Why AI in Insurance Isn’t Ready Yet | FrontRace’s Jack Siney

Speaker A: Jack, thank you very much for joining me on the initialtech talk. How are you doing?

Speaker B: Great, thanks for having me. Appreciate it so much.

Speaker A: My pleasure. Now, instead of me reading from the little bit of a few questions that I prepared for us, or not questions, actually topics that we'll try to navigate through. Um, how about you make an introduction of what's phone price? What do you guys do?

Speaker B: Uh, amen. Appreciate. Appreciate the opportunity to speak about what we're doing. Yeah, I'm serial entrepreneur and each one of my, uh, ventures has really spun out of the previous one. And so at our previous company we had a big sales team and uh, Covid happened, everybody went work from home. And so we started to develop a new management tool that helped us manage our sizable team which included, hey, what were people doing each day to be successful? And so in the old days, we all know if you had a really successful person on your team on the business side and you hired a new person, you'd say, oh, go shatter. Go shadow Gillard. He's great. I'll show you what to do. Or Bob or Susan or Mike and you know, you take a couple of things from each one and go off and, you know, conquer the world. Well, Covid, we're from home. Everybody went home. You can't send somebody, go sit in Susan living room and figure out what the heck she's doing. And so we had to develop some way to figure out, hey, what were our people doing? Not what they were doing day to day, but what were they doing to drive success. Because everything that was organic in an office environment was now gone. Right? And so that tool that led us to a nine figure exit from that company which was acquired was great. We actually then spun out and now made it available to other companies, so standardized it. And so what Frontrace does for organizations, we all know this. On the commercial side, we've invested so much in our tech stack, from CRM to pipeline management, to call, uh, conversation intelligence, all these tools. And if you go look at the stats, within the last 40 years we've added this huge tech stack, billions of dollars. We're no better today, forecasting, reaching forecasts than we were 40 years ago. And we're no better developing our team in its totality. If we have a team of 20 people, two, three or four of them are crushing it. And the other 15 or 16 people, we're trying to share what the best people are doing. And we're failing on both fronts, shockingly. And so what front race does is ties together the data, you have in your organization puts some AI analytics and starts to uncover what are those true drivers of success for your organization. It's really, we believe, the next wave of what your someone's tech stack they'll have on the business side of their operations will be.

Speaker A: I think that you're the tech stack for the human stack in a sense.

Speaker B: Amen.

Speaker A: No, because the way that one of the things that drove me crazy with COVID besides the distance, the fact that people started working from home, the fact that we became a little bit, um, not lazy, but we got super comfortable working from home and in this type of isolation. And with that you don't have the uh, water cooler conversations which are, uh. People think that it's a waste of time, but no, it's not just about gossip. It's actually the opportunity to have a conversation about work and the ability to recognize the people that you need to shadow and actually enabling the technology to shadow while you are not there. That's a power multiplier or especially.

Speaker B: And to see it.

Speaker A: Please bring it back.

Speaker B: Totally. When we were in an office, you'd see it happen impromptu, right? Somebody would have. Is having a great call or close a great deal. Or maybe it goes the other way. Somebody has a, uh, bad incident. But learning from that, sharing it. And in an organic office environment, people would see it, it would get shared automatically, as you mentioned, people would talk about it. But now if that happens in someone's home, office or wherever you're working from in an isolated environment, all of it kind of gets lost. The learnings of it. How do we get better? How do we share that? All of it gets really lost. And it's one of the reasons I believe we're still struggling to hit our stride, both because of AI and because we've never really regained our footing on, um, how do we figure out what's working? And more importantly, when you hire a great person, think about this. Forget bad hires, but when you hire a great person, they are like, how do I. How am m I successful here? Right? What do I do to be successful? How do I replicate success? What, what's worked for you in the past? How do I help you get to the next level? And so it's. Those are really hard questions. We, we serve people up kind of a sales manual or write a client service manual and say, this is how we do it. In reality, that manual is normally very outdated from what happens in the room.

Speaker A: I'm trying to remember now. I actually used that quote recently, uh, in a certain proposal I forgot the name, um, the book is From Good to Great. James Collin or Jim Collins, something like that.

Speaker B: Jim Collins I believe.

Speaker A: Jim Collins, yes. Thank you. And basically if you can do that, enable the good employee, become great. That's the thing. It's a different challenge dealing with the people who are lagging behind. Um, I remember when I was working for um, big tech companies, they are all kind of uh, drinking the koolaid because uh, which is actually a very important one because they truly understand that the value of the company itself, especially if you are a tech company, it's all about ip. As a result it means the quality and the knowledge and experience of your employees. So your engineers or scientists. My, my first career in my first, my first job in my early career was with Intel. They and that was a company. So we're talking about 2005. It's been a few days since then. Uh 21 years or 24,04 maybe.

Speaker B: Yeah.

Speaker A: It's a company that created by engineers for engineers, hardware engineers, semiconductors. And you see how they structure it from how do we make sure that we keep learning, developing the knowledge, the experience and if there are laggers, how do we bring them? How many opportunities opportunities do we give them so they can move forward and reach their goals and be aligned? Because you always have like the last 10%. Microsoft and Meta and other are uh, very notorious for cutting the last 10%. Okay guys, you failed over and over again.

Speaker B: Last 10% are out.

Speaker A: Yep, yep, last 10%. Uh, which kind of uh, always makes me laugh now with the AI and one of the big companies say oh we're going to fire now 10,000, 14, 15, 20,000 people and then go isn't that like your 10%? Isn't that part of your you know, really efficiency, nutrition. But uh, now because of you trying to push the AI narrative that is killing, disrupting and improving and making you more efficient. Um, without talking about uh, you know, investors, especially retail investors in the store, in the capital markets, um, it may or may not have some sort of an effect back to our.

Speaker B: But, but one of the drivers of that is it's an interesting dynamic with AI. Ah which is when we were younger if a public company did a massive rift like that their, their stock would get crushed and the market would say the management team did not know what the company was doing. Right. They over hired, they mis, forecast somehow they mismanaged the business. Hence we're going to devalue you. We may replace some of the members today in this weird crazy AI uh just like the dot com world. In this weird AI world, if you do a significant rift, not only do you save some money, your market cap actually goes up and the CEOs are being rewarded for doing these massive rifts. And so you know, more are coming because ultimately that's what drives their valuation, that drives their comp plan. So when the market is now rewarding these rifts, it's a problem.

Speaker A: Well, well I, because my day job is an investment banker, I need to be very careful anything that relates to investments. So folks, here are uh, all the disclaimer. Now nothing that we're talking about is an advice or something that relates to actual investments. Don't listen. It's pure entertainment. It's not even knowledge. We are just riffing about what we observe in things that we have nothing or no knowledge about and most of it is made up. So ignore it. It's not a financial advice period. Entertainment full. Okay, now that we kind of satisfied finra, um, it's all about controlling the narrative and how the analysts actually perceive it. And since more retail investors kind of were empowered by the different platform, uh Robinhood for example and others, uh, that they have their own incentives. Right. Um, AI introduces efficiency. So if you are firing, I don't know the 10%, does it mean that now you are more efficient so better multiplier, better pe, better whatever that you know the index that you need there actually it's revenue per employee, not per earning. That in this case, um, there are all kinds of different things that you can kind of play with. Um, but you know, uh, it's a question that how are we going to see that? And that will be my longest segue to talk about how you are helping insurance companies or to be precise insurance agents, agencies, insurance offices, because they don't have budgets. They are not the most uh, tech savvy organizations at the end of the day most of them are small businesses. They may write 10 million gross premium, 100 million in gross premium. However, they are not the most tech savvy businesses.

Speaker B: Yeah, I know, yeah, we were talking before the show. I think tech and AI has impacted the back end of the insurance industry a lot in processing claims and, and doing uh, risk risk assessments. A lot of tech in the back end. And the challenge that insurance has and, and honestly most businesses are having is how do we bring that the commercial side, how do we bring that Help help our sales reps to help our teams be more efficient. And I would just say the following I believe is true almost in majority of the companies certainly in the States, which is you have a set of reps. Let's just say you have 10 reps on your team. Two or three of them are crushing it. They're doing Amazing well, the 80, 20 rule. And the other seven to eight are struggling in some way. Some are just new, some have just, uh, hit a ceiling. Some need training in some aspect of the process, and it's really hard. As we were talking earlier, how do I, how do I train them up? How do I, how do I share best of, um. And what happens? The best people sometimes don't even know why they're good. You know, I mean, like, it's just instinctual. It's the Michael Jordan. I just do it. And so when you try to have them train the other people, that doesn't work either because they're like, just do what's common sense. And so with Frontrace, what we do for organizations is we put together all the data that you have. So we want organizations to have at least six months of historical data. So you need six months of data. We'll start to analyze it and start to share what the success look like. What are the exact steps not that's in your sales manual. These are the 22 steps we do to close up a policy. Normally the real world, it's double that many steps. Right? There's all these little questions that are answered, building relationships, like, what's the real process to issue, uh, a policy? And also look at the things that have turned out bad when we don't win or when someone goes somewhere else, or they don't sign or they delay, it goes under black hole. Why is that? And so that's what we share for organizations. We connect your data, uh, put some analytics on and start to share why, if you have two reps, similar pipeline, similar number of calls, similar activity, why is one outselling the other one by three times? And for years in sales, that's been like the magic question. Like, why is Susan outselling Bob by 3X? And we in management make up some answer that makes us sleep at night, but it's totally made up. Susan's just, she's just a better closer. She's from the industry, she does a better demo, whatever. We make up something that's not real and not replicable. And so that's what Front Race does, is fills in the gap. Okay, what, what does, what is the difference between those two people? And how can you replicate it?

Speaker A: Well, and then there are things that are outside of that. Uh, she looks better and she's Nicer she could be, it's like. Or as my wife keep uh, telling, keeps telling me, be kind, be nice, be kind. No, yeah, kind is to other people. But uh, just remember to be nice to people. They don't understand your sense of humor. And I think that mainly is about the sense of humor. That, that would be one of the

Speaker B: things, you know, I know you're being light hearted about it, but one of the things AI is bringing to the table are real measures of what have been really qualitative or subjective things in the past. And I will put scores to those and incorporate your personality type and start to give you at least a number. Not an exact measure necessarily, but a benchmark of like, hey, are you in the top third, the middle third or the bottom third? In being funny, in professionalism, in tonality, It's a real thing because if, if you don't realize that we're type A's, most salespeople are. Type A's are like, I'm doing everything Susan's doing. And you're like, no, you're not. Because it's 20 little things, right? The things that separate. It's 20 little things. It's not the big things. Everybody knows the big things. Everybody knows the policy, the pitch, the pricing, FAQs, uh, everybody knows that stuff. It's 20 little things that add up, which is why Susan's crushing it over everyone else. And so it's putting a light on all those things so you can start to narrow the gap. So it's not 2x, maybe it's just 1x where somebody gets closer. And that's, that's what we're trying to do for organizations across the country, including in the insurance vertical.

Speaker A: How do you start the engagement with them within the agencies?

Speaker B: That's a great question. So our first interaction is normally a, ah, standard kickoff call where we go through whatever the, whatever tech, whatever systems they have. Today most insurance firms seem to have a little less than other companies are in that two to six range. Um, of hey, what are you using for email? Are you tracking your calls? Do you do video conferences? Do you have a CRM? What are those systems? We'll then start to aggregate the data from that. We'll say, well, hey, we'll get the logins and aggregate and normalize the data and then literally, believe it or not, within a week, right after that, the AI will start to spit out learnings to the management team and the frontline folks to say, hey, these are the metrics that are most really important. And here's the best next step for every opportunity that you have pending. And so it happens really quickly. And I'll just say anecdotally, when we deliver the first set of data and first set of feedback, there's only two or three things that are great that people want to repeat and are awesome, and there's only two or three things that are really bad that no one's purposely screwing up their deal. But the reality is, uh, they're just not good. Some of the things that are happening.

Speaker A: Can you give an example?

Speaker B: Um, I, um, have numerous examples. I'll share one that I talk about a lot. Like one of the firms, instead of, uh, building relationship and building rapport with people, they had some reps that were defaulting to the technology, which were. They were either texting or emailing too much. And so one of our prospect firms, once they sent a seventh text, they never closed the deal. The seventh text was the death knell of the process because again, folks were defaulting instead of engaging with the prospect, answering their question, building rapport, they were. It's just easier to text. It's less threatening. Uh, you're not breaking up someone's day. But inevitably, how that firm worked, uh, the seventh text, they never closed a deal, never after a seventh text. And so there's contesting be a good part of what you do. It definitely can. But like all things, once it's leaned on too much or abused, it can then turn into a negative.

Speaker A: I love it. Uh, no, it's a great insight and I can talk about it for, you know, the marketing and that was one of the distribution. It took so long to adopt while everyone was crying. But the millennials, or who's after millennials? Gen Z. Gen Z. I don't know. I know they're all whatever, right? Alphas and betas, even millennials are now early 40s or something like that. And it was all that cry. It's like, well, you need to text because that's the only thing that they know. And still to close a deal, you need to have a conversation. You cannot over text. Uh, it's no text at all to over texting. And you need to have somewhere the person. There are things personally. But again, it's anecdotal, so it doesn't really. There are things that I don't want. I don't, I don't want to talk to anyone. Just give me a nice form, a good user experience. I'll, uh, close it in a few clicks on the website and let's move on. And then there are things.

Speaker B: I'll give you nothing, a little bit more information, but I'll give you nothing. So real. We all know this, but we've not been able to. I've never seen a dashboard on this. Right. Which is in the Northeast, what they allow and permit. And the process is very different than the Midwest. We all know this. We. You, uh, call somebody in Iowa, it's a very different conversation speed, tonality, how you can text than if you call somebody in New York City. And uh, it's just a different vibe. And so being able to analyze that because sometimes regionally what works. Maybe a lot of emojis and a slower introduction. It all works in the Midwest in certain areas, but in the Northeast they're like, tomorrow, let's go. What do you, why are you calling. What do you want? You better be on your A game. Right. And sometimes in the south has a different tonality and, and, and things they want to hear. And so, uh, being able to share that again, there's been this black hole. Why is Susan so great starting to shine light and starting to quantify. Why is she able to sell 3x more than everyone else is amazing. It's, it's an amazing process. And by the way, when we start with a client, no one has perfect information. It's not like the first day we do it, they're like, here's all the answers. What happens? We start to give you a baseline of data. We start saying, well, here are some, definitely two or three big things. But hey, do you know that you don't have any data on how your folks reach out on LinkedIn? Do you know you don't have any data on some of your calls because you're not um, recording your calls. And you know, in your in person meetings you may, you require your reps to manually input everything that just happened into your CRM or whatever your management system is. Well, how many sales reps, after a hour and a half in person meeting put the right things into their CRM? I mean, right? I mean solid. I mean it's ridiculous. So there's, there's technologies and stuff we can do now that can start to help people be more efficient. I think Salesforce put out a report in their, in their 2025 report in sales. They said sales reps are only selling, I think 39% of the time. Right. And the rest of it's all admin data input, clerical proposals. It's like, what. And so we have now some tools. What AI is going to open the door it's going to give you some tools and some access to things that were very painful for many, many years. The ability to standardize and connect and normalize your data. That would be five years ago. That would make somebody's eyes rolling back of their head. A much easier endeavor today to help companies really figure out what the heck's going on. And, and uh, who is it? Larry Ellison said. Larry Ellison said, hey, whatever AI model you love, if you love ChatGPT or Grok or Claude, we're talking about, like he said, they're all pulling from the same open data. It's the same open data they're pulling from. The magic is in your data, your firm's data, your team's data. And if you have more than six months of data, ideally you have two or three years of data. The answers are all in there. The answers of what works, what doesn't work. When you won, what did that look like? When you lost, what did that look like? The answers are in there. Just many people don't have access to the data. It's not in a queryable, normalized form. So it's a big problem.

Speaker A: Well, your title is a co founder. When I used to have the title of a co founder or a founder, part of the concept was always be raising almost every conversation. If you are not selling, you are raising money and it's the same higher layer that you have. And, and I just wonder that that tool can actually be very useful for founders who are all the time on raising money and in that attitude or relationship. Because we are not raising now, we'll be opening around later on. But you know, we want to have this relationship and let's talk about what's coming next. But do you define yourself as a startup or this is business number seven?

Speaker B: No, no, we're, we are definitely in startup mode. So we uh, the technology has been around for about two and a half years. It came out of our old company, but we've just been go to market for the last uh, eight to nine months. And so we're definitely still in the early stages.

Speaker A: There is a big difference between startup mode versus I'm a startup, I'm a tech startup.

Speaker B: Oh no, you better. Then you have to clarify, then you have to clarify for me.

Speaker A: Well, a startup usually will be a company that develops some sort of. Usually it's a technology. It can be many, many other things and they will be in, I would say development and some sort of debt for uh, they will be in the red for many, many years and will Just try to scale. Those will be many. On the software startup mode, we are on high alert, we are cranking, we are poor, we are trying to make something out of nothing and our product may be whatever that may be. And it's kind of a, I don't know at this point. It may be a cliche, but startup mode for sure. That's when you are hungry and you want to build a business.

Speaker B: Yeah, I was going to say we're definitely in startup mode, chasing any and

Speaker A: all things 100% building a business. That's the ABC, well, I would say of this country, it's all about let's build and have these great opportunities to do. Agree.

Speaker B: Totally agree.

Speaker A: No, um, so you're working with small businesses, you're consulting and providing them with your technology. Can you talk a little bit more about your proprietary technology that you offer? The different businesses that you.

Speaker B: Yeah, the right way to think about it without. I'm not on the tech side, but the right way to think about it from a functional standpoint is when we approach organization of um, um, insurance firm or any other firm, we don't replace anything. You have the piece of technology, the little layer that goes on top. Whatever systems you have today, it starts to automatically connect and normalize the data. And then we have some proprietary analytics, uh, that serve up these learnings. And so folks are like, hey, sometimes I'll just put it in cloud or I'll just use OpenAI. Well, the reality is Mark, uh, Cuban said it best this week. I love a great quote. He said the problem with AI on the business side is you ask the same question with the same data in the same LLM, you're going to get a different answer every time. It's true. It's true. That's the problem that, that is if it's a complex question, if it's not Today's Sunday, it'll get that. But anything that has two or three layers to it, hey, how much did we sell last month with this many activities every single time? And so that's really the issue. And the reason that is without being. I'm not the technologist, but the way LLMs work, the joins and the technology, when you start to do multifaceted strategic queries, they don't join. Right. Each time. Each time it's rebuilding and not joining correctly. And so we have some technology. One of it's called a, uh, uh, metric engine. One of it's called a time machine where we put some flags in the sand, allows the LLM to work correctly Every time. And we track that data over time to give you right answers. Like hooks you later. Of course you track over time. The reality is in the sales world, it's not that way. Salesforce, God bless. Love salesforce.com but if you have an opportunity in Salesforce, you can do this today. Put it in today and let's say in 32 days it changes. Now you cut it by 20%. Oh, uh, and then some more things happen. Then it goes back up 10%. Oh, and then some other things happen. You close it four months from now for 107% of what the first, um, proposal was put in, you have no way in Salesforce to go back and look at what changed, why it changed, and what were the variables at that time. All you know is it's different. And so being able to track those variables over time and see not only that they change, but why they change is some of the magic in the process. It's like not that it just changed. Why did it change? Did we go up because the CEO got involved? Did we go down because the number of strategic players was altered or your point of contact left the company? Whatever it is, unless we know what's causing the changes, we're really just flying blind. And so those are really all the things we deliver for companies.

Speaker A: What should they be aware, especially when it comes to AI, uh, and implementing it. Oh, and using it in small businesses.

Speaker B: Listen, it's not ready. So I know I'm yelling against the win here. Listen, on the tech side, the AI is amazing. What it's doing. It's help people program and do coding, create apps, and it's sick. It's sick what it's doing on the tech side. But listen, when it comes over the business side, it's not ready. It's like aol. We're in the first days of the Internet. It's aol. And everything we're using today is going to look antiquated three years from now. It's not ready. An example I would just give you Deloitte, big consulting firm, sent out a report last year where AI made up, uh, references, made up data. So it hallucinates. It's only 80% correct. Mark Cuban, every time you query it, it'll change the answer. If you push back, it'll give you a completely different answer. And so the thing for most companies to do today, instead of rushing out and getting an AI solution that's going to be antiquated two years from now, getting your house in order, get your, get your systems in order your data in order, understand your process. And what I mean by that, Understand your process. Listen, everybody on the sales side again has a process box. These are the 22 boxes we have to sell. And here's a little diamond. Yes, no. And you draw it all out. You say, this is. We meet somebody, then we go to their house and we talk about the policy and then we dump the documents. Then we contract that typically not how your best people are selling. Number one and two, there's way more steps typically in the process and no one knows. So then someone comes and says, let's just put an agent to it. Let's automate that process. Well, the reason it fails is you're only telling the agent to do 22 steps. And in reality it's actually 59 steps or 42 steps. And so you can't automate what you don't know. And so again, getting your data in order, really understanding your process, training up your people. If you go into 2027 by doing those tasks, you're going to Crush it with AI. The, the tools we're going to have in January 2027 are going to blow anything you're using away today. And so most companies aren't ready. They just want to go out and get chat GBT or Claude and, and make your emails a little better, create some marketing content and go, okay, well that's, that's not AI in your business. That's just a, uh, spell check on steroids, you know what I mean? So to really drive efficiencies, you need to have your data and your processes in order. And that's what frontrace helps you do. And so then we can plug in different AI tools over the next five years, plug one in. Oh, now it's antiquated. Pull that one back out, start to measure its impact. That's what we do for companies. That's going to be the critical factor. It's not the AI tool you plug in today because you probably won't use it two years from now.

Speaker A: That's actually a great value proposition for the different companies, especially those who don't really have, I will call it IT department, or let's call, uh, Joe. He knows a little bit computers and just turning on and turning. Did he try turning it off and on again, as they say, the IT crowd. Because your value proposition here is let us manage your AI stack, not just your tech stack, the AI stack. Because you need now new tools and someone that actually understand AI. And what's the latest and greatest. We used to See, the best example for that would be, you know, a decade, maybe two decades ago, you had to hire people who understand CRM and how we can make your sales process better. Now it's not that you can build your own CRM if you know how to write. Yeah, Vibe coding, but now it's the heydays of the product managers that until now the only thing that they could have done is write the product description or the requirements and hope that someone in engineering, the VP of engineering, will say, okay, we have done for you. You know, we'll add it into the pipeline or the, whatever that may be. This, um, and now your turn. You have all these, let's call it common available skills or tools. And uh, God forbid, we'll talk about agentic AI and what's its name? The Claw.

Speaker B: Yeah.

Speaker A: And we're going to do it for you because we know.

Speaker B: God bless you. God bless you, God bless you, God bless you. Yes, yes.

Speaker A: Um, because you're up to date, you know how it works and you can integrate it. It will take a few more years until everyone may be able to do it themselves in the right way and maybe even optimize on it. But at this point, yeah, we need someone who knows how to bring in the right tools and keep them up to date for the right task.

Speaker B: Uh, so many companies, you just mentioned the people part. Mhm. We've all known this in the past. There's technical managers.

Speaker A: Right.

Speaker B: People that are really good with tech and people managers. You're good with personalities. Well, the reality is going to be less personalities, no doubt. So getting their people ready, they're going to be more tech savvy than historically they've been like understanding, hey, what is an agent? How does it work? How could I use one? Where does it really fit? Where doesn't it fit? How do I use. What's the difference between Claude or OpenAI and when would I invoke one and when would I invoke the other? Those are real things. And so folks have to have a perspective on that or they'll just get washed over.

Speaker A: You know what, one of the things that you said, which is, uh, it's very interesting, I would love to see that a result of that experiment. And the experiment is very simple because you coined it. You think that you need 27 steps, but actually you need 53 or 57 steps for the entire process.

Speaker B: Unquestionable. Yeah.

Speaker A: And I would love to see the, that exercise or experiment that, uh, the company will sit down, review their process, understand that it's not 2020. Let's round it up, around it up 20 steps. Because within those steps you have like those micro steps that you actually take. Because Susan will send a thank you and we'll do a small research to understand who is the decision maker and who is the influencer on that decision maker and send them like a small note and that small search. That's a micro step. But it's very important because she mentioned something that may be relevant for the incentive of the other person because that's, and adding that into the process. And the question now you have 50 steps that you can replicate by that, uh, agent.

Speaker B: And what we do, what we do as sales leaders, we just make that one box. Build rapport with client.

Speaker A: Build rapport, absolutely.

Speaker B: What? Right. It's a box.

Speaker A: Trust and deliver.

Speaker B: Yeah, yeah.

Speaker A: Uh, well, so true. It was a pleasure chatting with you today. Thank you very much for sharing with us your knowledge of, you know, how the small businesses, those that don't have, uh, millions of dollars of budget to build.

Speaker B: Yeah, Amen.

Speaker A: To buy the big AI or to build their own LLM or wraparound LLM or cool shiny startups. But actually here is a business that provides you value by actually helping you to use AI.

Speaker B: And one of the things, Listen, we didn't talk about it. If you go to frontrace.com, frontrace.com upper right, it says, join the race. We'll do the initial assessment. We'll give you the data for free so you don't have to. If you're a small company, you're like, ah, ah, it's gotta be like, we'll literally give you the initial assessment and then if you want to keep going, you can. And if you don't want to, that's fine. But literally you'll, you'll be a million times smarter. No obligation, but it'll tell you, hey, where, where are you strong? Where are their holes in what you're doing and what are you ready to do?

Speaker A: So, so you just answered, how should people reach out to you? Who should reach out to you?

Speaker B: Yeah, uh, uh, really? Anybody that's at some executive level in an organization. If you're a team lead, if you're running a client service team, an ops team, and so if you're a frontline sales rep, if you want to contact your manager, have them. If you're unsure about how success happens in your company, contact your manager. We'll come do the assessment of your team and you'll start to again have insights. Why are the best people having success. It's like, it's the most amazing, humbly amazing tool. So you can go to frontrace. Com or you can go to my LinkedIn. Jack, sine s I n e y, you go to my LinkedIn and ping me there.

Speaker A: That's the best way. Jack, thank you very much for your time today. It was a pleasure.

Speaker B: I appreciate it so much. God bless.