How to Make Better Decisions in the Age of AI with Ricardo Valerdi

Hey, everyone, welcome back to Make It Happen Mondays, where we talk about sales, business, entrepreneurship, personal growth, mental health, and everything in between with guests who I truly respect and I think make a positive impact on the world around us. Now, today's guest is Ricardo Valerde. Growing up, his dad ran a satellite communications company, and Ricardo thought what they did was just magic. So we went into college to major in magic, otherwise known as electrical engineering, which is actually what my dad did. That curiosity took him from Motorola to SpaceX to teaching decision analysis to cadets at West Point. Today, he's a professor at the University of Arizona. His new book, The Five-Tool Team, takes the five-tool player from baseball scouting and turns it into a framework for making better decisions.

His point is that great decision makers build a team that covers all five tools. We get into why SpaceX wants to fail fast, what John Harbaugh told him about ignoring the models on fourth and one, the Costanza effect, which for you Seinfeld fans out there, you're going to love this one, and why the one skill AI can't replace is thinking past the first dominant. I hope you enjoyed this conversation as much as I did. Let's make it happen. Ricardo Valerde, welcome to the Make It Happen Monday podcast, my friend. How are you doing today? I'm doing great. Thanks for having me. Yeah, you are on the book tour, my friend. This is the day the book launches. So again, congrats for that. I know how much work goes into writing a book.

And so congrats. And now the fun begins, right? It's right. It's been planes, trains, and automobiles for a week. Yeah. And, but it's been, this is the moment I've been waiting for, right? For not just months, but probably years. So it's finally here. There you go. You asked for it. You got it, right? Here it comes. So I wanted to, I mean, look, I've looked through your background and you're engineering professors or, you know, sports scientists, nonprofit, hundreds of thousands of kids, SpaceX. I'm about to jump on a plane and one of the things, you know, sitting next to somebody on a plane, it's like, Hey, so, you know, what do you do? Just out of curiosity, when somebody asks you, because we're going to get into all that fun stuff, but when somebody asks you, what do you do?

What do you tell them? How much time you got? Is it a six hour flight? That's what I heard. But I mean, I try to relate it to something they might already be familiar with. So if we're traveling from like Los Angeles or to Phoenix or from Washington, D.C., right? They might be tied to a certain sports team, for instance. So we might talk about sports for a while, or we might talk about engineering, right? If I see them, like their shirt logo might have a certain company that is in the tech space or, or, or just like kind of talk about like, where are you going and what takes you there? And then there's usually some interesting link that you can, interesting thread that you can pull there. And I love talking about what people do and, and share with them what I do.

And it's always so much fun. Yeah. I mean, do you pick a based on, so you kind of qualify them before you tell them what you do say, so you can kind of be like, well, it's, it's funny because I, well, I do the same thing when it comes to like trade shows and stuff like that. When people come up and when I train reps, it's like when somebody comes up to you at a trade show and they ask that blanket statement, well, what do you do? You know, the answer is, well, we do a lot of stuff. Let me ask you real quick, like what company, what kind of company do you work for? What's your role in that company? And there's, okay, well, based on that, for people like you in that scenario, this is what we do versus for the people like this.

And this is, so you have kind of a Swiss army knife of things that you do. And I mean, I'm guessing right now you say you're an author, right? That you're out there, you know, with this or is kind of what's, what's today's flavor? That's a newer thing, right? Because the book comes out today, but I usually say, oh, I'm a teacher at a university. And, and that just kind of opens the door to, oh, like, what do you teach? And, um, or do you follow college sports or are you, you know, you're an engineer or tell me more about that. So, uh, there's usually a lot of branches that you can go down, but, but I think that's really my lead is I teach at the university of Arizona. And then, um, if they went to Arizona state, then it's, it's going to be a long flight.

Yeah, of course. Of course. Um, all right. So let's, let's talk origin story, man. Like you, you know, growing up, your dad was an electrical engineer, um, at NASA's, uh, space center, right? Johnson space center. Johnson. Yeah. Johnson space center. Um, my dad was an electrical engineer. I always said he was the smartest guy I've ever known in my life. You know, he actually worked at, you know, the AWACS, you know, that, uh, the plane, right. So he was one of the people who, you know, built that, um, worked at, you know, MITRE corporation here in Boston. And I just remember like, he, he was almost intimidatingly smart in the sense that he has his PhD and he taught calculus. And, you know, I think at an early age, I almost deferred intelligence.

Cause I'm like, I'm never going to be that smart. So I'm going to take a different route here. Um, like I'm going to be more, you know, sales oriented and business and everything else. And so what were the, like, walk me through kind of that influence in your career early on, cause you ended up going into school to teach. Right. So how did that shift? And then I want to get into kind of the shift of you talking about decision-making, right. And, and where that happened with your electrical engineer background. I was fortunate that, uh, my dad had worked in a lot of different technical fields. And by the time I got the like middle school, I started paying a little more attention, asking more questions.

He had started a small satellite communications company that then started growing pretty quickly. And they were basically offering, um, mobile phone service in third world countries, South America, Asia, and Africa. And it was fascinating going to, uh, to his office because you'd see all, all these like electronics racks of equipment, uh, that were connected somehow to a satellite that was somehow pointed to the sky. That's somehow carry electrical signals that were voice and data. Fascinating. I thought it was magic. So I was just always curious, like, how does this magic work? And, and that's how I got into electrical engineering. That's not necessarily why I stayed in electrical engineering, right?

It's, it's, there's always sort of two, two phases to, uh, to somebody's trajectory, but I was lucky because he explained a lot of the things that was exposed early on. And so when I, by the time I got to applying to colleges, I knew I'm going to major in magic, otherwise called electrical engineering. Hey, quick break here before we get back to it. Look, it's a weird market right now, and I would love your help growing this show and my business. So here are three ways you can help. First, the free one, leave a quick review wherever you're listening to this podcast or share this episode with somebody who needs to hear it. More listeners bring in the sponsors who make it possible for me to keep giving this content away for free.

Second, if you want to go deeper, sign up for my on-demand platform at learn.jbarrows.com. It has all of my content in one place. So you can learn it directly instead of piecing it together from the free stuff. Third, sales kickoff season's coming up. If you know a sales leader planning a kickoff, I'd really appreciate the referral. Send them to jbarrows.com or have them reach out to me directly on LinkedIn. Your support means more than you know. Now let's get back to the show. You did your dissertation on cost, like, I think, let me look at this. It was a cost model for a giant engineering programs. That's what you did your dissertation. That's right. Okay. First of all, why? What in the world did you make you choose that one?

And then I want to talk about how, like, you know, it went from being like a circuits, like how that works to actually how that modeled decision-making. So first of all, talk to me about why you chose that and how did that morph into your fascination with decision-making? Yeah, there's actually a really interesting progression there, because if you think of the world of electrical engineering, it's primarily hardware-based. So you're building circuits, you're building antennas, you're building hardware components that are performing some function. They're either, like, processing data or they're transmitting. They're moving data, right? Yes, there's software involved, for sure. That's a lot of the interesting intelligence and functions comes from software.

But primarily, let's just say, it's mostly hardware. So I worked in industry for a few years for Motorola in the radio communications division. And when you start to abstract, like, what do clients really need? They need solutions. They don't need hardware. They need a problem to be solved for them, right? In the case of 911 systems, they need reliable and high-quality communications. When somebody places a 911 call. So when you start thinking about systems, then, you start to really de-emphasize the hardware and the software and more about, like, well, what is the problem we're solving? What is the customer's requirement? And how are we delivering on that? So then you start talking about, well, what are the trade-offs between the technical things and the cost we're doing and the cost of what we're doing?

Because one of the things I learned in grad school is that a solution that's too expensive is not a solution. So think of, like, how that turns into solving a different type of problem, which is, well, how do we make cost-effective solutions for customers? Well, we have to be able to forecast the cost of what we're doing. Well, how do we forecast the cost? Well, we need to understand what are the requirements of the thing that we are designing, and can we come up with some cost estimate in the present of something that's going to be implemented in the future? So it's essentially a forecasting activity that occurs. And that's what I focus my dissertation on, is to help people make better decisions in the present about how they're going to implement the system in four, five, six years into the future.

All right. I got to pull on that thread for a minute, because I have this ROI, like, let's put a blanket kind of ROI conversation here. I've always kind of had a little bit of a hard problem with ROI as a sales rep. Most clients don't believe any of the numbers that you put in front of them as far as why it's always bullshit. And now, specifically, you said something that you're going to implement four, five, six years from now. I don't know too many people that can look four or five months, if not days ahead, based on how fast things are moving right now. And so how in the world are you adjusting your decision-making frameworks here in this environment where things like a good idea today could be irrelevant literally tomorrow if Claude updates something and does whatever, some kid out of their basement, vibe codes, whatever it is.

So how in the world right now are you approaching decision-making and investments in business and whatever, and building in this type of environment? Absolutely. So that's always, you're chasing the receding horizon, we call it. So the sun is setting, the sun is setting. You're always chasing it, always chasing it. At the time that I was in grad school, the big concern was Moore's Law. Remember, so that every 18 months, the speed of microprocessors doubled. And it's sort of a similar question of like, well, then how do we skate to where the puck is going? As opposed to like, well, in five years, we're going to be way behind if we make assumptions on today's technologies. That's always going to be the case.

Unmanned systems was sort of the next after Moore's Law. Like we're worried about how fast robots are evolving and how much autonomy is being given to machines and being taken away from humans. Well, how do we anticipate that? Well, you can do some of it, but you can't predict the future. So you have to basically only limit your decisions based on the reliable information you have today, knowing that you have to go add flexibility to systems so that in the future, they can they can basically absorb those changes and adapt them well. Let me give you like a really a physical example of what I mean. So say you're building a parking structure for cars and you don't know, you can't forecast with any certainty how many cars you're going to need to park in that parking structure with today's data.

But you think, well, I'm going to need at least 100, right? Because that's what the city is requiring you to do. But knowing that in the future, you might have to increase capacity, you build that parking structure with the foundation to sustain multiple floors. Even though right now you're going to overbuild. Because maybe the first couple of floors, like, did you really need, you know, columns that thick? Yes, we do. Because that adds flexibility to add more floors in the future. So think of just how that concept of flexibility, building in future flexibility could work into software products or hardware functionality, right? Yeah, I mean, I think that's kind of where my head's right now. It's like I used to train on very specific tactics and techniques and, you know, from a sales standpoint.

And those things come and go at this point. You know, what worked six months ago just ain't working. So what I'm doing now is training more agile frameworks, right? Like a framework so that people can test and try different things, but within the framework. So it's not just the wild, wild west. And so that framework of, you know, not necessarily the script of exactly what it should be, but the framework of guardrails and then, okay, let's get creative within that framework and test and test. I mean, a little bit of Six Sigma of A-B split testing and those type of things there. But, you know, agility right now I think is one of the biggest attributes of any successful company moving forward right now because you've got to be able to pivot.

You know, I lost my entire company in 2023 when the tech industry came to a screeching halt. I went from six million to zero. I went from 20 employees to two. And I look back on that and how painful that was. I'm actually to a certain degree glad because now I can be super agile and adjust based on where we are in this market and use AI, you know, without being worried about it. So are you seeing, are you seeing like you have a framework that you go through, which is the flash model that we'll get into in some details. Is that kind of your model for help, like framework, if you will, for helping people make decisions, not necessarily telling them the decision that they need to make? That's right.

So a couple of points to what you said. Love the agile philosophy. One of the embedded benefits of agile is that there's iterations and at each iteration, you can pivot or you can redirect or just reevaluate whether you're headed in the right place. So that iterative thinking originates in what's called in software development, the spiral development model, right? So you don't just write code without checking, are we headed in the right direction. But so that iterative process of design code test spiral model, the guy who invented the spiral development model was my PhD advisor, Barry Bain. He recently passed away, but you can imagine just the way of approaching how you build software in an iterative fashion kind of expanded to business in general.

You know, it didn't really stay with software because it's really just such a smart way to always be checking. Are we headed in the right direction? Do we need to shift? Do we need to pivot? Right. Now, the thing about that way of thinking, and I write about this in my book, the spiral model teaches you that there are certain questions that you ask in the first spiral. And then there's a different set of questions you ask in the second spiral. And then a slightly different set of questions in the third and the fourth and the fifth. And you never stop asking questions. They just become a little bit more refined. They become a little bit more focused. Right. And so, like, to put it in sort of your context, in your agile approach, maybe in the first round of agile, you're asking broader questions.

But as you move along, your questions are getting more surgical and surgical and surgical. Right. And you're landing at the solution, but you're pivoting adequately. And I love the Patriots Cup, by the way. They're a little hurt locker right now. But, yeah, like, I don't know what's going on with Drake May. That's a whole different conversation, but holy shit, he looks like a rookie again. But anyways, well, so, I mean, all right. So let's talk about this framework or decision making under pressure, because you took a little sabbatical. You went for MIT for a few years and then you'd bounce from there. And then you worked with SpaceX and the Army, right? West Point. That's right. And you were teaching decision analysis to cadets.

And, I mean, those are SpaceX and West Point. Those are two most high pressure kind of environments that you could map out, for crying out loud. So what did teaching there or working within those groups teach you about decision making under pressure and something that a lot of corporations get wrong or leadership within businesses get wrong? Yeah. Well, there's a couple of things that I learned from those experiences. The first one is, especially at SpaceX, the culture is it's okay to fail and you want to fail fast. Right. To really understand, like, what the limits are of what you can do. And you've seen all the videos of, like, rockets blowing up and, you know, crazy stuff. And a lot of, you know, those are tests.

They're not, like, the real launch. And you learn a whole lot from those experiences. You learn more from the failures than you do from the successes, right? Because if you were to always successfully launch a rocket, you'd get this, like, false sense of confidence. Like, oh, man, this is easy. I mean, it's easy, right? So I love the philosophy of understanding what learning you're going to get from failure. But also inspiring a culture where failing fast is the goal, right? And so, but I saw it. It's not like they're reckless, though. I mean, I saw it as a very calculated way of accelerating knowledge. Like, let's know, let's figure out the most we can figure out about this rocket and its capabilities.

And we're going to take it to the limit. That way, we understand, like, the performance envelope. That way, when we do launch an expensive payload or astronauts, we know that we need to operate within this performance envelope. And that's going to be safe. And that's going to be safe. That's going to be successful, right? So that was, to me, like, one of the big takeaways. One of the takeaways that I'll share from my time at West Point is that kids who enter, cadets who enter West Point, they may not realize it, but they're entering a team environment. To be successful in the military is you have to work at the team. And to me, that was, like, extremely evident when you see the progression from the freshmen all the way to the seniors.

They call them firsties, the seniors. Pleaves are the freshmen. And, you know, they just have their own culture of what they call each of the years. But you see the progression in cadets. And as you go farther and farther into the Army as officers and as leaders, you begin to realize that the people who get promoted into, you know, field officer and then flag officer are the people who understand that their job is to take care of their soldiers. And you lead by making decisions that are for the betterment of the unit and the betterment of the division or battalion or whatever you're in charge of. And I thought that was, like, so counter to what I thought the military was. I thought the military was a bunch of people just barking orders at each other and then you just have to follow them, you know.

But it's totally not that. And you don't see that unless you go to boot camp or you teach at West Point. So I chose the easier one of those things. Yeah, no kidding. I guess, I mean, because you work with a lot of kids through the science of sport and, you know, teaching math through baseball and those type of things. And that's like the trend I'm seeing outside of environment like West Point and those type of things is a very individualistic trend in the sense of, you know, social media and look at me. And so how do you instill? I mean, there's two components to the younger generation here, right? The teamwork component to it of being for somebody, you know, for the betterment of the team, but also, you know, the failure component.

And really getting, you know, accepting and learning from failure. And I find that, you know, maybe it's just me, you know, being old man John over here. But I find that the kids are way more afraid to fail right now or maybe parents are way too afraid to allow their children to fail. So how do you instill some of those values that you've learned from corporate into these kids of teamwork and putting the team above yourself and failing fast and learning from it? Are there ways that you can use like sports to translate that to kids and especially with what you're doing with math and how you're getting them to accept it? Absolutely. If you think of the ability to fail safely, it is all about the environment that's created, right?

Because so think of like maybe a coach you had in Little League. You could have probably had the kind of coach that I had, which is if you made a mistake, you got yelled at, right? But I also had coaches that were completely opposite, where if I made a mistake, the question was, what did you learn from that? And those are two completely different experiences, right? One makes you hate baseball. The other one makes you understand that baseball is actually a metaphor for life. That's where you learn a whole lot more about who you are as a person and you learn about failure. It becomes a feedback loop. And so what I learned from one of my coaches is sometimes you win, sometimes you learn, right?

But it just comes down to like what environment did that coach create? What environment did that army officer create? What environment did that executive at that company create, right? And to me, like, let me just tie it to the book, because my book is really about roster building. It's about creating an environment where you bring people together. And because of that environment, you make better decisions as a group. It's not an individual decision maker hero. It's a five tool team. I like that. So let's talk about this framework, right? It's Flash, right? F-L-A-S-H. And it's what focus, look, analyze, scrutinize, and humanize. That's right. Give or take. Walk me through, first of all, how did you come up with this?

And then I want to kind of pick apart each one of those areas. Specifically, I'm curious on the look and the humanize pieces. But walk me through this framework and how you came up with it, why you came up with it. And then let's kind of pick apart each section. Certainly. So a lot of the decision making books out there are really good at describing the problem. This is why we make bad decisions. Okay, got it. I didn't want to write that kind of book. I want to write the kind of book that gives you solutions about what you can do to be a better decision maker, right? So I want it to be prescriptive. I wanted to give people specific tools that they can draw from and say, okay, if I apply this tool, it's going to help me see the world differently.

And it's going to result in a better decision, right? Now, I wanted to be careful. I'm not telling people what to decide. I'm actually just providing them tools on how to think about it. So being a big baseball fan and analyst and now, you know, little league dad and softball dad, the concept of the five tool player in baseball is what I leaned on here. So, as you know, the five tools in baseball are hitting for average, hitting for power, running, fielding, and throwing. And this was all invented by Branch Rickey, who's the president of Brooklyn Dodgers. And he's the one who drafted Jackie Robinson from the Negro Leagues to Major League Baseball. Okay. So we're going back a few decades here.

So primarily, that's what Branch Rickey is known for is the Jackie Robinson story. But he developed the five tools for baseball. And they're still used today when scouts evaluate players. They have their little worksheet of the five tools. So I asked myself, well, is there something similar in decision making? Like, do good decision makers have these five tools? And the answer was yes, but it's not like an individual decision maker needs to be excellent at all of those five. What that person needs to actually do is build a team where the five tools exist. So it might be spread across two or three or five people, right? So that's how I came up with just the concept of five. Then I wanted to find an acronym that could fit together nicely.

And it just fell together after brainstorming and talking to friends. And so Flash landed nicely. I like to think of these five tools as collectively complete, like they form a complete set. There is a little bit of an overlap between them because they reinforce each other. But they're different enough where you can say, okay, I'm going to read Ricardo's book and I'm going to work on tool number one today. And you can just stop there and say, okay, how does tool number one help me today? And then you can skip to tool three or tool five, right? There isn't necessarily an order. But big picture, the more of these tools that you apply, the better decisions you're going to have. So focus, right, which is the first one.

That's all about figuring out before you debate what answers, right? You debate, are we asking the right questions? That's right. How do you know you're asking the right questions, especially if you're not experienced, right? Like say you're a young entrepreneur going headlong into trying to build a business and stuff and you don't know what you don't know. So how do you even know which questions to ask? Yeah. So I get people started with what I think are the important questions. And let me start with like a couple of really critical ones. And I think you'll agree that these matter a whole lot, regardless of what decision you're making. But question number one would be, who is making the decision?

Is it me? Is it my boss? Is it my wife? It's probably my wife, you know. But also, in terms of figuring out a decision, you also have to ask yourself, well, what metric are we trying to improve? Like, are we trying to increase sales? Are we trying to increase profitability? Are we trying to increase happiness? There's something we're optimizing here. Something that's driving, like, in the background. But just bring it to the foreground. And that question helps expose that. There's another important question that I think everybody needs to ask, which is, how are we going to make this decision? Like, are we going to apply some rule? Are we going to listen to our gut? Are we going to vote on it democratically?

Like, there are just so many ways of getting there that it's important. It's important to ask. And then I'll leave you with one last one, which is, you know, sometimes you're asked, hey, like, I want your opinion. I want you to do some analysis to figure out, you know, what to do next. One of the questions I'm always curious about is, has this decision already been made and you're just coming to me for validation? Or are you, are you actually interested in the answer? There you go. And that a lot of times, well, yeah, I know plenty of people that do that. They just come to validate their good idea. Right. And then they get very defensive when you give them feedback on it that isn't great. It's like, why'd you come to me if you're, if you already made this, right?

Interesting. So, because I think this is important right now with AI specifically, right? Because there's a lot of people, I still think that we're in the, the productivity slash efficiency phase of AI, not the results phase of AI, right? Where people are doing a lot of things to become really efficient in doing whatever that thing is. But I'm still not seeing the dial move at all when it comes to revenue or results, conversions, any of that stuff. So, it's almost like right now in the world of AI, there needs to be a pause of like, look, and I fall into this, right? Just because you can't doesn't mean you should, right? Like, I'm like, oh, look what else I can do. Look what else. But I got to start asking myself questions like, why does this matter?

What problem am I solving? What's the, you know, impact this is going to have? Is this going to produce any revenue? Or is this going to change anything fundamentally other than making us more efficient, which is a really hard thing to justify when you're dumping your time and money into this and you're not generating any better results? Yeah. I mean, I think you hit the nail on the head there. This is where the difference between efficiency and effectiveness is the most obvious. AI may make us super efficient, but is it efficient at the right thing that we want to accomplish? Is it being effective, right? So, yeah, we might be able to, you know, make photos of our cat wearing a space suit, you know, with just a simple prop.

Man, that would have taken me a long time if I would have had to do that by hand. But is it effective? Like, what are we doing here, you know? So, but ultimately for me, thinking about decision making, like AI will never replace the human decision maker because AI doesn't have the context. AI doesn't have empathy. But here's the other thing is AI, we cannot hold AI accountable. Whereas a human being, like, I want to, I want some, somebody accountable for that. Yeah. Totally. Like, sometimes that's me. Sometimes that's you. Well, it's funny. It was just, I mean, sidebar, but that's why I think, you know, crypto is, is not going to ever take off until that problem is solved. You know, they all talk about the blockchain, the blockchain, the blockchain.

It's like, well, yeah, good for you, the blockchain. But when I, when somebody steals my credit card and charges a bunch of shit, I call up Capital One. I say, give me my shit back. And there, so until there is the blockchain police that I can call when somebody steals my shit off the blockchain and doesn't just point, well, it's on the blockchain. I see it and can't actually rectify that problem and take responsibility and accountability for it. Crypto is never going to take off to the point where people like, you know, normal people are going to be acceptance of putting their money in there. And so that's why, like, I saw this huge push with all these sycophants and like, oh, my God, this is going to be great.

And at first I was into it too, but then I just kind of dawned on me. I'm like, wait a minute. There's a lot of nefarious characters out there. There's a lot of people who know way more about this than I do. This could be a Ponzi scheme. I'm going to put some money in there, but I'm not going to shift to this until there's somebody responsible for the phone call that I can make when I, if and when I get hacked, if and when I lose some money. And I don't think enough people are paying attention to that accountability factor because AI is not accountable. It lies all the time. And it happily tells you afterwards when you catch it, you're like, oh, it's like, oh, you're right. I'm so sorry. You know, it doesn't even apologize.

It's like, yeah, you're right. It's like, dude, I was about to make a really important decision based on that data. Right. Which, which actually brings us to the second point of look. And you talk about how, like, you know, so Flash FL look, looking at the data, but data lies. You and I could look at the same set of data and come up with vastly different. That's why I always love these polls and stuff like that people put on. I'm like, yes, you are positioning that. I could probably take that exact same data set and tell the exact opposite story in some way, shape or form. So how do you, how do you QC the data? How do you, how do you take two people's view of data and combine them into one that is actually logical?

Yeah. So let me use another baseball example. You ever, like, hear, like, these crazy statistics, like, this pitcher is the league leader on opening night on Wednesdays when the group is open and the wind is blowing 36 miles. And it's like, what? Yeah. Like, first of all, a point, completely useless data points. Like, in my, like, football does this all the time. I'm like, wait, what? Like, who gives a rat's ass about that data point? But like, and that's a dangerous thing with data that you can tell, you can basically narrate whatever story you want to tell. And I, and I give tons of examples of that in the book. But, but I think what's really important to remember too, is that data has its limitations because when you have data, it means that it describes something from the past.

But when you're trying to make a decision, you're trying to look to the future. So the data, you can extrapolate outside of the data set and say, well, what could this tell us about the next two, three, four years? Well, a little bit, but ultimately it's the experience, it's the human decision maker that can, that can make that leap and say, you know what? This is how I'm going to interpret that information, but I'm going to fill some gaps here. And then use my experience and use the information that I can see that the data cannot see to make that prediction, to make that forecast. This is the way we should go, right? So it's always like a really healthy combination of don't ignore the data.

It tells you something, but your experience in your gut is also a form of data. I was going to ask like kind of what's your theory or approach on gut versus data, right? Because, you know, I use a lot of, you know, in sales, they always say people buy on emotion. They back it up with fact, right? Which is true, right? Like it's an emotional, when you're making a decision, it's an emotional decision for the most part for most people. But on the flip side, you can get stuck in the analysis paralysis of working at the data before you make any decision. So what's your balance of gut versus data? Yeah. And so like these pressure environments that we were talking about earlier, let me give you like a basketball example to explain like cleanly how the data and then the experience and gut kind of go hand in hand.

So in decision making, we kind of like to think about or talk about this as like making decisions with your head versus your heart. Okay. So the head is kind of the more analytical part, the analysis part, and then your heart is you're reacting and you're using your experience. So think of like a, like a typical college basketball game. The college coaches and to some extent, the players will, will do scouting and they'll try to figure out, okay, who's our opponent that's coming in? So let's watch some film, let's watch some film, let's look at their tendencies, and let's figure out some plays that are going to counter them based on our personnel, how can we best counter the opposing team? So that's all kind of making decisions with your head.

You get to game time, the referee, you know, tips the ball up in the air and the clock starts. Now you're making decisions with your heart. You're reacting to what's going on. You're reacting to the crowd. You're reacting to how terrible the refs might be. You're reacting to the opposing team. You're reacting to trends. You're reacting to what's the foul count, right? Yes, all that preparation beforehand helped you get there and you're informed by it. You're not ignoring it, but you don't have time to run a spreadsheet during a timeout. You literally have 30 seconds and yet it's barely enough time to open the Sharpie pen or the, you know, the erasable pen and then, you know, drop a play. So you maybe have a little bit of time at halftime, but that's still, it's still very brief.

But then after the game, you're doing post-game analysis. You're sort of going back to the head, which is, okay, how did we do? Let's look at the stats. You're talking to, like, maybe the coach has to talk to the press, right, about, well, this is, you know, these are my players. This is what they did. This is what the opposing team did. This is kind of my reflection on what happened in the game. So you see the shift from, like, pre-game, head, in-game, heart, post-game, back to head, right? And then, like, put yourself in almost any situation. Like, you're going to a sales meeting. You do all your prep before that meeting. But then at the actual meeting, you're thinking on your feet. And you're reacting to maybe what your potential customer, maybe their reservations or their concerns or their questions.

So you're making decisions with your heart. And then afterwards, you call your colleague and say, okay, this is what we need to do to close this deal. It's interesting because I use, you know, like, Moneyball, right? It was actually funny. That was on last night. I was watching it last night. And what I use that example is, like, the Oakland A's versus the Boston Red Sox, right? Like, Billy Bean scienced out baseball, period. You know what I mean? And got them almost there. And this is my argument of science and art, right? I think, you know, sales, I think a lot of things should be a little bit more science than art. But not, you know, because the science lays the foundation, the structure, the process, allows the art form to thrive and be better.

But he completely discounted the art early days of Moneyball, right? And then what happened was the Red Sox bring Moneyball to the Red Sox. But then they get David Ortiz and Manny Ramirez, two of the most un-mathematically, like, because mathematics and data doesn't account for clutch. Like, there's no such thing as a clutch hitter in that world. And David Ortiz, I don't care what you say, when that man stood up to the plate and the money was, and the game was on the line, out of fucking note, like, he would every single time. Right field. Right field. Like, and Manny Ramirez was a savant. There was, like, there was nothing you could predict about what, how that kid could act and what he did.

And he was so unconventional. And so it's the, I think the Red Sox took the science of sabermetrics and Moneyball and added the art to it to allow them to win the World Series. Thank you. And it's the same thing I'm watching right now in Celtics, right? Is like, I love our coach, but I think he's a little bit too by the numbers. That's why we shoot more three-pointers than ever, because by the data, three-pointers are way more, you know, it's better to miss a bunch than, because you'll make it up in the back end. And then you watch the Celtics just, when they're in a slump, which happens, but data doesn't take that for granted, you know, doesn't bake that in. He just keeps saying, just shoot the three.

It'll eventually work itself out. And then you, we get smoked by 30 points because they didn't adjust in the game to the game. And they tried to go to, by the book of, nope, this play, the, sorry, this is what we mapped out. This is what their skills say. And this is what our skills. And that's what drives me fucking crazy. Excuse me about sports that are too analytical about it. I think you can get away with it in baseball because it's like a pitch, a hit, you know what I mean? That type of thing. But in fluid sports like basketball, I mean, it's the whole Mike Tyson thing. You can have a plan until you get punched in the mouth. And if you can't figure out what happens when you get punched in the mouth and adjust, you're not going to get up.

Yeah. And I think that my favorite example to your point is, is the, whether you go for it on fourth and one in football. Right. And I had a great conversation with John Harbaugh about this because I asked him like, Hey coach, like the models tell you, you should go for it on fourth and one almost every time. But, but, but you don't. Why? And, and his answer is, well, because the models are much more aggressive and the models are not seeing what I'm seeing. Bingo. That's it, man. So, I mean, I think we covered a couple of the other pieces, like the humanize, right? The, the analyze and then humanize and scrutinize the data. I want to, before we wrap things up here, I got to talk to you about the Costanza effect, being a Seinfeld fan myself, like, you know, I'm assuming this, this was based on the episode where George decided to make the opposite decision of everything that he, you know, like, so every decision he made, he was like, you know what, if that's what my decision is going to be, I'm going to make the opposite decision of that.

And he immediately got better. So could you explain to me, like, like, what's the, you know, is that the Costanza effect and how is that applicable in a business world when you like, and, and also how do you do that while still building trust with your team that you're not just, you know what I mean? And contrarian, just for contrarian sake, like, cause there's a trust factor here when people make decisions and then flip to the other side. So explain the, the effect, how it's applied and how it, how you do it, how you can do it in a way that builds trust with your organization. Certainly. So I actually, at one point while writing this book, thought that this would be a cool title, the Costanza effect.

Can't stand you. So, yeah, I mean, you, you, you described it correctly. It's, it's basically the idea that if, if you're doing, you're making certain types of decisions and just, it's not working out for you. Like you're still living in the basement of your parents' house and you don't have a job and you don't have a girlfriend. Like you got to change something here. And what's the definition of insanity? Same thing over and over. All right. So, so what I loved about that example, despite the fact that it's, you know, it's just a comedy, it's, but, but it makes you think like, sometimes you need like a Jerry Seinfeld in your life. Because Jerry Seinfeld tells George Costanza, George, you should just do the opposite and everything, instead of going wrong, it's going to go right.

And, and that's like an extremely refreshing reminder that like, we need friends and mentors and colleagues to sometimes just slap us in the face and say, look, you're not looking at this the right way. This is what you need to be doing. And sure, it's extreme to just like go completely opposite. But, but the point is like, he, he, somebody needs to get out of his loop, this reinforcing loop that he was not getting out of. Right. So in terms of the trust part, though, I think that you earn a lot of respect from people when you are open to new information and that new information is going to change how you make decisions. Like George could have just been a jerk about it and said, no, I'm right.

You're wrong, Jerry, you're wrong. But he, he was like, I'm tired of this. I need to change. I'm open to new information. And he was receptive to what his, his, his, his friend said. And look at that. It led to some really funny and cool results. Like that to me is, is like a microcosm of like, Hey, you know, I really respect people. That are open to new information, because think about it. If they were not right. Like that's really dangerous. I mean, that's, that's my problem with politics right now. It's like somebody, you know, if they make a decision, if they said something five years ago and now they're running for whatever, somebody brings up, well, five years ago, you said this and you just, you know, you, well, yeah.

Cause that's the data that I dealt with at the time. And I've learned over the past five years that that was wrong. And the, and the fact that we don't celebrate that, I mean, don't get me wrong. Flip-flopping is one thing, like just saying something like that's a totally different story, but for somebody to fundamentally change, like, you know, like if they're, you know, let's use the maximum, like a racist or a sexist back in the day, because that's the situation they grew up in. And that's all they saw, but then all of a sudden they realized, holy shit, like that is not what I want to be anymore because I've just been exposed to, you know, people in a different light. And now all of a sudden that tweet that they sent out five years ago, that was either sexist or racist or whatever it is, which don't get me wrong, massively wrong, but is now this, this dagger that get thrown at people as opposed to the benefit of the doubt saying, Hey, you know, you know, did you really learn from that?

Like, I'm curious, like, what did you learn from that and being interested in, in that evolution as opposed to chastising? Well, screw you because 10 years ago, this is who you were. And now because you're not that same person, or I think you're that same person, even if you've evolved and it drives me absolutely crazy that we don't give people the benefit of the doubt for changing their mind. Yeah. Yeah. People confuse leadership with being stern and rigid and, and, and, and fixed. Right. But, but, but, you know, leadership, I think is different. Leadership is, is like, Hey, best idea wins. Best idea in the internet. I might not be the one with the best idea. If you bring me a better idea and you bring me the evidence to back it up, you know what?

I got to be open to that. So let's finish on this. If there was one thing that you could tell organizations to, to, to change about how they're preparing people to make decisions. Or if you were to tell kids right now in a world of AI, you know, where AI has taken over a lot of different things, like what's the one skill you think is the most important to foster right now? The most important to develop right now in this world of AI, when knowledge is a commodity, you know, judgment is at a premium, but judgment comes with experience. And so that's my challenge right now is that a lot of this entry-level stuff that these kids, you know, that, that allowed you and I to develop the context, the grit, the acumen, all that stuff is now being ripped away because AI can do it better.

And so I think, you know, decision making and, and how you look at problems and how you look at things is going to be critical versus the actual data that it produces and the answers that it gives it to you. So what are some of the skills or what is the skill do you think is, is the most important one to, to hone and foster in this world that we're living in right now? I thought a lot about this because regardless of whether it's AI that we're concerned with now or 20 years from now, it's going to be something else, right? That I'll tell you universally, what will always be important in decision-making is thinking about the consequences of our decisions. And, and so it's not just the first domino, but it's what happens after the second and third domino fall, right?

And AI might give us like this immediate jolt of productivity, like, oh my gosh, look at how quickly I did that. But, okay, but did you do the right thing? Was that what was actually needed? And, and then how much of the conclusion are you leaving for AI to make versus you owning the decision and owning the outcome, right? So I think the, the consideration of the consequences of decisions, that AI won't do that. AI will not think beyond the first domino. AI will just give you the answer that it thinks you are going to like because it's the average correct answer. But you as a human being have to look beyond that and say, okay, well, say we do implement this solution. What happens then? And then what happens after that?

And what happens after that? So that simulation that occurs in somebody's mind, like that requires a human being that, that cannot be delegated to a large language model or, or some algorithm. Yeah, I think that's, it's, it's important to not outsource the total thinking to these tools. Cause man, you know, they, like you said, they lie a lot. And, and, and, and without that judgment, without the, I look, I think curiosity, right. Of, you know, of, of just being like curious why, you know, kind of the five whys of just getting down to the root cause and help me understand this versus just giving me the answer. Cause that's, that's kind of what the struggle I have right now is, is everybody just grabs the answer and goes and there's a right and a way and a wrong way to use this tool, at least in my opinion, because it's a superpower.

If you use it the right way, if you're curious and you're like, tell me more about this, give me an example of whatever, you know, that type of stuff, man, you could expedite your learning curve faster than you and I ever could when we were kids. But if you outsource your thinking to it, where it's like, cool, thanks for the answer, copy, paste, send, man, you're, you're replaceable in every way, shape and form. And I, and I worry that decision-making that, that judgment in decision-making is going to get reduced because it is so easy now to get an answer that sounds good. So, you know, hopefully give people some, some thought of how to use this the right way, as opposed to getting replaced by it.

Cause it's coming. Well, let's go back to the accountability part. It's even if AI helps you get 90% of the way there, the last 10% is you and you're, you're never going to tell your boss, Hey, well, you know, uh, chat GPT told me that this is what we should do. So, uh, well, if you do, you'll probably get fired almost on the spot. So that's right. Exactly. Cause your boss would say, well, then why do I need you? Why do I need you? Yeah. That's my point with sales reps is like, if chat GPT is going to write the proposal for you, do the prep for you, sit here in a conversation and give you topics to talk about and questions to ask. Like, why do I need to talk to the human to, to, why can't I just go straight to the AI?

And that's, you know, in the macro, that's what I keep saying is like, I'm not worried about all these tools that are coming for sales professionals or whatever the professional is. I'm worried about on the other side, when the customer, whoever that might be realizes they don't need us because if they know how to use it the right way, you know, a lot of times we're just friction asking our dumb questions that are self-service versus trying to help solve the problem. And so that, that dynamic right there is going to be an interesting shift, uh, with where we go because, you know, people say people buy from people. It's like, well, yeah, uh, if those people are valuable, if those people, you know, can bring value to a conversation.

Yeah, absolutely. But if, if I have an AI tool, that's going to answer every single question I have, help me analyze this, give me the, you know, worst case scenarios and all that other stuff. And then I got some rep who's walking me through a generic demo or, you know, asking me their dumb ass Bant questions and meta, like, like I'm going to buy from the computer. So I, I think people are holding onto this human factor and thinking that just because we're humans, we're always going to want to talk to humans. That's not the case. You want to talk to a human that brings value to your life, whether it's a relationship, whether it's, you know, data insights or judgment or whatever, you don't want to talk to a human that is not bringing value to the conversation.

Yeah. And a human that wants to understand your problem. Yes. Empathetic cares. Yeah. I call it the give a shit factor. It's the one thing that AI can't do. Yeah. It can't care. It can, it can seem like it cares. It can pretend like it cares, but fundamentally it cannot give a shit. And that's the one thing that we can do as humans. We can care. And that's the part that breeds curiosity, breeds, you know, decision, you know, questioning and all that other stuff in my opinion. So awesome, Ricardo. Well, look, where can people find out about the book? Where do you want to send them? And, and what can we do to help amplify the message here? Well, thank you. So the five tool team is, is available on your favorite online retailer.

My website is Ricardo Valerde.com. And, and I have an Instagram account for the book on it, at a five tool team. And I'm having a ton of fun just talking to people about how they can make better decisions, not just in sports, not just in life, in business, you know, everywhere. And these are universal concepts. They're going to be around for hundreds of years beyond, you know, your lifetime and my lifetime. Why not spend some time thinking about how you can get better at this? There you go. Love it. Awesome, Ricardo. Well, thank you so much for coming on the show, man. I really enjoyed the conversation. Likewise. Thank you so much. And go Sox. There we go. We'll see what happens tonight, man, against the Yankees.

We'll see if they can pull it off, man. Nobody gave them any, any chance early in the season. So the fact that they went on a heater, again, I'd like to actually look at the data behind them to see if there was some sabermetrics involved in putting this team together. Because this team did not look like they were going to be anywhere near what they are right now. And they got some, they got some Manny Ramirez guys, especially going on the mound tonight. This kid, I love him. Right. So anyways, yeah, we'll see how it goes. But thanks again for coming on. Thank you. Appreciate it. All right, everybody. Hope you enjoyed the conversation as much as I did. And like I said at the end of all these podcasts, go out there and make somebody smile.

Because no matter how bad your day is going or how bad you think it went, if you make somebody smile today, you know you had a good day. And the world needs a lot more of that right now. So thank you all very much. And I will see you on the other side. Hey, thanks again for listening to the show and for all your support throughout the years. This is one last reminder to check out my training programs at jbarrels.com or leave a five-star review on your favorite podcast platform or G2 if you've already gone through my training or sign up for my newsletter. Any of those will help ensure I can continue to bring you free content and great conversations like the ones you just heard without having to know you with sponsors.

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