AI Sales Revolution: Transforming Revenue Engines and Empowering Sellers with Kevin Surace

The sales robots have taken over and they're decimating deals left and right, destroying them right in their tracks. On the AI for Sales podcast, our mission is to empower everyday sellers to embrace AI superpowers to build new bridges and crush quota. Dr. Joel Laban said it best in the AI for sales book. In sales, time kills deals. In AI for sales, AI kills time. Join our hosts as we learn from the C suite of the world's fastest growing companies about how you can leverage AI today to transform your sales revenue engine while being the best version of you.

Welcome everyone, to another AI for Sales podcast. I'm Chad Burmeister and I'm your host. Today I'm with someone who has a lot of experience in this space. He also happens to be very interested in music. So I'm sure we may touch on that just a few minutes. Welcome to the show. Kevin Serrace is a technology Silicon Valley Tech CEO, keynote speaker with 94 worldwide patents and has been in Inc. Magazine. He was announced the entrepreneur of the year and innovator of the decade. So really pleasure to have you here. You have 94 times the number of patents that I have.

You have one. What is it? What's it? What is it?

I do have one. So I was the head of sales and marketing for a company called Connect and Sell.

Yeah.

And it's a system that calls multiple people at the same time and agents do the transfers. The part that we added on top, my patent is around coaching in that kind of an environment.

Oh, interesting. There you go.

Pretty cool.

One patent. Take it. Yeah, I didn't, I didn't intend to have any patents, but, you know, I. Something that drives me is waking up and finding real, real pain points. Real pain points that people are willing to pay for. And they exist in sales. Of course. They exist all over the place. I could, we could. All the sales teams that have reported to me, as good as some of them have been, you know, there's other people and you go, wow, you know, their close ratios are terrible. And so that's a pain point. I wish I could so well train them that their close ratios would double or triple versus what they walk in at. But we still use a lot of old processes for that. But that's changing. So there's pain points all over the place. I got to invent in the building material space. I invented soundproof drywall. We got to, I got to retrofit the Empire State Building in the New York Stock Exchange with higher value windows. But in, in technologies, I invented all of the core technology that ended up in Siri and Alexa and all of those AI virtual assistants, did AI building, building management systems. I've done AI quality assurance systems for software that can find bugs automatically. So I've been in the applied AI space for about 25 years now. Wow.

You know, I've been watching that show Suits probably like a lot of people on Netflix. And we got to the episode recently where they introduced the Donna. And, you know, that was kind of talking about serie versus Alexa versus the Donna. So pretty. Pretty interesting, you know, that it's come back out.

Yeah.

So I love to help kind of rewind the tape for our audience and go back, you know, where were you raised? What were you. What were you doing when you were a kid? Right. I try to help connect the doctor Dots to, you know, where. Where you were brought up, what you were doing to.

Now, I was brought up in upstate New York, outside of Syracuse, and my dad worked for General Electric in consumer electronics at the time when they were a powerhouse in that radios and tape recorders and stuff. I had great interest in electronics and a great interest in business. And I was 13 or 14, and I had a little business repairing radios and TVs and other things that were broken because I loved doing that and loved earning the 10 or 15 or $20 someone might give me for repair or $5 for repairing their TV. You know, it was. And then I made speakers for people and, you know, I just got kind of got involved more. More or less than that and really enjoyed solving problems. Right. Just enjoyed that. When I was much younger, I was building with Tinker toys, but I wasn't building simple little things. I would look at a Tinker toy and say, well, if I added motors to this, I could make a Ferris wheel that turned out of Tinker toys and things like that, or an elevator that went up and down. So I would build things like that and create things from nothing. I think that's great. I don't think kids get that anymore. They play games. But I think that's really great for your mind because you're problem solving. You're saying there's a pain point here, is that my beautiful layout of a state fair needs a Ferris wheel. How can I build a Ferris wheel out of the pieces I have around the house? Right. That works. And so you're problem solving and you're critically thinking, and those are really skills that we want across the board. And we want them in all our salespeople. We want our sales leaders. Right. We want critical thinking. Critical thinking skills, they don't teach them in school. They don't teach them mostly in university either. So you come out and you can be really book smart. And then there's a problem, like a customer problem that says the customer says, I've got the following problems. Is there anything your company could do for me? I hope the answer is yes and you figure out how to close that gap. Right. That's how we sell. That's how we sell anything. But no doesn't sell anything, number one. And number two, if you say, well, I don't know, maybe I can ask some people, it really says I don't have all those critical thinking skills to put all these pieces together for this client. But clients have pain points and we're here to give them solutions and, and solve their pain point. Just like I'll end with this, this part, just like going to the doctor, right? You go to the doctor and say, my knee hurts. And you hope that the doctor has a solution for your knee hurting. Maybe it's Tylenol doesn't. Maybe it's an operation, but that's what you want. I have a problem. You're the doctor. Same in sales, Right? That's the doctor. And the doctor is there to solve your pain, otherwise nobody gives you any money.

Yeah, that's right. Well, it's interesting, you bring up the thinking, critical thinking. I think of, you know, my son's in fourth year of college at Colorado School of Mines in the engineering department. And so when we play board games with them, you know, my wife and I are playing this basic board game. Well, there's an expansion pack and it makes you think well beyond the basics of the board game.

Right.

And, and it, and it can be difficult, but I love to see that level of critical thinking just like you're talking about.

Right.

You can do the Legos or you can build the windmills with the advanced motors and everything else.

So. Right.

Yeah. Getting into that level of thinking, I think that's what AI is enabling people to do now. So let's kind of start high level there. If you think about 30,000 foot view of what can AI solve. Right. Thinking you've been in a lot of it. What are the most exciting things that you see AI can solve as we go?

Well, first of all, we've had AI around us since the 50s. We've been working with this technology just getting better and better. And so a lot of people think today, oh, AI is finally here because they've seen ChatGPT and they use ChatGPT it's the first time they interfaced with something like that. But we've been using this for facial recognition and object recognition for a decade or more and, and in certain areas of the military, been using it for many, many decades, since the 80s. In fact, it was, I think in the 80s that we signed an agreement with Russia to not allow AI to automatically launch nuclear weapons because we knew in the 80s that was already bad. So we've had machine learning in a variety of ways, but with the cloud we've been able to create better, deeper, more capable algorithms. Until today, after a billion dollars of spend or so. You have OpenAI with ChatGPT and a bunch of other models now, right? We've got Bard, we've got Llama, and these models were able to take in a trillion tokens or a trillion phrases from the English language. If you take in a trillion phrases, you get really, really good at spitting out English language. You are not always really good at what the facts are. Now the reason for that is that these models are trained on fact, fiction, truths and untruths. And because they're trained in a way that was not supervised, it's called unsupervised, they actually don't know which one is true and not true. So what you do is you create the model. A lot of what it's going to spew out is not accurate, but the sentence structure is outstanding. And so what you do is then you hire a thousand people, you know, somewhere overseas to put rules around that, so that when certain things come out, it says, no, you're not going to say that. So at first these models would tell you they were in love with you and, and such, and they've, they've sort of, you know, roped that in. But what can it do? Almost anything a good assistant can do if you train it right. And so look at a lot of the lower level kinds of tasks. So one could certainly, and there's lots of startups doing this now, including Google actually has some capabilities now to automatically reply to emails. Now we've had automatic email replies for a decade, but now we're talking about very smart kinds of replies, right? Very thoughtful, based on a suite of data. How about customer support? Right? How about getting answers to two complicated questions that would be hard for anyone to know, but the AI can know. So there are lots of opportunities. I think there's opportunities for training salespeople and have an AI trainer because that's one cheaper and more consistent than a human trainer. And there's no reason we can't do that and really train them. Train them by listening to what they actually say. And then when you're off the call, come back and say the. I could say, let me. Let me tell you what went well there. And let me tell you probably what cues you didn't hear from the other side. Right? These are important things. I started out in sales, actually five years in sales before I was, you know, I had my technical degree and before I went off and did all those patents start in sales in consumer electronics sales in upstate New York at a company called Sounds Great. And we had, I don't know, 20 stores or something like that around, you know, sort of New England. And very early on, I was quite good and. But I wasn't number one. And so we had a trainer come in and train us a customized version of what was at the time essentially the Dale Carnegie sales course. And there's five steps of the sale. Open, qualification, conviction. Objection. Close. And to this day, those five steps still exist. And people have different. Oh, we got seven steps. We've got this much. In the end, you're gonna do those five things. But I'll give you. I'm gonna give you a really good lesson here that I learned. So the first time the trainer comes in, I'm like, in the top 10 of sales. I'm like, number nine, number 10 out of 250. I'm going, what can I learn? So I don't really pay any attention. I take the test at the end, and I score like 12 really badly. And my manager at the time, his name's Mike Rest, he comes up and he's very good, and he puts, you know, very gentle guy, puts his hand on my, you know, on my shoulders and says, you know, I know you thought you knew everything about sales and that in this store you're our top salesperson and all that, but can you imagine how much better you might have gotten had you paid attention to the course? And I go, yeah, that's right. You know, no hubris. Maybe I should have some humility here. So, you know, a couple months later, the guy comes back around and says, you know, we're going to train everyone again, Kevin, we're going to train you again because you sucked. And. And the best person in this course we're going to learn do. We're going to train the trainer. We're going to train the next set of trainers, right? So not only did I score 100 this time, because I really paid attention and I studied and did everything. And, you know, really did it the way the book said. But then I learned the course and then I've used that and I've trained salespeople throughout my both CEO and CTO careers, you know, on those five steps and what's important and how to think about it. It doesn't matter what you're selling. It doesn't matter what you're selling. The customer has a pain point. You need to learn that through qualification. And then you need to modify what you're going to sell them based on solving their pain point. Right. And there's a lot more to it, but you, you get the point. So lots of overlap from that. Right. Learning that and then saying, if I were AI, what would I want AI to do? I want AI to train my people. I want AI to listen to the calls. I want AI to do everything that if I could afford my very top salesperson to sit next to my very worst one, what would they do? They'd listen to every call, they'd review every email. They train, train, train, train, train to make the very worst salesperson as good as the best salesperson over some period of time. Now I can have AI do that.

Yeah. So that's obviously keeping human in the loop. I'm starting to see tools like Vocodia come out that actually have an AI bot that has the conversation. It's going to be interesting because the belief system of a seller is important. Right. And if you hire someone who doesn't have the belief they can be successful, then they're not going to be successful. You can train them as many times as you want, and it's. And it's hard for them to come on board with a computer and an AI I have a feeling that as it gets better and may have better listening skills, male, female, different accents, it's going to be real interesting to see where that picks up.

Yeah. So I have some thoughts about that. The first thing is that clearly if a company is selling a $50 item, an AI sales bot is going to become the norm. We can't afford to have humans do that work. Right. I'm selling a pillow for $50. You know, it's hard to enlist humans. And in fact, what we've been doing in the United States, we've been hiring humans in India to do that work. But certainly customer support, but also in sales. Right. Because we just can't. No one in the US is going to work for a dollar to an hour. And we can go to other countries, India, Vietnam, China, wherever. And they do, by the way, some Very interesting technology is, is real time language modification so that if you're in India and you're speaking, what the user is hearing is an American accented person in India. So you have a human person, but you no longer know where they are because the accent has been modified real time. So that's available. And of course that's controversial because what are we saying? We're saying I'm going to modify the way you sound so that people, people don't know you are where you are. And I'm not trying to say it's good or bad, I'm just saying it is, right? It exists. So that's the first thing. The second thing is, are we talking about a $50 item, a $500 item or a $500,000 item? Right? So in enterprise sales, we sell things that are $100,000 on up, right? That's what we sell. Those deals are done primarily in the end, in person. People looking across the table looking at Chad and saying, I trust you, Chad, because there will be bad days and there will be bugs and there will be issues. But Chad, I trust you'll be there for me. So, you know, you are my partner, right? You're my partner in this effort that we're going to spend all this money. I don't think you easily replace that for enterprise level sales, right? But for sales that are tens or hundreds of dollars, AI is going to be the sales people that you could afford to hire there, right? Because again, if you're selling a pillow for $50, the people that are going to come in and work for the wages that you have are probably not the best salespeople on the planet. Best salespeople on the planet are working for Oracle and they're making a million a year. I'm making it up. But yeah, pick your poison, right? And, and so, and, and so they know how to sell or they wouldn't be making a million a year or 800,000 or 500,000. Pick your poison. All right, the people making 30K can't make the million, you know, for whatever reason. So, so, you know, these things subdivide. So clearly AI is always going to take the lowest level, entry level, sort of, you know, one to five year experience positions. Why? Because those are the easiest ones that it can go after. I don't know, you know, where you ranked in sales when you were doing sales, et cetera. But you know, you work your way to build that reputation. You work your way with the eq, with your customers, you work your way around the product that takes years and years and years of experience and personal contacts. And we're not going to change that with AI probably in our lifetimes, because it's not how we buy. We buy with trust. Right. So that's my sense is $50, $100 items, AI is going to take all those jobs. Probably should. Yeah, with all due respect.

Right, well, and then the other part is, you know, if you're talking to the AI and there happens to be a trigger that says, oops, now I need a human, then the AI can say, hang on. And so my feeling there is that you'll get a higher quality. Right. I remember at a telco that I worked with recently, a voiceover IP company, Big One, they outsourced a lot of the jobs to Philippines, three to 500. And they kept a tiger team of 10 or 15 in the US and Denver, Colorado.

Right.

And so when it goes above threshold, then boom, it kicks over the team at 10 or 15.

Of course it does. Of course it does. And, and those people that are in Denver or wherever they were are your very best closers. And you knew that and you had the stats to prove that. And the other stuff was, you know, they were going to sell $100 item, so send it offshore because that's cheap. So, so then you say, well, what jobs are first going to be replaced? It's actually going to be the things we've already sent offshore. Yeah, they went to India, they went to Philippines, they went to Vietnam, and again, this is not a knock on the wonderful people who were working for us there or any of that. Those are the first ones that get replaced in customer service and in sales, primarily because we already decided as a company that we could afford to put those over there and our close ratios will be what they'll be and these are small potatoes, but we have to service them, et cetera, et cetera. Right. So we already decided that and we went there for cost. So now that you have AI come around and it's a hundredth the cost of that, you're still going to go for cost and the AI will probably.

High quality, if not the same or better.

In terms of same or better. Probably, probably better because you can keep tuning the AI and tweaking it. As you know, those CS and, and, and sales jobs offshore tend to have a high turnover. And, and because of that, you train people, but you got a 30% turnover. So they're. Everybody's gone in three years. You keep training and training, the AI just keeps getting better and better. You keep training it and it stays around, it gets better. So it learns from its losses, it learns from its wins and it continues to get better, it will continue to improve it's close ratios. Yeah. Until it's hit some limit of, you know, whatever a consumer will buy.

Kevin, talk to us a little bit about any of the active projects that you're working on. Whether it's sales related or not. I love to expose, just like you said, the accent converter, if you will, on a commerce. I had not heard of that till today. So anything that you're seeing that's bleeding edge that people should know about?

Well, I'm seeing all of these things. Right. So there's something like in Silicon Valley, a thousand startups a week doing something with generative AI, one of those per week is probably going to survive. Right. But it shows you how much it's opened people's minds to wow, I can now do this. And the reason for that isn't that we didn't know that you could build a large language model. It's because it was going to cost a billion dollars to do so and only a few companies could afford to do it. But now that they've done it and they're available, that base technology, that core is there and we can build on top of it and we can add value to the basic model. Right. That is fascinating. I mean it's fascinating. It's the first time we can all use something like that and just put it to work right now look, we've done this kind of thing in the past, by the way. We all, when calculators came around, started using calculators and then started using Excel when that happened now 30 plus years ago. Okay, we all use Excel for something. We don't really think much of it. That tool solved math. It solved math. When's the last time you did long division? Yeah, very fourth grade, fifth grade. I don't know. Why? Well, because we have a tool that does that for us. It's called Excel or it's called a calculator or whatever. And so we solved math decades ago and now we're solving language. And these will be regular tools in our life. We will use them at university, we will use them throughout our life. We will use them to write marketing documents. We write, we write sales emails with them. I use it even to write, I do 40, 50 keynotes a year on AI and generative AI and ChatGPT. I use it to write parts of my presentation. And I will credit the model, but I do it Because I have, I say this is what I really want to say. And when it says it, it says the, that prose is better than I could ever come up with. I, I, I don't, I, I never haven't read, you know, 5 million novels, right, where the sentence structure is just gorgeous and the words chosen are gorgeous. And I can tell when it's written by AI, but it's so good I just use it. And I'll credit the AI, right. I might edit it. So when you look at a sales email and you're really thinking about, here's all the inputs, what's the best thing I can say to close this deal? There is no question that asking an LLM, a ChatGPT or another model will probably come up with something better than you're going to come up with now. You will change it a little bit for your tone, but if you feed it all the information, what it comes back with is unbelievable. And so if you think about the time savings as a salesperson, right there, right there, without doing anything weird, just using signing up for chat GPT at 20 bucks a month, it'll do this. What's fascinating about that is that it used to take me sometimes in a sales email, 30, 40 minutes and some, and sometimes a blank screen for 10 of those minutes, right. What am I really going to say? And I reviewed everything in Salesforce or in HubSpot or whatever it is, and slowly I come around and I think I've crafted something. I don't know. It's the best I can do any, so, so an hour goes by. Okay. In one minute, as long as I can feed it all the inputs in one minute, I have a better output than I'll ever write. And now I can take 10 minutes to edit that and I'm done. Probably five times faster or more than writing it myself. This is a tool you will use all the time. Just like you use Excel all the time. Just like use Salesforce all the time or whatever your CRM is. Right? It's what we do.

Yes.

So what am I working on? I didn't answer that. I'm working on a lot of things. One of them is this. Finding bugs in software automatically using AI. That is a, that is a very heavy lift. A very heavy lift. Because the bar for QA is 100% and LLMs are not 100%. They're sort of. Here's a suggestion. LLMs work as a great suggestor. They don't work as well. When you say do this and it has to be Absolutely perfect. It doesn't exactly know what perfect is, so you have to build a lot more infrastructure around it, because there is no other gate past quality assurance. Quality assurance of your software is the final gate. So it must be correct. It can't say everything's good and it isn't. Okay, so there's that very high bar. Worked on it for 10 years. Millions of lines of code, 110 people working on the problem. This is a heavy, heavy lift problem. We got a cyber security company called Token Ring. Along with AI has come huge cybersecurity issues. So the phishing emails now are so good you don't know they're phishing emails. The. The stuff that's happening over the phone and in emails to get your MFA, and there's other tricks to get MF2FA and your codes from the phone and stuff there. That all works. So all two FA that you're familiar with has been hacked. All of it. In fact, every major ransomware hack today has your code. Either they got it electronically or. Or you gave it to them and you.

And you're worried about the Google authenticator that's on the fly. They've got access to that. The bad guys.

Oh, of course, yes. There's. There are methods to access it or methods to fool you into giving them the information.

Sure.

Okay. And whether it's Google Authenticator, Microsoft Authenticator, or codes that come on your phone, all of those are easy to hack and have been hacked. And there are well documented hacks for them. Okay, but there's, but, but there's a different technology called FIDO2 that is a highly encrypted technology. If you look up tokenring.com you'll see it's a ring that you wear and you put the ring on. And this is really for enterprises right now, Right? You put the ring on. It's tied to your fingerprint. Well, the ring has no numbers on it. So I can't call you and somehow convince you I'm the bank and that you're going to give me your code just this one time. Because there's no code to give. Everything's encrypted. There's nothing for your eyes. We take the human out of the equation, and you're just wearing a ring all day. And that ring identifies you as you. And once you take the ring off, it no longer works until you put it back on and it ties to your fingerprint. Very cool. Got it. Solve cyber security. 84% of ransomware hacks are because they hacked your credentials including your mfa. So there's that, and then there's another company that's doing some really wickedly great stuff around code compliance. So cities, 50,000 around the country or so, if you want a permit to build a house, a permit to build an apartment complex, permit to build a commercial building, you have to submit your plans and then they have to check it for fire code and safety code and all the different codes. Right. In some cities, that takes two years. It's that long of a wait. Mostly because you can't hire the people to do it. You can't get enough people to do it. Those people go work for an architect. They don't want to work for the city. So this is a real problem. And, and so this software will check your plans automatically for you using AI and, and come back and say the plans meet all of the criteria and hand that sort of test to the city so the city doesn't have to double check. Those just says, I trust the AI Now I have very little check. And it could take two years, down to a week or two. Right. That's a big deal. It's a great use of AI. There are lots of other things, but, you know, those are some of the really fun, harder problems to solve. The easy problems to solve are an AI sales trainer, you know, or an AI, you know, CS agent or things like that. Right. Or. Or AI going through your salesforce and picking the five best accounts that are most likely to close things like that. These are. These are pretty straightforward problems to solve. They don't take millions of lines of code.

Yes. Well, there's a guy that comes to mind, was just on the podcast not too long ago named Robert Peterson with Xenon Lab, and he came from New Relic as one of the top strategy guys there.

Yeah.

And they're doing the bug fix kind of thing they're calling it. They're kind of renaming a space, if you will, Outcome centric guidance that says, here's what I need to get to. But then if I look back to the bugs, where is the bug? Because right now people like my son are having to go scour through millions of lines of code to try to figure it out.

Yeah, what, what new Relook does is. Is called apm, and that's Application Performance Management. What APM does is it instruments an application and then monitors how it works in production or how it works somewhere, flags a problem and then tries to flag potentially where in the source code that problem is. What it does not know is what you intend to do. That's different. So QA is. Here are my thousand intentions of the way I want it to work. Let's make sure it works the way I want it to work. What apm, like New Relic and Dynatrace and a few others do is say, I don't know how you want it to work, but I can tell you that a user ran into a problem here because it got an error on the screen. So go dig that error out. Does that make sense? So these are two very important things and really different parts of the QA process for software.

Yes. Well, really enjoyed our conversation today. One more question for you. And that's kind of the future of where things are headed. You know, you've seen things transpire over 25 years. I think a lot's going to happen in five years, right? More in five years than happened in 25. What do you think?

It's always that way.

Yeah.

Right. At the pace of acceleration of technology creation and adoption, you know, each five years is outdoing the last 10 to 20 before. And this has been the case basically since the invention of the wheel. Right. We've continued to do this. So here's what I'd say. Everyone is always worried about. The first thing they do is, oh, it's gonna take my job. Look, first of all, we have 8 to 10 million open jobs in the U.S. okay? The first jobs it's taking are the ones we already shipped overseas. We already took those jobs, and we still have 8 million jobs open. Right? So we have a fundamental problem in the US Anyway because of our immigration rules that aren't gonna get fixed. Right. Congress, either side, isn't gonna deal with this. So that means that because of the declining birth rate over the last 20, 30, 40 years, we have a declining number of people entering the workforce and an increasing number of people leaving the workforce, leaving a huge gap. So whenever I speak at conferences, people say, what? You know, what's the number one thing I need to worry about? The number one thing you need to worry about is you have been doubling the size of your business every X years by doubling the employee base. You can't do that anymore. Because everyone in this room that I'm talking to, 500 companies, thinks they're going to double their business in 10 years. Except there's not double the employees anymore. That's not possible. That's not possible. There aren't more people to work. There's already eight and a half million open jobs. So if that's the case, you now have to step back and Say, okay, I have to fill that gap with AI. So I'm going to have just as many jobs 10 years from now as I do today. I'm going to be twice as productive and I'm going to be twice as large of a company. And that is going to be good for the economy. It's going to be good for my employees. And yes, I realize when I say that there's a gap and it takes time and trickle down and all when I get in the economics of it turns out bigger companies equal a bigger GDP is in the end better for.

Revenue per head means that that person could make a little bit more money. Everybody wins.

Yeah, everybody wins. The worst thing you got is less revenue per head. Okay, so more revenue per head. That means we have to double our productivity over the next 10 years. And that's how companies are going to double in size. And they're not going to do it with people for the first time. They're going, I can't add any more people. There's no more, no one to hire. That is the promise and that's the excitement. And so if I'm in sales or if I'm in any, or I'm in marketing, I'm in whatever it is. I absolutely want to be in front of this. I want to know about it. I want to use these tools. I want to use. Even if it's junk today, I don't care, right? I'm going to use it. I'm going to be a master of it. I'm going to lead the parade, right? Because the people who don't, the people I talk to, say, well, I don't know, I don't think it's for me. And I'm not, you know, I'm not going to use that newfangled. Well, then you're going to be left behind, right? There won't be a job for you because your next sales job is, is going to say, and, and you have experience prompting large language models, you know, prompt engineering for your sales efforts. And you, you know, just like 30 years ago, anyone in accounting, all of a sudden, if you didn't know Excel, you were, you were out of a job. Like there wasn't there were no jobs. What are you going to still write?

Well, a good example, Kevin, is that, you know, for 25 years I've managed inside sales teams, right? Highly productive. Well, over time I've learned that I can program the technology to do the work of the inside seller. And so if they can come to me, the source, using a large language model to build the Message, whether it's LinkedIn outreach, email, phone scripts, voicemails, and then at $500 a month or, you know, and that rate will continue to go down over time. Why spend 7 to $8,000 on an inside sales ad when you can spend 500 and have Chad overseen?

Oh, look, we. If you're going to use AI to one, have a chat, come back a chatbot, and two, generate your LinkedIn outreaches. The LinkedIn outreaches will be better than a human and the chat bot for the first instantiation will certainly be better than the human you could have put in that role. Right? Yeah. Not the next tier, but the first tier that is doable today. It's not hard. Everyone should do it. Why wouldn't you do that? Right. That is the way that outbound marketing, outbound lead gen is certainly going to work. Top of funnel. Right. That's the first thing that's going to happen. And remember, a lot of companies already are doing that offshore anyway and it's been okay. Right? So yeah, this is already better and you can do it today. You don't have to wait for anything.

Well, it's been an amazing conversation, Kevin. We've been talking with Kevin seres technology, obviously inventor, 94 patents. Kevin, if 1 of people get, if people want to get a hold of you, what would be the best way to find you?

Kevin? Sa.com.

All right, there we go. Simple. And we didn't talk about your music, but you will find a lot of music that Kevin's worked on over the years there.

If you, Kevin, space to race and Google it, you'll find, you know, 100 talks and lots of music and me conducting things and all kinds of stuff. It's been a pleasure, Chad.

Excellent. Well, stay at the bleeding edge. We need people like you in the world and appreciate you taking the time out of your morning from upstate New York to Chicago.

Absolutely. Happy to do it.

All right, everybody, thanks for joining another AI for Sales podcast. See you next time.

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