time.
162 00:23:21.310 ⇒ 00:23:26.510 Joshua Wiley: Our work, at least within this specific environment, is not…
163 00:23:26.700 ⇒ 00:23:36.299 Joshua Wiley: selling, right? It’s presenting them with option 1, 2, 3, 4, and the pros and cons of those things. And so…
164 00:23:40.120 ⇒ 00:23:43.690 Joshua Wiley: When it really comes down to it, It is…
165 00:23:44.370 ⇒ 00:23:46.920 Joshua Wiley: I’m sorry, can you repeat the question? For whatever reason, my…
166 00:23:47.240 ⇒ 00:24:00.550 Uttam Kumaran: Yeah, no, more about, like, what is the, like, engagement structure? Like, you know, I know in your role, kind of heard a little bit about your scope. I guess, like, who are all the players in the client? Are you interacting directly with the client? Are you setting roadmaps?
167 00:24:00.550 ⇒ 00:24:00.920 Joshua Wiley: Excuse me.
168 00:24:00.920 ⇒ 00:24:02.600 Uttam Kumaran: So, like, kind of like, yeah, like…
169 00:24:02.960 ⇒ 00:24:04.930 Joshua Wiley: Yeah, so we use,
170 00:24:05.070 ⇒ 00:24:18.040 Joshua Wiley: we use a JIRA instance to manage all of our work, and through that, we use sprint planning. We use, like, a Scrum-style sprint planning, and our engagement partners are,
171 00:24:18.120 ⇒ 00:24:25.340 Joshua Wiley: basically the IT department, and then an organizational component that oversees
172 00:24:26.930 ⇒ 00:24:34.379 Joshua Wiley: Operational improvements, recommendations, and strategy for this organization that we, that we support.
173 00:24:34.420 ⇒ 00:24:52.109 Joshua Wiley: And so, this is a lot of really, you know, high-level leadership, high-level IT leadership that we’re, consistently prioritizing different tasks with, whether it be, recurring reporting, dashboard development for new reporting, and…
174 00:24:52.110 ⇒ 00:24:56.070 Joshua Wiley: dashboard maintenance, and then also improvement of our ETL.
175 00:24:56.070 ⇒ 00:25:00.250 Joshua Wiley: So there, there’s… I’m on a team of…
176 00:25:00.770 ⇒ 00:25:17.719 Joshua Wiley: our team is about 20 right now for our specific component. Our overall team has lots of components, right? And their focus is beyond that of data and analytics, and that team is larger. That’s more like…
177 00:25:18.670 ⇒ 00:25:27.189 Joshua Wiley: 200 or 300 folks. So we’re a small component, but we drive a lot of the change and the impact to the client, because we’re the ones
178 00:25:27.230 ⇒ 00:25:45.310 Joshua Wiley: that really own the data, the ETL, the dashboards, and also the kind of deeper level analytics that we do through kind of leveraging that information. And so we’ve got, you know, folks that specialize in ETL, and folks that specialize in data visualization in Power BI.
179 00:25:45.340 ⇒ 00:25:49.499 Joshua Wiley: And then folks like myself who are more focused on special projects.
180 00:25:58.660 ⇒ 00:26:15.759 Uttam Kumaran: And then I guess another question I had is, like, within a specific engagement, are you setting, like, let’s take, like, the dashboard development or analysis piece? Are you conducting the analysis yourself, or are you working with, like, an analyst on your side, and then are you presenting that back to the client, or, like.
181 00:26:15.950 ⇒ 00:26:18.619 Uttam Kumaran: Walking through, like, the actual deliverable.
182 00:26:19.450 ⇒ 00:26:20.220 Joshua Wiley: So…
183 00:26:20.220 ⇒ 00:26:20.630 Uttam Kumaran: Or, like.
184 00:26:20.630 ⇒ 00:26:34.959 Joshua Wiley: Yeah, in the case of most of my tasks, I am the one pulling the data, conditioning, building visualizations, and putting it into a deliverable for the client. Many times, it’s an offline delivery.
185 00:26:34.960 ⇒ 00:26:43.940 Joshua Wiley: And our senior leadership are ones that end up doing the presentation, though I do have experience presenting to, you know, executive level
186 00:26:44.090 ⇒ 00:27:01.860 Joshua Wiley: clients within our organization. I stepped out of a portion of that role to focus more on, kind of, the analytics component and building the overall story that we present, as opposed to running some of our recurring presentations that we also deliver offline.
187 00:27:03.440 ⇒ 00:27:22.690 Uttam Kumaran: Yeah, and I guess, like, what… what are you thinking about, like, next? I mean, you’re in this sort of role where you’re leading, sort of, part of this massive, you know, engagement. Are you thinking about, like, hey, I want to go deeper onto analytics? Like, I want to learn more about, like, the end-to-end pipeline? Like, tell me about, like.
188 00:27:22.910 ⇒ 00:27:24.119 Uttam Kumaran: What you’re thinking.
189 00:27:24.540 ⇒ 00:27:31.930 Joshua Wiley: I think that that is… yeah, that’s kind of exactly what I’m thinking. I’d like to learn more about the technical components of what we do.
190 00:27:32.080 ⇒ 00:27:39.510 Joshua Wiley: more about, kind of, the ETL process, actually building and, and, building and scaling databases.
191 00:27:39.510 ⇒ 00:27:53.159 Joshua Wiley: I’d like to learn more, you know, sharpen my SQL abilities, which are relatively decent to begin with, but, and expand on that, right, into Python and AI development, things of that nature, so…
192 00:27:53.160 ⇒ 00:27:55.129 Joshua Wiley: You know, I’d like to see myself
193 00:27:55.370 ⇒ 00:28:01.760 Joshua Wiley: segue into a role where I, at minimum, have…
194 00:28:03.000 ⇒ 00:28:07.840 Joshua Wiley: a kind of low to medium understanding of the technology that I’m overseeing.
195 00:28:08.390 ⇒ 00:28:14.110 Joshua Wiley: Rollout of, or, you know, implementation of, or the sales of, that
196 00:28:14.800 ⇒ 00:28:23.930 Joshua Wiley: It’s going to create impactful change for, you know, whatever that client may be, and hopefully make a positive impact on the world along the way.
197 00:28:24.490 ⇒ 00:28:32.400 Joshua Wiley: So, kind of a balance of technical and ownership of process and leadership is where I kind of see myself going in the future.
198 00:28:34.590 ⇒ 00:28:39.209 Uttam Kumaran: Yeah, I think, Clarence, that’s… that’s sort of all the kind of questions I had. I don’t know if you had anything else.
199 00:28:41.280 ⇒ 00:28:57.769 Clarence Stone: Yeah, I just… I guess, like, one last fun question. You know, a lot of people, when they start using AI, they experience some sort of surprise or delight or frustration, right? So I’d love to hear about your experience with starting to integrate AI into your daily workflow.
200 00:28:58.140 ⇒ 00:29:00.949 Clarence Stone: Did you run into problems, or do you love it, do you hate it?
201 00:29:01.820 ⇒ 00:29:08.469 Joshua Wiley: I… have learned to really love it. It helps streamline what I do, and…
202 00:29:09.490 ⇒ 00:29:25.830 Joshua Wiley: kind of eliminates some of the busy work that goes into the analyses that we complete. So, you know, if you have a high-level idea, you want to express that in a presentation, feeding it very specific inputs tends to, in my experience.
203 00:29:25.890 ⇒ 00:29:36.809 Joshua Wiley: give you specific results, having it step into the role of a, you know, like an analyst within a contact center environment, or… and frame the information that you’re looking to present.
204 00:29:36.810 ⇒ 00:29:46.660 Joshua Wiley: In a way where a senior executive, you know, would find value in it and would be able to absorb that information in a very short period of time, say 30, 60, 90 seconds.
205 00:29:46.660 ⇒ 00:29:50.229 Joshua Wiley: Per slide. And so…
206 00:29:50.290 ⇒ 00:29:59.180 Joshua Wiley: you know, one of the pain points, obviously, is it’s computer generated, obviously. It is only as good as what you prompt it to do, and so really learning
207 00:29:59.610 ⇒ 00:30:14.200 Joshua Wiley: how it thinks, I think, is really important, and I don’t have a great understanding of how it thinks at the moment, but I have enough of an understanding to leverage it in my day-to-day work, and understand how to kind of tweak my prompts and get results that are going to
208 00:30:14.490 ⇒ 00:30:16.719 Joshua Wiley: Be helpful, as opposed to…
209 00:30:16.940 ⇒ 00:30:21.739 Joshua Wiley: you know, something that I have to do a bunch of manual edits to anyway, before I use.
210 00:30:22.100 ⇒ 00:30:24.509 Joshua Wiley: Granted, you know, review is important.
211 00:30:24.660 ⇒ 00:30:28.900 Joshua Wiley: It’s also nice to have it spit it out where it’s like, oh, this is perfect, you know what I mean?
212 00:30:32.100 ⇒ 00:30:40.669 Clarence Stone: Yeah, absolutely. Yeah, I love that you’re, you know, using it, and starting to get used to it, and still very cautious about it. Sorry, Yutam, I cut you off.
213 00:30:41.810 ⇒ 00:30:51.280 Uttam Kumaran: No, I had the same thing. I think that’s, like, it’s great that you’re starting to explore, like, yeah, those are all, like, I think a lot of the things that we’re also just trying to figure out ourselves, so…
214 00:30:53.050 ⇒ 00:30:53.970 Joshua Wiley: Absolutely.
215 00:31:00.360 ⇒ 00:31:06.329 Joshua Wiley: So, yeah, that being said, what other… do you have any other questions for me? I…
216 00:31:07.300 ⇒ 00:31:10.530 Joshua Wiley: This has been really helpful. I mean, I… go ahead.
217 00:31:11.450 ⇒ 00:31:19.859 Uttam Kumaran: Yeah, I think… I think that’s probably all, you know, we need on our side. As I mentioned, like, we’re sort of just figuring out this next layer of, like, leadership.
218 00:31:24.180 ⇒ 00:31:25.810 Joshua Wiley: You’re cutting out, I’m…
219 00:31:26.040 ⇒ 00:31:26.660 Uttam Kumaran: Bro.
220 00:31:27.730 ⇒ 00:31:29.310 Clarence Stone: Oh yeah, he is cutting out.
221 00:31:29.890 ⇒ 00:31:31.590 Uttam Kumaran: Am I cutting in and out? Can you hear me now?
222 00:31:31.590 ⇒ 00:31:33.169 Clarence Stone: There you go, you’re back, you’re back with Colina.
223 00:31:33.170 ⇒ 00:31:33.580 Uttam Kumaran: Okay.
224 00:31:33.580 ⇒ 00:31:34.529 Joshua Wiley: Yeah, I gotcha.
225 00:31:34.750 ⇒ 00:31:40.789 Uttam Kumaran: Okay, maybe, mainly I was just saying, like, we’re having several conversations about this
226 00:31:40.930 ⇒ 00:31:46.879 Uttam Kumaran: These new sets of roles, and sort of figuring out, like, what the responsibilities are, and…
227 00:31:47.010 ⇒ 00:31:52.539 Uttam Kumaran: I think we’re just starting this journey, so we’d love to just keep you on our short list.
228 00:31:52.610 ⇒ 00:32:09.400 Uttam Kumaran: And I think we’re… right now, Clarence and I are just figuring out, okay, how do we scale this company and build this next class of leaders? So, that’s kind of where we are. We’re not in a super heavy rush to sort of bring on a ton of people, especially… we don’t have, like, a really formal scope yet, but that’s sort of, like, where we are on our side.
229 00:32:09.400 ⇒ 00:32:20.079 Uttam Kumaran: So, if it’s okay with you, like, we would love to just stay in touch and, sort of let you know once we arrive on, like, okay, what is a potential role and the scope, and run it by you, and get your thoughts.
230 00:32:21.130 ⇒ 00:32:31.689 Joshua Wiley: Okay, and so right now, you’re… it sounds like you’re in very high-level planning, feeling out, how the marketplace is, and individuals within the market, and don’t have quite a…
231 00:32:32.040 ⇒ 00:32:39.429 Joshua Wiley: a firm expectation of what a role would look like, or kind of what it would entail, or do you have… Exactly right. Okay.
232 00:32:39.570 ⇒ 00:32:41.149 Joshua Wiley: Yeah, I’d love to be fantastic.
233 00:32:41.150 ⇒ 00:32:41.830 Uttam Kumaran: I, I…
234 00:32:42.190 ⇒ 00:32:53.529 Uttam Kumaran: Yeah, like, and I don’t want to, like, that’s… you kind of nailed it on the head, like, we’re having a lot of conversations with people. For me, it’s a long journey of figuring out, like, okay, what is the next
235 00:32:54.050 ⇒ 00:33:03.960 Uttam Kumaran: version of this organization look like. For Clarence, he has a lot of experience seeing what, you know, the high-level folks are doing, and so we’re gonna arrive somewhere in the middle.
236 00:33:03.960 ⇒ 00:33:18.300 Uttam Kumaran: But, you know, I think it’s just really great to have conversations, and again, I really, really appreciate the time, and as soon as we do sort of arrive on a scope, we’ll send it over to you and can totally get your feedback on if it aligns with sort of your goals and what you’re thinking about doing next.
237 00:33:18.850 ⇒ 00:33:21.369 Joshua Wiley: Yeah, I’d love that, and even if it’s just,
238 00:33:21.600 ⇒ 00:33:26.980 Joshua Wiley: brainstorming thing, if you guys, need another perspective, and, and, you know, I, I’m…
239 00:33:27.250 ⇒ 00:33:43.680 Joshua Wiley: that’s what I do for a living, right? I try to find creative solutions and help improve things within, at least within this organization, but, you know, within our own as well. So, you know, love to continue to stay in touch, whether it lands… That makes me really happy, I really appreciate that.
240 00:33:43.980 ⇒ 00:33:44.540 Joshua Wiley: Yeah.
241 00:33:46.720 ⇒ 00:33:48.039 Clarence Stone: Yeah, thanks so much, Josh.
242 00:33:48.710 ⇒ 00:33:58.780 Joshua Wiley: Yeah, it was great meeting you, too. I really appreciate your time, and in the meantime, if you have any questions for me, let me know, but I’m looking forward to, you know, just staying in touch, learning more, and
243 00:33:59.300 ⇒ 00:34:00.530 Joshua Wiley: And yeah.
244 00:34:00.650 ⇒ 00:34:01.910 Joshua Wiley: Hope you have a great day.
245 00:34:03.180 ⇒ 00:34:04.520 Uttam Kumaran: Perfect. Thank you so much.
246 00:34:04.520 ⇒ 00:34:05.240 Clarence Stone: Yep.
247 00:34:05.240 ⇒ 00:34:06.149 Joshua Wiley: Thanks so much.
248 00:34:06.320 ⇒ 00:34:06.820 Clarence Stone: Right.