(14) GTM Strategy: 5 Insights from 500 B2B SaaS Orgs (Jeremey Donovan, EVP Sales + CS @ Insight Partners) - YouTube https://www.youtube.com/watch?v=1H4Xvl8c4ds

Transcript: (00:00) Jeremy Donovan is the EVP of sales and CS at Insight Partners where he advises over 500 B2B SAS companies on their go to market strategy. >> So even this week I think I had six or seven conversations with CRO about what's working, what's not. Jeremy breaks down the counterintuitive trends he's seeing in the data. (00:17) >> One of the surprises in the survey was that some of the top performing companies are actually expanding their outbound sales teams, which blows the mind because everything you read on LinkedIn says the opposite. We dig into the build versus buy debate and why Jeremy thinks buying software right now is risky business. (00:34) >> I think a lot of buyers are afraid that they're going to be the dope who buys the point solution tool only to have one of the foundation LLM companies just be able to do that capability pretty quickly. >> We get into a ton of more interesting topics in this episode and I asked Jeremy what he thinks the best revenue leaders in the world get right. (00:52) when there's a CRO vacancy in our portfolio. That's the number one thing I'm looking for is >> Welcome to the revenue leadership podcast. Momentum is a powerful tool for turning sales and customer conversations into go to market intelligence. Using Genai, it extracts, analyzes, and automates customer intelligence across your GTM or the best part, it's not a new platform for your team to adopt. (01:15) It integrates seamlessly with your existing stack or it can straight up replace your conversational intelligence tool. At Owner, we ripped out Gong and went all in on momentum. It writes back to our Salesforce directly, captures forecast and churn risk, autofill CRM fields, shares product signals, and tracks sentiment. (01:34) Companies like Cursor, Zcaler, Ramp, and 11 Labs use it every day. Check it out with a free trial at momentum.io. Today's guest is Jeremy Donovan who is the EVP of sales and customer success at Insight Partners where he advises around 500 B2B SAS companies on GTM strategy and uh Jeremy's had an unusual career path started as a semiconductor engineer masters of science in uh masters in data science MBA from Booth and then almost 20 years uh on the in the analyst world at uh Gardner and GLG um where you were uh SVP of sales strategy at the end at (02:13) GLG and then operator side. So almost four years at sales loft across solutions engineering, rev ops and eventually revenue strategy and now uh in this role uh as an operating partner and and advising a bunch of these companies and insights. So really excited to unpack a bunch of those learnings and uh you have a really interesting vantage point as an analyst as somebody who's connected to port codes as somebody who's been an operator. (02:42) Um and one of the things that is great timing is you published uh a survey recently that we're going to unpack. It was a survey of 150 CRO's um and some uh findings around where AI is actually working uh where it's not how how folks should think about it next and uh really looking forward to this and uh being a big fan of your content for a long time as well. (03:05) So uh thanks for joining me. Your timing is good, too, by the way, because uh independent of scheduling this, we've actually been interviewing a lot of the CRO who responded that they had adopted with high value a bunch of different AI use cases. So, even this week, I think I had six or seven conversations with CRO about what they're doing. (03:24) So, a lot of fresh off the presses information about what's what's working, what's not, where people have adopted things or where people have even abandoned things. >> Awesome. Okay. Okay. Well, I'm looking forward to to digging into it all. So, as I mentioned, you know, you're you're advising a bunch of companies in the insight portfolio. (03:42) You've done this survey. You're you're closely connected to these CRO. And so, I'm curious at a high level, what do you see as the gap between the companies and leaders that are uh getting value out of AI versus the the ones that are coming up short today? >> Yeah. Well, I'll start by saying that I think it's it's early days, right? If you look back at the course of 2025, in early 2025, there was hype everywhere, right? Uh and and I pulled some >> some other survey data that folks had produced that was we're talking about astronomical improvements in win rates, (04:19) and productivity, right? And and I think as we got further into 2025, we we really landed in a the troth of disillusionment, if you will, in the hype cycle, right? I I saw a lot of stuff on LinkedIn and elsewhere where people were saying and podcasts also like that I listened to yours and tons of other ones where people were saying ah it's not really working in sales yet and I think like I'll start by saying you know that um at least with AI adoption from top performing company CRO of top performing companies versus CRO of like (04:54) you know average to below average performing companies there's not actually a huge difference in which use cases they're adopting >> and um in which order. So I would that's a I mean maybe it's disappointing but there's not a there's not like a silver bullet that's obvious yet in terms of what they're doing. (05:19) I would say that the ones who have been more successful talking anecdotally from the individual conversations we've been having with CRO is they are they've developed a system to farm ideas from the bottom up right because you had a lot of reps and other people who just got interested and excited and started testing things and then they would pluck the best ones of those and then start to build a a mechanism to syndicate that across the team. (05:46) Another one is Maybe stating the obvious that a lot of companies realize they had to shore up their data foundation >> and >> AI, right? The garbage in garbage out thing. If you don't have a clean data foundation, you're not going to get anywhere. I think the thing that is switching towards the end of 2025 and into 2026, and I'll pause. (06:07) >> The thing that is switching is I think you're seeing a lot more top down now versus bottoms up. Like we tried to do a lot of things bottoms up and there's only so much you can do. I I got to be careful with this one, right? Like I was going to say there's only so much you can do in in like the foundation LLMs, but every day what you can do gets more and more oppressive. (06:29) But when you get into agentic capabilities and things that require orchestration across different systems like if you want to get in your snowflake uh or whatever your you know uh um you know data brick snowflake whatever like your instance and by the way we are investors in data bricks I always have to disclose when we're a known investor in a company um but whatever your data warehouse is >> yeah uh whatever your data warehouse is or any other systems right like when that happens at any company that's of any scale like security gets involved (07:02) and you can't just point your you know random AE can't just can't just point their agent at those systems and have it mine data out. So I I I it's a hypothesis >> that like 2026 is going to be much more the year of top down sales AI innovation because you need that systems access in order to get the real value out of it. (07:28) Yeah, I I think there's a lot of truth there and our approach has been almost purely top down. Um, we have we made like really aggressive early investments in AI across the business and especially in GTM, but it's largely being run out of our data team and now we've got a few applied AI folks within that organization for that exact reason because you can only get so far with chat. (07:56) You can only get so far in a chat interface that has, you know, some limited MCP access to to certain things. But, you know, if you really want to get value, well, you need to be able to combine, I mean, this is the context graph thing that everybody's talking about today, but you need to be able to pull in information from Salesforce and potentially Snowflake and then do external research and then be able to tie these things together or else the outputs are are are just sort of thin. (08:21) And um one of the things that I I wanted to talk I wanted to ask you about is this idea of governance and orchestration. So it's it's a topic that we're talking about internally a lot. How have you seen and this ties into your top down hypothesis. How have you seen companies um be able to give the power of these tools to a broad cross-section of people without significant like security, privacy, governance governance concerns? This is this is like the topic dour. (08:58) I think >> I I mean it's the answer is they don't get around it, right? I mean I think there's I deal with two types of companies. Um and maybe we're companies in two dimensions. So the the companies I support are in the 0 to 100 million AR range. We have other teams my the team that I'm on supports 0 to 100. (09:16) We have other teams that do 100 million AR plus um of our 550 plus portfolio companies. So one dimension is like the size of the company and absolutely as the companies get bigger and certainly as you get over you know 20 25 million of ARR stuff is pretty locked down. Um and then the other dimension is whether or not these companies are serving regulated industries. (09:40) So one of the CIO I was talking to the other day um you know sells into financial services and um you know they we have others that sell into healthcare. We have others that sell into um uh you know other regulated industries. Doesn't matter how big they are they are locked down you know from the beginning. So I don't really I I think this is a you need to include security and privacy as your as your partner. (10:05) The good news and this is another best practice of you know like top performing companies and this gets at the top down thing also is that the the best fastest moving most agile companies have appointed a human who has good organizational influence and authority to be the AI point person for internal AI optimization. (10:27) It's really important to separate those things because right we're dealing with >> almost all of our portfolio companies are are B2B SAS. So we we also have to be really careful when we talk about AI that we're not talking you know whether we are talking about AI in the product >> or we are talking about AI for operational efficiency and and and growth right so in the case of of you know these companies they have a >> an AI zar basically and it's it's a bit of the old chief of staff you know there's still chiefs of staff floating (10:54) around but we're seeing a lot more aisars and those AIARS could have come out of revops they could have come out of PMOs project management organizations, right? They could have come out of finance like they're popping out of different places. They're just people who really, you know, they showed the passion early on and and then they're they're helping to to basically project manage these complex things including privacy and governance through um you know through through implementation and deployment. (11:23) >> Yeah. Anything else that you see in terms of AI adoption in in in the top performer versus average companies? Um, right now most people like it's it's it's not a there's not I would say like a huge difference. The uh some of the themes I guess that I'm seeing, you know, this a lot of the stuff is starting at the top of the funnel and moving its way its way down funnel. (11:49) So I mean that that could be a thing that the the companies who are more aggressive with this right are starting to not just do the top offunnel inbound lead engagement and outbound personalization and research and so on but they're moving into those midfunnel and bottom of funnel use cases like >> right >> figuring out risks and opportunities using it for RFP responses >> going way downf funnel into customer success to do churn signal detection right so >> it might just a matter of like yeah I I was about to say more of the use cases but I almost feel like doing fewer (12:23) better right now is a better approach than trying to scatter shot over a whole bunch of different use cases. Yeah, that's I that's what I hadn't explored in the data, but that there's a little bit of that I think is focus focus matters >> and and this is maybe me talking my own book, but how how much do you see because I think this was the your quote at the very end of that um survey uh news article. (12:52) How much do you think is just like the discipline like companies that are disciplined with execution and can project manage and deal with the details or winning versus versus not considering this is such a like systems engineering problem. >> Yeah. Yeah. And that well that's a bigger theme, right? Like I'm often asked, hey, if you were to go back into an operating role and you couldn't do any research, right? You just had to get in there and you had to do you know you could do run one play. (13:19) Um you know what what would you do? And the thing I've observed in working with these companies over the course of the past four years and then you know the prior whatever 25 30 years of my career is everybody knows AI or not AI like we all know the playbook. Um, I'm not going to tell you, no one's going to tell you the what or the why or even the how. (13:44) That is that you're not going to know. The the best companies have insanely disciplined execution. So, getting more specific, right? If I were to go into a company, what's like, you know, the some of the first things I would do? >> Um, one of them is just deal reviews. like if I could do one thing, it would be incredibly disciplined >> weekly deal reviews um using you know in the survey we asked what the most common sales and deal sales qualification and deal inspection framework is it's medic or medic or whatever variation you want (14:18) like especially if it's in a upper mid-market to enterprise sales context the first thing I would do is is like get rigorous deal inspections on medic you know number two incredibly disciplined pipeline generation, right? With time carved out to do that. >> Um, you know, uh, and you could kind of go number three is is ensure that you're really going after the ICP that you think you're going after because, you know, you could waste a lot of time on the wrong ICP, right? So you can go down the list but but so that disciplined (14:51) deal exec that sorry that disciplined execution thing when I I I do a lot of interviewing when there's a CRO vacancy in our portfolio and that's the number one thing I'm was looking for is I want to understand their operating rhythm um and and how much they adhere to that and I don't just ask them I do back channel reference checks and I go ask the teams that they worked on what was it like to be on that team because I really need to know what that discipline was that that's that's success and failure so it's the same with AI, it's like if you (15:19) just throw it out there and and you're not kind of, you know, passionate about making sure that people are using it >> and you're not using it a lot of times yourself, especially with these smaller companies, it's not going to stick. Another one of the CERO I was just interviewing um like he personally was writing a lot of the prompts and uh you know, you could argue that that's it's a smaller company, right? like a larger company CRO that may not be the best use of their time, but like in this smaller company, it was a good use of his time (15:50) and and that he he helps train and enable and set expectations that people are using this and that reinforcement is really critical. That's part of the discipline. I would argue it's a great use of site. Like I I do this myself, you know, like I'm uh we're moving into a clawed enterprise plan. (16:12) Uh for the express purpose, like my most governance and securityurities piece of it, but like my most um important feature is centralized skill management. So I can create and update skills and not just have to, you know, copy paste my markdown or send my markdown file to somebody >> because I'm I'm whenever I'm using my cloud skills, I'm using it, the output's not quite right. (16:36) And then in that same message, I'll be like, oh, actually like, can you update the skill to do this, that, and the other the other thing? and and being able to do that and then have that now be centrally improved for everybody using that same skill I think is really compelling because you know you think of the leverage AI gives you. (16:54) We're in performance review season right now. Performance reviews can normally take could could take you know 20 hours for a manager. They got eight reps. You want to do it right. And and yet you know we have all of our one-on-one transcripts. You've got all of the notes that you've taken about your rep. you've got all of your coaching sessions. (17:13) And so with a really well done prompt that you've iterated on, you know, that can save I think it'll save 75% of the time that it takes to do all this. I I still want, you know, the the managers to like hand edit and craft and make sure that it's really personal and tight, but but like there's a bunch of grunt work going and reviewing all your notes and remembering a bunch of stuff and and sort of synthesizing the themes. (17:40) Um, and so my it was my BDR director who created the prompt that then we shared that more broadly and like that that I think is an is a great use of time. >> Yeah. It's >> I Yeah. >> Yeah. I mean, as companies get bigger, I think it's just right like the CRO might have a right-hand person who they trust, you know, to do that stuff. (17:59) I was going to Yes. And what you were just saying, though, which is I would love for AI to be the death of the annual performance review. uh and instead right cuz cuz the AI can give you real time continuous feedback which is what's most valuable right I mean every right there's tons of research that practice in a feedback rich environment is what matters and the more you know the more immediate real time that feedback is the better so you know there's there's an increasing number you know I think the first wave of tools is kind of like it'll host (18:29) process your call right like there's a ton of conversation intelligence solutions that will do that >> and then you So maybe we're moving to a world where you get real time guided selling. You know, we've had that as as a promise for quite a long time. It's almost like augmented reality, right? That keeps coming coming and going. (18:50) But >> what if you're on a you're on a call and you've got a little side panel. >> I know co-pilot is a little bit, you know, charged term these days, but whatever an an agent helper, whatever you terminology you want to use. And if maybe it's not flooding you with information because that's going to be very very distracting, but maybe it's just giving you like >> just the nugget you need and and imagine it's subtle that it >> it understands if you maybe said if you misspoke on something, >> right? Or if you were pausing and heming (19:20) and hawing a little bit trying to remember something or you get asked a detailed technical question that you would need your sales engineer there for, but you don't want to delay the sales cycle. Like we're going to get there, right? I mean just like you know those of us old enough kind of remember the days of you know everything seem you you were really limited in compute in storage and communication networking bandwidth right and then all of a sudden compute seemed effectively free storage seemed effectively free so that that's (19:52) as I think about AI that's what I think about too is is like we we we will advance upon a world in not too long from Now when the the memory context window is effectively unlimited, >> when the use of the AI is effectively, you know, costless. Um, funny side story from, you know, just yesterday. (20:19) So, one of the things that we do is we um in the diligence process, right, we segment things and and my team and I will conduct a sales and customer success diligence on companies that we're about to invest in. And I was we were doing one and we we record the calls and I I wanted the transcript and our call recorder did not join properly. (20:42) But fortunately, we had a recording and we had the recording in video and audio but not the transcript. Okay. So, what did we do? Um, one of us like took the entire video and fed it into Gemini and got a transcript. >> Yeah. >> Um, and I was shocked like cuz that's a huge file and and within two minutes, right, he got a transcript out of that. (21:04) I used a service that I've been using forever that um, you know, has an AI transcription. So, my thing cost me $1450. >> His thing. >> Wow. uh which is you know like in two minutes I was f I was you know whatever uh and then his thing was free so like yeah uh this is again that you can take whatever uh this is an hourong video so I don't know how many gigabytes that is that you could just dump that into Gemini and like two minutes later get a transcript is pretty >> I don't know I'm I'm amazed if I maybe I shouldn't be but I'm amazed (21:40) >> I find myself I'm amazed every week I was just slacking with um or texting with a buddy last night cuz he was asking me cloud code questions and uh he was like, "Man, this is insane." I'm like, "We are living in a truly insane time." >> And >> yeah, it's it's crazy. >> It is. Yeah. (21:59) I don't I mean, you could stop me if you if uh if these stories are not relevant, but I have a buddy who built he he he like Yeah. using cloud code, he built kind of a revops claude agent, right, with tons and tons of of skills and he realized that you could you could basically build a virtual clone of yourself also using that. (22:25) So on on a on one of the weekends I was on a call with him and he onboarded me cuz I I still code and >> um uh so he onboarded me with his like kind of claude code package of stuff. >> Yeah. >> And then I down you know I pointed that at all my podcasts and transcripts and I have a revenue playbook and all these different things and like started asking it questions and it's kind of a virtual meet right. So yeah pretty incredible. (22:50) And then the other story just from this last week about how powerful this stuff is is um we get a lot of questions from portfolio companies when they're when they're um doing planning, right? So they want to figure out, okay, how much of my of my budget should go towards sales and customer success and marketing and R&D and this and that, right? So basically an income statement >> based on your rule of 40, your you know, your growth rate, your efficiency. (23:16) And so I wrote a little, you know, I did some some data analysis and I turned that into a calculator and I I did it first in PHP, very old school, but um you know, one of I just kind of had that available and then I said, "Okay, I'll convert this to JavaScript, right?" So I just dumped it into JavaScript >> and then lo and behold, because of our systems, this is an internal tool at this point. (23:41) Because of our systems, I can't run PHP or JavaScript. It needed to be in a Microsoft Power App. But I'm like, "Oh, I don't know how to do a Microsoft Power App." So, I just did the same. So, so I met with our engineering team. I said, "Okay, you know, like we're going to send this out to the to our, you know, outsource coding people. (23:55) It'll be about a month." >> I'm like, "Hold on, hold on." And I just put it into into uh um I was using cursor uh in agentic mode and I just said, "Hey, build a Microsoft Power App out of this." And again, whatever, 60 seconds later, I was done. So, yeah, it's it blows your it just blows your mind. (24:18) My VP of Revop built a similar type of agent that we call Cerebro and it's hooked into all of our stuff and you can you can just like point it at a Slack thread where it pulls out the business requirements, highlights what questions still need to be answered, writes the PRD, can actually go and build that thing in our sandbox environment. (24:36) Then you could go like do the testing and and deploy it. It's it's um we actually think it's going to like completely transform how we do rev ops >> um and allow the RevOps team to do like much more strategic interesting forwardlooking stuff because this can just like chew through the basics. >> Yeah. Um, but you have to gohead. >> There's a subtext of something that you're saying, by the way, which is like I I described I told you a bunch of stories of like, you know, we're whatever a buddy of mine built or I built. Same thing for you, right? Like (25:11) someone at the company or a buddy or you built and that was also a theme that came out of this, which is >> there's a lot more built, you know, than I've seen in past waves of of technology. Uh, we all kind of know it. I don't think I'm telling you anything you don't know. But yeah, the the the skew towards build is pretty surprising. (25:31) I mean, one of the spaces where we've seen uh a lot of like mid edfunnel use cases is in RFP response. >> Mhm. >> And right, there were a whole bunch of RFP response companies in the pre Gen AI days and they have morphed, right? and and they have >> strong solutions but it's now become I would say it's 50/50 now of people using you know packaged RFP software response software and and then the other 50% just building it you know using by by uploading you know they they they'll point it at their at their knowledge base and they'll point it at (26:10) their prior um >> RFP responses and they'll point it at their security guidelines and this and that, you know, and then boom, totally they're gone. >> So, yeah. >> So, I I want to there's a few threads I want to pick up. I I want to get back to to buy versus build. That that's a really interesting one. (26:29) But, um you were just talking about uh this idea of like augmentation and it's it's in the survey as well, like people are seeing success um people are seeing success augmenting reps versus, you know, this promise of replacement. And even when it goes to coming back to that conversation around performance reviews like I don't really want to pull a person out of that. (26:53) I don't want to pull the manager out of that. I actually want to think about what is the most elegant human in the loop so that the output is as human-driven as possible for with you know the the um craft and taste of the the sales leader embedded in it but like you know you've got all the advantage and scale of of AI. (27:17) So maybe just talk about what you saw in the in the survey results about like people being successful with augmentation versus automation thus far. >> Yeah, I mean it it definitely skews at this point towards towards augmentation. Um and it's the use cases that that you would expect, right? It's at the top of the funnel. It's research. (27:41) It's personalization. Um and take outbound prospecting for, you know, for a minute, right? So, one of the surprises in the survey was that like some of the top performing companies are actually expanding their outbound sales teams, which >> which blows the mind because everything you read on LinkedIn says the opposite, right? And I was that was mind-blowing to me. (28:06) And the reason I think there's a reason for that which is this whole correlation causation mixup which is I think it's because they're top performing companies that they are getting higher response rates to their outbound prospecting. So I don't think it's like the AI necessarily. I think it's that they've got great product market fit and then once they've got great product market fit, step on the gas, right? Like no no question about that. (28:28) Um, >> but it can also be this Jevans paradox of sales where you know if you're if AI makes every BDR twice as effective, the decision for me as a CRO isn't like, okay, great. I'm going to get the same number with half the reps. >> No, I'm going to say, "Okay, let's keep the same budgeted reps and get 2x 2x the funnel. (28:50) " Now the economics the economics of investing in outbound on a relative basis to um paid marketing or a bunch of these other channels that are you know quite saturated and it's you know being well well discussed you know it's it rational actors look at that and go oh let's allocate more budget here. >> Yeah I mean yeah yeah I mean my expectation is well let's stay on outbound and let's transition from it. (29:16) So like staying in outbound, right, is is is I probably been in the SDR game for I don't know 15 plus years, 20 years now. And you know, it was really shockingly effective early on and it really is pretty pretty hard right now if especially if you're you don't have obvious product market fit in a and a clear in a clear brand. (29:38) Um so what does that mean? It means that like the the the what I've seen is basically that the efficient threshold for where outbound sdring outbound prospecting in general works is just moving up dollar amount wise right so these days you probably get about um you know six qualified ops per SDR actually not probably we just batch this six qualified ops on average per SDR per month and if that's >> this is alpha like a pure alpha SDR can do six, five, six, you know, like it's not much more than that. (30:11) There are exceptions, right? If you have a great product market, there's exceptions, >> but like if that's the case and you know, you assume a you know, a certain a you know, a certain win rate of whatever 25% on that, which is probably still pretty optimistic even even for a qualified op. And you know, then what you find is it's the threshold is around 40 or 50k ACV for it to make sense for outcome prospects. (30:36) that threshold just keeps you know as the effectiveness drops and drops that that keeps marching marching up. The other comment about this outbound thing is is also benchmarking like something like 70ish% of all meetings get booked by a phone. Um so you're still doing the multi-touch thing right phone email social whatever but most of the ops are getting booked by a phone. (31:01) So that's one of the reasons why it's also an augmentation. Pulling this all together, the whole thread together is like that's why it's one of the augmentation things because it is currently not legal in the US and in Europe and probably elsewhere. I'm not sure what the regulations are in in Asia. It is not legal to call somebody with an AI if they have not opted into receiving the call. (31:23) Like you can do it if they're a customer, >> but um and if they've otherwise opted in, but cold outbound via via AI is a no no. >> You can't like you I think it's like I don't remember the exact dollar amount, but it's significant fine per call. >> Huge fines. >> It's like $5,000 or something per call. >> Per call. Yeah. Yeah. >> Yeah. Yeah. Per call. (31:43) >> And and I don't see that changing. It would just be I mean your phone would be now useless if you said AI calling is is allowed because every every smart company will say great well I can have a million calls be made every single month to every you know every part of our ICP. >> We'll do the same thing to the phone that they did to email, right? Like they'll just destroy it. (32:04) >> Yeah. And email is Yeah. you email is so bad now that you know I use superhuman for my email and it's great at screening but I miss a lot like superhuman h the the the bar has to be so high that I miss a lot of emails >> and it's just because my my inbox is torched >> yeah I think it's life I think it's life and then then people find other channels to you know to try to get to you if it's important >> what has that 70% Do you know how how that has trended over time. (32:38) Is 70% higher than it was five years ago? >> It has to be. I don't know the answer, but it it it has to be. >> I would think so. >> Yeah. Just because like back in the day, right? I mean, going back to the way way back evolution of this, if you put someone's I remember the first time I saw my first name in a subject line, like I read that email. (32:58) This is a long long time ago because there was no dynamic tags at that point. So the h some human had to type my name in. And then once I realized that dynamic tags came along and I the collect and also the collective universe of people then we said delete >> right and then and then and then the next wave that came around was a little video in your email >> and when I got the first video in the email saying like hey Jeremy blah blah blah blah you know like and holding the little whiteboard >> in front of them >> same deal right it's it's like wow they (33:29) took time to do that this is this is incredible and then you know AI could could insert their first name and whatever in there >> thing. Yeah. >> Yeah. Yeah. Then it's like so I think right I mean there's there's nothing what is that book? Uh Chaldini influence I think. >> Influence. Yeah. >> Yeah. (33:50) So it's like this is not a new thing. It's it's we will there's reciprocity like we will reciprocate when another human puts effort into engaging us in a thoughtful valuable way. And and the reverse is also true is like >> if you if you if you phone it in >> and uh and just like use AI to try to contact me, >> I'm going to be upset, right? Like >> yeah, >> not only am I not going to respond, I'm going to like >> I'm going to flag you as spam and and hope that your emails never come through and your calls never come through. (34:26) So yeah, this effortful thing really matters. I mean, you're probably you probably get this, too. Is um I don't know how many LinkedIn connections you have. It's probably like 200,000 or something crazy. >> 40, but uh Yeah. >> Yeah. So, but like, okay, you have 40,000 LinkedIn connections and you probably get SDR calls all the time. (34:46) How in the world do they not know who you are, right? Like, that's unforgivable. >> I know. I know. >> It's unforgivable. >> It is. It is really interesting. the I I come back to disciplined execution. It's just attention to detail >> and and you know there's some leader at that company that is not not paying attention to the the you know the specifics of of their system. (35:15) It's like, oh yeah, just like give give people a bunch of lists and let them call and say whatever and yeah and you know we do a bunch of work to we've got these machine learning models that score the leads and and its propensity to buy plus the deal size and then we enrich it with the information we want the BDR to use, you know, the the closest customer that they can name drop and you know the some analysis of that person's digital presence that the the BDR can can leverage and all of those little things just like add up. We we we say shavings (35:44) make a pile. Shout out to Shannon Malar who I got that from. Um and you you do all of these little things and and sort of it it stacks up, but it's >> you know it's a grind and it you need you need that that attention to make make it all work because I mean I I the effectiveness of outbound has such a massive gulf. (36:08) Like if you talk to the companies that are really doing this with a lot of precision. So like my team spends time with the ramp team because that's a team that that has a lot of diligence in terms of how they've built the system and so we like to to share notes. >> You know their economics are awesome on BDR and share on their behalf >> for sure. (36:31) But but then conversely, you go look at a bunch of companies, you're like, "Okay, so you are losing money on every single BDR and you've still been doing you're at this for like multiple years and so when you when I hear you say six qualified ops a month, you know that that's to me I look at that I'm like that's tough math. That's >> exactly we have some I mean we've had portfolio companies where you know let's say they had a 20k ACV >> and when we moved from we the collective we right like moved from growth at all cost to efficient growth (37:03) >> everyone started to look at that >> and and you know that that losing money on every call didn't make sense and we had a bunch of companies who you know if their ACV was too low they just got rid of the inbound sorry the outbound SDR and then took that money and redeployed it into inbound >> channels and redeployed it into channel partners >> and like went from there because it was just never >> if you have a 20k ACV it's just not going to work >> like it just can't you're you brand would have to be so exceptional (37:36) >> I mean but it's not just really good I mean like what really good at it means it's I don't think it's the quality of the SDR I think it's the qual it would be the brand and and >> the brand and the problem you're solving, you know, is this a problem that people care to solve? Like we're we're only an 8 to 10 8 to 10k ACV and our BDRs on a monthly basis generate 70 70K and closed one new revenue. (38:00) So we have like a more than 10x uh cost to close one ratio, but you know, we've dialed in the way. >> I'm sorry to interrupt, but it's velocity also, right? Like I assume you you have a one or two call kind of close, >> right? that like if you're square or you guys or whatever, you know, like boom, you got to velocity, then it works. (38:21) But you have to do the math. >> Yeah. Yeah. That that is this is something that I share with people. I think the the magic of our business is, you know, 8 to 10K on two calls over three days is pretty attractive. You you can you can make that work. But, you know, like I I I see this when people ask for advice, when founders ask for advice, like, "Oh, yeah. (38:44) my my ACV is like 17K. It's like a 45day sales cycle or like a 90day sales cycle. I'm like, >> dude, >> and there's a twoe p and all the rest of it. Like, it's just not work. I was like, you have to find a way to charge three times as much or this is a PLG business and you got to go like totally refactor because the it's it's this is sort of the Mark Rober stuff from science of scaling. (39:12) He talks about you know there's product market fit then there's go to market fit you like your business model needs to work with the right economics before you could scale but oftent times we skip we I see companies skip that like oh you know there is PMF our retention is okay >> and we can close some deals but you know like you do the your economics are never going to be amazing in the beginning but they have to be serviceable and and you have to have a clear path to to solving to solving for a you know three then 4x LTB CAC and and uh (39:48) >> I think like the point one of the meta things I see in a in a post AI world is like you can't win as a point solution there's too much competition every incumbent can spin up that feature and and so it's uh you got to play the platform game because the sales cycle like necessitates it >> yeah yeah yeah well and we saw that I mean another aspect Right to that it was in the survey we asked the cos about like how how important tech stack >> consolidation was which kind of speaks to the platform thing >> and it's it's it is I mean (40:22) >> it's confirming I think some of these that's what we wanted partly to was just confirm what what we were hearing out there but absolutely they're eager to they're eager to um you know to consolidate it's not just about money savings it's also about just tool explosion and >> yeah And you know a lot of times I think the reason you get shelfware is because you just forget that I need to log into the 20th system right and and use this thing and and you know to the extent that you can you can log into one thing (40:53) and get >> you know 10 >> different pieces of core functionality you know >> that that's key and you know we're seeing it already >> in rebek right like there's a bunch of companies out there who are uh who are consolidating and or and it could be a buy, you know, it could be a buy or a build like I can think of be really careful talking about companies but I can think of one company right where they had mostly built and you know they built out forecasting and they built out conversation intelligence and they built (41:22) out you know all the pieces and then I can think of a of a bunch um who who are buying their way and and merging their way into this. >> Yeah. So so let's uh hop into that topic. Uh it's certainly one I wanted to hit. So what what are the conditions that would tell a a revenue leader whether or not they should build or buy and what are the relative strengths and weaknesses of those >> at least on the on the AI side if it were me. (41:55) Um I mean frankly I'm probably going to build at this point. >> Yeah. >> The the I I I'll tell you why I I think I see more of the building uh on on rebek related stuff right now. I think one of the reason well first of all having built a lot of stuff in the past it's always faster and I'm being hyperbolic mo almost all of the time it's faster and better to build because you're going to build exactly what you need and you know it's just a lot less friction and that's only gotten faster now with with uh you know like vi agentic vibe coding it's just so (42:29) freakishly fast so you're going to build something faster and better and it's always been true independent of AI that a year later most of that stuff stops working because nobody maintained it, right? And and you know and the world just moved moved on. So like we're going to see that too. We're going to see a lot of stuff that gets built and then we're going to see a lot of stuff fall by the wayside as as things go. (42:51) But that's that's one thing is like it's why there's a preference I think for buy versus build. It's just so freakishly easy to to build and you're going to get a better solution. The other thing I think is like I think a lot of buyers are afraid that they're going to be the dope, you know, who who buys the point solution tool only to have one of the foundation LLM companies, you know, just be able to do that capability pretty quickly. (43:17) And we're seeing so much of that. I we talked about the RFP use case earlier, right? like you don't you know I mean there are instances of where the point solutions are great but there are plenty of instances where you can build a good enough solution you know with a with a DIY effort and and some prompts so I think there's there's just that like I don't want to buy the op the instantly obsolete technology so so to me those are the those are two big things but it does I think as these things that's this what I was saying with the top down thing also is as the (43:47) integrations become much more complex X um and potentially as the functionality, you know, becomes more heterogeneous, and I'll explain what I mean by that in a second, then the point solutions or platforms like the off-the-shelf stuff become more appealing. >> The heterogeneity um piece is we've been talking most of this conversation about text, right? generative AI. (44:12) >> But a lot of the Rev tech value is unlocked by predictive traditional predictive machine learning. And you know there are some solutions out there that are starting to put those things together and and like what what do I mean by that? Okay, let's take customer success. >> Um >> if you want to figure out like health and this could be true for opportunities also, but let's take let's take existing customers. (44:40) If you want to figure out customer health, that's a predictive generally a predictive AI problem. You might use some generative stuff to comb through transcripts and stuff like that. But like the the the algorithms that you use to figure out whether you know the probability that something's healthy or not. >> Yeah. Is an as a traditional ML. (44:59) All right. But now I'm going to take that and I'm going to say, okay, this particular uh account is not healthy. Now I got to put in now I got to generate a workflow. So now we get into agentic andor you know guided workflow mode and then I need to draft messages right on behalf of the CSMS either that would just go out or that they would review by way of augmentation. (45:22) Now we're in the generative world. So my my point is like that that that's the heterogeneity is like the heterogeneity of the traditional ML predictive AI as well as the gen AI like that stuff starts to get complicated and that's another reason why You might, you know, you might start to skew towards platforms and and point solutions just package software. Yeah. (45:48) And what is because I don't think every company can build like from what I've seen. And so what are what are the signals that you as a business can build versus like what type of companies should just you know buy even with the tradeoffs? >> Yeah. said some of the I I don't I have a perfect answer here, but like the more regulated you are, you probably should buy >> um because the because you want someone who's sock type 2 compliant and has done like all this stuff and if you try to build that it's going to be really (46:26) really difficult. I think the more you squeeze towards regulation, um I'm I'm waffling in my mind about the bigger you are, the more likely you are to want to buy something. Uh and and and the more likely you are also, by the way, to want a platform as opposed to a point solution. That that's definitely true. (46:49) But but um yeah, like if you're a big company, you know, you want to use your capital for what you do, right? So you want to use your capital for R&D of what you do instead of using it for you know the the operational efficiency side. >> Mh. >> So I think that's an instance of where you're also probably better off buying. Um little companies would you know whatever they're going to be scrappy and do what they're going to do but the bigger you get I think they're much more likely to buy. (47:22) And and do you think it the technical prowess of a business matters? like you know if you're if you are um selling a technical solution into dev and engineering or if you're cursor or uh ramp or rippling that have like really uh impressive technical teams is like that an indicator maybe build versus if you're like a more commercially driven organization like because I I feel like who's going to build this thing it's not traditional revops most of the time it's something else >> yeah but you'd have to carve off and that's what I was saying earlier and did (47:59) go through my mind. It's it's like, okay, you're you're like a bleeding edge tech company. Are you going to carve off a pod of engineers to build, you know, a ramp tech widget >> or are you just like, and those engineers are >> are so freakishly expensive right now? >> Yeah. >> Like, I don't think so. (48:20) I think you're gonna I think you're gonna if you're being economically rational about it, you're gonna buy. >> Yeah. >> Because Yeah. Yeah. It's an opportunity cost for you. I I think the people who don't consider that opportunity cost, you know, they they they should be. >> Mhm. Yeah. I have friends who who are leading go to market at some of the most sort of like technically impressive companies out there in in the AI space and I'm always surprised by how much they do buy. I like dude. (48:49) Yeah, >> you guys are XYZ company. Like why why wouldn't you just build it? Like we can't we can't get an ounce of engineering capacity because we're in a battle to the death with whoever. So I think that makes sense. >> Yeah. Yeah. I was I was at Sales Loft, you know, for four years before I joined this and my little disclosure insight still has a stake in it as do I personally. (49:08) Um >> but uh I I could not get engineering you know when I was running RevOps I could not get engineering resources >> and uh I will credit our CEO Kyle Porter >> uh and and our COO Rob Foreman they were like incredibly thoughtful about capital allocation and our CFO Chad Gold like incredibly thoughtful about capital allocation and like I completely completely respected their no. (49:40) So like I built as much as I could um because I I could code and you know I had at least one person on the team who you know had some coding uh background >> and and and but I coded a ton. Um and and then when I hit my limit, >> I I bought and then the other thing I did was I often prototyped which is another common thing, right? It's like you prototype >> with with internally and then you go outside. (50:09) So I did a lot where I would prototype and the whole point was like this thing should not go into production >> even in our internal environment. I'm just I'm just >> we're going to run it for six months and if it sticks then we're going to go out and find a vendor who can do this better. And sometimes we didn't and we just kept running that thing for for years. (50:27) But uh we probably shouldn't have like we probably should have tried to tried harder to go find a better >> Yeah. >> I I sort of think the answer is you need the you need somebody in RevOps or your data or bisops teams to go and say I'm the AI guy now. I'm going to go learn to be sort of a vibe code master. And so it's not engineering capacity, but it's, you know, somebody who is learning to be a pseudo AI engineer now because, you know, you you can do enough. (51:01) That that's like a little bit of how we've dealt with it. We've hired two applied AI people who are not traditional like um engineers by background but have like done a bunch and built a bunch and and um are just interested in these problems and it's and it's working because same sort of thing like I I think I would have problem I would have a hard time getting uh engineering capacity away from our core product just because of how how important it is. (51:29) A lot of us are probably in that boat and and I I do think it's a superpower, right? Is a lot of organizations, right? Um don't have somebody who who can code. >> Um and and it's I think it's trickier definitely trickier for them. I would say most revops organizations I deal with >> don't have someone who like they can they're learning vibe coding for sure but uh they don't you know they they can't code at scale. (52:00) >> Yeah. >> And not super scale just like enough for the roundoffs organization. >> Yeah. Um okay I want to I want to transition to one of these other um areas I wanted to pick out. So uh in that survey uh CRO's expect between a 5 to 15% productivity lift and so obviously very modest compared to the promises that like many of these vendors are touting um why why is the lift so low and and what should we actually expect? >> Yeah. (52:34) Yeah. I think that's the right thing. And I'll tell you like I I in my mind it's like I ask myself we're often asked to quantify the impact in our portfolio companies or we ask them to quantify the impact of AI and I I rail against that because >> because it's it's like what is the ROI of of sales enablement? We've been trying to quantify that for, you know, >> the the first super formal sales training or the like, sorry, the modern era, the first super formal sales training was Xerox in 1968. (53:09) And at least since then, we've been trying to quantify and then you can go back to, you know, uh um uh you uh who was it? It was uh not maybe Burrough's company. Oh, NCR. It was National Cash Register. NCR in the late 1800s like you go way way back. Anyway, um like I I just it's not quantifiable for operational efficiency. (53:33) So I think part of this is like I can't really say but I can say okay I gave everyone Chat TPT or Gemini or Claude or whatever and I think we're all like 10% more efficient. So I think there's a little bit of that hand wavy thing in there. The other piece is is >> you think about a CRO and and they're saying like what am I going to commit to to my CFO and my CEO and my board and and I I think they want to be really really conservative about like how much productivity lift because if I say okay AI is going to make me 10% more (54:07) productive that means that instead of hiring you know 10 people incrementally I'm going to hire nine. >> Yeah. and and that I'm I'm that much more likely to miss my, you know, to miss my target. I one of the CEOs I'm working with recently like on the on the the good news is he has he knows exactly how much people will produce month by month of their ramp and into their tenure experience like and he he nails his forecast like nobody else. (54:41) The bad news is the productivity is not, you know, he's not rule, they're not rule of 40 plus, you know, they're rule of five or something. >> A big reason they're rule of five is because that they're so like they're just not efficient. And uh you know, I think you need to get kind of but but my my reason for bringing that story up is like he he would not accept hiring 10% fewer people because he knows he'll miss his forecast by 10%. (55:12) So anyway, that's I think that's there's like a conservative conservatism there. Um I actually think that that like five to 15% whatever I think that's about right. I I personally wouldn't commit to something bigger. Nor do I see in the data like a discontinuous trend in efficiency. So one of the cool things of having all these portfolio companies is that we can track like net new ARR per ramped sales equivalent and there absolutely is a steady or even ARR you know net new ARR per FTE across companies and there absolutely is a steady slow and steady (55:50) productivity increase that has been happening we don't know if that's AI or not it could be other efficiency moves right because you can't know the exact cause here >> just coming out of the ZER era inefficiency isn't is >> yeah a lot of people are doing a lot of things to be more efficient but it at least it's improving probably AI is a factor there but there's no there's no kink in the curve yet >> so I I would be reluctant also to commit to something significant until the kink in the curve really starts to happen (56:22) >> the kick in the curve is scary by the way because it means like machines are replacing humans so from a social impact point of view that's that's Yeah. I mean, unless, going back to that Jebans paradox thing, unless reps getting more efficient means more companies are viable, the economics on on these roles get better and and people actually want as many or more of them. (56:48) I I I I think it's going to be a shifting more than anything. people are are roles are going to shift around an organization like you see you know large swaths of of customer support being uh compressed and eliminated but then oftent times those roles are showing up in in other places like the the head of McKenzie was talking about this they've they've reduced their like back office roles by 25% and increased their customerf facing strategy consulting roles by 25%. (57:22) So it's like a big job dislocation but like net net they're they're even but not growing. So >> yeah yeah yeah but not growing and they're like presumably revenue is increasing with flat headcount so it's like yeah >> yeah like Microsoft I can't remember the exact numbers but Microsoft has added like uh some number of billions of revenue and they've they've frozen headcount for four four something years. (57:48) Satcha was on was on Allin talking about this which was you know I think an indicator of where people are going to go. >> What and so what do you think it's going to take for some of the promise to be realized? Not like you know 10xing but like you know a 30% lift. What do you think needs to happen for that to be unlocked? >> I wish I had the crystal the crystal ball but I do think it's coming a little full circle in the conversation. (58:16) I I don't think it's going to happen through the bottoms up tinkering use case approach. Yeah. >> I I think the unlock is is like to go, you know, downstream into deals downstream into um customer success uh and and expansion and and leverage the multi-systems approach as opposed to me throwing prompts into a GPT. (58:46) I I think that's that's the precondition. I don't know what it's gonna I mean, if I knew, I probably would uh you know, go do that because I love Rev Tech. Uh but but I don't I don't know. I I don't know. I I do think it's a system it's a system solve though as opposed to a a prompting solve. >> Yeah. (59:09) Um so on the on this topic of um job displacement and and job changes um everybody's saying you know the SDR is going away the BDR is going away we haven't seen that at all yet um what do you think changes about that role over the next like or what would you actually more interesting question what would you change about that role to to maximize leverage and and uh and grow I I I've heard a few things that I I I steal 99% of stuff like I I don't want to take credit for stuff I've I've heard. (59:42) So I I've heard a couple things that I do agree with. Um both for SDRs and SES by the way. So >> it's worth it's worth chatting about both of those roles. um for SDRs outside of this correlation causation thing around like if you're a hot company then sure you step on the gas and you hire more outbound SDRs but otherwise I think I you know I I think you can absorb the outbound SDR into the AE's job >> and AE sorry an AI accelerates that right because now you can have discipline around um you know who you engage and how you engage engage them. It just by necessity (1:00:23) makes it easier to consolidate the job. Again, the other place it's happening for sure is in inbound SDRs. Like inbound SDRs are going away fast and they're going away fast because even independent of AI like just regular lead to account routing stuff. Why would you have an AE an SDR in the middle when you just want to get speed and just get people directly to AEES as fast as possible? And then in instances where like those are instances where you don't have a lot of tire kickers. (1:00:54) In instances where you have a lot of tire kickers, you do need to do a little pre-qualification. And there again because people have opted in like you can actually have the AI, >> you know, have the conversation with them in real time and qualify them in ever sophisticated ways before you send to an AE. (1:01:11) So that trend for sure is like absorbing into the AE. So I I think you know independent again of the product market fit thing like I I do think that the role is increasingly being absorbed back into AEES. And then the other one and I wish I can give credit to who where I heard it. It must have been on a podcast. (1:01:30) Somebody was talking about like the kind of a an evolution of the S of the sales engineer role >> that because of technology and augmentation of these things like AEES are going to assume more of the SE's responsibility and sort of the low to mid end part of what SES do and then at the super high end right you've got this trend towards forward deployed engineers towards FTEES of what sales engineers like could otherwise be doing. (1:02:04) So >> it does seem that AI is an interesting threat to the to the SE job as as the low end goes and gets absorbed into AEES and the highend you know is moving towards FTEEs. >> Yeah. And again, this is that same like dislocation from low value to high value that that I think we're seeing in a bunch of places because you know like the the FDE it's just a fancy word for like a solutions engineer, an onboarding engineer, whatever. (1:02:31) >> Exactly. But I when I think about how many tools we've bought where we like haven't felt like we've got the support we really wanted and I just wanted somebody to sit with us and really like go line by line through something. Um it feels like if companies can save on the low end and you don't have sees just like on a bunch of demos answering questions that the AEES really should know the answers to >> and those and those roles can be deep more deeply embedded than customers >> are that's like net net a huge win in (1:03:04) and >> need help. Yeah. I think it's it's the other thing by the way you was talk you asked me about like the unlock the unlock is also again I keep saying like I heard this from somebody else but like um so far what we've done is we've tried to optimize the jobs to be done within a within the existing role structure and within existing processes >> and and the unlock is to say okay maybe we don't have the roles Right. (1:03:37) And maybe we and maybe we need to to like not just make the existing process better, but we have to develop some new new processes that are, you know, more AI verse, AI friendly, whatever you want to whatever you want to call it. That's that, you know, is likely the other the other unlock here. (1:03:58) And and there that's real creativity and innovation, I think, to figure out what those you know, what those new process designs are going to look like. Th this was one of the things that uh was my favorite takeaway from my episode with Jordan Crawford last week. You know, he was talking about how you need to look at a role as a basket of tasks and then unbundle it completely and say, you know, what of these what of these tasks are AI tasks and what of these tasks are things that are good for humans? And then once you unbundle the tasks across, you know, call it three (1:04:30) roles, BDR, AE, solutions engineer, just to keep things easy. >> Yeah. You unbundle a bunch of stuff. You say all of these things are really just like AI tasks and we need to go build those workflows and and I like saying workflows more than like agent or like AI employee which I think is corny >> but like once you once you can have those done by those workflows then you know what are the tasks that are there and what is the what is the grouping that makes the most sense. (1:04:56) I I haven't heard somebody say the SDR role will get absorbed by AE. I think that's really interesting and and true. You know, if if you can have, you know, your co-pilot running with you on a demo, so when technical questions come up, you know, you can quickly hit a button and get answer get an answer to that uh from your little your your ride along. (1:05:19) That's a great way to to to improve the buying experience, make sales reps more valuable and more strategic and then have sees do like a different set of things like the deeply embedded for deployed engineer. And so I think we're going to go through some version of that this like unbundling. My yeah my process thing also I wanted to call back to something that you were you were kind of hinting at is and and the fact that like you might need different governance structures and operating structures. (1:05:52) So >> take um you know we have a lot of portfolio companies using clay. We're not invested in clay um but but we have tons of them using it for you know data enrichment and whatever. what we're hearing from them and I I you know I don't I don't know I'm not close enough but what we're hearing from them is is like it's not a tool currently for individual contributor like AEES and SDRs >> it's a tool that is some for someone in revops a go to market engineer or whatever and um I mean I know this from trying to consolidate certain things (1:06:24) like lead research and account you know moving accounts around and so on >> when you centralize that stuff like it's it's there's you you create risk. So I mean by way of story you know we this is ages ago but we centralized lead research >> and we had a guy who was like the manager of the lead research team and he had five or so librarians that we called they were librarians at the time but like lead researchers and you know most of them were contractors but but basically he was in the critical path >> and it and like (1:07:01) >> he he got sick um not bad you know like he recovered but he got sick for a week and a half >> and just knocked out of commission and when he got knocked out of commission our we we we went into like desperation mode because he was in the critical path and I think these go to like that's the thing with go to market engineers are also in the critical path so like I'd be really scared to consolidate with maybe just one person in that function I would be more likely to you know use an agency in that in that case, right? So that (1:07:36) there's backup and I don't have that. I don't have I was just talking about port to a portfolio company about this, you know, this week is like you kind of need an agency to to to cover your risk there. And and that's the kind of thing where I think it's hard, right? It's you got to plan for that and figure that out and that's complicated. (1:07:53) And I think that's part of the value realization of this is is is these types of of uh you know, centralizing some of these things is hard. >> Yeah. and and there's a good chance we're going to see that across the business. Like I I have a feeling that you're going to see much uh much more vertical organizations where leaders are way more impactful than they once were. (1:08:20) where if you are an AI native VP of sales and you can like much more tightly and broadly orchestrate your organization and be in more things. Um you're doing less busy work. Uh, I think you're going to see that like keyman risk accelerate because you're going to have, you know, certain people that are really AI native just be so much more high leverage and important than ever. (1:08:52) You're seeing this with engineers. >> Yeah. >> You know, the the dispersion of engineers from, you know, people always used to say the cliche, you know, a normal engineer versus a 10x engineer. Like that 10x engineer is a 20x engineer and maybe even like a 50x engineer now. And that seems hyperbolic, but like we have examples in owner where there's where there's engineers that like just ship an ungodly amount of stuff still at a at a very high quality bar because they've they've been, >> you know, uh they've been such great (1:09:23) adopters. Um and yeah, you're you're more reliant on those individuals now than ever. >> Yeah. And I think in RevOps too, right? I think you can have like a RevOps person who knows how to vibe code or and and to code and and to integrate systems, you know, like and has the collaboration ability to work with security and governance and, you know, understands how to make sure the stuff gets deployed and used. (1:09:50) Like that's all 10x Remops stuff. >> Yeah. >> And and um yeah, I I would presume I don't know. I would presume hard to find, but maybe every Rev ops person is like doing everything that they possibly can to get as smart as possible on the stuff as fast as possible. >> I can tell you that is not what I'm seeing and not not what I'm hearing from folks uh in the market. (1:10:15) Yeah, I'm I mean I'm getting I'm getting old and I try to keep up, you know, like I'm using cursor and uh but I one of my buddies who was walking me through cloud code like I realize I got to spend a weekend at least. >> Yeah. just kicking, you know, hitting my head against a wall and and mastering mastering claude code because um yeah, because I don't know you you mentioning uh Jordan Crawford, I think he talked about what is it like uh dangerous. (1:10:48) There's some mode you can put Claude in where it's like you're authorizing every permission >> dangerous authorization or something like that. So like I have to learn all this stuff. >> Yeah. But it's so fun like as soon as you as soon as you get things like the the basic infrastructure done, your MCP integrations are are working enough and you've got like a skill built, then like it's just it's just a blast. (1:11:14) I just want to spend every one of my non-scheduled hours doing this. I'm building like an AI chief of staff for myself right now that like sort of works. There's little pieces. It's it's not autonomous enough uh yet. Um, but you know, like I just see I I can see a path to making myself twice as productive. >> And people ask me all the time be like, "How do you have time for all this stuff? Podcast and this and that and the other." And I'm like, "It's just AI. (1:11:41) " Like I have work anything I do, >> anything I do repeatedly has, you know, prompt templates and snippets and projects within whether depending on the model. And now with this AI chief of staff, I'm like, I bet I can go to 50% fewer meetings. >> Yeah. cuz there's a lot of meetings I go to that I'm like, h like I said one or two helpful things here, but like I could have just read the outputs and then asked I I can just talk to the transcript. (1:12:08) I can just read the output, ask a couple questions to the transcript, message that person, hey, you know, I noticed this and this. Here's like a a little tweak on it. And that's sometimes all you need to do as a senior leader >> is just like just just just make sure nobody's making the wrong decision. Those are important changes. and then just like tweak people a little bit here and there. (1:12:30) And that's a lot of the job. And I spend 20 plus hours a week in big like orgwide meetings to do that. And I see a pretty clear path with this like AI chief of staff to be able to monitor all those things and high like my I'm I'm uploading a bunch of my decision-making frameworks and mental models and it learns from how I like give feedback in meetings to try to emulate me more and more to to like you know give myself that extra leverage. (1:12:58) >> There's there's a negative externality in this which a lot of people talk about which is the the damage potential damage it does on critical thinking. Um, and I'll give you a real example of this. Like one of our um, one of our portfolio companies >> um, was using a tool to I can't remember was homegrown or if they licensed one of the ones that's out there, but they were using a tool that, you know, took call recording transcripts, >> extracted the medic framework out of it >> and then pasted that into Salesforce. (1:13:30) >> And here's the good new like the good news, bad news. The good news is whatever they were doing again in-house versus tool did a perfect job >> near perfect job. The bad news is when the sales managers were doing deal reviews with the AEES in the words of the CRO he said they said uh she said actually it was one of our um women CRO's she said um the the AES were getting stupid >> like they couldn't they didn't know their deals because previously they were really thinking hard about you know the metrics the economic buy or the decision (1:14:09) criteria like blah blah blah like they were really thinking hard about it and and they just knew the deals much better and that they were on top of the gaps much better. >> So, you know, this CRO is ripping out that solution because it made her refs stupid. So, that you know, she actually like took an AI tool out of commission and and you know, there there probably is an augmented better way, but that full replacement of that was not was not effective. (1:14:38) Um, so it it just yeah, just I I worry about that too for myself is I now ask myself on everything, how might AI >> like how might I use AI to do this and I push it further and further like >> I've coded my whole life and I used to use AI just to like >> refine a function or a couple lines of code and now I just describe the entire program. (1:14:57) >> Yeah. Then it was autocomplete and then it was, you know, build a part of it and now it's just like yap >> and and tell what you I just write I use markdown to describe the entire program and I press go and then it works. >> Um but I worry like I maybe my critical thinking is working on the architecture side of >> just about to say this. Yeah. (1:15:15) >> Um but I feel I feel I try to I try to do something about this one by reading. I I always read a lot. So like I just can keep reading long form stuff and then I used to write and I'm thinking I gotta start writing again because that's another way to just keep the critical thinking muscle strong. >> Yeah, the I I I do think the critical thinking is just in a different way. (1:15:43) you're thinking, you know, engineers now spend some of their critical thinking time as product managers and as system, you know, as as solutions architects more than just like the engineer on their little piece. I feel like I'm I'm using more critical thinking than ever because I'm like really sweating through each component and how it's going to work and this like building this system um to eventually like let me work on higher levels of abstraction. (1:16:12) But it's very easy to go the other way and just like outsource your thinking to >> you know where I think there is high value is I remember um in the course of my career journey like I was a manager at a super young age >> and I messed that up royally it's too long of a story to go into like how how I messed it up and then I got put back as an individual contributor uh I was like 23 years old or something um and and then a few years later I had the opportunity Imagine again. (1:16:43) And I remember the hardest thing I was dealing with was like because I'm so technical and I love the work, you know, so much. I was having a really hard time delegating. And it was only when I was so overloaded with work that I had to delegate that I like I had the break I had the breakthrough. And um I think I I don't know if I can completely generalize that but I think like a lot of people who go from individual contributor to manager have to have that breakthrough somehow that they learn to you know they basically (1:17:13) learn to distribute >> the work and then be able to reassemble it. So the buildup here is I think that might be on the going to the critical thinking side like I think this may give a lot of people a lot of opportunities to build that skill sooner because if you start having to think about how to architect job distribution to a set of AIs that's just as complicated right if not more complicated than doing it with humans. (1:17:46) So that could be a a I'm trying to find the silver lining. Like that could be a really good silver lining to the critical thinking pieces is that we learn at a much earlier age and a much more sophisticated level how to >> how to like systems think. >> Yeah. >> About about this. >> Yeah. It's you know it's the same evolution that engineering went through that computer computer uh engineering went through like you used to have to actually like build things on the chip or like you know actually choose the the knobs and tubes and or the punch card uh (1:18:21) outline and then you you know okay now somebody else is going to >> uh program the chip and you just need to program at this layer of abstraction and now now it's a different layer and a different layer and so like you know because open source you can just, you know, get a bunch of the answers and you're just, you know, now building on top of that. (1:18:41) And so many people would say, >> well, that makes you a lazy uh software engineer. Well, but but it it it doesn't. We're just doing a different thing and now it's about managing agents and agent swarms and like all these other things. I I >> I'm I mean I'm an optimist at my heart. So, >> yeah. Thanks, Sam. >> I want to ask a couple quick hitters and then uh I can get you out of here on time. (1:19:06) Um what's the one thing that you think the market believes about AI in sales that you think is wrong? >> I right now I think it's that it's it's having a discontinuous impact on productivity, right? Like it's it's not yet having a discontinuous impact on productivity and we haven't figured it out yet. Um, and what have you changed your mind on in the last 18 months in AI? Um, I think the mind change for me was I felt at least from my personal usage or my work usage, >> um, >> I felt like 18 months ago or whatever, yeah, I forget what your exact time frame was, (1:19:52) but I felt like AI gave me really good answers on things that I didn't know well, but it when it was on something that I felt like I had deep subject matter expertise, I felt like it didn't give me a great answer. >> Mhm. >> And I've noticed since the last generation of models, AI is giving me answers that are as good as, if not better than things I would come up with myself, which is both incredibly satisfying and incredibly incredibly nerve-wracking and, you know, terrifying. (1:20:28) But yeah, so my my mind changes like >> the quality of the output, I think. and and even incredibly esoteric questions is is beyond what I would have thought it was capable of doing. >> Yeah. Um All right. So, now we're going to get into our quick fire. I'm curious to hear your answer on this one. What do you think separates a good CRO from like a truly great one? >> Disciplined, execution. That's it. (1:20:56) Nothing else. >> Love it. Um, what's the most common advice you give to like firsttime new VPs of sales or new like heads of sales? >> Um, I I give them advice on hiring that like >> be incredibly thoughtful about the the hiring process um and the dis and and the way that you structure your hiring because if you hire the wrong people, you will torpedo the business. Yeah. (1:21:32) Yeah. I've I you and I have always thought about this in a similar manner and when I read um leading sales development, I was like, "Oh, there's some good little there's some good extra nuggets." So, I I pulled in some of that stuff into our hiring system. >> It's that's actually one of the books I recommend the most for people managing BDR teams. (1:21:52) I think it's one of the single best books out there on building BDR teams because it is like such a systems engineering centric version. >> I appreciate. Yeah. Like if I do write another book, I'm going to take a similar approach. Um I really like I've studied sales hiring for so long and I still don't think I crack the code, you know, but definitely IQ >> is a I I I've definitely come to realize like assessing IQ is a humongous piece of that. (1:22:18) And then I think the other unlock is and we saw this actually going back to the CRO survey that you know the number one criteria that CRO look for is a track record of success and the only way you're really going to be able to assess a track record of success is through a back channel reference check or or having worked with that person before. (1:22:37) So yeah like I I I think yeah you can't just hire rent like and I've done a lot of data analysis. You just there are certain no nos of of people, you know, that um at least at this moment of their career, you shouldn't hire because it's you're just wasting time and money. >> Yeah. When we're going to do this big um analysis of all of our hiring and try to do a regression between the scorecards we use in the interview process versus rescorecarding everybody when they're enroll. Yeah. (1:23:08) I'll let you know when this is done. I'm I'm sure you'll be uh interested to see the output. >> I've done it a bunch of times. It's a great exercise. >> Yeah, it's so good >> and effective. >> What is the hardest lesson you've had to learn in your career? >> Uh I don't know if I saw that coming, but the hardest lesson I've had to learn in my career I think it's probably I'll tell by way of very quick story. (1:23:36) So, um, one day Kyle Porter, right, the the former CEO of Salesoft, came to me and he said, he said, "Jeremy, I don't think you really want to manage people. I think you want to build shit." And um, it was painful when he said it because I had been thinking about this for years. >> And, you know, I I went home and talked to my wife and and I I said, "Damn it, he's right. (1:24:05) " So I I I so I think what that gets at is just like accept it and different different stages of life, you know, like I I'm at a stage of life where that's where that is the case. Like I I want to build stuff and uh um and I there's a huge value in leading and managing people and architecting that, but it's just like not for me right now at this point in my life. (1:24:26) And yeah, that's that was hard. >> That was hard. Cool. >> Um what's the best thing you've read in the last 12 months? I I constantly reread it. River runs through it. Um Oh, >> I'm I'm an avid fly angler. I constantly reread um The Qualified Sales Leader, John McMahon's book. >> Uh I constantly reread a book called Art and Fear, which is just a book about art. (1:24:52) And even though I'm not an artist, it's still like I'm a creator. So yeah, I um I re I reread Ana Carenna. Like yeah, there's a whole bunch of books I re I reread. So yeah, cool. >> I read I'm like a book a week or a book every two week person. So it's sort of like I read a lot of >> a lot of stuff, but it's the stuff I reread that's the best. (1:25:11) >> Very cool. Um Jeremy, man, this was awesome. It it very much lived up to uh the hype I had in my head. Uh I I knew you'd have a ton of really insightful stuff to share and certainly you deliver it. So, I appreciate you doing this and uh >> uh yeah, would love to stay connected on other stuff moving forward as well. (1:25:32) >> Always, always, always. >> All right, man. Thank you. >> Thank you for listening to the Revenue Leadership Podcast. If you enjoyed it, don't forget to subscribe and you can find a link in the show notes. And be sure to leave a fivestar review, share it with your network, and please join me next Wednesday for another great conversation.