What does AI actually change once you move beyond the pilot phase and into the messy reality of live deployment? In this episode of AI at Work, I sit down with Jack Siney, CRO and co-founder of FrontRace, to separate operational truth from industry hype and explore what he calls the “Great Upheaval” already reshaping how organizations generate revenue, measure performance, and define success.
Drawing on experience from the U.S. Navy’s Blue Angels program, PwC, multiple startup exits, and now hands-on AI implementation across hundreds of companies, Jack offers a practitioner’s perspective on where AI is delivering immediate value and where it is still falling short. We talk about why so many expensive initiatives fail to move the needle, how legacy KPIs are pushing teams toward the wrong outcomes, and why most automation breaks because organizations never fully document the human steps they were trying to replicate.
A big part of our conversation focuses on sales leadership and the frontline reality. Jack explains how AI can finally resolve the long-standing mystery of why two reps with identical activity metrics produce wildly different results, how decision engines built on a company’s own historical data can guide next-best actions in real time, and why better data hygiene and process clarity matter more than buying another tool. At the same time, he is clear that today’s AI is an 80 percent solution that still demands human oversight, critical thinking, and constant tuning.
We also step back to look at the economic and cultural shift ahead. If productivity is no longer tied to headcount growth, what happens to the traditional link between company performance, employment, and spending power? And what mindset shifts do chief revenue officers and business leaders need to make right now to avoid incremental thinking and instead redesign how work gets done?
This is a grounded, candid conversation about readiness, responsibility, and real outcomes, recorded for leaders who want practical direction rather than another theory about the future of work. After listening, where do you see AI genuinely improving performance in your organization today, and where is it still a promise waiting to be fulfilled?
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Untitled - March 2, 2026
00:00:00 Neil: So thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do?
00:00:10 Jack: Sure, sure. Thanks, Neil. Thanks for the opportunity. Yeah. My name is Jack. I'm the CEO and co-founder of an AI startup, Front Race Inc. And so, um, I started my career literally in big organizations. I had the fortune enough to start my career with US Navy. I worked on the F-18 Blue Angels program to. And then, uh, took that opportunity and went to go work for PricewaterhouseCoopers and the consulting vein, some big, big organizations. And but since that time, I've been involved in an array of startups and, uh, and, uh, work in technology. And it's been great. I've been really fortunate. I've been blessed to have a couple of exits, uh, good exits from those opportunities. And so, uh, now in the AI space, like so many different folks trying to help companies deal with and implement technologies to help them be better and take advantage of this crazy, amazing time that we live in.
00:01:01 Neil: It really is. And you've had a fantastic career and enjoyed great success and enjoy seeking opportunities. And it feels like there's never been more opportunities.
00:01:10 Jack: Yeah.
00:01:11 Neil: Right now. Yeah. With with the AI that with AI and the future of work all up for debate. And I read before you joined me today that you described this moment as the great upheaval. So the great upheaval in AI and the workplace. So what are you seeing on the ground, whether it be sales teams or anything in the workplace that makes this feel such, such a structural shift rather than just another tech cycle that we've seen in the past with cloud and mobile, etc.. What's different this time and what are you seeing?
00:01:43 Jack: Sure. Yeah. I mean, some historical context. We're all familiar with the Great Depression, which took place about one hundred years ago and which really interested me. I don't I don't think we go through the a depression type event, but similarly we're one hundred years later. And as you mentioned, I've really classified as the great upheaval, which is I really think it's AI combined with robotics, and it's going to have this amazing change in almost every part of what we do. And that that combination, if any of your listeners are are I've spent a lot of time on X Twitter. If you've seen the reports are put out, I think it was, um, what Matt Schumer and then the Katrina report that was just put out. There's a lot of swirl around, hey, what's what's the future look like? And, and how will it impact what we do. And and there's some doomsday as you've seen, I'm sure some doomsday. It's all going to, you know, go to hell in a handbasket. Or some folks are like, no, no, it'll just be incrementally great. I for me, I think the biggest, most interesting thing that we've not really confronted before is the following, which is historically when companies are doing great, they're growing and inheriting. That is, they normally hire more people, their salaries go up and then people spend more money. Right. It's kind of that wheel of consumption that makes capitalism go. And what's going to be really interesting with this AI combined with robotics is going to be I think companies are going to be super efficient. As you hear Elon and all these people talk about, their productivity is going to be amazing, but it's not necessarily going to be tied now to having more employees. Right. That that that connection is going to be lost at some, whether that's a small change or a notable change as some folks have forecasted. But that change in economic cycle, I think, is going to cause economists and, and, and our markets to adjust accordingly. And I think that's the part that's going to cause this, this great upheaval in how we work, how we think about work, how we think about companies being profitable and being successful. And and we'll get through it. You know, again, there's some doomsday forecasts out there. We we've always adjusted with the with the world got through Covid that's disrupted everything. We'll get through it. But I, I really believe, say between the next five to seven years are going to be a dramatic change in how we work and how we look at success and how we as individuals think about, uh, how we get income and how we spend money and, and how that's going to play out. It's going to be really, really interesting. And I do think it's going to create this upheaval that we've we've not seen at scale ever in certainly in our lifetimes.
00:04:11 Neil: And one of the things that put you on my radar was your white paper, the fifteen AI truths that Every Sales leader will face.
00:04:18 Jack: Like somebody reading that, I appreciate that, I appreciate that.
00:04:21 Neil: So I'll also link to it for everybody listening so they can go check that out. But if we took a look at the insights in there and comparing with what everybody's talking about right now, which of those truths tend to surprise leaders the most once they move from experimentation to live deployment and trying to get out of that pilot purgatory that we've seen.
00:04:42 Jack: And then, you know, I appreciate that. I appreciate the shout out for the for the we just put out that AI truce for folks deal with going into this new year, going to the twenty twenty six. Now, you know, I think about it. The one that really sticks out to me, that kind of causes people sometimes to to pause is the, um, I'll get it right. It's is AI is optimizing the wrong metrics. And so I think what happens when folks think about AI, we have this we have these metrics that we've utilized for years. And measuring performance and measuring how many calls or what's the pipeline look like or, you know, whatever, whatever your company is utilizing. And the reality is that AI provides so many new opportunities and the ability not only to identify, but measure dynamics that we've not been able to before. And so things like quality or correlation or timing of things. Right. It's not just doing something, it's when you do it how a deal changes. If one thing changes, typically three or four other things change. And so a lot of folks are implementing AI and some metrics that they've always had before. And the reality is that AI is not going to provide us the ability to, again, identify and measure some new metrics. And so that can cause people some angst and really causes people to step back and have to look at how is my business being run? What are things I really care about? And so that part probably gets people's attention the most. Like, let's not just make what we've done historically better, faster, more efficient because some of that's honestly not the right thing or not the thing that's really driving success. It's some of these other variables we've not been able to measure previously. And so that's probably what gets the most conversation about oh, I should I really shouldn't be measuring these things. What what can or should I be measuring. And so I think in our list of of trends or truths coming this year, that's probably the one that gets the most conversation.
00:06:29 Neil: And I'd also love to throw something else into the mix here. I'm very fortunate I get to attend twenty plus, uh, tech conferences. Yeah, twenty plus tech conferences around the world. Very similar. A lot of the keynotes that I've seen there, but there's also somewhat of a gap between what many of them are promising and what operators are actually experiencing.
00:06:49 Jack: Yeah.
00:06:49 Neil: Amen. So where is AI genuinely improving sales performance today and and where is it still on delivering from what you're seeing.
00:06:58 Jack: Sure, sure. I think it's a great question. Is AI impacting some processes for companies? It clearly is. Yeah, it clearly is. But the numbers honestly aren't great from a hey are we having a big impact for the business. And so AI right now for me the things we see live on the ground. And by the way, I think we're in a unique position because we approach companies. We have no bias. We don't ask them to change their tech, and we have the opportunity to see AI and different industries. And so I think my perspective is somewhat unique as a consultant, probably is the best way to as we get to see a lot of things right now and a lot of different areas. But AI is no doubt crushing. And I would just say in very simplistic tasks, we all know this. Hey, if you're trying to write better, if you need content, can it do some basic outbound calling? It can. Can it clean up some of your emails and do summary of calls? It can. The I would just say really basic, straightforward task AI is crushing. No doubt. Yeah. But some of the stats that we probably all seen, the stats of, of what AI looked like. So I think the number that McKinsey and I think is just put out was like six hundred and fifty billion was spent on AI initiatives last year. I think that was the number from US companies. But but the sad reality on the back end is that ninety percent of them had not affected bottom lines for companies. It's not improving their success rate. It's not it's not bringing home an ROI that they're happy with. And I think the reality is that the complexity in a lot of the process, AI is still not great at, and not because AI is not good at it. But we as human beings that are setting it up and looking at process to invoke it to are don't fully understand all the nuances that happen in our process. And so we turn on some automation. It fails and we're like, why did that fail? And the reality, we didn't give it all the steps that are happening that human beings are doing intuitively. Sometimes we don't measure those properly or we don't allocate for those steps in between. And so AI is automating something that missing eight steps. You imagine if you work at McDonald's and you. Hey, here's how we make French fries. But we left out three steps that the person walked over, turned on the burner, poured out the oil and put it in the wrapper. Like, if you left that out, we would just start to do things and it wouldn't come out well. And so inevitably, that's what's happening in in complex processes or customized processes, which is the norm. We've just got to do a better job at documenting and and helping to automate those things. But right now, not great for any stat. Again, with no bias, AI is going to be invoked in a lot of places, but the net net outcome at the end of the day has not been great so far from the amount of money that's been spent, right? So.
00:09:41 Neil: And before you join me on the podcast today, I had a quick look on the front way. Front race website. Great website by the way. Very cool, very flashy. Thanks. I also immediately took away that you helped bring together teams real activity data, connect across systems, and then apply powerful AI to reveal what's working, what's not, and what's to do next. You also heightened real time decisions without sidelining human judgment. And that's a, um, the the phrase that really got me there. So what does that balance look like in practice during a a live deal cycle just to bring to life what we're talking about, what problems you're solving and and how we're not sidelining humans here.
00:10:21 Jack: Yeah. Yeah. You know, I think the real world part of AI is, is that we as companies. So forget AI for a second. We as we as company leaders, executives, we want to train our people. And hey, here's how we do things, right. Whatever that process is, whether it's client service or sales or whatever it is. Right? We have this thing. But the reality is typically, as companies, historically, they're very static. Hey, here's how we do our outbound go to market stuff. Here's how we do our demos. Here's how we close a deal. Here's our support model, here's our, uh, retention model. And we want to have these benchmarks. We have these KPIs that apply to these kind of processes we put in place. But again, the reality is that almost all of those are really dynamic and they're really customized. And even if it's small, like if a company is in a different vertical or a different size, or if they're experiencing something internally that's affecting them. And so it's never really matched. If you think about what we do today, we companies, we spend so much money on technology, think about CRM and all these sales enablement tools. It's an amazing amount of money. But if you study the results, we're not much better today, shockingly, at forecasting or if you have a sales team, it's still the almost the eighty over twenty rule. I have twenty. My twenty percent of my people are crushing it. The other eighty percent I'm trying to develop. We're not much better. We have a lot more technology. It's more automated. We're just not great at being better at the process. Forecasting even the best biggest companies would miss forecast. Our teams don't hit goals. We have all these metrics. And so the amazing part to me about AI, it now provides this ability to take in so many more dynamics and start to measure. We can now customize outcomes. We can customize next steps. In front race. We have this thing we create. We use a company's data. We take the best of AI. We leave folks with a decision engine so they can literally take some of the best of what they do corporately. Okay, which is amazing. You want to take your company's data, don't just sideline that or poo poo it. What's worked for you in the past? What what are things that did not work for you in the past? Take all those variables, then take the best of AI analytics. What's happening in the market? What's best of what are benchmarks so that you can start to analyze those things in a decision engine that's customized to your company and to the prospect that you're working with the company you're working with, so you can figure out what's the best next step for this opportunity today based on everything we know. And so that sounds logical, but the reality is we don't do that. We haven't done that historically very much because either we couldn't handle it intellectually. It was too many variables and too many moving variables. We didn't have all the data. We're missing parts of the data. So we made some poor decisions. Or, you know, no one loses a deal on purpose. But we do things that don't turn out well. And so I believe the wow factor about AI now is going to be we can literally take in all the moving pieces, put it in some Analytical Engine, and then let folks kind of have specific advice and data points that you can start the action. Right. And so that to me is really, really, really exciting. We've not get rid of kind of this the bias that we all show up with, right. We all show up with either, hey, this worked for me before, or this worked for me in my last company, or this worked for me Three weeks ago. Whatever it is. And so it'll get rid of those things and let you do the best thing for how dynamic you are working with a client in a particular vertical at a particular time of day, or a particular part of the year. So that part I think is really, really exciting. Nothing we've been able to like that we haven't had that capability like that in the past.
00:13:57 Neil: So and just to further bring that to life and give people listening, a valuable takeaway here, if we drill down on, let's say, a front line sales manager, how are you seeing AI? How are you seeing AI changing the role that they've got there, especially when it comes to coaching, forecasting, decision making, accountability, all those big responsibilities there. What kind of difference are you seeing AI making there or potential to make as well?
00:14:22 Jack: Sure. I want I want to share this dynamic. I think if anyone's led a sales team, you've experienced this. You have two sales reps. So let's just go out, you know, visually close your eyes. You have the story of two sales reps and they're great or they're good, you know, but they have the same metrics. You have two sales reps. They have the same amount of outbound calls. Same amount of meetings. The pipeline looks the same. They interact the same. There's their stats performance. Based on everything we've known in the legacy. How we manage a team are very, very similar. Make sense? You have two reps, A and B, everything look the same. However, one of those reps is outperforming the other one. Just say by three times right? They close deals. It's amazing. And in the history of sales management we sit around. If somebody's been that myself, I've had large team one hundred plus folks. We sit down when the when the CEO comes or the board comes. Why is that happening? We always we always go, well Susan's just better at sales. She just does that thing. She's she's great. She's personable. She's just magic. Right. And for years and years and years, we've tried to replicate that. We've tried to follow her around, follow what she does. How does she do it? Right. And so it's been it's been this black hole, as I mentioned earlier, we haven't we're no better at increasing the performance of our total team if we still have a lot of the eighty twenty rules. But what's happening today with AI, which is amazing, is that AI can start to both identify at your company because it's going to be different at every company and start to measure these kind of things. I would just say with the black hole of sales, the black hole of success, like, hey, Susan was just good at this. They're just a natural salesperson. They're just, you know, they're just having to they just know what to do at the right time. And the reality is that's not true. There are specific things that people are doing. We've just had a hard time measuring that previously. So let me give you some. One is timing. A lot of our CRMs and other sales enablement tools, it's not what you do, it's when you do it right. There's a lot. It's what's the right order, what time of day, when should it happen? It's just not what's done. It's the timing of when it's done right. So that's one I'll give you two or three. Another one is the correlation between things. Sometimes once you do something within a deal, or the construct of the deal changes, the value of the deal changes your main point of contact at a company changes. Well guess what? That typically changes two or three things down the road that a lot of sales reps sometimes don't change. They don't they don't. Oh this deal was x. Now it's half x. Well that changes this. Or our contact with the VP of sales. Now it's just a sales manager that changes the subsequent steps. So the correlation between once something happens and then what should I do next. Right. Can change the process. Relationships we start a deal. How did we get this deal started. If we have a deep relationship somebody made a referral that's different than if it came in via email. But did it come in via cold call? Did we get, um, somebody passes along because we did something great at another company being able to measure how we got the relationship, how in depth that relationship is right can affect what, how how sensitive they are to price. When should we give them the price? How long does the sales process go on? I'll give you one more. The construct of the company. We as a company. How are we doing overall typically right? If we're doing great, we're less open to modifying price or changing the deal around. If we're having a slow quarter, a slow year, we're typically more aggressive, right? We're like, oh, we really need this deal. Or if they represent. So I can now start to measure these qualities, I'll give you a quality. That's we somebody somebody can do two things, but I can now measure the quality of how that effort was done. And then inevitably somebody goes, you guys are measuring quality. How are you doing that? That seems very subjective. Well, how we measure quality at front race. Did the deal turn out well right. Who cares what Neil thinks? Who cares what Jack thinks? If somebody does something in a way that's counterintuitive to us as individuals, but every time they do it, they win. That's how we start to measure quality. Not what Neil thinks, not what Jack thinks, not what Larry did last week. Hey, when we did something, did it result? Did it come out well or not? Well. So those are myriad. There's there's probably about a dozen of those variables that can affect the outcome of deals. And so that's what AI provides now. So as you're measuring two people as a sales manager, AI is going to give you a series of metrics and be able to shine the light on the kind of always just kind of, you know, the it's kind of been the dark cloud of sales be like, oh, Susan's just been great at it. Well, now we can start to put more stats and more analytics, which is amazing as a sales manager. And then you can start to share that across your team. And I think that's going to be an amazing change for companies over the next five to ten years.
00:19:05 Neil: And for any leaders listening that want to unlock the opportunities that AI could bring. But they're also quietly concerned about readiness. What signals tell you that the sales organization is culturally and operationally prepared to adopt AI successfully? And I say that because there's so much emphasis on AI, on technology, the culture aspect of any tech project, that's the most important thing, isn't it?
00:19:30 Jack: Yes. Totally agree. You know, we were talking a little bit about this. I have two things that are probably a little counterintuitive. So folks that are ready to jump in are very anxious. We just. So my my first one is don't rush. Please don't rush into AI again. If you study, if you go look at any of the stats. Companies have spent hundreds of billions with a B dollars in just twenty twenty five with minimal results. They study, I think, McKinsey's latest study, only ten percent of companies are using AI at any high level production besides just some nominal tasks. And so so we're you're not behind, so you don't have to rush into ten pilots and figure out what's working. So that's number one. Two is before you rush into AI, just encourage companies to get their house in order, as the term I always use in two areas, there's really two areas that are super important. One is your data. So that we all know we've heard this garbage in, garbage out. If you have data in two or three systems, which most companies do you have a CRM, you have video conference, you have an email system, you have sales enablement tools. You have all these different places. You're monitoring and tracking data. You have to find a way to get that data both together. So you're looking at all of it and standardize, because sometimes an Apple in your CRM is an orange in your sales management tool and is a lemon in your finance tool. So being able to standardize and get your data together is huge, because that's the real wow factor of AI. Don't throw a lot of people can generic AI analytics, but the magic is analyzing your company's AI data, your companies. What's made you successful? When did you lose deals? What were the variables driving that? And so getting your data together and standardizing is huge. And then the second part of that, when we talk about getting your house in order, a lot of folks target certain processes like, hey, let's do AI here. Let's let's invoke it here. Well, the reality is a lot of leaders particularly think they know the process their teams are doing. And the reality is they don't. Most leaders miss anywhere from a third to fifty percent of what's really happening. And what I mean by that is the following. Almost every company has like just let's do sales, a sales process. Here's what we do. We send an email, then we send a text and then we do a, then we send a document. Then we do the pre demo demo. And we think we have this process chart that looks really, really nice. And we think, oh, it's twelve steps. And the reality when you follow what salespeople really do that's probably forty steps right. There's there's three more interactions that happen. They text at night. They're building relationships. They talk about the game. They share an article that's relevant. There's there's a myriad of things that happen that are not captured in the company's formal twelve step process. And so when you're going to target AI for something, we talk about process clarity is what we call it. Hey, go out, make sure you understand every step of what's happening, not just the steps the company put in the process chart, in your sales manual or in your client service manual. We have an eight step process. So this is what we do. The reality is almost all sales reps, all client service reps, all tech people are doing a myriad of small little things that get missed. And so they're not automated or they're not accounted for to be automated. Then we throw some agent on it and we go, oh, the agent's following the process. And you're like, why is it failing? You're like, yeah, it missed nine steps. It missed the other nine steps that Tom and Mary were doing. And so it's so critical in those two things. So you don't have to rush. Like let's not be laggard but be patient. Get your house in order. And then you have to once your data is good to go and you fully understand the process. AI can do amazing things, but those are where companies should start. Those, to me, are signals that they're ready to do great things. And AI will have a real impact in your investment. Do you make financially will turn into an investment in your bottom line?
00:23:20 Neil: And if we have a chief Revenue officer listening out there, what would what would be the main mindset shifts that they need to make to ensure that AI strengthens team effectiveness, rather than creating confusion or overreliance on automation. For for those people out there listening, very cautious on the bottom line, etc. and generating revenue, what mindset shift should they be focusing on?
00:23:44 Jack: Well great question. I think what there's no AI expert out there. So anybody that comes to you and be like, well, I know, please know that you're even if you just start today, you're you're only a smidgen less intelligent than most people are out there. I mean, it is the wild, wild West right now, and no one really knows what twenty thirty one is going to look like. That's that's the reality of it. But there are some things I think are really important so that folks will be successful. And they look at it mentally because that's half the battle. Things are going to change a lot. And and we we as human beings don't typically make good decisions when we we're suddenly impacted, right? When something suddenly happens, if you think about a storm coming your way and something happens, you're like, oh, we don't always respond well. So I think some things that are important are, um, don't accept incremental changes. Literally every system out there today says, oh, we have AI, we added AI, we have an AI component. And the reality is AI has the chance to change everything we do in an enormous way. And I think a lot of companies and a lot of software solutions, God bless, are are trying to take what they had and kind of just add to it. Right. And let's, let's just add to it. And one example I'll give you just happened this past week. Or, you know, one of the big, I think, Jamie Dimon, who leads one of the big banks here in the US, said, oh, we're going to we're the truck drivers, long haul truck drivers. Our technology is going to replace most of those people. You know, they're going to be, uh, robot drivers and they're going to be safer and be cheaper. And then he's like, well, those people go be Uber drivers or Lyft drivers. And and so even in that construct, he was saying it sounded like kind of pithy, oh, they're going to have to take this job and take this other job. Well, the reality is the Uber drivers and Lyft drivers. They're not going to be there either, because they're going to be in automatic, you know, all the driving is going to be automated at some point, so there's not going to be drivers there either. So we he was saying it kind of off the cuff, but he's like that job go to another job. Well that job's not going to be there either. And so just encourage people that do not accept incremental changes. AI is the chance for you to look at things. And we talked about earlier, it's going to shine a light on some areas that we haven't had data for. We haven't we haven't had analytics for. And so it's going to give folks a chance to kind of reimagine what they want to do and, and data points they've never had access to. So that would be one I'd encourage you to think bigger and think about, hey, what can happen? Two, I think for the foreseeable future, AI in my mind, the number I was using my team will tell you AI is like an eighty percent solution. Yeah. It's not it's nowhere near perfect. And so just realize that AI is going to help you do things, but you got to double check it a lot. A Deloitte I if you saw the story last year, Deloitte put out a major consulting firm, put out a major report and let the AI run wild. Right. They didn't monitor it. They literally gave a client report that had hallucinations from the AI, made up people and made up stats in a report, and they had to give the client back money. I don't know if you saw that story, but. So the AI is not in any way perfect. So don't have this thing like I'm going to turn it over to AI. It's just going to go it literally is an eighty percent solution right now. That's the number I always use, whether it's sixty or eighty. But it needs you can do it. You need to double check, and you need to make sure that someone's looking at it and checking the quality and constantly tweaking it and bring it back in. Right. Yeah. And then lastly, I would just tie in the thing that we just mentioned. Um, there's a lot of people in the market, um, that are selling AI and it'll just do this or just plug it in, it'll work. But to me, the magic for companies where they're going to have big efficiencies is demand that the AI tools you're going to look at and are considering are using your data as a company. You've been in business most companies for a long time. Can you slap an AI? Can you take, um, a ChatGPT or Claude and slap it on, slap it on some generic data and get some outputs? You can you can get some general feedback of what you should do, but that's normally not great for your company. The magic in AI is to demand that it takes the data you've had historically and analyzes it. What works for your company? What do we do well, when we do this, it works out well. When we do this, it doesn't work out well. Demand that you take what you've learned over all these years, and make sure that the AI is analyzing the good and the bad of it so that you can be better. And I think that is a hard, you know, easier said than done, hard, hard for folks to do. And people are running on. Just let me just put, you know, this. Give me my folks. Uh ChatGPT. Or let's we'll add Claude to our analytics and just do what it says. And you're like, it's just pulling from, as we all know, what's out there, readily available. It's the magic. Is incorporating your company's stuff good and bad. So that you get better in totality. And so those would be really the three things. As folks are thinking about jumping in, that can be really powerful and ensure that they have success and don't waste their financial investments.
00:28:38 Neil: Awesome. I think that's a powerful moment to end on. And before I let you go, where should people listening want to find out more information about anything we talked about today, your work and announcements coming out this year. Where would you like to point them? I will link to that, uh, fifteen AI Truth white paper. I'll link to that anywhere else you'd like me to point to?
00:28:58 Jack: No, no, I really appreciate that. Yeah, I'm a little commercial for, uh, I would just say we. I think our little slogan is, I believe it's the AI companies want. Because most companies, no one really knows. We talked about what's coming. And so front race is a piece of software doesn't replace anything you have that we add to your system that gives you like a digital analyst next to you that will tell you what you should be working on. What are the right success metrics? Here's a decision. And next to you, I think it is the thing most people want today. I say that really humbly. That's why we built it. It's like, don't replace all your stuff, people. The first step is like, analyze what you have today, standardize it and get some AI. Have your little digital AI assistant next to you. And I say that really humbly, but I believe it really is the thing that most people are looking for today. And folks can, um, go see that at Front Comm. We have a little button on the upper left that says join the race. You can put your information in there and folks can find me on LinkedIn. I'm on LinkedIn. Um, quite a bit, uh, love that platform. And and one last, my last little, uh, what is it pushes. You don't have to believe anything. Uh, I the thing I've tried to do in this venture, my. As a serial entrepreneur, you don't have to believe anything we say or I say. Even in this podcast, one of the things we'll do a front raise, what I've always wanted to do as a company, we do this, we'll actually give you the software, customize it, which is unheard of. Let you use it for a while before you ever pay for it. So if you, you know, like a lot of folks show up like we do this, we do this and they always want the money, or let me put you in a contract or, you know, give me your credit card or whatever it is, we'll give it to you. The thing about customize it, let you use it for a while before you ever pay for it. So if you don't think it's the most amazing thing, you'll just turn it off and never utilize it. And you get smarter about how you do things. That's how much I believe in what we're doing. And so that's how you can reach us again. Com and Jack s I n e y on LinkedIn.
00:30:50 Neil: Oh wow. It sounds like you've just made an offer that nobody can refuse. I like surprise, but I think everybody listening will feel that great upheaval that you've talked about in AI and the workplace right now. But what I love about what you're setting out to achieve here is helping teams move past the noise, focus on what truly drives outcome, and ultimately using AI to heighten real time decisions and most importantly, without sidelining human judgment. As you said a few times today, AI is about eighty percent there, but you need people alongside it. But thank you for shining a light on this. I'll include links for everybody and I'd be interested, uh, for if people check that out, what they think, if they take you up on your free offer as well, I'd love them to feed back to me what they thought. But thanks for joining me today.
00:31:35 Jack: Thanks, Neal. Appreciate the opportunity. God bless. Stay safe.