AI TODAY (Aired 03-12-2025) AI Beyond the Hype: Converging Tech for Business Growth

[00:00:26] Foreign welcome to AI Today. I'm your host, Dr. Alan Bideau. And this week we are looking at AI and the convergence with other technologies. [00:00:43] We've all seen a lot of headlines recently that AI is, you know, getting a, you know, a bad rap. In some cases, it's getting elevated. In other, you know, cases, it really depends on, on who's printing it and, and what sort of agenda that they have. But I'm gonna, I want to read a couple of headlines for you. It's first one is, AI startups raised $50 billion in 2025. And that's early, man. It's March, and some of these companies are already raising a significant amount of funds. But then also robotic firms structure struggle for funding. [00:01:21] That's interesting, isn't it? But then blockchain innovators are pivoting to AI. Another one makes you think, right? [00:01:34] So really the question that we have to look at is, is there a blind spot in our business strategy? [00:01:44] Are we so focused on AI that other transformational technologies that can work in your business are being left behind? [00:01:58] You know, AI dominates the headlines. We see it all the time, good or bad. But then some companies just seem to be missing a critical play, and we, we need to. We need to think a little bit harder about that, and we need to think beyond that, because the convergence of AI with other technologies is what's really going to separate you from the competition. You hear me talk about AI all the time, right? This is an AI show. [00:02:30] We're trying to say, okay, how can you use AI in your business so that it will accelerate everything else that you do? [00:02:40] But if you're not incorporating other breakthrough technologies that can support the activities that you have going on, then you are still missing a critical opportunity. What I want you to do, think, think of AI like we always do. Think of it as your brain. It's powerful, but it's incomplete. [00:03:01] Johnny Five said it best, right? You need input. [00:03:04] You've got to figure out a way that you can give the brain information using a whole bunch of senses, memory, and, you know, potentially your, your arms and your legs, right? I mean, so if you think about the senses that could be the Internet of, you know, technologies that are, that are out there, you know, the IoT field, mobile devices, you know, wireless connectivity, satellites, those kind of things. But then you've got blockchain that can be your memory, right? What you're trying to do with blockchain is, is that you're trying to make sure that you can track it from start to finish. And then be able to continue to track it as it moves along and really, you know, you know, democratize the, the data that's available to everybody. And it's, it's, it's true, truthful, and you can see it, right? And then from a robotics perspective, that's your arms and your legs and coupling all of those things together with AI is what really can be powerful. You know, if you think about, you know, for instance, AI driven medical diagnostics and I mean, they're groundbreaking. [00:04:20] But without those other technologies, wearable devices, you know, that can collect real time, you know, information or patient data or really blockchain that allows you to secure that data, the impact that you have is actually going to be limited. [00:04:37] And so what we're going to do today, we're going to look at some of those technologies and how they work together, how do they work with AI, how do they pull that information and how do they feed the beast so that you can continue to train your models, how do they drive you forward from your business? And we'll look at, you know, some, some IoT technologies as well as, you know, some, some technologies that are a little bit farther away but are really coming a lot sooner than, than we, than we want to think. So I'm going to help you get prepared for those. So we'll look at a couple of test cases and you know, how we can deep dive into those and see how you can benefit and then see the power of what that true convergence can look like. [00:05:24] But then also we're going to continue to talk about what we always talk about, the ethics around it. We'll hit on some of those. The ability to really maximize the effort that you're putting in and really realize that ROI that you're trying to get. From a business perspective, some of these technologies are very expensive, right? And so we've got to make sure that it's applied in the right way. Just like always, we've got to make sure that things are aligned with our business strategy, our business plan, our go to market strategy, and how we want our users to interact with those, you know, capabilities that we're providing. [00:06:06] And so at the end of this show, what I want you to think about, and this is really about every leader that's out there. [00:06:13] Is your AI strategy too siloed? [00:06:17] Is it part of your ecosystem or is it just a pilot project for you to work on and then try to figure out how you're going to use it? [00:06:27] Because if it's the latter, that's, that's not the best way to go. Like we always say, make sure that you're following your plan. Don't do AI just for the sake of AI. [00:06:40] Now, this week, we're saying don't just do AI if you have other technologies that you need to use that are already part of your strategy. So we want to explore how to build our ecosystem, how to build it out, how to scale it, how to make sure that AI is being treated like other technologies, and how you can take advantage of the coupling of all those technologies inside of your ecosystem. That's what we're going to explore today. I'm excited for the show. We're going to talk about some fun things, so stick around. We'll be back after a few short commercial messages. [00:07:33] Foreign welcome back to AI Today. I'm your host, Dr. Alan Badot. And this week we are talking about AI and other technologies that are converging to really, you know, supercharged, how you can use those in a, in a business environment. Oftentimes, we are so focused on AI that we forget about the other technologies that can accelerate, you know, either your customer experience or your, your performance or, you know, your, your ROI that you're looking for, you know, because without those other technologies, then you're really not able to scale or take advantage of the things that you're trying to do. [00:08:35] What we want to do here, we want to talk about using AI with IoT devices, really coupling that with 5 and 6G. In some cases, you're starting to see 6G rollout, and it's more like 5G plus, but that's debatable. And then also blockchain and our friend quantum computing, because all of those together are really going to reshape how we do business, how we interact with our customers, how quickly we can, you know, understand the environments that we're in, and how much more efficiently we can provide some service to our, you know, our customers or our end users. So if you think about AI's role, it's really, you're trying to take data that you're getting from sensors, and by sensors, I mean, you know, can be glasses, like my meta glasses that I have on, or traffic patterns or energy usage or whatever that is. [00:09:45] And you're, you're, you're trying to find patterns in the data, potentially, you're trying to process that data so that you can understand what it's doing, so you can somehow predict, you know, you know, better, better usage of that data or more cost efficiencies, whatever it is. [00:10:05] Now, 5G's rule 6G's rule is really just enabling that data to get there faster with a lower latency. Quantum computing is an ability to really try to process that, crunch the numbers on that data significantly faster. [00:10:26] There are some, you know, some studies that have just been released. There's a company in, in, in China, they were able to use their quantum computer, I think it's 276qubits or 274, something like that, that, you know, to compute data that would take a traditional supercomputer about a billion years to compute. [00:10:51] Now why is that? You know, we don't have a lot of time to go into that, but I can tell you, binary computing, zeros and ones, quantum computing, zeros, ones, and the distribution of all infinite numbers in between 0 and 1, right? And so you've got an infinite number of possibilities that you really have for, for that information to be computed. We'll take out the errors. All right, so that's a high. I know, I'm going to get emails about that, but we're trying to do this at a very high level. But, you know, that ability to crunch those numbers, move that data, store that data and get that signal, you know, faster to the AI is what's so powerful. Think about if you put a roadblock up in between any one of those, you're going to have a challenge. [00:11:38] You know, we see it already with like chat, GPT or some other things that are out there. You may have the answer, but you can't get the data quick enough or you can't send this, the, the response back fast enough. That requires other technologies to be able to do that. [00:11:57] And you know, as you're thinking about, okay, where does blockchain fit into it? Well, that's your ability to store the data in a much more secure manner so that everybody understands where the data came from. Everybody understands how they got the data. [00:12:13] Everybody understands that when somebody accesses the data, we know that somebody accessed the data. [00:12:21] It's that ability to, to really follow that data life cycle and accelerate it across every function of your system is, is key. Go into a store, use a credit card, and when they have a credit card machine that's so darn slow. What do you think? [00:12:42] Well, we all know what we think. We're thinking, oh man, that's, it's broken, it's slow, yada, yada, whatever that is. AI is the same way, anywhere along the chain that you have some sort of issue, it slows down. AI's ability to slow, to work efficiently. If you have bad data, it's not going to work as well as you expect it to. So you have to take into account all of those factors that touch AI in order to be successful. Now one thing we haven't talked about, skills. So what does that mean for your people? [00:13:24] Well, it means that you've got to start looking at folks that not only have subject matter expertise in either AI or analytics or IoT. It's going to be more hardware, 5G, 6G, it's going to be more networking, blockchain, it could be a whole bunch of other things. You've got to start looking for cross disciplinary people. [00:13:53] They have to have knowledge or the ability to apply knowledge in a lot of different fields. That's why I don't get, it's excited about large language models. Right, because that's one field in a few, you know, in a, in a, a sea of about 265 different types of AI that you can use. [00:14:14] It's the same thing when you apply those to other technologies. You have to have a team or at least, you know, some people that understand multiple portions of how you can apply the AI to their technologies, how you can, you know, really, you know, improve the input and the output for those ancillary technologies that are going to help AI be better. [00:14:40] You know, a great example, Siemens and you know, it's trains and you know, they've got AI teams that are using IoT devices for sensor maintenance and they've got, you know, of course five and 60 protocols that allow them to avoid, you know, the, the fragmentation portion of, of that. Now that, you know, in studies that they've, that they have, that they've released, they've been able to use AI to monitor how their sensors are performing. So as it's sending continuous amounts of data in a whole bunch of different things, whether it's, you know, the brake systems in their train, their trains or it's the temperature readings and those, you know, it's sending a whole bunch of different data to it, but it's also capturing how those sensors are performing. [00:15:37] And by having a nice continuous feedback loop as the AI is getting more information and providing better answers, those sensors are being optimized at the same time. Oh may, you know, let's, let's adjust the temperature, you know, let's make sure that the brakes are performing. That's a big one. Let's make sure the brakes are performing the way they should be. Right. And so having a strategy around the entire ecosystem is, is key. And we know this, you know, if if folks have dealt with any kind of logistics as well, supply chain type things. With blockchain, it's your ability to track from really from where it starts to where it finishes. [00:16:24] We're going to deep dive into that, into the next segment when we talk about how Walmart is using blockchain to be able to track their goods and services that they're providing in their entire store almost, and how they're using AI to help really drive some of those analytics and the performance and their cost efficiency. So stick with us. We'll be right back after a few short commercial messages. [00:17:22] Welcome back to AI Today. I'm your host, Dr. Alan Badot. And last segment we talked about the convergence of 5G with AI, with blockchain and with quantum computing. Now, quantum computing, of course, is a little bit more distant, but it's really just getting people to think about how you can push boundaries. You know, know, when you combine AI and quantum computing, that really is a tip of the spear type, you know, combination. And then, you know, what I mean by that is you are really pushing innovation with more like Star Trek in some cases. However, the reality is, is that the breakthroughs that we're seeing in quantum computing, it could be here quicker than a lot of folks realize. You know, the power of being able to compute so quickly really underscores the importance of winning that battle, making sure that we are not only maintaining a supremacy in AI, but also a supremacy in quantum computing. [00:18:35] Because if your AI models are, you know, great, but if somebody else's models are maybe a step below yours, but they can use a quantum computer to do all the calculations, then you are in deep trouble. There's no other way to put it. You're going to be behind. [00:18:55] So, you know, those, those sort of technologies are, are really just, you know, there that I want folks to think about. You don't have to deal with it today, but think, think about it and you know, start to start to at least look at some of those things because it, it could impact your business sooner rather than later. Now when you look at those tip of the spear type technologies, there are also, you know, some, some potential opportunities to couple AI with the ancillary, you know, technologies to really mitigate some of the risk that comes into those. [00:19:34] So think about a, you know, AI plus blockchain plus cyber security, right? And you're thinking about, okay, how am I going to protect my customers, how am I going to protect my data, my infrastructure, everything in what we have now. And it's really a hyper connected world. And so, you know, looking at those and let's peel the onion back a little bit and think about, you know, where can we enhance AI's potential and how can we continue to protect that. [00:20:06] So trust security in an AI era is, it's, it's, it's, it's hard, right? We've seen it. Hospitals are getting ransomware, casinos are getting ransomware, ordinary businesses, schools, financial institutions, you name it, they're getting ransomware. [00:20:27] And that's always because the hackers are usually one or two steps ahead of where, you know, the good guys are. And so as you couple things like zero day vulnerabilities, you've heard that before and if you, you know, if you haven't, pretty much what that means is, is that there's a big issue at Microsoft or some of the other providers where there's an attack or something that is impacting their network. It's the easiest way to think about it. I know I'm going to get emails about that too, but you know, that's not the exact definition. But for, for this context it's, it's good enough. [00:21:04] So using AI to help filter those, verify the datas, look at, you know, the usage of that data and who should be using it and who shouldn't, then that gives folks, you know, a little bit more insight into what they have to do on an everyday basis. Take for instance a hospital. [00:21:30] We've started to see more and more that somebody has been fired because they access data that they shouldn't have touched 10 years ago. It's almost impossible to see that five years ago. We start to see a little bit of it. You know, they were one offs and they were more with famous people. But today we can track that, we can make sure that our PII data, our medical records, our financial information is not exposed in certain environments and you know, leaking sensitive details about that. Hospitals, you know, sharing patient data across different AI models in a private manner allows them to comply with other regulations that are out there. Gdpr, what they have over in, in Europe is a perfect example of that. And that's where a success is really, you know, a, a good use case using AI to decentralize training platforms, you know, using blockchain to, you know, enable a secure, decentralized, you know, capability that's provided for data in an open marketplace. Meaning you can go out, you can grab the data, you can see where the data came from, you can see what is used, what artifacts are in there. All of that information is, is visible to everybody. That is on that blockchain. You know, Ocean Protocol is a perfect, you know, that's, that's primarily what they're used for. And so having those AI models train on global data sets without some sort of centralized ownership or, you know, or induced bias that can come out of that, or, you know, breaches of that data is very, very powerful. [00:23:24] It's your ability to use data that you have confidence in, use information that you know where it came from, it's used in a, in an ethical manner, then that is what makes AI even more transparent. So you've got your ability to move data faster, get data faster, compute the data faster, disseminate the data faster, make a decision faster when you use AI. [00:23:54] But now you can start to trust the data more and you can trust the models more. That's the important thing. [00:24:02] Just because I can get you an answer quickly does not mean that it's the right answer. But when you take all those other technologies and you put them together, then you start to build your case. [00:24:14] Now let's talk real world stuff. I know there's two, two examples that, you know, I'll talk through really quick. You know, Lockheed Martin, perfect example, F35. All the systems that they're building, right? [00:24:28] Largest defense contractor in the history of the world. You know, they use blockchain to do, you know, audit some of their AI decisions that they're having their systems make. [00:24:41] Why do they do that? Well, they're doing it because they're trying to ensure the compliance and prevent tampering associated with either the systems themselves or the data. You know, people, people will ask me all the time, they're like, well, you know, Dr. Bredell, how do you, how would you hack into a system that's using AI? I tell them I wouldn't hack into the system. There's no need to. I would poison the data, have the AI learn bad data, and then that's how I would go about it. [00:25:16] And when you think about that, that's kind of scary. [00:25:19] And so having ways that you can make sure that the data isn't tampered with, I can't do that if there's a blockchain or the data is on a blockchain, because as soon as I touch it and make a change to it, everybody else is going to know. [00:25:34] So being able to track that data all the way through is one of the reasons why Lockheed is using that kind of technology to do that. So powerful. And I know you think, you hear, you hear Bitcoin and you hear all you think about is the financial implications of it. But that's a small portion of what a true blockchain can do. Then when you add that to your credentials, your identity, you know, I talk about taking back our identities. Only way can we can take back our identities is by using a blockchain, using some sort of distributed ledger, coupling that with AI, and then that's how we'll get a solution that we're, we're trying to get. The last case study really is around Walmart. [00:26:23] They are using also blockchain, but they're tracking all of their stuff from the farm all the way to the shelf, which is pretty powerful. And then they use AI to analyze that data to predict, you know, whether they're going to have shortages, what kind of contamination risks are out there. You know, how do they, how do they do that? Well, why do they do that? Actually is a better question. But it's so that they can reduce the amount of things that are recalled. [00:26:50] One stat says that they've been able to reduce that 90%, which means that they are improving their speed on everything else, that they're bringing in some as high as 50% there. That's phenomenal. [00:27:05] That's using two technologies only in order to be able to improve what your operational efficiencies are. [00:27:15] That alone should highlight to you the potential benefits that you see. You see it and we see, we talk about AI all the time, you know, but using it as a bigger part of your strategy with technologies that you're already using has to be part of that discussion. So I want you to stick around. We'll be back after a few short commercial messages. [00:28:12] Welcome back to AI Today. I'm your host, Dr. Alan Bedot. And we have talked about technology convergence. We've talked about how AI is fantastic and can be a great time saver for things, but when you couple it with other technologies, that's where you see the most significant benefit we've talked about. You know, it's important for you to be able to use AI to do predictive analytics and analyze the data. But we've also said if you can move the data faster, if you can crunch the numbers a lot faster to analyze the data, and then you can also transport the data and get the information out there faster, then that entire life cycle is significantly more powerful than just AI by itself. And that's the key message I want you to think about. You know, it's hard for people to get past the AI by itself sometimes. You know, it's scary. I don't know how to use it. It's still new prompt engineering. I don't know where I can apply it. How are the users going to take that? There's so many questions that we're still working on and trying to get some of those answers. But having a solid foundational strategy of how you're using technology today, how AI can impact you today, and where you're going to go with your entire ecosystem, your, your networking, your user experience, you know, software, etc. Etc. Etc. With AI is really key. [00:29:48] Now. [00:29:50] There's some things, just some themes that I want to talk to you about, especially around what I call a convergence playbook that's taking AI, bringing AI in, understanding how it's going to impact your users, and then being able to say, okay, I have these other technologies though, that I'm using as well, or that I want to go to some influencers and I'm gonna, I'm gonna drop AI in to support them and we're gonna, we're gonna see what some of the results are, you know, associated with that. That's how you have to think about it, you know, and so you, you've heard, you know, Jen Gode talk about it, she's on our show often, you know, talking about infrastructure investments. [00:30:37] The last thing that you want to do is go out and buy a brand new, you know, shiny, you know, giant inf piece of infrastructure to support only your AI, you know, developments and things like that. That is, that's a single use system. It's expensive, it's not cost efficient. [00:31:03] Even, even if it is in the cloud, it's still not going to be cost efficient. You want to prioritize interoperable systems. Yes, interoperable systems, meaning like aws and their IoT and TwinMaker and some AI services with that. [00:31:21] Or you're using Azure and you want your data, you want your sensor information and then you want your AI to do compute. It's looking at how all of these systems can combine into whatever that ecosystem is that you're trying to build. [00:31:40] You know, Alton Brown, I don't know if you guys remember the Food Network with, you know, his, his shows that he used to have. He refused to buy single tool use items for his kitchen, so he wouldn't go out and buy something that would only work to mix things. He wanted dual use type appliances and, you know, tools. It's the same thing with AI. You've got to look at it in a broader context. Don't just go do AI again for the sake of doing AI. You've Got to do AI so that it's going to optimize what you're trying to do, how you're trying to apply it, where you're trying to impact your users. [00:32:23] You know, it's not easy, this is not an easy strategy. And I don't want folks to think that, oh, I can just drop it in and no, and you know, as long as it's aligned, everything will be, you know, roses and unicorns. Right? It doesn't work that way. There's no easy button. [00:32:39] But there are ways to think about it that will make it easier as you're trying to transition and you're trying to modernize. That fundamentally is power. [00:32:48] Now, I haven't even talked about the ethics piece yet. I'm going to spend a little bit of time around the ethics and the policies associated with that. You know, as you are looking at, you know, new laws that are coming, new data rights, you know, bill of rights that are, that are coming, an AI bill of rights that is potentially coming. You know, having only strategies like GDPR for AI is not enough. [00:33:24] We have to think about these cross cutting technologies in a more holistic manner. [00:33:35] How can AI manipulate IoT devices? How can AI manipulate financial information? How can AI manipulate your information? Your PII that is, you know, really what pretty much allows us to, to function in today's society. [00:33:58] We've got to go beyond that. [00:34:01] Folks have to start thinking about our, you know, our, our representatives have to start thinking about, you know, what are the impacts to other technologies or when you combine those technologies, what is their entire impact? [00:34:19] You know, on, on the government contracting side, we would talk about these things all the time. Talk about, oh, I've got this piece of data which by itself is not classified, but then I've got this other piece of data that is also not classified. But when I put those two together and I start to do analysis of that, that's very classified because you're taking separate pieces and you're making a, you know, a much more powerful tool set, AI exact same way. But we're not thinking about it that way, but we should and we need to start thinking about that because the ramifications are going to be huge. [00:35:02] All right, so that's going to be something that we'll, we'll do a deep dive on that. I think we'll do an entire show on that in the near future. Just talking about how these laws are going to start to have to change so that they're less technology focused and more really impact focused to the user Community, Because I think that's going to be a big driver as we move forward, not only with our breakthroughs, but also with some of the challenges that we're going to be running into. [00:35:38] Now, when you take the technologies and you take the ethics and the legal piece of it, you take all the processes, it still boils down to one thing. Humans, right? [00:35:54] We are the ones that are driving this. [00:35:57] So from a talent perspective and from, you know, the folks that you're looking to hire, you've got to continue to reshape what that looks like. [00:36:09] Do I need a junior person, a senior person, whatever that is, if it's coupled with AI, plus, you know, something else, some other technology? [00:36:21] Do I need to upskill my, my staff? Do I need to have them not only learn how to use AI, but learn some of these other technologies that are out there to help me, you know, really take advantage of that? Do I need to partner with somebody else? Do I need to bring in a Microsoft Team or, you know, maybe, you know, have some boot camps around, you know, those sort of technologies that are available in Microsoft that we're just not using? [00:36:48] Well, the answer is probably yes. You've got to broaden that, that, that scope. You've got to look at everything that you can in order to be successful. If you only tie yourself to AI, then that's going to get you a little bit farther than others. But when folks start to figure out, oh, I can, I can tie this to my logistics, I can tie this to another sensor, and we're only adding more sensors, right? We're not taking away sensors. [00:37:18] Taking advantage of that is going to be really the holy grail from a, a customer experience perspective and a business perspective. You've got to look at am I hiring now for connector roles, meaning it's a professional that can be fluent in a couple of different domains, whether that's AI and robotics, blockchain, VR, quantum, whatever that is, they've got to have more skill sets. That also means our educational institutions need to do a better job training folks. [00:37:52] I know when I was a cto, I would tell people I want, I want people that will work hard. I'll teach them what I need them to learn because the educational institutions were not doing that. You know, they have you so focused on, oh, you're a software engineer and, oh, you are a electrical engineer, and, you know, there are bits and pieces in between that I'd love for both of you all to learn, but you can't learn that until you get some operational experience. [00:38:18] We've got to start preparing, folks to be able to do those sort of things. You've got to be multi skilled, cross functional. You know, capabilities are, are going to be the way of the future with AI because now you've got an assistant that you can ask to do some of those things, but it only gets you so far. You still need somebody to say, yeah, I trust that solution, that's the right solution. But again, combining the technologies together and then adding a human that is skilled in those, again, another upskill, another, another win for us, what we're trying to do. So think about that because here's, here's the reality, folks. We already know that you can't put the AI back in the bottle. It's there, it's out. You got to deal with it and be in business or don't deal with it. Put your head in the sand and you'll be out of business pretty soon. [00:39:13] So same thing with convergence. It is not optional. It's not because other people are continuing to move the ball forward with those other technologies and they're using AI to get there. [00:39:28] You know, AI alone is, it's really a hammer looking for nails. It's the best way to do it. When you pair it with other technologies, again, IoT blockchain, even human, you know, our ingenuity that we have, you know, it's a toolkit that allows you to either build or rebuild, you know, toward the future. [00:39:54] And so chasing the next chat GPT, chasing the next big model that comes out there, you know, you're better off putting your energy into figuring out ways that you can build those bridges in between, try to build, you know, your, your capabilities. So it doesn't matter what the new model is. It's really the impact to the user and the devices that they're going to receive your information on and the type of information that you want to get so that they'll buy your stuff. Right? I mean, that is what we're trying to do. [00:40:37] You know, looking at text, you know, tech stacks every week. I see it all the time with, you know, how we're hiring and what those hiring patterns are starting to look like. And you know, that is, that's going to be also something very important for you to start to build into your business plan. So think about if you were, or you know, think about if you could pick one other technology with AI, what would that technology be that you would want to integrate into your business? [00:41:12] It could be a lot of different things, right? If it's supply chain, it could Be quantum because you're trying to optimize, you know, some of the delivery patterns that are out there. But whatever that is, it's up to you, of course, but it's got to be meaningful. It's got to align with your business practices, it's got to align with what you're trying to do, and it's got to align with your strategic direction and your roadmap that you're going. Okay. [00:41:38] At a great, great meeting over in, in the UK last week with some folks that are thinking like that. They're thinking about, oh, geez, you know, if I use AI, I can do this with my logistics team and I can do this with my service team, and I can do this with another one of my teams, my operations teams. And, you know, Dr. Allen, do you sell robots, too? That's the kind of thinking that we need to have. We need to be as aggressive as we can. [00:42:08] As aggressive as, you know, you. You feel comfortable. I'm pretty aggressive when it comes to AI, but it's aggressive as you feel comfortable. And then you've got to have trust in people that understand how to help you modernize your business, how to help you get there. And they're not just selling things and, and overselling and over promising, because that's not success. All right, so I want to thank everybody for watching this week. Think about, call it a challenge. Think about another technology that you can, that you can use. Come to my website, send a question in. If I bring it on, if I talk about it on air, let's, let's maybe do a segment or two with some folks that have these questions, and we'll do some deep dives around that because I think that kind of stuff will be a lot of fun. So, you know, thank you again. I look forward to having a great conversation again next week. And remember, don't be afraid to take that first step. It's all you got to do. Thanks, and we'll see you next week. [00:43:14] This has been a NOW Media Network's feature presentation. All rights reserved.