CRO Roundtable — CRO Strategy in the Agentic Era

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Julia Nimchinski: Thank you so much. Thank you, Jean and Robin, and we are transitioning to our next panel, CRO Roundtable, hosted by Matt Darrow, the CEO and co-founder of Vivint. Welcome to the show, Matt. How are you doing?

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Matt Darrow: Well, thanks, Julia. Thanks, thanks for having me to host the panel and our guests here. How’s my audio doing as we, as we close Day 2 for you? Can you hear me loud and clear?

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Julia Nimchinski: Excellent.

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Matt Darrow: All right, fantastic. I see, I see our panel coming in. I’ve got Trevor, Allison, Steve, alright, I think we’re just, waiting on, Vinesh here, just so he didn’t bombard in the last session, and then, and then we’ll get going.

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Julia Nimchinski: Yeah, let’s do a quick round of introductions.

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Julia Nimchinski: Allison, let’s start with you.

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Matt Darrow: Well, yeah, Julia, so for the audience out there, just for the context of today’s panel, as you mentioned, so Matt Dara here, co-founder, CEO of Vivin, I’ll be leading the discussion today, and our discussion’s topic is CRO Strategy in the Agentic Era. So, we’ve got this incredible lineup of speakers today who are going to share real-world examples of AI creating real-world results for sales teams.

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Matt Darrow: My goal is to make this the audience’s most favorite session of your summit, Julia. We’ve got a big, a tall order at our hand, but we’re going to be discussing this topic in a variety of ways. We’re going to do some Q&A, we’re going to do part debate, it’s all going to be meaningful insights, and that’s enough of me, because the whole panel is here. So let’s kick off with some of the introductions you mentioned, and Allison, we’ll actually start with you.

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Matt Darrow: If you want to just talk a little bit about yourself and the company that you’re currently at.

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Allison Grieb: Good afternoon, everyone. My name is Allison Grieb. I’m the Chief Sales Officer at PayStand. PayStand is a really unique and disruptive AR automation and payments company.

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Allison Grieb: We are helping companies, reduce fees to transact by the complacency, that people have around all the fees that it costs to move money around.

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Allison Grieb: And we’re doing it in a really unique way. Our Felix platform is built on the blockchain, so we have a nice crypto flair here at Paystand, and we’re sort of set up for a really exciting future as stablecoins and Bitcoin and cryptocurrency become more and more prominent.

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Allison Grieb: In the future.

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Matt Darrow: Thanks for joining us, Allison, and I love about your team, too, not only some of the size and scope, but the scale and the broadness of the team that you run as well. And honestly, the sales leader community is rather small when it comes to tech, and when I turn it over to Trevor for an introduction… actually, Trevor, you and Allison crossed paths back in the day, too.

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Trevor Jett: We did, we did. We ate some of the same dirt, or cashed some of the same RSUs together, and time… time’s passed.

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Trevor Jett: So, nice to meet everyone. Trevor Jett, CRO here at Vivin. So, I work with Matt today, I worked with Allison at Anaplan in the past, so it is a small community. Vivint is an AI sales teammate that helps our customers in the mid part of the sales cycle provide always-on expert,

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Trevor Jett: capability to our sales reps, to our SEs, and to our CSMs, and pleasure to be here today.

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Matt Darrow: Thanks, Trevor, and just like you and Allison kind of go way back, I’m gonna turn it over to Vinesh, who I’ve had the good pleasure of actually working with about 15 years ago, and since that time, Vinesh has gone on to lead some pretty incredible sales organizations. Welcome, Vinesh, a little background about yourself.

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Vinesh Vis: Thanks, Matt. Glad to be here. So, my name is Vinish Fiss, Chief Revenue Officer at Smarsh. I’ve been in a variety of revenue and sales leadership roles over the last 20 years, and you’re making me feel old, Matt, saying 15 years ago, but it’s been a great ride. So, Smarsh is a leader when it comes to

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Vinesh Vis: Communications, data, and intelligence, and so we work with about 6,500 customers across the globe. We capture, we archive, and surveil that data for a variety of customers and regulated industries, predominantly.

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Vinesh Vis: Agentic AI is very near and dear to our hearts. In fact, we just introduced our first, agent, intelligent agent, if you go out and read about Smarsh, with a lot more to come, some material ROI for our customers in the world of Agentic AI, and we’re hiring. Hopefully, we’ll have a chance to continue to grow going in next year, so pleasure to be here. Thanks again, Matt.

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Matt Darrow: Fantastic. Thanks, Vinesh. And next up, actually, Francis, I’d love for you to introduce yourself. You’ve got a fascinating background as well, where you’re actually one of the panelists that has held both a role in sort of being a founder and a product leader, as well as a sales leader, but tell us a little bit more.

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Francis Brero: Yeah, absolutely. Thanks for having me. Excited to be here. Yeah, so, because the founder of Mad Kudu, started, as a sales leader, and then when we started getting reps that were, closing consistently and hitting quota, I transitioned to a CPO role, running the product and engineering team. Mad Kudu, was a tool that was intended to bring

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Francis Brero: All of the data, to the fingertips of the reps to make it easy for them to not have to navigate across 17 different tabs whenever they’re trying to book a meeting or talk to someone.

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Francis Brero: And since then, joined

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Francis Brero: HG Insights, where I’m running the AI strategy, and it’s all about how do we

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Francis Brero: bring, go-to-market, to the agentic future. So, very excited for the conversation today.

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Matt Darrow: Thank you, Francis. Okay, Steve, bring us home. While we’ve got four panelists that are sort of in-seat in the function, it’s a function that you’ve held multiple times, but then also you’ve got this amazing purview that I was really excited to bring to the panel today, too. So, over to you, Steve.

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Stephen D’Angelo: Sure, thank you, thanks for having me. Steve D’Angelo, partner with BCG,

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Stephen D’Angelo: Boston Consulting Group, as you probably know, is a global strategy consulting company, 12, 15 billion in annual revenue. My focus at BCG is in our next-gen sales organization. So what we do is we go into companies that are typically

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Stephen D’Angelo: Half a… half a billion and above.

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Stephen D’Angelo: And we go and do sales transformation work with them, where we look at all go-to-market framework items, like sales execution, sales process, how should you use technology, how do you be doing pricing, what’s your hiring model like, so sales organizations that look to transform, that’s what we go do in advisory work.

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Stephen D’Angelo: Prior to BCG, as Matt said, I was in the tech space for 35 years.

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Stephen D’Angelo: I’ve been a chief revenue officer several times, building high-performance global sales teams, been president of a publicly traded company, CEO of a mid-stage company. So, my background is really as an operator, so I bring that operating experience to these consulting engagements that we do here at PCG.

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Stephen D’Angelo: So, look forward to the conversation here and giving you some of the insights that I see multiple companies engaging in as it pertains to AI.

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Matt Darrow: Fantastic, Steve. So, Julia, I said at the beginning, our goal is to make this the top session for HSC, especially on day 2. I think we’re off to a good start with the panel that we’ve assembled for the topic of CRO strategy, the Sagentic era, so let’s get into it.

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Matt Darrow: The audience now knows, sort of, a little bit of who’s who here that’s going to be speaking. So let’s start with the number one thing on the mind of nearly every sales leader that I get to speak with, but also the audience, which is, where have you guys seen actual success rolling and leveraging AI across your teams? Allison, I’d like to turn it over to you to kick us off here, because you’ve done some incredible things on the sales development side, but maybe you can start us off there.

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Allison Grieb: Yeah, so in my role, I have responsibility for all of the account executives, the SDRs, the channel team, and the revenue operations function. And, you know, of course, in my seat, not everything’s always working super well at once. That would be

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Allison Grieb: too much to ask, but our SDR team, you know, they were doing really well. We had a team of about 20, people, and they were cranking, so we were making over 150 dials a day, and just not getting the results. It was really a struggle for them to start booking meetings, and so we really had to sort of stop and think differently, right? Think differently, and I think some of

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Allison Grieb: us who have been in this seat doing this a long time, sort of, we know what has worked well in the past, and that’s something that’s been a real challenge for this new evolution, this new AI, like, how do we think differently and really challenge what worked historically to think differently?

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Allison Grieb: We went out and did some investigations, some trials with various SDR AI capability, you know, whether we’re doing an agent or other things, but what we really focused on was leveraging a couple of different solutions. We leveraged Clay.ai to be able to clean

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Allison Grieb: are,

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Allison Grieb: database, we use HubSpot, so we were able to clean our database and actually enrich it using Clay.8i, meaning we could surface up better lists that were enriched with ICP information, better contact information, and give a better opportunity to the people actually making those dials if they were calling someone that was going to be

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Allison Grieb: a good fit for what we do here at PayStand.

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Allison Grieb: The other thing we did is we decided to reduce the force of our SDRs, so we took it down to about 25% of the original size, and we leveraged a solution called Artisan. And what Artisan does is it goes out and actually warms up

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Allison Grieb: your contacts, warms up leads for you, and we were doing Artisan sort of in a vacuum for a while, and we were seeing some traction. Takes a little while to, to kind of get things up and running.

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Allison Grieb: But what we saw, a real success factor was having Artisan do some of the legwork ahead of time, and then

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Allison Grieb: putting in our sequences where our remaining fewer SDRs were calling in after they had been touched by Artisan, and we were getting almost

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Allison Grieb: we were actually… we were getting twice the results where we were with the bigger team, with a much smaller team leveraging AI. So, that’s been a really, impactful, opportunity for us, where we’ve been able to truly see results.

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Allison Grieb: But it’s not all AI, right? So we’re still needing that human touch to convert those opportunities, but we’re doing a lot of legwork ahead of time to make sure we’re surfacing the right opportunities to our SDRs.

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Matt Darrow: Awesome, and thanks for sharing that, really great sort of top-of-funnel use case using two established vendors in the market to actually give you those real results. Francis, I’d love to turn it over to you as well, because while Allison sort of teed up some vendors that are helping

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Matt Darrow: drive results at the top of the funnel, you’ve actually, especially with your product background as well, you’ve actually built some tools that are materially helping the sales organizations that you work with. Can you talk a little bit about that?

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Francis Brero: Sure. Yeah, and I’ve struggled with a lot of the off-the-shelf tools out there for various reasons, but one of the things that was interesting to me is that when I started interacting with ChatGPT, I realized that

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Francis Brero: the UX and the way we interact with the product was actually the biggest change, more than anything. And so, I started thinking, like, what are some of the internal processes that are broken that we could fix with this new user experience? And one of the big things is, you know, QBRs, like, quarterly retros that you run with the sales team. We’re trying to figure out what went well, what didn’t, like, where did we lack enablement, all that kind of stuff.

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Francis Brero: those meetings are absolutely horrible, generally. You have a team of, like, 20 reps, like, half of them are doing something else, like, very few are listening, and the few that are listening

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Francis Brero: just, like, take over the conversation, and it’s a monologue for, like, 10 minutes, and everybody’s rolling their eyes. And so the idea was to say, well, why don’t we extract into a prompt

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Francis Brero: what is the information we’re trying to get? Like, what worked well? What didn’t? Like, why did we struggle at this particular point in the funnel? So essentially, like, running some analysis ahead of time to figure out

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Francis Brero: how our funnel conversion was going, highlight the points where we wanted to dig a little bit deeper, and then give each rep the individual prompt, and tell them, have a one-on-one session with ChatGPT in the advanced voice mode, so that you’re just talking to the computer.

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Francis Brero: And then, at the end, run this other prompt, which will basically, like, get a full transcript and summary, and send it back to me, and I’ll summarize the retro.

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Francis Brero: And the beauty of this was that

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Francis Brero: We got much more detailed insights as to what was working and what wasn’t.

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Francis Brero: And the feedback from the reps was pretty unanimous that the experience was just radically different. They felt like it was a safe space where they could share the things that weren’t working, the things that were working. Like, the sycophancy of the AI tools actually was a positive, because, like, every time you say something, ChatGP’s like.

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Francis Brero: oh my god, that is so brilliant, you’re a genius, like, tell me more! And that actually, like, served a really good purpose for the sake of getting the reps to talk more, and to share more. And so the level of insight, the…

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Francis Brero: spread of insight across the entire team that we got was pretty extraordinary, and… and I would say, like, that’s one of the core use cases internally that we’ve built, in, like, changing the user experience of some of these

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Francis Brero: I guess, like, context-heavy meetings.

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Matt Darrow: Yeah, I’ve seen so much, even working with account executives too, Francis, that having those different modalities really unlocks engagements from the sales rep community at large.

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Matt Darrow: Where, like, typing text isn’t sort of the reaction that a lot of folks normally need to go to. I think it’s an amazing tool that you use, just by using voice to drive this.

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Francis Brero: I’ll be honest, like, salespeople are good at talking.

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Matt Darrow: I know, we’re off to a good start here, too. Trevor, drop us down further in the funnel. What about yourself?

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Trevor Jett: Yeah, great. Yeah, we also have experienced sort of the top of funnel. There’s a lot of tools that do that. With drinking our own champagne in the middle of the funnel, I think, was where we saw the most profound changes.

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Trevor Jett: Someone that Alice and I both used to work with years ago used to say, if you’re selling something, it should increase revenue, decrease cost, or make a material change in the way that you operate. And so, when I look at a lot of these tools and trying to figure out, like, really where the value is, including our own, it’s in that

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Trevor Jett: that determination, like, is it changing the way that you do business? So in the middle of the funnel, like, what we’ve seen is that we are able to do a lot less work

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Trevor Jett: by having an agent come in, our agent come in and do a lot of that work for us. So things like live call assist, where we’ve got always-on expertise, we can save a lot of cycles having to go back and get things answered later, we can answer them in real time.

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Trevor Jett: deliverables to the customer, whether that’s a value study, a value case that gets generated. You don’t have to wait a week, you don’t have to have a head do that. We can do that, like, with an agent, and it’s frankly better.

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Trevor Jett: And even internally, as we… we do things that we need to do in the sales cycle from a leadership perspective, whether you follow Spiced or MedPick.

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Trevor Jett: You know, building those things out, instead of having a rep do it at one point in time, this can be automated and can be better, because it’s not your rep telling you something, it’s a disinterested, objective third party that’s actually making those determinations.

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Trevor Jett: And what we end up doing is just a lot less time in tools. You know, we don’t go into Salesforce anymore, we don’t watch Gong calls anymore.

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Trevor Jett: one of the last firms that I was with, we actually measured and put a leaderboard on for, like, the number of hours that everyone spent watching Gong calls, and

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Trevor Jett: And for clarity, the larger the number, the better. And now I look at that as being just positively ridiculous. Like, if I had a rep of mine

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Trevor Jett: or an SE that was spending 5 or 10 hours a week watching Gong calls, like, I think I would be furious about that. Like, now we don’t need to go do that. All of those things are done for us in the middle of the sales cycle. We can save all of those, all of those minutes and put them towards calling customers and selling deals.

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Matt Darrow: Fantastic. Yeah, and changing, changing those sort of, normal standard operating practices. Spot on, Trevor. So, those are very specific. Steve, take us wide. How about yourself, right? You’ve got this purview of, of all these different companies that you work with. What are you seeing across, across a broad spectrum?

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Stephen D’Angelo: I’m seeing, I’d say, 3 core areas. One has to do with talent. One of the biggest challenges sales organizations have had for many, many years and continues to have is

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Stephen D’Angelo: do I have the right talent, and am I enabling my talent? You know, in days gone by, if someone didn’t make a number one quarter, the next quarter, you’re pushing them out. These days, through the advent of AI, we’re able to identify, through data modeling

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Stephen D’Angelo: what are the behavioral attributes, behavioral traits, and skills that are required to be a top performer for a specific sales organization? And by modeling those top performers, and then modeling those that are not performing at the highest level.

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Stephen D’Angelo: we’re able to identify where the very specific enablement has to take place. So we’re seeing more and more companies using large language models, using AI to enhance their talent, enable their talent.

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Stephen D’Angelo: The second area is in the RFP responses. We’re seeing a ton of this, where, you know, RFPs have always been a very difficult thing from a time perspective, making sure you’re answering them correctly. The very simple process of uploading an RFP that you got from a prospective client into your large language model, and having that

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Stephen D’Angelo: AI create the response. It is amazing how accurate the responses are.

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Stephen D’Angelo: How kind of informative the responses are, so we’re seeing companies cut 80-90% time off of what it takes to respond to an RFP.

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Stephen D’Angelo: And then the final area is real-time coaching, and this is more for inside sales. So I’m inside sales, I’ve got a Zoom call going on, the AI engine is listening, it’s transcribing, but most important, there’s a section on the screen where it’s providing the salesperson advice

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Stephen D’Angelo: As to, you should be going down this path. The prospect said this, position this value proposition.

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Stephen D’Angelo: And it does take a little bit of time for the rep to kind of be able to maneuver this, having a live conversation, getting the coaching there, digitally, utilizing that information, but those are the three areas, and they’re having really dramatically… a dramatic positive impact on… on some of the performance.

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Matt Darrow: That’s, and Vinesh, I know that when you and I were prepping a little bit beforehand, RFP was, was one of yours as well, but bring us home. What are some of the other areas as well that, you’re starting to see results?

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Vinesh Vis: Yeah, I can only imagine, Matt, when you were my sales engineer 15 years ago, if you had RFPs that could be automated with the GenTech AI, how that would be. So, you’d be playing a lot more golf if, if we went back in the day. So, very similar to what Steve was talking about, yeah, we use a tool called Responsive, formerly RFDIO,

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Vinesh Vis: to, to help with our RFP automation. It’s a fantastic tool, and it’s a fantastic use case for us. That’s definitely number one. Number two would be, so when we think about,

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Vinesh Vis: just general research on our accounts. Our reps have started playing around with Perplexity, ChatGPT, Gemini, you name it, and so that… we’re still in the infancy stages, I would call it, of exploring which tools are going to be best and how do we programatize it.

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Vinesh Vis: The third, and just for context, so a big part of what I cover and what we do in the enterprise business unit is large deals, large customers, longer sales cycles, and so that’s why RFPs and research play a big part. But the third piece is we recently launched in partnership with our SFA provider, our CRM provider, Salesforce.com, an AI agent specifically for customer support called Archie.

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Vinesh Vis: And it’s working really well for us, and because, in our case, we’ve made 15 acquisitions over 12 years, we cross-sell and upsell into our existing install base a lot, and so being able to provide the right customer support experience only increases our velocity of being able to upsell and cross-sell in the future. So it’s just as you think about, kind of, one mind and one company across any organization the audience is working with, you know, the benefit to sales

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Vinesh Vis: And the benefits to go-to-market can come from a variety of different ways across the organization as well.

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Matt Darrow: That’s awesome, Manish. You know, Julie, it’s kind of funny having, being on this panel with you, because I remember, like, maybe 6 to 9 months ago, you and I were doing another one of these, and it was so nascent, the use cases for sales, there was so much just around

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Matt Darrow: doing basic research or email writing, and now we’ve heard everything from sales coaching, RFP response generation, completely changing the way that people are doing top-of-funnel, mid-funnel use cases. These are all fantastic examples, and I’m glad you guys all shared them, because it’s not theory.

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Matt Darrow: everybody’s moved beyond, maybe we should do it, and you guys are all doing it in practice at some of the largest organizations on the planet as well. So, I want to take us to, like, what’s next? So let’s stick with you, Venet, since you closed us out on the last one, but what are the areas that you guys are looking forward to, sort of, leveraging AI for the next series of maybe use cases or investments?

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Vinesh Vis: Yeah, for sure. So…

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Vinesh Vis: Matt and others kind of talked about the fact that there is an ability to kind of get ahead when it comes to writing emails and outbounding and such as well, too. When we think about using the Perplexity or ChatGPT to go do some initial research, how that translates into a broader account plan is where I think I see this going really well, especially for folks who cover enterprise accounts. Going through and creating a truly robust account plan can take a lot of human hours, and so doing more of that through AI

1647
04:48:54.720 –> 04:49:02.199
Vinesh Vis: I think it’s going to make people a lot more efficient, and it’s going to give sales reps an ability to get faster RAM time, faster time to productivity as well, too.

1648
04:49:02.200 –> 04:49:25.450
Vinesh Vis: Another area that I see, and what we’ve already started to kind of think about, is operational insights. And so, we all operate with the sales operations, revenue operations team that is giving us insights. We might be using, you know, more modern tools that give us insights as well, but it still requires a human to go and extract what… extract what insights you want to make decisions. And so, I think the world that we’re going to all see moving towards is being able to say.

1649
04:49:25.450 –> 04:49:33.490
Vinesh Vis: How can an agent help me to make decisions when I don’t even know that I need to make decisions? So it’s leading me one step ahead versus me pulling it alongside things.

1650
04:49:33.490 –> 04:49:45.919
Vinesh Vis: And the same, I think, is going to apply down at the sales rep level as well, too, when you think about next best action for a sales rep in any of their prospecting or any of their deal cycles. If you have an agent as a virtual teammate that’s telling you what to do next.

1651
04:49:45.920 –> 04:50:03.269
Vinesh Vis: Think about how powerful that can be. It’s going to compress sales cycles, lower the customer acquisition cost, and everybody benefits in that world. Second kind of bucket I’d see is, even beyond just sales, when you think about our partnerships with marketing, and just the website, and how we actually engage with our customers and prospects on the front end.

1652
04:50:03.270 –> 04:50:13.630
Vinesh Vis: We’re moving from a world where search engine optimization was key, to now, all of a sudden a world where it’s really about the generative AI channels, where people are doing their research. So we’re going from, hey, how…

1653
04:50:13.630 –> 04:50:36.410
Vinesh Vis: trying to use my website to be able to actually engage a client to, okay, how logical and productive are the answers that are coming from these generative AI channels when somebody’s doing a search? So just the basic text and output that’s coming from a chat GPT, etc, is going to be what leads somebody to whether or not they want to engage with you as a vendor. So thinking with that mindset first, especially as you’re growing bottoms up for companies that are just getting started.

1654
04:50:36.410 –> 04:50:44.809
Vinesh Vis: is going to be very, very important. The other area I would look at is, as you think about the overall sales process that you might engage with.

1655
04:50:44.980 –> 04:50:51.469
Vinesh Vis: how a customer expects to engage with a sales rep and a vendor, I think, is going to evolve very, very quickly, and what I mean by that is.

1656
04:50:51.630 –> 04:51:07.829
Vinesh Vis: We all know that customers don’t want to talk to a sales rep unless they have to, and more importantly, they’re going to want to talk to an agent, if that’s a better option. So, especially when a rep is either unavailable or maybe doesn’t always have the answer right at their fingertips, the more that we can train an agent to answer questions, whether it’s on behalf of

1657
04:51:07.830 –> 04:51:28.249
Vinesh Vis: a sales engineer, product, professional services. Think about even the sheer contracting process. If a customer has a question about pricing or certain terms and conditions, being able to ask an agent those questions saves time for the customer, saves time for the rep. Everybody makes money that way. And so, it’s much more efficiency. And I think where we’re going to go, the last piece I’ll kind of close with here, Matt and team, is

1658
04:51:28.460 –> 04:51:36.440
Vinesh Vis: I think we all need to be prepared that in 12 to 18 months, if not less, anytime we’re hiring for top talent, back to what even Steve was talking about as well, too.

1659
04:51:36.850 –> 04:51:54.969
Vinesh Vis: account executives are going to ask and expect for you as a vendor, hey, what investments are you making in go-to-market AI to make me more productive as a seller? If we don’t have a good answer there, if we don’t have a path for a seller there, you know, that’s on us right now to be able to start making those changes. So getting in front of it right now for the next 12 to 18 months is going to be key.

1660
04:51:55.900 –> 04:52:09.299
Matt Darrow: Thanks, Vinesh. And you’re already stepping 3 clicks into the future with some of this different ways that buyers are going to engage. I think, Steve, I want to turn it over to you, because you have a name for this. I think you called it Conversational AI, maybe, when we were chatting, but maybe you want to hit a little bit more about that.

1661
04:52:09.300 –> 04:52:18.379
Stephen D’Angelo: Yeah, it’s in alignment with what Vinesh just talked about. So, we are seeing and helping deploy, in several instances, this concept of conversational AI.

1662
04:52:18.520 –> 04:52:35.900
Stephen D’Angelo: And to what Vinesh just mentioned, prospects don’t want to talk to salespeople until they’re ready. You know, the statistics have changed over the years. It was, well, prospects go through 30% of their buy process, then 50% of their buy process. We’re seeing 60-65% of the time

1663
04:52:36.470 –> 04:52:54.329
Stephen D’Angelo: that the prospect is going through their buy process, which means you have to deploy some kind of technology to educate them. They want self-service. They want conversational AI, and what we’re doing, and what we’re seeing a lot of, is allowing a prospect to engage with your digital agent to

1664
04:52:54.700 –> 04:53:13.910
Stephen D’Angelo: Ask questions, have a dialogue. It’s almost as though a real person is having that dialogue. As a matter of fact, before this panel, I just had a conversation with a company that’s deploying some technology for one of our clients. It’s Synchrony is the name of the company. They have an AI agent that does full discovery.

1665
04:53:13.910 –> 04:53:25.360
Stephen D’Angelo: Need assessment, full discovery. Depending on the question or answer that they get, that agent is smart enough to now go ask another kinds of questions, to assess hot buttons, assess needs, assess requirements.

1666
04:53:25.360 –> 04:53:40.839
Stephen D’Angelo: And then what’s happening, of course, in the background, all this data is being gathered and synchronized, so that when the prospect does raise their hand, and Steve D’Angelo, the rep, is now going to go speak to Matt, the prospect, I am furnished with all of this information about the dialogue that took place.

1667
04:53:40.840 –> 04:53:55.570
Stephen D’Angelo: what are the needs, what are the areas you’re focusing in on, etc. It’s even now getting to a point where the first demos, where SEs are typically dragged into these first, sometimes unqualified demos, conversational AI is able to do those demos.

1668
04:53:55.570 –> 04:54:01.220
Stephen D’Angelo: So, while it’s early in that area, we’re starting to see it move pretty quickly.

1669
04:54:01.620 –> 04:54:23.520
Matt Darrow: Yeah, I think, just from my chair as a technology builder, being able to layer some of these new advancements into our own services, the tech is moving fast, but the other thing too, Steve, is, like, culturally, companies are also moving quickly to saying, this is a real mode of operation that’s going to be a big deal, and that’s actually kind of where I wanted to go next, because

1670
04:54:23.790 –> 04:54:48.569
Matt Darrow: we’re all talking about AI being poised to do so much more than just help book a meeting or help do outbound, and as AI becomes so much more capable to do more and more of the real work, that’s gonna kind of radically start to reshape and change the sales organization, including potentially introducing new roles, changing the size and scope of existing roles. Allison, I’d love to throw it over to you, because when it comes to new roles.

1671
04:54:48.740 –> 04:54:52.710
Matt Darrow: That’s something that I think you guys are experimenting with in a big way.

1672
04:54:53.030 –> 04:55:06.809
Allison Grieb: Yeah, absolutely. Thanks, Matt. So we, we were at an off-site, maybe 6 months ago, and, you know, leadership, we were all saying, we need to do more AI, what are we… more AI, and everyone just kind of looked around at each other and said.

1673
04:55:06.850 –> 04:55:21.990
Allison Grieb: well, I’m using ChatGPT, you know, and it just isn’t cutting it, right? So, I think the hardest thing is, as an executive, as a leader, you know, to be intentional about carving time to…

1674
04:55:22.230 –> 04:55:25.020
Allison Grieb: Investigate, research, test.

1675
04:55:25.110 –> 04:55:34.479
Allison Grieb: And it’s not something that, you know, people who are doing their day jobs have an opportunity to do, so we’ve introduced the concept of a go-to-market engineer.

1676
04:55:34.530 –> 04:55:48.340
Allison Grieb: Right? And these go-to-market engineers actually live in each of the organizations, so I’ve got someone in go-to-market that is gonna sit with me, that’s going to be my AI guru.

1677
04:55:48.340 –> 04:55:55.110
Allison Grieb: Right? Who has a dotted line to our head of engineering, and is gonna make sure that the work that we’re doing

1678
04:55:55.110 –> 04:56:09.519
Allison Grieb: inside of go-to-market aligns with what’s happening in customer operations, and payment processing, and in support, and that we’re not doing duplicative, because I think all of us that have been at early-stage companies have

1679
04:56:09.520 –> 04:56:17.479
Allison Grieb: Seen companies go out and buy 25 technologies, and then all of a sudden you have all these silos of information, of business, and…

1680
04:56:17.480 –> 04:56:26.459
Allison Grieb: Nothing talks to each other, so this is our way of getting really far ahead of this, and also being really intentional about, we want to do more.

1681
04:56:26.520 –> 04:56:39.900
Allison Grieb: But we need the right people with the right mindset to go and look for these, and those are engineers. They’re not necessarily, you know, my sales ops leader that drives a forecast is not necessarily the right person. That’s the customer.

1682
04:56:39.900 –> 04:56:47.239
Matt Darrow: Right? So we’re really, being intentional about these go-to-market engineers and putting them across the organization.

1683
04:56:47.330 –> 04:56:56.949
Allison Grieb: So that we can find new paths, because there’s… there’s still a lot of AI fluff out there. I think everybody says AI and go-to-market now, but what’s real?

1684
04:56:56.950 –> 04:57:07.970
Allison Grieb: And even with some of the products and companies that we’re working with, you know, my sales… my SDR leader is on the phone with the artisan CTO all the time.

1685
04:57:07.990 –> 04:57:25.140
Allison Grieb: Right? Here’s our feedback, here’s what’s working well. And, like, there’s a good collaboration, but that takes time and energy, too. But because we want to be on the forefront of AI, and we want to rewrite, you know, what sales and go-to-market looks like leveraging AI, you know, we want to be intentional about it.

1686
04:57:25.690 –> 04:57:35.229
Matt Darrow: Well, and I think your point about partnering with a vendor like Play has helped you guys achieve results, and obviously they’ve been a big champion for this new role.

1687
04:57:35.230 –> 04:57:51.419
Matt Darrow: It’s like the hottest new role in GTM at the time, too. Allison, maybe second click for you would be, you know, how do you think about holding these folks maybe accountable, responsible, the projects that they do? So, if anybody else that’s listening in the audience sort of wanted to bring this role on.

1688
04:57:51.420 –> 04:57:56.510
Matt Darrow: How do they maybe give them the right levels of responsibility so they’re actually driving impact?

1689
04:57:56.930 –> 04:58:10.679
Allison Grieb: Well, I think all of us, you know, no matter how much AI we have, we’re still all in charge of revenue. So, revenue is still king, right? So, it’s making sure that we design measurable metrics for each

1690
04:58:10.680 –> 04:58:29.849
Allison Grieb: solution that we’re driving, right? So, we can say we’re, you know, leveraging something, but if it’s not actually driving results. So, for example, you know, with Clay, you know, I had a list of 5,000 customers or target accounts that had been sitting in my database for 7 months.

1691
04:58:29.900 –> 04:58:36.340
Allison Grieb: Right? We put clay on it, and we were able to clean and enrich that in a week.

1692
04:58:37.310 –> 04:58:55.970
Allison Grieb: And so now those have been activated, and we’re now calling and leveraging those solutions, or those contacts, and actually being able to go faster. So it’s really being, so for the go-to-market engineer, we will have various projects that will have various KPIs and metrics.

1693
04:58:55.970 –> 04:59:01.299
Allison Grieb: But all with revenue, all with all of the metrics that ladder up to revenue.

1694
04:59:01.820 –> 04:59:18.000
Matt Darrow: That’s awesome. Thanks. Thanks, Allison. Now, Steve, just from the clients that you’re working with, so Allison’s talking about new role coming into the sales organization, how about the existing sales organizations at large? With the companies that you’re working and consulting across, what are you guys expecting to happen?

1695
04:59:18.760 –> 04:59:29.229
Stephen D’Angelo: Yeah, and I’ll answer it in a second. I think one of the things Allison said was important about, you know, CROs were still responsible for revenue. I will share with all the CROs

1696
04:59:29.230 –> 04:59:43.980
Stephen D’Angelo: just about every client that I’m involved in at the executive level, CEO, board level, they are looking to reduce sales operating costs. They certainly want to drive revenue, but they want to reduce sales operating costs. It’s a constant focus.

1697
04:59:44.060 –> 04:59:52.550
Stephen D’Angelo: And the way that AI is impacting that is sales organizations, sales team size will definitely shrink.

1698
04:59:52.630 –> 04:59:58.289
Stephen D’Angelo: As… as you talk about conversational AI that I just shared, and you think about

1699
04:59:58.350 –> 05:00:16.439
Stephen D’Angelo: prospects engaging with digital agents early in the sales cycle, maybe through 60% of their buy process, we don’t need as many SDRs, we don’t need as many solution engineers. And I know this is a controversial topic, but the reality is AI will allow us

1700
05:00:16.440 –> 05:00:30.500
Stephen D’Angelo: to shrink our sales organization, we will operate much more efficiently, we’ll have a much lower operational cost, but yet it won’t reduce the focus on revenue generation, of course. But that’s how I see it’s changing. There will be a reduction in size.

1701
05:00:30.690 –> 05:00:35.439
Allison Grieb: And Steve, I just want to add to that. I feel like,

1702
05:00:35.640 –> 05:00:48.480
Allison Grieb: we’ve all been trained over the last 4 or 5 years, you know, in this profitability, right? And it used to be growth at all costs, right? And then it became, well, no, you gotta run the business.

1703
05:00:48.530 –> 05:01:05.099
Allison Grieb: with profitability in mind, and so I think some of us are used to more juggling that, and so it’s been… it’s been good training wheels for what you’re talking about, to come, but it is going to be, you know, an efficiency play, and how do I get more out of each resource?

1704
05:01:06.050 –> 05:01:30.330
Matt Darrow: That’s been a big fundamental mantra of AI in general, is that disruption of what the workforce is doing. I actually want to come back to this, because I think this topic, we can probably take 2 or 3 clicks deeper, just talk about operational formulas and how people plan for that. So I’m going to double back on this, but first, Francis, what about the humans, man? You and I were talking about it, right? You’ve got… there’s still a place for that, and

1705
05:01:30.330 –> 05:01:42.050
Matt Darrow: Maybe, maybe you want to, if you could address, for us a little bit about, like, where do the humans still fit? How is it still a differentiator? Will it be a differentiator? I’d love to hear your take on that.

1706
05:01:42.610 –> 05:01:44.500
Francis Brero: Yeah, I think the…

1707
05:01:44.690 –> 05:02:01.200
Francis Brero: the way I see it is very similar to what happened when we got mass email and, like, email automation. I think the efficiency boost that we’re getting today from AI is just… it’s a… you know, we’re gonna look back at this, and it’s gonna be a blip in history, right? It’s…

1708
05:02:01.290 –> 05:02:02.759
Francis Brero: It’s good that…

1709
05:02:02.820 –> 05:02:20.239
Francis Brero: people should be using it today, but I think sales leaders need to have very deep conversations with their CEOs, their CTOs, as to what’s next, right? The same way that it’s always easy to predict, like, first order changes when there’s a big transformation. I think when

1710
05:02:20.240 –> 05:02:34.919
Francis Brero: we invented, like, the gas engine for cars. It was easy to predict that there were going to be gas stations everywhere in the U.S. What was harder to predict was the Walmart model. We were going to design and architect our cities around use of the car, and that would allow a business model

1711
05:02:34.920 –> 05:02:45.089
Francis Brero: of a corner store that was built outside of a city, massive surface, lower cost, and, like, driving people there. I think no one is denying that today.

1712
05:02:45.300 –> 05:03:02.840
Francis Brero: you should use AI and some automation to do your outbound. I think the true question is, what happens a year from now when every single one of your competitors is using Artisan, or Piper, or whatever, like, introduced name of new AI SDR coming out there? Like, then.

1713
05:03:02.840 –> 05:03:06.449
Francis Brero: How does your sales team differentiate from them?

1714
05:03:06.450 –> 05:03:19.090
Francis Brero: And I think that is a very, very important question that companies need to be asking themselves, because the short-term gain that we’re getting from AI SDRs and, like, AI in the sales process is undeniable.

1715
05:03:19.090 –> 05:03:26.950
Francis Brero: And something that we need… we should be jumping on, but thinking about what’s next is critical and actually, I would say, existential.

1716
05:03:26.950 –> 05:03:41.730
Francis Brero: And I think that’s where the human connection and reps actually building nurturing relationships with the deals that they’re closing, it’s what… it’s gonna allow them to then, in the future, say, hey, I’m working on this deal with company A,

1717
05:03:41.730 –> 05:03:53.230
Francis Brero: you know, would you be willing, former or customer, to be a reference to these people? Because AI slop is real. Everybody can generate fake videos, fake testimonials, fake images.

1718
05:03:53.280 –> 05:04:12.499
Francis Brero: The only way buyers are going to trust something is real and delivers the results that your reps are going to suggest they do is by talking to someone who’s actually using it. And the only way that happens is if that customer still has a relationship with the AE that closed them and is willing to take a reference call. And I think the value of that

1719
05:04:13.040 –> 05:04:22.629
Francis Brero: you know, network from a sales perspective, and the Rolodex, if you will, is gonna make a gigantic comeback, and I worry a little bit that our current

1720
05:04:22.920 –> 05:04:40.329
Francis Brero: batch of, you know, reps that are very, very comfortable in the digital world. Like, I go to these conferences, and I… I can tell the AEs are not comfortable as much in, like, you know, in-person conferences like it used to be, and they’re, like, more comfortable behind a Zoom meeting with, like, a cute little background.

1721
05:04:40.670 –> 05:04:45.640
Francis Brero: that, to me, I think is, like, scary and should be scary for all leaders, because the…

1722
05:04:45.780 –> 05:04:59.479
Francis Brero: at least my opinions, one person’s opinion, but the big differentiating factor that you can bring into your team is that human connection and driving word of mouth and brand through references. That’s something that

1723
05:04:59.560 –> 05:05:07.390
Francis Brero: cannot be cheated through with AI. Everything else can be and will be easier to do in the next 12 months.

1724
05:05:07.390 –> 05:05:28.449
Matt Darrow: there’s a big point of the, just the feeling, and I often think about, or I talk to my team about, like, if you have two equally qualified candidates that can do the exact same skill set, well, who do you bring into your organization? It’s the one that has the best culture fit. So I think that there’s a lot that still hasn’t been talked about on the AI front when we get into

1725
05:05:28.450 –> 05:05:41.549
Matt Darrow: humans interacting with AI services, especially in sales, where personality and culture is going to be an incredibly important dynamic, too, as a differentiation mode for so many clients as well. That’s what’s going to give you difference of feeling.

1726
05:05:42.460 –> 05:06:06.079
Matt Darrow: Just to double back, too, I see we’ve got some audience questions, so we’ll hit those as well, so thanks for… thanks for engaging crowd. If you throw those up there, we’ll get to them. But, I did want to talk about, if the teams get smaller, humans still remain important, Francis, I’m with you. How does the planning change? I think so many folks that might be listening to this, and the panelists here, we’ve, for over the last 20 years, everybody has sort of the same spreadsheet from their CFO,

1727
05:06:06.080 –> 05:06:20.299
Matt Darrow: Around, we’re gonna grow this much, so therefore we need this many heads, and this much quota, and this much undersigned, and this much ramp time. Vinesh, I want to throw it over to you first, around, how do you think about if AE productivity starts to change, how does your planning cycle change?

1728
05:06:21.480 –> 05:06:25.530
Vinesh Vis: Yeah, for sure. So, I would agree with what Francis was talking about first, but…

1729
05:06:25.550 –> 05:06:33.879
Vinesh Vis: you know, as far as the human relationships will differentiate, I think it’s so key, and as you think about just the overall productivity model to what Allison was talking about as well, too.

1730
05:06:33.880 –> 05:06:46.069
Vinesh Vis: You know, I’ve been always in a private equity-backed software company, that’s the DNA that I’ve grown up with, but you’re seeing a lot of folks that are in venture capital-backed companies that are looking for that combination of growth and profitability as well, too.

1731
05:06:46.070 –> 05:07:08.539
Vinesh Vis: I think Agentech AI is going to be a key piece of how that gets done. So normally, somebody might take, at the start of a year, okay, I’ve got an Excel spreadsheet, and we’re going to build up a capacity plan of how many account executives, how many, you know, sales engineers, etc, do I need, and what productivity can I get out of each of those individuals? And when we start to think about where the efficiency gains are through each stage of the sales process, to my earlier point as well, too.

1732
05:07:08.540 –> 05:07:16.119
Vinesh Vis: But we can start to create more productivity out of that same seller, and get a higher capacity, which is a higher quota as a result of it as well, too.

1733
05:07:16.120 –> 05:07:39.789
Vinesh Vis: Well, for reps, I mean, the ones that really do start to shine when it comes to human relationships, if they had an agent that could take care of more of the administrative type of tasks, engaging with the customer, for more tactical-type use cases, then that’s going to allow them to do more with less. It allows them to cover more accounts, generate more demands, it improves their forecast accuracy as well, too, because agents will be able to kind of feed that data into a more cohesive experience.

1734
05:07:39.790 –> 05:07:43.390
Vinesh Vis: And so, as we look at that from an investor standpoint as well, too.

1735
05:07:43.390 –> 05:07:59.890
Vinesh Vis: all of a sudden now, you know, in the private equity world, what traditionally was the rule of 30 or the rule of 40 starts to go up to a rule of 70, rule of 80, because you can actually drive that much more growth and profitability with, with agents, and just overall gentic AI stretching across go-to-market, but all facets of the business as well, too.

1736
05:08:00.210 –> 05:08:15.059
Matt Darrow: So, and Steve, some of this put in practice, so in Vanessa’s point, if everybody’s getting more productive, quotas are going up, in the wild, am I just expected to sell more with my same comp plan, or how is that starting to change for leaders as well?

1737
05:08:15.450 –> 05:08:22.379
Stephen D’Angelo: So, since there are these efficiencies by using AI, sales organizations shrinking.

1738
05:08:22.440 –> 05:08:38.830
Stephen D’Angelo: Believe it or not, and this is going to be music to everybody’s ears, CFOs that I work with are saying, we can afford to actually enrich our comp plan, because we are more efficient, we are reducing some of our overall sales operational costs.

1739
05:08:38.830 –> 05:08:45.719
Stephen D’Angelo: We want to keep our star players, because we’ve invested so much in sales enablement, we don’t want them leaving.

1740
05:08:45.720 –> 05:08:52.719
Stephen D’Angelo: we could now start to tweak our comp plans and start paying them more aggressively for the top performers. So…

1741
05:08:52.720 –> 05:09:06.859
Stephen D’Angelo: we tend to look at technology that it tends to kind of hurt, reducing sales size, sales team size, you know, am I going to be as important? Here’s an example where, for those that are performing, they’re going to start to experience

1742
05:09:06.860 –> 05:09:12.739
Stephen D’Angelo: More aggressive comp plans, being paid more, because again, sales operation cost allows it.

1743
05:09:13.150 –> 05:09:35.549
Matt Darrow: Well, and Steve, at the top of the session, too, you talked about some real-life use cases around, like, coaching, hiring profile, everything else, where normally an enterprise might have a zero, 25, 50, 100%, sort of, sort of a ramp tier over the coming quarters. I think, can you talk a little bit about, is that materially changing on how you’re doing the operational planning for your clients, where, to Venetia’s point.

1744
05:09:35.550 –> 05:09:44.760
Matt Darrow: Like, the new formula has maybe these differences of higher quotas, different territories, faster time to ramps, but what’s actually being put into practice?

1745
05:09:44.760 –> 05:09:50.299
Stephen D’Angelo: Yeah, so the, the… the focus on the ICP

1746
05:09:50.310 –> 05:09:58.620
Stephen D’Angelo: In the territory environment, we’re finding that they are starting to allow territories to shrink.

1747
05:09:58.620 –> 05:10:10.889
Stephen D’Angelo: but still getting a lot more yield out of those territories because they’re getting a lot more sales activity than just a rep engaging. The fact that they’re using conversational AI in order to nurture opportunities

1748
05:10:10.890 –> 05:10:18.170
Stephen D’Angelo: we’re starting to see them… see organizations adjust how they’re planning out their territories. Still getting a little bit shrinkage.

1749
05:10:18.180 –> 05:10:20.369
Stephen D’Angelo: But you’re getting a whole lot more yield out of it.

1750
05:10:22.070 –> 05:10:46.709
Matt Darrow: Well, this is a, yeah, to your guys’ point, too, it’s like, it is… it is great for the business with operational efficiency, that was your CFO-CEO discussion, but then for the rep, it’s not great, my comp plan didn’t change, but my expectations went up in a much bigger way. Right. And for most folks here, running the whole revenue org, it is very broad. Allison, you mentioned you have channel, you have SDR, right? You guys probably have SEs, and you have CSMs, too.

1751
05:10:46.730 –> 05:11:05.180
Matt Darrow: Trevor, nobody’s spoken about the ratio yet. This is another cost of sale, AEs and SEs. I want to call on you, actually, because way back in your day, you were an SE at a prior life before you moved into the wild world of sales and sales leadership. How’s that ratio going to change as well, as AI can do more?

1752
05:11:05.390 –> 05:11:09.450
Trevor Jett: And it was way, way back. Our first product was Fire, that’s how long ago it was.

1753
05:11:10.450 –> 05:11:21.849
Trevor Jett: Yeah, I remember back in those days, like, the… someone told me that the, the expected ramp time was going to be a year before I quote-unquote, would not stink up the room, and, like, that’s an entirely different expectation today.

1754
05:11:21.850 –> 05:11:38.290
Trevor Jett: I think the… a few things are in motion now. Like, the things that kept people from getting to critical mass in their roles as an SE, an AE, or whatever, was access to expertise, because we went to formal training. We flew to formal training. It would be ridiculous to do now.

1755
05:11:38.290 –> 05:11:57.739
Trevor Jett: like, so those things happen a lot faster now than they ever did before. So now we can ramp these things up a lot faster. The AE in the mix on the go-to-market team has access to expertise that they did not have access to before. And we typically think of that ratio as the AE to SE ratio.

1756
05:11:57.740 –> 05:12:01.719
Trevor Jett: And so that number’s gonna change completely, because now AEs can

1757
05:12:01.720 –> 05:12:21.819
Trevor Jett: either do their own demo, or I think it was Steven that pointed out, they’re not even going to do the first demo. Like, the first demo is going to happen agentically, and they’re going to be that much farther along in the process. But that access to expertise is going to change things, both from an SE perspective and from an overlay perspective. And that’s the thing that I don’t think many people have contemplated.

1758
05:12:21.820 –> 05:12:33.049
Trevor Jett: If you go back to my resume, I worked for some big blue companies that were known for backing up the school bus whenever we’d go to a meeting, because there was, like, anybody that knew anything about this thing would show up.

1759
05:12:33.050 –> 05:12:51.320
Trevor Jett: And I’m wondering if we… we don’t need the school bus anymore, because now we can go in and say, I’m going to be an expert… expert enough on these things, in these subject areas, in these verticals. I think that’s going to change the… the ratios entirely, and back to Steven’s point again, we’re going to hire fewer people as a result.

1760
05:12:51.590 –> 05:13:07.359
Matt Darrow: now, as these orgs change, and all these new tools, new workflows get adopted, Francis, I actually wanted to tee it over to you, also, just because of some of your interesting bent being on, on a product and engineering focus, too, is…

1761
05:13:07.360 –> 05:13:26.640
Matt Darrow: what seems like science friction starts to become a reality, but what do you still need to be maybe aware of from a data security perspective? So for the audience out there that’s listening about, oh my god, like, this future is here, it’s arriving, I want to go and try these things, but there’s new attack vectors that are opening up, so…

1762
05:13:26.640 –> 05:13:38.589
Matt Darrow: maybe what are some of the examples that you’ve seen that maybe you wouldn’t have expected as AI rolls out more? And then, what is the advice that you have for folks evaluating tools that they might seriously want to consider?

1763
05:13:39.310 –> 05:13:46.149
Francis Brero: Yeah, I think, this is something, again, that every sales leader, revenue leader needs to be

1764
05:13:46.150 –> 05:14:04.349
Francis Brero: discussing with their CIO, CISO, CTO, like, what is our strategy around this? So, I started with a very, very simple experiment. I updated my LinkedIn About section. At the very bottom of it, I added some reference to Nick Cage.

1765
05:14:04.350 –> 05:14:18.349
Francis Brero: And one of my employees did the same thing and said that he was the master of dad jokes, and then we look at how many dad jokes do we receive in the AI-generated Connect messages we get on LinkedIn. That’s a kind attack, and it’s a fun attack.

1766
05:14:18.480 –> 05:14:20.010
Francis Brero: The thing is.

1767
05:14:20.120 –> 05:14:33.479
Francis Brero: I’m now work… I’ve started building a prototype that’s very different and a lot more scary, where if you start having AI agents that look at your calendar, I can send you an invite to your calendar, you don’t need to accept it.

1768
05:14:33.570 –> 05:14:47.929
Francis Brero: In the description of that invite, I can inject a malicious prompt to make sure that when the AI agent is gonna read it, it’s gonna set new instructions to send me all of the emails of people you’re meeting with in the next months directly to my inbox.

1769
05:14:48.100 –> 05:15:01.520
Francis Brero: And the challenge here is that where, in the past, when we were buying software from vendors, the software was deterministic. It had clear guardrails of, like, it’s supposed to do this. This way, it was easy to test it.

1770
05:15:01.800 –> 05:15:08.189
Francis Brero: with LLM-powered systems, you’re now entering a world where

1771
05:15:08.460 –> 05:15:19.560
Francis Brero: The system might work the way you expect it to 99% of the time, but anyone smart enough and understanding how this system works can actually jailbreak it and

1772
05:15:19.560 –> 05:15:37.230
Francis Brero: the consequences can be disastrous. And this is where we go back into, like, this is almost, like, brand-killing type of error, right? If, like, now imagine all of your reps all of a sudden are sending, like, insulting emails to their customers and prospects because someone figured out a way to inject a prompt

1773
05:15:37.230 –> 05:15:49.019
Francis Brero: into your outbounding system, that becomes really scary. And this is something that few of the vendors out there are talking about, few of the CISOs have experience around, because all of this is very new.

1774
05:15:49.020 –> 05:16:02.649
Francis Brero: So, again, like, I’m playing a bit of the card of the doomer and gloomer here, because, like, this is my field of work, so I spend a lot of time in it. I’m very excited about the future, but I also want to balance a little bit of the reality here, right? Like.

1775
05:16:02.650 –> 05:16:13.280
Francis Brero: there’s a lot of things that don’t work the way, people market them to work around these AI systems. Like, we call them reasoning models, just to be very clear.

1776
05:16:13.280 –> 05:16:25.609
Francis Brero: All the research papers show that what you get in the reasoning model, ChatGPT, Claude, all that stuff, that is not their actual reasoning. This is the model pretending to reason so that you’re convinced

1777
05:16:25.630 –> 05:16:43.959
Francis Brero: by the reasoning to believe the answer they gave you. That is literally how it is designed. The actual reasoning that’s going on in the background is different, and we have, like, proof that that’s the case. So, it is very, very important that you think about what are the guardrails, what are the things that we put in place.

1778
05:16:44.040 –> 05:16:48.119
Francis Brero: To make sure that, yes, we accelerate, we improve efficiency.

1779
05:16:48.400 –> 05:16:53.079
Francis Brero: But, you know, we… we stay away from catastrophic failure. And I…

1780
05:16:53.190 –> 05:17:03.709
Francis Brero: I think just those few examples, I think, are important to bear in mind, because, yeah, you don’t want to be the brand that, you know, becomes the canari in the coal mine.

1781
05:17:03.950 –> 05:17:19.320
Matt Darrow: Yep. Some of the new, yeah, even compliance audit certifications that are coming out as well are going to help with this, but to your point about making sure that there’s a strong plan with the CIO, CISO, that are going to evaluate these technologies, spot on, because the attacks are completely different.

1782
05:17:19.320 –> 05:17:37.759
Matt Darrow: With 10 left, I do want to get to the fun debate topics, but before I do, Julia’s been kindly enough posting some, audience Q&A into the chat, and we’ve got a couple ones that have had some plus ones on it, so I actually want to hit this for anybody that wants to take it. We’ve got a question around, hey, customers spend…

1783
05:17:37.760 –> 05:17:45.070
Matt Darrow: is… is potentially down, so should we expect to see the sales productivity gains with AI

1784
05:17:45.070 –> 05:17:57.789
Matt Darrow: lowering the cost for customers to encourage them to spend, so therefore things get cheaper, so they’ll potentially go and buy, or are there any other different ways that AI is helping motivate customers to spend?

1785
05:17:57.920 –> 05:17:59.890
Matt Darrow: Anybody feel strongly want to take that one?

1786
05:18:01.450 –> 05:18:03.820
Stephen D’Angelo: Yeah, I’m happy to jump in.

1787
05:18:04.030 –> 05:18:06.240
Stephen D’Angelo: I’m sorry, Allison, if you wanted to go ahead first?

1788
05:18:06.960 –> 05:18:14.239
Stephen D’Angelo: Okay, so… The… the trend of customer spend going down

1789
05:18:14.490 –> 05:18:34.040
Stephen D’Angelo: I don’t see that ending with a however. As we know in the SaaS space, it all comes down to that value proposition that you are delivering, right? That could get spend to kind of stabilize or even start to go up. However, I’d look at this a little bit differently.

1790
05:18:34.200 –> 05:18:48.900
Stephen D’Angelo: If we are shrinking our sales organization a little bit, if we’re becoming more efficient by using conversational agents to move prospects through the cycle, sales reps will be closing more deals. And while spend goes down.

1791
05:18:48.900 –> 05:19:05.979
Stephen D’Angelo: and the compensation plans, as I mentioned, are going to get more aggressive, it won’t have as much of a negative impact on the reps. So they’re going to close more deals, they’re going to have a stronger comp plan, the time that they’re engaged in a prospect is going to be less, because the digital agent took that prospect through.

1792
05:19:05.980 –> 05:19:09.940
Stephen D’Angelo: The challenge of spend going down does not become as much of a challenge.

1793
05:19:13.340 –> 05:19:21.700
Allison Grieb: Go for it, Allison. Yeah, so, I’m really lucky here at Paystand, because our solution,

1794
05:19:21.870 –> 05:19:25.579
Allison Grieb: Doesn’t have to have, sort of, value…

1795
05:19:25.770 –> 05:19:28.759
Allison Grieb: driven because we have a hard cost ROI.

1796
05:19:28.810 –> 05:19:40.240
Allison Grieb: So, being able… but one of the things that’s difficult is actually gathering the information from our prospects for all the inputs that we need from an ROI.

1797
05:19:40.240 –> 05:19:52.030
Allison Grieb: So, that’s an area where I think, you know, AI could help us go faster, where sometimes we’re on a 2-3 week live. We’re a velocity business, so our sales cycles are super fast.

1798
05:19:52.130 –> 05:20:06.579
Allison Grieb: But being able to collect that information and get that information a lot faster, where we’re justifying up front the spend, and I think a lot of us in our careers have seen the role of a value engineer.

1799
05:20:06.700 –> 05:20:25.750
Allison Grieb: Right? Who has been creating sort of the value story and scope to help position, you know, whether it’s manual hours saved, whether it’s hard cost hours saved, like, that role I can really see helping us get faster to a value, right? Because I think you’re not gonna have

1800
05:20:25.750 –> 05:20:32.070
Allison Grieb: Customers where spend is down, spending a lot of time if the value is not apparent up front.

1801
05:20:32.070 –> 05:20:42.699
Allison Grieb: So I think this is an area where AI could really help us get to that value faster, and create a stickier reason to move forward.

1802
05:20:43.860 –> 05:20:55.450
Matt Darrow: Thanks, guys. Other audience question before we lose them, it’s always good to make sure we, we’re giving the people what they want here, is, any, anybody have use cases for competitive analysis?

1803
05:20:55.690 –> 05:21:00.260
Matt Darrow: Anybody been dabbling in this for, examples that maybe they want to share?

1804
05:21:01.450 –> 05:21:02.550
Trevor Jett: I could take that one.

1805
05:21:02.620 –> 05:21:03.560
Trevor Jett: Alright. Good.

1806
05:21:03.600 –> 05:21:21.920
Trevor Jett: Sure. I mean, that’s… that’s something that happens, I think the net-net is it happens a lot faster than it used to be. Like, it used to be something that would go through a whole process, and then we’d build a battle card, and then the next quarter, they’d send out the thing, and we’d know what to say. With Agentic AI, specifically, that rides along with you, like, now that’s built in immediately so that

1807
05:21:21.920 –> 05:21:40.209
Trevor Jett: it just becomes part of the support that the agent gives the salesperson in the sales cycle as what they say, and they can vet it out, and that’s where a lot of the enablement is happening today. So we can find it, and then just very simply put it into the process, now everybody’s going to get it, and it’ll be part of what everyone sees.

1808
05:21:41.750 –> 05:21:45.320
Matt Darrow: I, I, I, I, I feel like we could,

1809
05:21:45.340 –> 05:22:08.899
Matt Darrow: well, we could have used 2 hours, Julie, instead of just 1, but I want to be mindful of the remaining time, and even though I really had my eyes set on getting a debate lined up here, we’re going to run out of time for that, so I figured what I’d do is we’ll do a closing circle in the last 5 minutes here to just go around the horn for everybody here to say, you know, for the audience that’s out there listening to all of this, we’ve covered a lot of ground.

1810
05:22:08.900 –> 05:22:17.599
Matt Darrow: from where sales teams are going, how they’re changing, how comp plans are changing, how real-life use cases are being applied. So after listening to all this.

1811
05:22:17.600 –> 05:22:27.799
Matt Darrow: maybe everybody could just boil it down to the single piece of advice that you want to give a sales leader that’s in the audience today. Vinesh, I got you on my screen first, so we’ll start with you.

1812
05:22:29.010 –> 05:22:38.559
Vinesh Vis: Yeah, I’ll try to hit both at the same time. So, I think from a debate perspective, we’re going to talk about the ideal stack, and also it kind of goes into the closing point, which is if you look back many years ago.

1813
05:22:38.560 –> 05:23:02.159
Vinesh Vis: you know, similar to Trevor starting with Big Blue, I started with Big Red. There was the, hey, go buy Siebel and everything comes around it. Then you had Salesforce and all the ecosystem around it that kind of came about. I think we’re entering a world now, this is unprecedented, where it’s going to be really a, you know, an agentic AI-based stack that kind of builds ground up. That is going to be very interesting to see how that influences vendors that we probably deal with today versus with the vendors we deal with tomorrow.

1814
05:23:02.170 –> 05:23:23.720
Vinesh Vis: But the one key thing I would give the audience is start with the end in mind. If you don’t map out the customer journey and understand what are you trying to solve for, back to what Allison was saying around know what metrics and ROI you’re really trying to get out of leveraging, you know, any type of an agentic AI solution end-to-end, map out the process, then kind of back into the solutions that make sense, and hopefully it helps you drive your business forward.

1815
05:23:24.100 –> 05:23:28.889
Matt Darrow: Fantastic, Vinesh, thank you. Allison, how about, how about yourself? One thing for the audience.

1816
05:23:29.390 –> 05:23:32.740
Allison Grieb: Yeah, I, be intentional.

1817
05:23:33.030 –> 05:23:41.850
Allison Grieb: Right? I think be intentional and prescriptive about how can we leverage AI to do better. I think…

1818
05:23:41.850 –> 05:23:59.000
Allison Grieb: I don’t like to approach it from a cost-cutting, head-cutting, that’s not my style, but how do I create efficiencies? How do I increase productivity? How do I serve up the right at-bats for my AEs to knock down? And I think we talked a little bit, Matt, in our prep, but

1819
05:23:59.060 –> 05:24:00.030
Allison Grieb: you know.

1820
05:24:00.070 –> 05:24:08.610
Allison Grieb: where, traditionally, I would be looking to double my sales force year over year, you know, I don’t know that I need to do that.

1821
05:24:08.610 –> 05:24:26.659
Allison Grieb: I need to create efficiencies for the team that I’ve got, and put them in a position to potentially take over the sales cycle in the middle of the cycle, and knock it down. And their at-bats are, you know, they’re starting their at-bats with the bases loaded.

1822
05:24:26.660 –> 05:24:37.920
Allison Grieb: So, it gives everybody a chance to win faster, but I do believe that you have to be intentional, you have to take the time, you have to do the research, because there still is fluff out there.

1823
05:24:37.920 –> 05:24:50.860
Allison Grieb: And you’ve got to, you know, talk to peers, right? And listen to things like this, where people are actually doing it, because we want to crowdsource and share what’s really working, because AI will become a competitive advantage.

1824
05:24:51.400 –> 05:24:55.289
Matt Darrow: Fantastic. Thanks, Allison. Steve, how about over to you?

1825
05:24:55.290 –> 05:24:57.159
Stephen D’Angelo: Yeah, my suggestion is…

1826
05:24:57.340 –> 05:25:10.169
Stephen D’Angelo: kind of just do it. You know, I would say that we as sales leaders, we get caught up in the day-to-day, week-to-week, quarter-to-quarter, chasing deals, making sure we make our number, and while this is a very…

1827
05:25:10.170 –> 05:25:18.090
Stephen D’Angelo: informative, fun, healthy conversation about technology. There is sometimes the situation where sales leaders

1828
05:25:18.380 –> 05:25:32.949
Stephen D’Angelo: don’t focus as much on the efficiency technology, and they’re just chasing opportunities. Again, make sure that you’re focusing on it. Pick the right one that’s gonna… that’s gonna solve an important problem for you. Conversational AI is big. That would be my suggestion.

1829
05:25:33.590 –> 05:25:34.420
Matt Darrow: Awesome.

1830
05:25:34.540 –> 05:25:35.810
Matt Darrow: Trevor, over to you.

1831
05:25:36.330 –> 05:25:51.630
Trevor Jett: Yeah, I think I would just zoom out a little bit for sales leaders that are of a certain age and say, believe it. You know, come down on the right side of history. Don’t be Dan from the movie Wall Street, where he’s like, oh, there’s so many monitors and computers, I don’t know what to do. Like, this is how it’s going to be.

1832
05:25:51.630 –> 05:25:56.530
Trevor Jett: And more importantly, like, the next generation of sales reps, I forget who pointed out, like.

1833
05:25:56.560 –> 05:26:16.190
Trevor Jett: they aren’t going to just benefit from it, they’re going to expect it. Like, if you don’t have these things, they’re not going to go to work for you, because you’re going to be this dinosaur of a company. So, you gotta stay… stay up to date on these things, make it work, but don’t just dismiss it, like, oh, AI is a… like, it’s not. This is how your life is going to be, this is how our organizations are going to be.

1834
05:26:17.040 –> 05:26:29.640
Matt Darrow: Awesome. Yeah, I think that was, that was Venetia’s, point around, the, the, the reps being hired are going to be, holding the leaders to task to make sure that they’re going to be enabled the right way. Francis, bring us…

1835
05:26:30.090 –> 05:26:49.900
Francis Brero: Maybe two things. I’ll answer the… at least the question on the… on, spending. I think I would encourage sales leaders to think about OPEX and CAPEX in the sense that we’re seeing, like, larger organizations, like, revisit some of the AI investments more as CAPEX, and if your product delivers

1836
05:26:50.040 –> 05:27:09.950
Francis Brero: like, meaningful assets, and they’re accumulating knowledge that can be reused and, like, create a, you know, benefit, like, over time, I think it is something that a lot more CFOs are willing to hear. I think even, like, from a go-to-market strategy, like, using that to understand, like, the spend on, like, pure OPEX software might be going down, but overall, like, we’re seeing CapEx go up. So that’s, like.

1837
05:27:09.950 –> 05:27:14.459
Francis Brero: one slight nuance on… on spend is down, I would debate that one a little bit. But then.

1838
05:27:14.510 –> 05:27:18.590
Francis Brero: I’ll just go with another contrarian take here a little bit, is…

1839
05:27:18.590 –> 05:27:38.340
Francis Brero: As a leader, I think it is absolutely critical that you change a little bit the way that you manage people in today’s day and age with AI. Instead of just looking at the outcome, you need to look at the process. Today, it is way too easy to bamboozle everyone by putting a great-looking output that comes straight out of ChatGPT,

1840
05:27:38.340 –> 05:27:53.159
Francis Brero: But if you don’t coach your team through the process of, like, how did they decompose the problem to smaller steps, how do they address every single one of the problems, you’re gonna have a team that’s gonna drift in quality over time, and when you realize it, it’s gonna be too late. So, I think…

1841
05:27:53.530 –> 05:28:06.540
Francis Brero: leaders really have to go into the, how did you get to this, and not just, like, what did you get? That’s a big change that needs to happen, and you should not let your employees just, like, ship a bunch of ChatGPT AI slop.

1842
05:28:07.350 –> 05:28:11.879
Matt Darrow: Thanks, Francis. Julia, how’d we do? Hour on the nose? Killer panel.

1843
05:28:12.140 –> 05:28:27.420
Julia Nimchinski: What a compelling session. Thank you so much for hosting, Matt, and thank you to all panelists. And last question for you. What are you doing in the SLM land? I thought that that would be your gem in particular.

1844
05:28:30.330 –> 05:28:31.749
Francis Brero: on… Oh, this…

1845
05:28:31.750 –> 05:28:34.659
Julia Nimchinski: Yeah, small language models, Matt.

1846
05:28:36.430 –> 05:29:00.369
Matt Darrow: Oh, for us, so our team is a little different, where we actually have our own reasoning model specifically for sales. So one of the things that Vivint’s been focused on is that LLMs are really great for general purpose, what is the next best word that I think Francis mentioned will sort of dupe you into think it’s reasoning, but it’s really not. So if you’re ever going to get the level of fidelity that you need for, as Steve mentioned.

1847
05:29:00.370 –> 05:29:24.410
Matt Darrow: to have conversational AI actually talk to a client without being a liability, it needs vertical-specific reasoning that will allow it to perform well in high-stakes arenas. That’s been our focus, that’s a big part of our core IP at Vivint, is we build reasoning models for sales that allow us to extend what LLMs are capable of doing that make this type of future possible.

1848
05:29:25.790 –> 05:29:29.370
Julia Nimchinski: Awesome, and how can our community support you the best?

1849
05:29:31.040 –> 05:29:50.889
Matt Darrow: Supporting for us best would be, two things. One, come to Vivin.com and reach out to, myself and Trevor. We’ll be able to love to have a conversation and carry that forward. And the second thing is to just engage with us on LinkedIn. You can follow me. I’m always posting about these topics as well, so two simple takeaways.