Agentic GTM Methodology: From Experimentation to Operating Discipline — CEO Roundtable

Julia Nimchinski:
Awesome, thank you so much again, and we are transitioning to our CEO Roundtable, and I’ll start one. We have Oren Zena, founder of the CRO Collective, who’s no stranger to the community, and author of What TROs actually do? But a roundtable. We’re going to be talking about Agentec GTM methodology. Welcome back to the show, Warren.
What’s the latest and greatest, and what’s in your Agendic OS?

Warren Zenna:
Well, lots going on. You know, the CRO world is topsy-turvy with all the stuff we’re gonna talk about today. But, I appreciate you having me here, and this great panel. We had a really great, for the correct call, so I’m looking forward to this. I’ll jump right in, if you don’t mind.

Julia Nimchinski:
Let’s do it.

Warren Zenna:
Okay. So, first of all, everyone, thanks for being here. I’m Warren Zen, I’m the founder of the CRO Collective. For the past 6 years or so, I’ve been advising Chief Revenue Officers and Founders And as you can appreciate, in the last year, the only thing anybody wants to talk about is AI.
Particularly talking about revenue agents and revenue tech, it’s just… and we have to tell everybody here how huge it is.
I think the… interesting thing, though, is I’m seeing that people are deploying things like SDR agents and chatbots and intent tools and intelligence platforms, but, you know, it’s sort of… no longer an experiment, you know, it’s really here now, but I think what doesn’t exist, though, is a method, and it’s just not just a process problem, it’s really a definition problem.
So we had a great call, we talked about this, every one of us, and we talked about, like, what’s the way in which these agents actually work? how independent can they be, and what are they? So, I’m going to introduce the people. We have this great, great panel. So we have, Stav Levy.
Ulta CEO and founder, Max Greenwald from Warmly AI, CEO of that company, Sridhar Pedeni. go-to-market buddy, Steve Davis, CEO of Brevardi, and, Rashid Kataria, from Centralized, CEO and Founder.
So, why don’t you guys introduce each other, you know, just briefly, give a little overview of what you do, and this way we can, let you all speak a bit more intelligently about what you guys are up to.

Stav Levi Neumark
Cool.

Steve Davis:
Steve Davis, CEO of Proverity. Proverity offers a pre-sales platform to automate proof of concepts and help you increase your technical win rate. Utilizing AI, and we’re an AI-native platform, which we’re going to talk about, I’m sure, in Spades today. Stuff.

Stav Levi Neumark
Yes, oh, hi, I’m Stav, co-founder and CEO of Alta. Alta is an AI go-to-market system that helps over 200 businesses to find, engage, win the right customers.
We do it through 3 agents, 3 AI agents, an AI SDR, an AI inbound and calling agent, and an AI revOps layer, all built on one intelligence layer.
The system connects to over 50 data sources, like CRM, product data, using omnichannel, like SMS, WhatsApp, LinkedIn, calls, and all end-to-end go-to-market flow, closed-loop, measured by outcome and ROI.

Warren Zenna:
Great. Okay, why don’t you go next, Max?

Maximus Greenwald
Hey everyone, Max, founder and CEO of Warmly. Warmly.ai is an autonomous revenue agent, that basically warms up your TAM, so we understand who is in market to buy at any given time with our intent signals.
And then we outbound them via ads, email, LinkedIn, and chat, and then we drive them to your website, where we close them with our inbound SDR agent that kind of forms almost like a digital twin of yourself on your website, to help you book more meetings.

Warren Zenna:
Thanks, and Rashid, why don’t you go next?

Rachit Kataria | Centralize:
Yeah, some familiar faces here, and I see again, Warren and Sav and a few others. I’m Ratchet, co-founder, CEO of Centralize. You can think of us as relationship intelligence for enterprise revenue teams.
So, think of us as agents that automatically build relationship maps and show you who you know, who you’re missing, and how to get there, basically helping you operationalize multi-threading, and arguably almost navigating the most important asset in your deals, which is people.
We were the placemat to do it, almost like a ways, if you will, turn-by-turn of where to go next, and who to get to, to ultimately get the path to sign done.

Warren Zenna:
Excellent. And Sritar, why don’t you go next?

Sreedhar Peddineni:
Hi everyone, Sridhineni, co-founder and CEO of GTM Buddy. We are a revenue activation platform that activates and reps in their daily workflows, activating the training and content that’s created by enablement teams. We also extend that further by providing an AI teammate that helps with many jobs to be done in the revenue workflows.

Warren Zenna:
Okay, great. So as you can see, we sort of have, like, a full, like, funnel represented here, which is intentional, so we can talk about how this all affects everything. So I have a kind of a lightning round question, like, to start with, and that is… If someone came to you today and said, Define an agent. What would your definition be?
Let me start with you, Ratchet.

Rachit Kataria | Centralize:
Yeah, it’s an interesting… framing where it’s like, there’s a lot of AI-native companies, and agents versus being AIs, it’s kind of… it’s unique. I think our take on this is very much, an agent tells you what to do next without being asked. That’s really the baseline of it.
It knows how to interpret context that humans are unable to do at scale, and give you the right next best action, if you will, or an action to take on your behalf, or an insight that you wouldn’t have otherwise had. Unless you’d spent maybe an inordinate amount of hours, or maybe it’s just untenable altogether to do.
But TLDR tells you something to do that you… you didn’t have to ask it to do.

Warren Zenna:
Okay. Staff, what do you think? What’s an agent?

Stav Levi Neumark
So there is, like, the more kind of a technical definition that maybe I will take in the definitional part. Basically an agent that is something that have a goal, and it have, the goal is, like, the system prompt.
It have skills and tools, which is like, you know, keys to certain places, API, and things you’d be able to do. It have memory, and context, and files, there are type of memories, but, but memory.
And it’s have an engine, like a brain, which is the model that it runs, and all built to deliver some outcome. So, it can be very simple or very complex, but the goal is to deliver an outcome.

Warren Zenna:
Okay. Max, what do you think? What’s an agent?

Maximus Greenwald
I think it’s an undercover spy that helps governments foil plots of terrorists.

Warren Zenna:
Alright, great. Well, we figured it out, so… great. Congratulations. Okay, it’s Steve, what do you think? What’s an agent?

Steve Davis:
Yeah, anything that is proactive and autonomous, right? That’s how I always think of an agent. If it’s proactively thinking for me, it’s doing something for me, without me having to interact in it, proactive and autonomous, that’s agentic.

Warren Zenna:
Okay, great. And Sudhar, what do you say?

Sreedhar Peddineni:
Great, great question. I’ll start off by saying what I think Agent is not. A lot of people talk about creation of a custom JPT or a chatbot of some sorts. Where I ask a question, and I get an answer. They would call that an agent. Think of McKinsey’s 30,000 agents working as part of their workforce.
with that out of the way, I think of Agent as, A software program of some sorts that activates upon a trigger. A trigger is a… it could be an event in your CRM, could be somebody filled out a form, there’s an event of some sorts. It triggers, and it accepts inputs. Inputs can be structured or non-structured.
It knows that… what it is supposed to do, what’s the goal of the agent. It is able to reason about it. Creates an action plan, And goes out and enhances that inputs that it has gotten. And then executes its plan of action. and ends its flow by performing an action.
An action can be updating your CRM, Creating a document for you, or it could be handing off that output to the next agent in the workflow.

Warren Zenna:
Got it. Okay, great. You know, Steve, I recall in our initial dialogue we had. you said something like, if it’s not continuously learning or autonomously acting, then it’s not an agent, right? So, it’s more like a process, actually, more than just, like, a question and answer type thing, which I tend to agree.
Well, walk us through where your company draws that line. Like, what does an actual agent do in a pre-sales workflow versus what’s just an automation? What’s… what… how do you make that distinction?

Steve Davis:
Yeah, it’s a great, great question. I think when we look at it, the agent where it starts and stops, again, it’s continuously learning, it’s getting more intelligent, but it’s very focused on automating mundane tasks. Right? So, and I think about what an SE, a sales engineer, is doing.
If they’re spending 70% of their time updating Excel, taking Slack information, putting it back into notes, those are mundane tasks, adding no strategy, or no strategic value.
That’s where we kind of start and stop with it, but it’s intelligently learning, so it’s learning how you interact with all the data and where you want it put, and then what the next step should be. But where it stops is when there’s strategic thinking needed.
I want the SE working on a multi-million dollar deal to be very highly involved in making those decisions, so I’m very much on the philosophy of it’s AI with human in the loop. I am not all about replacing humans, especially not yet, but that’s my start and stop.
So, really focus on the mundane tasks, the manual, parts of your job, and then stop at that, I need a strategic thinking decision here. That’s where the human comes in.

Warren Zenna:
Gotcha. So, Stav, your agents, Katie and Alex and Luna, they’re transparent without being AI. Kind of, in a world where everyone’s debating what is an agent, how does transparency about AI identity change the conversation? Like, you said you need to redesign the data foundation for deploying agents, so how do you know when the data’s ready?

Stav Levi Neumark
So, there is, like, maybe two parts, of this question, the… I will start with the transparency part. So first of all, it is a decision of our customer if they want to be, totally transparent, or not.
We, as someone that gives also service, to our customers, for specific agents, like calling agents, we recommend to be very transparent on it and to find the right, use cases on it. So I think it’s a lot about, like, AI transformation.
In these days, and to understand what, agent can actually deliver value, and… there is, like, maybe three types of, of way to deliver value, so one is, like, replacing something that a human being would do, and this is, like, maybe on the inbound SDRs, examples, for customers that have a lot of leads that are coming to their website, and they need to take care of those leads.
And in those cases, there are… there is no reason not to be transparent, about, about using an AI, because, first of all, there is, like, a very big, advantage, for the AI over there, that it’s a… you can talk with them and understand, all the questions you have, and it can talk with any language and transform to any language you prefer, to talk with, and it’s also available 24-7, so it’s, it’s… there is no reason not to be transparent about it, and it can deliver value.
So this is the first type, like, the one that places. There is the one that empowers, so it’s more like kind of a co-pilot, and it empowers salespeople, also SDR, and… and this is more of the messaging part, so, like, to, to help people, reps.
write better messaging and in the right time, in the right audience, and there, it’s not acting as AI, it’s acting as the rep, but the rep is reviewing it and approving it.
So, there, by the way, the data doesn’t have to, be, like, we can take a bit more risks Over there, in order to, to, to, to make better outcomes. And the third part is more kind of AI that’s inventing new roles, right?
So it’s replacing, augmenting, or inventing, new strategies that wasn’t exist before. And over there, it’s an experiment land, of, low-risk, high-value, tests that we’re doing with companies, and, So yeah, this is the third part.
About the data infrastructure, it’s a whole other subject, but I think that there is no… part of the AI transformation is to understand which data and knowledge we can prepare in order for the agent to use and make good and better go-to-market flows.
And… and it really depends on the type of the company, depends on the type of the risk they are willing to take, depends on, a lot of different things on the go-to market, with every… it’s not one-size-fits-all.
In this, and, and it is require service, as part of, really embed this data, into the, into the go-to-market, agents.

Warren Zenna:
That’s an interesting, good segue, because in the conversation we had, nobody was arguing about full autonomy as the goal. Instead, really, everyone kept coming back to value. You know, Sritar, you actually even mentioned very clearly that, you know, more autonomy doesn’t necessarily translate to more value.
And Steve, you were really clear about enterprise deals. Like, humans make the strategic decisions and handle… the agents do the busy work, right?

Steve Davis:
Yep.

Warren Zenna:
I would say, like, if we could dig into autonomy isn’t the goal, and value is, then how do you decide where autonomy creates value, and where it creates risk? Because I think this is risky right now, what we’re doing, and it’s fine, but how do you… manage between pushing autonomy to the point… I mean, I’m already in the situation.
I’m using autonomous tools, particularly around just scheduling, and I see it makes mistakes that have implications for my clients, and I’m really angry about it. But I took that risk. Because I deployed this thing into my system, and I’ve let it run, and I’m sort of, in a way, a beta user for a lot of things right now.
I have a small company, so maybe the risk is less, but I mean, someone like Steve, maybe Raheit, you know. what is it that you guys do to determine how far you’re gonna push these things? And where’s the risk calculus for you guys? I’m curious. Ratchet, why don’t…

Rachit Kataria | Centralize:
Yeah, I can jump in really quick on this. To Salv’s point, I think it depends on the customer’s risk here. Like, what’s the workflow that you’re fitting into?
I think, Steve, you talked about enterprise, and that is arguably the highest risk area, where if you’re burning relationships, you’re potentially burning six-, seven-figure dollar deals that you can’t afford to mess up. And I think there’s a.

Steve Davis:
That’s right.

Rachit Kataria | Centralize:
There’s, like, it’s like, you’re almost like… I think the industry’s over-indexing on this idea that agents have to be end-to-end autonomous. Like, that’s not actually, I think, the reality. It should be more, where is the judgment of the agent creating leverage that wouldn’t be possible prior?
And then how are you showing up with that leverage in the day-to-day? I mean, for us, like, our customers are literally the highest risk ones. We sell to enterprise revenue teams, and so you cannot be out here blasting TAMs with AISDRs. You should not be using those, I think, in that world.
It’s all about strategically throwing darts and creating that right messaging. So I think… It’s a question of what is the workflow that you’re automating, and how quickly can you… do something that wasn’t feasible prior. I mean, if I can share our workflow, it’s… it’s the thing that you’d ask every rep to do in a QBR.
It’s making that amazing work chart that is who reports to who, who has influence, who has power, that goes stale in a week. the second someone leaves that company, it becomes shelf where no one looks at that thing ever again.
Everyone pretends to be strategic for that day, and then goes back to the, you know, just like folklore of asking people questions. But if it were a living, breathing picture that agents can actually do for you in a world that AAUs just frankly shouldn’t, or SCRs shouldn’t. Then, all of a sudden, you have leverage to act strategically.
And the risk is not, hey, I’m gonna go send a message to 50 people at this company and burn them. It’s. you’re giving a place of work, almost like this place of strategy you can work off of, that you never would have even got into as a baseline, if an agent wasn’t working behind the scenes for you. So that’s our version of, like, where do we stop?
It’s, hey, let’s get you the last mile, but you’re putting the puzzle pieces together, because that is the whole job of humans, kind of, in the world that we’re heading towards, as opposed to let’s just send everything for you on your behalf, and it’s, like, all automated, all 24-7.
So that’s kind of our take, but again, to Sava’s point, it’s based on how much risk you’re willing to take, and… I think we can push it as far as we can, but the humans… we exist for a reason in go-to-market, I think it’s to orchestrate the relationships, and that’s kind of, like, as close as you can get to.

Steve Davis:
Yeah, Warren, I’ll jump in there. Yeah, please. Something that we kind of put a really hard line on, you talk about risk, there’s internal risk collecting data. If you mess up, it’s okay. Like, you gotta learn from it, you’re gonna get smarter. Data that is going to your prospect is high risk.
And that’s where we’re really careful, because if we have a multi-million dollar deal, and we’re running a proof of concept, and we screw up and send bad data to the prospect. that can risk the entire deal. So, right now, we probably err on the side of caution. We kind of… we throw caution to the wind, like, anything internal data, try it.
Like, let’s see what comes back, if there’s hallucinations, if there’s intelligence, like, let’s… let’s test that out. before any of that data gets pushed out to your end prospect, we’re much more careful about that right now. Now, we’ll get to a point where I’m sure all of us will have these amazing agents that may make no mistakes.
But for right now, that’s how we look at it. It’s… we’re willing to take and push the boundaries, take a lot of risk, push the boundaries on internal processing data. Anything going to your end customer or prospect, it’s much more high risk, and we’re much more careful about.

Sreedhar Peddineni:
Yeah, adding my perspective to this, I think perspective of autonomy is a decision that, the way we think about it, it’s a decision that our customers should be able to take. We don’t decide for them. Let me talk about it with two concrete examples of workflows, agentic workflows that we’re talking about. So, problem statement.
A lot of customers that we serve, they invest millions of dollars in creating training content for their GTM teams. They invest a lot of money in creating these programs, and get the reps to Enroll and follow up, whether they have taken the courses, and so on, right?
And there’s this ongoing struggle about, hey, my managers are not, coaching the reps the way we expect them to. This is the business problem statement. Now, with an agentic workflow, what we’re able to do today, you could do it with my product, or you could instrument it outside, but it’s possible today.
that I could look at a rep’s performance over the past quarter, past month, past week, whatever the case may be. Look at all the conversations that the rep has had. Pull out those call transcripts. Get my organization’s competency framework. And compare notes. Assess the rep’s performance with objective data. Now, identify what is rep doing well.
AI identifies that, based on this competency framework that you have given me. Rep is doing this well, and these are areas for improvement. Now we have a decision to make. It’s actually a fork. I could notify the skill gap analysis that was done to the manager.
Saying that, hey manager, your teammate, you have a one-on-one this week, here is the information that you can use to make more data-driven and objective coaching to your teammate. In addition. We can also identify from our training repository and content repository, identify the information that moves the needle for that particular individual.
Now I have another choice to make. I can ask the creative workflow in a way that, go ahead and assign this training, AI assigns the training. Or I could say that, hey, identifies the training courses that a rep could make, could take, And… Ask for the human in the loop, do you want me to assign these courses?
Whether AI sunset or not is a choice, the way you define your workflow. And if you’re seeing that…

Warren Zenna:
fence.

Sreedhar Peddineni:
Initially, you do it manually, and if you’re comfortable, it’s nailing it Each time, then you automate that process.

Warren Zenna:
Got it.

Sreedhar Peddineni:
It sounds…

Warren Zenna:
It’s like it’s more like, not so much about whether we want to do this, it’s whether the discipline is there to make sure you understand how to navigate both the data and the operating. So I want to, if you don’t mind, I’d like to bring this into, like, more of a framework here. So, let’s make this practical.
Imagine there’s a CRO in the audience right now, and they run a $50 million revenue organization, and they have AI tools deployed in, like, 3 or 4 places, let’s say. Like, maybe, you know, a couple of revenue tools, maybe some SDR-based things, and some listening platforms, etc, etc. And the CEO asks him.
Why isn’t all of this AI translating into faster growth? So I’m just curious, each one of you, maybe Max, we’ll start with you, what would your answer be? What’s the operating discipline that they might be missing? Why they’re not able to display or show how it’s actually having revenue impact. Without selling your product.

Maximus Greenwald
And without knowing both their tech stack, their ICP, I’ll keep it very general, I’d say when we find that our customers are coming to us saying, we’ve tried to deploy some AI, and it’s sort of not obvious, and that it’s working, probably… well, I actually, I think 100% of our customers see productivity gains.
I don’t know if anyone feels differently with their customer base, but, like. any AI tool you implement should, in theory, have productivity gains internally.
But we talked a little bit earlier about this sort of external world of Getting it right, right place, right time, right message, when it comes to prospects, or getting, external to the organization.
And I think that the lack of productivity gains, or the lack of successful AI implementation, probably comes from… needing to… again, I don’t know the specific tools they implemented, but probably part of it is needing to review the results and iterate, and have human-in-the-loop verification of answers.
An example might be if you, use an AI chat to, and upload all your, like, help center docs, and then, hope that it just answers questions perfectly to a prospect.
you’ll notice differences between how a human would respond and how the AI would respond, and you want to basically try to merge those things together over time through having some sort of adversarial testing, or having, yeah, just the ability to, like, rinse and repeat 150 times. So I think that would drive more toward actual revenue impact.
Trying to think of another example where… without getting to the specifics of that particular business organization, I mean, the… I guess the obvious one is just lack of alignment with your humans, on, like, where we’re deploying these things, and how that flows in across the org.
I think there’s, like, a lot of, entrenched human process that we, as, like, leaders of organizations, don’t realize happen on the day-to-day, and so something I’ve advised, like, revenue leaders in the past is, like.
If you… especially if you’re remote, like, you have to go physically in person and hang out with some of your SDRs and AEs and just watch them work for a day.
And, you’ll be really surprised at, like, some of the habits they’ve developed, and patterns that they, have in order to get their job done, but from your vantage point, you’re like, wait a minute, like, I didn’t realize, like, that’s how an SDR uses Sales Nav effectively, and creates a briefing for their AE. We could do this way faster.
You know, I put in this tool in place, but just because you put the tool in place. Doesn’t mean you sort of trained everyone the right way.

Warren Zenna:
Gotcha. Stav, what do you think?

Stav Levi Neumark
Double…

Warren Zenna:
might be going on.

Stav Levi Neumark
double down on two of the things that Mark said, like, one is test, test, test, like, I think that… the bigger… we talked about risk before, the bigger risk, is not to deploy and implement and scale using AI agents, and not, like, boost your performance.
And I think that the second double down on what Max said is sit down with your SDR, with your AE for one day, and watch what they are doing. And then you realize what is the risk that’s being taken when you’re not finding those places, and go back a little bit more on the risk part.
I think that part of our job when companies come to talk to us and think how they can do this transformation is to think with them where is the areas that they will allow AI to be more autonomous.
In order to make those tests, and in order to learn and to fail fast where the risk is low, and in order to create learns that they will change their business and help them deliver more outcomes. So… So double down on the tests, and listen, and look at the reps in action.

Warren Zenna:
Gotcha. Steve, what do you think? What does CRO need to do differently to demonstrate to his board and CEO that this is actually making a difference? You’re on mute. Here. You’re on mute, Steve.

Rachit Kataria | Centralize:
Dave, you’re muted. You’re muted.

Steve Davis:
That was such a good thing I said, too.

Warren Zenna:
Okay, my greatest hits are on music.

Steve Davis:
I mean, that’s my life.

Warren Zenna:
I have an entire mute tape, it’s brilliant. It’s okay, yeah. Yep.

Steve Davis:
probably have a different take on this a little bit. As a former, kind of, 6-year CRO, now CEO, I will be pretty brutal on salespeople and CROs and VP of sales and all this stuff. I want tangible… Okay, great. Like, what is getting me… I don’t care as much about efficiency, I’m sorry. Like, what are you doing to get me to technical win faster?
What are you doing to get me to increase my revenue and decrease my CAC? And so, when I’m, you know, CRO of a $50 million company, I’m looking at every specific piece of data. That’s how I determine what’s working. I don’t care if you need one agent, 5 agents, is it helping me do one of my three metrics that I report to the board with? Right?
Everything else, you say, oh, but we saved 13 hours. So what? Did that 13 hours allow you to work on more proof of concepts that allowed you to close more technical wins? Okay. That’s great. You saved some headcount? But did that saving headcount now just cost me and people to work on strategic deals?
You really gotta dig under the covers of the data, and that’s what I’m doing internally. Even our own tools we put in, I’m like, great, so it saved me a headcount. And was that worth it? What did that lead to? And I try to always bring back everything we’re doing at Gentip, process-wise, our own product for our own customers. What is it leading to?
What is the goal? And for us, Proverity, it’s get to technical and faster. So everything we put into our product should be helping our customers get to technical and faster. Tools I’m implementing at Proverity internally, I do the same thing. Look, we’re all… we all report to a board. Right?
I’ve got my 3 big metrics I look at every day, and if we’re not putting in processor agents to help me do that. All the bells and whistles, I don’t care about. And… and so that’s… I just look at it probably from a… that kind of standpoint of. you know, oh, that’s a cool tool? Yeah, that’s fine.
You know, that’s… I don’t have time, like, that’s great. I don’t mean to sound flippant, there are a lot of cool tools out there. Does it help me achieve these three goals to run my business? I look at everything that way.

Warren Zenna:
Yeah, thank you, Steve, it’s great. Sounds to me… There needs to be some sort of, attribution metric for AI that will be created that we could, like, much like we tried to do in the ad tech space, right? Like, how do you know that advertising works? I mean, there’s probably a thousand attribution models that were created.
None of them actually ended up working, but I tried. But I think it’s an important thing to have some sort of benchmark around what is the attribution modeling for AI implementation, and I don’t know, we’re not there yet, right? And I agree with you, I don’t know, productivity seems like it should.
But I’m also noticing, and I’m sure you all are too, that what AI’s done, aside from making things easier, is now two things happen. One is you work more. I mean, I’m in front of this crap all the time now, because I can do so much of it.
I find I’m actually working more, even though I might be getting more done, I’m working more, so time… sort of became, because I can work faster, now I’m gonna do more to get faster. And the second thing is. you know, the cost model is not the same, because it’s costing money to use these systems in different ways.
The consumption model is a different model, too, so there’s other costs that leak out while you’re using them. I think this is an interesting thing. But then that goes to the accountability side, is… who owns this, right? So, if you have a revenue operation, and you’re putting out AI, and you’re running across the organization.
Is this a CEO-led function? Is it a CRO-led function? Is it a CTO-led function? Or is there, like, a new, God forbid, like, chief of AI function? I’m curious what you all think, like, what… who’s the… where does this sit? Because there needs to be… this has to sit somewhere. I haven’t answered, I’m just curious what you all think.
I’m gonna leave it open to jump in.

Sreedhar Peddineni:
So I’d like to take that. So… many founders and other CEOs that I talk to, it’s a… A repeating observation, now. that most of them are consuming information about AI and the possibilities secondhand. hey, somebody in my team did this, or did that. We have purchased 10 different AI tools, and so on.
It’s almost like, it feels like, you know, they’re reading about swimming, they want to learn swimming. But they’re reading about swimming. They’re not actually getting out there in the pool and actually swimming. Okay. That seems to be a really, really big gap. And, given that the transformation that we are living through.
is not like anything that we have ever seen in our lives. I’m an older guy, I’ve been on this planet for 5 plus decades now, but the change that we are seeing right now, I have not seen this kind of a change before. And we can’t treat it like… You know, we’ll pass through this as well.
Leaders need to roll up their sleeves, and understand the potential, and needs to experience it. This kind of goes back into your previous question about, you know, as a CRO, okay, is CRO accountable for AI point solutions, and okay, we bought X number of solutions and proved the accountability? Or is it aug-wide leadership responsibility?
To understand the potential. And how to deploy that. Buying a fancy new AI tool is not going to solve that. What are the workflows we are going to automate? improve, or create new possibilities. That level of understanding, the visual understanding, comes through when you start experiencing it.
And I feel that leadership is not doing enough of that. I’m generalizing it a bit, but it seems to be fairly consistent.

Warren Zenna:
Gotcha. Stav, CRO, CTO, AI expert, who runs this damn thing?

Stav Levi Neumark
So I’m going to say, for me, for my company, it’s a bit different from what I think it’s going to be. We’re, like, 40-people company, so maybe it’s also a different stage for many of the companies that are our customers.

Warren Zenna:
Think about maybe your clients, right? The clients, the people you’re dealing with, who’s responsible for what you’re doing on their side.

Stav Levi Neumark
So, in the end, it’s the person that owns the outcome. So, I think CRO owns the outcome of the revenue department, so it needs to be under the CRO, but I think there is two different types of AI agents that have been deployed in the company, so one is, like.
a service that you buy a system that is, that is responsible for the outcome, so it’s, like, actually using a service, and there is, like, building an AI agent internally.
So, for us, I have, like, a team of AI engineers that are building AI agents internally for different departments and sits under me, like, under the CEO, because we have, like, the importance of the sharing knowledge Between the team is more important, and the outcome for every department is still, like, the department is not huge, so it’s still very contained.
And then the outcome of every department is really under my outcome anyway, so it’s not that there is conflict of interest.
So I think in smaller effort, if you have not a lot of building agent that you’re building yourself, so it is good that it will sit under, like, I don’t know, CEO, CRO, depends, on what is the goal, but eventually, like, I would expect our finance person to own the finance agent’s tool that he’s using, and the CRO to own the the go-to-market agents that he’s deploying.
So, yeah.

Warren Zenna:
Ratchet, what do you think?

Rachit Kataria | Centralize:
Yeah, I like the ending point there on, eventually every vertical should be the owner of their AI rollouts at the end of the day, unless you’re big enough that you have a CIO, and all tooling goes up to them, and then, you know, it’s more of, like, a broader decision.
I think maybe zooming back a little bit to something that we were talking about around the workflows. I think a lot of people think that agents are gonna show up, and just the fact that you have one is solving all your problems, like you bought one, life’s good, when in reality.

Stav Levi Neumark
Oh, it’s not?

Rachit Kataria | Centralize:
You’d think so, I mean, life would be very nice. But it’s like, everyone jokes about this, right? Like, sellers are lazy, because there’s just so much to do. You have, like, so much you have to get done. You, like, intentionally just want to be at the point of value as fast as you can.
And I think in our cases, like, the best customers we have it’s not just the CRO, I don’t even mention this yet, it’s like, RevOps are the unsung heroes in the corner here. They’re getting bombarded with, like, 50 tools a day, and have no clue what to make of the world that’s happening and evolving underneath them.
Don’t know if they should invest right now, and the world’s gonna be better in 2 years, so why even make a bet today if, like, the next best thing’s around the corner? And they have to kind of sift through all this. So, I think the recurring theme is workflow. It’s like, the best companies I’ve seen that roll out successfully, even with us.
It is the CRO working hand-in-hand with the head of RevOps to understand exactly what they’re solving for. Like, what are they automating that today is not feasible, prior, that an agent can take care of?
And if they can figure that out, and maybe it’s running a POC that validates that, it’s about, kind of the users on the ground also confirming it actually did pre- and post-fix that workflow. you have a very, very high chance of success, and it’s sticky, and everyone’s happy.
Versus, I think, Max, you might have alluded to this, like, if you don’t show up and know that your team has a certain workflow that they’re not doing, and then drop a tool and expect they’re just gonna do it, like, that is not how the world ends up working out.
I think people get scared of saying that AI is, like, not change management, when in reality, I think there’s a lot of change management involved, but it’s leadership down to make it very clear, like. your philosophy has to be that everything is going to be… if it can get automated to help you out, it will.
That is a change you need to be thinking about, top-down, and then that is what you have to lean into from the rep, from the manager, from the CRO, up to leadership. And ensuring that you identify the workflows where you are forcing that change management, basically. But if you do, like, it works out quite well, I think.
But if you’re not thinking in that way. It’ll probably fail.

Warren Zenna:
Gotcha.

Maximus Greenwald
One issue.

Warren Zenna:
Steve, on your end, like, your big enterprise clients, who is the person that you’re dealing with most of the time on their side? Who runs this stuff at these big companies?

Steve Davis:
Yeah, so it’s a little bit of what Ratchet and Stav said, so I don’t want to repeat too much, but I totally agree. I’m selling to the CRO or the VP of Sales Engineers, sometimes the VP of RevOps. The key thing I’ll add to it, they own it. I agree with that 100%. But they’re too busy, they’re not vibe coding, they’re not putting in these tools.
They’re just… they’re playing around with it. I’m all… the guy’s like, I vibe-coded QuickBooks this weekend. I’m like, awesome, great, good for you. Like, but they’re not putting it in. They own it, totally agree with Ratchet and Stav. They own it. CRO, VP of SEs, there’s a go-to-market engineering expert.
underneath that they’ve hired or should hire, and these enterprise companies, I’m seeing more and more of that. Now. whether they exist under VP of RevOps or RevOps, they might. but they exist under each department. There’s an expert who’s trying to figure out what they can build themselves or deploy.
from, you know, like, our software companies, right? What do they deploy? Then it gets into the next conversation of build versus buy, and I’ve been fighting that for the last, you know, couple of months. Yeah, yes, you could build it, and this is the same conversation that’s been happening for 30 years. It’s just the next iteration of it.
It’s all it is. And it’s like, yep, you can build it, and it’s easier to build it now than it was 3-4 years ago. Should you be building it? Why are you building this tool? Why aren’t you building things to help you sell the product you make? Right?
And that’s not building out an SDR, building out better outreach, building out… it’s using our tools to do that while you focus on the ability to manage your company and sell your product. Right?
And so, again, long-winded way of saying, totally agree, CRO, whoever that VP owns it, but they’re gonna have people under the covers who are doing that work, and that’s who we’ll talk to as well.

Warren Zenna:
Gotcha. Max, what do you think?

Stav Levi Neumark
One sentence on the…

Warren Zenna:
Please, let’s talk ahead, yeah.

Stav Levi Neumark
the… my favorite type of customers are the ones that’s coming, that’s saying that they tried to build it themselves. Yeah. So, they know already, like, what does it take, and yeah, so…

Steve Davis:
I love that.

Warren Zenna:
No problem.

Steve Davis:
That’s a great prospect.

Rachit Kataria | Centralize:
Definitely agree.

Warren Zenna:
Max, who owns this, you think?

Maximus Greenwald
So, beginning of the year, I made everyone at my company a manager, and I said, you’re all now managers of your own agent fleet. Congratulations, you know, no training whatsoever, you’re now a manager.
And my expectation really is that they’ll start to build their own ICs that can help serve them in their roles, which call them sub-agents or power tools, whatever you want, doesn’t matter. So that’s in just terms of empowering people.
In terms of who owns this on the go-to-market side, I actually think that the go-to-market roles, are gonna evolve a little slower than the internal roles in companies, probably because of the fear of messing with stuff that affects the most powerful part of a company, which is its revenue.
I love this piece, I think it was by Lenny Riccitsky, that basically talks about how internally, we’re gonna see a merger of engineers, product managers, and designers into one role we call the creator.
And I thought that was, like, a really beautiful way to describe, you know, the merger of what used to be sliced into many different roles, but now we can all do together with one person with AI underneath them. I think we’re gonna see the same in go-to-market, but it’s not obvious what’s happening.
You know, the word go-to-market engineer seems to be some sort of merger between demand gen, rev ops, sometimes SDR Manager, it just depends.
And whether the title… what the title is, I don’t really care, but I think that there is going to be a, consolidation of responsibilities for a human manager of agent responsibilities, and that will then answer your question, but I think we’re in the early innings of figuring out what those roles are.
So for now, I guess it’s just easy to say CRO, because that’s, like, the top of the chain, but I think we’ll see subspecialties within that.

Stav Levi Neumark
equivalent to the creator is the relationshiper, so this is the equivalent for the go-to-market.

Warren Zenna:
Oh, that’s a nice segue to my next question. Which is, like, all your products sort of represent a different phase of the funnel. And a lot of the product companies you work with are going to be working across all different phases, and maybe interact with all your different products, conceivably, across that funnel.
So, when’s the future gonna come where all your products talk to each other? Or are you guys just all creating a bunch of silos? And what are we gonna do about that?

Sreedhar Peddineni:
That’s something… Sorry. Good.

Warren Zenna:
Please, go ahead, go. There’s no, no right, no record.

Sreedhar Peddineni:
Yeah, the… The tooling for, connecting these, all of these disparate silos has already come out at least a year ago with MCPs. And it’s going to become… we’re all used to hearing about APIs. Now we are talking about the world of MCPs, where these tools can talk to each other, and it’s a reality today.
but when I’m looking at integrating with… we try to integrate with other MCPs, and we built our own MCP, but when I was looking at it objectively. started realizing that how thoughtfully did we construct MCP? Is it more about checking a box versus what can I achieve with the MCP?
And when we’re connecting with, let’s say, Gong’s MCP versus Salesforce MCP, it’s not, the same experience. It’s like apples to oranges. But that’s going to be a problem that’s going to get solved. If you have read about Cloud Mesh, for example, they talk about it’s one… going to be the build versus buy question as well.
You can build it all in Cloud. What’s actually happening? Everybody has MCPs. And cloud can understand MCPs, and it can interact with multiple things, and you can automate your entire workflows. So, MCP’s strategy of a company is going to be mission critical, not only the MCPs that we are building, but of MCPs of the tools that we buy.
That’s going to be the connective tissue.

Warren Zenna:
Okay, I’d love more thoughts on this.
I mean, it seems like it’s gonna be a big spaghetti junction, and there’s gonna be a point where everything’s gonna have to connect, and… I also see the possibility that all this stuff would just get grabbed up by the big, large language models, and they’ll bundle together some big solution, end-to-end, and everyone gets all pushed out.
It becomes a difference between a trusted enterprise that can run things safely and can have the litigation power to take my… if they screw things up. Or I’ll go with the little guys who are more nimble and make better products and take a risk and try and tie them together.
Do we see that conversion’s coming, or is it going to continue to be this sort of, like, you know, single-point solution world where we try and link everything together? I’m just curious what your thoughts are on that.

Stav Levi Neumark
I think we’re all going to be, competitors, eventually. Maybe a different, approach. So I think that without being end-to-end system that compounds from learning, full loop, closed loop. It will be very hard to deliver true outcome and ROI.
And it’s more about, like, so it’s, like, more about, kind of, full service, the differentiation between company will be more, like, in the go-to-market space will be more what is the, or what is the type of customers that they are service, that they are serving, what is the industry. What is the vertical?
Or that it’s going to be, infra. So it’s infra services, infrastructure that go-to-market engineer will build with it something. So, this is where I think it’s going. It depends… I don’t know how much time is it going to take.
It might be, like, end of 26, or it might be, closer than that, or a bit more far, but this is where I think it’s going.

Steve Davis:
Yeah, I’ll just say, like, the history of Silicon Valley always ends up playing the same movie.

Stav Levi Neumark
I don’t know.

Steve Davis:
I don’t care what bubble it is, the dot-com bubble, the SaaS bubble, now we’re in the AI bubble. It always starts off like this, there’s great point solutions, there’s silos of data, and then they consolidate, and whoever builds the platform wins. There’ll be multiple platforms. But that’s how it ends up happening.
So if you can win your silo and show you’re the best point solution, you’ll be part of the winning platform. If not, you’ll probably be left behind. It’s… I mean, I’ve just seen it every single time. And so… and there’s a reason for it. People don’t like to integrate, even though integrating’s easier now.
whether it’s MCP or OpenClaw, whatever people are using, you know, from yesterday, changed yesterday. MCP’s dead yesterday, now it’s OpenClaw. You know, at the end of the day, customers want ease, they often want one throat to choke, and it ends up going in that direction after a year or two.
And I think, Warren, what you’ll see a year from now, you know, I believe in agent swarms, I do think it’s going to happen, it’s not reality today, but who wouldn’t want your agents learning from each other and talking to each other?
And then eventually, you’re in your silo, those agents talk to the other silos, and oh, lo and behold, we have a platform instead of all these just siloed feature, offerings, so…

Sreedhar Peddineni:
I think there’s one difference, Steve. On the movie that’s coming out right now, it’s not done yet. So, I would say, going by history, it’s going to be the Microsofts and the Oracles, and these are the companies, Googles, they’re going to win.
But with what’s happening with Microsoft’s Copilot success, or lack thereof, or AgentPost that’s been around for a long, long time, and moves that the likes of Anthropic is making right now, of late. And these companies are, like, they’re not, they’re valued already in hundreds of billions of dollars.
So, probably, in this movie, the difference probably is going to be, could anthropic emerge as the single point… single solution that integrates it all, as opposed to the usual suspects, like a Microsoft.

Stav Levi Neumark
I don’t know the answer to me. the new browser, you know?
Like, so, like, you know the concept, I think Sam Altman said it, like, liquidity product, that, that it’s, like, before there was browser, like, the first form of product in the dot-com bubble was, like, extensions for browsers, right? Like, this, a lot of, like, pinned, links.
And then, like, there was, like, web application, so… so it’s also, like, a chance that Claude is changing the interface of, like, co-work, is going to have, like, liquid product lives inside of it.
And I think that the question of… there is a lot of, like, question about whether everything is going to be skills, or, like, is it going to be UI, exist in the world, right?
And this is also a question that always comes back, but I think that terminal, like, UI is needed, and it’s also, like, going to be part of, of the future, but just in a different form, more in the kind of a… maybe design system form, or… So, I think that the product will just change their forms to a form we don’t really know, yet.
I think Google Disco. Have a great video on that, that they’re showing, like, their vision of how products in the future, are going to be. So, yes, maybe the form, we don’t, we don’t know yet.

Warren Zenna:
And Max, what do you think? Are we gonna all be linked together, or is it gonna get all globbed up by the big three, or whatever they’re called, today? I don’t know.

Maximus Greenwald
Your guess is as good as mine, man. I think the, the… I mean, we spend a lot of time thinking about the future of the website, and I think that we have historically had humans navigate a browser to go look at the front door of different businesses to evaluate things.
And I think in a full agent-to-agent world of evaluation, you may not need the, like, traditional B2B SaaS, marketing splash page anymore.
And so what’s gonna be important is, as any business, basically having a… ability to take all your information, put your best foot forward, and then let that be crawled by both humans and agents in a way that’s valuable and accessible. So, I think the answer is probably it will go to… Or if I had to take a bet, I’ll put it on Anthropic.

Warren Zenna:
Okay.

Steve Davis:
Got it.

Warren Zenna:
Well, so far, I guess that seems to make sense. We’ll see, right? So, I’m curious, how many of you are currently running AI agents in production right now? All of you, okay. I would imagine. So, could you define… What those agents actually do in one sentence.

Maximus Greenwald
I think it depends if you’re calling your agent a replacement for an employee or a title, right? Like, we have an AI SDR, but, like, what does that really mean? Or if your agents are tools that are used by humans at a sub, like, sub-agent level.
So, I guess to do it in a single sentence, and we have many in production, but Our AI SDR agent mimics the actions that a SDR takes across the channels they use to engage prospects to try to hit quota every month.

Warren Zenna:
Okay, nice job. Anybody else?

Sreedhar Peddineni:
Our weekly pipeline reviews are automated by looking at what deals are in play, extract what happened, what’s the current deal health assessment. Where we have the conversations move to more of… more qualitative conversations as opposed to subjective conversations. Weekly pipeline interviews, that’s just one of the many.

Warren Zenna:
Okay.

Stav Levi Neumark
Maybe I think…

Warren Zenna:
Yeah, Ratchet.

Stav Levi Neumark
Go ahead.

Rachit Kataria | Centralize:
Yeah, and this is more for, like, how we’re using it, or what we’re building? Curious, which framing?

Maximus Greenwald
Oh. I don’t know.

Warren Zenna:
No, I’m using it today, what does it do in one sentence?

Rachit Kataria | Centralize:
Sure. I mean, I think we’d all be terrible CEOs if we didn’t use our own products, so we automate our own deal reviews. Like, you just wake up to exactly who you talk to, who you’re missing, how you got there. We shouldn’t have to think about any time I know if my team’s in the right rooms or not.
we… we aren’t pulling up spreadsheets, we’re not looking at docs. It should be something that’s thinking for you to answer that question. So at least as it relates to any one-on-one deal review team selling. We just wake up to the answer, because the agent’s already figured it out under the hood.

Warren Zenna:
Gotcha. Steve?

Steve Davis:
Yeah, I mean, I can still watch its thunder and, like, and just in my own product, obviously, we’re using it, like, so, you know, we’ve got our whole host of prospects, they’re all doing proof of concepts on our proof-of-concept software. Right, so we use agents to help automate that. Internally, outreach intelligence.
Like, all… again, I’m so focused on mundane tasks, right? So… the old days of, I’m gonna go into LinkedIn, and I’m gonna look at the account, and I’m gonna find the people, and build an org chart. There’s no reason to do that anymore. So, have the agents do that, save all that time, and now we’re doing proper outreach that much faster.

Warren Zenna:
Great. If your AI agent Could give… you can give it one new capability that it doesn’t have today, which… what would it be?

Stav Levi Neumark
I think that’s something that wasn’t, accessible before, but now is accessible, is the ability to query… create good SQLs, query data. So it’s just, like, past few weeks. Claude had a great breakthrough on that.
So it’s a capability that they just revealed, of creating SQLs that’s actually amazing.

Warren Zenna:
Okay.

Stav Levi Neumark
Hmm.

Rachit Kataria | Centralize:
I think if agents can actually, to, the definition of memory, actually have memory at the scale of everything that’s happened till date, and actually compare across what is similar to what you’re working on right now, and infer that type of reasoning to almost, like.
The brain that’s actually doing pattern matching for you, and telling you where to go next, but…

Warren Zenna:
Yeah.

Rachit Kataria | Centralize:
Generally, that’s evolving over time.

Warren Zenna:
The content context window is a major issue.

Rachit Kataria | Centralize:
Yeah, if we can solve that problem, these things become, like, insanely powerful.

Warren Zenna:
It’ll be solved. They’re gonna compound that. I’ve seen something came out a couple weeks ago that solves this in a really weird way, but I agree, it’s a mess.

Warren Zenna:
We got a question from the audience, I want to make sure we get to it, which is, Would you draw a line between an agent that essentially is a microservice, like a task automation, versus a macro-digital employee that replaces a human? Like, what’s the line you draw between those two things agnically?

Stav Levi Neumark
I probably have an unpopular opinion here, that I don’t think that this line is interesting. I think that if a tool using, like. LLM or not, or, or if it’s, like, an automation or an undeterministic way to build it. It’s not the interesting part.
The interesting part is what is the outcome, but I let anyone else to answer the, actually to answer the question.

Maximus Greenwald
Yeah, I think…

Warren Zenna:
What do you think?

Maximus Greenwald
Well, for all… for all time, like, a, Anything, automated was a microservice, because you automate, like, one task. And then anything that, like, is human-based is undeterministic, because I choose when I do work, I choose if I want to use this tool or that tool, and I have my own kind of decision tree in my head.
And so, it’s just, in the last 5 months, you are starting to get into a world where you have An agent that can command a bunch of microservices, or sub-agents, if you want to call them, that can replace or automate, and be… undeterministic in how it decides what it does, and so, it’s sort of just a new concept I think we’re wrestling with.
I just see it more as, like, a marketing play, honestly, which is like, are you selling a product, or are you selling a digital employee?
Like, digital employee, people are… well, vendors are trying to go that way because you can charge more for it, because if a salary is, whatever, like, $100K a year, then, like, to be able to sell 100K a year software is actually pretty hard, you have to build something pretty great.
So if you, like, are selling, oh, well, you’re hiring for a whatever, you can just hire this thing instead, you really just… it’s more of a marketing play than it is, like, a technical concept.

Warren Zenna:
Gotcha. Right. Any other answer to that question? I mean, I think, like, Steve, what you said in the beginning was really good. I think it’s something about it. You leave it alone, and it does stuff on its own, with some rules. you know, it’s sort of like, you deploy it, and you walk away. I tend to agree that’s an agent for me.
It’s out there working on it on my behalf, within certain framework and certain guardrails. Whereas in task automation, yeah, I would… I don’t know, I’m not sure I was asked, but I make a distinction between an agent and an automation.
They’re not the same in some respects, like, sort of think of it as something that is out there figuring something out, you know, in a way. Well, this was great. Any other follow-up questions anybody has, you can put them in the chat, but we have about, 3 minutes left, before this great panel is over. I think at the end.
What we really come down to, the follow-up here, is… Agents are, much more defined now, I think, than they used to be. I think we’re getting closer to knowing what that is. I would say right now, incentives seem to win. The CRO or the revenue leader owns this right now, because they’re incentivized to.
They’re the ones who want to get the outcomes, but I think, frankly, it’ll probably end up with IT or something, because there’s going to be security issues and other stuff like that, that ultimately is going to take over.
you know, I think that right now, it is an incentive-based ownership, and I think the CRO probably is the one who really should be thinking about this stuff most. And in terms of a risk, you know, I completely get it. It’s about making sure you have the right relationship with your clients, and they know that you’re sort of experimenting with them.
You have to have a really different kind of relationship with them. You’re kind of playing, you know, chess with them in a way. We have a question. If you have multiple teams building, how do you wake certain… make certain you don’t end up with multiple agents burning credits trying to do the same thing?
And what happens when the guardrails change frequently? Can agents still be effective? So I think the first one’s interesting. How do you end up with multiple agents burning credits trying to do the same thing?

Steve Davis:
Yeah, talk to any enterprise, they have that problem.
So it’s a great question, they all have that problem right now, so whoever invents the, the guardrail software to make sure everyone can’t build their own stuff, build burning tokens, yeah, solve that problem, you’ll be pretty rich pretty soon, but… Every enterprise company we talk to, every department’s vibecoding, they’re doing this stuff, and half of them are creating the same things, same tools.

Sreedhar Peddineni:
I’d be happy to have that problem, organization.

Steve Davis:
No, you’re.

Sreedhar Peddineni:
We are building it for the right reason, at least we have the basics ready. We are an AI additive company already. Now, it’s more about alignment. That’s a good problem to have.

Warren Zenna:
Yeah, I agree, agree.

Stav Levi Neumark
Yeah, I encourage, like, if it’s two people in the company coming and say they want to solve the same issue, but they have a different perspective on it, I would say go for it, both of you, and collaborate in the future if it’s connected, and mostly the outcomes and the, like, if someone really owns it and owns the goal and the outcome.
So, the ROI is much, much, much more, many times from the credits it’s spent. So, it’s a… it’s a great problem to have, in my opinion. Got it.

Warren Zenna:
All right, well, I think we’re at time. I’m assuming that we are. I know we had an hour, it’s at 1 o’clock, so… Hey, Julia!

Julia Nimchinski:
Hey, Warren, amazing panel, thank you again, and before we wrap this up, can we do just a minute of shameless plugs? All of you? Let’s start with you, Warren. How can our community support you? Sure.

Warren Zenna:
Thank you, yeah, so, you know, the CRO Collective, we, focus specifically on Chief Revenue Officers and their success. We, help them develop their skills, and, we’ve defined the role, I think, most sharply. And we also build CRO-ready organizations.
These are companies that are looking for a CRO, or are actually in the process of deploying one, and make sure they’re ready for one so that the CRO gets the full potential of the role. Thanks for having me, Julia.

Julia Nimchinski:
Awesome, always a pleasure. Let’s just do a quick one. Max, how about yourself?

Maximus Greenwald
If you want warm leads, come to Warmly.ai.

Julia Nimchinski:
Rasheet.

Rachit Kataria | Centralize:
If you’re trying to multi-thread, break into enterprise accounts, and make sure you’re in the right rooms, we are the place to remap all that out automatically.

Julia Nimchinski:
Staff.

Stav Levi Neumark
You want to transform your go-to-market and boost it with AI, so AltHQ.com.

Julia Nimchinski:
Steve?

Steve Davis:
If your company is running proof of concepts to sell your product, come talk to us.

Julia Nimchinski:
Sridhar?

Sreedhar Peddineni:
If you want your reps to run their deals effectively and get to that clothesline faster, come to GTM, buddy.

Julia Nimchinski:
All of this. Amazing. Alright. Picking it off.

Warren Zenna:
Thanks again.

Julia Nimchinski:
Diane.

Stav Levi Neumark
Word.

Sreedhar Peddineni:
Thank you.

Julia Nimchinski:
Thanks.

Rachit Kataria | Centralize:
Appreciate it.

Warren Zenna:
Everybody, thank you.