AI GTM Pulse Debrief

Skaled’s AI GTM Pulse survey was conducted in April 2026 to understand how GTM teams are using and operationalizing AI.

In this debrief, I bring those insights together, connecting the dots across usage, automations, ownership, and measurement. The debrief reframes AI as GTM integration and shows what it takes to move from never-ending experimentation loops to real impact.

• Hear the current pulse on AI in GTM. Not adoption, not how many hours saved, not doing more with less… a pulse on how AI is actually showing up in processes and workflows. 
• Realize the delta between usage vs adoption, real automation, and pilot success.
• Get an interpretation of what the data actually means inside GTM orgs.
• Gain the six things GTM leaders and teams can do right now to move from experimentation loops to real GTM impact.

If by the end of this debrief you’re thinking “we’re stuck experimenting, and not scaling,” these are your next steps:

1. See where you stand today with the AI GTM Maturity Assessment
2. Then get the AI GTM Strategy Playbook: PLAN
3. Book time with Skaled to put the plan into action

Transcript

Why This Survey Exists

SPEAKER_00 0:01

All right, what's going on, everybody? Uh, excited for uh today's conversation. I've not done a live uh webinar session in a little bit, so I'm very excited to walk you all through a lot of the data that we've been working on as a team. Uh, you know, one of the things that we've really been focused on as an organization is just, you know, making sure that we're doing everything that we can, you know, to bring you all, not just from the hundreds and hundreds of conversations that I have on a quarterly basis with different VPs of sales and CROs. Also, like what is the data saying about where AI is going and where we see the areas of opportunity, where we see these areas of focus for organizations, you know, today is not just about observations, it's about data and what we're seeing in the market, you know, where we see the areas of opportunity based on where the market is today and where the market's going. Um, appreciate it. We got a lot of folks piling in here. So um we're gonna just jump into it. What I always encourage with this is this is your first time. I feel like I haven't been live in a while, so this might be your first time seeing me, maybe or hearing about the work we do. Uh I used to do quite a bit of these and uh look forward to doing many, many more. So we're gonna jump into today. Today is all about the results of our AI uh go-to-market survey. And one of the things that we wanted to do is start to marry together our observations from working with clients, uh, running uh conversations with customers, really to provide for you, hopefully, over the next call it 25-30 minutes, a blueprint from what the data says on how you can move from point A to point B. In about 15 minutes, I'm gonna get into like some really meaty stuff on like the tactical pull through. So make sure you stick around for that in particular. And if you're watching this on replay, um, thank you for watching. Thanks for tuning in. I think you're gonna hopefully get a lot of value out of this. As always, I very much want to encourage people. I see um Becca uh hopped in here as well. If you have questions, please feel free to just jump in. Jump in, ask the questions. I have this pulled up on this screen. I've got the survey pulled up over here. So without further ado, let's jump into it. Should be a fun walkthrough. And I think hopefully you're gonna see a lot of data points that you know might feel familiar too. There's probably a lot in here where you'll look and say, yeah, that that feels a lot like me. You know, that feels a lot like where we're at right now. And that's that's okay. That's a good thing. So let's jump into it.

What AI Maturity Really Means

SPEAKER_00 2:44

If you're not familiar with me, I'm Jake Dunlap. I'm the CEO of Scale Consulting and uh AI company called Journey. Uh, we work with hundreds of organizations every year helping them to optimize revenue performance, whether that's top of funnel, middle funnel, current customer growth, everything in between, systems, uh processes, and AI. You know, we were very early to the AI game. We were doing workshops on Chat GPT in 2023. I did the first ever LinkedIn learning course on ChatGPT for sales back in 2023. Uh, we've been building agents since 2024. We've built hundreds of agents. And so we've learned a lot, and a lot of our failures, you can learn from those as we get into this. So for us, look, our goal was a lot of the things that we hear from clients around how things are working, where are we at with AI, we wanted to try to understand where people are actually at. Because if we understand where people are actually at, we can start to pot pat part, put together a much more clear roadmap of how to get from point A to point B. And so what we did is we sat down and we said, how can we make this very simple for people too, you know, in terms of not taking up time? And we came up with eight questions that help us to judge, let's call it like AI maturity and then AI adoption as a part of that. And so our goal here was, you know, we had a few hypotheses around from our from our qualitative observations of where we thought people would be. And I think what'll be fun is to kind of walk through what we actually observed and then what the remedy is, you know, for a lot of these. And so today I'm gonna pressure test some of the the noise that you might hear in the industry. AI adoptions here, AI adoptions there, everybody's doing it, nobody's doing it. I'm gonna talk about the gaps, and then most importantly, where I want to try to spend a most of our time is what to do next. We actually have a survey, it's 100% free. You can fill it out, and maybe Becca, we can drop that now as well, too, where you can get kind of your quick lay of the land. And we and again, this isn't based on our opinion. This is not based on, well, based on where you're putting it, you're at step three or whatever. This is based on the data of like, hey, where are world-class organizations today? And then maybe where can we where can we uh focus on to get from point A to point B. And I think that'll be a really good leave behind, you know, uh for you to take a look at after this. Uh, we also have our AI go-to-market playbook, which is really the findings and just a tactical way that you can move from current state to there. So a couple of really cool deliverables. I'll drop that one toward the end as a part of it. So

The Adoption Myth And The 6% Reality

SPEAKER_00 5:25

we saw a lot of stats in the market, right? 93% of teams, you know, are already using it. Um, Pavilion said 95%. 95% of people are using it. Um, but then you see all these ROI, and it was really cool, like from looking at outside data too, you know, 5% from MIP, MIT, 6% from um, you know, Pavilion. They just launched lap uh put that out a couple weeks ago. A lot of leaders thinking they're fully integrated. And candidly, what this tells me is most leaders don't know what's possible. That I think a lot of people in leadership are still treating AI like a checkbox, like, oh yeah, we're using this tool, we're using gong, we're using ABC, and not really understanding what fully integrated needs, because this is the punchline. Six percent of actual uh individual contributors think that their companies have a fully integrated AI workflow. And again, I think a lot of this comes down to just not understanding the art of possible. And so the assumptions that we had is individual usage is high. Every company we talk to, the power users are the frontline folks. They're the people in the trenches, understanding what's happening. So this is one of the assumptions that I had coming into this. Overall team adoption is a little bit behind. Um, real automation, not just you know, scoring a call or something like that, but really taking a meaningful chunk off of people's plate is not happening as much. That was a big one that I had. Uh, the pilots, we get this all the time. We've got companies with AI councils, and they've got all these pilots all over. They never actually get deployed. Um, and that's kind of what I've observed in my conversations. Lack of ROI, we're not doing that. Ownership is all over the place, and measurement is a missing link, right? These kind of ROI and measurement um are a missing link as a part of it. So, this is what whenever we sat down and said, hey, based on what I'm seeing in the market, this is what I see is happening. And so what we wanted to do is well, shoot, is this what people are actually, you know, is this what people are actually saying or doing as a part of this?

High Usage Without Embedded Workflows

SPEAKER_00 7:42

So, one, okay, how often are we using AI? Look at this. Almost every task, 41%. Daily, 45%, right? So literally 86% of people are using AI on a daily basis. Uh, this doesn't this doesn't did not surprise me too much. Now, how we're using it is is maybe the the bigger question here and and the areas of opportunity that we might have to optimize how we're using it. So usage is high, which is great. But let's talk about institutionalizing it, right? So I'm using it, but do I have any clue what I'm doing? And I I was talking to a team of of uh sellers at one of our clients, and and one of the things I said to them is like, look, your bosses also don't know. So I think a lot of the gap that I see in this embedding here, right? Most of our tools have some AI, some process, informal, not at all. So again, what you're seeing here is most of the market, right? Um, has these AI workflows, but they're just not embedded. And I think that goes back to some of the other things I'm gonna talk about. That, you know, we're everyone's still we're still experimenting. Let's just be honest. Like we're still very much experimenting as a part of this. Um, and we you know, we're trying to figure out how to actually make this thing work. So this is perfect. We have our first question, and it's perfectly timed. So again, I highly encourage you. You can you can leave as an anonymous question as well or call it out. The question that that I have here is you know, we have pretty strong individual AI usage across the team, which means it should be easy to implement across processes, but we're struggling. Is this usually a process issue, an ownership issue, or something else? This is wonderful question, anonymous attendee. And it's the exact question that you should be asking. I think I'm gonna get through some of the reasons why in more detail as we go through this. I will give you my very clear take on what we see when we step into organizations that are at this stage, is they don't have a North Star. And what I mean by that is uh two things. One, what's the actual business problem we're trying to solve? What's the process issue we're trying to solve? And is that actually tied to what's the biggest business need? What I see a lot of these AI projects are focused on is a little bit, oh, we're gonna get a little bit better at enrichment. We're gonna use clay. Well, great, but I cannot tell you. We had a client, I very specifically remember this conversation from about a month and a half ago, all in on clay. Then we looked at like the clay score and the intent signals, and you talk to the sales team, they're like, Yeah, we don't trust any of that. And so I think a lot of this has there's a disconnect between AI things that we think are cool and what the actual bottleneck is in the revenue org. It could be on the marketing side, it could be sales, it could be success and account management, SDR, and really making sure that we tie it to that. So, great question. I'm gonna answer some of these other questions um throughout, but again, AI initiative should be tied to what's what's the biggest bottleneck we're having, and will it actually solve that problem? Great question. Keep them coming. I love the QA. Uh so

Automations That Remove Busywork

SPEAKER_00 11:04

here we are. Do AI automation uh do AI automations complete go-to-market tasks for you? All right. I love this. 33% said yes. That's awesome. Right. Um, that means they're using automations or GPTs or something to do the boring stuff. Like in 2026, you should never be logging information to your CRM. Between your call recording and email, I can just do it for you. You know, that's one of the more common automations that we come in and do for companies is you know, immediately call recording updates very specific details, not like what Gong does and some of these other ones, but very specific details that are relevant to your business into the CRM, automatically pre-writes the email for you. It's sitting in your Gmail, your Outlook for you, automatically will score it against your rubric and then send that to Slack or to wherever you live. So I love to see that we have a third of people that are using some version of automations, but that means that there's a lot of people that aren't. And I think a lot of people, because we're just so used to doing all these things manually, the idea that, like, wait, well, AI can't quite do that. We've got to get over that hump. And so that's a big one there. And so I think for me, when I think about integration, it's like what I just mentioned before. And that's just like one very simple example. And guess what? You can use that same use case on the account management side. I finish a call with a customer, it automatically updates CRM, it automatically updates health score, right? All of these things are doable today. And so, for a lot of organizations, again, when you prioritize your roadmap based on the business needs, that's when you see big impact. So, and really starting to think about, ah, that's good enough. I think that's the other attitude I see. We deploy one automation, we say, Oh, that's good. And we don't have this kind of ongoing roadmap. Well, that was v1. Well, now v2, right? So that first automation I mentioned around grabbing the call recording, doing the CRM work, doing the follow-up email, doing the scoring. Well, what else can I do with that? Well, I can automatically put into a dashboard one-on-one prep from a manager. Great. Now my manager knows these are the three things we should talk about in our one-to-ones. We're actually having productive one-to-ones. So each one of these, it's it's it's a continual development. I think a lot of organizations stop when they get to something that's okay and don't look at this as like truly integrating to where you want to be as a part of it. So, next set of questions here.

Why Rollouts Stall Inside Companies

SPEAKER_00 13:39

What stage best describes your company's AI rollout? Nearly half are still experimenting. Right. And now, look, the numbers are better. I can tell you, we we were doing some surveys back in 2024. The numbers are better, right? We've got 22% said, hey, there there is something that's you know getting automated. Um, 20, you know, 25% said, hey, we've got three or four different AI use cases here as a part of it. And then you know, some people are in, you know, piloting. So again, most organizations are not really focusing on how you know this goes down to that first question. We don't have a follow-through plan. We implement an AI solution, and it's like, and then when the experiment works, this is how we deploy, and that's why having this kind of singular AI roadmap, and that's a lot of the work we do. We've got our AI jumpstart program, and and the reason we developed this late summer last year is we realized companies needed two things. They needed a focused way to deploy initially, and then they need a roadmap. We use a sauna for that. So, hey, we're gonna pick these three automations that are tied to these two biggest bottlenecks, and then we're gonna build a roadmap for each department, right? As a part of that. So that's really why so many people are still stuck in it in experimenting, is that is they don't really have a deployment uh plan. You know, there's a a mediocre at best deployment plan. Uh, we got a question here. The question is, let me pull it up. This is taking up my chat. I think the question was around, yes, we'll share all the stats and the deck uh with everybody after. So stay tuned. You will get that as a part of this. Okay, why why did it stall? No leadership push. I gotta tell you, I see this everywhere. I think many, many VPs, CROs, and CEOs do not understand that this is as big as the internet. We have to retrain the way that people work with AI as a part of every process. And so they look at their pipeline issues, their close rates, their churn issues, and they still see AI as a tool and not a new way of working. And so there's no leadership push one. This didn't surprise me. Poor data quality. All right, I'm gonna say something very controversial here. Poor data quality is a cop out. Poor data quality is one of the biggest reasons I see companies continue to kick the can down the road and not get started. My friends, a lot of the things that the like all the automations I just described for you that are gonna make your team 20% more productive, the data doesn't matter. We're the the the people that say poor data quality, it's like I want my reporting to be more accurate. Well, that's great. But how about we make our people 20% more productive right now? And yes, let's clean up the data. But the fact that a third of people, and I think this feels about right. The, you know, whenever we work with companies, well, we got to get our data cleaned up first. It's like, no, you don't. Like that, there is a million other things I can do that your people are doing manually right now that you need to fix now. Now you need to fix this thing, right? Let's change the way they do ABC, let's change the way they interact with customers, let's get this data source that they'll actually use and care about, etc. Let's not, you know, wait for data to be a part of this. No success metric. I already talked about this one a little bit, not surprising there. And then obviously, frontline buy-in low. It's not surprising that this is last. So if your team is telling you poor data quality is why they're not moving forward with AI, it's a cop out. And you can say I heard it on a webinar from Jake Dunlap, and they do hundreds of these, you know, conversations, and we do tens and tens of these deployments every year. So use me as a part of that. You know, when I look at these things, again, I just don't think execs know. I think this is a cop-out, right? Yes, this if CRM hygiene is poor. What this stops me from being able to do is leverage AI to get like really cool, unique insights, which don't get me wrong, I want to get those really cool and unique insights. You know, one of the things we we've developed is this concept of the co-pilot, the cockpit that your rep sits down in, it's all fully AI enabled. They've got their own clothes rate at the top, etc. Um, that's awesome, but that's not gonna stop me just because my data is bad, um, from doing all these other things as a part of this, right? I'm gonna can consistently try to um find ways that I could optimize people's work without the right data as a part of it. Um, so success metrics, you know, we've got to tie these things to ROI. Um, so this is a good one. Uh Shannon brought this up, so shout out to Shannon. Um, she said, hey, we recently got Claude and there was no pilot, no deployment plan for our commercial team. Um, the guidance is still yet to come on how to use it, and you know, there's not an AI team to manage this as a part of it. Shannon, you are not alone. I I we didn't ask that question. I mean, we did ask like the structured rollout question. Most of the companies, and by most, I'm gonna say at least 80% of the companies that we come into. What's your AI strategy? Well, it's so funny how things shifted from Chat GBT to Claude. You know, if you if you look back to last year, like last November, December, for example, I felt like Chat GBT was coming like Band-Aid, your Kleenex, you know, like when you referred to your chat, it was like, oh yeah, chat GPT it. And man, Claude, because of Claude Code, I think, in particular, in the first half of this year, has just and now what your man, what what what leadership is doing, Shannon, is they heard about Claude, someone on their board said, guys, we gotta get Claude. Like, you know, you know, you know, our AI strategy, we gotta get Claude. Like everybody's using it. The reality is, again, without the deployment plan, we're still using it as a basic chatbot. And I think a lot of this comes from the lack of time internally, that most companies don't have anybody in the go-to-market team who has the time to invest because they're stretched so so thin to actually make sure that these things get deployed properly. And two is expertise. I have to tell you, as a consulting company, we've got 40 plus consultants in the business and about 15 or so. This is all they do. AI deployment, AI deployment, you know, that's all they do. And and even for our team to keep up, you know, even for our team to keep up, it is difficult. Like it's not easy for us because at literally, this happened two weeks ago, week and a half ago. Claude drops four six. You're like, okay, well, what does this do to everything? And so I think companies have to realize, and I'm not saying this because I run a consulting firm, but you know, we bring in outside experts too into our firm for certain things. I think that this is just a very difficult thing to do in-house when people are stretched too thin and they don't really understand like the art of possible. And let's get into this here too, which kind of dovetails into that.

Ownership Problems And Missing Metrics

SPEAKER_00 21:12

Who owns the AI initiative? Multiple owners, 50%. Um, sales ops, marketing ops, 23%, RevOps, 19%, IT security. I actually love this. I love that it's only 10%. It was much higher a year ago. But the issue is we still have two, we have one, no clear roadmap. You know, what what we we have a client um who we've been working with for over a year, and finally what we were able to get to is you know, we've got the CMO, they have a CO CMO and a CRO, CRO oversees success as well. And we have our exec counsel, but the CRO is in charge. And I'll get I'll get into some. Of the fixes as a part of that here as well. But you have to have that person. I gotta tell you, these are the companies I'm probably most worried about right here. Multiple owners, I can fix this. When we've got, let me do some quick math here. Quick math is 42% of ownership in the ops department. Again, that's why that's why you hear poor data quality as why we're not moving forward with AI. My friends, the ops department is not comp the way the revenue department is. If the ops department, if you don't generate more leads, that doesn't usually impact their bonus. If you don't close more deals, doesn't impact their bonus. If customers churn, doesn't impact their bonus. Maybe if they've got a company performance thing in there. But again, this isn't about it's this isn't another technology. I think that that maybe that that's the point I'm trying to make. We're giving it to ops because we think it's a technology. If you don't have a business unit leader tied to the initiative, it will fail. Or it will focus on solving the wrong problem. And again, we're outsourcing this knowledge to ops and they don't know what the bottleneck is in the RevWorg. So that's why, well, we if we had better enrichment, if we had, well, if we can just integrate this, it's like you have to have a business unit leader attached. I'll talk about that uh here in a little bit. Um, AI tied usage measures. Uh, 17% have it clearly defined. I don't think that that's a big shocker. Um, I think one of the biggest issues that I'm seeing in in the market as a whole is revenue leaders across the board and CEOs, I'm calling you out as well, too, struggle to understand a massive increase in productivity as an ROI metric. That's that's one part of it. And then two goes back to what I said at the very beginning. We aren't tying the AI initiative to a business impact initiative so we can measure it over the next six months. The first one is a real problem because very simple question. I'll ask all of you. All of you use HubSpot, Salesforce. If you're on finance, you use Net Suite. What's the ROI of Salesforce? What's the ROI of Salesforce? What's the ROI of HubSpot? How do you prove ROI on Salesforce? Well, it's obviously better than us doing this in Google Sheets. It's obviously better than us doing this like this. That is AI. It's the same thing. You could have your team manually logging calls, manually sending follow-up emails, doing these things, or I can just automate all that with AI. It's obviously better. And I think because uh because we struggle to really believe productivity gains the same way we believe revenue gains, that is a big limiting belief. And that is why, again, these metrics numbers are not being impacted. It's those two reasons. One, we're not measuring productivity. Rep productivity is one of the five top metrics that we measure when we work with client. We call them gains metrics, sales team, or it's really revenue team productivity, is the S. Right? So you got to measure both as a part of that. So for me, my my fix for this is that when you're going to implement an AI initiative, you need a department lead for each group that is tied to this. They can be partnered with an ops person. I really don't care. But you need each department needs to have a lead. That's the only path forward. You need a business expert who understands the business in the bottlenecks that can help to champion this. Every single one of our engagements, we work directly with the head of marketing, the head of sales, the head of account management, or RevOps. We've got RevOps dashboards and cockpits too, the things I can automate for you to clean up your sales force would would blow your mind. Um you have to have business unit leaders. And then you have to have a senior revenue leader at the top that help that can work across groups. That's it. It's the only way this works as a part of it. So the question QA's piling up here. So let me take a quick QA here as a part of that. Let's see what we got. All right, first question. Uh, we're running into this internally. Who should actually own AI? RevOps. Well, I just I answered part of that for you, so I think I I went a little deep there. So the per each department should own the AI strategy for the business use cases in their department with a singular leader. And they could live in any group. I really don't give a shit who it is. Because here's what happens if you just run in three silos or three or four silos, this group is gonna start to create a lot of stuff that's actually applicable to this group. Right? And so if you've got this group running and they've already built this automation, this integration, this whatever, it's like, ooh, let me take the guts of that. I'm gonna plop it over here, put our spin on it, and now we're running together faster. So I I really don't think RevOps can own it. I think you need a senior business person, is just the reality of it. Um okay, Michael asked a good one here. Uh, he said, Hey, do you see AI performance metrics embedded directly in comp plans or added as spifs? No. Um, I do not think going back to some of these previous slides. I don't think most organizations are doing a good enough job of measuring productivity or measuring a business impact to tie it to any type of comp. Um, because again, we're thinking of it as like, oh, we've got better enrichment, this should happen. Not like, oh, we've improved the way they work and now they're 5% more productive. Are we measuring the productivity? So I think what you'll see is maybe a version of this, which is that quotas and things will go up and there will be less people. Um, I'm gonna say it, if you're a mediocre to low performer and you're manually doing things, I can have a high performer who's super AI enabled, and I can just give them a bigger number, and it's easier for them to hit because they have a lot of these. So I think what you're gonna see is more expectation that the AI initiatives are leading to these either business metric impact or productivity gains to show return on investment as a part of it. Great question. And then I see I've got one more in here. I'm gonna come back to this one. Um, so this is our AI maturity curve. Um, you can see here like where people are, like Wild, Wild West, assistance in pockets, connected workflows, automated insights. Uh, so most people, two-thirds are still early. This is kind of how we think about this. And and like I said, we have um after this, uh, we'll drop a link here in about I think 10 or 15 minutes when we wrap up. That will you can get access to the full playbook that you know, we talk about from the data how our company is actually moving up this ladder as a part of it. Um, so the hypothesis that I had in the beginning are here, right? Individual usage high, 86%, not surprise, right? Shannon's example of, you know, well, we got Claude and I use it. So, so people are using it. Team adoption struggling, right? Similar to that. It's again, I really appreciate you sharing kind of your current use case. Um, very much so what we're seeing. 23% are just informal adoption. Some have some embedded things. Um, look, it does seem like, you know, I I don't know if this is wrong, wrong, but it does seem like companies are doing some amount of automations, you know, and again, like I think those really differ from company to companies, but still we've got 64% that aren't there, but 33% is higher than what I expected. So I missed on that one. Pilots poorly defined. Yep, six percent are running as structured. I mean, again, that this is the number. This goes back to these AI deployments. If you don't have an AI roadmap, if you don't have a business unit leader who is tied to the initiative, it's never gonna, it's never, you're never, you're not gonna see the impact, right? And you're not gonna have the ability to pull through the pilots, which kind of brings these two, you know, together. Ownership is unclear. You know, people have owners. I I don't think they're the right owners because that's I think because ownership is unclear for half the companies, for the other half, I think the reason the other half aren't seeing ROI or pilots running or measurement being tracked is because of who you have owning it. If I'm invested as a senior sales leader in solving the challenges in my team to drive more sales, if I'm a marketing leader and I'm trying to figure out ways to get better at running campaigns quickly, running targeted campaigns at account, putting out content, they are invested in the problem because that's how they're compensated. Right. And so I think you know, ownership is unclear. I'll say I was like partially right on this one in particular. Um, and then this is a really good question here, anonymous attendee. And then I'm gonna get into some of like the solves um here next, said, is the leading use case for AI in RevOps forecasting. Um, isn't this still the correlation? I would answer and, meaning, yeah, RevOps is supposed to be responsible for forecasting, but that's not why RevOps was invented. RevOps was invented to see across the revenue organization. That's why we don't, you know, people don't call it sales ops or marketing ops or CS ops anymore. And identify the bottlenecks in the business. We have we have turned RevOps, and that's why, again, like so many companies, you know, almost 50, what probably 45% of our business is working with companies to create to get the RevOps in a place where it's like driving business value. The whole goal of RevOps is to unlock revenue. It is not to build a random report or dashboard or forecast. Yeah, forecast, you know, you know who cares about the forecast? The people up here. You know who co who I'm not saying that obviously as a rep you care about your forecast, but there are issues that are happening that are stopping the revenue machine from working. RevOps should be able to pinpoint those things, fix, deploy the patch. Again, have the data yes to forecast, but but it should be fixing problems and from marketing to success and everything in between. So I think part of this is just how RevOps teams are um being utilized today. And going back to the goal of these organizations was not glorified order takers to answer tickets and build dashboards and report um as a part of it. Um I'm gonna talk a little bit about more about AI forecasting here, so make in like a couple of minutes, so stay tuned for that.

Six Steps To Move From Pilots To Impact

SPEAKER_00 33:08

So I'm gonna walk through kind of six steps um on how to kind of like move from point A to point B. So step one, I've already kind of teased this, is we have to have an owner that is a GTM leader. They understand revenue, they understand what it is, and we have to have department leads. Now, ops can support that person in helping to make all this stuff happen. Okay. But and ops has its own roadmap too. Ops has its own roadmap, right? Define what pilot means. If this is successful, we are going to do insert thing. This is our deployment plan. This is why a lot of tech tech implementations fail too, is that we deploy it and then there's no real like adoption plan as a part of it. So, again, what goes into really good pilot? We pick one workflow. We usually will pick two or three, but they're very clearly defined. One team, 30 to 45 days, usually is what we shoot for to deploy. Clear success metric, decision to go live. You do this, you're gonna start cranking on AI. Next is you have to tie AI to revenue metrics. And I will argue that productivity is a revenue metric. We we track it as one of the top five metrics on top of you know pipeline growth, uh, sales cycle, win rate, and NRR net retention rate, right? Churn plus you know, churn growth, current base minus churn plus growth as a part of it. Um one, do not build on top of bad data. This goes back to AI forecasting, right? So you can build a lot of the AI things that you want to do, but if you want to, this is this is the point I wanted to make on the AI forecasting. If you want to get really tight and much better and more granular, you have to be able to fix the data to get the reporting piece right. It should not stop you though from moving forward. So if we want to get better reporting, this is great. This is what we can do to get there. Okay, this is another one. You overcomplicate things. Let's start fancy. Oh, we want to do this, this, and this, or even, you know, oh, there's this magic tool. It's gonna come in from fairyland and it's gonna just sprinkle its dust on our org, and now we're AI-ified. Like, that's not how this works. Okay, like I trust me, we have done enough of these and you know, scene where they went a little bit left as well, too, as well as go right and you know, turn out great. That start with these very basic use cases that are bogging down your team or can unlock revenue and and just laser focus there. And then as you have your AI strategy and roadmap, you kind of have the next things up as a part of it. Transformation has to be at the top. It's already at the bottom. You guys saw the numbers. 86% of people on the front lines are using this every day. The reason the transformation isn't happening is you, CEO. It's you, VP of sales, CRO, CMO, head of customer success. You are not investing enough in this and realizing that this is transformational for the way that your team does their job. You are still looking at this as a cool new technology and not we used to work like this and now we work like this. That's where I use my 1996 analogy. There, there was a world, I was not in the workplace, I do have gray hair when the internet came out. Okay. Um, but someone had to retrain, like, hey, Brenda, like, we don't send faxes anymore or write handwritten letters. We use this thing called email. And and people laugh when I make that analogy, but it's the truth. Somebody had to stop and say, hey guys, we're not going to the phone book anymore. We're using Google. Like, we're not using this type of communication, we're doing this. We're not writing down our forecast. We're doing it this way. We're imp, we're gonna we're gonna write the notes in CRM. And and that has to come top down. That you have to realize we are changing the way that human beings solve problems and work. We are not deploying a new technology. And if you are looking at this as another technology and not like, hey, it is my duty as a senior executive in this company to retrain a workforce on how to work, you're thinking about this the wrong way. And so, you know, look, for all of you on the front lines who are feeling this, you got to figure it out for your own self. You've got to say, hey, here's how I work. Boom, I'm gonna start to fix AI this way. And the playbook we have will help be just as applicable, you know, for you as well, too. So, what do we got next? So take the assessment. Becca just dropped the assessment there. Um, hang on, everyone, because I'm gonna show you all some pretty cool stuff. Um, so we've got some links. Start here. Take this little um assessment that we put together. It's basically like a combination of best in class of like what we've seen, coupled with like the data that helps to tell you like where you're at, and then get this playbook. It's a hundred percent free. Like, you know, obviously, if you need help to deploy it and you're like, Jake, this is great. Can you help us get started? You know, make sure to hit me up on LinkedIn or reach out on scaled and you know, contact us, etc. My email is jake at scale.com. So you can just reach out to me directly if you need help deploying. But if you've got the people and you're ready to rock, this is it. I prom like more than any again, unquantitative, because there's not really any competition in our deals. We have done more of these GTM deployments than maybe anybody that are structured, tied to ROI, tied to business metrics. And that is what is included here in this playbook. And and I want to show you all, I think that this would be a lot of fun to just show you all like where this thing is headed, because I think you'll you'll actually get a lot of value out of seeing, like, okay, Jake, yeah, this is great. But you know, like what's a really great AI deployment, you know, look like. Um, so you know, when we're thinking

The North Star AI Cockpit Demo

SPEAKER_00 39:20

about, you know, like what we're doing, like how we're actually doing things, um, you know, what actually should this thing look like. I think that this could be kind of fun uh for you all to see as a part of that. And I'm gonna pull up a couple examples here and I'm gonna share my screen as well. Um let's go with this one. Yeah, here's a couple of like really like some fun ones um that you all can that I can show you all as a part of like what people are doing. Um just built this one. This one's pretty dope. Um, let me show you where this is headed. I think that this will be helpful for you all to kind of see what the North Star is. Um, I hadn't planned to do this, so I'm going off script a little bit here, Becca, but I think it'll be helpful. This is what an AI-enabled workflow looks like. So this is an example of a cockpit that an AE can sit down in and it just tells you what to do. It says, hey, based on what you're prospecting, based on what you're doing, just go do this. So Q2S, okay, great. I go here, it's already integrated with my email, it's integrated with my calendar, it's integrated with enrichment, and it just tells me what to do. And then I can literally like send the email from the dashboard. Literally from there. I'm like, okay, great. Now I've got to go back into prospecting mode. Okay, well, what are the signals? Okay, the signals were this. Okay, great. Hey, I saw your post about four-hour Comcast outage. You know, blah, blah, blah, blah, blah, blah, blah. Send. Send on LinkedIn. The future is you as a rep don't have to think about what to do, right? Obviously, you're gonna turn your brain on, and then I can use Claude over here to basically take action. Like this is the future. And then I've got my Claude embedded. The future is no clicks. I don't click around from tool to tool, I don't click around from thing to thing, right? Um, you know, here's an example of like uh a RevOps one. Uh, let me find a better one. I feel like I had one, yeah. Yeah, where I as a a leader, um, let's see, I've got a good sales manager one I can show you all. Yeah, sales manager. Here's a good one. As a sales manager, right? What's my team's pipeline? How are my people doing? Dig in, take action, email the rep. I don't need to bug you 24-7 and have worthless meetings. Okay, hey, we've got our one-on-one coming up. Here are the things that we should talk about. This is the future. All your AI, when you want to get to an AI to get to step five, you have to start to really think about AI not being a series of uh automations. It's building toward this, that there's a new way that people work. You know, whether you're in RevOps, we've got some cool like marketing op ones. Let me pull one of those up for you guys. Like, imagine this world. So, this is a marketing cockpit for this company, new homestar. We've got a realtor appreciation brunch coming up. Yep, I do. We need to create content for that. I want to push that to gamma. I don't know if you all know gamma or not, but it's something that we use a lot of. And and this isn't a direct integration. You you could make this a direct integration. I literally go, I paste it in, and if I'm a marketer, in less than I don't know how long it's taken me so far, 25 seconds. I have this. I have this deck, and I'm done. This is the new way to work if you're in marketing. Same thing in sales. I just have it there for you. I've automatically written it. I can take action, I can see responses, the intent, the intent signals are there. It pulls in all the account data that's relevant. But I'm literally building this in real time. How long would it have taken your average marketer to build a deck for this? Three five hours? Guys, like this is the future. We are changing the way that people work. It's not done yet, it'll keep going. It's got 10 slides to make. But those are just some good examples. Managers, AEs, we've got account management cockpits, like all of the above. Right? And this is the future. So, you know, if you want to think of your North Star, this is it as a part of it. Um, as you know, as we kind of look at like what's next, what's next. Um, so um a couple questions in QA before we wrap up. Uh, how is this pulling in? So this is a UI. That pulls in CRM, email, calendar, um, enrichment, different enrichment signals, and um CRM calendar, oh, call recording. Call recording. That's kind of the power of five. But again, if you're an account manager, it pulls in Zendesk, Planhat, like whatever your tool stack is. So this is the ability to have these containers that based on the role, we can visualize, you know, we can visualize these either in a separate web app or inside Salesforce or HubSpot. It's easier, it's just much more flexible inside of a uh a web app. So what you're doing is you're basically orientating this toward, hey, what are our intent signals? Like what are the things that we need to do as a part of this? You know, um, who how should we take action from here? So I don't have to click between 50 systems. I can say, hey, which dark dark fiber prospects are stalled? What's the recommended unstick move? So you're integrating the whole Claude or Chat GPT or Gemini or co-pilot experience into one place to where me as a rep now, I just work. I don't have to think about what to do next. Oh shoot, I forgot to follow up with Rick. It just it just doesn't happen. So this is the future. If you guys want to know what AI transformation looks like, it's that every single person in your company has this. And we don't switch between 55 systems anymore, we focus on what we can control. So that's what I got for you guys. That's the plan. Um, I'll take this last question, though, because I

AI Forecasting Without Stage Fantasy

SPEAKER_00 45:30

like it. It says for RevOps example, won't LLMs produce inconsistent calculations? It's probabilistic. Um, that's all solvable. Um, it's actually really, really solvable, but based on the constraints that you put on the LLM. So depending on how you program your agents behind the scenes in terms of their um, you know, ability to, you know, hallucinate, you could say, it's actually very workaroundable in 2026. In 2024, not so much. In 2025, not so much. Because a lot of what we're doing is we're pulling from CRM data. We have actuals there. I have your actual win rate. I have your actual sales cycle for you as an individual rep. I've got your average deal size. And if I have the actuals that I've pulled from real data and I'm just calculating those to do a roll-up to like a VP level, then then it's not really um the the lat the the hallucinations are very unlikely, is my answer, um, as a part of it. And that's why, again, to do the more reporting pieces, like I said, that's why we're focused on helping companies on like, yes, a thousand percent all of you should be using AI to forecast versus your linear sales journey. Like that, that era's dead. The era is dead where we should be forecasting based on deals in stage four versus stage two, because every seller knows, every account manager knows, every BDR knows, every marketer knows. This deal looks like it's in stage four because they check these five boxes, but this deal is technically in stage two. But I've had two meetings, they've got a real initiative, they've got five people on every meeting, but it's in stage two by our definition. AI fixes that. It's like, okay, if these are the criteria that are most important, it's like this one I'm gonna forecast at an 84, this one's at a 75, and we're gonna stop, we're gonna move away from these six-step sales journeys, and we're gonna move toward how do I get people from a 60 to a 75. And it's like, okay, these are the things that I'm missing. Because it can also take into account everything that happened in your email and your calendar, etc., to give you the score. So it, you know, it'll only get better from here. So that's

Act Now Plus Free Assessment

SPEAKER_00 47:31

what I got. I'm gonna take a breath, have a drink of water. And I hope this is helpful. I know a lot of you are kind of sitting there like, man, there's a lot here. I trust me, I know there's a lot here. But if you now is the time, you have to act. You have to do something to start to move toward what I just showed you. Because if you don't start now organizations that aren't as good as you, maybe they have inferior people, a mediocre product, but they're able to out-execute and out-deploy and out give people business outcomes, are gonna win. Very rarely does the absolute best product end up being the actual winner. Many times it's like a pretty close, but all the other things that happened around it. And if you as an individual become exponentially more effective, you're gonna hit your numbers a lot faster. So that's what I got for you, everybody. Really appreciate you tuning in. Uh, we will share all the links to everything. Make sure you go do the assessment, share the assessment around with your team. Um, we can do a team roll up as well, too. Um and yes, human in the loop. You can see here everything we're doing, human in the loop. That's why we use the term cockpit. It's like your F1 car. There's a driver. You sit in, the buttons, this, they tell you what to do. And you, but you still gotta you got still got to hold on and drive this thing to be an excellent expert seller or SDR or marketer or account manager in 2026. So appreciate y'all. Have a great rest of your week and make sure to stay tuned uh for everything to go out via email. If you need help with this, make sure to reach out. Thanks again.