SPOTLIGHT: AI Is Making Sales Reps 60% Faster to Revenue | with Justin Shriber, CEO & Co-Founder @ Terret AI

0:12Hey everybody, it's Sam Jacobs and you're listening to a special episode of Topline Spotlight. If you're not familiar, Topline Spotlight is a segment of the Topline podcast where we dive deep with an operator that's on the front lines today to talk about a challenge or an obstacle that they've solved in the last 12 to 24 months and hear how AI-empowered operators are solving problems on the front lines. And today, we're excited to have as my guest, Justin Schreiber. He is the CEO of Taring, the company focused on deploying AI to modernize revenue management for the past three decades. He's focused on delivering solutions that unlock new levels of productivity, growth, and profitability for revenue teams.

0:58Before joining Territ, he served as chief marketing officer at Entra and People AI and VP of global marketing at LinkedIn. During his career, he's led product sales and marketing organizations at both startups and large companies such as Siebel and Oracle, and he started his career at McKinsey & Company, and he lives in Salt Lake City where there's not enough snow. Justin, welcome to Topline Spotlight. Thanks, Sam. It's great to be on the show. We're excited to have you. So I gave a little bit, I nodded to what Territ does, but why don't you, in your words, we want to make sure that people understand the product, the company, and what you all are doing and the problem you're trying to solve.

1:34So give us a little bit of background on what is the company that you're building? Yeah. So at a high level, we have built what I call an answer to action engine. We leverage AI to capture all of the revenue data associated with the products and services that you sell. And we can basically mine that data, not only to tell you what the key trends are, but what the root cause of those trends are. And I think what's novel is we're also able to translate that into operationalized workflow. So once you know what the answer is, we're able to deliver that payload at the moments that matter and actually change behavior to accelerate growth, compress sales cycles, increase win rates.

2:15So give us some specific, like what's your ideal use case? Who's your ICP? You know, when you're on the phone with a prospect and your eyes light up because you feel like we can really solve this problem, what is that problem? Yeah, so first of all, our ICP tends to be about 50 sellers or above. And we've got customers that run all the way up to several thousand sellers. Complex B2B sales, where you've got multiple interactions with key stakeholders and a complex process. In terms of the use cases that we attack, anything right now that a RevOps team is trying to figure out in order to improve the sales productivity or the enablement team is trying to implement in order to impact sales productivity is a great use case for us.

3:09Two that we tend to start with. The first is I want to know why I'm losing. I want to know what my closers are doing differently. I want a playbook that actually bottles up that secret sauce. And then I want a way to make sure that the rest of the team is adopting that. So it's the win-loss analysis into the playbook. And then if you're a CRO, the use case we love to tackle is the CRO who says, all right, I can tell you what my forecast is. I can give you a number. I don't have the color commentary behind it. I don't know what the headwinds, the tailwinds, what happened in the big deal. I got to ask my RevOps team and they go away for a couple of days and come back.

3:49We can nail that in a couple of minutes for you. I love that. I'm sure before we dive into the challenge that you solved, you also must have incredible data across your customers segments and across the universe of customers that you're working with. What's changed over the last couple of years as we've seen Gen. AI really become more a part of the go-to-market motion and probably impacting how buyers buy as well? Well, I think a couple of things. Not surprisingly, we're actually now starting to see the revenue per rep increase as companies are able to drive more revenue across reps. That productivity probably stems from the fact that one, reps are spending less time on non-revenue generating issues.

4:38And two, they're just getting better at their trade. And we're early days in this, but I definitely have some data where the savvy companies, the early adopters, are able to point to the fact that that productivity number is going up. What I'm finding is that companies, at least that we're working with, aren't necessarily laying off a lot of frontline sellers, although you're starting to see that in some of the bleeding-edge companies. What they're doing is tabling the need to hire new people, and they're asking their existing folks to deliver more in the form of greater quota. From a buy side perspective, what I'm seeing is the sales cycle is getting compressed.

5:22And the reason for that is companies are using agents to do a lot of the early late work, not only in terms of mapping what they need to what customers offer or vendors offer, but they're also doing a lot of validation on the front end. Obviously, combing sites like G2, other feedback, but they're also finding out who's using it and having conversations before they even enter the sales cycle. Wow. Just accelerating all of the trends that were prevalent for the last, you know, 25 years, so to speak. So, you know, this, this is a short show about a business problem that you've solved in the last 12 to 24 months, wide ranging, you know, you got a lot of freedom and leeway there in terms of what you want to talk about, but give us a sense for what is that business problem?

6:08What were the circumstances surrounding the issue? And then how'd you tackle it? Yeah, I'm the, the problem that we had to tackle and actually tackling this led us to productize a solution that we'd originally planned to deploy internally stems all the way back to when I was at McKinsey. I had an incredible amount of pride in what the team and I put together. You know, we'd be on site for six months, grinding away, working with people, come up with what we all thought was a great set of recommendations that introduced competitive moats, all of the things that you learn about in business school.

6:43And we get a kind of a pat on the head and a thank queue, be shown out one door. And then we'd watch basically as our PowerPoint got thrown up on the shelf with all the other strategy decks that were, you know, introduced over the years. And what I realized then was getting to the answer is not really the answer. And companies are spending way too much time and waiting way too long to come up with strategies they never actually take advantage of. And once I became a founder myself, I empathized with that because things are moving quickly and it's really hard to change behavior and also taking into account all those uncontrollable variables.

7:24And so really the first problem that I asked was, all right, this has been a problem for decades. Could we use AI to actually solve the problem? And so one side is getting to the right answer and the other is using that answer to change outcome. In terms of getting to the right answer, we started by saying, first of all, let's accumulate all the data. It's kind of what consultants do anyways. They walk around and ask you and they're interviewing everybody. We could do that a lot faster and more efficiently. Aggregate all your revenue data, whether it's in CRM or a data warehouse or a call transcript, and then really quickly get to a smart answer.

8:07Not only what is the number, what are the underlying root causes? The second part was the more challenging part, which is once you have the answer, how do you solve it? And that's where we realized that this idea of trying to enable a sales team is an antiquated model. I was a head of sales. You know, I'd pull my team out of the field for a week, put them in a conference room somewhere, watch as they tried to digest the information and then literally flush it out of their brains at the bar that night. It's why do we do this? You know, I asked myself that what reps really need, what managers really need is in the moment when something is matters, they are acutely focused.

8:50If I'm about to talk to you, Sam, about a problem that you have, my spidey senses are heightened. And all of a sudden, I'm trying to remember all this stuff about how to sell. And so the insight was, let's use workflow, let's use automations, let's use these agentic processes to figure out what moment you're in right now and selectively feed you information in an appropriate way. And it's the combination of those two, getting to the right answer fast, the root cause, and serving it up in the moment, whether it's on a call or if you're a manager talking to a rep, serving it up in the moment that was really moving the needle for us.

9:27And that's where we internally started to unlock a lot of exciting results. Like what's an example of a great result that you got from sort of this realization? Well, we hired a rep and he was a smart guy, but he had never done the kind of complex selling. So we made a bet that, you know, the brains and the hard work and hustle would outweigh the experience. and we basically got him plugged into the system. And what he was able to do very quickly is number one, benefit from what our best sellers were doing, essentially tap into the playbook, but then we saw him very quickly apply that. And so in terms of the ramp time, his time to first revenue, it was about 60 % of what we had seen historically.

10:17And we also saw his close rates very quickly start to ramp to the point where, you know, he was performing like an experienced rep. And this is kind of the nirvana state for what every sales organization is going after. Wow. So how did you, what was the biggest challenge and sort of, is this an entire product redesign? You know, is that how you thought about, okay, and is the key innovation, hey, we need to meet people where they are. So we kind of have to be multimodal, plug into existing workflows, and then deliver them the right information at the right time so they can make better decisions.

10:53Was that an architectural insight besides just a strategic insight? Like, how did you solve the challenges? There was definitely an architectural insight. When we started to get into this, we quickly realized that the underlying revenue graph that needed to be built was actually pretty complex. You're pulling data from highly sensitive systems, whether it's a data warehouse, your CRM, and basically asking AI to run through it and come up with insights and then serve those insights up. We realized quickly that you had security considerations that you had to take into account. Absolutely, you didn't want the AI to backdoor into a system and start to expose data in an inappropriate way.

11:37There were also accuracy issues. We know that these models are ripe with hallucination. The biggest issue we saw is when you ask it a numerical question. What's my win rate? What's actually happening on the back end is those systems are jumping from, or the LLMs are jumping from one system to another, connecting the dots willy-nilly. And every time you ask the question, we were getting a different number. And then the third thing was just the scale. If you are not inefficient at prompting the LLMs, you burn tokens so fast. And it quickly becomes a situation where you're spending more money than the value you're getting out of it.

12:17So it was being able to build a revenue graph that one was secure, two was accurate, and three that was scalable. That became the essence of the challenge that we had to solve. solve. You know, today you can build MCPs in any system that you want, but that's not the answer because basically what you're telling the LLM is I'm going to give you one of these puzzle pieces and then I'm going to ask you a really complicated question and see if you can come back with an answer. Guess how high quality that answer is. So that was the architectural insight. And then the kind of the operational insight is exactly what you said.

12:55Multimodal, meet people where they are. it's not about proprietary apps anymore. It's about Slack. It's about email. It's about if you've got a call recording system, serving it up on the call. That makes sense. You're at the cutting edge, the bleeding edge of all of these AI companies. And I guess thinking about what you've learned from this experience, but also candidly, just trying to peek into your perspective on what's going to happen over the course of, you know, the next couple of years. granted nobody really knows but when you think about um the this buy versus build uh debate you know there's there's one perspective which is all of these enterprises now that they have replid or maybe you know at a more uh at a more uh primitive level you know a cursor or cloud code you know but one way or the other maybe they think they're going to build their own crms their own revenue intelligence platforms.

13:53Then there's another group of people that would say, yeah, but what are they going to do to get it into production? How are they going to maintain it? What about security concerns? It's not so easy. When you think about this buy versus build debate, how do you think about discussing with vendors what their strategy should be? Yeah. I think that as a company, you have to make an honest assessment of where you stand. This is complex. The other dimension, though, that is a reality is it's evolving quickly. I mean, if you go back to, you know, the year 2000 when the internet was coming on the scene or when mobile was coming on the scene, big data was coming up, there is a couple of years of infrastructure build where we're just literally figuring out the underlying chassis that's required to deliver value, and we're building that.

14:45When you are in the state of infrastructure build, it is an incredibly fluid time. And the last thing that you want to do is make big bets on one particular technology that could become obsolete a month or two months from now. And so whether you build or buy, I think what's really important is to retain flexibility. Don't tether yourself to one LLM. Don't tether yourself to one particular data structure. And if you have the internal chops to be able to navigate these straits, then, you know, I think there's a good case to be made for why you should do that. Most companies are kind of early stage.

15:27They're much more nascent. And one of the things that I always tell customers is, like, that's our job. We have our finger on the pulse of where the market's going. We work with all the LLMs. We work with open source technologies. We are explicitly focused on creating a flexible platform that will not become obsolete over time. You could try to figure this out yourself, and then you're going to have to figure out all the things I talked about. security, accuracy, scalability, or you can let us handle that. The advantage, I think, to working with a vendor that's nimble is you still have options.

16:01You can get out-of-the-box capability and a platform on top of which you can build your own agents. And so at the end of the day, I kind of feel like it's a hybrid where you're going to use some agents out of the box. You're going to build some of your own agents. And the underlying platform that you use really is going to come down to the internal chops that you've got. And speaking of internal chops, the other big debate is centralized AI deployment, well, production and deployment versus decentralized. One world is give everybody a replet or a lovable or a Vercel account or a cursor or cloud code account and say, go build stuff, you know, go do more AI.

16:47There's another world where you centralize, and obviously I'll be leading the witness because I'm sort of partial to the latter, but you find people that have the capability and the aptitude and the potential to develop into high-quality AI engineers, and then they build internal, you know, they build the stuff internal. Whatever you choose to build internally, they're building it, learning from the process that they're developing, learning from the prompting that they're developing, learning from how to stitch different systems together so that they can deploy things on behalf of marketing or customer success or sales.

17:22Where do you fall out in that sort of debate? Yeah, Sam, I'll actually put in a plug for Pavilion. I was just out at a dinner in San Francisco, and this is what we talked about. There's a scene in Jurassic Park, the original Jurassic Park, where the dinosaurs all break out of their cages, and that's when all bedlam breaks loose. And the conversation at this dinner last week was exactly that. We've empowered all of our people with all of these agents. And I feel like I'm in a Jurassic Park movie now where they're all coming at me. And the specific scene that everybody talked about is I'm sitting in a meeting, everybody's got their recommendations and they've got their data set, which has been generated through the agents they're using and none of the data matches.

18:09And we spend more time now in this meeting asking, well, where did the data come from? How did you get to that? And it's kind of derailed the overall conversation about like, what should we do as a company? That said, I think that the value of the agentic world is that you do have these companions with you that are helping you to do work. I think ultimately the answer is a centralized model that creates these playgrounds for different functions, puts appropriate constraints in place so that no matter how you ask the question about revenue, it's going to come back consistently. No matter what question you ask, it's going to deliver the appropriate data for you based on your role and your security access.

18:50And it's going to do that in an efficient way. The other thing people said was, as soon as we turned our team loose, they started burning tokens like we couldn't believe it. And what we realized is that they're very inefficient at token usage. So the advantage of centralizing is you're able to optimize the systems and use tokens efficiently to make sure that the output outweighs the cost of generating that output. Yeah, and 100%. And also, it's a leap that we wouldn't make in many other contexts where all of a sudden we give some nuclear weapon to a toddler. It's not a perfect metaphor. but you're sort of like, yeah, figure it out.

19:36Here's nuclear energy, like go do something with it. And I think that the use cases that people are coming up with are sometimes directing resources towards bad design so that you're, you're building these Frankenstein monsters of, you know, stitching together notion to some other database to Google sheets in such a way that you're like, why would we do that? We wouldn't do that. You know, it's sort of resource misallocation to your point because it's not yielding the result from some central strategic plan. So I agree with you. So we're almost at the end of our time together and we want to pay it forward.

20:12And so the way that we do that is we try to figure out what's inspiring you right now. It could be mentors or other sales leaders. It could be books you've read, podcasts you're listening to, but it's a pretty open-ended question. But in terms of paying it forward and thinking about what do you think might inspire the audience? What comes to mind? I'll throw out two things. One, I am really into this podcast right now called History of Rock and Roll and 500 Songs. Oh, that's cool. This guy does an incredible job of going back to some of the seminal music of rock and roll, and he gives you the backstory.

20:49And, you know, I always say, if I had to summarize it, it all goes back to the blues. But what I love about this is one, it gives the insight into the creative minds of people like Jimmy Page and Mick Jagger. And what you realize is that the creative process is a building process where one idea inspires another idea that inspires another idea. And inevitably, these artists throw themselves into this pool of other artists that are coming up with ideas and they're just experimenting. And the things that we take advantage of or that we take for granted today didn't exist 50 or 60 years ago. It was somebody saying, oh, that's cool.

21:31Well, what if we did it this way? And as a founder, I feel like being willing to associate with other creatives and experiment is so powerful. And one idea builds on another idea. So for me, it's kind of just unlocked my mind. And the other thing anybody that's been at a startup knows it can be tough. You know, you get up on some days and you're like, help me out of bed. Like, I feel like I'm just pinned down under the weight of all the things that I'm facing. I love the book Endurance by Ernest Shackleton about Ernest Shackleton. Like the dude was on an iceberg and he got his team back to civilization despite insurmountable odds.

22:16And I'm like, man, if Shackleton could get through that, I can get through today. So it's just a great inspirational book. That is a great book. The comment you made about rock and roll and musicians reminds me, I'm reading Bill Gurley's book, Running Down a Dream. And he's got a whole section about how you got to go where the action is. And to the point, you know, if they didn't have the Internet, then Jimmy Page and Robert Plant had to hang out with each other and all the other artists in London so that they could get that experience. So it's a good reminder that you've got to be, you are sort of the sum of the people that you hang out with.

22:50So you've got to make sure that you're hanging out with interesting, creative people. Justin, it's been awesome having you on the show. People probably are intrigued. What's the right way to reach out to you? What's the right way to demo Territ? Is it Territ Nexus? Is that the full product name? Yeah, the name of our product is Nexus, our AI product. The name of the company is Territ.ai. Hit me up on LinkedIn. in, just DM me and I'm happy to get back and chat a little bit more about the things that we've talked about. You can always hit the site as well if you want more information and we're happy to provide it there.

23:21Awesome. Justin, thanks for being our guest on Topline Spotlight. Great to see you. Sam, it's been great. Thank you.

23:34Okay. And against all odds, you're still here. look if you want more Topline check out the Topline newsletter at topline.beehive.com beehive is spelled weird it is b-e-e-h-i-i-v.com topline.beehive.com or if you're a video person because video is great check us out on youtube topline-media is what you want have a delightful day everybody

24:07Thank you.