---
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

_Shaadik of LambdaTest on treating RevOps like a general physician — root causes, not symptoms_

**Episode 38 · The LeanScale Podcast**  
Shaadik, Senior Manager, Revenue Operations, LambdaTest · Hosted by Anthony Enrico  
Published October 29, 2025 · Updated July 22, 2026 · 00:29:00  
Canonical: https://leanscale-knowledge-hub.netlify.app/podcast/shaadik-think-like-a-doctor/

**Topics:** Revenue Operations · AI in GTM · GTM Strategy · Demand Generation · Outbound & Sales Development


## Executive summary

Everyone in RevOps spends their day catching symptoms: a reporting request, a new-tool ask, something 'broken' in the systems. The trap is treating the symptom instead of the disease. In this episode, Anthony Enrico sits down with Shaadik, Senior Manager of Revenue Operations at LambdaTest — the cloud-based software-testing platform — to make the case that the best operators think like doctors. RevOps, Shaadik argues, is the business's 'general physician': a generalist with no single specialty who fields problems from strategy, marketing, sales, and the C-suite every day, and whose job is to peel back the layers of the onion until the real root cause shows up.

The heart of the conversation is a repeatable diagnostic method. Because RevOps sees the whole business, it's tempting to jump to a conclusion the moment someone describes a problem — and that's exactly how you misdiagnose. Shaadik's antidote is a three-step process: first give the person the comfort to talk (you learn things a biased, know-it-all posture would suppress), then validate the hypothesis quietly in the background against the data, then triangulate with other stakeholders across people, process, and platform. Only then do you know where the problem actually lives — and it's often not where it was reported. When an SDR says conversion is down, you don't fix the sequence; you decompose the funnel (lead source, volume, region and quality, marketing activity, routing, scoring, product pitch) and benchmark whether it's hitting 20% of reps or the whole board.

The back half turns to preventative care — the RevOps equivalent of diet, exercise, and an annual checkup. Shaadik runs four defenses: adopt automation and AI to keep leaders out of low-value work; invest in learning and development so the team actually understands how the GTM machine fits together; enforce strict data hygiene ('junk in, junk out') by restricting who can write to core systems; and run weekly/biweekly checks with real-time reports, fixing leaks on the spot rather than waiting for next quarter. Anthony adds his own ritual — spending unstructured time, a coffee (or a glass of wine) with your data — because it almost always surfaces something worth actioning. On AI specifically, Shaadik is pragmatic: point it at the highest-ROI, well-scoped jobs — ICP and account scoring across hundreds of thousands of CRM records, TAM research, and CRM cleanliness — but only on clean data, because you can't learn on bad data. The episode closes on the operator's real edge: being a genuine people person and staying relentlessly curious, because no two days in RevOps are ever the same.


## Key takeaways

1. **Treat RevOps like a general physician — cure the disease, not the symptom** — RevOps has no single specialty; it fields problems from every function every day. Like a doctor facing a fever with many possible causes, the operator's job is to dig past the presenting complaint to the underlying issue instead of patching what's visible.
   _Why it matters:_ Resist the reflex to satisfy the literal request (a report, a tool, a quick fix). Diagnose first — the reported problem is usually a symptom of something deeper in process, people, or platform.
   _For:_ RevOps Leaders, Revenue Executives

2. **Let the person talk first — your prior knowledge is a bias, not an answer** — Because RevOps sees the whole business, it often 'knows' the problem before the person finishes describing it. Shaadik deliberately lets people speak so they don't inherit his bias, which surfaces context and side-effects an expert posture would suppress.
   _Why it matters:_ Make people comfortable enough to over-share. The tangential detail they mention — 'irrelevant' to their function — is frequently the thread that leads to the real cause and builds the relationship you'll need to fix it.
   _For:_ RevOps Leaders

3. **Follow a three-step diagnosis: listen, validate, triangulate** — Step one, give comfort and let them talk. Step two, validate your hypothesis quietly against the data in the background. Step three, talk to other stakeholders across the platform, process, and functions. Only after triangulation do you know where the problem actually lives.
   _Why it matters:_ Codify the method so it survives you. It reliably reveals that a problem 'owned' by one AE or function is actually systemic — or vice versa — before you commit resources to the wrong fix.
   _For:_ RevOps Leaders, Revenue Executives

4. **Decompose the symptom into workflow steps, then benchmark the blast radius** — When conversion drops, break it into stages — lead source, volume, region and quality, marketing activity, routing, scoring, and whether reps are pitching the right (possibly newly launched) product — then ask whether it hits ~20% of reps or is symptomatic across the board.
   _Why it matters:_ This turns a vague complaint into a locatable defect and tells you whether it's a person, a process, a knowledge gap, or a systems problem — so the fix matches the cause.
   _For:_ RevOps Leaders, Sales Leaders, Marketing Leaders

5. **Solve in three dimensions — people, process, platform — not just numbers** — Shaadik repeatedly frames solutioning as a mix of people, process, and platform. Everything can't be numbers when you're running a business; relationship-building and human touch are part of the diagnosis and the cure.
   _Why it matters:_ A dashboard rarely fixes the problem alone. Pair the data with stakeholder alignment and process/system changes, or the same symptom returns next quarter.
   _For:_ RevOps Leaders, Revenue Executives

6. **Preventative care beats firefighting: automation, L&D, hygiene, and regular checks** — Shaadik's four defenses are adopting automation/AI to keep leaders out of low-value work, investing in learning and development so the team understands how GTM actually fits together, enforcing data hygiene, and running weekly/biweekly checks with real-time reports.
   _Why it matters:_ Build the annual-checkup equivalent into your operating cadence. Most fires are preventable if you monitor inputs and educate the team before problems reach your desk.
   _For:_ RevOps Leaders, Founders

7. **Guard the front door: junk in, junk out** — Everyone loves data until it becomes a mess to clean. Shaadik restricts who can write to core systems — CRM, billing, subscription, product, and lead-gen — so the data that originates there stays trustworthy. The simple metaphor: eat junk and you'll get sick more often.
   _Why it matters:_ Tighten permissions and validation at the point of data creation. Clean inputs are the precondition for every downstream decision — and for any AI you layer on top.
   _For:_ RevOps Leaders, Founders

8. **Fix leaks on the spot — in a startup, a quarter is a lifetime** — A lead mis-scored 9-out-of-10 when it should be a 3 shouldn't wait for next month. Numbers reset to zero every quarter, so a problem that appears in April must be solved that same week, not in July. Today's small inbound can become one of your largest accounts.
   _Why it matters:_ Empower a dedicated person and real-time reports to catch the last few percent of leakage and remediate immediately — the opportunity cost of waiting is invisible but real.
   _For:_ RevOps Leaders, Revenue Executives

9. **Spend unstructured time with your data** — Beyond formal checks, Anthony and Shaadik both carve out agenda-less time — a cup of coffee with your data in the morning, a glass of wine with it in the evening, three quiet hours on a Friday — just exploring reports and Slack signal to sense how the business feels.
   _Why it matters:_ Schedule it. Nearly every time, this open-ended review surfaces something interesting or worth actioning before anyone brings it to you as a problem.
   _For:_ RevOps Leaders, Revenue Executives

10. **Use AI where it pays now — scoring, research, and hygiene — but only on clean data** — LambdaTest's early AI wins are scoring whole accounts (not just leads) against historical win patterns, using AI as a knowledge bank for TAM research, and cleaning the CRM. Every solution has a time cost and a dollar cost, so Shaadik picks the highest-ROI, well-scoped jobs — and insists you can't learn on bad data.
   _Why it matters:_ Sequence AI: fix data first, then automate the highest-return, narrowly scoped tasks. Be ruthless about the only two assets you're spending — time and money.
   _For:_ RevOps Leaders, Founders, Revenue Executives

11. **The RevOps edge is a people person plus relentless curiosity** — Two skills carry a RevOps career: being a genuine people person (you work with extroverted sellers and senior cross-functional leaders all day) and curiosity paired with a doer attitude. No two days are the same, so you learn business faster here than almost anywhere.
   _Why it matters:_ Hire and develop for empathy and learning appetite over any single tool skill. It's character-building and not for the weak-willed, but it's the fastest seat for learning how a business really works.
   _For:_ RevOps Leaders, Founders


## Frameworks

### RevOps as the General Physician (00:46)

**Definition:** RevOps is the business's family-clinic generalist — no single specialty, but a stream of problems from every function daily. Its job is to diagnose root causes by stepping into each function's shoes, not to treat the presenting symptom.

Because RevOps works to eliminate silos and sees the whole business, it can gauge whether an issue is even worth solving now or will resolve on its own (a seasonal 'cold' that needs no medicine). When something is worthwhile, it connects the dots across functions to find the real cause.

### Peel-the-Onion First-Principles Diagnosis (02:22)

**Definition:** Take a reported symptom and break it into workflows and steps from first principles — for a conversion drop: lead source, count, region/quality, marketing activity, routing, scoring, and product pitch — then benchmark whether it's isolated (~20% of reps) or across the board.

The method separates a knowledge gap (reps not versed in a newly launched product the marketing generated leads for) from a systems issue (routing/scoring) from a targeting issue. It mixes the why, the how, and the what across people, process, and platform.

### The Three-Step Diagnosis (Listen, Validate, Triangulate) (05:05)

**Definition:** Step 1: give the person comfort and let them talk (avoid seeding your bias). Step 2: validate the hypothesis quietly against the data in the background. Step 3: talk to other stakeholders of the platform, process, and functions to triangulate where the problem truly lies.

Front-loading empathy prevents the classic RevOps failure of jumping to conclusions from prior pattern-matching. Triangulation reveals whether a problem attributed to one AE or function is actually systemic across the board.

### People, Process, Platform (03:50)

**Definition:** RevOps solutioning is a blend of people, process, and platform — never numbers alone. The revenue outcome can come through personal relationships, process, or systems, and usually a combination.

Shaadik returns to this triad throughout: relationship-building and human touch sit alongside data and systems in both diagnosis and remedy, because you cannot run a business on numbers alone.

### Preventative Care for RevOps (10:25)

**Definition:** Four defenses that stop problems before they surface: (1) automation and AI to keep leaders out of low-value work, (2) learning and development so the team understands how the GTM machine fits together, (3) data hygiene with restrictive write-access to core systems, and (4) weekly/biweekly checks with real-time reports and fix-on-the-spot remediation.

This is RevOps' version of diet, exercise, and an annual checkup. L&D here means understanding how outbound and inbound interact, how AEs and AMs coordinate, and how partnerships fit — not just product or pitch training.

### Coffee (or Wine) With Your Data (16:26)

**Definition:** A deliberate, agenda-less block of time spent exploring the data — the opportunity module, lead behavior, Slack signal — just to sense how the business is behaving, without a specific question to answer.

Shaadik spends about three quiet hours on Friday evenings; Anthony frames it as a coffee with your data in the morning or a glass of wine in the evening. Both say it nearly always surfaces something interesting or worth actioning before it becomes a reported problem.

### The RevOps Operator Skillset (People Person + Curiosity) (23:37)

**Definition:** The two skills that carry a RevOps career: being a genuine people person who can build relationships with extroverted sellers and senior cross-functional leaders, and curiosity paired with a doer attitude — because the problems are new every day.

Unlike specialists in finance or marketing who go deep on one craft, RevOps must be adaptable and flexible across capacity planning, quotas, territories, and tooling. It's a co-pilot seat next to the CRO and the fastest way to learn how a business actually works.


## Quotes

_Speakers inferred from an undiarized transcript — verify before attributing._

> "RevOps is more of a general physician — your family clinic. No specialty over there, but a lot of problems every day."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (00:46)

> "Everything in RevOps gives you a perspective of the whole business. So we try to step into their shoes to understand what they're looking for — is there even something worth solving as of now, or will it resolve on its own?"
>
> — Shaadik, The LeanScale Podcast Ep. 38 (01:34)

> "It's a mix of breaking down the problems and understanding — not just from number systems, but incorporating process, people, and platform in the solutioning."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (03:50)

> "I really like the metaphor of RevOps being the general physician. You may come into the doctor with a fever, and there could be a number of causes of what's causing that fever."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 38 (04:27)

> "First of all, we let them talk. We don't want them to have a bias with our knowledge — because RevOps has a very wide perspective, we might even know the problem beforehand."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (05:05)

> "Everything cannot be numbers when you're trying to run a business. There has to be a human touch as well."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (06:57)

> "Once you've done that triangulation of the problem, you know where the problem is actually lying — and it might not just be a problem with that particular AE or function. It might be across the board."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (08:28)

> "I assume I already know everything that's going on, so when somebody brings a problem to me, I may jump to conclusions — and you really miss some of the details and nuances if you don't take the time."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 38 (09:06)

> "Simple metaphor: junk in, junk out. You eat junk, you'll fall sick more."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (11:59)

> "In a growth startup you should not wait for a quarter. Every day counts. A problem arising in April has to be solved in that very week of April."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (14:18)

> "It's really important that you have a cup of coffee with your data in the morning, or a glass of wine with your data in the evening — and every single time I do that, I find something worth actioning against."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 38 (17:06)

> "We have hundreds of thousands of records in the CRM. We can't ask sales to run in every direction — being in strategy, we have to give them a direction."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (18:40)

> "The advent of AI — the most important thing right now is to have your data in place. You can't learn on bad data."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (20:51)

> "It's not about data — it's about getting the outcome for the business."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (23:37)

> "You need not be high on coffee or wine. You can be high on data and operations."
>
> — Shaadik, The LeanScale Podcast Ep. 38 (25:46)

> "In the US there are over 12 million cases that are misdiagnosed, and about a third of them lead to further harm. I'm thinking how much of that happens in RevOps too."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 38 (27:09)


## Practical advice by role

### RevOps Leaders

- When someone brings you a problem, let them talk before you diagnose — your wide perspective is a bias risk, not a shortcut. Give them the comfort to over-share.
- Run the three steps every time: give comfort and listen, validate your hypothesis quietly against the data, then triangulate with other stakeholders before committing a fix.
- Decompose every symptom into workflow steps (source, volume, quality, routing, scoring, pitch) and benchmark whether it's ~20% of reps or across the board.
- Build preventative care into your cadence: automation/AI, L&D on how the GTM machine works, strict data hygiene, and weekly/biweekly checks with fix-on-the-spot remediation.
- Schedule agenda-less time with your data — you'll almost always find something worth actioning before it's reported.

### Founders

- Tighten who can write to your core systems (CRM, billing, subscription, lead-gen) early — junk in, junk out compounds as you scale.
- Invest in learning and development beyond product and pitch: your team needs to understand how outbound, inbound, AEs, AMs, and partnerships actually interact.
- Recognize the empathy-driven, high-touch RevOps approach has a ceiling around 1,000-2,000 people, after which you must fund a larger, more data-driven function and stop changing GTM every three-to-six months.
- Sequence AI investment: fix data first, then fund the highest-ROI, narrowly scoped projects — every tool carries a time cost and a dollar cost.

### Sales Leaders

- Give RevOps the room to listen without judgment; the AE who feels safe explaining why they'll miss quota hands you the real diagnosis (bad leads, coverage, win-rate, funnel).
- Don't accept 'the sequence is broken' at face value — push for the decomposed funnel view before changing messaging or tools.
- Fix mis-scored or mis-routed leads the same week; a lead scored 9 that should be a 3 is quiet opportunity cost that a quarterly review will never catch.

### Marketing Leaders

- When conversion or lead quality drops, check whether a recent marketing push on a new product outran the reps' knowledge of it — a common, fixable disconnect.
- Treat lead source, region, and organization quality as first-class diagnostic inputs; a volume or quality shift upstream shows up as a sales symptom downstream.


## AI takeaways

**Thesis:** AI in RevOps is most useful for saving the only two assets that matter — time and money — but only on top of clean data and a clear direction. Point it at the highest-ROI, well-scoped jobs (account/ICP scoring, TAM research, CRM hygiene) and keep humans on empathy, judgment, and stakeholder trust.

- **Start with data in place** — 'You can't learn on bad data.' AI's first job at LambdaTest is CRM hygiene — finding flawed assignments and cleaning records — because everything downstream is junk-in, junk-out.
- **Score accounts, not just leads** — With hundreds of thousands of CRM records, AI (ChatGPT / OpenAI) rates whole accounts against the industries, regions, and firmographics you've historically won, and scrapes sites and LinkedIn for ICP, role, and tech-stack fit.
- **AI as a knowledge bank** — Use AI to size TAM and surface adjacent markets a human can't research at scale, turning research time and dollar cost into a targeting strategy.
- **Mind the time-and-dollar cost** — Every AI solution carries a time cost and a dollar cost, and there are too many options — be ruthless about where you spend, and make data hygiene the P0 because AI projects take time and bandwidth.
- **Function-specific tooling** — Beyond in-house builds, tools like Clay integrate ~20 enrichment sources, and point solutions increasingly target solutions engineering, CS health scoring, and consumption analytics for upsell, cross-sell, and churn prevention.

**Agent & automation ideas**

- An ICP/account-scoring agent that ranks the full CRM against historical win patterns (industry, region, headcount, prior competitor tech) and routes only high-fit accounts to sales.
- A CRM-hygiene agent that continuously flags mis-scored leads, bad routing, and assignment errors before they cause quarterly revenue leakage.
- A 'Friday-evening' monitoring agent that summarizes the opportunity module, lead behavior, and Slack signal so a RevOps leader can spot what's breaking with no set agenda.


## Operations takeaways

### Revenue operations

- **Diagnose, don't prescribe.** Treat root causes like a physician: listen, form a hypothesis, and validate before you build a report or buy a tool.
- **Three-step method.** Give the person comfort to talk, validate quietly in the background, then triangulate with stakeholders across people, process, and platform.
- **Preventative care.** Automation/AI + learning & development + data hygiene + weekly/biweekly checks keep problems from ever reaching your desk.
- **Guard the inputs.** Restrict who can write to core systems (CRM, billing, subscription, lead-gen). Junk in, junk out.
- **Fix on the spot.** Don't wait for next quarter — a mis-scored lead gets fixed the same week. Every day counts in a growth startup.
- **Time with your data.** Schedule agenda-less exploration of the data; you'll almost always find something worth actioning.

### Pipeline & marketing ops

- **Decompose the funnel.** When SDR conversion drops, break it into steps: lead source, volume, region/quality, marketing activity, routing, scoring, and product pitch.
- **Benchmark the blast radius.** Ask whether the issue hits ~20% of reps or is symptomatic across the board before deciding it's a person, process, or system problem.
- **Watch for knowledge gaps.** A new-product launch can create a disconnect between the marketing that generated a lead and reps not yet versed in that product.
- **Plug small leaks.** A lead mis-scored 9/10 instead of 3/10 is quiet opportunity cost; today's tiny inbound can become tomorrow's largest account.

### Customer operations

- **Farming needs consumption data.** Post-sale account managers and technical sellers rely on consumption data, not just business data, to spot expansion, churn, and downgrade risk.
- **AI across the lifecycle.** Point solutions increasingly cover CS health scoring and consumption analytics for upsell, cross-sell, and churn prevention.


## Metrics mentioned

| Value | Metric | Context |
| --- | --- | --- |
| 12M+ (≈1/3 cause further harm) | US misdiagnosed cases / year | Anthony's medical analogy for how often RevOps 'misdiagnoses' by treating symptoms instead of root causes. |
| hundreds of $ to top account in 5 years | Inbound land to largest customer | Why plugging revenue leakage matters — a tiny inbound lead compounded into one of LambdaTest's largest customers, an opportunity cost that quarterly-quota thinking hides. |
| hundreds of thousands | CRM records | The scale of prospect data that forces AI-driven account/ICP scoring rather than manual targeting. |
| every 14 days (biweekly) | Formal check cadence | The preventative 'checkup' cadence, complemented by real-time reports and fix-on-the-spot remediation. |
| ~3 hours, Friday evenings | Personal data review | Shaadik's quiet, agenda-less time reviewing the opportunity module, lead behavior, and Slack signal. |
| ~1,000-2,000 employees | Empathy-to-data crossover | The scale at which the high-touch, empathy-led approach must give way to a larger, more data-driven RevOps function. |
| ~20 sources vs. 1 | Enrichment sources via Clay | Modern enrichment integrates roughly 20 data sources instead of depending on a single source of truth. |


## Entities mentioned

- **LambdaTest** (company) — Shaadik's employer; he is Senior Manager of Revenue Operations at the cloud-based software-testing platform, running RevOps for a fast-scaling growth-stage business and its early AI experiments (ICP/account scoring, CRM hygiene). · https://leanscale-knowledge-hub.netlify.app/company/lambdatest/
- **LeanScale** (company) — Used by Shaadik as an illustrative example of AI-as-knowledge-bank for TAM research — that a company like LeanScale may have adjacent businesses/markets AI could surface to inform an account strategy. · https://leanscale-knowledge-hub.netlify.app/company/leanscale/
- **OpenAI** (company) — Named ('open air') as the provider behind the ChatGPT knowledge layer LambdaTest uses for account scoring and research. · https://leanscale-knowledge-hub.netlify.app/company/openai/
- **Shaadik** (person, guest) — Senior Manager of Revenue Operations at LambdaTest; treats RevOps as the 'general physician' of the business — root causes, not symptoms. · https://leanscale-knowledge-hub.netlify.app/guest/shaadik/
- **Anthony Enrico** (person, host) — Co-founder of LeanScale and host of The LeanScale Podcast. · https://leanscale-knowledge-hub.netlify.app/guest/anthony-enrico/
- **ChatGPT** (tool, AI Assistant) — Referenced ('chat GPT, basically OpenAI') as the knowledge layer LambdaTest feeds data to for scoring accounts against historical win patterns and for TAM research.
- **Clay** (tool, GTM Data / Enrichment) — Cited as a market tool that does strong research and enrichment — integrating ~20 sources of information instead of depending on a single source.


## FAQ

**Q: What does it mean to 'think like a doctor' in RevOps?**

A: It means treating RevOps as the business's general physician: fielding problems from every function, resisting the urge to jump to conclusions, and digging past the presenting symptom to the root cause before prescribing a fix. Just as a fever can have many causes, a 'broken sequence' can be a messaging, tool, product, targeting, routing, or knowledge problem — and quick patches that treat the symptom let the real issue persist.

**Q: How should RevOps diagnose a problem someone brings to them?**

A: Let the person talk first, without seeding them with your own assumptions — RevOps' wide perspective is a bias risk, not a shortcut. Make them comfortable enough to share context that seems irrelevant to their function, because that detail often points to the real cause. Then validate your hypothesis against the data and triangulate with other stakeholders before committing to a fix.

**Q: What is the three-step process for finding a root cause in RevOps?**

A: Step one, give the person the comfort to talk and listen without judgment. Step two, validate your hypothesis quietly in the background against the data. Step three, talk to other stakeholders across the platform, process, and functions to triangulate where the problem actually lives. Only after triangulation do you know whether it's isolated to one rep or systemic across the board.

**Q: How do you do 'preventative care' in RevOps?**

A: Run four defenses: adopt automation and AI to keep leaders out of low-value work; invest in learning and development so the team understands how outbound, inbound, AEs, AMs, and partnerships interact; enforce data hygiene by restricting who can write to core systems; and run weekly or biweekly checks with real-time reports, fixing any leakage on the spot rather than waiting for the next quarter.

**Q: Why does data hygiene matter so much for RevOps and AI?**

A: Because it's junk in, junk out. If bad data enters your CRM, billing, subscription, product, or lead-gen systems, every downstream report and decision inherits the error — and AI makes it worse, since you can't learn on bad data. The fix is to restrict who can create or edit data at the source and to make cleaning the CRM the first AI project, before advanced analytics.

**Q: Where is AI actually useful in RevOps today?**

A: In three high-ROI, well-scoped places: scoring whole accounts (not just leads) against the industries, regions, and firmographics you've historically won; using AI as a knowledge bank to size TAM and surface adjacent markets; and cleaning the CRM by finding flawed assignments. Every solution has a time cost and a dollar cost, so pick the highest-return jobs and run them on clean data.

**Q: What skills do you need to succeed in RevOps?**

A: Two above all: being a genuine people person, because you work with extroverted sellers and senior cross-functional leaders every day and the job is about outcomes, not just numbers; and curiosity paired with a doer attitude, because the problems are new every morning. Adaptability and flexibility across capacity planning, quotas, territories, and tooling round it out — RevOps is a fast, character-building way to learn how a business really works.


## Timeline

- **00:00** — Symptoms vs. root cause: the daily RevOps trap
- **00:46** — RevOps as the general physician
- **02:22** — Peeling the onion: break a symptom into workflow steps
- **04:27** — The fever metaphor and a diagnostic punch list
- **05:05** — The three-step diagnosis: listen, validate, triangulate
- **09:06** — Empathy first — don't jump to conclusions
- **09:41** — Scaling the approach past 1,000 people
- **10:25** — Preventative care for RevOps
- **11:11** — Data hygiene: junk in, junk out
- **12:45** — Learning & development: how the GTM machine works
- **13:34** — Weekly checks and fixing leaks on the spot
- **16:26** — Coffee (or wine) with your data
- **17:06** — Leveraging AI: ICP and account scoring
- **20:51** — Clean data first: CRM hygiene as the P0
- **21:42** — Third-party tools and function-specific AI
- **23:00** — Career advice: be a people person, stay curious
- **27:09** — Misdiagnosis in medicine — and in RevOps
- **28:20** — Preventative care recap and close


## Related episodes

- **Ep. 95: Why AI Means More RevOps Hires, Not Fewer** (Jimmy O'Halloran, VP GTM Strategy & Ops at New Relic) — The field-operator model of RevOps and the 'AI makes humans superhuman' thesis, expanded by a two-decade operator. · https://leanscale-knowledge-hub.netlify.app/podcast/jimmy-ohalloran-new-relic-revops-consumption-revenue/
- **Ep. 85: Why AI + GTM Engineers Can't Replace RevOps** (Tessa Whittaker) — The sibling argument — AI can execute a strategist's judgment but can't replace the diagnostic thinking at the center of this episode. · https://leanscale-knowledge-hub.netlify.app/podcast/tessa-whittaker-ai-gtm-engineers-revops/
- **Ep. 88: Why AI Won't Close Your Biggest Deals** (Michael Kiernan, CRO at Nextdoor) — A human-plus-agentic view of AI in GTM that pairs with Shaadik's 'people person + AI on clean data' stance. · https://leanscale-knowledge-hub.netlify.app/podcast/michael-kiernan-nextdoor-ai-wont-close-deals/
- **Ep. 15: Where Should RevOps Report?** (LeanScale) — Org-design companion to RevOps sitting as the 'middle layer' between the C-suite and operations. · https://leanscale-knowledge-hub.netlify.app/podcast/cameron-legge-where-revops-report/
- **Ep. 6: Why Your Forecast Is Broken** (LeanScale) — Foundational data-driven RevOps episode underneath the preventative-monitoring and leakage themes here. · https://leanscale-knowledge-hub.netlify.app/podcast/why-your-forecast-is-broken/


## Full transcript

_Machine-transcribed and not diarized; speaker attribution is inferred._  
_Transcript only, as a separate file: https://leanscale-knowledge-hub.netlify.app/podcast/shaadik-think-like-a-doctor/transcript.md_

### 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.


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