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The secret sauce to your sales success? It’s what happens before the sale. It’s the planning, the strategy, the leadership. And it’s more than demo automation. It’s the thoughtful work that connects people, processes, and performance. If you want strong revenue, high retention, and shorter sales cycles, the pre-work—centered around the human—still makes the dream work. But you already know that.
The Unexpected Lever is your partner in growing revenue by doing what great sales leaders do best. Combining vision with execution. Brought to you by Vivun, this show highlights the people and peers behind the brands who understand what it takes to build and lead high-performing sales teams. You’re not just preparing for the sale—you’re unlocking potential.
Join us as we share stories of sales leaders who make a difference, their challenges, their wins, and the human connections that drive results, one solution at a time.
Jarod Greene (00:00):
Hey everybody. Welcome to The Unexpected Lever, this show where we talk to some of the best B2B minds in the world. We're talking about levers that move revenue across people, across process, and across platforms. This season, we're exploring one of the biggest shifts happening in all three, the rise of AI in sales. Not the hype, not the abstract theory, not the pie in the sky stuff. No, we're focused on real life, practical advice from leaders who are already on the journey. They're sharing the wins, the mistakes, the learnings, the lessons learned so that we can all level up on our AI journey. I'm your host, Jarod Greene. And today I'm joined by someone who brings a deep go- to-market and operational mindset to the conversation. Scott Peyser, Senior Vice President of Operation at CBTS. Scott spent more than two decades helping companies scale, modernize, and transform the way they go to market.
(00:56):
And today he's currently leading operational strategy for a company that is a major force in technology, infrastructure, modernization. The managed service is Scott, welcome to the Unexpected Lever. Great to have you here.
Scott Peyser (01:06):
Jarod, great to be here. Great to be. In this new format, I'm excited for what we're going to do. And thank you for getting my name right. So many people get it wrong and they don't even ask. So thank you for that.
Jarod Greene (01:16):
I understand. I can relate. I get a lot of Jarod's and it is what it is. So before we get to the AI journey, I'd love to start with CBTS. What do you guys do? Why does it matter? And this is your moment. Give the shameless plug.
Scott Peyser (01:29):
Yeah, I love that. So CBTS is traditionally a value added reseller. We started as primarily in a product resale business and we've grown to $1.3 billion. And today a majority of our work is in the services space. So helping customers drive outcome-based value in professional services and also providing a managed services offering. So we have a services first mentality and approach, and we focus in technology pillars like app and security and infrastructure and digital workplace and cloud. And AI is obviously a part. AI automation is a part of just about everything we do in the business. And in a perfect world, what we do is we are helping to design, build, and operate the technology infrastructure that's fueling growth and innovation for our customers.
Jarod Greene (02:17):
That's awesome. Thanks for sharing that. Appreciate that. I'm sure folks notifying you will drop that in the link. All right. Before we get started with the AI journey, here's the question. I promise we would get to, what is the oddest or most unexpected thing you or have seen someone else use AI for personally or professionally?
Scott Peyser (02:36):
Yeah, I think that the neatest one, and I still haven't figured out how to do it myself, is they've used it for territory planning. So essentially loading in their ICP and their account models and using that, using AI to help define how to build the patches for their sellers. I think it's been a fine heart when you think about propensity and intent. And obviously you use a lot of insight and data to drive that, but the ability to bring that model together in a way in which it doesn't rely on your sales ops person to think about building the territories. I think it's a unique application of AI.
Jarod Greene (03:14):
I think that's very cool as well. And I'm sure folks are excited to do it, especially at this time of year. We'll record in November. A lot of folks are in that kind of fiscal planning for next year season. And so I'm sure it just resonates.
Scott Peyser (03:25):
I'll give a shout out to Mark Malloy who actually built it. If you look up Mark Malloy on LinkedIn, he actually posted his entire prompt for it. So it's a really neat way to start to leverage AI.
Jarod Greene (03:37):
Absolutely. Shout out to Mark. We will post that here in the clips and the show notes. And so yeah, it's always interesting to see the one thing or the odd thing or the neat thing to reveal how we're kind of casually and seamlessly embedding AI into the workflow. So follow-up question there. What was the trigger moment for you? Sitting from the operational seat, what was the moment where you recognized that this was a thing, it was happening and there was a point where you needed to get your head and hands around it?
Scott Peyser (04:08):
God, it was actually as a result of a job change I made. So I spent 21 years at Dell, and in my last job there, I got to lead an incredible team of services and DealDesk folks globally. And in order to run that business, I actually had a gentleman on my team who built my version of a revenue orchestration platform, but it was in spreadsheets and it required him to update it every single day. And it took two hours to update it and it wasn't real time, but it gave me what I needed. I left Dell and went to UiPath, and it's when I was first introduced to the concept of actually a SaaS offer in this space that did all this for us. Sat on top of Salesforce, it gave you live perspective, it did the insight and the automation. It told me where to go look.
(04:55):
And that's when I realized where I think there was potential and opportunity. And that was an interesting intersection because I was out of place driving automation, leveraging a tool that focused on insights. And I think for me, the definition of AI is where insight meets action. And so that was at this interesting sort of melding of concepts that really opened my eyes to the possibilities.
Jarod Greene (05:22):
That's awesome. You feel more as a pull, a push, a combination of the two? You're there, but you got there in an interesting way.
Scott Peyser (05:31):
I don't know. I think professionally, it's a pull. Personally, it was a little bit more of a push. In the workplace, we're driving hard to use it in any way we can effectively. Personally, I don't think I take advantage of it the way I should, just to show my vulnerability. I found Kayak AI two weeks ago to simplify my travel.That's a unique way in which it exists and I should take advantage of it, but I don't normally.
Jarod Greene (06:00):
Yeah. We can go offline and go to 100 UC. I probably use it way too much, GPT in particular, down to even for travel, like a pack list. I'm going to Boston next week. What should I pack? And it'll kind of understand the weather. It understands how long I'm going to be there because I told it. And something that I used to sit down and write a list for is now generated. I'm 90% of the way there. So if it could actually do the packing, I actually get the robots to do different day, but something that used to take me five, 10 minutes of just mental energy to think about, I can just do in a couple seconds, have the list and keep me straight as I put my stuff on the side.
Scott Peyser (06:36):
My kids are there. I've got a 20, a 19 and a 15-year-old. All three of them, they figured out how to hack life with it. So I'm learning maybe a little bit more from them in the personal usage.
Jarod Greene (06:46):
Okay. All right. We'll keep each other honest on the journey. And speaking of the journey, when the team there started to integrate AI, started using a scale, what were some of the use cases they started to explore? You talked about Mark and the territory plan, but were there other people that needed to be brought along for the journey? Was this something you led? Was this something that was brought to your attention? What was that like for the folks at the org?
Scott Peyser (07:10):
Yeah, I've been fortunate. And I think that's accelerated my journey when you work at a place, when you work at a company that's driving automation and then you work at another company that AI is embedded in essentially everything we do, it's part of the fiber of the business. So the change management for us was probably easy. I should say easier than it might've been other places, but it still required people to think a little bit differently. So for instance, how do you build an automated sales program? Typically, you sit down with your enablement team and you think about, okay, what's the hot topic of the day? What product are we trying to push? Now we don't need to do that because that was an example where I could sit with the enablement teams and say, "Hey, here's the data we have. Let's feed the data.
(07:55):
Let's let it help us make those decisions and refine our process and help us build our message." So typically it is bringing some people along. What I have found is it is also generational. I'm older. My generation is still trying to figure this out. I've built my champions in organizations with a different generation. Those in the mid- 30s, early 40s, in the 20s, they tend to be more used to this. They're willing to embrace some of this change. And so I build my champions that way, and then it tends to bring along the rest of the organization.
Jarod Greene (08:27):
When you talk about a culture of it, is this a situation where everyone gets a license to ChatGPT? Everyone has Gemini, everyone has Claude, or is this a combination of that? And within the tool sets you use, there's the kind of AI features and capabilities. What's it look like to apply the culture of, "Yep, let's go use it when it comes to providing the tools and the resources to do exactly that.”
Scott Peyser (08:54):
Yeah. At my last company, everyone had a tool and we leveraged a little bit of everything, whether it was Chat or Claude, we leveraged Glean as well, which is an incredible agent to be able to use across the business. At CBTS, we are in the business of both using, but also servicing our customers around AI, helping them understand the infrastructure required, the data hygiene that needs to take place, quality of the inputs, as well as helping them with building the use cases. And so internally, we have essentially an AI zar who's responsible for this work and drives our thinking around strategy, drives the content we build, driving the standards that we have. And so yeah, we're starting to look at what is the premier model or primary model we want to use and how do we then expose that to our teams for access?
(09:45):
Because right now, and I think we're not alone in this and many companies, it is really an individual choice in what platform they use, which LLM they want to leverage. We're trying to build the capability or essentially buy the capability in anything we do when we're evaluating an ERP program. And one of the key levers is, what do they have? What's the quality of the agent and the interaction? Chatbots aren't good enough anymore. You have to be able to say, "I want to see this data and present it to me this way." And for the systems that we're leveraging every day to do that work for us.
Jarod Greene (10:19):
Yeah, absolutely. And again, from the top, it doesn't sound like, and maybe I'm just inferring too much, doesn't sound like what your AI zared is an issue of you understand what data quality means, you understand pieces of the change management, you're building the culture. What's been the kind of experimentation like? What have been some of the ways in which the employees there, particularly the ones in the field, have kind of had their aha moment through that kind of managed experience or the ability to get them the tools and give them free rein to do things? Were there any aha moments that you were privy to where a rep or a team kind of unlocked something that they didn't think was possible?
Scott Peyser (11:00):
Yeah, I do think there's a fine balance. This is kind of like the next generation of shadow IT. That happen where you want people to push, but you've got to make sure that you're thinking around data security and privacy and we're working in regulated industries. So that's really important to consider in this journey and the decisions that we go make. I think there's all kinds of aha moments depending on your sales methodology. Many of our team uses Miller Hyman or now KF. And we had a team member use AI to build an application for us to manage the content for our environment. There's a super aha moment where the use of the methodology became more approachable for a variety of our teams to go invest in. We use it a ton around account planning and account intelligence. There's this unique tool called Crystal Knows. I don't know if you know about that. No. Yeah. So Crystal Knows will give you a personality profile based on someone's digital footprint.
(12:00):
And so we ran it on myself. I've done DISC. I know exactly what my DISC profile is. Crystal Knows gave me my DISC profile and it was 95% dead on with what I've done myself. That's another way where to help enable executive alignment and executive readiness for conversations, we can run those kinds of reports. So I think it's this mode of experimentation and then figuring out what sticks and what we sort of wash out.
Jarod Greene (12:26):
Yeah. It's been phenomenal to see, and I asked that question because I feel like I've seen the widest range of use cases ever when it comes to this because there are guardrails, as you mentioned, there are real security protocols and risks that we got to manage and mitigate. But I've also seen with the proper guardrails, people do some incredible things. We talked about personality assessments and there are projects that I personally would've never gotten off the ground or never even started without the acceleration AI provides. For example, you bring up personality assessment. If I wanted to cross reference sales success and attainment and risk factors amongst how healthy the sales team is by five other factors, that's a project that I'd give to RevOps and they just kind of do one of those. Not because it's not important because it feels like a science experiment and it's got to rise to the top of the queue amongst other really important priorities.
(13:24):
But these are things we can get, again, 95% down the path of to start to cross-reference datasets without being a data scientist or a data engineer or data architect. So there are things that I might have inklings around that I want to test and see if there's a there there that have just been shrunk dramatically in terms of the time to execute, the time to make it make sense, the time to maybe present the data in a space and then give it to someone else and they have time to go do it now. So it just unlocks just a ton of productivity, but more importantly, just a ton of things that I don't think I'd ever get around to even trying to put together because I didn't have the time, I didn't have the skill. And sometimes I just didn't have the desire to go fight across those battle lines.
Scott Peyser (14:11):
We just did an exercise. I don't even think my team knows that I did it this way, but we're redefining our ICP. I worked with GPT-5, fed in a bunch of the information and guidance and modeling and combined that with publicly available information and just interfaced with it over and over continuing to refine my prompts and my questions and the information we were looking for to build an ICP that was exactly where we think we needed to land.
Jarod Greene (14:39):
Yeah. Great validation. Sounds like that is one of the successes you're proud of and you should be. I think that's incredible. What advice would you give to someone, because I told you before, believe it or not, there are some folks still on the sideline. There are some folks who have not gone in. And so what advice would you give sales leaders, operational leaders, go- to-market leaders about getting started on their AI journey?
Scott Peyser (15:00):
One, you have to be focused on it. I think you can get lost in all the noise that's out in market. So one, focus. Two is start small. And three, try to demystify it a little bit, which is at the end of the day, AI, again, I talked about insights meet action, but at the end of the day, it's about automation and productivity and quality more than anything else. And so figure out how to get started there. There's basics and focus and some of it is just predictive action. Okay. We have this set of data, especially in sales and revenue organizations. We have these accounts, we have this data, we have this insight, what place should we run and where should we go run it? Just sort of starting there. And those types of experiences tend to bring people along. So demystify it, focus your efforts, and be certain not to fall into the trap of using all these one-offs.
(15:55):
There's so much AI and automation that's being built into the platforms you already use. Go leverage that. It's good enough that it doesn't require a specialized tool to get the value.
Jarod Greene (16:07):
Absolutely. Great advice. And then one more if we're in the business secret sharing. What are you looking ahead to do? What's on the horizon and how do you think AI will help you achieve those?
Scott Peyser (16:18):
Yeah. So I talked about some of the things already that we're in the process of doing. We talked about the work we're doing on ERP and how we think that embedded AI in a platform can make a wild difference in the success of the team and deliver productivity when you make that kind of a change. And we're going to do a lot of things around the sales organization as well in terms of refining ... I talked about the ICP, refining our account planning process, driving prescriptive plays and prescriptive actions, leveraging that insights. And we're doing a lot of work around just predictive ... It seems basic to a lot of people, but predictive forecasting. It is the most basic use of AI that is undervalued. And so building that into our rigor and process and deal scoring and a whole host of those things exist as opportunities for us.
Jarod Greene (17:04):
That's awesome. Well, best of luck to you. Appreciate the time, Scott. This was fantastic. Thanks for sharing your perspective, your insights, your experiences, and most importantly, the lessons learned all the way. For everyone listening, this is exactly why we do this show. We want to surface the real, the practical, get these battle tested insights from leaders who are living the reality every single day, not just talking about it. So if you enjoyed this episode, follow, like, and share and leave us a review. We'd love to hear your feedback. I'm Jarod and I'll see you next time.
(17:36)
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