Episode 14: Making AI Work: Operationalizing AI Across Healthcare, MedTech, and Life Sciences

But in starting to stand back in 2003, it's crazy to talk about 23 years of doing this with the same company I found my passion clearly and love of it. We focused on health care because there was always that sense of giving back and in 2003 we had different challenges, there was a lack of data and health care has always been the first industry to embrace technology and thank you X-rays, your MRIs, there's a lot of technology about it, but data was always sort of a little behind the curve, but fast forward 20 plus years, the world's changing, everybody's aware of AI, it's making a significant impact at health care is in fact one of the leading industries in embracing these AI technologies today. Welcome to Playbook AI Partners Podcast, the show that turns AI from overwhelming into action of all. Your host Sandy Kibling is the chief playmaker, helping business owners and teams stop chasing shiny tools and start using AI in a way that actually moves the numbers, saving time, reducing busy work and driving growth. Each episode features industry experts and real world tactics providing clear, do this next place, simple AI workflows and practical guard routes so you can use AI safely, execute with confidence and get results, let's run the play. Hello everyone and before we get into the show, I wanted to cover just a couple of things. Number one, our AI solution Mini Locker. This is a series of quick 20 minute courses that you can learn a new skill, you can learn about a new tool, you can learn how to solve a problem, whether that's email overload or learning about your digital navigator and teaching AI about your business so you have more refined or customized prompts, whether you're looking for marketing strategy or creating a newsletter and saving up to 15 to 20 hours a week. So make sure and check that out. If you're not sure of what skill you want to learn, there's also the NoFumble AI 3 pack and the Playmaker 5 pack which these are pre-bundled courses that help you whether you want to learn automations or social media, how to enhance that, they are bundle courses to help you accomplish that goal. Make sure and check it out, they are affordable and again, who can't be 20 minutes of time to learn a new skill. Finally, I want to talk about the huddle, the huddle is our community and one of the things that I have found is to stay current on AI and to figure out all the tools and all of the things you need to know without overwhelm community really is best. In our community we do two live sessions a month talking about a tool or a current trend to help you stay on top of things and we also do quarterly boot camps. This quarter and we are talking, it is currently June, 2026, we are doing a boot camp on the digital navigator, talking about how you can really teach AI about your business, really setting things up to get those customized responses and I'm going to show you how you can save that 15 to 20 hours a time. Through July through August 15, it is free. You can come in, experience two live sessions as well as participate in the boot camp to get a flavor of what it is like and access to the AI solution mini locker. I will drop a link to that in the show notes so check it out, hopefully I will see you there. And now on with the show. Well, hello everyone and welcome to the show enabling healthcare, Medtech and life science organizations to operationalize AI from strategy to scalable execution. AI has the power to transform healthcare, Medtech and life sciences but only when it moves beyond ideas into real workflows. So how can organizations turn AI strategy into scalable responsible execution? To get into this topic today, I have Richard Kedziura, also known as RJ on the show. RJ is a co-founder of Astenda Solutions and is over 25 years of software product design, development and management experience. At Astenda, he focuses on people and process management and provides strategic, technical direction, guidance and innovative insights into creating cost effective digital health solutions that make a difference in people's lives helping them live, live longer and healthier. His work is one multiple awards, including the HEMs, Davies award and recently the Health 2.0 Outstanding Leadership Award. Welcome to the show RJ. Thank you for having me. I'm looking forward to that conversation. Absolutely. Well, let's get into it. So why don't you share with us your journey and how it's led you on the path today you're on with Astenda Solutions and AI? My journey actually starts decades ago. Interestingly enough, as there was a teenager in high school and even in the college, my thought was I'm going to go and get a PhD in AI. It's hard to believe people are like, AI really existed way back in the '90s. Yes, it probably even in the '70s AI was a topic. It's really hit its stride in the last few years. But through the '90s, I got going in various different consulting areas and accounting systems and just really found my passion and software development. But in starting Astenda back in 2003, which is crazy to talk about 23 years of doing this with the same company, I found my passion clearly and love on it. We focused on healthcare because there was always that sense of giving back. And in 2003, we had different challenges. There was a lack of data. Healthcare has always been the first industry to embrace technology. You think your X-rays, your MRIs, there's a lot of technology at all. But data was always sort of a little behind the curve. But fast forward 20 plus years, the world's changing. Everybody's aware of AI, it's making a significant impact. And healthcare is, in fact, one of the leading industries in embracing these AI technologies today. Still lots of challenges, but they are on the forefront of adopting AI solutions. About getting your PhD in AI, because you're right, it is all of a sudden, it's just yesterday or last year, we may have heard about AI, but it certainly hasn't over the past, I think toward the end of the year and this year, it seems to me that it's really just picked up, like just in significant momentum and being a topic of conversation. So it's hard to think that it's been around a while. And I kind of laugh, I mean, you've been on my healthcare podcast. People talk about 20 years of experience, but AI, most people don't have that. Because it's been around, but I think people, it seems to me to maybe to sum it up, are people are embracing it more. So I find that interesting, but I'm glad you've been in it to win it, so to speak for a while. Now you also meet a comment about healthcare and that healthcare seems to be one of the industries that's really embracing AI. Not to say I'm glad I'm sending, but healthcare sometimes it always seems a little bit behind in embracing technology, at least from what I've seen. Tell me more about healthcare embracing AI today. It's in two specific categories and even more so in the first one, and the idea of ambient listening and taking notes. So your healthcare practitioners, doctors, nurses, they didn't get into healthcare to stare at an electronic medical record while you're talking to a patient. That's not why they want to do this. And so we can take this technology, it can listen to the conversation, can transcribe the notes, and do a very good job of that, such that the healthcare professionals can focus on the patient, why they got into healthcare. So that's the first use case that's really been very quickly adopted by the industry. It's been fascinating even seeing that progress over the last couple of years as I've gone to various healthcare technologies, you have 10 different startups all with their ambient listening technology. You talk to them as like, what's different about yours and what's different about yours? It was a big difference. They were all just trying to make their mark in the world. And now the EMRs are adapting that technology, bringing it into themselves. So it doesn't make someone to challenge for some of the smaller startups, how are they implementing the ambient listening? And you think about large language models, the open AI and the chat GPTs, and they aren't known for hallucination, making facts up and not being 100% accurate. And that is looked upon by a lot. You see criticism in many industries, including healthcare, like, it's going to listen to this conversation. How's it going to transcribe an accurate note? It is true. The pushback that I don't think is recognized enough is that us, people, humans, we're not perfect either. So a very well-accepted use case in the world of healthcare is scribes. Is people listening to these conversations? There are stats out there that the human scribes are not perfect. And if you implement a review process, there's notes can be improved, they can be made better. Same thing with our language technology that we can then use those, yes, it might make mistakes, but then we implement those review processes that we already have in place. It can improve the quality of the notes and save time. Some of the most recent research is talking about doctors and. situation, saving as much as an hour to a day. That's a phenomenal amount of time. The other use cases are radiology reading images. Some of the earliest AI approved technologies on the area of radiology and looking at X-rays and we are using it at a stand-on, some projects for retinal looking at retinopathy, images, diabetic retinopathy, doing go-no, go decision making. They're still very much a human in the loop to validate what it's seeing and helping them in the process, but it's speeding up the ability to read those images. Very much an accepted use case. And then now, even from an admin perspective and billing, revenue cycle perspective. Interesting from that perspective is AI is increasing that billing, that's being sent off to the insurance company. And what I found fascinating at some point here, we're going to get an AI submitting the bill. And the insurance company using an AI to be like, "Hey, is this valve?" You know, these two AI is talking to each other, which is a whole lot of world challenges, but that's where we're going. Now, I find that interesting, you know, the retinopathy, we probably talked about this last time too, but I've often wondered because other people can still be skeptical about that kind of thing. But, you know, I think back about a retina specialist, you know, you think about if you go into a retina, which I do, but you go in and you literally see your retina specialist for like three minutes, if you're going in for an appointment, that's all you get, three to five minutes. And that's just the traditional system, but I have often wondered over the years, they take scans every time you're there, and I've often wondered, you know, what if you could take those scans over the period of years, look at those and see if there's some anomaly or something to help because I laughingly asked my provider last time I was in there, I was like, "Have you ever looked at my scans over the past five years?" It goes, "No, don't have time." Yeah, they're busy. They do. But I'm amazed and intrigued by what you're talking about the diabetic retinopathy, and also being able, I would think, to be able to take scans over a period of time to a point that a doctor doesn't have time to do, and maybe, or maybe not, but you never know, uncovering that anomaly. So I think if we can embrace that, that would really be so awesome and saving time because we know our traditional healthcare system is fragmented and broken in many ways, so we can, if we could use AI to help. So on that note, what are the biggest risks or concerns that you're seeing today that people actually, not risk, maybe hesitancy, I should say, people have great ideas they want to implement, but somehow getting from that idea to creating an actual workflow that works. I mean, there's just hesitancy or somewhere along the way the wheels fall off. What are your thoughts on that? Yeah, there's a couple of things going on there. The first challenge is overcoming that hesitancy. Like, can we implement this? AI solution? Is it the appropriate thing to do? Am I going to lose a job because of this? Is it big concern across many industries, particularly healthcare, and we saw that early on, radiology, I mentioned, with some of those earliest FDA-approved use cases, and there was prognosticators that are like, it's going to decimate the radiology industry and we're not going to, you know, why bother studying this? That's proven to not be true. We need those radiologists more than ever because they don't just read images. They provide so many other services and values, and that's what we're seeing across multiple industries. You know, there's also the same sort of thing. It's, you know, I'm in software development. You're not going to need software developers because AI can write the code. Yes, AI can write the code, but you still need somebody to tell it what to do and make sure that it's doing it in the right way in a secure way, and that's what we're seeing in healthcare. So first, you have to overcome that fear uncertainty of doubt of like, if we're going to implement the AI, do I even have a job anymore? Yes. You're still going to have a job, which is important. People using hospitals using the AI are going to start replacing the people that aren't. It's making a difference or seeing it in the productivity metrics, the efficiency metrics. It is making a difference. So you really have to start on this path. With the second thing is change management, and this is not unique to AI systems and implementing AI systems. You need to think about change management, thinking about that workflow. How do you implement it in part of the workflow? You know, it's done. We've done a lot of work in remote patient, monitoring, in chronic disease management. All of those things is like, great, we can create a standalone solution, but if it's not part of the workflow, it's never going to be adapted by the healthcare professionals, the providers. It needs to be part of that workflow. So you have to think about how you're implementing AI to be in that workflow, have access to all of the data. That's one of those big challenges still today. You know, I talked about 20 years ago, is gaining access to the data, like getting the data was difficult. Today, we had so much data. It's probably overwhelming between medical record systems, just the level of knowledge in journals, wearable data that, you know, I wear an R-arrang, wear an Apple Watch, you can generate all sorts of information about you as an individual, and then go to your doctor and be like, hey, what do you think? Getting access to that data in the workflow is very important. The AI can help you interpret that data and understand it. We're still struggling from an operationalization perspective of how to connect all of these systems. You have legacy EMR systems and getting all the wearable data. It's changing, it's rapid. It's not a technology problem. It's that operationalization. It's the people problem. So I think those are the big challenges. Overcoming the fear and the workflow issues, change management, making sure that that's incorporated into the process, and then the integration and inoperability of data is what we're seeing. But continue to make great struts. What's the winning question, right? How do you solve that problem? You go into an organization. You see these issues. How do you help solve that problem? I know it's a big question, but how do you get people from that hesitancy to, I'm ready. Let's go. We know there's some challenges, but we see the benefits. How do you get people from that big fear to implementation? Yeah, I think the first good staff is education, and I would start at the top. You're executives, the board of directors, department leads, provide that education such that they can message the rest of the staff within the hospital to provide that assurance and understanding of what this actually means and why you're doing it. Education, number one, alignment is the second thing. Across that executive board, including the technology staff, what are we doing? What are the risks? How are we dealing with these and embracing the idea of change? One of those challenges above and beyond everything that we're talking about is just the rapidness of change in AI now, sort of overwhelming. There's this idea of like, if I'm developing a solution in AI, I can't do it today. I just wait three months until we have to do it. That's how fast some of these technologies are advancing. Education is a key experiment. Is that alignment then to experiment with these solutions getting about there to learn those lessons? We are all learning these lessons of how to implement these systems and what these new challenges are. There's the two keys and the third I would add in that change management. It's like, how are you messaging us within the organization, making a part of the workflow and making sure that everybody on the team is a long word with what you're doing. Your comments about the change management take me back to the day back when, 1999, 2000, when SAP Enterprise Resource Planning System was so big and I was actually in one of the big five as it were at that time consulting firms and that was my job, change management. There was an actual position in job for that because so many people didn't want to embrace the implementation of a new system for all the things that you've mentioned. Fear of job loss, transition, more workload, learning something new. That hasn't changed. I think what has changed is the rapid pace in which AI is moving and trying to get people to move a little bit quicker through that change management process which is no small feat for sure. With that said, why don't you tell us more about the stint of solutions, what you guys are doing, more specifically service offerings and maybe a when case that you can share with listeners? Yeah, absolutely. Estenda is a custom software development data and AI consulting organization. We partner with large corporations, hospitals, health systems, medical device providers and some startups too along the way to develop new tools and technologies to take advantage of data and AI to improve that patient health and wellness. Our typical projects are an MD and a PhD. As we're exploring new avenues, striking new grounds, it's like the doctor can have an idea, PhD can have an idea but it's one thing to have an idea, another thing to implement and improve that it actually improves patient health. health and wellness. You know, we've been part of many R&D projects where it's like, okay, that didn't move the needle in the way we thought it would be. How do we adapt? How do we change and advance that such that it does? One of the recent interesting projects that we got through the NIH National Institute of Health here, we got an SBIR grant to the small business innovative research grant, very fortunate to get this. And the idea is to implement a domain specific repository for smell test data. You don't think about the loss of your sense of smell, and it didn't get a lot of attention until COVID happened when everybody started losing their senses. As I wait a minute, so now there's a renewed energy around advancing the science of smell loss and smell testing. The loss of sense of smell can be an indicator of brain issues, Parkinson's, Alzheimer's, which makes it fascinating. So there are many smell tests out there available in the market. It's not a new industry, but it's how do we bring all this data together and then apply AI on top of that to learn from this information to, again, improve the health and wellness of our fellow humans. There's been a fascinating project as we're working with different PhDs and MDs out there to bring all of this data together and one of those challenges is like, can you smell chocolate or rose or gasoline, grass kind of thing? And what does that actually mean? So that's just a quick example of a major product that we're working on all these days, which is just so much fun to work on. So let me just ask you a question. Let's say if you could have given me a comparison, the time it would take to make some progress on that manually and versus using AI. I mean, is it a three months different, a six months difference in terms of getting some valid data that you can share? It's interesting because this project we're using AI in so many ways from a software development perspective, from a writing requirements, from writing test cases, using it to, you know, so you have all these different smell tests and different research projects and clinical protocols, take that data and store it in different methods. You know, if you look at, there's one of the tests that's called SNIP and STECS, another is the Sentinel test. And it's even though it's the same test across different institutions, and research protocols, you're going to store that data in different passions. So when you want to bring this together, create, put it into a domain specific repository, especially you can compare across these different research protocols, you have to clean up that data. And it's a long science around cleaning up data, but now you can take AI and say, here's one data set, here's the other data set. I want to match this. And it does a lot of the work for you. It just accelerates the process so much, you know, so we enjoy using it from, you know, that software development perspective, it really accelerates our ability to experiment in, you know, in early stages historically, when we were in the design phases of a project, we would use, you know, can the thing, the different things where you're drawing pictures, and then presenting that and working with the client. Now we can create usable interfaces so quickly with AI, we're doing that. It's like, hey, here's the idea. Here's an interface. Here's something to talk about. Now that gets you to the early stages to be able to talk about it. Then you have to implement, you know, what we know is, is good software development practices to make something that is implementable in production that does apply to, you know, the HIPAA privacy guidelines and cybersecurity guidelines. AI is capable of doing this thing, but you have to guide it in the proper ways to make sure that that actually happens. You can get a work and prototype pretty quickly, but it takes a little bit longer to get something that is HIPAA compliant, cybersecurity compliant. And then, as I said, on the back end of that data management aspects, it helps accelerate that. And then, what we're doing with this is once we have that repository, now you as a scientist and an end user don't have to know the technical programming languages to look at that data. You can just talk to it. It's like, you can talk to a chat TVT, you can talk to the data, and start exploring it and picking it apart, and seeing what new patterns and trends you can find in that data, just like you do with chat TVT. It's just fascinating. Yeah, I love the word accelerate. It's hard always pinpointing a timeframe on it, but just amazing what you can do. Now, let me ask, as you're going through this process, we know in the world of healthcare, there's a lot of regulatory requirements and HIPAA and things like that that you mentioned. But to companies that are listening and maybe have that concern about data and preserving what, you know, in the data that you get out, we talked about hallucinations. What sort of protocols do you have in place or did that human in the loop to check that data to make sure that everything is accurate? Do you guys have a process for that to kind of put those fears or concerns that listeners have at ease? Yeah, as we work with healthcare professionals and they may want to use these AI models, say, chat TVT, I'll just generally say, you know, open AI at chat TVT, but there's many models out there. They have created targeted systems for healthcare medical professions to use. You want to, they will sign business associated agreements. So, you know, if you put your data as an individual, like, say, me, put my data in chat TVT, that's not protected by HIPAA. It's when the healthcare provider comes into the equation that HIPAA comes to play. So, you do want to be cautious about putting your identifiable information into the systems to protect your privacy, the healthcare practitioners. This is definitely something they do at an institutional level, but make sure that they sign those business associated agreements which pulls in those same protections through to the open AI organizations. That's sort of step one. The next two things, one, you have to guard against hallucinations, biases is very important in the world of AI because it's training on data that we've created, processes that we've created, we as humans have biases. That's then in turn reflected in the AI systems out there. So, you have to be cautious about those and think about those challenges. And then with AI, it's called drift. You know, these systems learn and change over time. So, you want to make sure that it's staying in alignment with your goals, what you're trying to accomplish, and that's not drifting away from where you think it's supposed to be operating, and that it's staying on target. But even from that human perspective, I think most importantly, I think that all of these are risks, all of these are important. I think the biggest risk in the use of AI systems is if we move forward as a society, is critical thinking. How is it impacting our ability to think and learn and understand what's happening? These AI systems are designed to please you. They're going to tell you what you want to hear. So, you have to guard against that. And they sound very confident. So, when you see something, when it tells you something, and you see it in the research, it's like, we humans have the tendency to be like, oh, yep, that's right. Well, you need to think critically. And is there really right? Is there really accurate? Oh, and you have to guard against that. Yeah, wow, that's really interesting. I haven't thought about that, but it's so from that angle, so I appreciate you bringing that forward. Well, since we last talked, you've written a book. Tell us about your book. So, one of the fascinating things that I've been doing over the last two years is I've thought about writing a book for a long time. So, I sat down and was like, what speaks to me as an individual and what do I want to put out into the world? And I bring to the other my experiences of being that entrepreneur and driving the business. But also, my passion for racing and triathlon. And the key element of all of this is the idea of energy. And I think the time management industry has failed us. It's just not working for us. And it contributes to what I think is the time management deathspire. As you get more efficient thinking about time management, what's the next thing you do? It's like, oh, look at that. I just found an hour of time. You try and do something else. You try and take on more work. So, you know, not only do you just, you think you're more productive, you're earning yourself out by constantly trying to do that next day. AI is just increasing this pattern and trend because AI is a boom to our productivity. It does improve our productivity. But it's like, wait a minute, I can do one more thing. And that just is leading to burnout. So, I think first, we have to think about how we use our energy and our day-to-day life to get things done. So, I wrote a book called productive harmony. And it's all about in the 21st century and this digital age, the information age where so much of our work is cognitive-based. How do we have to get that done? You only have 24 hours a day. You cannot create more time. And if you're lucky, you're sleeping seven or eight hours, you know, that you have even less time. you can't change that. You can change how you apply your energy. First, improving your ability to have more energy by eating better, moving more sleeping and getting that rest. But then guarding your mental capacity, how do you make decisions to be easier to eliminate ones that you don't have to do? One of those fascinating stories is I started this down this journey. I was like, where did time management even come from? It's been around for a long time. The industrial age, we started clocks, such that we could get all the factory workers to the factory at the same time. These are one of the big drivers of time. There was a gentleman, Frederick Taylor. Taylor is sort of the science that came out of it. He's a godfather of scientific management. In the early 1900s, Bethlehem steel hired him to make his workers more efficient. So he'd get that stopwatch and figure out I say, okay, he'd take two steps here, three steps here. If we move this, if you left this particular steel being this way, you're going to be more efficient. And this really kicked off what I think of that as a modern day time management movement. What everybody has forgotten is that he was fired because he was burning people out. He wasn't improving productivity, but we fall sort of like, pin this and say, oh, time management, it's the greatest thing. The person that really started this movement was fired because he failed in that job, which is just amazing. So that just drive this whole idea of energy management. Think about that energy. Yeah. When you have, I think I have more energy in the morning. That's like, you want to do things that require more creativity, more thinking, you know, do those in the morning. After lunch, after you've had those couple of pieces of pizza, that burger, you know, your energy is going to go down. It's going to dip. That's when you do that administrative stuff that doesn't require as much, much focus. That's it. It's a fascinating topic. So productive harmony. And the second aspect of that is the harmony. A lot of people talk about balance. Balance implies equal, all things the equalist. It just doesn't work in today's society. Go go go go with your kids and jobs, different opportunities. Kind of like finding balance just doesn't work. You're going to have these abs of flows of energy and things that you have to pay attention to. And that's where that harmony comes in. It's like, okay, you can be more productive. We need some of that time to arrest. You don't have to use it to take on like the next challenge. So. Yeah. Well, also, well, we'll make sure linked to it. And there as well, it sounds like a fascinating topic on that. Now, as we draw to a close, I always like to leave the final thoughts that you would like to leave listeners with about a stand of solutions and/or AI, future of or anything that you'd like to profound thoughts because you have a lot of them. I always appreciate our conversations, but that you like to leave listeners with. Yeah, always, you know, from the extend of perspective, I was looking for that next opportunity. How can we help people embrace data and AI? You know, love those challenges. And so we are working on that. I was looking for the next customer, the next person to help, whether it's grand-based or, you know, startup, you know, just love having those conversations. It doesn't have to lead anywhere, but it's like, let's have a conversation about it. So I was willing to do that. From the productive harmony perspective of getting the book out there, that's been an interesting journey. And my one key takeaway for you, when I learned what I was learning this, it was like so fascinating. I implemented it immediately. Just having your cell phone, even if you silence everything in the room with you, has an impact on your productivity. If you really need to focus and get stuff done, put your cell phone in another room. As I was writing this book in the weekends, and I implemented that, it was just so eye-opening to me. I was amazed. You know, I was reading the research around this. And they talk about a 20% improvement in your productivity and ability to focus by moving your cell phone out of the room. Because even having it next to you is just a structure. Your users are like, oh, what am I missing out? Or you might have a silence, but then it buzzes. Oh, what's happening? You know, oh, let me just check in on Facebook or Instagram or TikTok. And then 20 minutes later, you're like, wait, what am I doing? I'll put it in another room and you'll see the difference. Now, so true on that end. Well, lastly, what do you think the future of AI is in healthcare? I think it's going to do amazing things. I think there's a lot to embrace and figure out here because it is accelerating so fast. We don't have enough humans to be able to help all of the other humans that need help. Whether it's mental health or weight loss or just what did I see recently? Maternal care, like for pregnancy care. They're talking about implementing various robots, which is a whole nother story, but robotic ultra sounds. You know, when you just don't have those humans available to be able to expand the care, that's how we're going to make a difference in the world because, you know, as the population ages, a lot of those elders are the doctors, the nurses, the healthcare professionals that are going to be retiring. And there's more and more people that need healthcare. The AI technologies are going to help us expand that capability and improve the health of most of our populations as we control it and make sure that it's done well. Will it reduce the cost? I hope so. I think in the short term, it has a potentially, you know, increase those costs, which nobody wants to hear. A lot of the AI ring out is subsidized by venture capital money kind of thing. So sort of waiting for that to transition out. There's a lot of contrast around data centers and energy use. That's got to wash out over the next couple years and see what that is. But in the long term, I think it'll make a difference. Well, we hope so. Well, RJ has always has been a pleasure having you on the show today. I just am always fascinated to talk with you and and your wealth of information. We'll make sure, again, link to stand a solutions and your book remind me the name again. Adaptive Harmony. Adaptive Harmony. What we all need. So how can I forget? We'll make sure and link to that as well. Good luck on getting that out there. And again, thank you for your time and expertise. Great. Love the conversation. What an eye opening episode with RJ and talking about how AI is changing healthcare. Make sure and check out a stand a solutions and the show notes to learn more about what they are doing. Also, there will be a link there for RJ's book, Productive Harmony. Make sure and check it out. In our next episode, I have George Rivera on the show in a world where AI is another tool that is competing in and or taking away from our time. George is bringing such enlightenment to help successful dad founders or any founder for that matter escape the trap of building a business that depends on them for everything. After scaling multiple companies to eight and nine figures, he realized success was quietly costing him the moments that mattered most at home. Today, he helps founders buy back 10 to 20 hours a week building businesses that run without them. So they don't miss what matters most. Make sure and join us for that episode. Until next time, take action, execute and let's run the play. [Music] , yeah. [Music] [Music] [Music] [Music] [Music] [Music] (upbeat music)