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Why the Top 20% of Your Sales Team Is About to Pull Even Further Ahead · Revenue Problem Solvers

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The person who is not a affluent, even if that person
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has 10% extra, you know, track record, that would, I would
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discount that. Welcome back everybody to the
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Revenue Problem Solvers Podcast, where we look to crack the code
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and growing your business by tackling the challenges to keep
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revenue leaders up at night. I'm Carl Owens and on today's
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episode we're going to dive into the topic of AI and revenue.
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Now today's episode is brought to you by the great team at
0:25
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They help B2B companies get found earlier in the buying
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The result? Fewer cold calls, better
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Visit virtualcauseway.com and don't forget to mention the show
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for a special rate now. My guest today is a Serial SAS
0:59
founder, a category builder, and someone who spent his entire
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career rethinking how companies actually drive revenue.
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My guest today is Sridhar Pedininini.
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Most people who build a category defining company move on and
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build something adjacent. Well, he did that twice and then
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decided the real problem was hiding somewhere else entirely.
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He Co founded Gain Site and served as its CTO for eight
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years, helping create the infrastructure behind what we
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now call customer success. And before that he Co founded
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Host Analytics which is now planful and helped reshape how
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finance teams plan and operate at scale.
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Two companies, 2 categories, built from the technical side by
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someone who understand the plumbing or anyone else did.
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Now here's what makes his current work interesting.
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After watching sales team struggle with enablement up
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close across multiple companies. He didn't just build another
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training platform, he built GTM Buddy around a different
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diagnosis entirely. Your reps don't have a content
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problem, they have a context problem.
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Which I love by the way. I'll come back to that another
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time. And in an AI driven world,
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solving that distinction might be the difference between
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growing your revenue and growing your headcount.
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Sweetheart, welcome to the podcast and thank you so much
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for joining us. Thank you so much for the kind
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words call. Great to be here.
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Awesome. I've been looking forward to
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this one because we had a great conversation to start.
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So in in for those of you, for those that maybe don't know you,
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can you give us a little bit of a context about your world?
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What what industries or verticals do you work in?
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What are the selling motions? What size companies do you work
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with? Great.
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Thanks again Call for the for the introduction.
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And I'm a serial entrepreneur and I'm currently building my
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third company GTM Buddy. We are a revenue activation
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platform that basically looks at how do you leverage your
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existing investments on enablement content and training
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and actually activate that in the reps workflow to elevate the
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reps performance and outcomes. That's what we do and we
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predominantly focus on tech vertical currently looking at
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broadening the into other verticals as well.
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And from a company size perspective, we are typically in
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mid market to lower end of the enterprise.
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That's our sweet spot. Nice.
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OK. And they would make sense
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because those size teams will typically have enablement, they
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will typically have larger, larger sales team and etcetera.
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So makes it makes total sense. So to kick us or to get us
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started and just as we, as I have asked everybody how we got
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today's topic as we had a really great chat, really great chat.
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And I asked him, I said, what's a successful revenue initiative
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you've either LED or an existing challenge you've either solved
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or currently solving? And we had a really fun
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conversation, which actually started a LinkedIn post, which
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was last week. And it became clear that it was
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the link between AI fluency and revenue capacity.
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So we could take this in a whole bunch of different ways, but
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maybe let's start here. The typical hiring reaction to
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scaling a number is just hire more reps, But is that the right
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reflex today, or should we be rethinking revenue capacity
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altogether? Great question, Carl.
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We in the in the leadership teams and when you're presented
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to the board, if I were to go to the board and say that I'm going
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to grow revenue by more, adding more headcount, how do we shut
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down? So growth at any cost is behind
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us. Now the new mantra a lot of
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companies is about capital efficient growth is the new one.
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So everybody's looking at it very, very closely.
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And at the heart of this in terms of when people OK, do more
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with less capital efficient growth, any which form you take
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it, keep not looking for breaking the, the trend that
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we're all familiar with. You know, there was, there's
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this triangle in, in product engineering world predominantly
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as you say that between quality, quantity and time of delivery,
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you need to pick any two of them.
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That was the traditional choice. Either I ship it at an
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acceptable quality, but it's going to take time, or I'll do
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it fast and I'll do a lot, build a lot, but quality might go for
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a toss. This was a choice that we are
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trained to make, but what's happening with AI is that now
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all of a sudden, to a person who is AI fluent, they're able to
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achieve all three. I can do a lot of great stuff
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and in terms of quantity do it quickly as well and at high
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quality. So that has fundamental
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implications for capacity. If you were to take that,
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translate that into revenue capacity, we're all trained to
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to think that OK, what's, what's the, the, how many deals can a
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Rep work on at a given point in time.
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Depending on my sales motion, I might say OK, reps capacity is
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about 20 concurrent deals. That's the current capacity.
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Now what if you're able to move that to 25, yet the Rep is not
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dropping balls and is able to do exceedingly better job compared
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to what he or she is used to doing.
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Now you're really talking about addressing that capacity problem
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at at the root of it. Oh, sorry, it's interesting
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because, well, the first, when you said it, like I immediately
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think of that there's AII don't know if you've seen a sign in a
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diner. It's like you can have it good,
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you can have it fast, you can have it cheap, but you can't
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have all three, right. It's sort of where you are, but
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there, there's, there's two different approaches or two
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different talk tracks. That almost seems like in this,
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this revenue capacity conversation, there's one of how
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much can the reps handle? And then there's also the second
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part of it of like a mathematical business direction
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of like what's our revenue per headcount and our revenue and
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like how do we actually get to the number by utilizing quotas
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and attainment and like you said, some of the deals as well
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as the number of leads. So it almost seems like we have
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two different talk tracks that we could potentially go down
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today. Yep, Yep, Yep, exactly.
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So let's let's build on, you know, on the revenue capacity.
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What exactly is capacity? It's amount of work, let let's
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just say amount units of work that's needed to produce a
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dollar of outcome. OK.
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So if I were to use that, OK, let's say that the dollar of
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outcome is we want all of our reps to hit their quotas and go
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beyond, let's call that as the outcome that we're looking for.
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Now hitting that would require a we get our reps to win more or
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win faster. If these two and I take away the
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the dredge walk that the time sinks if you will along the way,
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the productivity boosters. If I'm able to address these
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three, then I'm in a Golden State of addressing the capacity
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problem. Now, if you look at all of the,
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the, the, a lot of the software use cases that we come across, a
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lot of times we talk about the, the time savers.
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What is it that I, I was, I'm doing it manually that I can
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automate away. That's where we everybody gets
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started. There's nothing wrong in that
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time. That does give some time back.
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And the expectation is that reps are going to use that time to
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close more deals because that's time given to them.
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So that adds some part of the capacity, OK, Now when you start
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thinking about, OK, how do I get the reps to win more and winning
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more is adding to my capacity, I'm going to hit that company
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level revenue number with fewer reps.
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Now you're starting to think, OK, winning more requires
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multiple things to happen. That includes, OK, here's the
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Rep doing the right things at the right time in terms of
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engaging with the buyers. Is the Rep able to connect with
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with the buying team, able to multi thread efficiently, able
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to talk the the buyers language and connect with the buyer
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personnel son on on the buying committee and so on.
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Like so that's in your comments. You talked about what we have is
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a context problem and a Rep to do the right things at the right
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time as as he or she is pursuing the deal.
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How does how do we equip that Rep with the right context and
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contextualized information which helps the Rep to be more
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effective? This makes sense.
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It totally does because there's, I use the, I try to use this
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regularly, the content context. So I typically say marketing is
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content, sales is context and you have to bring the content,
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the context of that content to the conversation at the right
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time. And one of the things actually,
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when working with, with David Bloom, who's the founder of
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Level Jump, that was one of the things it was actually part of
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the thesis is as you're in the opportunity, it'll actually
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surface you relevant content for the contextual conversation.
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So, you know, a deal a, a war room, here's a customer story
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or, you know, here's a deck or whatever it is.
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As you change your stage, that information would be surfaced to
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you in the moment. So that was actually part of the
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original hypothesis. So I'm all for and I'm totally
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agree with like that content context conversation.
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But then comes in maybe the the there there's a there is also
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another question that you sort of alluded to or comment you
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alluded to earlier is like, you know, having the the removing
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the time wasters. So there's we're, we're in a bit
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of a trust recession around like is this information actually
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factual, right. So there is a little bit, there
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is some of that today where we're like, is it actually true,
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can I trust it etcetera. So there's still a little bit of
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human validation. I think that's happening.
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So as that context is served up to the Rep for them to share the
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content with the customer, are you seeing any pushback by teams
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saying, you know what, Sridhar, I don't know that I can trust
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that this is actually the right thing in the right time.
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How do you handle that conversation?
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Yeah. So I think that problem is if we
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were to compare same time last year, the problems
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hallucinations with AI is cooking up stuff.
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I think it has ready used to certain extent.
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So we're broadly we're on the the right part here.
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The extent of hallucinations have reduced that.
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That seems to be an objective fact in terms of the fact that
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as I see it experience it myself and what I hear from the other
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people as well and two human in the loop is something that's not
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going away anytime soon. If you're making mission
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critical decisions, then AI is is going to be your copilot.
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It can help you, but we would not want to give the total
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freedom to blindly trust AI. And so take it with a pinch of
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salt. It'll get you 90% there.
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If that mindset is that if in order to get to 90% I had to
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spend hours and hours of effort. But you can get to 90% very
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quickly if you know how to leverage AI the right way, but
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spend that extra 10% or 5% of of the effort to ensure that you're
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polishing it, applying the human intellect on top of it.
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Right. I, I really like that you said
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the human in loop, because I've used this analogy of the, the,
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you know, it's the I sandwich, you know, AI, the human tells AI
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what to do and then the human validates whether AI is correct
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or not. And I, I think that that is that
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that still is where we are because like you said, like can
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you really trust it? So it does bring up the question
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of like what impact does the human judgement have going
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forward? Is it more important or is it
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less important as these models and these AI platforms scale?
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Yeah. I think the question really is
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it kind of goes back to the conversion that we had a few
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days ago around the AI literacy. The question that that
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constantly battling with, I come across 2 spectrums of people.
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There's this constant skeptic who says that I don't trust AI,
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it's going to hallucinate. It gives me wrong answers all
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the time have given upon it. He can only do rudimentary
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tasks. And on the other extreme, I have
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this total believer in terms of like, OK, whatever, OK, I
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believe this is true because, you know, ChatGPT told me, told
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me so. So I'm maybe dramatizing, but
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not too much. People are at somewhere along
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this continuum, people who are skeptics, who refuse to believe
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what AI is capable of and that they don't believe it, they
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don't trust it. 2 total believers.
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The reality is somewhere in between and in order for you to
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leverage and get the value out of AI, how many people are
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really fluent in terms of how do I leverage AI to reduce
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hallucinations? How do I make it more grounded?
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What kind of context engineering should I know?
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What's the role of skills? What what's a plug in?
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What's happening with cloud versus open AI?
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That that debate, especially over the past few months, how
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many people are investing their time to understand, you know, we
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we all read in the newspapers about, you know, Co work
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released and globally, the stock markets crashed globally and we
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all talk about, you know, the the AGI coming in, AI taking
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over and all of that. We all worry about that.
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But how many people have really used Co work and try to build
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their point of view in terms of why is Co work different from a
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clock chat? That simple question which hit
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all the news headlines, how many people have invested time in
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that? Yeah.
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So those are questions that are on my mind.
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And I tried asking many people. And the reality is something
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like even a clock work I'm leaving or technical people who
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are using it for coding tasks. I'm not talking about that.
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I'm talking about business people, sales leaders, How many
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people have used Go Work for meaningful tasks?
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Probably that number is not more than maybe 10 percent, 20%.
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I I think I'm already stretching it.
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It's it I'm not sure that you are over stretching it because
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like I'm I'm a member of Pavilion and one of the courses
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that I actually just finished was AI and go to market and how
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that's changed and what's changed.
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And there is they helped. They actually showed a stat and
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said this time last year, the number one, let's just call it
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chatbot for the sake of simple conversation.
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The number one chatbot used was, was ChatGPT.
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The one that's made huge jumps is actually Gemini, which
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actually kind of aligns because how many companies are built
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their, their back end? They're using the Google suite,
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right, as an example, versus, you know, Microsoft is, has
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their own option as well. And then you have clawed and
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you're clawed and you have all these different options that are
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available now, perplexity, etcetera.
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And So what they showed though, and one of the things they took
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away from it is a year ago was Claude or it was anthropic or
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perplexity nearly as popular then than it is today.
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And I think the answer is no. And because Chachi PT was
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everything. And that was the, that was the
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term like if you want to say Velcro, right, it's not.
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Instead of saying hook and loop, you say, oh, it's just Velcro.
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But it is like that's the brand name.
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It's the Kleenex. Oh, I'll just put it into Chachi
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PT. So we're starting to see
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companies deploy and make their choice, which I also think is an
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interesting one that we're going to come to because as you're
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starting to determine and pick your AI copilot of sorts, maybe
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not Co pilot's the wrong word because that's Microsoft's your
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AI, your AI chatbot of sorts. There's a lot of, there's a lot
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of things that are happening in the background.
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In my opinion. I, I do think that we're going
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to start to see an AI officer and I think you're going to have
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to have that. But the question of, of the AI
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officer is what's the skill set? And I don't, and I haven't heard
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anybody say it yet, But the, the question of that is like, as an
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AI officer, is the skill set like a CTO?
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Is it more of a CIO to make sure it's, you know, connect and you
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know, it's, it's not bringing things?
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Is it more of a revenue leader? Is it the seat?
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Like who's the person that's looking at it and saying this is
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how AI is going to operate for us?
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Because some of the other tools and even going through that AI
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class, some of the my counterparts said, well, I can
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only use Gemini. I can't use the others.
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I'm locked out. So there's that.
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That's also one of the the challenges too.
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Oh. Of curiosity call.
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When did this course take place? I finished it like a month ago
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within a month. That's wow.
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Wow. So for those of us in the the
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frontier of AI, building AI products and and so on, the
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company that's truly making waves is Anthropic.
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No one else even comes close. Right.
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Yeah. And you're describing a world a
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lot of companies, I'm assuming that a lot, a number of them are
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leadership level people, VPS and above.
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Yeah. So if BPS are saying that, OK,
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I'm stuck with Gemini, that's the only thing that's allowed
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and or ChatGPT and not really looking at what's happening
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elsewhere, right, there is a problem that needs to be
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addressed. So let's go back to what we
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started off with. We talked about capacity
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problem. We are not doing AI for AI sake.
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We want to address the capacity problem.
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It's a objective fact that we don't have as much money as we
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had in the heydays of 2122. That's the the near universal
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truth for most companies out there right now.
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We have to do more with less. And there there is a possibility
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that AI can help us. Now we have a, it's not the
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content problem, the context problem like we talked about.
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Now within the context problem, if I want a Rep to do a
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fantastic job in terms of executing on the deals and win
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more than the Rep is becoming some level of a context engineer
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engineering information that's relevant for a prospect.
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So I'm not trying to say that Rep is going to become AI prompt
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engineer and all of that, but for the for the sake of
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discussion, what is context engineering?
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I'm able to leverage tools of at my choice and I input or ingest
21:12
all that I know about a particular deal.
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And I have the account formographics, technographics
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and people data that I have access to, all the call
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recordings that I have access to.
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And I have similar customers that I have sold to in the past,
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including the similar personals that I have sold to in the past.
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How do I process this information and engineer the
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deal to move forward? How, how do I look at creation
21:38
of a presentation for the next meeting that's rooted in what
21:42
was discussed in the last meeting, the objections that
21:47
were raised and a deck that does not look for it's, it's a deck
21:51
that was, you know, there's something called a slide remix
21:54
in enablement platforms, which is basically, you start with
21:57
three decks and pull some slides from different slide decks and
22:01
put together a deck and that's personalized for you.
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But in today's world, a context engineer can actually say, OK,
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here's what we discussed in the prior calls.
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Here is the information about my company.
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Now let's create a totally personalized deck for, for this
22:17
prospect. There's something that's
22:19
possible with, let's say the likes of any LLM Gemini can do
22:24
it. And in terms of building out the
22:25
slide outline, but you need to download all the call
22:27
transcripts and give it all the context it needs.
22:32
Then you ask it for creating a presentation outline.
22:35
It can do it for you, then it can potentially do a
22:38
presentation for you, or you can connect it to other tools.
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That's why you're talking about things like MCP starting to come
22:45
into play the moment you introduce OK, here are the hops
22:50
that you need to jump through to context engineer a deck creation
22:55
for a prospect. Very very few people are
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motivated to do it. I don't have time to do all
23:01
that, but an AI native Rep has a tool kit that has I'm not
23:09
selling my wares here GTM buddy here you could do it build it on
23:13
your own on your LLM of choice 1 needs to know what what is an
23:19
MCP? How can I connect systems and
23:23
these are these are not as difficult as they used to be
23:26
before. Right.
23:28
Yeah, I I agree. Yeah, Connect the systems, get
23:32
the data and then I apply my business sense, business human
23:37
judgement to create this artefact called a deck that I
23:41
want to share that's extremely personalized for this particular
23:44
prospect. I'm building a business case
23:47
based on the the the conversation that I've had with
23:49
my champion to enable my champion to sell internally.
23:53
Right. These are things that are
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possible and you systematize that in GTM money.
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But it's possible for you to build.
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It does require. Sorry, it makes, it makes total
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sense. And just because you said it and
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I just want to make sure that I, I say that the point accurately.
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So the, the trend, the, the trend from last year to this
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year. So Chachi PT dominated the
24:16
Google trends of the market share of of 90, call it 92% is a
24:21
round Number. And then in January of 2026, so
24:24
one year later, Chachi PT dropped to let's call it 60% for
24:29
a round Number. And Gemini in that same period
24:32
jumped from again round numbers 6% to 32%.
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Now Claude was .91%, so it was just under 1% and then it jumped
24:43
to 6%. So we're starting to see a a
24:45
difference of of who's using her.
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So that was just the market share in terms of and that, but
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that was in January and we're now in March, right?
24:52
Like by the time when we're recording this, I know it's
24:54
going to come out later, but in March, look at all the moves
24:57
that Antopics made. We started to see, we saw
25:00
commercials at the Super. I don't know if you watch the
25:02
Super Bowl, but there was a Super Bowl commercial as an
25:04
example, right? So you're starting to see that.
25:06
And then now you're hearing like Claude cowork and Claude chat
25:09
and you're hearing these in the progress, which I think is
25:11
great. But it does also go back to the
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question that I should ask you. He says you're talking about
25:17
that AI native Rep. And so like somebody like, you
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know, I'll say, I'll just use myself as an example.
25:23
I'm I like to learn and I want to try to stay on top of things,
25:28
but who inside the company now is going to teach that Rep how
25:33
to do exactly what you just said?
25:35
That's $1,000,000 question Carl, that's $1,000,000 question.
25:40
I was on some panels recently and also I just fairly large
25:46
audiences as part of, you know, an event that I was
25:49
participating in all in the last few weeks now, and it's really
25:59
the state of the union amongst the leadership is kind of
26:04
divided right now. We are giving tools to the
26:07
people and people are experimenting and they're coming
26:10
back and showing us what what they have come up with.
26:13
That experimentation phase is still on.
26:18
Now, if as a leader, am I experimenting myself to form a
26:23
point of view that answer, the answer to that is in a majority
26:28
of the cases, no. I'm dependent on second hand
26:33
information. It's almost like I am reading
26:37
about how to swim. I'm not diving into the pool.
26:40
As a leader, how do I learn to swim?
26:47
And the common stance is that we don't have the time.
26:50
We have the business to run. I don't have time to do this
26:55
stuff. I'm, I'm not saying it's not
26:57
correct, but given the extent of transformation that's happening
27:02
and the impact on our lives and on our organizations that
27:06
something has got together. But is that something where a
27:09
leadership goes on a retreat for maybe 2 days or three days and
27:15
does things? I don't know.
27:16
That's something that's doable, right?
27:20
So let's go back to, to one example, right?
27:23
You're talking about Pavilion, a very respected organization and
27:27
leadership. Yeah, we're talking about
27:29
ChatGPT at 60%, Gemini at 32% and Claude at 6%.
27:35
Totally believe that. And that was in January.
27:38
But I guarantee if we run those numbers now, I would be willing
27:41
to bet that ChatGPT has dropped under 40.
27:44
My opinion, I have no facts, but it's just the sort of gut feel
27:48
from people that I talked to. ChatGPT is sliding, Lot is
27:53
taking a jump and Gemini and all the stuff that they're doing
27:56
with nano banana and some of these other things that they're
27:58
doing. I, I think it might, I might be
28:00
holding and perplexity continuing to gain some momentum
28:03
as well. It's just a gut feel.
28:05
I can't, I don't have any hard numbers on it.
28:07
And this is in the moment. But I think that there's, but
28:10
that was like one of the, in the context behind why they were
28:13
saying this is they just wanted to show the evolution of
28:17
platforms within 12 months. And then you start to see what's
28:21
happened later. And I think one of like one of
28:23
the challenges that we're talking about is we did not get
28:28
a playbook for how to use AI because that playbook that is
28:33
it's happening in front of all of our eyes.
28:35
And so as all of us are learning together, all of us are trying
28:38
to figure things out. There's all these different
28:40
tools and which tool? Oh, there's this one.
28:42
If you did you know about that one?
28:43
No, I didn't. Oh, there's all these other
28:44
ones. So I can see that it's a bit of
28:47
a dizzying headspace right now to try to stay on top of it.
28:50
But I also think that there should be a point inside of all
28:54
companies that the executives or or the leaders or whoever, there
28:59
should be something to say. What have you built this quarter
29:03
that is leveraging AI for you to stay on top of it?
29:07
And it could be part of your, you know, what success looks
29:11
like or it could be part of your quarterly bone or whatever.
29:14
But I do agree with you that there should be something that's
29:17
built in because if you don't ever pay attention and if you
29:20
don't make the time, you'll never make the time and you'll
29:24
tell your teams to do it. But it's just like, as, as it
29:29
was said to me is like, if you're going to teach people how
29:31
to cartwheel, I need to know that you know how to cartwheel.
29:34
Exactly. Exactly.
29:37
Right. So I, I think that there's a bit
29:39
of a challenger and and bringing it all the way back to the top,
29:41
the very part of this topic of the around the Rep, the revenue
29:44
side of things. So if I'm a Rep, I'm going to
29:48
experiment myself and I'm going to figure things out.
29:50
And those reps are the ones that are probably part caving or
29:54
paving, I should say, paving their own path for whatever the
29:58
next World looks like. Maybe they become a serial
30:00
entrepreneur like yourself and they build a company or
30:03
whatever, but they're going to find other ways, but they have
30:05
to learn it. And then there's the question
30:08
underneath of that though, is like, OK, great.
30:10
They figure something out. Who's who's the one that has
30:13
ownership of it or are we just allowing is the mandate on maybe
30:19
not the mandate, but is the expectation for any new Rep
30:22
joining company today to say you're going to be experimenting
30:26
and you're going to tell me what you found?
30:28
Yep, Yep. So the we we're actively looking
30:33
for hiring reps right now. And one of the first questions
30:37
that I asked when I get on a call, how AI native are you
30:42
don't may not exactly ask that, but what tools are you using?
30:45
Can you describe your, your tool usage or how is it helping you?
30:50
Can you give me realistic anecdotes of how how you
30:54
leverage GI personally, professionally, whatever the
30:57
case may be in terms of are you pushing the envelope on that?
31:02
And a lot of us people are taking ownership of their
31:04
personal learning and growth and they're and organizations to be
31:09
fair or giving the budget in terms of buying the tools.
31:12
A lot of companies do purchase some tools.
31:14
Like it's really hard to find a company that does not give
31:17
access to any AI tools to their everyone.
31:20
Everybody's doing that. The question is giving it's
31:22
structure. That's where the problem.
31:25
Also, you know, a person in the front lines, very enthusiastic,
31:30
young, you know, energetic, want to learn, build a career is
31:34
investing time to learn AI. But that person does not
31:37
necessarily have the, the context the that leadership has
31:42
on what moves the needle, what initiatives to prioritize on
31:46
versus what does not know. So wait, how do you translate
31:51
that wisdom that exists in the in the heads of the people on
31:54
the leadership team in terms of informing the frontline
31:58
initiatives that are taking place?
32:00
How do you build that bridge? That is the question because I,
32:04
I like to say you can't, you can't buy experience.
32:07
You just have to, you have to live it.
32:09
And sometimes you can buy experience by bringing people
32:11
in. But then it brings actually the
32:13
question that there's a, there's a question.
32:14
So if the expectation and, and I, I'm, I'm going to ask it from
32:19
a question of curiosity and not a question of like, it's, I'm
32:23
not asking it from a, a pointed question.
32:24
So please, please, everything I'm about to say is if I'm a Rep
32:29
interviewing and I get another call with you and and you say,
32:33
Hey, you know, what are you doing from an AI perspective?
32:35
And I say, well, you know what street RI run AI run a podcast
32:39
and I use opus clips and it's helping me do this.
32:41
And I use Claude and I use this. I use it for these different
32:44
things. And then I'm also building, you
32:46
know, my slide decks using Gemini.
32:48
There's some great things I'm using.
32:50
Well, whatever I'm I say a whole bunch of things to you.
32:54
Does my value as a Rep increase? And because now I have a a level
33:01
of knowledge or sophistication that maybe some of the other
33:04
reps don't have. And is there a correlation
33:07
between AI fluency and compensation going to a new
33:11
company? Absolutely.
33:13
So clearly we're not saying that what we look for in
33:17
traditionally in a Rep that's not going away.
33:20
The basics of how, how the selling skills, the track record
33:23
of success and all that continues to be important,
33:27
right? We added one more layer of
33:29
filtering in terms of AI. So given two people who have a,
33:35
a strong track record of success over the years and one is AI
33:41
fluent, the other isn't then the, the person who is not AI
33:47
fluent, even if that person has 10% extra, you know, track
33:52
record, that would, I would discount that.
33:55
The primary reason for that call is that if one is selling GTM,
33:59
but it's platform today, we are selling into the AI
34:03
transformation budgets at companies.
34:07
And I'm not selling a content management system, I'm not
34:10
selling a learning management system.
34:12
Yes, we do have a CMS. We do have an LMS, but that's not the
34:15
core value proposition. The, the value in a content
34:18
library is like long gone. You need a CMS, you need an LMS,
34:22
fine. But what you do with it in terms
34:26
of equipping your revenue team to drive the outcomes that you
34:31
want, that that's the vision that we're selling, that also
34:34
the capabilities that we're selling and those capabilities
34:38
leverage the likes of, you know, an MCP.
34:41
An MCP is nothing but OK, you want to integrate your data
34:45
together from different systems. And you know what, MCP is the
34:49
new standard that's out there and layer in the fact that we
34:54
are both an MCP host and an MCP client.
34:56
What it really means is that you can connect GTM body to other
34:59
systems and we can pull in data from other systems.
35:03
We can assimilate all of that information to build a business
35:06
case, build a, a, a presentation that a Rep can produce
35:12
automatically by clicking a button.
35:14
And it is contextualizing all that information for you, you
35:19
know, and if the prospect asks you a question in terms of how
35:22
you, how are you doing it, then you start talking about, OK, we
35:25
do have skills that we have purpose built for this.
35:29
And skills is a is a emerging standard as of December of 2025,
35:34
that's all of three months ago. And all of that is product ties
35:38
into GTM body platform. You know what this is what I
35:40
would say. I'm not selling the the AI base
35:44
here, but in today's world, if you want to contextualize the
35:47
information at scale across the 150 reps that I have without
35:52
expecting everybody to become, you know, fluent AI engineers,
35:58
then I need to have a system, a fabric that stitches things
36:02
together. That's GTM money.
36:04
Now, if I want a Rep to sell that vision, a Rep should know
36:10
what all of these things stand for.
36:12
What's the older, way earlier way of doing things and what's
36:16
the new way of doing things? Right.
36:20
And I think there's an underlying content, an
36:25
underlying theme is if you have those two reps and somebody is
36:30
more AI fluent, what it tells me is that they're curious.
36:33
They have a higher level of curiosity than others, right?
36:37
Because to me, it's one of the thing.
36:39
And actually Mark Roberts wrote in the sales acceleration
36:42
formula and everything, like curiosity became one of his core
36:46
and the core themes of am I going to hire you or not?
36:49
Because if you can learn and if you're curious, you will do
36:52
great inside of our company. And when you're going to talk to
36:55
those AI leaders, I have to think the curiosity of like,
36:58
what did you do? Hey, did you know that I used, I
37:01
used raplet and I built this thing and it did this thing.
37:03
Oh, no, I didn't. Hey, actually I built a whole
37:06
thing using Manus and my workflow.
37:07
I did this. Oh, interesting.
37:09
Like there's a curiosity, but there's also a lessons learned
37:12
because, you know, we got to continue.
37:14
We have more today than ever. If you're not learning, you are
37:20
falling behind so fast. Cheers to that.
37:23
Hey it's interesting, I use exact same framing on a LinkedIn
37:28
post so a few days ago. You can teach AI, you can teach
37:32
a lot of different things, but you can't teach curiosity.
37:36
Yeah, and actually John Barrows was on my my podcast.
37:39
He was last year and we had a really great chat.
37:42
And that was one of the things is like there's a difference
37:45
because he goes, he goes, Carl, just you and I then it was it
37:48
was fun. But if if you were to go back
37:50
and listen to what he goes, but there's a business acumen that
37:52
you have and I have. And there's also like, well,
37:54
what does that mean? And is this really the truth?
37:57
Like there's a genuine curiosity to pressure test whether it's
38:00
correct or not. So that's going to bring me to a
38:04
question that I'm, I've sort of been hearing in my ears as we've
38:08
been talking, but it's this, it's like everything you're,
38:11
you're talking about of like connecting these, the MCP
38:13
servers and you know how I'm able to orchestrate this
38:16
information and populate it and like all in the same time.
38:19
So is AI going to make average reps great or does it make great
38:25
reps unstoppable? Or is there somewhere in
38:28
between? I think great reps unstoppable
38:32
is is a given and in average Rep better is definitely a
38:41
possibility. Because one of the things that
38:43
I'm sort of thinking about is as we're talking about is like,
38:46
well, if the best AI we or the best Rep we have is utilizing
38:50
the certain workflow. And if I'm an underperforming
38:53
Rep and I just say, you know what, I'm going to copy the
38:55
workflow of this person and it's all automagically, I'll say that
38:59
automagically going to make me a top performing Rep.
39:03
Is that something that I should be thinking is, is that the way
39:06
that I should be thinking? Or should I be asking the
39:08
question to those reps saying like, what is it that you're
39:11
doing that I'm not? Yeah.
39:13
So I think my answer was not really the answer that most
39:18
people want to hear. But the question that I have is
39:21
why is somebody an average Rep? Is the person average because of
39:26
lack of curiosity, lack of drive, of initiative?
39:29
Though if the person is average because of those reasons, giving
39:33
AI will help the person to do more stuff and it is possible,
39:39
very much possible that the Rep will produce more.
39:43
But is that going to be persistent?
39:45
Like is somebody average because of this behavioural traits?
39:51
By giving AI to them, yes they could produce incrementally
39:54
better results. But during that time, the people
39:57
who have the right traits of being good.
40:00
Or diligent about their work and are also curious the gap is that
40:04
going to to reduce. So we we're thinking about, OK,
40:08
an average person is learning, leveraging AI growing, but the
40:11
reality is the great person is also leveraging AI and is that
40:16
delta going to get bigger? The gap is going to get bigger
40:19
or smaller. That's anybody's guess.
40:22
My guess is that the gap is going to get wider.
40:26
It's a great trip. Is likely to be using AI far
40:28
more fluently compared to an average.
40:31
Right. Because they're going to be
40:32
curious. And so now as we're going
40:35
through that and actually candidly, this is one of the
40:38
questions I, I, I try to give planted questions and I
40:41
everything's curious after that. Like it's just me being in the
40:44
moment and being curious of the talk track.
40:47
But as you just as we're here in the conversation, there's a
40:51
question that I asked you at the beginning and I think is
40:53
relevant now. And we talked about like raising
40:55
revenue capacity per Rep. So if the AI today is going to
41:00
deliver on raising that revenue capacity per Rep, what's
41:04
actually going, what now breaks? Is it, how do we hire?
41:07
Is it how do we compensate or how do we structure teams?
41:13
Can you come again on the? Yeah.
41:15
So if AI is going to deliver on everything we just said, it's
41:18
going to level up. We got curiosity, the reps are
41:20
going to find a system. They've done all these things.
41:23
Now we're seeing them being incredibly successful, our
41:26
revenue per headcount and our how did you say to the
41:30
beginning, I think you said the efficient capital growth.
41:33
So now we're we're moving away from headcount growth to
41:36
efficient capital growth. But now I'm seeing these reps
41:40
crush it. So for forsake of conversation,
41:42
every one of my reps are at 180% of quota plus well, now I'm
41:47
paying out higher Commission checks.
41:48
So if AI is going to deliver on everything we talked about, what
41:53
now breaks? Is it how we hire, we got to be
41:56
faster, slower? Is it, how do we compensate well
41:59
we have to change Commission plans because they're actually
42:01
using other tools or is it, how do we structure the teams where
42:05
maybe they're SMB mid market enterprise or they have a bigger
42:08
patch or they have more, they have a broader vertical or maybe
42:12
they do multiple like what is it that breaks when we start
42:15
thinking about revenue capacity per Rep?
42:17
I think the compensation will be the the first one to be
42:20
revisited primarily because of Commission checks of the AI
42:25
native sellers. If it's not already diverging
42:29
from the rest of the the the pack, they will start diverging
42:34
a very, very significant level. And if you start seeing that in
42:38
all the Commission plans that have been created for in a
42:41
previous year of where people are not leveraging AI, then
42:45
that's probably going to be revisited.
42:47
First, that it's a hunch. I don't have any data to to back
42:53
this up because let's, let's get one thing to just make sure that
43:00
I'm coming across the right way. AI is not doing the selling, but
43:04
people who are leveraging the AI can do a lot more than what they
43:09
are used, they were used to doing and produce much better
43:13
outcomes. That's the scenario.
43:14
That I agreed. I agree.
43:17
Now, in that scenario, there's a pack that starts separating away
43:20
from the rest of the group. For a while, organizations will
43:23
be cool with it because they're producing.
43:26
But when that pack starts getting larger, others are
43:29
looking at them and the air proficiency inches up, the pack
43:35
becomes larger, then your Commission checks are going to
43:38
get larger. That's a hypothesis at this
43:42
point. Any seller listening to this?
43:45
I hope that's the case. Let's just say it that way.
43:47
I hope your Commission checks grow.
43:49
Yep, Yep, I'll be happy with all of my reps commissions getting
43:53
fatter by the day. We all want that problem.
43:57
That's a good problem to have. That's.
43:58
Right. Yep, Yep.
43:59
That's right. And that automatically is going
44:02
to have an impact in terms of the way we hire.
44:06
So I'm, I'm, I'm going to, am I going to say, OK, we're growing
44:10
that you said 180% of the quota. Now the question becomes like,
44:14
am I reducing the Commission checks?
44:15
So am I increasing the quota because of the tools and all of
44:18
that, That question will come. Now if I'm increasing my quota,
44:21
for example, for the sake of this question, then I'm going to
44:24
hire more people to hit the this company level revenue number
44:28
that's interrelated decision to be made, right?
44:33
And in the way in terms of a structure, the all, am I going
44:36
to have like a lot of companies or really talking about AI
44:39
employees? There's a lot of hype right now.
44:43
But if you're saying that OK, 30 percent, 40% of my jobs to be
44:47
done can be done by AI, then does the org hierarchy include
44:53
some AI assistance? It's a it's a great question.
44:57
Jason Lemkin has talked a lot about this.
44:59
I don't know if you see any of his post the CEO saster.
45:02
It's like, you know, I think his I think his the post that I'm
45:05
going to refer to is I'm going to I'm going to butcher it.
45:08
But anyways, the, the, the the generalist of it is he had reps
45:12
and two of them, they quit on the floor at an event and he
45:16
said, look, I'm not doing this. It cost so much to get these
45:19
people here and etcetera. So he's building a team of just
45:22
agents and he was like, it's such a quiet floor of sorts.
45:26
So as you're, and maybe there is a question there and maybe that
45:29
is part of it. Do you build a, are companies
45:32
now being built by I'm an account executive, I'm maybe not
45:36
going to bring in an SDR or sales development Rep or
45:39
business development Rep because I'm going to deploy an AI agent
45:42
that's going to be the your counterpart that's going to
45:45
enable you to be more successful and that's the investment we
45:47
place on you as you join. Is that a conversation that
45:51
happens? That conversation is already
45:54
happening to some extent, especially on the AIXDR probably
46:00
is one of the early use cases for AI replacing the humans kind
46:05
of a conversation. It went through it's own boom
46:07
and bust cycle and there seems to be a revival underway right
46:11
now for certain use cases with some level of pragmatism baked
46:14
in. But Azure video function is some
46:17
is an example, a prime example of where where AI is at least
46:23
people are having conversions about can AI do this job instead
46:26
of a human. It makes sense because some of
46:29
them, I don't want to say they're not bad because it XDR
46:32
role is the hardest role in sales.
46:35
I truly think that's the case. But then there's also the the
46:38
question of like with the tool evolution of the sales lofts,
46:42
the outreaches of the world that are automating some of those
46:45
things and enabling kind of quote UN quote, push a button.
46:48
And some of the things that we're seeing with clay and, and
46:51
how that whole modern workforce is changing from a text stack
46:54
perspective to get yourself in front of people.
46:57
And if anybody's even paying attention on LinkedIn, how many
47:00
times do you see DM or put the word Claude or put the word AI
47:05
agent? And I'll send you my framework,
47:08
right? How many of those do you see on
47:09
a day-to-day basis? Yeah, that's the modern click
47:16
bites that we see all over length and now.
47:18
It's happening so often and it's it's trying to look, make you
47:21
think a little bit differently. And I hope people do think
47:24
differently of using it to your advantage and not thinking this
47:26
is going to replace me, because I do think like, like you said
47:29
at the beginning, the human and loop is important.
47:31
Sweetheart, I just looked at the time and we are 40, almost 48
47:35
minutes. And this has been a lot of fun.
47:37
And I'm sure we could talk for a lot longer.
47:39
But if everybody likes new, nobody likes different.
47:41
And when you're doing both of the same time, they can either
47:44
cause friction or uncertainty. So for somebody in that's just
47:47
listened to this and they're thinking about how can I improve
47:50
my, my AI fluency or my even my AI revenue per headcount?
47:56
What are two or three actionable things that you can tell them to
48:00
do on Monday morning for them to put them on a path to be more
48:04
productive? So I'm assuming that addressing
48:09
the revenue team here. So first thing that I would say
48:14
is that look at your active deals.
48:18
I'm talking about a Rep here. Look at your active deals, get
48:22
all that call transcripts together and create a threat.
48:29
See if you have cloud, for example, get them all into
48:33
cloud. If you have can figure out the
48:35
connectors, build that out. Get get your call transcripts
48:40
and try to have a conversation on how you can move the deals
48:43
forward. What's your next best step?
48:47
And this is something that you could do today over the weekend.
48:51
And if you have the connector setup, it's probably a few
48:53
minutes walk. If not, it'll take a few hours
48:56
of course. Right, great.
49:03
So if somebody wants to learn a little bit more or they say,
49:07
hey, you know what, I really want to pick the conversation
49:09
up. Sweetheart is super smart and I
49:12
enjoyed his his views and they want to learn more.
49:15
Where would you suggest that they try to to find you?
49:19
So LinkedIn is the best and and also company website GTMRE dot
49:24
AI. OK, awesome.
49:28
If you, if you can't tell, Sridhar is incredibly passionate
49:31
about this topic and you know the whole GTM buddy and
49:35
everything he's talking about, you should check him out because
49:38
it is really interesting what they're doing and, and how
49:40
they're doing it. And if you can't tell he's, he's
49:42
clearly passionate about this because there's been a multiple
49:45
times in the conversation where you really leaned in.
49:47
And so it was this has been a lot of fun.
49:49
We've not had a chance to, we, we don't know each other outside
49:52
of a couple of of quick conversations.
49:53
But I've really, I appreciate, I really appreciate your view on,
49:57
on the world of AI and, and I'm sure that we could have spoken
50:00
for many hours on this topic. Thank you so much for having me
50:04
call. Truly enjoyed our conversations.
50:07
Yeah, me too. That's going to wrap up another
50:09
episode of the Revenue Problem Solvers podcast.
50:11
Obviously want to thanks Radar here for sharing these
50:13
incredibly invaluable insights. And there, as you heard, there's
50:16
some strategy you can put in action.
50:17
Remember, success leaves clues, and every revenue problem has a
50:20
solution. Sometimes you just need the
50:23
right conversation to find it. It's Carl with brand new problem
50:26
solvers helping you turn your obstacles into your
50:28
opportunities. Thanks for investing your time
50:30
with us today. We'll see you in the next
50:31
episode.