How to become one with technology and marry it with human capability

Rajesh Bhattad: 1800s, there are these firehouses in New York, fire engines are pulled by men, there is one pandemic, cholera comes, the city is wiped out. And they forced the, as necessity was the mother of invention, they put horses to pull these engines. 50 years later, the motor engine has already been invented. Now New York Firehouse is sending off its last five horses to replace them by motors, and the newspapers are flushed with headlines saying, this is impossible, the horses will get back, motor engine will dramatically fail. That never happened. People were skeptical of all of these innovations, but they moved on to live with it. I think the same thing, the same fate, RevOps and any GenAI consumer, either you could be threatened by what you do not really understand, or you poke holes into it, try to understand it, evolve with it.

Danny Wasserman: This is Reveal: The Revenue Intelligence Podcast, here to help go-to-market leaders do one thing, stop guessing. If you’re ready to unlock reality and reach your full potential, then this show is for you. I’m Danny Wasserman, coming to you from the Gong Studios. Howdy, howdy, howdy, welcome back to Reveal in the Gong Studios with Danny the Rev Wasserman with a really cool episode this week coming to a friend of mine, a RevOps brethren coming to us from India. So yes, we have a transcontinental transmission of brilliance coming from former head of RevOps Strategy and Solutions at RevSure AI. We have Rajesh Bhattad in the house.

What I love so much is that over the years, if not decades, that Rajesh has been in RevOps, what he’s telling us to do is to embrace the changes that are inevitable, that are bound to come, especially in all of the hype, in all of the buzz, in all of the mania that is AI right now. And while he’s telling us, rather than resist or reject all those changes, to embrace them, he is saying that you embrace them with balance. That especially in the case of AI, well, it’s neither stealing nor taking you away from work, nor is it absolutely allowing you to take your hands off the wheel. Really what he’s telling us is that facts from AI don’t solely drive decision making. More crucially in fact, is the complementary nature of storytellers who can connect the dots between the facts that AI is increasingly giving us access to with our ability to also determine how do we stitch all of these various components of the entire tapestry of truth together. Well, data and AI can boost efficiency, while it’s human involvement that will always play a role in the analogy he uses. I’m starting to steal, Rajesh, your thunder. But for him, technology and AI will always be the Robin to a seller or a RevOps Batman driving leading role. I’ve said too much. So with that said, it’s time for me to put a cork in it and leave you with this. DJ, spin that.

Danny Wasserman: Ladies and gents of Reveal, welcome back to this week’s episode, back in the Gong Studios. You’ve got Danny, the Rev Wasserman here, talking about all things Rev Intelligence. In the house, we’ve got a guy that was doing RevOps before RevOps was cool. In the house, we’ve got a guy that was doing GenAI before we even knew what the hell that was. In the house, I’m going to tell you, we have someone that has, yes, done RevOps, he’s done SalesOps, he’s done CS Ops, he’s done PartnerOps. And over that tenure of a career, well before we even knew the phrase, the rise of RevOps, this guy was recognized on LinkedIn as one of the top SalesOps voices in that ecosystem. Most recently having been at the helm as the head of Strategy and Solutions for RevSure, now an advisor and an actual facilitator for those disciples of his. We’ve got Rajesh Bhattad coming to us for this week’s episode. Rajesh, welcome to Reveal.

Rajesh Bhattad: Thanks, Danny. Excited to be here.

Danny Wasserman: Well, I am thrilled to pick your brain because as I alluded to in your introduction, you were doing RevOps before we gave a damn about RevOps, and now we can’t escape you guys. So I want to understand, in the journey that’s taken you to this point where people are seeking out your thought leadership, what were you doing? And then from there, I’d love to segue into what is the intersection between everything you’ve experienced to date and the tipping point probably in the last, we’ll say, maybe 18 to 36 months of GenAI being part and parcel with the profession. So let’s start with your background and then transition there.

Rajesh Bhattad: Absolutely, yeah. I started my career as a business analyst, moved into a sales operations role. And to be very honest, candid, this was all accidental. I did not design. It was all fate, it’s all divine calling, which pushed me into operations. So I moved into a SalesOps role at a media entertainment organization, and that was my first foray as a .NET engineer. Trained, I was a technical guy. And then I’m an accidental SalesOps guy, and suddenly I’m all numbers, all spreadsheets, and the first three months are pathetic to be very honest. And someone who’s never experienced numbers, does not understand EBITDA, does not understand what conversions, what forecast is. The first three months were tough, and then eventually at the end of four quarters, I appreciated the function. So much so that I was also the debtor’s guy for the organization. And my organization was going through a merger, and we in fact had a written-off debt of a multimillion-dollar written-off debt from the past merger. And one of the highlights of that point of my career, which also taught me, when you go outside of your day job, people appreciate, go beyond what is told. I was able to get $69,000 of written-off debts, and my vice president who I reported into, loved that so much, they sent me on a flight to meet our MD CEO saying, “You know what, here’s one of those guys who’s gone out of their jobs.” And that was also a flip in my career. They pushed me, they gave me more responsibilities.

So I eventually then moved from SalesOps at Media Entertainment to an organization which was telecommunications, and my original job was three-member team for four and a half thousand people vertical. I moved into a new organization, two-member team, entire two-member team for 800+ people, so suddenly the responsibilities are. That’s also my first experience with Salesforce, and then I start working on Salesforce. The ecosystem opens up, the possibilities open up. I start getting a little more technical, analytical, and then I foray into my first startup. This was a cloud communication startup, it’s a scratch to scale SalesOps, and it’s a one-member team for the entire 250-member startup. And it’s a scratch-to-scale, that really brings a lot of uncertainties to my work, but also pushed a lot of my boundaries. And I think SalesOps back then, there’s the point where I really become more technical, more forecasting. Until then, it is very transactional, a work comes to you, a number comes to you, a QBA comes to you, you’re reacting.

That’s the point I realized SalesOps is like a bellboy on the Titanic. It’s not my job to tell we hit the iceberg, it’s my job really to tell there’s an iceberg coming, here are two propositions, we go left, we go right, we avoid that. You become appreciative of the facts. Suddenly you feel that yes, you are a time teller in a data stream. You go back and forth. I mean, I don’t know if there are fans of Loki on your podcast hearing and you’ll appreciate how much of the destiny could be changed if you are appreciative of the numbers to influence your future.

So yes, I then moved from a cloud communications role to a PartnerOps, SalesOps, CS Ops, which is true RevOps now in a digital adoption. And my most recent stint was at an EIB startup, which is building for the RevOps for CROs, CMOs. It’s nearly a 20-year journey.

Danny Wasserman: So in the decades you’ve been doing this, you’ve seen a lot of what works well, you’ve seen also a lot of things that really suck that could take an organization. We’ll get to that. But one of the things I find fascinating about your journey is this toggling back and forth between learning the craft of operations, but having this inherent technical bend. And why I think about the tech bend that you bring in your pedigree to the equation is, we’re at this intersection where, okay, you cannot avoid the involvement of AI, specifically generative AI in anything we’re doing now, that is here to stay and fighting and resisting it is futile. So I’m wondering when you talk so much about, okay, we want to be predictive, we want to be proactive, I think you called it, you don’t want to just be this passive bellboy on the Titanic that reacts. But how do you harmonize what inherently cannot be singularly dictated or predicted by GenAI, but complimented by human intuition? Give us your hot take or your authoritative position on what is that balancing act.

Rajesh Bhattad: A couple of things. One, yes, the last 18 months have been super hot with GenAI. One quick check, one quick call-up that I want to make is most people tend to be driven by the markets and marketing. What I mean by that, like a couple of Dreamforce ago, go to any booth, pipeline was the hot take. Everyone had pipeline plastered on their booths. Why? Not everyone really influenced your pipeline, but pipeline is a lead magnet, people want to come to your booth, and they might not really be delivering pipeline. And the last Dreamforce and the latest one proved that one, almost every booth had GenAI written. And probably they don’t deliver on that one, but hey, you know what, marketing said so, it delivers. So what? And if you really peel off a lot of applications that build GenAI, they’re really if this, then that. That is the construct of the algorithm in the backend.

Yes, people could argue GenAI in the backend is a complicated if-else algorithm. But then it is very probabilistic, it learns from its own outcomes and takes that as an input and delivers. If I had to say 18 months of amalgamation, if I’ve learned a thing or two, how GenAI compliments and why some RevOps people are appreciative and some are very defensive, two things really stand out. One of them being is our solutions have, I mean the last few quarters, few years, there have been a platitude of solutions. Suddenly the GTM team feels overwhelmed. You know what, here is a CRM, go do these four things. Here’s a solution for forecasting, this is what you do. Here’s a sales engagement solution, do this. There have been a plethora of learnings, a lot of psychological studies done that every context switch cost affects your teams. The productivity goes down for us. In fact, there is a study that tells every time your, let’s say my rep basically is filling X, Y, Z on Salesforce and then they have to go to outreach to do X, Y, Z, there is this context switch cost. Of course, at that minuscule level you don’t really worry about, but when you start looking at compounding effects of that, good or bad, you start seeing there is so much time lost. I think this is where you start seeing GenAI’s finally plugging. In a way, if you see GenAI does two things really well, there is one, it improvises on what you’re doing, or it stitches the entire analytical layer which was not existing for you. Either you are becoming more efficient or it’s improving the quality. For example, a good example to take in here is my previous experience at RevSure.

One of the things you start seeing is that there is so much data and it’s all in silos. Let’s say a CMO wants to really understand this one single question, do I invest in Dreamforce this year? Let’s say it’s two months before Dreamforce and they’re taking the final call. Do I buy a booth? It’s a quarter of a million dollar booth. Do I do, do I not? What are the data points that have for you? At best you’d say, you know what, we actually generated 4, 000 leads last year, so we should do that. That means nothing really unless you’re able to tie to revenue. And we are starting to see a solution like RevSure for that where there are alternative solutions also, which are really tying up all data points saying, you know what, you did 4,000 leads, all good. But hey, none of them, hardly 10 percentage of them translated into late stage negotiation, or hardly five percentage of that translated into revenue. What you’re really trying to do now is, there is a UI layer of a GenAI solution where you ask, do I invest in inaudible this year or not?

And the solution goes through multiple data points to tell you, yes you should. You invested a thousand dollars and you got in $2, 000, it was 2x. Here are the team costs. Assuming you have that data point for the solution to pick in, for the AI to pick in, that is the outcome we’re trying to really go to. But I think most startups today struggle with the data itself. If I think one place where GenAI fails today is the absence of data, most startups really don’t have data. Either they have captured data in a very unstructured format that the AI cannot read into, or they have this data that it’s not available at all. If you never captured who you lost, your most significant deal, presume you have Gong for that matter and if Gong is capturing all that information, unless you have pushed that to CRM, which is where you probably have tied up all the information, that information is not going to come.

Danny Wasserman: When you talk about the biggest issue for most organizations with GenAI is they don’t have the data. Well, that is a symptom of another root cause, is that disparate siloed catalog of data sets, is that because we’re coming out of an era where everyone bought everything under the sun, which was also causing, as you described, the switching costs and the swivel chair effect that was causing a lack of productivity. Well, are we all coming out the other side of this glut in tech purchase and GenAI, which is promising to be more predictive? It actually can’t totally be unleashed until we resolve this other root cause. Is that a fair conclusion? Or do you have another hypothesis as to how we got to such a disparate set of data sources that aren’t talking to each other?

Rajesh Bhattad: That’s one of the reasons. You’re right on the fact that economic was flush with money, so people had money to spend too. Everyone wanted to be the first one out there irrespective of the cost it took, VC sporting money, there was flush money, everyone did that. Let me give you an example. One of my past experiences, this is a post-covid era, markets are flush with money and we are nearly a 10 million ERR organization. Surprise, surprise, we spent $800,000 on GTM Tech Stack. Makes no sense. At this point in time, it sounds ridiculous. And the same organization then goes ups and downs, struggles. And can you believe now? Now, the GTM Tech Stack as it stands, that I understand, is $50,000. So to validate the point you said, yes, there was this flush money, everyone sitting on so much money. Everyone said, you know what, go buy this tool, this brand new product. That is one.

The second system of course I think has got to do with the operations themselves. DevOps as a tribe, I take some of that blame as a tribe because it’s on us really to advise organizations saying what is important, what is not. Most organizations think RevOps, they think, you know what, start with the project, implement CRM. What do I need next? Implement PLG motion next. You’re working really project by project. That’s not how you typically would work. Two RevOps could foresee every single thing I do. Does it touch revenue? So much of that in fact, if a CRO or a founder has to tell me, can you introduce these two fields? Unless I cannot directly or indirectly correlate how that adds to revenue, I probably would not do that. I’d ask them to justify that. There’s one reason you saw, there’s this plethora of tools that came, then now that you have bought, you have a justification to do. So you forced your GTM teams, your customers really, to use them. And now we suddenly are in a macroeconomic situation where you’re forcing reps to cut down and justifying why they don’t require. The same people who said you needed it, are the same people who say why you don’t need it. It’s a funny situation to be, but it’s the reality.

Danny Wasserman: Is that an erosion of your credibility where your whip sign reps were 24 months ago, you were saying use this for this purpose and that for that purpose, and we all of a sudden find ourselves juggling 12 applications. 24 months goes by and you say, what the hell are you doing? You only need three things. Have you had to contend with reps looking at you, Rajesh, and be like, WTF?

Rajesh Bhattad: We did go through that situation, yes. To be very honest. Two outcomes, one of the reasons you see. Here is a fact. This is a fact. I’ve seen the RevOps team in India in this subcontinent, I’ve worked with RevOps leaders in the US, and one realization I have is most RevOps team still don’t have a budget of their own. I mean, RevOps is amazing. You want to sell to DevOps, all good. Most DevOps teams still carve out a budget either out of sales or marketing, which means a lot of decision making also rolls down from sales and marketing. Which means the authority that you thought RevOps has, probably they might not have as much authority. And I’ve gone through that phase where I had to buy a couple of solutions because my CRO was hell-bent. He said, “You know what, I need these two solutions,” no questions asked. And no matter my justification. I have those Slack screenshots I sent justifying why you don’t need this. And eight months in, he’s filed, the application’s out.

Danny Wasserman: Yeah.

Rajesh Bhattad: I use that very screenshot saying, I kept telling and it wouldn’t work. At some point you hit that wall, you hit that ceiling of decision making authority. So yes, I’ve been through that phase, but adding that to the very GenAI revolution that’s happening, in my mind, the best solution out there would be ones that are not visible to the GTM. They don’t have to see that. All they would care about is, will I hit my quota this quarter? Maybe it’s a chatbot. And I would say yes, or I’d say no, you don’t have pipe coverage. You are an EMEA SMB rep, you are heavily dependent on inbound. The inbound NQLs dropped 15 days ago, which based on the cycle means, in 45 days you step into a new quarter, your pipe coverage is going to go down by 2.2x, you’re not going to hit your quota. And then all the rep cares, how do we mitigate this? How do I hit my quota? Three things, go talk to the CMO, increase your NQLs because they’re inbound heavy. Try increasing your win rates, try increasing your conversion rates. Now, those are the possibilities that a good GenAI solution looks like. It’s going to shadow, it’s like the Robin to your Batman. It cannot be the entire end spectrum, it cannot take your attention from the day job that you’re doing, it has to complement, it has to compound what you’re doing.

Danny Wasserman: New tools can help salespeople, but human intuition and validation are still essential. Here’s something from Rajesh. AI is here and it’s influencing every aspect of business from data analysis to decision-making. Using AI to analyze data can greatly improve efficiency and quality, but it’s equally important to maintain balance with human intuition. A report from McKinsey supports Rajesh’s theory and found that 80% of the tasks that AI will automate in the future still require some degree of human oversight or intervention. AIs ongoing limitations are in comprehending and reacting to the nuance of human behavior and interaction, which will always be a factor alongside technology. There’s no foolproof replacement or panacea for human instinct. Let’s get back to Rajesh and hear a little bit more about how he advises us on striking that balance.

Danny Wasserman: Well, the analogy you talk about is the Batman and Robin, and inherently we have the superhero with a sidekick. And I appreciate that you are associating GenAI’s role in the equation not as the leading man or woman, but as the sidekick. So let’s presume, cool, we have moved past all of the siloed data issues, we have moved past all of the consternation left from a glut of tech stack that reps are pissed about and now we’re in what is, I think hopefully what we could describe as, the point at which the dust is finally beginning to settle. What are the points of friction that when GenAI, appropriately deemed Robin the sidekick, is implemented? What is pie in the sky for you, Rajesh, when GenAI actually does unlock some of those points of friction or those bottlenecks or those gateways that you, as you describe on your LinkedIn profile, are attempting to mitigate, you want to obliterate the friction. Give us the ideal scenario.

Rajesh Bhattad: Think of this. In operations, there are three pillars you’re trying to really build for in RevOps. In my opinion, this is how I look. There is one, the data infrastructure layer, which is the entire tech stack. You’re re-architecting the CRM, trying to make sure that we know why we lost, what product, one, what are the reasons, what pricing works, what were the margins, so on and so forth. This is also the place where you are capturing what’s the longest monologue of my best winning rep versus the worst. This is also the place where you’re trying to see what was the forecast versus actual achievement quarter on quarter. Is there a pattern emerging? That’s the data infrastructure really.

Then the second part is the process part, process excellence. Think of this way, in geopolitics, there’s a beautiful word, this phrase is called, no man’s land. What that really means is, between two countries there is a disputed territory, and hence because of the dispute, it’s never developed. And you will see no man’s land in a lot of the go-to-market motions. Take for example, an SDR at the end of the day is incentivized to create a qualified opportunity. And the SDR, let’s say I’m a prospect, and the SDR bombards me with reach outs, they wish me on my birthday, my anniversary, they say, you’re Japan trip was awesome, Rajesh, I loved Tokyo. They might never have been. And I’m like, okay, your solution makes sense, I would love to see a demo.

And then there is radio silence because hey, SDR was paid to create a qualified op, they created a qualified op. And there is a meeting seven days later, and the A only picks from the first meeting. You start seeing this is a no man’s land. This is also a drop-off between the first meeting scheduled by the SDR versus first meeting picked by the A. And you see that between SDR to A handshake, A to C handshake. So RevOps also solves for this entire process layer. And the third one of course is the analysis. This is where you’re trying to tell, how many MQLs do I require to hit a hundred wins in EMEA, enterprise, APAC, SMB, all of those, and this is where you start stitching, this is where I start seeing GenAI starting to play a role. One, of course, is who’s looking into all of these data?

To answer one simple question, today if I have to answer this question, if someone comes to me, Rajesh as an ops, tell me how many MQLs do I need? An America’s enterprise rep. Now, Rajesh has to go to Salesforce where all the data is. And probably I’m also putting all or some of this data on spreadsheets, see what a churn looks like, see what my inbound data is, make sure all the inbound that marketing told is actually seeping into my CRM, make sure the MQLs, they set RMQLs. Of course the MQL leads scores keeps changing, marketing keeps evolving. The definition of MQL, go figure, what’s the definition of MQLs in each of the cohorts that I’m using? Figure that, and then eventually get all this data in one place, put on a spreadsheet and then say, hey, you know what, it takes you, for every deal you close, you require like 17 MQLs. That’s an exercise in itself. Now, you start seeing a GenAI solution probably is a chat interface where I just say, in fact, I don’t have to say, the rep does not have to depend on RevOps. It’s a sensor. The rep just asks, how many MQLs do I need? And the solution tells, these many MQLs. All they have to care is how many MQLs, how was my conversion rate? Am I hitting? Bam, bam, bam. So a good GenAI solution will start bridging this data silos in itself.

Danny Wasserman: And when you describe the analysis that you just computed for that AE, none of that per se is rocket science. It is not as though that is pushing the boundaries of your quantitative competencies. It’s just that this is one of maybe hundreds if not thousands of sellers who have the exact same question and you as a single point of failure do not have the bandwidth to run a thousand unique analyses. The question then becomes, okay, if it’s not rocket science but the GenAI alternative to Rajesh being the sort of Wizard of Oz, this can do everything that you just described in a self-serve way. Does that then actually work you out of a job or does someone need to be pulling on the marionettes strings and that is now actually your new role in RevOps? It has less to do with seller facing, or I don’t know, business leader facing stuff. You’re purely tinkering with the models than the leaders in the seller self-serve. Is that the fate of your profession?

Rajesh Bhattad: Potentially, possibly. Let me tell you this one. One of the things I’m very sure and I keep telling people I meet in RevOps, if you are in the research, a lot of RevOps teams have these research folks on the team whose day job is to research the accounts. I’ve seen these teams extension of SDR teams also. Now, these people should be the most worried that they’re going to be out of jobs very soon. That’s the easiest piece of the pie. Like Salesforce launched, it’s AI copilot, they’re doing all of that. ZoomInfo naturally has their own scoops. It’s a natural extension of the job they’re doing. And the build desk teams, I’m really worried for the build desk teams. If you have a checklist to maintain, that’s easily replaceable, and a GenAI solution is going to maintain that checklist for you, nobody has to be. So you’re right in the way if whatever is repetitive, whatever was just an issue with the bandwidth, I cannot take it’s just a ticket, will be easily replaceable.

So what the outcome for me looks like, RevOps teams are going to get smaller and smaller. It cannot be a 10-member team, probably potentially it’s just a two-member team whose day job is one, yes, see the outcomes. One of the things I’ve seen with early models, data models, GenAI models is that the outcome of those models has been faulty because they tap into wrong data or maybe they’re looking at averages and get it wrong. Presumably it’s all on how you feed the data, maybe you’re not looking at very specific parameters. There are multiple, even in forecasting, there are multiple parameters. When did the rep log the deal? Did they move it timely? What are the stages it is going through? Hey, when did you last change your stages? And what is the forecast? What is the internal forecasting to the stage? Is that the actual forecast? Hey, what is the lead score that your demand gen thought and what is actual? There could be multiple parameters that someone has to still validate. There is going to be a long period of time until GenAI matures where someone has to validate that the models are correct, they’re looking at the right data. Even as it stands, yes, nobody’s trusting GenAI’s outcome. Hey, what’s my average deal score? How many MQLs? It’s not that the rep is going to go, boom, okay, 17 MQLs, all I care is 17. No, they’re still going to ping a RevOps person, can you validate? Can you give me the source? How did we arrive at this? So yes, so for the longest period of time, RevOps will have to babysit these models. You have to get technical, you have to hone. It’s a chicken and egg problem. You could feel you are getting extinct or you could live with it, see new possibilities, evolve with it.

Think of this, one of my good examples when I think about this is the firehouses in New York. 1800s, there are these firehouses in New York and the engine, the fire engines are pulled by men really. Practically, it’s the men pulling. There is one pandemic, cholera comes mid-1800s, the city’s wiped out. And they’re forced, as necessity was the mother of invention, they put horses to pull these engines. 50 years later, the motor engine has already been invented. Now, New York firehouses is sending off its last five horses to replace them by motors and the newspapers are flushed with headlines saying, this is impossible, the horses will get back, motor engine will dramatically fail. All of that. That never happened. Now, people were skeptical of all of these innovation, but they moved on to live with it. I think the same thing, the same fate awaits RevOps and any GenAI consumer, either you could be threatened by what you do not really understand or you poke holes into it, try to understand it, evolve with it.

Danny Wasserman: I think about your description of crisis being the mother of all innovation or invention, and either you evolve with it or it steamrolls you. And a real-life example for me, so the producers of this podcast are saying, “Danny, do you have a TikTok?” And I’m like, “No, no. I am in my thirties. And why the hell would I ever bother with TikTok?” And I catch myself saying to them like, what the hell am I doing? I got to get with the program. It’s here to stay whether I’m participating or not. So rather than reluctantly or stubbornly burying my head in the sand, I got to figure out how the hell I put this podcast and my own persona into the medium where the transactions are taking place. So I love your description about, if you as a RevOps participant right now are operating from a checklist, that’s going to be replaced inevitably. And that’s not meant to be a bully or a fearmonger, but the writing’s on the wall. So it’s either a shape-up or ship-out reckoning. And again, not trying to be cruel or condescending, but why not shape up and learn how to harness how you tune all these GenAI models so that you remain relevant. That is a choice that you can make. And you could also make the choice, well, I want to fiercely hold onto my checklist because that’s what I know and that’s what I’m comfortable with. And that seems to be, as you’ve pointed out, a certifiable mistake. Would you agree with that conclusion?

Rajesh Bhattad: Absolutely. I’m not sure if you’ve watched the Dune. Have you watched the movie, Dune?

Danny Wasserman: No.

Rajesh Bhattad: I read the books. And Dune, basically has the story, I’m not going to give this spoilers away, but there is a spoiler, so if you’re reading the third book, maybe not be here the next 20 seconds. Now, Dune has this monster worm in the deserts. The show, it’s a sci-fi. There’s this monster worm, and the desert tribes worship, they call it Shai-Hulud. It’s the monster worm, but it’s the god of the desert. Eventually, you figure that our protagonist, the hero, Leto, who has to change the destiny of the planet, he chooses to ride the worm, he chooses to become one with the worm. Now, people fear the worm for the longest period of time. And then he has this divine intervention, he realizes he has this, I mean, in a way he’s kind of a RevOps now that I think, because he has experience of all the past incarnations, so he taps into their learnings. Eventually, to come to this conclusion, I have to become one with the worm, so I become the demigod to change the destiny of the planet. So it’s in a way that way you could fear the worm, you could ride the worm, or you could adapt, become metamorphosis with the worm. You become the demigod.

Danny Wasserman: It is funny that you draw this parallel with Dune and becoming the worm, because I use this reference all the time in previous episodes, but it feels fitting to draw its association before we tap out for the day. An enablement leader, enablement is far less technical than RevOps. Typically, we do not come by those things as naturally as you do. He talks about, well, with AI, I can harness the power of that in the same way Iron Man harnesses the power of technology, becomes one with the technology. Or I can foolishly attempt to resist the machines much in the same way in the beginning in the movie, The Terminator, they feared it. And we all know what happened in The Terminator, we all know how we celebrate Iron Man. So I just love that you and I in our own respective movie analogies, we have roughly the same conclusion, which is we have to become one with it. That, I don’t know, resistance is futile, if we think about another Star Wars reference and the analogy there. So I like that. But looking at the clock, Rajesh, this has been awesome and thank you for illuminating for us what goes into RevOps. And for our RevOps listeners out there, I really hope you take away all of the sage advice from someone who’s been doing this for 20 years across sales, CS, partners, and so forth.

So if you’ve listened to the podcast before, Rajesh, you know the last question I’m going to ask, which is this: Well, if you could describe sales in just one word, what would it be?

Rajesh Bhattad: A storyteller who could live with the rejections day in and day out until they’d see this day of life. The reason I tell this simply because I stepped into the shoes of sales in the last year. I took my last stint, which was a little divergence from traditional DevOps, if you see go-to-market teams are my customers. And I had this revelation where I said to myself, it is one thing to hypothesize behind numbers, tell the sales teams this is what you should do. But then I also had this realization maybe the foot soldier who gets shot feels very differently. And that’s why I took the last divergent role where I was sitting with machine learning teams, designing these models, GenAI models, all of that, but I was also doing sales. That was a non-negotiable with the role that I took, and I realized two things that stood out to me. The product exists. There are competitors out there. What is the differentiator? One, of course, your product could be very massively different, but turns out the way you position it is all in the hands of sales. One of the examples I could give you from my previous experiences, one of the best sales reps I have worked with, I’m watching, surprisingly, I was watching this recording on Gong. I’m a new RevOps guy on the floor and I’m trying to learn what the company does, and I’m listening to his Gong call and seeing, and he says this, you know what, I think the lady’s name was Jessica, I still remember. Yeah. He said, “Jessica, whatever you’re seeing on the screen, it’s not me typing. You see, my hands are up,” he raises. It’s all robotic process automation, it’s RPA. The system’s doing that. And I see, this is how it’s differentiated. By then, I had already seen good 15, 20 videos, and this was the only one that stood out. Everyone was basically explaining, you know what, RPA basically means X, Y, Z, this is what goes into the technology. People are yawning, board. But this guy said, whatever you see, see my hands, they’re up. It’s magic, it’s RPA, it’s the system in action. So that’s the whole storytelling narrative. But storytelling as a word has been squeezed out, has been misused so much people probably feel no more magic when they associate storytelling with sales. But for me, having seen that, yes, it’s still a massive differentiator. And rejection, if you can live out day in and day out of rejection, it’s in it for you. That’s also my experience the last year, rejection is not for me, so I can never be a good sales guy. I’d probably be a good storyteller, but rejection plus storytelling is equal to sales. One of them, it’s not on our condition, it’s pure and conditioned.

Danny Wasserman: I love that answer and I relate it back to an enablement. We shop around a lot for different sales methodologies. There’s a host of whether you’re a Sandler shop or a challenger shop or a corporate vision shop, all of them, if you boil and distill them down to their bare essential components, are espousing the same crap. But what brings them to life, when you’re in a training where you’re arguably hearing the same things you’ve always heard over and over again because the shelf life of innovation has been compressed, and who is actually bringing any original thought leadership anymore that isn’t somehow recycled or repackaged from what they’ve heard before. I don’t fault people or begrudge them for doing that, but what elevates a methodology or what elevates a facilitator in training has everything to do with what you just described. How do they stitch together the story in a way that doesn’t force me to have to do backflips to understand where they’re at, doesn’t give me the inclination to want to poke holes or scrutinize everything they’re saying. But that is a craft that comes into our profession that is perhaps trivialized or overlooked. And your ability to simplify what is arguably a complex ecosystem equation into something that’s a delightful experience for me to ingest, like you described, I’m not even typing, I’m just waving my hands in front of you. That isn’t rocket science, but what brilliance underlined the simplicity of that tactic. So I love that, I don’t know, point of dismount in the episode, Rajesh.

Rajesh Bhattad: We all have a learning. What is Disneyland, or for that matter, Universal Studio raking billions and billions of dollars? In fact, it’s a known fact Disney does not make as much money from the movies as much from the parks. Parks are, I mean, that’s the positive in that PNL, movies are, in fact, most of them negative. Simply, it’s gamification in life. Anyone who understands hormones, human body hormones, anyone who gets Daniel Kahneman, Nobel Laureate who won prize for his behavioral psychology.

Danny Wasserman: Yes.

Rajesh Bhattad: Peak-end rule. People are not going to remember, even for the podcast, I probably feel most people probably might not remember the entire 60 minutes, but they’re going to remember two things. One was the peak moment, and the last few minutes that we talked. That is going to be the recency bias they’ll carry with. Yeah. So most salespeople are very good behavioral psychologists, I feel.

Danny Wasserman: I agree with that. Well, Rajesh, this has been a ton of fun. Thanks so much for joining the studios. Listeners, if you are hoping to learn from someone who has seen it all, be sure to subscribe to Rajesh who’s frequently posting on LinkedIn. As I mentioned before in the introduction, here’s someone who has been recognized by that forum as one of the top voices in SalesOps, Rajesh Bhattad. Thanks so much for joining Reveal.

Rajesh Bhattad: Thanks, Danny. I enjoyed our conversation.

Danny Wasserman: Thanks so much for listening to this episode of Reveal. If you want more resources on how revenue intelligence can help you create high performing sales teams, then head on over to Gong.io. And if you like what you heard, well give us that five-star review on Apple Podcasts, Spotify, or wherever you may listen.