November 6, 2026 – AI Playbook Eric Siegel and Treasure Map to Joy Stacie Shifflett and a Quick 10 w Kristen Prinz

00:04 Intro 1: In the AM, broadcasting from AM and FM stations around the country. Welcome to the Small Business Administration Award-winning School for Startups Radio, while we talk all things small business and entrepreneurship. Now, here is your host, the guy that believes anyone can be a successful entrepreneur, because entrepreneurship is not about creativity, risk or passion, Jim Beach.

00:23 Jim Beach: Hello, everyone, welcome to another exciting edition of school for startups radio. I hope you’re out there making a difference in the world in your life. You know, it’s up to you where you end up. It’s up to you if you end up unhappy. It’s up to me if I end up unhappy. I have to make the decisions. You have to make the decisions to do something different.

00:50 Jim Beach: And I’m talking to myself, to remind myself just as much as I am talking to you, every single day I have to get up and make the decision to be productive, and I invite you to do the same. Anyway, you got a great show for you today. First up, welcoming back Eric Siegel. He is one of the AI super geniuses, and he has a company with the best name ever, Gooder AI. I absolutely love it.

01:20 Jim Beach: He has a new book out, also on A. I excited to learn about that. Then Stacie Shifflett is with us, to talk about joy and how we can create a map, to joy in our own lives. And if we’re not happy again. It’s part of our own decision. Again, I’m speaking to myself. And then Kristen Prinz is on the last show, but we didn’t get her quick 10 in. So we’re going to play quick 10 today at the end of the show as well.

01:53 Jim Beach: My favorite feature. Thanks for being with us. We got a great show. Let’s go ahead and get started right now. Here we go.

02:02 Jim Beach: I’m very excited to welcome back to the show a professor who will hopefully understand AI a little better than us and can explain parts of it. Please welcome Dr. Eric Siegel back to the show. He was a guest a couple of years ago, is the co-founder and CEO of a company called Gooder AI. Which could have been said better, but I guess that URL wasn’t available, but you know what? I’ll never forget it now. Gooder AI.

02:31 Jim Beach: It is designed to help close the gap between the ability of AI and the actual real business value. He is author of 2 important books. One is called the The AI Playbook mastering the rare art of machine learning deployment, and also the author of predictive analytics, the power to predict who will click, buy, lie, or die. Wow, that’s quite the title. Eric, welcome back to the show. Dr. Siegel, how are you doing?

02:57 Eric Siegel: Thanks, Jim. It’s great to be here. I’m doing good. How about you?

03:02 Jim Beach: I’m good. Do you remember the game Kiss, Marry, or Die?

03:06 Eric Siegel: Sure, of course.

03:08 Jim Beach: You added another one now. Click, buy, lie, or die. What a…

03:11 Eric Siegel: Yeah, that’s right. It’s a, it, it ascends.

03:16 Jim Beach: Well, that’s a class book. I mean, that is a textbook designed for students, isn’t it? A course book?

03:22 Eric Siegel: No, it’s used by 100s of universities, but you know, so are Malcolm Gladwell books. I wrote it in a pop science, uh, mess- manner, because machine learning, learning from data to predict, who’s going to click, buy, lie, or die, commit an act of fraud. Uh, which train wheel is going to break. I know you know someone involved in that area. Um, uh, any outcome or behavior that’s valuable for an organization, right? This is the, that’s the holy grail for informing micro decisions and managing micro risks.

03:56 Eric Siegel: It’s the type of AI you turn to, to improve your existing large scale operations.

04:01 Jim Beach: All right. So, you’ve developed Gooder is an add-on to other AI or is this a totally separate AI?

04:09 Eric Siegel: It’s not the AI. It’s asking how good the AI is. And yes, indeed, we want to help make the AI gooder, um, as my toddler might put it, um, uh, you know, this is our company and both of my books pertain to predictive AI. That’s not the kind of AI that’s making most of the headlines, but it is making most of the returns these days. It’s the, it’s older but not old school. Most of its value is still largely untapped.

04:40 Eric Siegel: And what we do, what we do at Gooder is, if you’re doing one of these projects, you want to like use your fraud detection team more effectively, or target your marketing more effectively, or make a more judicious choice of which, um, credit card applicants to approve, which insurance claims to, to, to fast track. There’s there’s like 1000s of use cases. Because you predict something and that helps you make decisions for each individual case. But whatever those kinds of predictive AI project you may be doing.

05:13 Eric Siegel: What really matters is how much value you’re going to get, when you start to use it, that is to say, to use the term of art in deployment, also known as operationalization, going to production. Um, the launch of the rocket rather than just the rocket science. And in general, these projects get um, stalled after the rocket science. And cosmetically, right, theatrically, everyone loves AI because they love the core rocket science and it is the coolest. Kind of stem, right?

05:44 Eric Siegel: I mean, there’s, so so many pop science books about all, you know, uh, astronomy and all different kinds of fields of science and nobody had really written one about machine learning. Um I think it’s the coolest science. Everyone should be able to understand it. Um, Uh, but beyond getting exciting, excited about that core technology, let’s get it to actually deliver value. Don’t just develop or generate potential value, but realize the value, and that only happens by improving the way things operate.

06:16 Eric Siegel: Things only improve if they change. There’s a change management. Um, struggle. That’s what my second book, as, as you mentioned, The AI Playbook that they’re in the subtitle, um, mastering the rare art of machine learning deployment. And then, and within that realm, focusing on, well, how much money will I make?

06:35 Eric Siegel: Or whatever business metric matters, if you’re a healthcare organization, that could be some other KPI, but whatever the metric that matters to the company, not just sort of an arcane abstract sense, that, that, that it can predict better than guessing, which is what data scientists almost exclusively focus on their technical metrics. Even accuracy is only a technical metric. There’s a bunch of other ones, precision recall, a lot of them. But they don’t, they tell you that the model’s potentially valuable.

07:06 Eric Siegel: This this thing that can predict it’s potentially valuable. They don’t tell you anything about how much value there is. And by making that transparent, and that’s what we do at Gooder AI. We’re a software company, by making that clear and visualizing it, you can navigate to value. If you’re not, if you’re not measuring the value, you can’t be navigating to it, and you can’t be selling it, selling it, internally at a company is absolutely key.

07:35 Eric Siegel: It’s generally what’s missing because the stakeholders, you know, perhaps the non-data professional, the non-quant, uh, a little bit less nerdy than some of us who are in charge of the operation is meant to be improved with prediction. They don’t necessarily understand everything. And the data scientists aren’t speaking their language.

07:55 Eric Siegel: But if you speak their language in terms of really straightforward business goals, like profit, other monetary performance metrics or whatever the KPI or business metrically matters, then you can sell it, you can convince these, uh, powers that be and partners internally at your company to actually greenlight authorize the deployment. That’s what’s usually missing most of these projects still to this date, so that they succeed technically but they don’t get deployed.

08:23 Jim Beach: I had a guest on, and I’m sorry, I don’t remember her name, but she was a PhD and her work was peer reviewed that had come up with 96% of projects, AI projects at the corporate, I think she said 5000 level. Never get implemented, that they get designed and talked about and praised, but actually never get implemented, and that the CEO knows it, and the CEO knows that the CIO is lying and that we’re all in on it.

08:53 Jim Beach: We’re all lying together because we all know that we’ll get fired if we’re not doing it. And hence, one of the major problems. Do you agree with that number and predicted reason?

09:05 Eric Siegel: No, not quite, it’s not quite that dire in my, and I’ve been involved with some of the industry research. I’d say it’s more 15%, 20%. But that’s still pretty dismal, considering that this, let’s, as I mentioned, older but not old school, but sort of the original, when it comes to commercial use of machine learning, when it comes to industrial use of AI, this is the original, predictive AI, otherwise known as predictive analytics, um, and it’s been around for decades and decades, right?

09:37 Eric Siegel: And in the 1960s, we were first trying to predict risk scores on who’s, uh, going to be a risky debtor or, um, you know, how should we target marketing, who’s likely to respond? That really started decades ago. So, there’s this seemingly abominable divide between tech and biz, but I strongly believe it’s not abominable ultimately. It’s just, we got, you know, if the if the business stakeholders, um, get their hands a little bit dirty.

10:06 Eric Siegel: Just delve into some semi-technical, really accessible, interesting pertinent side of it, right, on the level covered in my book. What’s predicted, how, what are you going to do about it and how well is, is it going to work? Um, You know, that’s going to make all the difference in the world and we can realize so much more of the promise and potential of this technology.

10:31 Jim Beach: So what do they need to be doing differently? What does the CIO need to do differently?

10:38 Eric Siegel: Well, the answer to that question, was the reason that I, um, wrote the book the The AI Playbook? Um, and there’s, I also have a Harvard Business Review article that very briefly outlines it. A 6 step practice. It’s called BizML, the business practice, we’re running machine learning projects. I should probably also mention that the book’s website is BizML dot com, B I Z ML dot com. Um, and the 6 steps run the team, um, through the project to successful deployment, right?

11:10 Eric Siegel: So get past the awesome, exciting part about how great the technology is, uh, go beyond the theatrics where to the degree an organization is satisfied. I don’t know if they’re lying very often the way you described. I imagine they are sometimes, but more generally, it may be a little more innuendo than that, but suffice it to say, we’re often, more often than not, unfortunately, stuck on that theatrical side, right?

11:37 Eric Siegel: We got a certifiable expert, your data scientist, your data professional machine learning engineer, you know, preparing the data, making a sound predictive model, developing it from the training data. Um, And everyone sleeps well at night because you’re using the best kind of technology. You’re doing the best cutting edge type of project that promises to improve existing large scale operations, one of the last potential points of differentiating from your customer who’s also doing large scale operations. Um, uh, you know, so everyone can celebrate it.

12:10 Eric Siegel: You can talk about it with the board. The fact that it hasn’t quite been deployed yet, and that the one that we were just talking about a year or 2 go, actually never deployed either. That’s very, uh, quietly, and I, I don’t, these days, the chatbots, you love to use the word quietly so much, and now I’m self-conscious when I say it, but it’s quietly swept under the rug. Um, uh, and the industry’s kind of become adept at that.

12:42 Eric Siegel: But, you know, at some point value matters. At some point there’s going to be hell to pay, there’s going to be a reckoning. Executors are going to get wise or too many pieces of this house of cards going to fall apart. Um, but there’s no reason it has that this has to be the status quo.

13:03 Eric Siegel: So if we run a project that facilitates deep collaboration between tech and biz, between the quant, the machine learning professional and and the person in charge of the operations that are going to be improved with it, who’s going to ultimately green light, the changes the operations. So they’re made more effective or efficient by way of using probabilities to decide, hey, should I automatically block this transaction? It’s potentially fraudulent? Should I expand the cost of a human, pull them in the loop to audit potentially fraud?

13:36 Eric Siegel: Whatever the choice is and there’s so many places where we’re playing these numbers games, played a little more effectively. The fact is, there’s every reason to believe that it, it, it can be very valuable. And of course, even if a mino- only a minority of these projects actually succeed. That’s still lots of successes. It’s great track record, even though it’s, it’s very small compared to what it could be, right? 15% of many projects succeeding is a lot of success.

14:07 Eric Siegel: Um, So getting them on the same page, facilitating deep collaboration, speaking the same language, talking in terms of the business value, not only in terms of arcane technical metrics of pure predictive performance. Right? That’s, that’s what I offer with the business, uh, ML framework, which by the way, isn’t rocket science at all. And in fact, a lot of it isn’t original thinking by me. What’s original thinking by me, is I came up with an awesome buzzword for it.

14:38 Eric Siegel: That is to say, I’m trying to brand it because most people out there. Don’t, most, you know, business practitioners don’t realize that you need a very particular specialized practice framework paradigm, playbook for running these projects, if you’re hoping to beat the odds and actually successfully get it deployed. Let alone know the name the name of one in particular. Um, so let’s, let’s make it a little spiffy.

15:04 Eric Siegel: Let’s put it all in one place, and at the same time, you know, ramp up, the business readers, technical readers need it too, because this speaks to a business practice that’s not uniformly observed. But that the business readers get that semi-technical understanding so that they can participate in and and play a greatly needed role towards getting these projects to start to succeed much more routinely.

15:30 Jim Beach: Can you go through the 6 steps, the value target performance?

15:34 Eric Siegel: Yeah, sure. So, the first 3 are the pre-production, and they’re concerned with what’s predicted how well and what’s done about it. Not quite in that order. Um, but we don’t need to get into all the details. I want to give the listeners like a just a basic sense of this. So what’s predicted and what’s done about it? That’s what defines a predictive project. Right? Who’s going to buy if I contact them with this sending them this marketing brochure?

16:04 Eric Siegel: Which customer is going to cancel if if left alone? Uh, which help, um, which restaurant is likely to show a healthcare, uh, excuse me, a health code violation, if inspected, those are obviously the ones that we should spend the more time, uh, inspecting, higher priority. Then you can think of all of these, or many of these you can think of predict a negative thing in order to potentially intervene. It’s just a form of triage, right?

16:34 Eric Siegel: Um, And where do we spend the time and effort of a limited resource, such as human auditors, of a fraud, of a potentially fraudulent transactions, non-compliant restaurants, building code inspection, fire hazards, manhole covers at risk of a dangerous incident, such as explosion, whatever it is? So, what’s predicted and what’s done about it? Real simple concept and that’s what defines the project.

16:58 Eric Siegel: And then, of course, how well is it predicted, but rather than sticking only to, um, the standard, almost universal technical metrics that just tell you the pure predictive performance, um, right, which is never going to be a ma- magic crystal ball. Who’s going to click, buy, lie, or die? We, we’re no matter how sophisticated tech gets. We’re never going to have a magic crystal ball. But we have the next best thing, which are the odds, otherwise known as probabilities.

17:29 Eric Siegel: Um, and those are the things that we can use to play the number, all these different numbers games that much more effectively. But rather than just looking at sort of how well is it calculating these odds abstractly, let’s say, hey, what if we use these odds and drew the line here and said, hey, I’m going to spend human time auditing the top one. 22% of these 10000000 payment card transactions over the next 3 months. What would happen financially?

17:59 Eric Siegel: Would we, how much money would we save by catching fraud before it happened or recouping it after it happened? And how much money would we spend having human auditors on that many cases, which potentially are going to be a much more effective use of their time than if you didn’t have a way to do prediction and play the odds in this way, right? That is to say, you know, finding fraud in a lot of these things, finding certain diseases.

18:30 Eric Siegel: A lot of these types of detection or prediction, goals that are kind of needle in a haystack, makes a haystack smaller. That’s what you’re doing by playing these odds. But let’s calculate how much money it’s worth, if we did it, if we use these predictions in this particular way over the next few months or whatever the process cycle might be. So that, instead of just sticking with how good is it? Abstractly, it’s how good is it for the organization or for the business?

19:03 Eric Siegel: So again, those 3 things. What’s predicted, how well or how good is it? And then what do you do about each individual prediction? That defines the project. So the first 3 steps breaks that down accordingly. Get everyone on the same page in agreement, much, much better chance the thing’s going to deploy, because what typically happens without that is that the data scientist goes to the business counterpart and says, hey, guess what? I made the predictive model to predict which customers are likely to cancel.

19:35 Eric Siegel: Now you can target, you know, you can offer $100 off the next month of subscription, you know, uh, to these highest risky ones, and the, very abstractly what was going on, but they don’t realize, Oh, wait, now I have to invest in changing operations or in a new operation or campaign, and I have to somehow trust that this idea which seems so great on paper is going to pan out financially, they they weren’t.

20:04 Eric Siegel: They weren’t uh, sort of indoctrinated and and um, and socialized properly and they didn’t get to put in their ¢2 on the details. So that’s what’s generally missing. The other 3, so those, that, that’s the pre-production, the other 3 steps out of 6 are the same technical steps have always existed since the 60s, which is prepare the training data, train the model, over that data. That’s the actual machine learning part of, so it’s the penultimate step there.

20:34 Eric Siegel: We didn’t even do any machine learning to step 5 out of 6. Train the model. Um and then deploy it. So the last one is where you’re actually realizing value, right? The deployment is where you’re going to actually start using the predicted model to put odds on each case and then use those odds, that probability level risk level, to make the decision for that case.

21:00 Jim Beach: All right, excellent. And what will the cheat sheet help us do?

21:04 Eric Siegel: The cheat sheet, you know, this book is whatever, 280 pages. I mean I have it right here. Um, the cheat sheet’s just one side of one page and it’s just list of 6 steps that I just, what it’ll do is much more succinctly list the 6 steps than I just did. Uh, Oh no, there’s 216 pages up until my bio here in the book.

21:29 Jim Beach: And the conclusion, is it a blueprint for us to follow?

21:34 Eric Siegel: Yeah, the conclusion covers a bunch of additional uh, aspects to the process. Um, um, because the process itself, you know, cult, culminates with that. Value, creation, not value creation, but value, capture, value realization of actually getting deployed. Um, then you’re, that, that only if you get that far, does, do you stand to actually gain from the project? Um, but that, that’s not actually the end of the story. It’s literally just the beginning. You started doing operations in a new way.

22:05 Eric Siegel: Now you need to monitor and do upkeep. So there’s some particulars there that are certainly, um, Uh, are necessary. You want to keep the thing alive. You’ve come that far, which is rare. That’s that’s awesome. But overall, I focus on just getting there in the first place because that’s where we usually flop.

22:25 Jim Beach: Right. So, for us small business owners. What advice would you give us? Play with it until they can learn agents themselves. What about that? Advice.

22:35 Eric Siegel: Well, you know, I, I, everything we’ve talked about so far is the stuff that people, unless they’re really immersed in the industry, almost never hear about. Everything we’ve been talking about so far is predictive AI, whereas what you hear about with chat bots, large language models, um, uh, is generative AI.

22:55 Eric Siegel: Um, which is also built with machine learning, but machine learning is essentially predicting what should the next word be in the sentence, uh, or token, really, but similar, um, or how should I change this pixel as I iteratively draw this new picture? It’s, it’s different.

23:12 Eric Siegel: It has to do with literally the generation of a new content item and the, and the, it does that so adeptly and then that can be used in all different ways, including so-called agentic, um, projects, although I, I tend to eschew the word agent because I feel like it oversells the technology. Um, it implies that we’re kind of potentially give agency to a machine, but I think that the people run the world, and that’s not any risk of changing.

23:43 Eric Siegel: Um, and that this is a tool and all all technology will remain nothing but a tool. Um, now, it’s amazing what it can do, and it’s uncanny how seemingly human like it is. It’s extremely valuable. I tend to criticize the hype, which is not the same thing as criticizing technology. Um, I was in natural language processing research at Columbia University for 6 years and in the 90s and I never thought I would see what we see today in terms of the capabilities of these things.

24:16 Eric Siegel: Um, uh, but you do need to be really realistic. So for both kinds of AI, predictive and generative, you need to have a particular problem that you’re going to solve, a particular value proposition, a particular operation that you’re going to either create a new or an, or existing operation that you’re going to improve. There has to be some way that you’re going to use it. Um, the, on the predictive side, it doesn’t matter how large the organization is necessarily.

24:47 Eric Siegel: What matters is how large the operation is. And if it’s being conducted enough times, then there’ll be enough data from which to learn, there’ll be enough opportunities to benefit by tweaking it, you know, by making it more effective and efficient by targeting it predictively. Um, so let’s say you’re a small company and you send one holiday mail order catalog per year and that’s your whole business, right?

25:13 Eric Siegel: But if you’re sending it to 500,000 prospects, then, you know, you’ve got to, you probably have enough data to really benefit from this technology of targeting, learning from uh, test mail or learning from last year’s mail um, to target this year’s. Um, but in general, yeah, so the, the hype kind of promises a degree of automation or autonomy, um, that is generally infeasible. It’s overpromising, that’s the definition of hype. Um, and it’s very important in, to be real cong-.

25:44 Eric Siegel: I mean, the antidote to hype, is focus on concrete value propositions. Um, so instead of leading with the technology, lead with the problem.

25:53 Jim Beach: That is great advice. All right, let’s go, silly. Now, you’ve heard the book, uh, if we build it, they, we all die. What do you think the chances are of something bad happening from this, you know, one of the Terminator scenarios.

26:09 Eric Siegel: Um, zero.

26:10 Jim Beach: Really?

26:11 Eric Siegel: I mean, yep, it’s a ghost story. It’s the epitome of hype. You’re saying, hey, this thing is going to get so smart. I mean, there’s so many aspects of that mythology that do nothing. It’s ironic because it sounds negative and of course it would be negative if anybody died, let alone everybody dying. That’s, that’s negative, but ironically, it’s actually serving to pump up the positive. If it’s going to so smart, it’s just going to kill everybody.

26:40 Eric Siegel: Um, it’s probably pretty good at targeting your marketing as well. Um, so, one person calls that critta hype, like criticism slash hype. Um, I guess that’s a portmanteau. Um, and um, uh, yeah, so, uh, you know, it, it’s, it’s the story that Mary Shelley would have written if she knew about algorithms, and I don’t think we’re going to reincarnate the dead any sooner, uh, as well. It’s fiction. Um, the Terminator movies are fun.

27:09 Jim Beach: That’s right. She wrote she wrote Frankenstein.

27:12 Eric Siegel: Yes. So we’re talking about Frankenstein’s monster. Um, and uh, or the Terminator, right? The Schwarzenegger robot. And, you know, and in, in, in Terminator 2, he said, my CPU is in, in that process, so, and, but it’s not just that he said that, it’s not just that he said that, right? It’s like really compelling fiction. And in most science fiction has artificial general intelligence, right?

27:37 Eric Siegel: It has a machine that, that all the characters in the movie anthropomorphized, and that you and the audience go right along for the ride, and it’s a sort of, it’s a fictional conceit that works really well in fiction, and it turns out, extends very well to very effectively overpromising and hyping the technology.

27:58 Jim Beach: Great stuff. You really brought that down back to zero. Eric, how do we get in touch to find out more and get a copy of the book?

28:08 Eric Siegel: Uh, well, thanks for asking, Jim. Yeah, the books URL is BizML. Remember, that’s the business practice for running machine learning projects successfully, so they deploy. BizML dot com. There’s a bunch of info about me on that website as well, and my other book, Predictive Analytics. Um. And uh, so yeah. Keep tabs, let us know any questions if, uh, if you’re actually gonna embark on a predictive AI project.

28:35 Jim Beach: Great. Thank you so much, Eric. Really appreciate it and we’d love to get you back in a year or 2 and get the next update.

28:45 Eric Siegel: My pleasure, Jim. Thank you so much.

28:47 Jim Beach: And we will be right back.

28:49 Jim Beach: We are back, and again, thank you so very much for being with us. Very excited to introduce another great entrepreneur to the show and on a topic that I think we need to focus a little bit more on. Please welcome Stacie Shifflett to the show. She is the founder and architect of modern consciousness. We will learn all about that, and she has a new book out called Treasure Map to Joy, One Woman’s Journey from Disruption to Joy. Stacie, welcome to the show.

29:22 Jim Beach: How are you doing today?

29:24 Stacie Shifflett: I’m doing great, Jim, thanks for having me.

29:26 Jim Beach: Thank you. So tell us about your journey. What was the disruption and what was the joy? Tell us about what went on.

29:35 Stacie Shifflett: Oh gosh, I’ve I’ve had a lot of disruptions in life, you know, who hasn’t. I think one of the most life-changing ones for me was divorce after 28 years of marriage, which set me on, um, you know, a very deep personal development journey and having the brain that works the way mine does. Uh, that journey was certainly to learn more about myself and see if I could better understand the circumstances that I found myself in, but also very much toward learning more about the human condition.

30:09 Stacie Shifflett: Because I think the more we know about what makes us human. Um, helps us to better understand ourselves, and I think understanding ourselves is key to a joyful life.

30:20 Jim Beach: Or is that the key to despair? Maybe you don’t want to know yourself. Maybe that’s the joy. I think that’s my model right there is to not know myself so that I don’t sound or I don’t get stressed.

30:35 Stacie Shifflett: Oh gosh, I I wholeheartedly disagree.

30:36 Jim Beach: All right, so, um, I’m very sorry about the divorce. I’ve also been divorced and it is horrible. It is so painful. Um, I don’t think people who have not been through it can understand how uh, disruptive it is and uh, how just uh, pulls the rug out from underneath you, everything you know disappears. And so I’m very sorry to hear about it. Was it his fault or your fault or let’s just damn him. I can’t believe what…

31:07 Stacie Shifflett: Yeah, let’s just go mutual. It was, um, it was a decision I needed to make and sometimes the right decision is the hardest. It was a very difficult decision and it needed to be made. So, I made it. Yeah, it was tough.

31:24 Jim Beach: And how did eventually you find joy from that?

31:27 Stacie Shifflett: Well, you know, you you adjust to life. Um, I I meet so many people and, you know, women in particular can stay stuck in the story of the divorce and feeling like a victim. Um, and I understand that. You know, I’ve had, I’ve had things happen in my life where I’ve, I sort of felt victimized myself. But, you know, even if that is the circumstance, staying there is not really a good place to be, right?

31:56 Stacie Shifflett: So I firmly believe in changing your story, changing your life and moving forward. Um, and, uh, it’s important. I mean, I lost, I lost my first child as well, many, many, many years ago. Um, so through the divorce, I kept telling myself, if I could survive that, I could survive this. Um, and I did certainly learn some lessons, um, through the death of my first child, which took years to overcome. Um, and quite frankly, it took a couple years to overcome the divorce as well.

32:30 Stacie Shifflett: But yeah, you either you stay in a miserable place or you make the decision to get out of that space, you know, and that decision is solely ours and we have to take the action to do that.

32:44 Jim Beach: That is very true. So the decision to escape the pain. How did you find the exit, the the way? I got 2 kittens. Yes. That has really helped.

32:55 Stacie Shifflett: Two kittens helps. Well, I did get a divorce dog.

32:59 Jim Beach: Yeah.

33:00 Stacie Shifflett: I did do that. Our family, our family pet had died, I don’t know, a couple of years earlier, maybe. But yes, I did. I you know, I did everything I thought I was supposed to be doing, right? And it was everything I was supposed to be doing at the time. We make the best choices we can with what we have. I didn’t have to worry financially. Um, you know, I was successful in my own right.

33:29 Stacie Shifflett: I was able to get out, get a home, reestablish myself, amp up my social life, so on and so forth. I was traveling and having a great time and, you know, doing some dulling of the pain as well, if you know what I mean. Um, you know, because yeah, pretty much out, you know, drinking having a good time.

33:51 Jim Beach: I was, is that alcohol or one night stands?

33:55 Stacie Shifflett: It was alcohol. Yes You know, out with friends and drinking a little too much. And, um, you know, it was becoming that was becoming the habit and becoming the norm. And uh, at some and and honestly, it was about 2 years in when I really realized that, you know, life was not just going to organically recreate itself. I had to remind myself of all that I had accomplished through my life and a lot of that was done through right action and through my action.

34:28 Stacie Shifflett: And so I, you know, basically was tired of being angry, I was tired of feeling resentful. Even though, you know, sometimes we have good reasons to feel those things. And I needed to move beyond that. So, uh, so I made the decision to do that and and that’s what I did. And and I did it by launching myself into personal development.

34:51 Stacie Shifflett: Um, you know, one of the initial things I did, uh, after meeting with some, you know, a local coach here in the town that I was in, um, you know, and starting to read and starting to study a little bit. Is I enrolled as a platinum partner with Tony Robbins for 2 years. Um, and I learned an enormous amount through that, um, through that interaction, right, through, through those 2 years of traveling the world and hearing all about personal development and business development and spiritual development.

35:25 Stacie Shifflett: And, you know, you name it. Tony kind of covers, covers all bases. Um, But I explored far beyond that as well. And um, you know, through that process. Um, you know, I started making sense of the things that I was learning, applying them to my life, um, not really outsourcing my thinking, um, because I know myself better than anybody else. I bring the lived context to my experience. Um, and, you know, started uh, intentionally.

35:54 Stacie Shifflett: I guess changing myself a little bit, you know, some of the early things I did was, you know, stop making assumptions. Um, I learned how to control emotional triggers, right? And put a little bit of different spin on that for me. Um, and, you know, that’s where this concept of treasure map to joy, quite frankly, came up was when I was doing trigger work and I had had started modern consciousness at that point.

36:23 Stacie Shifflett: Um, and to me, triggers uh, calming your emotional triggers are waypoints on your treasure map to joy. You know, when we allow external things to influence us internally, uh, it doesn’t always uh, result in the best solution for ourselves, right? It wastes a lot of a lot of time and energy and emotion. Um, so that’s how treasure map to joy sort of came about. Um, and that has become something that is, you know, central to my my business here at modern consciousness.

36:55 Jim Beach: So tell us about that. What services do you offer? What are the, uh, things that we can get buy and can we buy happiness?

37:04 Stacie Shifflett: Can you buy happiness? You can buy understanding. You, you can, you can do the work to gain the self- understanding. Um, you know, I truly think that that our greatest personal asset is internal peace. I think the way that you get there, the prerequisite of that is knowledge, but it’s actually applying that knowledge in a way that’s sufficient and effective in your life. And you do it just a little bit at a time. So I have, um, 2 programs.

37:35 Stacie Shifflett: I have something that I call the clarity sequence, which is something really you do yourself. I am a master at asking questions. I think if you ask the wrong question, you get the wrong answer. So through the clarity sequence, I ask a lot of questions from 2 perspectives. And I didn’t, you know, when I was seeking, um, assistance and help in doing my journey and looking at coaching programs that others were offering, you know, that you could get certified and so on and so forth.

38:08 Stacie Shifflett: One thing that I really saw missing was doing a deep holistic evaluation of your life and really understanding where you are right now, what your life experience is right now. So I call that assessment. And then part 2 of that of the clarity sequence is called aspirations. And that invites you to look at your life in a different way and say, okay, how would I like to experience life? Not in the context of external achievements, but internally, emotionally, how do you want to feel?

38:41 Stacie Shifflett: How do you want to feel at your work? How do you want to feel in your relationships, so on and so forth? So I think that oftentimes we step into situations. We think we have a problem, and we’re very good at solving that problem, but the problem we’re trying to solve is possibly not the problem that we have.

39:04 Stacie Shifflett: So I think it’s very important to get a good understanding of your current situation and your current experience in life, which is my assessment, and also a good understanding of what you would prefer to be experiencing. What would you ideally like to be experiencing that’s in a realistic realm? And um, Also, what is most important to you because we lose track of that over time.

39:29 Stacie Shifflett: We end up executing against decisions we made years ago, sometimes decades, are the momentum of our life is created by our activities by our calendar, and we lose track of intentional living.

39:42 Jim Beach: That is so very true. I, uh, during some of my times of depression have, uh, totally been unable to execute on any plan and the days that I have felt better are the days that I have done something. And there’s nothing more important than doing one or 2 things a day just to try to battle out of that. Um, Very, very. So what are some of the other things in addition to the dog that we can do to get out of this rut.

40:14 Stacie Shifflett: Um, you know, I’m, I’m, I’m, I’m pretty convinced that it’s understanding ourselves. Um, you know, I’ve I’ve done I’ve got a theory on joy and um, one of the things that I’ve been looking at deeply is, Just how much our relationship with joy helps us understand our relationship with our life and how we’re experiencing life. I think there are a number of orientations. One is not better than the other. One is not preferred over the other. They all have strengths and they all have a shadow.

40:48 Stacie Shifflett: Like even if joy is available to you when life is difficult, which I think joy can be, um, you know, if you’ve ever sat through someone in the dying process, even in the midst of all that grief, we can still laugh about memories with that person. Um, so, you know, I think that’s evidence that joy can be available even when life is difficult. That orientation I call internally rooted.

41:15 Stacie Shifflett: And, you know, but the shadow side of that is that you could underestimate your own capacity and overlook your own needs, right? Because you’re like, oh, that’s fine. I’m joyful. You know, oh, that’s okay. That’s okay. So, you might be overlooking parts of yourself because you’ve come so good at handling it. Um, there’s another orientation that’s deferred joy, which is where we’ll say, okay, I’m going to enjoy life when, right? We have scheduled joy for a later time in life. The strengths of those individuals.

41:48 Stacie Shifflett: They have a lot of commitment and perseverance. They’re reliable. They’re responsible. But they always find another reason to postpone joy. And what if the conditions that you’re waiting for never arrive. So I truly believe that if we slow down enough in life and allow ourselves to enjoy intermittent pockets of joy throughout the day, which can be as simple as something for me lately, the hummingbirds are here, right? So I can sit outside on my deck for 5 minutes and enjoy the hummingbirds.

42:20 Stacie Shifflett: The thing is, I’m not on my phone. I am present with the experience. And so I have put aside, you know, I’m not sitting there thinking about things. What happened yesterday, what I need to do tomorrow, what I need to do for that afternoon. I can schedule joy into my day. So, I, you know, I think that’s one way that you can do it is, is schedule some fun, enjoy in your day. Even if even if you can only muster 10 or 15 minutes of it.

42:53 Stacie Shifflett: Um, you know, if you’re terribly depressed. Sometimes for me, watching a funny movie does great. If you need to cry, that’s okay too. You know, watch a movie that brings you to tears to help you release that emotion. There’s nothing wrong with our emotions. There’s signals. We need to process them. They’re human. Engage with them in a healthy way.

43:17 Jim Beach: All right, great advice. What are some of the signals that were getting better? Do we just know when we’re getting better?

43:25 Stacie Shifflett: You actually do, and I have experienced this myself. Um, you know, especially, you know, when you become aware of something and you say, okay, that’s a thinking pattern that I might want to replace. Um, and You consciously do that, right? So you kind of catch yourself with your awareness when that thought comes up or when you’re about to react in a certain way or what have you, and you sort of course correct as you go along.

43:55 Stacie Shifflett: And as you do that for a while, it becomes second nature, like any habit, right? Thing is a habit. And then one day, and this has happened to me, um, you know, you’re in a situation and you respond in a particular way and you go, oh, that was me. Right? Like that. Oh, wow, that was me. And it was totally natural. It was not the me from 2 months ago or even a month ago. But it is the me today.

44:26 Stacie Shifflett: And so you have incorporated that way of thinking or that behavior so that you have embodied it and it becomes more natural for you. As it becomes more natural, you have to think about it less. And then, you know, I’ll pick something else and work on that. So um, just doing that a little bit of the time. And of course, I’ve been doing this for years now, so it’s very, a lot of things are very natural for me.

44:57 Stacie Shifflett: I’m not perfect at it, I can tell you that. But, um, you know, it, it, it makes a big difference and you just kind of know because you have that epiphany that like, Oh my God, that was me just then. And it’s such a good feeling.

45:15 Jim Beach: Yes. Almost like getting your taxes done.

45:16 Stacie Shifflett: Yes. That might be more relief. But yes, it does feel good to get a, to get a good, a big project finished, yes.

45:27 Jim Beach: Yeah, I the only way I’ve been able to get out of depression is to work. And I know that isn’t what people want to hear. They want to, the only way to get out of depression is to watch TV. 9 days in a row. You know, uh, the only thing that makes me better is work. And once you identify that, that’s easy because you can always find more work to do as an entrepreneur.

45:55 Stacie Shifflett: Yeah, we have to figure out what is going to help us improve, you know? Um, I’ll share a story briefly with you, um, if I can, if we have time, um, when, okay, when my son died, and and I think it’s just such a good example. Um, and it’s been years, decades. Okay? So I can I can talk about this. Um, my doctors had recommended me to go to a group counseling session for parents that had lost children. Children of all ages for different reasons.

46:28 Stacie Shifflett: And I did go and I only went once. And I remember it to this day, even though it was decades ago. During the course of that meeting, there was a gentleman that stood up and talked to the group. It had been 5 years since their daughter died. And his wife could still not leave the house to go to the grocery store. And I could understand that 110% because in that point in my journey, I didn’t want to leave the house either.

47:00 Stacie Shifflett: I didn’t want to go back to work. God forbid somebody else knock on the door and leave flowers. You know, any of that. I just couldn’t deal with any of it. But I left, um, with a couple of thoughts, and it took me quite a while to execute against them, uh, but they were very meaningful me to me. One was, oh gosh, do I want to be in this emotional space in 5 years? And the answer to that was no.

47:31 Stacie Shifflett: Um, the other thing that that brought up for me was, okay, um, and and this is how sometimes when we can reframe something, it helps us, um, move beyond it. So my child died very young and a month old. So I did not have a home that that child had inhabited, right? He hadn’t been home. So I could go home and I mean, although I had to clean the nursery out and all that, he had never been at the house.

48:02 Stacie Shifflett: So, I told myself, you know, I could tell myself I was robbed of all the memories of that child, which was the first thing that I went through, right? And then that became, okay, maybe I have a blessing here because I don’t have those memories, right? Every time I walk in the kitchen, I won’t be thinking of how cute they were helping me make cookies at the kitchen counter. Or every time we put the Christmas tree up, I won’t have the memories of that child.

48:36 Stacie Shifflett: Um, and so that’s kind of what I latched onto because that served me better at healing. Um, then believing that I had been uh, cheated. Uh, and had all of that taken away from me. So, um, it’s a, it’s, it’s a big example and, But I think it’s a good one. I think it really shows does not mean that it was easy by any means, not by any stretch of the imagination.

49:04 Stacie Shifflett: You know, even 2 years after he died, I could see a commercial on TV that would move me to tears, right? Because we still have a lot of emotion, um, to deal with and release, um, but those things helped me.

49:19 Jim Beach: Great, great stuff. Very powerful story, Stacie, thank you for sharing that. We are on time now. How do we get in touch? Get a copy. Reach out.

49:30 Stacie Shifflett: Yes, the book, Treasure Map to Joy. You can get on amazon.com and other, you know, online retailers. And you can contact me. You can connect with me. You can find my social media channels, everything that I have to offer, including some free resources, on my website at modernconsciousness.com.

49:48 Jim Beach: Fantastic. Thank you so much for being with us, Stacie, and opening up and sharing so much of your life. Godspeed to you and I wish you eternal happiness now on.

50:00 Stacie Shifflett: Thank you, Jim. You too.

50:02 Jim Beach: And we will be right back.

STATION BUMPER

50:04 Station Bumper — Unidentified Voice: Well, that’s a that’s a wonderful question.

50:07 Station Bumper — Unidentified Voice: Oh, my gosh, I love the opportunity to do this.

50:11 Station Bumper — Unidentified Voice: Thank you, Jim.

50:12 Station Bumper — Unidentified Voice: Wow, that’s a great one.

50:14 Station Bumper — Unidentified Voice: You know, that is a phenomenal question.

50:17 Station Bumper — Unidentified Voice: That’s a great question, and I don’t have a great answer.

50:21 Station Bumper — Unidentified Voice: That’s a great question.

50:23 Station Bumper — Unidentified Voice: Oh, that is such a loaded question, and that’s actually a really good question.

50:28 Station Bumper — Unidentified Voice: School for startups radio.

QUICK 10 | KRISTEN PRINZ

50:30 Jim Beach: We are back and have another brave contestant willing to play the quick ten. Please welcome Kristen Prinz to the show. She is a negotiations attorney out of Chicago and has just written a new book on how to negotiate better. I heard you want to play the Quick 10.

50:48 Kristen Prinz: Yes, I do. I want to see how I do on it.

50:53 Jim Beach: All right. Are you currently sober? I’m required by state law to ask.

50:58 Kristen Prinz: Yes, I am currently sober.

50:59 Jim Beach: Okay. Do you want to accept the standard wager?

51:03 Kristen Prinz: What is the standard wager?

51:04 Jim Beach: The bet that everyone else made.

51:07 Kristen Prinz: I need to know more. What is that?

51:10 Jim Beach: The bet that everyone else made. Just say yes.

51:12 Kristen Prinz: Well, what do I have? Yes. Okay, fine, yes.

51:16 Jim Beach: Number one. Your favorite creativity hack.

51:19 Kristen Prinz: Step away for 5 to 10 minutes and take 5 deep breaths.

51:23 Jim Beach: Number two, favorite bootstrapping trick.

51:24 Kristen Prinz: Yeah, ultra focus. I’m doing one thing. Really, really, really, really well and get to know your market better than anyone else.

51:33 Jim Beach: Number three, name your top passions.

51:35 Kristen Prinz: Reading travel exercise. Humans, and my number one passion is seeing others reach their potential. So family friends, clients, strangers, everyone.

51:43 Jim Beach: Number four, the first three steps in starting a business are…

51:44 Kristen Prinz: Have an idea with a name, talk about it and ask questions from everyone, and start charging money for your product or service. After that you can do everything else.

52:00 Jim Beach: Number five, the best way to get your first real customer is.

52:04 Kristen Prinz: Solve a real problem you see in the world. Tell everyone about it and require payment for the solution.

52:12 Jim Beach: Number six, your dreamiest technology is.

52:14 Kristen Prinz: Uh, my assistant and I set up an AI phone call, reminder that calls me 15 minutes before every meeting and it is my dreamiest technology.

52:24 Jim Beach: Number seven, best entrepreneur advice.

52:25 Kristen Prinz: Learn from failures, celebrate the wins.

52:28 Jim Beach: Number eight, worst entrepreneurial mistake.

52:30 Kristen Prinz: Getting distracted by what everyone else thinks or believes.

52:32 Jim Beach: Number nine, favorite entrepreneur and why?

52:35 Kristen Prinz: Ooh, uh, Oprah, because she built a whole business around human stories. And how we can be better.

52:42 Jim Beach: Number 10, favorite superhero.

52:43 Kristen Prinz: Oh, Elastigirl from the Incredibles.

52:46 Jim Beach: All right, while we calculate your score and find out the winner of the wager, how do we get in touch with you and get a copy of your book?

52:56 Kristen Prinz: You go on Amazon and order it right now for pre-order and it will come out November third.

53:04 Jim Beach: That was yesterday. Your score, Kristen.

53:05 Kristen Prinz: I’m so sorry. Oh, I’m devastated.

53:08 Jim Beach: You have to have a 94 95 to win our little games and you got a 94. Apparently one of our judges was for Milwaukee and didn’t really like some of your Chicago comments and stuff. So unfortunately, we win the wager and as always, we’re playing for a Tesla. So you owe us a Tesla.

53:28 Kristen Prinz: I look forward to receiving that soon.

53:31 Jim Beach: All right, it’s on the way. It’s used and I think it’s broken down too, but the tow truck will arrive shortly.

53:39 Kristen Prinz: All the Teslas have broken down.

53:41 Jim Beach: We get 3 or 4 of them together, we make one good one. Well, I was wrong about that. We are out of time for today. Thanks for being with us. Go tell your friends, go make a million dollars. Bye now. Have a great day.