Data-Driven Humanity: Joe Kokinda's Vision for the Future

The Intersection of AI and Humanity: In Conversation with Joe Kokinda

In this episode of The Human Code podcast, host Don Finley welcomes Joe Kokinda, a seasoned executive and visionary in data analytics and AI-driven business insights. They explore Joe's approach to transforming data into valuable assets that improve customer experiences and healthcare outcomes. Joe shares examples from retail and connected health industries, demonstrating how AI can enhance everyday lives. The discussion covers the practical application of AI, future consumer and business interactions with artificial intelligence, and the importance of personalized data in healthcare. Joe offers valuable advice for those looking to integrate AI and data strategies into their businesses.

00:00 Introduction to The Human Code 

00:49 Meet Joe Kokinda: Data Visionary 

01:52 Joe's Journey: From Data to Humanity 

03:35 Retail Revolution: Personalized Marketing 

06:04 AI in Action: Real-World Applications 

10:59 Future of AI: Balancing Cost and Value 

16:31 Connected Health: Revolutionizing Patient Care 

30:28 Practical Advice for AI Enthusiasts 

34:01 Conclusion and Sponsor Message

Sponsored by FINdustries
Hosted by Don Finley

Transcript

Don Finley: 0:00

Welcome to The Human Code, the podcast where technology meets humanity, and the future is shaped by the leaders and innovators of today. I'm your host, Don Finley, inviting you on a journey through the fascinating world of tech, leadership, and personal growth. Here, we delve into the stories of visionary minds, Who are not only driving technological advancement, but also embodying the personal journeys and insights that inspire us all. Each episode, we explore the intersections where human ingenuity meets the cutting edge of technology, unpacking the experiences, challenges, and triumphs that define our era. So, whether you are a tech enthusiast, an inspiring entrepreneur, or simply curious about the human narratives behind the digital revolution, you're in the right place. Welcome to The Human Code. Today on the human code. We're joined by Joe Kenda. A seasoned executive and visionary and data analytics and AI driven business insights with a focus on transforming data, into valuable assets. Joe's approach goes beyond numbers. He believes in making data work for humanity, whether it's improving customer experiences at your favorite fast food drive through or helping healthcare providers create better patient journeys. As the author of building the information asset, Joe shares his unique perspectives. On how AI can drive sustainable growth by aligning with core business goals. Join us as we explore how Joe's strategies bring both humanity and practicality to the world of AI and analytics, creating solutions that not only support business objectives, but also enhance our everyday lives. I got Joe Kokinda here. He's a long time acquaintance. We used to work together about 15 years ago. and so it's really nice to reconnect with you on the show. Joe's had a rich career that is going to lead to some interesting conversations. and so Joe, I just want to open it up by A, thanking you and B, what got you interested in the intersection of humanity and technology?

Joe Kokinda: 2:01

Don, just to get started, thank you. I appreciate the opportunity. It's been such an exciting time over the past number of years since we've worked together. And for me, the connection between humanity and technology has been data. So you'll hear a lot about this, especially from every business and in making investments and trying to figure out how do we help the people? In every business to do their jobs better, to be more effective, to help their customers, all of that is humanity. So humanity is driving forward, whether it be healthcare, patient data, it could be retail data with customers and you go about your day and you buy food and you buy groceries and you do a number of things as a customer and in all facets of your life. So the humanity itself is propped up by technology. It's not necessarily the hardware behind the scenes. It's the data that makes that connection. So to me, that's the passion. That's what I love to do. And that's why I've been in this business, for my whole career.

Don Finley: 3:02

I absolutely love it. It's the thing that touched on me is that in the essence of it, we are data in ourselves, Like we are a set of DNA that kind of like instructs proteins how to be created and cells in that. And like, when it comes down to that essence of who we are, there is a data component to what it is to be human and also like living in this world. But at the same time, you just spun it around to saying, Hey, no, we're collecting it in a different fashion, but everything that we're doing is to, improve the quality of life, to improve the experience around some sort of human relationship. And that's a really cool space. Are there any projects or anything that you feel entirely empowered by, in that space of taking data and helping to improve both businesses and people's lives?

Joe Kokinda: 3:47

Yeah, absolutely. I have two really great examples. Just one related to retail and some work that I did previously on some really cool projects that brought together humanity and technology. And then I have another really great example with connected health. So the future of health care is a huge topic. And again, from that perspective, all of it focuses on the patient journey. So I could share Two really good examples. So the first one, let me share is this version of something that's called in the industry, suggestive sell. The idea is one to one marketing. Has been something that every industry has been going after. So when you log into a web browser, everyone within the browser is trying to provide you either a promotion or a coupon or something, Hey, let me get you, to buy something else while you're here. So we worked on a solution for basically what's in the QSR space. So quick service restaurants or fast food restaurants, in the drive through. Part of that is bringing together all of the data for all of the transactions, the order that you've placed, so the items that you've chosen, and then other information related to either your mobile app or your customer profile, where for the particular customer, a specific recommendation of something they might like, That's very specific to them that they would like to buy. We could show that rather than showing some random promotion, which happens now everywhere you go, it's a poster. It's just something that runs on promo. This is the most personalized experience. Now, why is that good for the person, for the customer? The point is, it's very qualified. It's a highly probable suggestion that's presented directly to you and personalized. And in most cases, when we went through and building that solution, you're producing results that you can feed into, through a machine learning process back to the models to produce better recommendations. So it's actually. Builds on itself. It's core artificial intelligence in the field. And this is one of those areas, again, you're coming up and you're just trying to get some food to feed your family or feed yourself for the evening, but at that process, the data is helping drive a better experience for you, faster time through the drive thru, maybe other food that you didn't know that you might want, or healthy choices and other things like that. So again, really cool example, I think, where you bring those two pieces together.

Don Finley: 6:00

I, and I love this example too, cause it plays out in many different ways. And I know we talked about this a little bit on the pre show as far as like the structure of it. but bringing it back to that, AI umbrella, on one side right now, we have LLMs that fall into this and, given a significant number of parameters, they tend to show some like greater capacity around like reasoning and logic that are somewhat like emergent. And then on the other side of the AI spectrum, we have expert systems, Which is just for the audience. An expert system is essentially having, your subject matter expert come in and define the rules that this behaves by or creating a structure that like you can define those rules as you go. Whereas I think What you're talking about is somewhat different. It's not an, it's not an LLM, which is straight, just like transformer like making it happen, but you have a different architecture under the hood and a different like feedback loop cycle that runs into that. Is there any way that you could expand upon like how this is different? Because Point that I'm also trying to drive is like LLMs are incredibly expensive to operate from an inference and also training capacity. And expert systems are fairly static in the intelligence that they have, but you have a solution that is somewhere in the middle, I would say, as far as both the inference cost and also the opportunity for creating value is still just as high as anything else.

Joe Kokinda: 7:28

Yeah, absolutely. So if you think about the concept of either proof of concept or prototyping artificial intelligence that's applied in the field that actually produces revenue for a business and feeds back in and supports the cost associated with the LLM or data set that's described, that's what the future will have in every industry. So what we've seen, especially with chat GPT, Put in place for everyone to use. It's allowed everyone to understand, okay, if I'm typing something in, I can get a result. Something scrolling on the screen is interesting. So behind the scenes, all the investments in the LLM are bottom line. I'm trying to establish big sets of data that I can train to do something. So in this situation that I described, we're doing the something. The idea is that's another approach, not producing an LLM first and going to find some way to fund it and apply it. We're applying it in the field with real people, changing marketing in real time to then go back to and establish an LLM as part of the retail market. Industry at length or at a whole, So it's a different way to get to it. And like I said, it depends on where you side. a good example was when the, like data warehousing first came out. you see all the books and the headlines was, we spent 90 percent of our money lost because we just built a data warehouse. But we didn't know how we're going to use it and why we built it this way. And we had no customers and we found out, we learned a lot from this process. So a lot of times when you're building out the architecture and warehouses and stuff, and you don't have any applications, you really are again, taking a stab in the dark and that's where you lose costs. So I think again, at the end of the day, a little bit of both is the right answer.

Don Finley: 9:15

And I think you're hitting on a, progression, we do get projects for AI coming across the table, but going back to 2018, we were getting projects that were coming across the table where people wanted to implement some form of AI. And the rage was basically like, long, short term, LSTMs, Like RNNs, CNNs, working in those models of it, but we would always tell people look, let's take something and get your data pipeline working first so that you can feed the model knowing what you want to feed it. And then more importantly though, let's understand the goal that you're trying to achieve because no matter what we end up building, what we put in that middle sort of stage, That model itself, it could be something that we go and just use AWS bedrock. It could be something that we build custom, but if you start with bedrock and it works, then keep it. you don't have to make the additional investment, but then if you run out of capability of that model, you already have the pipeline going in. You know how your data flows. through the system and you know, the goal that you're trying to achieve and what you're hitting on is you can actually get value out of all of those steps in between going from like an expert system to, and I hate this because it's not really a progression, they're different tools for different jobs, but in the same token, if you've run into different capabilities that you need to, Add, you're there. And, the question that I have for you is, you're going from having, a recommendation engine of how people are working, getting that feedback, getting a loop, seeing the data that people are using, seeing other peripheral information to bring in based on the transactions that you're trying to accomplish. but additionally, you talked about adding LLMs to the solution. where do you see the consumer's interaction and the business's interaction with these AIs in the next five years?

Joe Kokinda: 11:09

Yeah. So I think in this example, just as I described it for

Don Finley: 11:13

Yeah.

Joe Kokinda: 11:14

it's, if I'm sitting on a computer and I have a screen and I have a browser. That's the known world as we knew it for marketing. The idea was you look at how much analysis about a click and a click through and a click through rate, this whole version of that moment. I will ask you, how much time do you believe that you sit in front of that computer screen? How much time do you sit in front of the next screen? And then how much time do you actually. Go about your day in a capacity that you're not sitting there. And all of those moments in the future are moments where that data will allow for that one to one marketing. So we've talked about retail a lot. So this is the idea of it's food, the grocery store, the pharmacy, all of the doctor's office. So there's all these versions of every industry is looking at you as either their patient or their customer or something, but the idea is for you in the future. AI will be happening. There will be companies building out the core components like I described this one for suggestive cell, but that will exist in many places. So right now it's non existent, Everyone's staring at a screen. They're typing into the screen, all of that. the phone calls and the call centers and all of this will be able to change and there will be interactions that you'll be able to have that you won't even know the software is running. So the best applications are the ones where you're not actually having to prompt it, but it's working for you. So I'll leave you with another type of example. So one of the other things which no one sees as part of this, especially with an LLM, you have to sit back and think about the known. Because the known is what you're looking for, going against, and the unknown data that's not collected. So I say that, for example, the idea is if you have, again, in an LLM, data that's already collected, Is what's going to be built in and modeled and managed and then producing. as there are limitations when you're working and you're prompting, whether it's through voice or through typing and interaction with a data set, the question is what data sets are included a lot of times, the time limit, Hey, this is only good as of, a year ago, but what about the other data sets that could. change specifically the value of what's there and what's collected. So I say that because there's other parts of this, to your question of how do I interact with it? Sensors and sensor technology and vision. So the idea is you'll see cameras and things coming up. We have cameras overhead. Another great example, if you've ever been into the Amazon, the new stores they have that are, pretty heavily tech, There's no

Don Finley: 13:51

Yeah.

Joe Kokinda: 13:51

Think about. Why? Really is it the checkout that we're trying to help the customer? I say that because if you look up at the ceiling, there's cameras and cameras. And what do you think they're recording? All the data of where you move in the store and how you operate and where you go and all the customer flow and everything. So that's a new set of data. Now think about it in every industry. What if you started to collect data that you never had before that can feed into your models or a broader LLM that's either an industry vertical or specific to your business. It's going to change that whole dynamic or opportunity changes, I think for everyone. So everyone that's leading a company today, like the idea is, and you mentioned this, AI is a strategy you need to have. If you're running any business, you have to have a response. What's my strategy. Everyone's gripping at straws because they're transitioning from the way we did business before, but we're in a new world and business is going to change given the fact that again, data we have, data we can go get and things we can

Don Finley: 14:51

And to hit on that point of the data that you now have and how you can improve your decision making ability on it, we briefly touched pre show about some of the work that I did in the vending industry. And for one of our clients who had, I think they had, it was about 500, 000 machines across the nation. And so we stuck our device in there that gave them the information as far as when the machine was working, what it was selling, But then on the backend, we optimize their schedules to say, Hey, you only need to visit these machines, Cause they were over servicing some, under servicing others. And when they would leave their warehouse. They would only bring the product that they needed to service those machines that day because we knew exactly what was empty that allowed that customer to cut down their carbon emissions in that division by a third. Also, instead of rolling around with, large trucks, they started going to like larger sprinter vans and further cut their emissions as well. But additionally, knowing when a machine was running out of something and when it was broken so that they could go fix it, increase sales by 20%. And this isn't a, this isn't an AI model that we were using. We were doing really simple kind of like calculations because of how fresh the data was. but that's the power of data and being able to make decisions. And I'm sure that like even squeezing out a couple of percentage points. in a business adds to considerable amount of like opportunity and growth for, companies that are really, moving on a razor's edge of margin.

Joe Kokinda: 16:30

Yeah. that's a great example. And as you describe that, I have that other example too, that I can share with you that I think really makes that connection between the humanity and technology, just as we talked about, and you mentioned it with vending machines and retail and restaurants and drive thru and grocery, the other side of this, every single person is connected to the health system and there are companies and industries that have built it. A whole world for a patient that exists, but may not be patient driven. And if you've been a patient for anything, you might appreciate that. So I worked on an initiative, which is just a strategic initiative in developing connected health. What it means is as a patient creating another experience. So in your home. Having sensors. So for instance, if you're elderly or you have someone in the home and there's movement, if they've fallen down, you could get alerts that something's happened in the home. So the idea is the home environment becomes an environment for data, for your healthcare. Let's say you just had surgery and you come home. If you have aging parents, you understand this thoroughly. it's really valuable. So having that particular, like, so there's home products, security products that exist in the home Wire the home to have it have data that's you never had before. The second part is what if you had content on your TV? Let's say you had surgery and you could see week one, you had videos, you had all the content, you had a community, a social community on your TV, that big box thing that you watch, whatever movies you watch. What if it was a conduit for a library of content that was relevant to your condition, your diagnosis, your particular situation, and then again, you could connect with your doctor. The third part is doctor's office visits. What if you knew the wait time that you were dwelling in a doctor's office? what if you had stats on that doctor, but all the stuff you have, like on a LinkedIn or on a Yelp with understanding something like that. There's a lot of data sets that as a patient. Using that technology to help you understand better, how do I care for myself? And if you look at that's just based on a diagnosis. What if you took all that preventative stuff beforehand, like you may see it on TikTok or Instagram and people are publishing tons of content. the way it used to work is you would walk into a geographically tied in doctor who sat somewhere, let's say they sat in Philadelphia. And they had a view of their world and that was the world. But right now you don't have to have that. All of that data, if the pharmaceutical company shared data or the broader set of data, all of that data, if it existed, think about how it could help somebody. So for example, if you had a what if you could see all of the patients? Who've had that diagnosis, what particular medication, what was the result? The probability for you given your genetic code or your makeup to have success rather than trying this medication, trying this one. So I think for me and working on a connected health project, expand it. The way I saw the future of healthcare is so exciting because the way things have been for us as we went through this, especially with data, you have claims data, insurance companies, and you have the pharma has their data. And it really wasn't patient driven until I worked on connected health. And I saw, wow, the future is so exciting because Building out LLMs just in healthcare can be like unbelievable, you know what I'm saying? So that's, again, exciting example.

Don Finley: 19:50

we say this in a number of areas that like, you've got to be your own advocate. And especially in the healthcare environment. And I, fortunately haven't had any like significant medical issues, knock on wood kind of approach for at least like the last 10 years. But in one situation, I was working with probably four or five doctors. And You're trying to compile the information from all of the experts and they all have their own narrow focus of blinders, but you're putting it together and to have an AI that could also walk beside you in that, journey to help you understand the differences between the two or like just how it goes together. It is a world I really look forward to,

Joe Kokinda: 20:35

Yeah. So you said it. I think the point is, from a use case perspective right now, still have this version of you have to go and get and prompt and put everything together. but. The opportunity. We have the technology. We have data. We have at least the desire, especially with AI becoming more than a chess, a version of, you have an AI that can beat someone in chess. that's fun. And that's interesting. But this one is, everybody knows this as a use case, Just as I mentioned, the retail one, if you're driving around and you have a personalized experience when you're doing something, that's great. And people really do that as well. But this one, everyone knows or has a relative. Everyone has parents, everyone has medical issues. And if you've been doing it, you realize, wow, there's so much opportunity. To do so much for the patient that AI could literally behind the scenes provide you that agent, whether it's on your device, it could be in your home or holistically, it could be something that you interact with just conversationally, just like in the movies that, Behind the scenes, it could do all of those connections with you and make sure that it can connect you to people who have had this. It can give you libraries of content you can watch at home. And, like again, so that's one of those I'm passionate about as well and excited about.

Don Finley: 21:53

love that you're going there. So we have definitely more conversations outside of the podcast to have. Cause one of the things that I've been working on is agentic AI, So giving the LLM a task and having it figure out how to do it, but I've been doing it on myself. and it started where I was basically taking local models and telling it to I need you to behave like Carl Young. And we're going to talk about my family. and seeing like what actually goes from it, but then I realized I could actually create a set of nine or 10 different coaches. as AI. And one focuses on mindset. One focuses on nutrition. Another focuses on like working out, sleep analysis, all focused on different areas. And I even have a spiritual one that kind of goes and then one that helps on, aggregating and being the, Orchestra or the conductor of the experience and working between them But they all have tools that they go out and can read data sets to offer Suggestions and I'm building it for me right because it's still in its infancy But that experience of being able to talk and just say the ideas that I'm having and for it to dictate it and store it and then go back to it and be able to like compile that information along with what's happening in email, what's happening on the calendar, where's my private data set of documents that it can pull from to be like, Hey, this is something that, you need to be looking at in the near future. it really, it helps to take away some of the complexity of life.

Joe Kokinda: 23:25

Yeah. So that's so cool. And just as you said that, even thinking in the example that I shared. So right now, when you go to the doctor, you're going to say, he's what's wrong, my ear hurts and if I turn my head like this, my neck that, so you're describing something, then they send you for a blood test so think about how medieval that is from a data perspective, because the idea is, and you said this, What about your Apple watch? What about all of the data about you that has to, it's here, fill out this paper form that's melted because it's 400 copies on top of, you go into there. You're like, guys, how far in the past are we living? And I said this, we have the technology. We have the opportunity. And again, I think given what's happening with LLMs, ChatGBT and the driver of AI being as exciting, if you can get that funding in some capacity, that your data. The two pieces of data, which I mentioned earlier, you don't have. Data about you that's collected. Again, you could see all of your data and you're collecting it on it. Let's say you have your Apple watch as an example, or Fitbit. That set of data becomes part of what you described, What you're building. And then the other side, which I mentioned, you don't know about everyone else who is in the same position as you. You could read WEMMD and somebody's yeah, I had these four particular items and it's from three years ago. Why is that so distant and obscure? The idea is all of these people, let's just say again, whether it's the state of Pennsylvania, whether it's a population of people that exist in the United States or around the world, what if you personally could know? There are 75 people in the past year who have had exactly the diagnosis that you've had given these particular things. 50 percent of those people have given this particular medication. 30 percent of those people have been cured. You could see statistically, wouldn't you just listen, I just found out I got this diagnosis, bam, here's everything from a data perspective. Not from the. Point of view of a doctor who lives in Wynwood somewhere, who's been going there and people like the person. Step back and it's from all of the people who have had a diagnosis, who have tried a bunch of medications from a bunch of pharmaceutical companies, who have produced results. is possible. And again, what you just said, it's something that

Don Finley: 25:47

and I love like the hyper personalization to Hey, here's what's going on for you. and I get my vitamins from a company called Viome. Okay. you send them blood, stool, saliva. And they basically analyzed the entire digestive track of what's going on in your body to basically say Hey, here's the foods you should be eating and here's the foods that you shouldn't be. And it was so shocking as far as what I thought was a superfood was actually detrimental to me. And like being able to like make those determinations for what is it about you specifically that can actually help you in this case? Not just like what, like you said, the guy in Wynwood who's been, serving that population forever. And it's not impossible to stay up to date on all the fields of general practice that kind of are moving. And so we now have the data sets to offer this up in a new way of both preventative and also like aftercare for any diagnosis. That's really cool.

Joe Kokinda: 26:53

Yeah. So you touched on another point. Again, as we talked about, there's the first part of a conversation that we discussed, and then once you talk about it, the second part, which is said, prevention, just as you described it, the idea is everyone walks around trying to figure out what to eat. So again, same concept. You go to the doctor and they're like, Hey, your cholesterol's high. Why couldn't there be. Cause that data exists. Like here's the diet. You can actually have your favorite, whether it's whole foods, drop the stuff off at your house. You can have the medication, the food, but you go about it like, let me go and find out what other people have tried. Let me get tests. You're looking for a prescription of what you should eat. Why couldn't there be that as a core business, given the data behind the scenes, and you said it, and I'm glad you said it because that connects it where then holistically. You could change your trajectory and you could plot that on a, by the way, you are on a trajectory towards cancer. Given your genetic makeup, everybody in your population who has eaten these foods, and it could tell you, here's your prescription to stay away from a diagnosis. That's the end state. And again, I hope that as this stuff becomes, funded and these political barriers can get broken down, that the patient. Can become the core of that. and you could connect those two pieces because as we talked about, that's definitely one of the most exciting areas of, I believe, applying this, the technology and humanity. And think about the comfort that you can provide humanity. Think about all that you can do for every single family on the planet. At some point, if this stuff comes together, it's not buying and selling, TVs or something. This is real, It

Don Finley: 28:37

And

Joe Kokinda: 28:37

person.

Don Finley: 28:38

that's what I love is this opportunity for us to shift how we're actually living. Because you look at any metric that we have to measure, what the population is and they're either flatlining or going down, The average lifespan of American now has actually decreased over the last year or two years or since COVID. I can't quite recall when that decrease actually happened. but then also for at least the last decade, we've had, job satisfaction is in the garbage. Like people are miserable at their jobs and that doesn't seem to be changing. The other stat that I saw yesterday was in the last five years, Depression and anxiety have quadrupled in the male population. And there's trends that I think we can reverse by becoming more patient focused, by becoming personalized to people. But additionally, we're seeing it in the use of like LLMs and like chatbots in the corporate space. Microsoft was saying that 90 percent of people reported a higher job satisfaction. When they were allowed to use LLMs. in their space. Now, you also look at this from like a productivity standpoint. The individual numbers on productivity outside of a specific use case aren't there yet, But we are seeing higher job satisfaction. So that's like a nice move in the direction of being able to apply yourself to this, having a counterparty. I always describe it as having a really knowledgeable intern. will basically able to grab any information that you possibly can, but at the same point, like you need your intelligence to be applied to these situations. what I love about what we've talked about today is that it's all been incredibly positive. It's all been about finding new use cases for how we can apply these technologies. into these spaces. for anybody that's, just getting started on this path of understanding data or, getting involved with creating their own AI strategies, any recommendation that you have for people, in that capacity, whether it's new in their career, old in their career, looking at running the company, Like in making investments on that side, or in general, just love to hear your parting words on this.

Joe Kokinda: 30:50

Yeah, absolutely. as of the most valuable ways for me personally To understand and at least experience something is to do it. And as part of ChatGPT and everything that's happening from that seed that's been started, it's and getting involved in doing. So going online, getting involved with all of that is a really critical part. So another example I've shared with people, if you've never done, an online game. where you're virtually playing a video game with other people. World of Warcraft or Fortnite or all these games. It seems, again for our generation, may seem like something that's not worth any of your time. But part of you understanding data and seeing how a world is brought up from nothing and then create it through animation changes your perspective. So engaging in something like that's very useful. Going to a drive thru, In the example that I shared with you in suggestive cells. So looking and experiencing the world through the lens of data is one of the most valuable things. And again, it may sound either simple or maybe not even on track for training yourself, but those are one key element is doing the things that I described. The second part is engaging with people, either at conferences or through, dialogue like we've had. Engaging with people on these topics to begin to understand the areas that make sense to you personally and areas you'd like to go after. And the third piece for me, which I always think is as important as almost anything, is understanding data itself. So whether it's training yourself about databases and data structures or doing something online to get involved. Because think about it, if you take a step back, anything that you tell me about AI, What if I took away all of the data? What is it now? It's a box that you're typing into that doesn't respond. It's a microphone you're talking to that has no response because it has nothing behind the scenes. So, one of the core pieces to understand is not necessarily What to say and how to interact with it. But behind the scenes, what is it? So if you need to go into again, you go, okay, what is it? So I tell this, like if you've never been to a big box store, and I said, Target is just the best experience and you've never been there, so how would you go about. Agreeing with me, So you go in there, you'd have to have a purpose. You get a shopping list, you do your thing, you try to build something or buy some clothes. So you create use cases, you go through, you come out. We have a better conversation. And then at the step two is, okay, how do we make it better? and then you may go to some competitors and see what some of the other companies are doing and how we make that. So there's that iterative experience that you go through, but I gave a couple of quick starters, get involved with specific. things, whether they're software, interactions, personalization, that are leading edge tech and humanity type things that we described, even in health care. The second part again is making sure that you can do some kind of interaction or training or conferences. And then the third part again is just being involved in those things, as you move forward and understanding the data side of it or thinking about the data. So for me, those are the three big

Don Finley: 34:01

Joe, that incredible advice. Thank you so much for, spending the time with us today and really looking forward to it.

Joe Kokinda: 34:08

Don, it was great. I really appreciate it. I enjoy the conversation. Thank you so much.

Don Finley: 34:12

Thank you for tuning into The Human Code, sponsored by FINdustries, where we harness AI to elevate your business. By improving operational efficiency and accelerating growth, we turn opportunities into reality. Let FINdustries be your guide to AI mastery, making success inevitable. Explore how at FINdustries. co.