11x or 0x? What it really takes to make agentic AI work

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Julia Nimchinski: Elias Blake. We are transitioning to our next session and welcome. Mary Shea

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Julia Nimchinski: Murray is an operator, analyst innovator, ex Forrester and outreach and the session. This session got

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Julia Nimchinski: probably really a lot of traction, if not most so super excited, to kick things off. Mary.

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Julia Nimchinski: how are you doing.

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Mary Shea: I’m doing great. It’s wonderful to be here, and I’m pretty excited about the next couple of days with you all some fabulous and important topics to cover.

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Julia Nimchinski: Amazing. Let’s do a quick round of introductions.

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matt cooley: Quick heads up on my side. Usher had something come up, so he was apologizes. He can’t be here. I’m his co-founder at bounty, so I will be filling in last minute scratch. But it’s nice to meet all y’all.

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Mary Shea: Fantastic thanks for joining us.

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matt cooley: Of course.

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Julia Nimchinski: What’s next?

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Matt Millen: I can jump in. Hello, everybody! My name is Matt Millen.

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Matt Millen: I am the co-founder of Reggie AI.

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Matt Millen: Reggie has been a pioneer bringing. Go to market Team AI to go to market teams the last 4 years for the 1st AI scp where predictable outreach meets AI agents. One thing I will say I’ve been selling leading sales teams for 30 years.

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Matt Millen: and I’ve had the advantage of watching technology coming into the sales process. And you start to see patterns as new technology cycles come in, it starts to solve some problems, and then it creates the new generation of problems that need to be solved. And once again, here we are with AI solving a lot of problems for us and also helping us think a little differently as we move forward with the technology. So it’s great to be here with everybody.

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Mary Shea: Awesome. Thank you so much, Matt. And yes, there are always trade offs right. But I look forward to digging into some of those topics a little bit more deeply as we get into things here. Who’s next.

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matt cooley: I apologize. I didn’t give you an introduction. I was just making the statement that I this is not my face on the screen. I’m Matt Cooley. I’ve been in Sas for about 25 years now work with companies like do the 0 to 120 with new relic. Mix panel.

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matt cooley: quip, and then salesforce by way of quip, ended up joining bounty in 2022 as co-founder as we relaunch company, and we bounty focuses on basically

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matt cooley: we started in the in the position of actually using AI plus humans to deliver outcomes for Sdrs and sales folks that can go and and find, like custom outreach at scale for customers. And we’re quickly moving in the direction of also include, inclusive of the marketer and how they can actually use account based marketing through AI and humans to be more effective in a holistic marketing and sales approach

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matt cooley: so awesome.

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Mary Shea: Yeah. Great to great to see you, Matt. Thanks for joining us.

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matt cooley: Sure.

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Mary Shea: And I think we’ve got stuff next next to you as well. I remember you from our last session.

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Mary Shea: Yes, how are you? Great.

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Stav Levi: And I’m with a cocktail, thanks to John session, and it’s already 9 pm. Here. So I’m going to be a bit drunk, thanks to John and the cocktail session. I see you have one, too, so I’m staff co-founder and CEO of Alta.

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Stav Levi: I’ll take an AI data-driven revenue workforce we have Revops and Sdr. That work best together, and our approach is data-driven AI Sdrs that helps with outbound work, and my background is one of the 1st [email protected]. I built over there a tool called Big Brain.

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Stav Levi: that basically did it before the Llm revolution. So it was like 10 weeks for revenue employees. And then my last job over there I led the growth organization where I had the privilege to to use all of this amazing amazing tools that we built internally. So yeah, we’re taking a lot of those concepts into Alta.

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Mary Shea: Awesome. Thank you so much, Staff and, Sam, you’re up next.

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Sam Stallings: Hey, guys, I’m Sam, co-founder and Cpo of artisan. We’re also doing an AI Sdr, ava. She automates the entire outbound sales process before artisan. I was working at Ibm on the Watson team, which is one of the 1st agents that won Jeopardy and now, excited to be working on agents.

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Mary Shea: Awesome. Thank you so much. And I think, Rohan, we need to hear from you.

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Stav Levi: You’re on mute.

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Rohan Suri: Very classic nice to meet everyone. I’m I’m Rohan. I’m a founder and a chief product officer at Nooks. Next is a series. B Company A startup that builds AI sales assistance. For you know, Str and Ae teams one of the unique things about nooks is we focus on not just email, but also calling. So we have big focus on the phone as a channel for outbound and build agents around calling as well.

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Rohan Suri: And my background is, I studied AI Research at Stanford, and then been doing it since. So.

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Mary Shea: Awesome. Thanks, Rohan and Julia. I think I got everybody right. I haven’t missed anyone. And, as usual, this is a blue chip panel. Did you want to say a few words before we kick off? Julia?

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Julia Nimchinski: I just realized. Sam, did you introduce yourself.

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Mary Shea: I got Tim. Yep.

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Julia Nimchinski: Yeah, yeah. Awesome. No? Then let’s just kick things off.

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Mary Shea: Let’s do it. And I love the cocktails, coffee and water vibe that we going get going on here. It’s a true global conversation, which is always amazing. So Julia talked a little bit about my background, and I’m not here to give any reveals. But I am in stealth mode, and I can’t wait to share my next venture with all of you. And hopefully, one of these

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Mary Shea: future conversations. I’ll be a panelist who knows but harkening back to my time at Forrester when I was an analyst there in 2015, I had this really exciting vision about what the world was going to be like for the b 2 b sales rep, and that vision was in the midst of

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Mary Shea: my friend Andy Hoare, saying, You know it was the death of the B. 2 B. Salesman or salesperson, and we see that that has not happened. But I was just reading some research by Dr. Howard Dover, a friend of ours from University of Texas, who said, in the last 2 years

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Mary Shea: we’ve seen the decline of sales reps by about 2 million, and so we are entering in a new generation of selling and buying that is going to be supported by agents on both sides. And I’m really excited to kind of explore some of the technologies that you are using

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Mary Shea: some of the processes and ways that you’re activating and collaborating with AI agents. I have a view that we will have an agent on every team, whether that’s a startup or go to market. Sdr. Marketing. And what have you? And it isn’t so much the disintermediation of the human. But it’s the collaboration and connection between the agent and the human. And our role as a human is going to be different.

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Mary Shea: We’re going to curate. We’re going to evaluate. We’re going to analyze, but we’ll be able to do so much more, so much more quickly. And so I’m looking to delve into that today. Let’s start with Matt Millen.

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Mary Shea: You know, one of the things I think that’s overwhelming for professionals is. There’s so much buzz about AI and Agentic AI. And you don’t really know where to start in terms of some of the tools like, How are go to market teams? Or how should Cros Cmos start to think about evaluating some of these different options, to bring into their teams and organizations without getting overwhelmed. What kind of advice do you have.

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Matt Millen: Yeah, it’s a great great question, Mary. I would say.

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Matt Millen: you know one of the things, and all of us on this panel, you know, pipelines are always been a problem and always been an opportunity for revenue teams. I think what’s different is that now, workflow is the problem like sales workflow is broken. 75% of sales leaders want more rep activity. 77% of sellers don’t complete their tasks. 66% of reps are drowning in their tools, and what we’ve created is doing, you know, less, with more.

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Matt Millen: And like, we’ve got to start with the workflow first, st not not the solution. But really go back to the workflow of how can you effectively generate pipeline today? And and I think there’s like 2 very discrete workflows that we focus on, you know the first, st the 1st is the tier, one workflow, and that’s where you put your best reps on your best accounts, and they do their best work.

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Matt Millen: and AI agents can absolutely assist reps. You know, Mary, you talked about like the thinking side and the creative side of of the work. And you know, the left brain, right brain. And really, how can we leverage AI to do more of that left brain work, the list building the research and really free the rep up to be creative, empathetic, and have conversations so that it’s that tier one workflow where.

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Matt Millen: you know, companies are very focused on either their best prospects or those inbound hand. Raisers like those are your highest converting now. They’re also like your lowest volume.

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Matt Millen: like you only have so many of them. So you have to be great every time.

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Matt Millen: The other side of that equation is this other workflow that has been long overlooked, which is the long tail of your Tim.

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Matt Millen: And really, that’s the mass number of

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Matt Millen: tier, 2 tier, 3 tier, 4 leads low prioritization, and oftentimes whereization is being ignored, signals are getting dropped or ignored. And the beauty is is that AI today can help on both sides of that equation. We can free the reps up to do more of their best work with their tier. One opportunities

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Matt Millen: and agents can work that long tail of the tam where you have lower conversion, but a much higher number of prospects to work signals to induce and then act upon and then feed those reps as the signals warrant some direct activity.

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Matt Millen: So I think that’s a great place to start. And I think revenue leaders are, you know, really looking at their workflow first, st because they understand that what their reps are doing today is broken, and we have to fix that first.st

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Mary Shea: Yeah, great great points. I’ve taken 2 2 threads from what what you’ve said. Matt, one, which is, don’t go out and buy a bunch of tools until you’ve kind of buttoned down on your internal processes and workflows and and have those really tightly locked in. So the tech isn’t going to solve your problems. If you have messy workflows, and and you’re not really clear on how to engage right? So I don’t want to put words in your mouth, but it

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Mary Shea: is that fair to say.

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Matt Millen: It is fair to say, and and again, really focusing on how you want your reps to spend the day, and how can you liberate them to be great.

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Mary Shea: I love that

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Mary Shea: liberate your reps. You know. One of the things I thought about back in 2015 was that

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Mary Shea: the role of the sales. Rep was going to become much more rewarding in the future, and today is the future than they had been in the past, because a lot of the rote activities that were really boring the heavy lifting, the repetitive nature of the role, the you know, continuous rejection. Not that the rejection will ever go away in totality, but that

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Mary Shea: what you described to me sounds like a quintessential business role like you’re you’re freed up. You’re working with the AI. You’re pointing it in the right direction, and you’re allowing yourself to have more creativity and more more ways to engage with personalization, empathy and deepen your connections with buyers, I think, is what you’re saying. So I wonder, you know, if we’ll see.

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Mary Shea: I want to see who wants to answer this question. But if we’ll see a different type of profile wanting to go into sales than we’ve seen in the past. Is it going to be, you know, maybe people from tier one business schools, or I don’t know who has a perspective on this Matt Cooley, you wanna weigh in. Do you think we’ll see a different type of rep wanting to pursue b 2 b sales than we have in the past.

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matt cooley: I don’t. I don’t. I don’t think so. I think the roles will change. The entry points will change. I mean that sales is inherently in in somebody’s being right. There’s certain, you know, personality types that are great for sales. And you’re still dealing with people. And I still believe that people buy from people I don’t think people will ever buy directly from AI. I do think that there’s the old product led Saas models that.

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matt cooley: yes, like Atlassian in the early days. Earlier days they could. They claim they didn’t have salespeople, but they really did. It was just behind the scenes. And so there’s always a human touch point and

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matt cooley: you know, the average deal size of a lead or a deal that came in with human touch was 3 times the size of a deal that came in on its own, and the churn rate was a lot lot higher, right? So I think that human touch is going to be important. I think the entry point for sales with with AI around Sdr support

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matt cooley: it will be junior sales roles versus like out a lot less outbound. The Bdr role in general is, is is dead in a way that not AI is going to fully replace it. But it is. It was totally broken because you couldn’t get quality outreach out at scale. So what it turned into was a bunch of

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matt cooley: cadences through outreach and gift carding to come to Demos. And like we’re sitting and waiting for those Demos. But the laws of averages these are all unqualified, Demos, and it trickled all the way down into the into the the sales funnel and ultimately into closed deals. So the conversion rates suffered. So what I’ve seen is AI, and we believe that

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matt cooley: we believe in what we’ve seen, and I’ve been a buyer of it before dumping AI directly on a customer sales team is ineffective, right? And

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matt cooley: it will get you 70% of the way there. But it’s just like Chat Gpt. If I’m going to use Chat Gpt for anything. I’m still going to do 30% of the tweaking to make it sound human because it’s robotic. And it’s obvious, right? And people have AI fatigue today.

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matt cooley: So what we do is we have a team of individuals in between the delivery of AI to a customer. We believe that our AI plus our humans will get you to judgment calls on outcomes.

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matt cooley: Right? 5% of the work is on you instead of 70% of the work is on you. And we’ve seen a lot of success with that. And so I don’t think the type of person coming into sales is going to change. I think the ramp time into really selling is going to change. And it’s like this is the way I think about it is.

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matt cooley: it’s the old saying of like one person’s trash is another person’s treasure. You take the bottom 30, 40% of what Bdrs are doing today, and you shift them up right. The sales team. The sales reps bottom 40%, they shift up and you get everybody in the organization gets closer to the customer.

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matt cooley: Right? And I I just, I just believe that’s a super important part, because in the last 10 years I’ve seen too much talking about doing the work and not doing the work right too much talking about and preparing to get on a customer call and not actually making the call, and it’s become completely bogged down. But I do think AI is important, but I do think there’s an inner like. There’s an intersecting point between. It reaches

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matt cooley: your customer sales team that has to be tweaked by humans, and we do both at bounty and if you, if if that is the strategy, you can then put folks right out of college into sales, because now they’re just making judgment calls.

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matt cooley: and then you can share the best practices around what’s working and what’s not working, based on how they tweaked it. And now you can onboard people faster, right? And so the ramp time for somebody new coming into a company, the workforce or into a sales role is a lot shorter, right? So I don’t think the personality changes. I think the the job changes out of college, which is exciting. That’s why they went into sales.

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matt cooley: and Bdr rolls hard. This is why it’s got a 1 year tenure. Either get promoted or you move.

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Mary Shea: Yeah, it’s it’s, you know, it is lifeblood of any selling organization. And it is the most difficult role that’s out there. I’ve done the role. And I’ve done some demand Gen. Consulting lately, and you know it. It is really challenging, especially with information overload and folks being, you know, sort of

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Mary Shea: immune to Linkedin in mails and emails. And it’s just really challenging to cut through the noise. I do agree with you that there’s a level of customization that absolutely has to happen with every interaction in AI. If you leave it as is, it’s just not going to work the human piece of it is so crucial.

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Mary Shea: Stav. I know you’ve got a kind of a point of view on this topic. I I’d love to have you have. Have you weigh in like? How do you see? You know, I guess I could go at it a little bit more with Matt, because I do. I do actually think

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Mary Shea: the demographic of of the persona of the salesperson is going to change a little bit. I think the

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Mary Shea: sales organization is going to be smaller. I think we’re going to have less of the specialization across the cycle, but not about me. I want to hear what you guys are thinking. So step. What? What do you? What are you thinking around, AI, and how that’s going to change the dynamics of the selling organization?

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Stav Levi: So I think you cannot compare like, and say that all of the sales world are kind of the same, because there are like transactional sales, and there are named accounts, and each one of them like operates completely, differently. And

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Stav Levi: what I think is that, for example, transactional sales is going to change dramatically like in the next few years. I think, like AI is going to take over much more of the work over there, and maybe like more of an Ops. People will be kind of the new sales, people of businesses doing transactional sales, and I think that more and more sales reps are going to be

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Stav Levi: like kind of transitioned into more kind of a field sales, or named account approach by being more in personal touch with the account, but can manage much more account in parallel, because they have, like someone that help them, and they can do only like the human connection. Like, be kind of a strategic advisor to

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Stav Levi: the customer and using AI to support his workflow.

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Stav Levi: So so I do think that the the people that searching like sales world, it’s also like different from company to company like when I was in Monday. So in the beginning we had a lot of like transactional sales later on, it’s like, so it was very different type of people in the sales. Then later on, and yeah, so so yeah, I think basically, that’s it.

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Mary Shea: Yeah, I mean great point. We we talk about b 2 b sales as if it’s a monolith. And it’s obviously not right. You’ve got high volume. You’ve got transactional, you have enterprise. You’ve got plg, so you know. Thanks for bringing that up, I think, on the high velocity transactional type of selling motion. The salespeople are gonna have more of a micro marketers, mindset. Really, that’s kind of what you’re doing. Right? You’re micro marketing. And then on the Enterprise side.

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Mary Shea: I think you’ve got much more time to be really thoughtful and to really take that advisor consultative role to a next level in partnering with the AI and moving off all of the downstream activities that typically would burn all your time. Right? So so great points on on different type of selling motion. Sam, I’d love to get you. I’d love to get you in here. Thinking about

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Mary Shea: some of the types of clients that I work with and the demand Gen. Work that I’m currently doing. Most everyone wants to bring in an AI agent to amp up that process. But what I find is people don’t exactly know where to start. How do you know if your you know your team, your org is ready to take that next step and embrace AI in your outbound selling motion.

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Sam Stallings: Yeah, I think there’s really 2 things. And we really felt this at the beginning is we were product led to begin with. And then we transitioned to sales led. So what we learned from that was companies that don’t have product market fit yet. They do want to do outbound, but they’re not quite ready to do that yet, and they’re not going to see the results that they’re expecting. So that’s 1 thing I think a good signal is like tech stack maturity. So if you’re already using these automation tools like outreach

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Sam Stallings: and any other sales tools, clay and you kind of want to stitch them together. You’re wasting time connecting the dots between them. I think that’s like a good moment to come. Look at an AI Sdr. And put that into place with your team.

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Mary Shea: Yeah, awesome, awesome. Thank you for that. So so, Rohan. You know one of one of the challenges that I have, and I spend a lot of time looking at technology is just kind of keeping up

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Mary Shea: with the pace of change. And some of the new tools that are available. Like, what kind of advice do you have for business leaders and go to market leaders in particular for staying on top of what’s what’s possible today as well as what’s coming down the path in the future.

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Rohan Suri: Yeah, I think it’s really interesting. I mean, this is why kind of the AI space is so fun and exciting. It’s like new models are coming out like literally, every you know, every month. What is possible today isn’t is different than what was possible 2 months ago. And what’s impossible right now. And so I think it’s actually created quite a bit of a challenge for for buyers to kind of almost decide like, you know, where do I, where do I invest? What’s possible? What isn’t possible?

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Rohan Suri: So I think there’s a couple of things that come to mind.

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Rohan Suri: one is at nooks. We’ve actually.

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Rohan Suri: we actually invest in doing trials with all of our customers. So I think it kind of breaks through a lot of the skepticism around AI and seeing, hey, like, we actually try to show results with people. So like, you know, that helps buyers kind of build trust. And into the solution, I think the other thing is that, like as an organization in general, you just need to be experimenting all the time. And so you need to.

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Rohan Suri: I I can speak. I mean, we have a lot of founders here, I’m sure, like, you know, one of the things that we do at nooks is, we have like, really short. We don’t plan more than a quarter out, because, you know, with the technology changing so fast, I mean, you have broad strokes. But you know, you can’t actually, you know, do that far planning. So it’s actually kind of created an interesting challenge for AI companies in general. In terms of like roadmap planning with the new models coming out so.

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Mary Shea: Yeah, it’s funny that it’s interesting that you say that because I spent a lot of time following what Dr. Lisa writes about Dr. Lisa Palmer, who’s a leading expert in the world of AI. And she says, if you’re you know, you’ve built your corporate business plan, your strategic plan, your go to market plan, and you have your annual plan in place, and you haven’t updated it within at least 3 to 6 months. You know you’re well behind, because so much is changing. So I think we have to be a lot more dynamic and pivot

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Mary Shea: have those plans, but be willing to break them, as we learn and embrace new opportunities.

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Rohan Suri: Yeah. And we’ve seen and we’ve seen actually like, that’s 1 of the things that I think buyers also look for is like they almost like want that help from a vendor. It’s like like, who can I work with that, I know, can just like keep us, you know, in the latest. And so we’ve actually seen that as part of the valuations is like, you know, is this a company and team that I think will be that partnership down the road. And so that’s another another thing. I think we’ve seen as more and more of a trend because of how fast AI is going.

714
01:55:09.140 –> 01:55:28.790
Mary Shea: Yeah, absolutely. So I’d love to just kind of talk about buying and selling at sort of a higher level and and understanding that you know there are different buying and selling motions across the board. But how do you all really think that AI is going to play a role in the buying and selling process.

715
01:55:29.050 –> 01:55:31.610
Mary Shea: Yeah, today. And in in the future.

716
01:55:32.710 –> 01:55:34.670
Mary Shea: Stav, you wanna take that on.

717
01:55:35.580 –> 01:55:50.243
Stav Levi: Yeah, so I can start by, maybe. Looking at this meeting, and then see how many of the of the people of this meeting are not real people in their agent. I don’t know if you see it in the, in the like, in the in the list of participants.

718
01:55:50.560 –> 01:56:01.679
Stav Levi: and I think, like one of the things that’s going to be dramatically changed. It’s like that there are going to be assistant that is going to actually be an active part of the conversation.

719
01:56:02.036 –> 01:56:31.660
Stav Levi: That is going to help you doing the sales like like I said before, like you’re going to be the selling motion, going to be more like a strategic advisor. And if you need, like, kind of a solution engineer that knows the product the best and like, know all the features, and of course, with the roadmap that changing every month, and like the like, Rohan said, and the plans are changing a lot. So it’s going to be much more hard to keep up of like.

720
01:56:31.660 –> 01:56:56.510
Stav Levi: what am I selling? What is the capabilities like? Everything is going to change really fast? So I think, like, this is something that’s going to be dramatically changed. We see it in Alta that we are using the conversational data to feed in the outreach process, like at the beginning of the funnel. So like, learn from this very precious

721
01:56:56.510 –> 01:57:01.789
Stav Levi: data of like conversational, that selling like a

722
01:57:01.790 –> 01:57:23.549
Stav Levi: the pain points that are being tackled in a conversation, and the proof point that the sales people using in a conversation to use this information for better outreach and better messaging and better targeting.

723
01:57:23.550 –> 01:57:47.829
Stav Levi: The data can be much more in use and accessible throughout this process. And I really like what Matt said in the beginning of like where to start like where a company is going to start with like. So it’s like small experiments on the long tail of theirs, and then, like putting into their largest larger processes, because, like the

724
01:57:47.850 –> 01:58:01.841
Stav Levi: also like buying and selling process is like going to change a bit. You want to experiment on the on the small portion, because we’re like we’re going to a phase where AI product which is

725
01:58:02.230 –> 01:58:28.890
Stav Levi: little bit less predictable in the old version of Sas. And you don’t know exactly how well they’re going to impact your process. And you want to know. And this is like we are seeing it like, I think like in the in the businesses adoption. And like, I think, most Sdr. That I’ve spoken with doesn’t use a tool like us like doesn’t use any AI, Sdr, and they’re using. But they’re using a lot of AI, they’re using today many tools.

726
01:58:28.890 –> 01:58:44.869
Stav Levi: So I think when we are moving to an AI product and the selling process is going to look differently. And I really connected to the Matt’s point of like starting like testing some low risk areas

727
01:58:45.598 –> 01:58:58.989
Stav Levi: and then like move on to to like a real buying. So it’s like experimental kind of revenue, and and moving to like a real retention and customer usage.

728
01:58:59.300 –> 01:59:16.579
Mary Shea: Yeah. So I think it certainly makes sense to start with a tight, you know, bookended type of experiment. Right? So you’re not going to your straight to your tier one opportunities and and experimenting. But looking at some of the opportunities that might be

729
01:59:17.460 –> 01:59:33.329
Mary Shea: you know, sitting a little bit stale, and seeing if you can rejuvenate them, and so on and so forth. I’d like to dig in a little bit deeper to the data. You know, we always talk about data. But I’d like to unravel that a little bit more like what you mentioned. You know you can

730
01:59:33.600 –> 01:59:57.880
Mary Shea: get better messaging. You can improve your value proposition, and you can improve customer engagement through really doing deeper analytics on data. So how? How would you do that? And is it? Is it? You know, evaluating call information and objections and tell us a little bit about how you would use the data specifically to improve a customer Interaction.

731
01:59:58.380 –> 02:00:24.549
Stav Levi: Yeah, so so there is like, 1st year data, I think, is the most obvious, unused and many times crappy data that companies have, like their Crm as an example. And I think many times when people coming to products like us, they’re expecting kind of a magic to happen like, or is it like work in a magic, or is it not not working at all?

732
02:00:24.550 –> 02:00:47.380
Stav Levi: And I think that the heavy lifting of like understanding what kind of campaign you want to build what kind of hypothesis you’re coming with. So you need in order to do it in a right way, you need to have kind of understanding of what business is working well for them today, what businesses are less successful which targeted persona, they’re usually targeting.

733
02:00:47.890 –> 02:01:11.979
Stav Levi: So this is like an example for the 1st tier Crm data. There is like also a lot of knowledge that they’re like sales enablement and knowledge that the company is nurturing, which is like blog posts and success stories. So there’s a lot of content and and sales enablement materials that they wrote for

734
02:01:11.980 –> 02:01:25.010
Stav Levi: the salespeople to use. So we we like one of the things we’re doing when company onboarding with us is 1st of all connecting to their crm, connecting to their sales enablement materials

735
02:01:25.010 –> 02:01:26.820
Stav Levi: and train

736
02:01:26.820 –> 02:01:39.560
Stav Levi: Katie, which is our aisdr based on their data. And then Luna, which is a revops, is feeding the hypothesis, giving the insights and actually help building a campaign and a better messaging.

737
02:01:39.984 –> 02:02:02.469
Stav Levi: So so it’s like, prioritization is part of it like, who should I send first? st Because in the end, like, you cannot send an endless amount of emails or Linkedin connection or phone calls every day. So you must cherry, pick the ones even with ais. There is not like infinite amount of

738
02:02:02.470 –> 02:02:24.540
Stav Levi: you don’t want to spray and pray like you want to prioritize. Who is doing the right audience like who is the right audience the right intent, like the the right timing for them to buy. And for in order to do that, you need to get like what a real Sdr. Will do today, which is, use perplexity to understand, like the customer.

739
02:02:24.540 –> 02:02:26.200
Mary Shea: Shoes. And yeah.

740
02:02:26.200 –> 02:02:55.279
Stav Levi: Yeah. So so I talked to many Sdrs like lately that did not use AI Sdr tools, because I want to understand, like what they’re doing that works great and a lot of them using like researching tool, like perplexity, for example, and and doing like all kind of research about what they can understand from 3rd party data and internal data like working with their internal team and wrap ups to understand what is their hypothesis? Who is the targeting on is that they got to manage, etc.

741
02:02:55.960 –> 02:03:11.030
Mary Shea: Yeah, what? What you just said. Staff sort of brings me back to Matt Cooley’s comment, which is, you know, very short ramp up times and junior folks being able to jump in and be very successful quickly. We’ve never been able to crack that code. And I think we can now, because

742
02:03:11.030 –> 02:03:27.699
Mary Shea: basically, if you just go to your whether that’s perplexity, or Claude, or whatever it is you use, you can immediately understand the issues of the persona the industry, and drop in a couple of current articles and come up with phenomenal outreach. That is

743
02:03:27.700 –> 02:03:30.200
Mary Shea: perfect for that individual persona

744
02:03:30.538 –> 02:03:39.749
Mary Shea: and I’ve been experimenting a lot with that lately, and I think the quality is pretty high, especially when you start to edit in sort of the human

745
02:03:40.052 –> 02:03:43.340
Mary Shea: component in the language as well. But I want to put a philosopher.

746
02:03:43.340 –> 02:03:54.930
Stav Levi: You cannot do it, you cannot do it in mass. By the way, if you want to target, for example, I don’t know 1,000 people in in 1 h. It will be super expensive, like.

747
02:03:54.930 –> 02:03:57.110
Mary Shea: Totally expensive, right?

748
02:03:57.110 –> 02:03:59.240
Stav Levi: Elements that you’re you’re targeting.

749
02:03:59.240 –> 02:04:12.762
Mary Shea: Yeah, the the compute costs are are scary. I’m I’m learning a lot about that right now. So you do have to be targeted, or else you can run up a pretty quick bill with your provider. But I I wanna

02:04:13.280 –> 02:04:17.770
Mary Shea: just put a philosophical question out there and and feel free. Anyone jump in. But

751
02:04:18.430 –> 02:04:21.389
Mary Shea: if if you’re on the receiving end of

752
02:04:21.580 –> 02:04:26.790
Mary Shea: an outbound communication, it could be email, DM, or even text.

753
02:04:27.130 –> 02:04:32.340
Mary Shea: And and it really touches on your

754
02:04:32.690 –> 02:04:40.720
Mary Shea: challenge at the moment, or a potential upside opportunity. But you kind of know it was probably generated by AI. Do you care.

755
02:04:41.730 –> 02:04:42.580
matt cooley: I do.

756
02:04:42.690 –> 02:05:02.450
matt cooley: I do again. It goes back to what I said around. People still buy from people. It’s creepy to me, and I delete it immediately right? And the thing that AI can do like again. AI is going to take you only so far right. And if you’re just sending flat AI emails directly out to customers. It’s going to sound like it.

757
02:05:02.480 –> 02:05:15.340
Mary Shea: I’m not saying, like flat, with no editing of, let’s just make the assumption that everyone understands that at a minimum. You’ve got to add 30% of humanity or personalization in. But you know, it’s almost like

758
02:05:15.600 –> 02:05:32.410
Mary Shea: the outbound is so perfectly attached to my needs and concerns that I’m like, I’m sure this rep went to, you know, Claude, and pulled this out. Are you gonna delete it? Are you gonna hold that against them, or are you gonna be like. Well, this this really relates to what I’m worrying about.

759
02:05:32.710 –> 02:05:49.230
matt cooley: I think it comes down to. Are you gonna open it? Right? So. And you can tell on the subject line like for me. There’s like a few reasons why I would open an email right? Very rarely is it trying to like in a standard format, connect some level of like

760
02:05:49.330 –> 02:05:57.060
matt cooley: generic solution, their solution tied to what our company cares about. You’re gonna either make me laugh.

761
02:05:57.230 –> 02:06:13.400
matt cooley: You’re going to stroke my ego somehow. You’re going to connect with something in my personal life like where I went to college, or I played baseball. Or if you went to the same college, you’re going to create Fomo or anxiety for me. But you’re not going to catch me on some.

762
02:06:13.540 –> 02:06:18.989
matt cooley: you know, headline subject subject line at all. But if

763
02:06:21.240 –> 02:06:27.643
matt cooley: yeah, and it’s clever. I mean, it’s just the truth, though. I mean, like, I had an email come in the other day that

764
02:06:28.020 –> 02:06:42.540
matt cooley: we’re actually experiment experimenting with the sentiment that goes out on emails. It was a Haiku. And I was like, this is really clever. Now, the return rates on people actually responding to emails are so low, but that’s 1. If I was going to, I would respond to.

765
02:06:42.540 –> 02:06:49.070
Mary Shea: So. So that’s clever. But you know the rep didn’t write the Haiku, and do you care.

766
02:06:49.510 –> 02:06:53.790
matt cooley: Well, potentially like, if if it sounds like AI,

767
02:06:54.350 –> 02:06:59.845
matt cooley: I think it’s actually you know what I I don’t care in that situation because they thought about it differently.

768
02:07:01.060 –> 02:07:06.110
Mary Shea: Some creativity, some personalization, and some connection to who you are.

769
02:07:06.110 –> 02:07:24.580
matt cooley: That is absolutely personalization. Right? It’s a choice on how they used AI differently rather than just auto generating something that’s just not gonna connect to anything that I care about right. They could even use AI to say, Here’s Matt’s Linkedin. Find me something personal that’s cool that he might care about.

770
02:07:24.580 –> 02:07:25.080
Mary Shea: Yeah.

771
02:07:25.080 –> 02:07:33.030
matt cooley: That’s personalization, right? It’s just the person before the AI. So I would open those much more often than I would anything else.

772
02:07:33.030 –> 02:07:39.650
Mary Shea: I guess what you’re saying is, you’re okay with it, but they better use it to its fullest extent to make me feel

773
02:07:40.860 –> 02:07:42.659
Mary Shea: connected as a human.

774
02:07:42.660 –> 02:07:57.489
matt cooley: Exactly. And I I would know in those scenarios like the Haiku, was someone’s choice, right? And so they were thinking about it, whether they were using it in a way to connect their solution to the business value of my my solution and my company

775
02:07:58.280 –> 02:08:06.219
matt cooley: I’m okay with. I’m okay with that approach as long as it is thoughtful. And it’s different and thoughtful and different has to come from humans.

776
02:08:06.700 –> 02:08:18.789
Mary Shea: Okay, I like that. But it’s actually thoughtful and different, probably coming from the curation and the prompting rather than the actual output. But yeah, that Rohan. Do. Do you want to weigh in? I felt like you had.

777
02:08:18.790 –> 02:08:19.878
Rohan Suri: Yeah, no. I think

778
02:08:20.150 –> 02:08:20.900
Mary Shea: Yeah.

779
02:08:20.900 –> 02:08:44.490
Rohan Suri: Yeah, I think ultimately like the buy. I think it does matter whether it looks AI. And then the reason is, people just have these like mental spam filters that like will just remove things. And so it’s it’s less about whether it’s AI or not. Or does it? Does it look different. And does it like catch my attention? And AI can output those things. And and to Matt’s point, I think you have to. You know it matters, and how creative you are, and and like the same.

780
02:08:44.490 –> 02:09:01.949
Rohan Suri: you know, very few plays will work consistently because they get saturated, and you have to try new things. And so I think, you know, people think that like, you know, AI Outreach is just like once you just turn it on, you can just leave it and let it run. And no, you have to basically be constantly working it, using it and like.

781
02:09:01.950 –> 02:09:03.629
Mary Shea: You have to mind it. Yeah.

782
02:09:03.630 –> 02:09:19.469
Rohan Suri: I’ll give an example, one of our top converting plays that we ran last last quarter, that it was a really simple email. But we are, you know, our enterprise. Sdrs, they would actually get very unique insights on orgs by calling into

783
02:09:19.780 –> 02:09:43.600
Rohan Suri: Ics at the company. And so, you know, we obviously sell to sales teams. So they call reps at team and get information. What’s tech stack? They’re using what problems are facing their Q 4 initiatives. And then they would email their boss. And when you get an email that’s like from someone’s bot, you know that says, Hey, I spoke to these 3 people at your org. And it’s like hyper, personalized, relevant to what you care about at your role

784
02:09:43.840 –> 02:09:46.149
Rohan Suri: that had like an insanely high open rate.

785
02:09:46.300 –> 02:09:54.109
Rohan Suri: But if you’re just going to do it, based on like what you see in their Linkedin like. Likely. What you see in Linkedin is probably not that relevant and so

786
02:09:54.608 –> 02:10:15.580
Rohan Suri: I think AI can help with the with the outreach notes can actually help automate some of those plays, and we do it quite consistently. But it’s all about like, I think Staff said it well, is like the data that comes in has to be. You can only personalize so much as to what data you have. And so if you’re like doing a personalization based on publicly available data, you’re probably going to sound like everyone else that’s outbounding.

787
02:10:15.580 –> 02:10:16.240
Mary Shea: Yeah.

788
02:10:17.090 –> 02:10:18.060
Matt Millen: Hey! Mary!

789
02:10:18.060 –> 02:10:18.750
Mary Shea: Yeah.

790
02:10:19.650 –> 02:10:24.650
Matt Millen: So I want to start by with the rep first, st not the AI first.st

791
02:10:24.910 –> 02:10:25.310
Mary Shea: Okay.

792
02:10:25.310 –> 02:10:34.039
Matt Millen: And I’m sure we have all seen Sdrs take 2030 min to construct a wonderful email that never gets opened.

793
02:10:35.940 –> 02:10:47.660
Matt Millen: And I think the question is in today’s technology age, like, do we still want to spend 20 to 30 min on the perfect email. Can a human write a better email than AI? Sure they can.

794
02:10:47.940 –> 02:10:49.930
Matt Millen: But is it worth it?

795
02:10:50.370 –> 02:10:58.949
Matt Millen: And I think what we wanted? And the AI has gotten to a point where it is good enough to be acceptable to open the door.

796
02:10:59.160 –> 02:11:04.310
Matt Millen: You know, research will say, even if they don’t respond to the email, they’re more likely to pick up the phone when you call them.

797
02:11:05.540 –> 02:11:11.989
Matt Millen: And you know whether it’s Haiku or whether somebody wants to be entertained. But I I think.

798
02:11:12.110 –> 02:11:22.800
Matt Millen: like we went through a personalization phase that get over overhyped. And people try to over personalize. And I think what matters most is, are you relevant to me.

799
02:11:23.140 –> 02:11:28.339
Matt Millen: You know all of our inboxes are full, you know. I’ve got way too many unread.

800
02:11:29.130 –> 02:11:34.289
Matt Millen: but I’ve always got time for something that’s relevant to who I am and what I’m doing.

801
02:11:34.440 –> 02:11:43.470
Matt Millen: And it’s not about where I went to school. It’s like, can somebody help me solve the problems that I’m currently trying to solve yes or no.

802
02:11:43.650 –> 02:12:05.070
Matt Millen: And I think the mind is open, like you know what you’re working on, and you know what you’re willing to receive. And I think when, whether it’s AI or human beings, or some combination, take the time to put relevant messaging in front of the right persona on challenges or problems they’re likely to be facing or solving.

803
02:12:05.210 –> 02:12:07.290
Matt Millen: That’s where I think email

804
02:12:07.710 –> 02:12:24.579
Matt Millen: can help and not hurt and not over spam and over, complicate and dilute the process. And AI can help with this today. And I really think back to some of the earlier conversations, we just need to rethink how we want the humans in our organization to spend the time

805
02:12:25.280 –> 02:12:52.629
Matt Millen: and is getting that extra 10% really worth it. Given the open rates today. So I just think these are all just considerations for our brand, you know, for how, for our company culture, how we choose to interact with our clients. How do we want to represent ourselves in the market. But I would start with the rep, and like, do we really want to be spending that much time writing emails today when there’s technology that can generate relevance and impact.

806
02:12:53.390 –> 02:12:58.189
Mary Shea: Well, I agree they absolutely shouldn’t be right, but they’ve got to have that

807
02:13:00.240 –> 02:13:06.479
Mary Shea: I don’t know. Find a way to crack that code of relevancy. And I think what we’re hearing from others is that

808
02:13:07.080 –> 02:13:21.789
Mary Shea: basic information that’s publicly available? That’s personalized isn’t doesn’t quite get us all the way there. Right? So whether that’s a stronger connection to who I am personally or really deeply understanding what I’m worrying about.

809
02:13:21.890 –> 02:13:24.919
Mary Shea: that’s what’s gonna get you to the opens.

810
02:13:25.090 –> 02:13:32.840
matt cooley: Let me clarify something because I actually, I don’t disagree the context. The content of the email needs to be more direct.

811
02:13:32.980 –> 02:13:48.750
matt cooley: If you don’t open the email, the content doesn’t matter right? And so what’s gonna get me to open the email is the funny thing is the personal thing is the okay, this is all it’s like that. What’s the credit card company where everybody’s swiping through? And someone pays cash

812
02:13:49.050 –> 02:13:53.500
matt cooley: right? You almost want to get you almost want. You want that pause moment. It’s like, Wow, that’s different.

813
02:13:53.910 –> 02:14:00.470
matt cooley: right? Because I get in this. In the morning. I get in the same in inbox. As you do. Probably, Matt, and it’s just like

814
02:14:00.670 –> 02:14:05.610
matt cooley: they all look the same. Whoa! That one’s different. Nothing else matters unless they open it

815
02:14:06.440 –> 02:14:10.950
matt cooley: right? So that’s where you have to be creative. And you’ve got like 4 words.

816
02:14:11.340 –> 02:14:14.148
Mary Shea: I know it’s really challenging.

817
02:14:14.840 –> 02:14:40.950
Mary Shea: well, we’ve talked a lot about email. I think there’s some real fatigue out there. And and we’re all challenged with getting those open rates. But it’s possible to get them for sure. I absolutely to. Matt Millen’s comment, will open an email that is a direct hit to what I’m worrying about or what I’m thinking about, and I respond to it. I may be different than other executives, but I certainly try to respond to as many as I can.

818
02:14:40.990 –> 02:14:56.940
Mary Shea: But let’s talk about like phone, what? What? And and you know, DM, and and other social social platforms that are out there. How do you see AI agents playing a role? In in the call?

819
02:14:57.610 –> 02:14:59.209
Mary Shea: Anyone want to jump in.

820
02:14:59.410 –> 02:15:21.820
Stav Levi: I think I have something that is like combining the 2 subjects, this one and the previous, that we had an Ab test recently with our phone like our calling agent, and we like did for half of the people saying, It’s an AI agent and half of the people it didn’t say, and I think what we saw was like amazed us

821
02:15:21.820 –> 02:15:45.149
Stav Levi: that we understood that people have talked really differently, much more direct. Tell their intent immediately ask much more question when they knew that they are talking to an AI. And I think that in some cases, and this is where the places that we need to look at AI agent is advantage even within the customer perspective.

822
02:15:45.150 –> 02:16:03.699
Stav Levi: So so I think, like, in terms of phone calls, it’s like, in some of the cases. We see it like performing much better than emails, because their response rate are obviously much higher. But I think it’s also like

823
02:16:03.770 –> 02:16:33.769
Stav Levi: a lot of issues around like legal and policies in different countries, places that we are, we can take responsibility places that we cannot take responsibility. If this person wasn’t ever engaged with the company so calls like AI agents for calls, works amazingly for some use cases. But it’s like much more challenging in terms of like the the process around it. So yeah.

824
02:16:34.709 –> 02:16:36.389
Mary Shea: Thank you. Thank you for that.

825
02:16:36.841 –> 02:17:02.539
Mary Shea: I want to take a couple of audience questions before we wrap at the top of the hour, and we’ve got one in right now from Kenny, who says I’m curious. If there’s any data that shows personalized outreach based on you as a person background. School interests performs better than personalization based on your role or you know, business role or customer journey, and so on and so forth.

826
02:17:03.529 –> 02:17:07.388
Mary Shea: We’ve we’ve sort of touched on this. But does anyone have any?

827
02:17:08.099 –> 02:17:14.739
Mary Shea: I don’t know results that that has anyone ever done any ab testing on on anything like this on the panel.

828
02:17:15.059 –> 02:17:29.629
matt cooley: I haven’t seen any data. I mean, mine is just my my personal feeling of having done this years. So that’s it. It could. It’s it’s my opinion on it. But I haven’t seen anything that’s broken down. I’ve never done an A B test on it, or anything like that.

829
02:17:29.910 –> 02:17:45.180
Mary Shea: Yeah, I think it depends on the individual human. It’s funny, because, you know, Matt is like, I want, I want that connection. I want them to know where I went to college, what my last blog was, where you know, whatever the last point of view, I had on a podcast was.

830
02:17:45.680 –> 02:17:47.459
Mary Shea: I’m more of like.

831
02:17:47.830 –> 02:18:01.739
Mary Shea: I just want someone to help me solve my business problems now. And that’s just a I guess personality difference between the both of us. So it’s kind of hard to say categorically what works. I guess you have to kind of experiment a little bit right.

832
02:18:03.959 –> 02:18:07.789
Mary Shea: Any other thoughts on on this topic, Sam Rohan.

833
02:18:07.790 –> 02:18:10.949
Rohan Suri: I mean, I’ll say, like the data generally shows

834
02:18:11.850 –> 02:18:25.030
Rohan Suri: like relevancy matters more. But, like, you know, like your job, title only goes so far to show whether it’s relevant. Right, like, you know, is this a closed loss that they spoke to you like 6 months ago? And and now you know.

835
02:18:25.930 –> 02:18:50.780
Rohan Suri: And now now is a good time to reach out. So it’s like, it’s not just like surface level. You do really want to understand like is this something relevant to their to their business? I think Matt has a good point that, like, you know, you do need to get people to open it in the 1st place. So you can get creative and get funny and like things like that does help with open rates. And so you know, data shows that, generally speaking, at least, like what we’ve seen with our customers is like it does help. Now you have to do the trade off like

836
02:18:51.241 –> 02:19:01.389
Rohan Suri: other than I was saying, it’s not worth your reps. 30 min personalizing based on open rates. And so, like, you know, there’s there’s a there’s a balance between the 2. But yeah.

837
02:19:02.500 –> 02:19:13.949
Mary Shea: Yeah. So so I think, you know, I’ve written a million perfect emails that probably have never been opened in in some cases. So what is the secret sauce for the subject line?

838
02:19:16.420 –> 02:19:25.930
Stav Levi: I think there’s trends, and it’s changed like dramatically every time, like I think, like we can, I can talk about the current trends. Maybe like we have.

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02:19:25.930 –> 02:19:26.830
Mary Shea: Yeah, that’d be great.

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02:19:26.830 –> 02:19:38.339
Stav Levi: Like, there is one that is like like an intro email. But it’s not a real intro. So it’s like the name of the company, and like the name of your company. And the the second one is like

841
02:19:38.500 –> 02:19:58.550
Stav Levi: like just a lower case is like quick question like this is like a pretty old one, but like always like work, kind of and there is like also another trend. We did tag multiple people from the same company like.

842
02:19:58.550 –> 02:19:59.130
Mary Shea: Oh, yeah.

843
02:19:59.570 –> 02:20:06.800
Stav Levi: So this is like, actually like, kind of trends. And it’s like, always, like, updating the the

844
02:20:07.577 –> 02:20:11.039
Stav Levi: the the trends. And and yeah.

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02:20:11.260 –> 02:20:21.739
matt cooley: I. I have one, and it’s so. It’s so salesy, but it it still kind of catches my eye. But it is. It’s older, too, is which? Where they put the fake reply, Dot, Dot.

846
02:20:23.530 –> 02:20:35.869
matt cooley: And it just looks like, Oh, did I email this person? I’m gonna open it. And I almost always open those until, like the the reply after the reply sign, it’s just like totally generic, right? But

847
02:20:36.000 –> 02:20:42.010
matt cooley: that’s just an old tactic that I still think kind of works. If you’re creative after that fake report.

848
02:20:42.350 –> 02:20:42.810
Mary Shea: Yeah, it’s.

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02:20:42.810 –> 02:20:50.320
Matt Millen: That’s the other thing that you know. It’s not just about the subject line anymore, because many people have a 1 or 2 line preview.

850
02:20:50.320 –> 02:20:51.090
Matt Millen: Yeah.

851
02:20:51.090 –> 02:20:51.540
Mary Shea: Right.

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02:20:51.540 –> 02:21:08.759
Matt Millen: Emails. So even if it’s a very catchy subject line, you very quickly know you’re being solicited. You know, I know that we’ve, you know, worked our models in a way where the 1st 2 lines are actually the most important, because, you know, can you hook them once you get past the subject line of relevancy.

853
02:21:08.760 –> 02:21:09.230
Stav Levi: Yeah.

854
02:21:09.230 –> 02:21:12.849
Matt Millen: But I think we just need to be thinking, you know, more deeply.

855
02:21:13.120 –> 02:21:31.750
Matt Millen: you know, in terms of, you know, are the communications that we’re sending out in the 1st place, are they the right messages to the right people at the right time? And one of the things that we didn’t talk about was really the incorporation of signals today, and how the AI is allowing us to use all kinds of signals in a way

856
02:21:31.790 –> 02:21:57.529
Matt Millen: that marketing used to invest in, and sales used to ignore, and things would fall on the floor between the 2 teams. And like today, there’s just so much more capability and execution between sales and marketing, and and using like what’s going on, and who to talk to at the right time in the right channel. And I think that’s changing the game in a big way. We didn’t really catch it today. But like it’s, it’s changing this conversation in real time.

857
02:21:58.850 –> 02:22:03.039
matt cooley: The one we talked a ton about email, right? And I just feel like

858
02:22:03.500 –> 02:22:23.118
matt cooley: the the AI companies in our stick to generating email outcomes are. The ones are probably going to die in the end, right? And there’s different ways of reaching out to customers today. You know, I talked a little bit about us moving in the marketing direction. You know, we’ve we’re launching as a part of our next version of bounty.

859
02:22:23.560 –> 02:22:34.160
matt cooley: custom micro sites for individual buyer personas at those companies and based on how your solution and it looks like their website, right?

860
02:22:34.160 –> 02:22:35.020
Mary Shea: Oh, yeah.

861
02:22:35.020 –> 02:22:51.760
matt cooley: Not just at the company level. It’s at the buyer persona level. What they care about in their role as it ties back to the business objectives of the company and how your solution helps them personally, that’s the future email is not going anywhere. It’s not going to take us anywhere. And the open

862
02:22:52.670 –> 02:22:58.819
matt cooley: interactive, you know. And this is, you need to be interactive in a different way. And that’s where the future is.

863
02:22:59.600 –> 02:23:18.919
Mary Shea: Yeah, I agree. I think you know, there’s an exhaustion around email that’s happening for almost 10 years now, because we’ve gotten so savvy at using it. But people aren’t really even using email anymore. Like when I communicate with my team, it’s all on an app, right? And there’s only an email, if it’s something long or

864
02:23:18.980 –> 02:23:43.174
Mary Shea: more formal. Right? So I I think that’s that’s right. And I love the custom. Microsoft sites and the deal rooms and and having things in my own brand as a buyer. I agree with you that that is the way of the future. Well, I know we’ve got to wrap up shortly, but I’d love to do kind of a speed dating finale here if we could go through each person. We’ll start with you, Matt millenn tell us.

865
02:23:43.520 –> 02:23:52.160
Mary Shea: you know, what kind of AI tools are you experimenting with right now? And I’d love to go through everyone and and get a bunch of tools out there.

866
02:23:52.160 –> 02:23:53.751
Matt Millen: Well, we’re using our own

867
02:23:54.070 –> 02:23:55.269
Mary Shea: Of course. Right sure.

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02:23:55.270 –> 02:24:17.000
Matt Millen: But I’ll share one quick thing and then and pass the baton is that there’s a there’s a big shift in tools right now. Historically, sales teams have organized around the tool. So before there were tools, we knocked on doors with the birth of the call center, we organized around the phones. With the birth of sales engagement we started with role specialization and the birth of the Sdr.

869
02:24:17.120 –> 02:24:30.850
Matt Millen: But it’s different with AI, with AI. You don’t organize around it. You organize with it. And it’s technology as a teammate and reps are working side by side with AI to optimize both sides of the equation.

870
02:24:30.970 –> 02:24:33.710
Matt Millen: So I would just implore everybody

871
02:24:33.900 –> 02:24:40.869
Matt Millen: to start thinking very differently around that you bring technology into your revenue organization as you go forward.

872
02:24:41.320 –> 02:24:43.539
Mary Shea: Awesome. Thank you, Matt. Rohan.

873
02:24:44.678 –> 02:25:11.821
Rohan Suri: Cool. Yeah, I mean, we obviously we use next as well internally. I’ll add in that cool. I think that email channel is almost you know, it’s just one of the channels for our customers. Phone is actually like 80% plus a pipeline. So I do think think you know, don’t. Don’t think just emails. That’s the most obvious one that can be generated with AI. But you know, think how you can get in touch with people in person. Phone is a really strong channel. So there’s all sorts of ways you can use outbound

874
02:25:12.210 –> 02:25:16.459
Rohan Suri: But yeah, we use. We use. Obviously, Nox. Internally, it generates more than more than

875
02:25:16.940 –> 02:25:19.569
Rohan Suri: 70 to 70% of our pipeline last quarter. So.

876
02:25:19.980 –> 02:25:24.620
Mary Shea: Awesome. Awesome, Sam, what do you experimenting with?

877
02:25:25.340 –> 02:25:37.800
Mary Shea: Yeah. So I lead the engineering and product team. So mine’s gonna be a little bit different. But we use cursor very heavily. It’s sped up our engineering team quite a lot, that’s 1. And then on the sales side, we use, of course.

878
02:25:38.890 –> 02:25:40.820
Mary Shea: awesome stuff.

879
02:25:41.340 –> 02:25:45.869
Stav Levi: So lately. I’m obsessed with fin by intercom.

880
02:25:46.412 –> 02:25:48.420
Stav Levi: Like the support agent.

881
02:25:48.450 –> 02:26:16.199
Stav Levi: Na, 10, creating a lot of internal workflows like within a 10. It’s amazing tool. Whoever not used it like, go and try it like you can build your own AI agent like for small tasks, like company updates and stuff like that. Of course, we use like our own tool, like heavily users. Yeah, like cursor. All of our, I think this is most of it.

882
02:26:16.920 –> 02:26:18.160
Mary Shea: Matt, Cooley.

883
02:26:18.920 –> 02:26:44.389
matt cooley: I think I’m just gonna follow suit. I think we’re all gonna say the same thing as we are on dog food and the reality of the world, though most people don’t. It’s crazy to think about that is, I’ll ask questions on a sales call, I’m like, Well, how do you use it? And it’s just an awkward pause like, how do you not use your own solution? If you, if you believe in it, we use our own we also had built this like backbone of our company. We we call Gen. Nodes. It allows for us to spin up

884
02:26:44.734 –> 02:26:56.780
matt cooley: new companies quickly. We have narrative, we have lead talk. They’re all for different purpose purposes. They’re very point solutions. But because we can do that externally, we can do it internally. So we build quick

885
02:26:56.820 –> 02:27:00.529
matt cooley: and yeah, we don’t outsource any. AI.

886
02:27:01.980 –> 02:27:24.400
Mary Shea: Perfect, perfect, and then I’ll share mine. It won’t be any surprise to anybody. But I’ve been experimenting with some of the deep research tools. They’re really expensive on a monthly basis. But the depth of research and information you can acquire. And then I’ve been putting it onto the notes so I can get it in podcast format and listen to my research.

887
02:27:24.853 –> 02:27:36.200
Mary Shea: Findings with 2 people talking. And it’s it’s incredible. So it’s great to be able to assimilate information through reading, and also just listening.

888
02:27:36.340 –> 02:27:37.040
Mary Shea: So

889
02:27:37.840 –> 02:27:51.320
Mary Shea: more to come there. But I’m going to pass the baton back to Julia. Thank everyone on the panel for a really dynamic discussion, and I hope that the audience has learned some new things as a result of today’s session.

890
02:27:51.990 –> 02:27:53.240
Julia Nimchinski: Brilliant session is over.

891
02:27:53.240 –> 02:27:53.760
Stav Levi: Understood.

892
02:27:53.760 –> 02:28:02.790
Julia Nimchinski: Thank you so much, Mary. Thank you so much to all of our panelists, and we are transitioning to our next panel. Welcome to the show mark organ.