00:01.94 Robert Karel RevOps is the team behind the dashboards, the ones who make sure the CRM is clean, the forecasts are running, and the reports are ready for the Monday morning pipeline review. But AI is raising the bar on what leadership expects from their ops teams, and the gap between what's expected and what's doable is getting harder to ignore. 00:20.84 Robert Karel CROs don't want spreadsheets. They want insights and actionable recommendations from their ops teams. And they expect AI to enable faster and more trustworthy insights. No matter if your rev ops, marketing ops, sales ops, or in a similar role, you're probably realizing your data foundations, governance, and infrastructure are not playing well with your AI tools. Today, I'm sitting down with two guests who have been living this problem from different angles. Darwin Singson, who has spent years building revenue enablement and AI-powered sales automation systems at companies like Eventbrite and Sindio, and Naresh Govindaraj, CEO and founder of AIdeaBlocks, who's building the infrastructure layer designed to close the gap between AI tools and trusted GTM intelligence. 01:59.64 Robert Karel Darwin, Naresh, and I have known one another for many years, going back to our time together at Informatica. And I was flattered when Naresh asked if I can provide occasional go-to-market coaching as he scales out idea blocks. 02:10.42 Robert Karel As you guys know, I love solving data challenges and I'm looking forward to this chat. So Darwin, Naresh, welcome. 02:17.82 Naresh Thank you. 02:17.89 Darwin Singson Hey, thanks for having us. 02:17.70 Robert Karel I'm gonna.. Oh, my pleasure. So let's start with some backgrounds in each you. So Darwin, we'll start with you. Just kind of walk us through your revenue enablement and ops journey. 02:29.50 Darwin Singson So I spent a career. Two decades in sales and development revenue productivity. At companies like Salesforce, Automation Anywhere, Sendio and Eventbrite that you mentioned, and where we all met at Informatica, where the bulk my career basically was taking place. 02:47.83 Darwin Singson Through that evolution. It was all about, and the beginning, was all about how do we help reps be more productive? But now it's evolving from that to how do we now help reps not just be more productive, but also provide the insights and the ability to do your jobs more effectively, more efficiently. And with ai that has come to fruition. So as you mentioned, my last two jobs at Sendio and Eventbrite, I started building out automation and AI bots to do just that. So I've always believed that, 03:22.65 Darwin Singson The best enablement isn't about teaching people to work harder. It's about moving the work that shouldn't exist. And so that evolved my whole thinking from just training to like the broader RevOps operation and productivity around that. 03:41.02 Robert Karel Very cool. I look forward to kind of digging deeper into, some of the challenges on the front line. Naresh, kind of same thing. I've known you for many years back to the Informatica days, but tell us a little bit about your background and what led you to founding IdeaBlox. 03:49.84 Naresh Yeah. 03:53.64 Naresh Yeah. 03:58.97 Naresh Sure. So i as I spent most of my career working on data platforms. I held them product management, leadership and engineering roles at companies like Informatica, Trifacta and Alteryx. And al tricks and what I've observed lately since the whole chat GPT sort of evolution is that AI has fundamentally changed how users will interact with data. So we're already seeing that in enterprises and it's democratized where a sales rep can upload a spreadsheet and ask a question and they have an answer. So things that could take weeks or days or weeks to get done 04:44.54 Naresh Can be done in minutes. So that's But at the same time, it feels like just from a data platform perspective that there is a new data platform that can evolve to cater the needs of this new AI-driven era. So that's one. And then idea blocks itself, what we are focused on is really a couple of areas. So one is how do you get reliable, repeatable results from AI? Which seems to be a challenge and I'm sure we'll discuss that more. And also how do you make sure that the enterprise operation knowledge or context is available 05:21.24 Naresh For for AI so that it can really answer questions on the enterprise behalf. So these are couple of areas that we are trying to solve. That idea block 05:32.60 Robert Karel Excellent. Yeah, know it's going to be a good conversation because i think everyone's beginning to realize that AI is awesome, but you can't really trust it. And that's the whole title of our episode is specifically around RevOps and, helping the chief revenue officer and go to market leaders. 05:43.25 Darwin Singson Bye. 05:50.67 Robert Karel Make good decisions, you can't just rely on AI, but you can't just rely on the old ways of doing data management either. So I'm looking forward to this. 05:59.38 Naresh Yes. 06:00.76 Robert Karel So let's start with the state of RevOps. So Darwin, you've been on the front line for many years, you've seen a lot of the changes, but let's just foundationally, especially if folks are maybe adjacent to, but are in living the day to day of a RevOps team member. What are they on the hook for today inside most companies, whether it be, scale ups or larger enterprises? 06:27.38 Darwin Singson So I mean, so the role of rev ops fundamentally has changed. Or is dramatically changing or changing now. They're no longer just reporting on the business. They're expected to influence it. 06:42.55 Darwin Singson Rev ops still owns the infrastructure. They still own forecasting, the pipeline, the CRM governance, story territory management and reporting. But the expectations have changed dramatically. 06:54.00 Darwin Singson RevOps has become sort of like now the organization's decision support function. that They're basically, the RevOps used to report the business, but now they're expected to help run the business, which is kind of fundamentally different from what RevOps was in the past, which was, let's build out those dashboards. 07:17.78 Darwin Singson But now let's. 07:17.94 Robert Karel It was backward looking versus forward looking. 07:19.96 Naresh Mm-hmm. 07:20.55 Darwin Singson Yeah. Current and backwards looking, but now revolve to expect it to like, and that's not just build out the dashboard, but what's the reasons behind some of the numbers behind the dashboards? Give me the, why is that happening? The why behind 07:34.94 Robert Karel Are there some kind of newer asks with expectations with CROs like that CROs are now expecting RevOps to answer some tougher questions. 07:47.61 Robert Karel You have any examples that you can provide some color on what do those some of those new asks look like? 07:53.59 Darwin Singson So Yeah, so from what I'm seeing now, leadership is they're not asking for more data, that's for sure. They're asking for more confidence. They're asking questions like why is the pipeline slowing? Which deals are truly at risk? 08:09.52 Darwin Singson What's hurting the win rates? What Which managers need more coaching? What should we do? What should we do? What should we be doing next week or next month? And so They're not asking for another dashboard, but what they're needing is confidence in the decision that they need to make. 08:28.82 Darwin Singson And that's kind of where RevOps is kind of throwing, just throwing in AI in there to kind of help them with that. And we can go into it little bit more and I'm sure we will, but that's where we were also run into problems. 08:43.54 Robert Karel Yeah, let's and let's dig further in there, Darwin. Is So across every function and discipline, ai has had this hyped expectation it's going to make everything smarter and faster. 08:57.02 Robert Karel And I think we've all come to realize that it's delivering on that promise in some ways and it's failing in that promise in others. So on the RevOps specific side of the business, so what are some examples of where it's actually really helping? 09:13.27 Robert Karel And then on the other side, I'll ask, where is it falling down? 09:18.23 Darwin Singson Yeah, yeah. So I mean, like, REVA or AI is already delivering a lot of value. 09:26.41 Naresh Okay 09:27.26 Darwin Singson But it can't compensate for the inconsistent data or undefined business rules. Where AI is shining is things like summarizing information, identifying trends, accelerating research, producing first drafts of analysis, even surfacing recommendations. 09:45.82 Darwin Singson Where it's struggling is like things like conflicting data, duplicate records, different revenue definitions, tribal knowledge, spreadsheets that are data-driven processes. So, a lot of, I wrote a blog on this that I just posted today. It to It's on my LinkedIn profile, but I mean, basically AI doesn't create the trust. 10:09.72 Darwin Singson It scales whatever trust already exists. And I think, Naresh, you can speak to this a lot more in terms of some of the conflicts around data. 10:18.52 Naresh Yeah. As Darwin put well, AI is already deployed and showing a lot of value already. That said, there is still some lack of trust among leaders on the outputs that they get from ai And some of the challenges are AI is probabilistic, just like to a good extent, even humans are probabilistic. So expecting sort of repeated exact answers from AI is difficult. It's just the way AI works. So it needs to be deployed in right ways to really use it to a benefit. The other part is that I think lot of initiatives that have involved AI 11:03.29 Naresh Has not provided all the context that AI needs. So it's like your enterprise knowledge, like it's a tribal knowledge, and the information that's on people's heads or in Slack or emails, how do you make that available to AI so that it has all the information and the context needed to give the right answer? So that's been part of the challenge. And also data quality. You talked about the duplicate data. How AI may not know that, so it's going to make a judgment on poor data. So a lot of it is our responsibility in how we deploy AI and fill these gaps so that we get more trusted results. 11:42.62 Robert Karel Yeah, and I think we' we've all spent a lot of our careers in the data management in space dealing with data quality and data governance concerns. And I think this is the funny thing that AI has not introduced the concept of inconsistent data answers. 11:59.03 Robert Karel We have for years, well before ChatCPT launched and people were just using their traditional BI analytic tools or spreadsheets to ask the same question and get 10 different answers. 12:09.15 Darwin Singson Thank you. 12:13.64 Naresh Mm-hmm. Mm-hmm. Mm-hmm. 12:14.28 Robert Karel That was often caused by poor data quality or inconsistent definitions. And what do you call a discount? What do you call an active customer? Like How is that defined? Going back to your policies and definitions that you're talking about. And so all that AI seems to be doing is making those really inconsistent definitions more visible to everyone because but it's making the same mistakes that an analyst would make when they didn't have clarity on definitions. 12:46.70 Robert Karel So what do you see, either one of you on.. 12:47.49 Naresh That's. 12:54.25 Robert Karel Where's the inconsistent data coming from? Because there's it's been 20, 30 years of vendors trying to solve this data consistency problem. Naresh, I'll kind of start with you is what's the status of where of what that market looks like and what have we done well in getting some better consistency in our insights? 13:19.48 Robert Karel And where are those black holes where we're still getting challenges that, put the AI thing aside for now, because that's just the accelerator of all the bad answers. 13:31.15 Naresh Yeah, yeah. Yeah, no, it's true. The need for trusted data and data quality has always been there. Like maybe for decades and there are platforms or products that are focused on data quality, data governance. 13:47.46 Naresh And part of the challenge that enterprise have is that disparate systems, you have like hundreds SaaS applications, you have the same definition of customer that's repeated, it's not aligned. 14:00.10 Naresh So to a good extent that landscape is complex enough. So you have data duplication, insufficient information, not all data is available to everybody. 14:06.57 Robert Karel You 14:14.67 Naresh So the landscape makes it tricky to sort of give you a data plane that is clean and trustworthy for you to start with so enterprises are grappling with the problem for a while so but now there's an urgency to address the problem because now you want to have ai to sit on top of it i think what is working i mean it feels like enterprise has the pieces of solution available to solve the problem but they're not integrated so if you have like data catalog, data governance products, you have ETL products, you have BI products, you have your data warehouse. 14:55.35 Naresh And so each one solves a specific problem, but to bring it all together to provide a holistic solution is a big challenge. And then to take that and make it available for the RevOps sales ops team so they can quickly get answers is also a challenge. 15:03.30 Robert Karel Yep. 15:10.29 Naresh So that's sort of my perspective. I'm sure Darwin can add to it. 15:17.37 Darwin Singson There's so many data is spread across a lot of outside of the core systems. And for RevOps, the core systems, the CRM, but some of the most valuable sort of GTM data lives outside of that. Things like marketing spreadsheets, event attendee list, partner reports, gong conversations are gold, are golden these days. Product usage guides, pricing files. 15:48.25 Darwin Singson So most of the data, the most valuable data usually isn't missing. It's just somewhere that nobody's looking. And that's kind of the huge sort of hole that RevOps is facing right now. It's not just, 16:05.65 Darwin Singson That's just and Let's just be the admin for CRM now. It's not it's beyond that. 16:10.90 Robert Karel Yeah, and that is a great example because, all of this disparate spreadsheets where these one off events or these smaller partners that you don't have like some integrated system transactional workflows with, which is meant most of them. It's kind of the concept, Neresh, that you brought up in the past around the business edge. 16:33.82 Robert Karel The concept that there's a lot of core critical business that lives maybe outside of that central infrastructure, that core IT governance. 16:41.34 Naresh Yeah. 16:42.78 Robert Karel And there's not one person listening that doesn't know what we're talking about when we say there's valuable data that lives in spreadsheets. 16:48.46 Naresh Yeah. 16:50.90 Robert Karel And the gone calls and other unstructured or semi-structured thing, that's a whole other ballgame too. But even just the simple concept of, is there important data that you're using to run business that's still in spreadsheets in 2026? 17:03.92 Darwin Singson You 17:07.03 Robert Karel Absolutely. 17:07.44 Naresh Yes, it is. 17:07.63 Robert Karel So Noresh, can you talk a little bit more about your business edge concept and kind what? 17:09.82 Naresh Yeah. Yeah. So I mean, the business edge concept was, and I'm also thinking of it as the operational edge. So this is where there are sales ops, marketing ops. This is where they operate. And it's not necessarily know within central IT, because central IT has a separate important function to run your data warehouse, your application integration, thoughts and so forth. 17:34.61 Naresh But RevOps SalesRub, they need that agility. They need answers tomorrow, or they need and they need to get back to the sales rep today. So you can So they need to be lot more agile. So for that reason, these teams have operated at the edge, so slightly outside of IT. They may consume some services from IT, but they are bringing data from Slack, from email from emails, from all your spreadsheets. And then putting it all together to get that answer to the business exit. 18:05.37 Naresh So that's the business edge. But now I think what's interesting is that with AI, the power of the edge has more. So now you don't need to be a data engineer to come up with a nice automation using AI. You can build an AI agent using point and click methods. You can write a plot skill to automate how AI works on your messages from email, CRM data, et cetera. So it's so what that's led is 18:38.78 Naresh Is that there is a growth. There is a belief that I can do more among these teams. And there's also expectation from sales leadership, et cetera, to say, hey, we need more efficiency. And so that's growing this edge. So there is this proliferation of use of AI, which is good, but at the same time, i think a lot of these teams hit the same problem. Like, how do I get repeatable? How do get trustable they trusted data? So they're running into some of these issues. So it is a growing operational or business edge that we are dealing with. It's 19:11.96 Naresh But yeah it 19:14.23 Robert Karel Yeah, i think it's interesting because.. And I'm going to ask you in a moment, Darwin, about what percentage of your ops work is this operational edge type of data. But I think in the old world, let's call it before AI, you either.. 19:34.58 Robert Karel Worked with central IT or these the central infrastructure to get this data into the infrastructure and you had to wait, you had to put a ticket in, you had to figure out how to get it done, or you just ran analytics directly from the spreadsheet or the data was very siloed. 19:51.00 Robert Karel Whereas AI, it seems like the risk is it's now easier for folks to build processes and build agents that are using this data 19:57.38 Naresh Mm-hmm. 20:04.15 Robert Karel In an ungoverned fashion, but making it have more impact. Is that a fair statement, Darwin? 20:11.19 Darwin Singson Yeah, yeah. That's fair. Fair i want to know what the percentages are in terms of how much of that out of the box or what we call it the business edge yeah that RevOps is using. I would say i'd probably closer to more than 50% for sure. I'd say 80. 20:26.90 Darwin Singson But But yeah, very fair statement for sure. 20:31.19 Robert Karel So what happens today when you feed, let's call it ungoverned data, we're all data geeks here, which means for the uninitiated data that hasn't had standards and policies and rules applied to define what you should do with it, and in AI speak, it's the context, the business context that AI needs to help create better results. It's just the data portion of that. So if you're putting ungoverned data into an AI agent, ask it to produce insights for the CRO. 21:06.74 Robert Karel What's happening, Darwin? 21:08.82 Darwin Singson Short answers, you automate uncertainty. AI will confidently give you the charts, the insights, the forecast recommendations. 21:19.90 Darwin Singson But if the underlying data is inconsistent, then the output becomes difficult to trust. Bad data doesn't become good because of AI. 21:30.75 Darwin Singson AI touched it. It just becomes faster. 21:34.33 Robert Karel Yeah, and to Naresh's point, which, not everyone may understand the difference of probabilistic versus deterministic, but in a nutshell, answer and then Naresh will ask you to kind of go deeper in a second, but it's if you use dead bad data and you run a report in Tableau, you're going to get the same bad answer, but it's going to be the same and consistent bad answer. 21:44.27 Naresh Thank you. 21:54.53 Darwin Singson Right. 21:57.14 Robert Karel With ai if you ask it a question, it's going to give you a different bad answer every time. That's and it's all based on some logic within the AI's LLM. 22:08.77 Robert Karel But because a probabilistic nature means it's taking all these different things into account. And as things shift, how it to answers the question shifts. 22:14.25 Naresh Mm-hmm. 22:17.48 Robert Karel So it's even worse because it's not the bad answer, but at least it's consistent. It's the bad answer and you can't repeat it. So ra I'll ask you to expand on that and then ask the question of how come a lot of these enterprise data platforms that folks have invested, 22:24.44 Darwin Singson Right. 22:33.66 Robert Karel Millions and millions in over the years. How come they're not solving this problem yet today? 22:40.82 Naresh Yeah, going back the original question on bad data is bad results. The interesting thing was before AI, if a human looked at data, they could tell it's maybe bad. And say, this is not a report I can share with the execs. 22:59.42 Naresh Now ai doesn't have that eye it's going to work with what it has and it's in most cases it just give you a confident answer so that is to darwin's point it's going to accelerate how in this bad and incorrect information gets to others which is not a good thing so the i mean your so your second question about enterprise data platforms and where they are I mean, lot of them are evolving and AI is becoming core to what they do, and which is a which is good overall for the whole entire enterprise. Some of the challenges that we talked a little bit about earlier still alex exist. So there are multiple pieces to the enterprise data stack. So it is So that causes a challenge because you have 23:51.16 Naresh Data catalog, where your policies are defined, your ETL systems, your BI systems. How do you share information across these systems has always been a challenge. 24:01.95 Naresh But now how you build agents that work across these systems and share the same enterprise knowledge is is a harder problem in a sense. 24:02.32 Robert Karel Mm-hmm. 24:13.91 Naresh So that's one part of the challenge. The other thing is that you can see that the new interface is becoming cloud or a co-pilot, that maybe chat GPT in the future, but the expectation for the RevOps, SalesOps, and even other teams is that cloud has become so powerful, it's game-changing. It's inter intelligent, it thinks like you, it can solve problems like you. So that's becoming the interface. So now the challenge a little bit for the enterprise platforms is that, how do I switch from the classic pipeline interface or BI interface and all into cloud? So that's, that's the heaviness that goes with it. It also introduces a challenge for them. 24:58.87 Robert Karel Makes sense. I want to go to more from a practitioner standpoint. 25:00.84 Naresh Thank 25:02.63 Robert Karel So yeah, the overconfidence of AI and how it just makes everything look so right when it's so wrong in many cases. But Darwin, you've been talking a lot about the go-to-market engineer role, which is kind of this emerging concept and whether it'll be an actual job title or whether it's just a role that many different job titles fall into. Love your opinion on that too. But you've been playing that role. 25:28.25 Robert Karel So what does this job look like and what's different about it than the old school ops rules? 25:35.38 Darwin Singson Yeah, yeah i kind of just evolved or even fell into it almost accidentally playing that role because as I evolved in enablement from the typical training function into more evolving enablement to automation and AI driven, 25:55.10 Darwin Singson I kind of fell into that role because I then had to start developing automation and to make revenue production better. So the GTM engineer is is a new role that I think it's not, it's a new role that kind of straddles that. They combine someone who has not just rev ops experience, 26:15.70 Darwin Singson But also automation, AI, process design, business analysis. Instead of manually producing reports, they're building systems that continuously generate trusted insights. This is sort of, the RevOps person, you'll still need the RevOps person to do what they've typically been doing, maintaining and the functions around the CRM. 26:39.64 Darwin Singson But what's straddling the data that the RevOps produce and the insights that CROs need. And that's where the GTM engineer comes in. And that's where AI comes in heavily as well. So I'm seeing that the role come up a lot more. 26:58.62 Darwin Singson And I think it's something right now that just kind of lower level roles. But right now it's starting to evolve to be a bigger position within sort of that rev ops space. And it's again, it's sort of the glue that holds all of the data and just the data and the insights together. 27:15.93 Naresh Yep. Mm-hmm. Okay 27:17.05 Robert Karel And what do you think in terms of career pathing? Are there new skills or experiences that organizations are going to look for as they start to prioritize and up level? As you're saying, it's kind of a frontline role now, but you have a sense that this is going to be a more senior, more strategic role over time. What are those kind of core skills that folks that may be interested in this path should focus on? 27:45.75 Darwin Singson Yeah. Yeah. For most companies, there's an evolutionary process. For most companies, they probably don't know they need a GTM engineer until the rev ops person starts losing time because it's like, wait a minute, beyond my regular day job, I now have to do this, other things to help you in this in your decision. So, so they're looking for, 28:06.97 Darwin Singson My thought is the best candidates definitely would need to know of the revenue flow, the sales processes, marketing as well, because of the lead generation aspect of it. 28:18.68 Darwin Singson They need to know internal just.. Deal for processes and the data within that right, but on the flip side, they need to know AI automation orchestration I think rev ops, when you going forward is going to be sort of the orchestration layer within the revenue. 28:39.50 Darwin Singson Production or revenue ecosystem i think that's where it would be evolving to it makes sense but they also i mean this person would also need to obviously understand governance so it's not just the data the systems the workflow but more importantly they need to understand the business behind it. 28:58.99 Darwin Singson And so someone that has sort of both of those layers, not so much analytical, which is RevOps typically has had, but it's also the business side of it. 29:07.64 Robert Karel Man 29:09.66 Darwin Singson And that's where I can be applied. 29:12.47 Robert Karel And Naresh, want to ask you just a similar thing around this go-to-market engineer. 29:14.19 Naresh Mm-hmm. 29:17.30 Robert Karel At AIdeaBlocks, you're not just focused on RevOps. You're really like all these different functional ops roles like marketing ops, sales ops, RevOps, et cetera. And when you talk about a go-to-market engineer, it's really having someone, maybe a role that's now spanning, 29:36.06 Robert Karel Kind of the marketing ops, which also so often focused on the buyer's journey, the customer journey, all the way then to sales and rev ops, which really focused more on the from lead to opportunity to close that sales funnel. So how are you thinking about kind of supporting all of these different ops roles as kind of maybe the lines between these different functions and stages are blending? 30:03.86 Naresh Yeah, that's a good observation. It's like, do you have the sales ops, marketing ops, rev ops. From a data and a process perspective, fortunately, the solution that you provide is similar, is same for all of them. It's really what type of process they're involved in, whether it's scattering leads or is it, how where are my revenue leaks? 30:30.10 Naresh So depending on what problem I'm solving, it's a different workflow, essentially pointing to a different system. But what's common among all of this, and I think what Darwin also mentioned, is that AI has changed the role of the DevOps and maybe has brought in the notion of a GTM engineer. Because now using natural language, these GTM engineers can really build that orchestration, build the automation. And I think their goal is to really automate what you would normally just be a very repeatable process. If you're bringing leads every day and you need to clean the leads, you need to de-dupe the leads, hey, why not AI do that. So having a platform that allows my marketing ops to do that, having sales ops to churn out reports so that every Monday morning, that is something delivered to the sales reps. So it's providing those tools, providing the ability to build those orchestrations, the skills, in a platform. Now it's all possible, everything in natural language and all that. To me, it's really exciting to be in the ops team now because all of a sudden you don't need to be a Java programmer to build an automation. You just need to know your business and you need to know natural language and know how to build a cloud skill and you're good to go. So 31:55.35 Robert Karel Yeah, and it sounds like, Darwin, that there's, historically been collaboration, but oftentimes a real distinction of roles and a little bit of a wall between, let's say, marketing ops and rev ops. 32:10.20 Robert Karel This is what you're responsible for. 32:10.68 Naresh Thank you. 32:13.16 Robert Karel You're providing insights to the CMO to provide the ELT and the board. And RevOps is doing it for the CRO. And they have other responsibilities, but everyone's responsible for the same goal of revenue. 32:23.64 Robert Karel So are you seeing maybe improved performance? Collaboration and evolution of marketing ops and rev ops, it doesn't need to converge into one organization. That's an organizational question. Has' nothing to do with the skills I'm talking about. As you guys have both pointed out, the need to understand the business and that these things are not mutually exclusive processes. These are things that are incredibly intertwined handoffs. So what do you, what do you think about kind of that collaboration over time? 32:57.82 Darwin Singson Yeah, I think the GTM engineers is kind of breaking down and breaking down those silos. Between the ops functions within organizations, because they're now providing the insights within the data that they produce. 33:11.86 Darwin Singson So I think some of that, I think part of that, the role of the, 33:12.66 Naresh Mm-hmm. 33:16.44 Darwin Singson GTM engineer will be just that. How do I make sense of like when I was, what I was doing was basically taking numbers out of marketing ops from Gong, from CRM, and then providing insights around those three things. 33:31.51 Darwin Singson And then in some cases i brought information back to marketing where i took gong conversations winning messages and said hey here are the winning messages marketing this is what you should be pushing out so it works both ways i think there's even a broader things that's shaping within The RevOps, i don't know if it's within the RevOps organization, but I think that's where it would lie. But the bigger broadening thing that's also shaping up what you're seeing a lot of larger companies do is they're creating a revenue intelligence function. 34:04.79 Darwin Singson And that's a bigger, broader function. I would think where GTM engineers would fall under and maybe in the future, where Vops will fall under as well. 34:06.59 Robert Karel Mm-hmm. 34:12.30 Darwin Singson But the revenue intelligence is a bigger, broader that looks at not just the 34:12.64 Naresh Mm-hmm 34:17.94 Darwin Singson What we'd see up front, but the backend of those agents and tools that we're developing. How How do we develop the backend architecture and infrastructures that AI and automation kind of work more effectively? And that's where I think the revenue intelligence role is starting to come to fruition. And again, what much larger companies because they have the capacity to do that. 34:42.74 Naresh Hmm. 34:43.16 Robert Karel Yeah, and I'm going to segue in a moment into just that. I wanna to get I want folks to be able to kind of understand the actual systems and tools architecture for all of this, because it is complicated and there's some central systems. There are one-off tools and utilities that people use. But before you do, I'm going to ask Darwin for you to make a prediction. 35:05.62 Robert Karel You've talked about RevOps kind of Moving from providing snapshots of what today looks like to the CRO and letting the CRO use that data to figure out for themselves what the heck's going on to actually providing guidance to the CRO about here's what's happening and why it's happening. 35:26.78 Robert Karel How far do you think we are? Because AI is moving fast. 35:33.76 Darwin Singson Yeah. 35:33.98 Robert Karel I think i'll put a bold statement out there. Data management is not moving fast. Everyone's known the problem for decades. Often they call it the number one priority is to improve data quality and improve reporting and insights. 35:43.27 Naresh Mm-hmm. 35:47.45 Robert Karel And yet it's remained the number one priority for 20 years because people have not put the right discipline. So do you think that AI is going to be a catalyst to finally get data the focus and party it deserves? Or do you think we're going to still be in that same hamster wheel of indifference? We're to do just enough to get the AI but then let it fall again. What's your prediction? 36:14.04 Darwin Singson I'm kind of falling to the camp of right now, people are, it's a shiny object. And people are taking it on and they're building these agents, which are so easily built these days. And so that's all front end stuff. 36:32.18 Darwin Singson So I think that's what's happening now is they're building a lot of front end stuff and now they're going, and now they're having like an oh, oh moment, and oh like an oh shit moment where it's like, wait a minute. 36:43.16 Darwin Singson As we talked about earlier, all the front-end stuff is great. Ai is doing its job, but it's pushing out bad data or bad information. And so Eventually, the lagging part is always going to be with the bottom line data management, unfortunately. And so I think it's always going to be lagging. 37:00.98 Darwin Singson The larger companies will have the resources and capital to fix it, the smaller companies, a lot of them would just struggle with it and will continue to struggle. 37:05.45 Naresh Thanks. 37:09.99 Darwin Singson And there's a lot of smaller companies coming up now that are trying to help in that regard. And so I think that the shiny object will still be key and upfront, but unfortunately, that's not going to solve the underlying deeper problem, which is the, the architecture behind it 37:32.44 Robert Karel Yeah. And let's so want let's talk about tools. I'm going to start with Darwin very quickly and then move to Naresh because this is your area of expertise. But Darwin, we know this cloud and Gemini and Cobalt, there's lots of new AI tools that people are using. 37:47.83 Robert Karel Everyone knows about those. Everyone's favorite changes every month. I want to talk about foundationally, without the AI tools, what have been the key tools that apps systems that made up the majority of RevOps life? What are those key systems, whether they're the operation edge stuff or the core? I'd love to just get a summary of, in your experience at the companies you've worked for, at least, what are some of those key systems that really you spent the most of your time playing with? 38:20.79 Darwin Singson From from a RevOps standpoint, it's the CRM. It's And now things like Outlook and Gong, the data within those organizations, of obviously analytics tools like Tableau are still mainstay. 38:37.75 Darwin Singson But now there are companies like Glean and other companies. And of course, backing up, that the major AI providers like Tatchypt and Cloud and Perplexity to some extent are really where they're trying to build things on to analyze the data that they're getting from the CRM tools and some of the other tools, the mainstays tools. 38:47.96 Robert Karel Thanks. 38:58.42 Robert Karel Os 39:05.02 Darwin Singson But there are now tools like Glean and other tools like that are helping, that are kind of advancing that even further. Like Glean, for instance, can go in and look at Gong conversations and connect it with CRM data, and make sense of those two together. 39:22.83 Robert Karel Mm-hmm. 39:26.20 Darwin Singson Win rates and conversations, what does that look like? So I think that's more and more coming to fruition. But again, that takes more time off and away from RevOps from doing the things that they originally were intended to do. 39:40.28 Darwin Singson Hence, the need for someone like a GTM engineer to come up to actually, now can you connect these two and provide insights?. 39:48.69 Robert Karel Yeah, no, the, the slew of new potential solutions implies that the existing ones aren't doing what it needs, but sometimes they are, but the shiny object syndrome gets in the way of productivity. 39:49.70 Naresh Yeah. 40:00.41 Robert Karel So that we all know about that. 40:02.55 Darwin Singson Yeah, you still need the data that comes out of CRM and Gong, or you need the reliable data that comes out of CRM. 40:02.71 Robert Karel 40:09.27 Darwin Singson And Often it's not reliable in some, depending on the input that the reps put in it. But But a lot of new tools that are coming out now are then now analyzing that data and making sense of it. And that's where the extra job comes in. 40:25.24 Robert Karel Makes sense. 40:26.32 Naresh Yeah. 40:26.82 Robert Karel Naresh, let's talk really about or what is a sufficient or i don't even want to get the best in class, but what does what does a reference architecture for a well-built go-to-market data stack look like. So what are some of the foundational pieces that no matter what anyone needs to do, if they haven't done this, they have to foundationally get started? And then we'll talk about kind of where some of the gaps are. 41:02.39 Naresh That's because of AI, there is a shift to democratization. So any platform you build must be easy to use, preferably natural language driven. So and as long as you understand your business, your data, you can build what the process that you want. And there are a few key pieces to build a platform for today. In the sense that Secure connectivity is a given. 41:32.86 Naresh Trusted a ways to connect to your CRM system, your yeah ERP system, your Google Drive, all of that needs to be in place. That's table stakes. 41:44.50 Naresh And so beyond that, I think more and more what operational teams will need is that the shared operational knowledge. Is that the context, you could call it the the operation layer or repository. And this is not just. It's not like a set of documents where you can just load and say, hey, here you go. These are all my policies. AI follow it. It needs to be broken down into pieces of knowledge that AI can retrieve and use in context for the right problem. So 42:18.46 Naresh So this includes things like not just policies, but schema definitions, business terms, metrics definitions, data flows, data quality logic, mapping logic. And even things like skills. So these are all artifacts that AI should be able to, it in the fingertip, make a call, preferably through something like MCP, which is the new ways for AI to talk to back-end systems. 42:45.78 Naresh So that's a key part. The other key part is, either we talked about deterministic, probabilistic with and deterministic processing. 42:56.37 Robert Karel Mm-hmm. Mm-hmm. 42:57.53 Naresh Not all process needs to be run by AI. So there are certain parts where when it comes to summarization, and where ai needs to step in and do the right thing but data processing for a good part is deterministic so you need a deterministic engine a lot of the classic platforms provide that already for the ops team they're looking for something lightweight that they can control from cloud to be able to run, hey, show me my updated pipeline. 43:26.50 Naresh What does that look like? What's the weighted pipeline look like? So they want flows that they can build through cloud that's run in the backend through deterministic process and returns kind of trusted deterministic answers. 43:40.69 Naresh So that's important. 43:41.31 Robert Karel Mm-hmm. 43:42.20 Naresh Data quality, we talked about that, is a key piece. So the good thing is that you we talked about some of the bottlenecks with data quality, how come it's not been addressed over the years, that we could actually probably use AI to help with data quality as well, to fix, to identify duplicate information, to fix the formats of dates or whatever that is. So But data quality is key, you need that layer to get the consistent answers. 44:14.71 Naresh So it's a lot of, it's these, it's like the shared operational knowledge, which is the context of the deterministic engine and then data quality and then secure connectivity. 44:26.62 Naresh And all of that, and hopefully in a unified platform, because for RevOps, SalesOps team, they can stitch like multiple platforms together. 44:27.63 Robert Karel Yep. 44:36.21 Naresh They want one place where they can log in and access everything. And hopefully, it's also a way we talked about SalesOps, RevOps, MarketingOps, being able to collaborate. 44:46.50 Naresh It could be a platform where these teams can come together because their workflows do overlap in some ways. So those are essentially component components of a newer framework right now that will go into this new stack essentially. 45:05.08 Robert Karel Yeah, so you're what you're saying is, if I got this right, is the core components like your ETL tools, your data quality tools, your data lakes, analytic platforms, reporting tools, that's still foundational architecture. 45:21.67 Robert Karel You need these things as any enterprise would. 45:22.14 Naresh Yes. 45:25.05 Naresh Yes. 45:25.07 Robert Karel But there's a lot of things that fall through the gaps, especially for fast moving ops teams. They don't necessarily have the usability or the access rights or whatever to actually leverage a lot of the things that are in these core systems for their fast moving, GTM engineering processes that building. 45:43.50 Naresh Yeah. 45:46.14 Robert Karel So mention a little bit about how does idea blocks work with this existing infrastructure? What gaps is specifically idea blocks filling for in that environment? 45:59.10 Naresh Yeah, so I mean, so we talked about the business edge or the operational edge where the ops teams are working. So today the tools they have is essentially Excel and maybe a SQL editor. So they're very simplistic. They get the work done, but it's not agile enough. 46:18.10 Naresh And now they have Cloud as an interface where you can generate SQL to do things. But what AIdeaBlocks provides this is a platform where you can bring your business rules, policy definitions, you have a deterministic engine, and you have the connectivity and data quality capabilities all in one, where an ops team can easily use Cloud as an interface and access your data source, define your workflow to bring leads in, right or route leads, and then share that the results with business users or other members in the team, other GTM engineers and so forth. 46:59.13 Naresh And then also address data quality all in one unified platform, which is natural language driven. So the goal is really to simplify and use AI as much as possible, but use deterministic engines where they are applicable. Or So it's at the end, the goal is getting that trusted results so that you can repeat and automate for that. 47:25.69 Naresh 47:26.74 Robert Karel So the best determinists, again, probabilistic is kind of the goal here is. 47:29.56 Naresh Yeah it's that it's a marriage you wanna kind of handle manage that sort of divide and that the shift from one to the other 47:39.19 Robert Karel Yeah. And, and it's I'll do a little public service announcement here is because there's a lot of. Technology talk about probabilistic bad deterministic good. It's like, no, that's not the way it is. It's they're both valuable for a specific need and it's rarely one or the other. 47:59.16 Robert Karel The challenge is deterministic doesn't get you the really interesting insights that AI can bring. Because it's just fact-based. So it is the combination that will eventually lead us and the future GTM engineer discipline as a whole into hopefully some pretty cool territory. 48:20.79 Naresh Exactly. 48:20.82 Robert Karel Let me let's end this with one final question. Which is how to hold the conversation, and Darwin, we'll start with you, with the CRO who has high expectations, but is very skeptical of what AI is coming from the team, like what AI is delivering and that the team is presenting to them. So what's the conversation that you would have with your CRO to get them off the edge? 48:53.40 Darwin Singson Yeah, i mean the they shouldn't be asking whether the AI is working. They should be asking whether the answers are trustworthy, questions that and Like, can we reproduce this tomorrow? Can finance validate this? 49:09.30 Darwin Singson Where did this data come from? Which business rules were applied? Can we explain every step. The goal isn't to automate the work so much, but it's to automate the certainty. 49:22.10 Darwin Singson And so, and that's what CROs are looking for. And that's more and more falling in the hands of RevOps. 49:30.25 Robert Karel Makes sense. Daresh, any final thoughts there? 49:34.20 Naresh No, I completely agree with the Darwin. It's about trust and I like automating the certainty. It's And I think a good part of that is use deterministic capabilities as much as possible, because that's how you get to it. 49:52.76 Naresh And use AI where it's applicable for some things like summarization. And but at the end of the day, CRO, it doesn't matter to them whether using AI or not, but they want you to be productive and get them the right answers. So it's, it's how do you do that? 50:09.13 Naresh Yeah. 50:09.34 Robert Karel The ends justify the means, give them trusted insights that they can make decisions on. However you got to it is the right thing to do. So 50:18.73 Darwin Singson Right. 50:19.38 Robert Karel That's great. Well, thank you both. Im and up I'll post the link to idea blocks as well as the link to Darwin's awesome article. And really, Darwin did a great job kind of walking through a day in the life, which is really valuable. It really kind of opened my eyes to kind of a lot of the challenges. 50:38.39 Robert Karel That RevOps folks are facing today, which i've been adjacent to, but didn't have not experienced directly. So thank you for sharing that. And thank you both for your time today. 50:49.57 Darwin Singson Thanks, Rob. 50:49.91 Naresh Thank you. 50:50.02 Darwin Singson I appreciate it. 50:50.62 Naresh That's great. Thank you. Thanks, Rob. 50:52.13 Darwin Singson This is fun. 50:52.02 Robert Karel Take care.