In this episode of The Next Next, host Jason Jacobs interviews Chris Olson, CEO of Mach 1. Mach 1 helps companies integrate AI to transform their business operations. Originally a spin-out from traceVision, Mach 1 utilizes AI technology to automate and streamline functions, significantly reducing costs. Trace, initially focused on personalizing sports video footage using AI, faced operational challenges due to high costs. By implementing AI agents, Chris managed to cut costs by 80%, which led to the creation of Mach 1 to help other companies do the same. The discussion covers Mach 1's journey, lessons learned, and Chris's advice on implementing AI in business operations effectively. The episode also touches on the cultural implications of adopting AI and the future of SaaS.
Transforming Business Operations with AI: A Conversation with Chris Olson, CEO of Mach 1
In this episode of The Next Next, host Jason Jacobs converses with Chris Olson, CEO of Mach 1. Chris discusses Mach 1's mission to transform businesses by integrating AI agents into their operations. Originating as a spin-out from traceVision, Mach 1 leverages AI to automate tasks and significantly reduce operational costs. The conversation delves into Chris's journey from Trace sports to launching Mach 1, exploring the integration of AI in sales, customer support, and overall business efficiencies. They also discuss the broader implications of AI adoption in business, the challenges, cultural shifts, and future opportunities.
00:00 Introduction to Chris Olson and Mach 1
00:14 The Origin Story of Mach 1
00:36 Chris Olson's Role at Trace
01:47 The Spin-Out to Mach 1
01:57 Overview of the Episode
02:23 Host Introduction and Show Purpose
03:59 Chris Olson's Background and Venture Capital Experience
04:24 Trace's Early AI Innovations
05:13 Transition to Youth Sports
08:16 Joining Trace Full-Time
13:04 Trace's Go-To-Market Strategy
17:14 Growth and Expansion of Trace
20:59 Leveraging AI for Operational Efficiency
26:57 Building In-House AI Tools
33:17 Scaling Down with AI Agents
36:15 Employee Satisfaction and Cultural Lessons
36:35 Managing Internal Dynamics with AI
37:06 Trace's Business Model and AI Integration
38:50 Communicating AI Changes to Employees
40:01 Challenges and Downsides of AI Transition
41:42 Broader Applications of AI Beyond Trace
43:31 Structuring the Spin-Out
47:47 Early Days of Mach One
49:52 Future of SaaS and AI Integration
57:18 Managing AI in Business Operations
01:05:57 Education and Support for AI Transition
01:07:45 Conclusion and Contact Information
Jason Jacobs: [00:00:00] Today on The Next Next, our guest is Chris Olson, CEO of Mach 1. Mach one partners with companies to transform their businesses by enabling the integration of AI agents into their operations. Now the origin story of Mach One is super interesting.
It was actually a spin out from a company called Trace Vision. And within Trace Vision there's an operating company called Trace. Trace is a pioneer in analyzing and personalizing sports video footage using ai. If they're able to capture the game the way you wanna watch it, by focusing on the player that matters to you.
Now, Chris was the general manager of Trace's sports business, and he oversaw their go-to market functions, including sales, marketing, support, and business operations. And he built a very successful business. They had a lot of customers, a lot of revenue, and a great product. The problem is that the cost structure was outta whack, and so they needed to find a way to strip out a bunch of [00:01:00] costs in order to make trace an ongoing offering that made sense.
So in partnership with the CEO of Trace Vision, David Lokshin, uh, Chris started looking into how AI could help and ended up. Using agents to automate a whole bunch of functions, stripping out like 80% of the costs. And fortunately, or unfortunately, a number of the employees too. Uh. They now operate with a much leaner team and have a much healthier business.
And they were so successful that the board was blown away and they started asking them to, to talk to and help out other companies in their portfolios. So as they were talking to these other companies, the other companies said, well, it's great what you've done, but can I just use your technology instead of reinventing the wheel?
So Chris went to David. Spun out the business into Mach One, and now they take what they learned and implemented at Trace and do it for other customers ongoing. We cover a lot in this episode, including [00:02:00] that whole journey, what Chris learned along the way, where he started, how he ultimately found his footing, how he structured the spin out, what they're doing for other customers, and also Chris's advice for anyone who's thinking about how to do similar.
Can't wait to get into it. But before we get started.
I'm Jason Jacobs, and this is The Next Next. It's not really a show, it's more of a learning journey to explore how founders can build ambitious companies while being present for family and not compromising flexibility and control, and also how emerging AI tools can assist with that. Each week we bring on guests who are at the tip of the spear on redefining how ambitious companies get built, and selfishly the goal is for this to help me better understand how to do that myself.
While bringing all of you along for the ride, not sure where this is gonna go, but it's gonna be [00:03:00] fun.
Okay, Chris Olson, welcome to the show.
Chris Olson: Thanks for having me, Jason.
Jason Jacobs: Thanks for coming. I was looking forward to this one. You were introduced to me through David Lokshin and what David's doing is interesting and you used to work with David doing what David's doing, and now you spun out from there and are doing something else based on the, what you did inside of what you're doing, which is, and both of those things are actually pretty relevant to what I'm doing, and we'll explain all of that.
But those, that, that makes for a, a pretty relevant discussion for me, which always gets me excited.
Chris Olson: that's cool. Yeah, no, that's a pretty good synopsis.
Jason Jacobs: Yeah. So maybe for starters I'm trying to think. Yeah maybe talk a bit about what you were doing. At Trace and then we can get into what you started to do with Trace that ended up leading to what you're doing at Mach one. Does that make sense?
Chris Olson: Yeah, that makes total sense. I think [00:04:00] maybe a little background on how I even got to trace is somewhat interesting, which is I was working in venture capital inside in Los Angeles. This would've been about from 2011, 2015, and I met David. And his co-founder, who is his father Anat tool auction.
And at the time they were building an action sports company, but it was really an action sports company that was like truly an AI company. What they were trying to do is use all this sensor data to make sense of the world. And we got really excited about what they were thinking. This would've been probably in the 2012, 2013 timeline.
We ended up making an investment in their company. I got to know David very well and became a really good friend, really good mentor for me. And he was really focused on how do I make sense of the world through all this sensor data. And one of the discoveries that they had was sensor data is actually pretty good at.
Giving you an understanding of what's going on with the world, especially when, as it relates to video. One of the technologies that they invented [00:05:00] was basically how do you give the computers the ability to see the world and understand what's going on. And at the time, like I said, they were in the action sports world.
They were doing things in snowboarding and skiing and surfing. But maybe four or five years after our initial investment, they had stumbled on how this could get applied in the sports world. Specifically in this youth sports world where they started filming youth sports, specifically soccer and using computer vision and algorithms to understand what those what the players were doing on the field, who was who what was interesting about the game and what was interesting about the, what the players were doing.
And they decided that it was a compelling enough product to start selling it. This would've been, I think around 2019 that they
Jason Jacobs: And Chris sorry to cut you off but what were they selling and to whom? When they were focused more on the snowboarding, skiing,
Chris Olson: Yeah, it was a little puck, a little sensor puck that they would put on their skis or their snowboard or their [00:06:00] surfboard. And it had a bunch of sensor technology in there like GPS and gyroscopes and they would be able, they would track all of your movements down the mountain. They would be able to see like speed that you were getting on, on, on your surfboard.
But the real unlock for them is when they started using that data to make sense of the GoPro footage that most skiers snowboarders were also wearing. So one of their insights was that they were sharing all of these, let's call it metrics, and people would come to them and say. This would be so cool because it would tell me exactly when I should create a highlight instead of my GoPro co row video.
And they're like that's really an interesting idea. Yeah, we absolutely know what what part of the mountain you actually probably care to see. And that was the unlock for them. And so they applied that sort of thinking and technology and process to the sports world as well.
Jason Jacobs: Got it.
Chris Olson: So yeah, it was just a puck that they would sell. And then it had a bunch of software that would allow you to upload your GoPro footage and it would start doing some of those editing editing processes. That was the [00:07:00] action sports world.
Jason Jacobs: great. And then before I rudely cut you off, you were just starting to get into what what you then started to do in youth sports or I don't know if it was you or if you still were in an investor capacity at this point in the story.
Chris Olson: At this point I was still on the sideline. I do remember, I was staying at David's house one, one night and he came home with fifa and I was like, you don't play video games. What are you doing with fifa? And he goes we're starting to think about how our technology could be applied to sports outside of, surfing and skiing and snowboarding.
And he is I just wanna see you know what a camera looks like when it's when you're playing soccer and I keep getting told that FIFA has the best panning of soccer imaginable. So he's I just wanna learn more. And yes, at this time I was still on the sidelines.
And then something like six months, a year later he was, he came back and was like, Hey, look, we're going full in on this soccer thing. We think this is gonna be an incredible product. We've got the camera and we're gonna start selling this to customers. So at this time, I was still on the sidelines, but I was actually looking for my [00:08:00] next gig.
I was wrapping up at another startup. I had hit my four years with them and looking to do something a little differently. And that's when he said, Hey, look, we're about to launch this. We've got a few customers but we want to go big and we're gonna be building this business from the ground up.
And that was a mid 2019. And so that's ultimately when I ended up joining them joining Trace in a full-time capacity to help them build the business around this new sports youth sports offering.
Jason Jacobs: Great. And so when you came in what type of customers did. You have and what were they buying? And then it'd be great to just understand maybe some of the the early innings of your tenure there in terms of what you walked into, what you did first, how things proceeded, what you learned along the way.
Any color on any of that would be awesome.
Chris Olson: Yeah, definitely. So at the time it was a camera that they were selling to. For the most part they were a hundred se a hundred percent focused on soccer. They were training their computer vision model to really understand the game of soccer. So [00:09:00] it made sense to be hyper-focused on one sport. 'cause the product was gonna be a lot better if they could do that.
So they were selling a camera to soccer parents to, soccer moms, soccer dad. They were also selling it to the clubs as well, because clubs were really looking to advance their technology stack and what they provided to their players. It was all at the youth level. So you would lease the camera from them.
And then at the time, the players actually wore GPS sensors as well, coming back to the earlier days of the action sports world, which is you would strap this little GPS sensor around your ankle. And that's what allowed the, let's call it all that sensor data to be matched up with the video.
And provide some insights into what was going on in the video. So you would go film this game with their camera. The players would wear this sensor, and then you would go home, you would upload the footage to trace, and about a day later you would have all of the footage automatically broken down for each player on the field.
So rather than, let's call her Lucy watching 90 minutes of raw game footage. She was able to [00:10:00] watch 20 minutes of the footage that meant the most to her because it was her footage, it was when she was in the play, when she was in the video. And it was a really compelling product because 90 minutes of you soccer is not the most exciting footage, but 20 minutes of video that has your child in it is very compelling.
And so that's what they brought initially to the scene and it blew a lot of people away. So that, that was the initial offering.
Jason Jacobs: And why was the trace provided, camera needed? How is it different than a normal camera?
Chris Olson: Yeah, it was really because they needed to be in control over, the whole field being filmed at the same time. So what they did was they actually one of the features of it was you didn't need a camera operator. You didn't need someone panning with the ball or with the action.
Their camera filmed 180 degrees, so it's all the entire field. And then they use their technology to pan around the field so that as a mom or dad, I don't have to watch this game behind a camera. I get to set it [00:11:00] up, forget it, watch the game, and then come back once the game's over. It added for a better experience and it paired well with the technology of being able to match the sensor data to the cameras that they controlled.
Jason Jacobs: Huh and just ballpark, what time period was this?
Chris Olson: This was still 2019.
Jason Jacobs: Okay. And so in terms of the the analysis of the data, and so helping the computers to see as you said, how much was that leveraging any of the rise of the LLMs or otherwise? Or was this all in-house and if you were starting it today, same question. Would you do it the same as you did it then, or has the technology changed such that you might do things differently and if so, how?
Chris Olson: Yeah, so trace was on the forefront of let's call it the machine learning space. Like they were building neural nets to build the algorithms to do all of this player detection. So at the time, LLMs were not on the scene, they were not available. This was something Trace was doing in-house.
This is, proprietary to [00:12:00] them. They built a deep technical bench to be able to power and build these models, these neural net models to be able to understand. Video in a way that sort of, the tagline is to help computers see, so this was all in-house. This was like, I truly believe that, this team was a pioneer on that front.
So would we be, would we do d things differently? Yes. They've, trace has always been on the firm forefront of ai. So they are constantly looking at ways to adopt AI and advancements in AI to do their business better, which is ultimately how I got, to, to my. The place where I am with Mach one, which was, at the time once we saw the rise of these LLMs, we figured out how to actually intro introduce that level of technology into our business operation.
So we were leveraging all this AI technology on the technical and the product side. But we were doing a lot of things traditionally on the business side. And so over time we started to really drink our own Kool-Aid in terms of how can we move AI across our entire operations and not just be [00:13:00] not just have it be in technology world or the product world.
Jason Jacobs: And so when you when you initially got there, how did you go to market at the time? Did you have a Salesforce? Who were they selling to? How did people find out about you? And then it'd be great to understand just how things went from there. And then we can of course, switch gears and get into, how.
When you started picking up this other element that you were just alluding to and then how that transition went.
Chris Olson: Yeah, definitely. So when I got there trace had done a small acquisition of a company called Stream Sports. And Stream Sports was a company that you could hire and they would come film your game and then they would manually with human editors break down the footage. The market was screaming for this, where there was these types of businesses.
And so Stream Sports had a healthy business providing the service. You could call them up, say, Hey, I'm gonna be out this field, come film it, and then I wanna highlight reel of my kid a week later or something like that. So to bootstrap their go [00:14:00] to market Motion Trace acquired that Stream Sports co company and say, Hey, rather than having us.
Come out to the film field and film this one particular game. What if you paid for this service and had a camera shipped to you and you would be in control over the games that you filmed? And so when I got there, they were leveraging that sort of sales force that, that, that motion to be able to start delivering it.
And it worked really well. Yeah, they had a small sales team that was taking a lot of leads from this one particular AC company that they acquired. And then they were starting to do all the traditional go to market motions, spinning up marketing cold calling all that, all of that good stuff.
So that's where we were when I first joined them, which yeah, was about mid 2019.
Jason Jacobs: And what was it typically teams or families? And also why was it to get better? Was it for highlight reels for social media? What was it for to show to scouts and coaches? Like what was the motivation for people or teams to take out their wallets?
Chris Olson: Yeah, at the time it was a relatively expensive product. The first iteration of [00:15:00] it was a team product. You needed all of the teams' buy-in to be able to one, afford it, but to make the technology work really well. So for the most part, it was teams. The reason why was pretty varied. I would say a big reason why people do this is because they're trying to go to college and they need a highlight reel to be able to send to coaches.
And so that was definitely a big priority. I would say player development was really good. One thing that we used to talk about all the time was when you saw yourself on footage, there was a lot of aha moments, which is we always in our head think what we look like.
But when we actually see it, there's a lot of aha moments and coaches really like showing that to players and giving them that de development edge. And then a third reason which ended up becoming a lot bigger than we had ever anticipated were. These are cherished moments, like watching your 12-year-old kid score a goal or watch just watching your, 13-year-old play youth sports is something that you'll want for the rest of your life.
And we underestimated how valuable that was to parents. And we learned that lesson pretty [00:16:00] quickly.
Jason Jacobs: And as more facilities get cameras built in, the rise of, in the hockey world, I don't know about soccer world, but in the hockey world, there's like live barn and black bear sports and things like that. Is that, are those partner opportunities, is that competition? Are they trying to build their own system that pulls the clips and does all that stuff?
Could you guys be powering that? Like how do you think about that landscape and does it help or hurt.
Chris Olson: Yeah, so for us we always liked the idea of a mobile camera. It worked really well in soccer because you're constantly changing fields. But we always noticed that there were facilities that would do these big installs. At the end of the day, the camera, I. Was really just a gateway to being able to allow people to film more.
So Trace is not in the camera business. They don't think of themselves as a camera company. They really think of themselves as a computer vision company. So it's hard to say what those relationships could look like in the future, but I do see a world where trace's, computer vision technology could be powering a lot.
A lot of other providers of camera providers [00:17:00] or these other sport providers as well. I'm no longer with them, so I don't have a lot of di understanding of what that direction is, but that does seem like it could be a part of their future being that layer where they can process any style of video that you give it and make it make sense.
Jason Jacobs: So what phase did you take the business to, and at what point did you start shifting your duties within the firm?
Chris Olson: Yeah. When I first joined, maybe they were doing about. A hundred to $200,000 in a RR. So this would've been in 2019. And up until I think we exited 2022 and the tens of millions of revenue. And so the company had grown pretty significantly over the course of three years.
Jason Jacobs: that's in sports or across everything.
Chris Olson: Just in sports. So this was just the sports business, which I managed. So at that time we had really grown our entire go-to market operation. We had something like, in 2023, we had something around 25 SDRs, 20 CSMs something along the lines of 20 [00:18:00] support people. So we had really grown the business because revenue was justifying it.
We were you. Growing, leaps and bounds every year in terms of customers. And yeah, we built up our operations and that was my primary res, primary responsibility, was growing our go-to market motion in a in a way that supported the business. So seeing, overseeing sales, overseeing marketing, and overseeing customer success and support,
Jason Jacobs: Where did that come from? Going from one or 200 k in a RR to to what did you say? Tens of millions of revenue in three years. How did that happen?
Chris Olson: I think it mostly the product was amazing, or, building a product early on is obviously very tough. But Trace had stumbled into a, really good product market fit at that time. This was something that parents were, I. Clamoring for this is, they were tired of filming games.
They were tired of editing games and they were willing to pay for a service that would do that at a high quality and a high caliber. And Trace had, this perfect magic sauce to be able to [00:19:00] do these things and parents quickly found out about it. There's a viral nature to trace because there would be a camera sitting at the side of the field, so everyone would ask, what's that thing do?
And of course you could share the footage with people so that all also had some virality to it as well. So it was just a great product for a really healthy market.
Jason Jacobs: And I mean with that kind of growth, did you feel like you were capping out or why? Why why focus on other things and also what implications should we infer from from trace's sports focus if any.
Chris Olson: Yeah, I would say that the trace sports business continues to grow at a very healthy rate. But I think at the end of the day David and the team over there, they just have high ambitions. They really believe that this technology can be applied to a lot of different areas. And so their goal over time was how do we democratize these tools and how do we introduce these tools to other markets?
Sports is a a very healthy endeavor. It's built a really great business, but once again, they just have [00:20:00] very high ambitions for how this technology could be applied. And so that's ultimately one of the reasons why, I got to where I am, which was in 2023. David came to me and said, I really want us to be in control of our own destiny.
I really want us to be able to have the time and the space to take this technology and everything that we've learned from the sports business and reinvest back into the business so that we can apply this to other verticals, whether it be retail or security. Obviously if you can give, if you can deliver on this promise of helping computers see it makes sense of the world.
There's just tons of application. So in 2023, he came to me and said, what can we do to make our operations even more efficient so that we are in control of our own destiny, we can be cashflow positive. And that's ultimately why I set off on the endeavor of how do we introduce AI into our operations so that we can see know huge benefits and efficiencies and be able to give David.
And the rest of the trace team, the ability to go off and make new investments in some of these other opportunities.
Jason Jacobs: Got it. And what was your [00:21:00] experience with AI prior to taking on this challenge within the firm?
Chris Olson: Not a lot of experience. Outside of just like sitting in, so on some engineering and math meetings every now and then being lost for the most part. I was all, I have a very, somewhat technical background but not to the extent that made me prepared for machine learning or some of these other AI initiatives.
I, I was closely following this new trend of LLMs in 2022, in 2023, but prior to that, not a ton of experience.
Jason Jacobs: Huh. And what about how much visibility and understanding did you have across the overall company's operations prior to stepping into this role? Because I would imagine if you were gonna try to drive costs out and help the company get to profitability using ai, that's not just the sports part, but that's everything right.
Chris Olson: Yeah, it was everything. But the sports part was the biggest, let's say, let's call it expense contributor. That's where they had, the most sales people, the most support people, the most customer success people. So from a [00:22:00] capital expense standpoint, the sports business was the biggest driver of expenses.
And overseeing that meant that I had a lot of responsibility into sort of all of the line items that would make the company more efficient or not.
Jason Jacobs: And it'd be great in no particular order, but it'd be great to get a better understanding of how these functions were operating. The before and after. Like here's how they were operating, here's how they ultimately operated, and then here's how we figured out how to get there, determined what to do, and then got things done.
Chris Olson: Yeah, I would say they looked trace Trac in, 20 20, 20 21, 20 22. Looked a lot like your traditional SaaS company. You had SDRs that were doing, handling inbound leads, doing out outbound activities. Their primary focus was to set up a demo for an account executive. The account executive was primarily responsible for getting a group of parents or coaches or clubs.
A deeper understanding of the product and what it could do for them. And [00:23:00] then. Delivering a sales journey and then ultimately trying to get them this camera. So from, on a sales perspective, very traditional SaaS process. On the customer success side, very similar. Our success managers were primarily re responsible for retention and also for customer experience.
Like most CSMs, they were handling a decent amount of support, but we did have a support organization. This was a a technology product, so software and hardware. And our market, what I would say wasn't the most tech savvy. So there was a healthy amount of support that needed to be provided via customer success folks, but also normal support people.
So once again I really believe that Trace looked like a very traditional SaaS company. Of course there's that hardware component there, but for the most part they were, they were really just a traditional SaaS organization at the time.
Jason Jacobs: Okay, so you made that assessment, then what happened next?
Chris Olson: Yeah. What happened next was, the rise of LLMs and what we saw from them from [00:24:00] a an intelligent standpoint. The one thing that we always, that always struck us about this was we were selling to consumers, and if we were selling to consumers we should just be able to have more casual conversations with them about the product and ultimately get them to purchase.
So for a long time we were saying to ourselves, could we get people to buy on a website? Yeah. We need to talk to them. We understand that they, this is a big purchase. They want to have a conversation with someone, but can we start driving them to make a decision a little bit quicker and then ultimately make the decision on the website?
So we're always inching towards that even without the l leveraging AI inside of our operations. And when we saw some signs of that, then we started saying to ourselves, okay, I. If we can have, a more, let's call it conversational experience where it wasn't a full on demo over zoom and then we can get these people to buy on the website.
We think we could probably do this over SMS. We think we could have these conversations over SMS because if we were ever introduced AI at the time, the AI was not in [00:25:00] a place where the voice stuff was, nearly the quality it is today. And so once again, we just started taking these toes.
Could we get our SDRs to have conversations with our prospective customers over SMS? Could we change it from a demo to just a longer form conversation over SMS or over the phone? And then when they were ready to purchase, could we walk them through an online purchasing experience? So we started taking all of these steps, and then finally one day we just said, one thing that we know about our customers is that they like to talk to us at all times of the day.
They're soccer parents. They're soccer coaches. Most of 'em work nine to five jobs. Talking to them during the day is really challenging. They like to talk to us at nights and on the weekend. What if we had some sort of AI or large language model that could talk to them after hours and provide a level of service or support in the sales process that was outside of our traditional sales hours.
And that's where we started. We said to ourselves, our goal is going to be give people something to talk to over SMS on [00:26:00] nights and weekends and see where that gets us. And so we went out and looked at a lot of different solutions that might help us be able to provide this, let's call it LLM, to our customers on nights and weekends.
And the one thing that. We, the one thing that changed our perspective on the buy side of it was we felt very confident in our sales process. We were, had a healthy business. It was growing really well, and we believed in what we needed to talk to our customers about what they cared about.
We really wanted a lot of control over that sales process. And the problem with going at the time, going out and buying a system was they were trying to push their opinions about how sales should be done, whether it was like scheduling or the types of conversations it could have.
And we just said, no, that's not gonna work for us. We need something that can really mold to our sales operations and our structure and how we do sales and the systems that we use to do sales. And so we made the decision, hey, we're gonna try to [00:27:00] build something in-house to be a tool for our sales operations to use, to be able to have conversations with people on nights and weekends.
And we spent about two, three months building a little homegrown tool to help us do that. And that was the, let's call it, version 0.1 of what is now Mach one.
Jason Jacobs: So what did you use to build that tool, and what did you learn from that process?
Chris Olson: Yeah, so we were using the Frontier model. So at the time this would've been like GPT-3 0.5 as a foundational model for the brain of the ai. But the tools around it and what you needed to do, to actually manage that LLM give it information, given knowledge that was all homegrown. So it was myself and an engineer that sort of built our own little application that allowed us to integrate this large language model.
Where we started off was. This isn't really an engineering problem, this isn't really a product problem. This is something that our head of sales is going to need to be able to manage on a day-to-day basis. So it's not so much we need a [00:28:00] really smart model, but we need tools for our operators to manage their functions and their roles just like they would a human.
So if this thing's gonna be talking to prospective customers on nights and weekends, like our SDRs are, we have to train this thing like an SDR, and then once we train it like an SDR, we then have to manage it like an SDR. We have to be able to see what types of interactions we're having. We have to be able to con, continue to train it, continue to give it new information.
Our PRI product was changing, our pricing was changing. So we really brought the perspective of from the get go, this has to ultimately be managed by our head of sales to be able to find success in this product from an ongoing standpoint. So early on we were really focused on how do we build those tools so that our head of sales could ultimately be responsible for this moving forward.
Jason Jacobs: What was the initial reaction of the head of sales when you first started talking about this, and how involved was he or she in the in the creation process of the tools?
Chris Olson: Yeah, it'd be funny to actually go back and really get his true opinion about what he was thinking, but my [00:29:00] understanding at the time was. He was a little reluctant, primarily because it was just a new system that he would've had to learn and something new that he was gonna have to manage. So being in a startup, he was already managing a lot of people and managing a lot of different systems.
But along the way we were really trying to show him that, look, we want this to be a tool that makes your life easier and gives you, the confidence and the control that this thing is gonna do, what you want it to do, when you want it to do that. And once we started showing him some of these early iterations of what we thinking he became let's call it a big champion of this product.
So he was definitely involved in giving us a lot of feedback and a lot of thoughts on what it would be like to manage an AI in sales. And once again, that was tremendous for us because we were really starting from the get go of this is gonna have to be something he manages. He has to be bought in.
So we have to have the tools that he thinks he'll need to be able to manage this thing to success.
Jason Jacobs: So having been through that what advice do [00:30:00] you have for people assessing. Whether to build out a system like this themselves or use the off the shelf offerings, what's the best way to figure out which one you should do and are there criteria that one might utilize to help inform that assessment?
I.
Chris Olson: Yeah. I think for us the lesson was we felt very confident in our processes and our functions. And we didn't want to change those and we didn't want to, we just wanted something that was flexible and fit into our systems. And I would, I advise people and say, you shouldn't have to go and change and fundamentally change your business to be able to adopt these tools.
You want something that can share your opinions and not say, Hey, this is how you should do sales, or this is how you should do support, or this is how you should do internal operations. You should be able to go to a tool and say, Hey, these are the services that we use, these are the functions and roles that we have.
Will you fit into our workflows? And if that's the case, then I think they could be a really good solution. But if you have to upend a [00:31:00] lot of things and rebuild things from the ground up I think that could be a lot, binding off a lot more than you can chew.
Jason Jacobs: And then from a management standpoint when it does come to, like you said, understanding what's going on with these agent SDRs and what's working and what's not working and giving them updated information as pricing models change or price tiered change or customer focus changes or whatever.
Is that stuff that the, can the head of sales get in and just do that himself or or is there somebody from product who's become the point person on that? Just from structurally how does that get managed?
Chris Olson: 100% by the operator of that particular function. So in this case, the head of sales has full control over his his sales agent. That is a crucial step, I believe. I don't think that, a head of product or head of engineering or an engineering should be running these functions. We have very talented sales support success operators.
We have very talented [00:32:00] operators for reasons. And those people should be fully in control and they should have tools to be able to control their agents that are now taking on responsibilities, functions, and roles inside their organization. And so that's a core belief of how we think that these AI systems will get adopted inside of businesses.
There's a lot of developer tools for engineers. There's a lot of different styles of AI tools that can find their way inside of an organization. But when you start thinking about how your operation, your businesses operations are go