---
title: "Think Like a Doctor: Diagnosing Broken GTM Systems"
episode: 38
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Shaadik"
guest_title: "Senior Manager, Revenue Operations, LambdaTest"
date_published: 2025-10-29
date_modified: 2026-07-22
duration: 00:29:00
word_count: 4733
topics: ["revenue-operations", "ai-in-gtm", "gtm-strategy", "demand-generation", "outbound-sales"]
canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/shaadik-think-like-a-doctor/
source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# Think Like a Doctor: Diagnosing Broken GTM Systems — Full Transcript

> Episode 38 of The LeanScale Podcast, with Shaadik.
> Published October 29, 2025 · 00:29:00 · 4,733 words.
> Machine-transcribed and **not diarized** — speaker attribution is inferred, so verify
> attribution against the audio before quoting a specific person.
> Structured breakdown: https://leanscale-knowledge-hub.netlify.app/podcast/shaadik-think-like-a-doctor/

## 00:00 — Symptoms vs. root cause: the daily RevOps trap

**[0:00]** So in the daily life of everyone in RevOps, it's really, really common that you are getting the symptoms of something that's wrong every day. So somebody's coming to you with a reporting request. They want to implement a new tool. Something is broken in their systems. And it tends to be more that there's something deeper going on. And Shaadik, I'm really excited that you're here today. Being the senior manager over at LambdaTest, I'm sure you run into this all of the time. But I'm hoping we can spend some time talking about how do you peel back the layers of the onion and dig a little bit deeper to find the root cause and make sure you're not just

## 00:46 — RevOps as the general physician

**[0:46]** treating symptoms, but you're treating what's actually causing the issue in the first place. Hi, Anthony. Thank you for having me over here. So yes, RevOps is more of a general physician, your family clinics. So no speciality over there, but a lot of problems every day. One thing that I've experienced over time is to think from the first principle approach. So at one day, it would be a problem from your head of strategy. Other day, it might be a problem from your head of marketing, head of CRO, head of executive manager. So RevOps manages different functions, right? The ideal way is to put yourself in the shoes and see what they are thinking.

**[1:34]** So a lot of times, RevOps works in consolidation, trying to eliminate silos in the business, right? But every function head, they have very specific, their own function KPIs. Everything in RevOps gives you a perspective of the whole business. So in that context, we try to step in their shoes to understand what they are looking for. Is there something even worth solving as of now, or just with some time it will be automatically resolved? Like in most, might be just a normal cold, do season change and might not even need some medicines over there. So that is the approach we have here, but yes, if something worthwhile, we do try to connect the dots, right?

## 02:22 — Peeling the onion: break a symptom into workflow steps

**[2:22]** So for example, your SDRs can come and say, my conversion is not happening great, right? And that can be a problem, a simple, let's go what's happening with your leads, right? But you have to break them into workflows and steps, for example, the very first tablet, the source of lead, is there any problem? Has the count gone down? Has the lead from good quality regions or good quality organizations have gone down? Was there any marketing activity that happened, which might have impacted in a negative way? So all those questions, then you see the data flow right in your CRM systems, because you're

**[3:10]** having that lead routing system, you have your lead scoring systems, then you try to understand are your SDRs pitching the right product? It might be that you launched a new product, right? So every start or every new age tech company is launching a lot of variation through the product, right? And at times it might be just a knowledge gap, you know, for example, you would have launched a new product and your team might not be well versed, but the lead that is coming might be coming through a marketing effort on the new product, right? So there might be a disconnect. So we try to have those dots attached, where is the gap? And then we try to benchmark.

**[3:50]** Is it something only happening with, you know, 20% of the folks, or is it something symptomatic across the board? So all those kinds of, you know, the why, the how, and the what, and the first principle, it's like a mix of breaking down the problems and understanding, not just from number systems, but incorporating process, people, and platform in the solutioning. No, that makes a ton of sense. And I think I really like the metaphor of RevOps being the general physician, where, you know, you may come into the doctor with a fever, and there could be a number of causes of what's causing that fever.

## 04:27 — The fever metaphor and a diagnostic punch list

**[4:27]** So it could be a viral infection, it could be bacterial infection, it could be, you know, a number of things that are going on. And I think that comes up a lot in RevOps. It's like you said, hey, we're having trouble converting this sequence, perhaps. Well, is it the messaging? Is it the tool? Is it the product? Is it the person? No. Is it the target that we're going after? So what process do you follow when somebody presents a problem like that? They bring up, hey, I'm having an issue. Do you have some form of like punch list that you go through to make sure you're covering all the bases? Just like a doctor would.

## 05:05 — The three-step diagnosis: listen, validate, triangulate

**[5:05]** They would ask like, well, are you feeling this? Are you feeling that? How, how do you go about that? So we would just be a doctor, like you mentioned, right? So if anyone is coming with a problem, so first of all, we'll try to let them talk. We don't want them to have a bias with our knowledge, right? Because RevOps has a very wide perspective. We might even know the problem beforehand, right? We might know that there's a flu breakout in the company and we know that this is going to happen. It's just that they might be coming with the same problem that we already know and trying to solve the background.

**[5:37]** So the first thing is to be a good listener, you know, make them comfortable, right? So your account executives or your other functions, right? If they go to see suit, they might have this thought process that they might be judged, right? They might be questioned if they try to help. They might not be that confident, right, that it might come back on them. But a RevOps is more like a middle layer, right? Between the C suit and the operations, if we give them the comfort, let's talk, what's the problem? So they might even give you insights, for example, which are not relevant to the function, right? Which might be indirectly impacting the pod.

**[6:17]** So, for example, an account executive comes to you and say, you know, I'm not just able to meet my quota. I feel we already know that they may not be able to meet the quota. And then we might even know the answer is a problem with your outbound funnel or inbound funnel or your availability or your win ratio has gone down, but we will let them speak to understand why they think they are not able to achieve the quota, right? This, first of all, gives a relationship building, right? Yeah. We have to be comfortable. But relationship building is important, right? Everything cannot be numbers when you're trying to run a business.

**[6:57]** There has to be human touch as well. So a lot of times they would validate our idea, what we are thinking in the background, and we would get extra ideas as well. For example, an account executive come in saying, I might not be able to meet my target disquarter because I think the outbound team aligned to my set of accounts might not be getting the right leads. So this somehow can validate what we are doing in the background. It's like a check for ourselves, right? As well, our systems are proactiveness working fine. So that is one step you let them stop. Second is you do a validation in the background as well.

**[7:44]** So if you see a lot of about folks, they are hiring entry-level analyst as well in the teams, right? To have a check on the data part. Data has grown a lot. And every week, there are a lot of rules of engagement coming into picture. There are lots of new territories being designed. Every quarter is a strategy. I'm talking more from the growth startup mindset, right? Not some organizations, which are like a startup, which has grown, got some investment. So step one, give them comfort, step two, validate in background, step three, talk to other stakeholders of the platform process of functions, right?

**[8:28]** Once you have done that, you know, triangulation of the problem, you would know actually where the problem is lying and it might not just be a problem with that particular E or AM or function. It might be, you know, across the board. That is the three steps that we ideally follow. I really like that you put listening, building empathy at the front of that. I think I've fallen into this trap too. I assume I already know everything that's going on. So when somebody brings a problem to me, I may jump to conclusions and I'm assuming that, you know, a lot of people in RevOps will end up doing the same thing.

## 09:06 — Empathy first — don't jump to conclusions

**[9:06]** Just because you have a lot of access to data, you've probably seen a lot of these problems before you're already looking for these things. But I think you really, really miss some of those details and nuances. If you don't take the time, just like a doctor, if you go see a doctor and they're rushing you through the process and they're trying to diagnose you quickly, but they're not really listening to the full story, they can absolutely miss a few things. Yeah, I just think that's so important in life in general, but I think in this context. And then I also like doing some of the legwork your own, okay, hop in, do your own research,

## 09:41 — Scaling the approach past 1,000 people

**[9:41]** come up with your own hypothesis of what might be going on. And then a big part of RevOps is always socializing and building alignment between stakeholders. So you have to have a special skill set to be able to do that too. Yeah, so that has helped us so far, but yeah, as we scale, you know, that might be a solution when you're a mid stage growth startup. But if you go to 1000 people company-wide, you might start losing on that empathy touch 1000, 2000 folks company. And then you would ideally would have to be more data-driven, more proactive. And that idea would be solved by hiring a larger RevOps function, more investment in

## 10:25 — Preventative care for RevOps

**[10:25]** tools and technologies and not changing of GTM every six months or three months. That would be a more stable street for the business. No, it makes a ton of sense. Yeah. Sticking with the physician metaphor, what measures do you take to do preventative care? So what is your version of diet, exercise, getting your annual checkup, your annual blood work? What does that look like to make sure you're preventing these things from popping up before somebody comes and brings it to you? Yeah. So that's a big problem to be solved and to an extent we are solving it as well, right? It might not be 100% but to an extent.

## 11:11 — Data hygiene: junk in, junk out

**[11:11]** First is to a lot of EIs happening these days, right? A lot of EIs happening. First is to adopt technology that is coming your way. So lately I started to see RevOps more of a productivity slash efficiency slash project management function as well. Where I don't want my time, my CRO's time going into the deities. So automation and technology specifically the advent of AI, which can understand a lot of context right before things happen. That is one thing. So on that case, what we are trying to do, we are trying to implement data hygiene practices. We are trying to make workflows bit restrictive, you know, everyone should not have the option

**[11:59]** to do everything right tightening those systems where the data actually originates from be it your billing, subscription, product data, your CRM, your lead gen tools, your creation of leaves in systems, all those we are trying to have a tight knit crap. Earlier when you start, everyone likes data, right? Unless it becomes a mess to be cleaned, right? So yeah, we are trying to have a first level of restriction or what flows into systems because simple metaphor, right? Junk in, junk out. You eat your presents and burger, you'll fall sick more, right? So that's one thing to it. Second is we're trying to have this LND perspective into picture.

## 12:45 — Learning & development: how the GTM machine works

**[12:45]** So a lot of companies, they do not invest in learning and development. When I say learning and development, it does not mean having learned about your product demo or your sales pitch, right? It's more about learning how the organization GTM works. How an outbound interacts with an inbound? How your ease and aims interact? How do you coordinate with your partnerships? But trying to have that understanding of business across the parts. So one was your technology and automation. Second is the learning and development. And third obviously is our by the key checks. So we have basically, you know, just to check the last 5% of leakage, right?

## 13:34 — Weekly checks and fixing leaks on the spot

**[13:34]** We have dedicated a person over there, got them reports, real time reports, up and running. We have defined the loopholes already that we may say may come still because even after all the automation and the learning and development and then quick fixes. Now there's another fourth aspect to it, no matter how much you do coverage, right? There will be gaps and the ideal solution is to fix them on the spot. So if you say, you know, I don't know why this lead got assigned as 9 out of 10 in waiting, it should only be 3. So we don't wait for next quarter or next month, fix things, right?

**[14:18]** Just in a startup, in a growth startup, in an active startup should not wait for a quarter, right? Every day counts. Yeah. Quarter is basically like a couple of years in startup timeframe. So for us, every quarter we have a fresh metric, existing metric, but the numbers get reset, right? It's always zero on your meter again. So you cannot wait a problem arising in April to be solved in July. It has to be solved in that very week of April, a number of machine power, manpower, technology. It has to be solved. So I'll give you an example why. So one of the largest customers happens to be an inbound lead just starting with hundreds

**[15:06]** of dollars and now they are one of the largest customers for us in just five years. So those kind of revenue leakages might not look bad at that time because you're meeting your quarterly quota, okay, I'm done, I'm set. But over the period, you might have an opportunity cost aligned to that, right? So we are trying to mitigate that as well. So yeah, automation and technology, learning and development, right? From the business sense, GTM perspective and your weekly and biweekly checks as well with the actual human person, because again, as I mentioned, right, a lot of this changing in DevOps, right?

**[15:47]** A lot of territory management, capacity planning, quota, leads, marketing, investors coming in and out. So things are moving very fast and like large organizations, right? So we do have a biweekly as in every 14 day we do a formal check as when everything is running fine. And if you ask me personally, I would spend time mostly on a Friday evening, you know, three hours in peace, just summarizing what's happening at the opportunity module, how the leads are behaving, what's happening in the Slack, that all gives you a gist how the business is happening. Is there too much sound on Slack? Something is broken.

## 16:26 — Coffee (or wine) with your data

**[16:26]** So you have to be a silent listener when even not talking to an actual person, but just monitoring your Slack send, you know, I miss Teams, that's one aspect to it. No, I like that a lot. And I think a lot of people don't appreciate that as much, or they may feel like that's not being productive. But I think it's really important that you have a cup of coffee with your data in the morning, or you have a glass of wine with your data in the evening. And you don't have a particular agenda. You're just going through things and seeing how the data looks, seeing how it feels, building some new reports, new ways of looking at the data you have access to.

## 17:06 — Leveraging AI: ICP and account scoring

**[17:06]** And every single time I do that, I find something that's interesting or worth actioning against. Totally. So one thing that I think we run into a lot is just how do we leverage AI for doing that particular job or helping to prevent issues? So you mentioned layering AI into your workflows. It can mean a lot of things, specifically what have you found successful and as specific as you can be. Are you using chat GPT in a certain way? Are you using some pre-built AI tools in certain ways? What's been most successful for you? Yeah. So we have been experimenting a lot with AI.

**[17:53]** Some in-house solutions, some third party solutions, and before I come to the solution, there's a problem to it. There are so many solutions out there that it takes up a lot of time without any ROI. Every solution out there will have a time cost and a dollar cost associated to it. So you have to be very mindful where you put your time and money, the only two assets in the world, time and money. According to my current organization at Lambattest, so we are trying to have AI in terms of a small case, what we have done is scoring your ICPs, your accounts, not the customers, but the prospects. I love that. We have a large set of data.

**[18:40]** We have hundreds of thousands of records in CRN. Now we want to make sure we have a large target. We want to make sure that our sales achieve the target and we cannot ask them to run in every direction. Being in strategy, we have to give them a direction. This is where you have to go. Now even for that, we have to have some knowledge with us. So AI is helping us in chat GPT basically open air in terms of knowledge, right? We can feed data to it and help it lead accounts that we should target based on a historic, what industries we have cracked, what regions we have cracked, what were the number of employees they had, right?

**[19:28]** And even scrap the websites and LinkedIn to an extent, right, to understand if they even have the right ICPs and roles in us for our company, right, for our product, do they have the right technology? Have they ever used any of the competitions previous, right? Like people do mention like we have used such extool in previous organizations, so we know that's a good fit for us, right? We're trying to not just rate or score our lead, lead can be a part of the higher account target, right? We're trying to rate the whole account itself. So that is one aspect of idea is to save time and money. I have a more have a dedicated outreach.

**[20:11]** Another way we are trying to use AI and is again, knowledge bank. So if you see a lot of times you would want to understand what's your time, what's the total market that you can sell into, right? But human capacity is limited, right? You cannot keep on doing research here every time. So we can use, for example, lean scale is there, right? So you might have another business. So if AI can provide us with that information, you know, that lean scale has other businesses as well with different names and different features. So that would give us a strategy. We crack into lean scale and then we crack into other markets, into other little companies,

## 20:51 — Clean data first: CRM hygiene as the P0

**[20:51]** that's one way. And then what I think the advent of AI is the most important as of now is to have your data in place. Yes. Yeah, you have to have clean data to run into work. Yes. You can't learn on bad data. Yeah. So we are trying to use AI for a lot of hygiene CRM, right? Finding flow in assignments. And then that's the P0 that we've taken as of now because AI projects take time and bandwidth. These two, three things that are targeting as of now and in future we have, you know, in mind for advanced analytics, you know, if you have something, you know, there are a lot of tools now in the market, you don't have them in-house tools like your clay, who

## 21:42 — Third-party tools and function-specific AI

**[21:42]** do a lot of pretty good research for you, what are fall enrichment, right? Earlier you were just dependent on one source of information. Now you can integrate 20 sources of information and decide which one works better, right? And then there are tools which can help you in terms of, you know, finding solutions, for example, dedicated to a specific function in your pod. So these are the tools not in-house, but in the market, what we're exploring, specific to solutions engineering, specific to customer success, health scorecards, advanced analytics. Because if you see in sales, there's another function apart from hunting, what we call

**[22:23]** account executive, there's a function called farming as well, right? Home managers and your core technical sales folks, which come basically post sales into adoption and all, right? They just do not rely on business data, they rely on consumption data as well. Are they going to chill for you? There will be any potential for expansion, right? Do you see that switching job and joining a new company? Do you see more people getting invited in the organization inside the product? So all this kind of insight, you know, so AI is having a wide, vast impact across functions.

**[22:58]** It's not just, you know, sales at high level, but at the very minute level, your SDR, your lead routing, your ICP scoring, your consumption data analytics, right? For upsell, cross-sell, preventing churn, downgrades, all those kind of things. So you've had such an extensive career and background in RevOps, I'm curious, a lot of our listeners, they're early in their career in RevOps, or they're navigating how to get to the next step. What led you to where you are today, and is there anything you attribute your success to? Yes, I think one skill or other two skills I would say, right? You have to be a people person, right?

## 23:00 — Career advice: be a people person, stay curious

**[23:37]** Even if you don't like it, you have to turn it on anyway, because they're going to work with a lot of sales folks, right? And they are highly extrovert, so you have to have good relationships with them. It's not like I mentioned in the starting of the session, right? It's not about data, it's about the getting the outcome for the business, right? Revenue operations has the very term revenue unit, right? So either through personal relations, process, people platform, you have to get that outcome. So you have to work a lot of, with a lot of senior folks, a lot of cost functionality, marketing sales, finance, investors, HR, everywhere, right?

**[24:17]** And then within sales you'll have to work with different functions. That's one thing, people skills. Second would be curiosity. So if you go to other functions, they are very knowledge focused, right? Finance. A person doing, you know, account receivable has mass treatment, right? But if you tell them go do an enable to start scratching their heads. Similarly, a person in marketing, right, who's doing ABM, they are great at ABM, right? But the moment you ask them go and start doing capacity planning, they'll again start scratching their head. Revops is a role of, I've been in Revops for almost two to three years, core Revops, since Revops originated.

**[25:03]** And I don't recall any topics being seen, to be honest. No, it's always different. It's very different every morning. You will have plenty of problems to solve. So you have to be a problem solver, a people person, understand a bit of tech stack. And that all basically comes with the mentality. You are curious and a doer attitude, you know. You cannot say, I do not know, I'll not do it. The problem is a new in the business. You go and learn, you talk to people, you do that work. You study, you explore, do demos, take demos, then implement. So I think you have to be curious, you have to be adaptable, and you have to be flexible

**[25:46]** along with those people's skills. So those are the core. And people should go into Revops, I would say rather. To an extent, it gives you that adrenaline rush as well. You need not be high on coffee or wine. You can be high on data and operations as well at times. So yeah, for people looking to go exponentially, learning about how business actually works, getting that pilot seat, that co-pilot seat along with the CRO is an amazing experience. And what all the, what do you say, effort and hardship, I mean the word hardship specifically, because it is tough, but you will enjoy it, but enjoys it. Yeah, it's absolutely character building.

**[26:29]** And I think it's not for the weak willed, but when you get to the other side of it, or as you continue to build your career, I absolutely agree. There's no better way to learn business in general. And get a little bit of everything, like you said, you've got to be a people person, you're going to have to do a lot of internal stakeholder management, you have to be really proficient with data, with systems or tools, your designing process, and you're solving problems, which I think is a ton of fun. Well, I really love how you laid this out. I appreciate the metaphor, you know, especially digging deeper to make sure you're not just

## 27:09 — Misdiagnosis in medicine — and in RevOps

**[27:09]** treating symptoms, but you're treating the root cause of what's going on. And I think sticking with that metaphor in the US, there's over 12 million cases that are misdiagnosed and about a third of them lead to further harm. And I'm thinking how much of that happens in RevOps too, where people aren't listening enough, people aren't digging deeper enough, you're hearing something on the surface, they try to solve it with a quick solution or bringing in a tool, but maybe it's more of a structure or process issue or a messaging issue, and not really giving the opportunity to solve what's at the core.

**[27:45]** So I think the process you laid out of, listen, do your own research, go around and work with other key stakeholders to get a holistic view of what's going on and then solve it. And then all of the preventative care that you talked about. Have a cup of coffee with your data, have a glass of wine with your data, try to figure out what's going on before people bring the problem to you, set up your AI, you're reporting your systems and make sure that you have everything in place to monitor what's going on. I think just like with your health, the best thing you can do is diet, exercise, get your

## 28:20 — Preventative care recap and close

**[28:20]** annual checkup and you should be doing that in RevOps with your data as well. Totally. Well, Shattak, thank you so much for being here. Thank you so much for taking the time. I really appreciate the conversation. I know our listeners will pick up a lot of insights that are really valuable. And as you go throughout your career and you continue to put more wins under your belt in RevOps, we'd love to have you back. It was a pleasure being on a session with you, Anthony. And thank you for the opportunity and it was a great conversation. Thank you. Thank you. Have a great day. Bye.