Episode Transcript
[00:00:00] Speaker A: SA Foreign I'm Alan Badot and welcome back to AI Today. Here we're exploring AI's potential. We're looking at the ethical challenges and we're looking at how we can apply it to numerous industries so that our, our viewers can get a strategic advantage in their own businesses as well as get back some time in their own lives. AI is one of those technologies that is really life changing, especially if you use it every day like I do. It can help facilitate an awful lot of things. So you know, I'm glad that you're able to join us this week and especially with the topic that we have. We have Mr. Ben Nybert back from DB Arrow, DBT Arrow and he's the chief of staff over there. Recently been been hired. Ben has, has a comprehensive background in, you know, aeronautics and consulting and he's been been at this for about 25 years and he's consulted to really some amazing companies as well as different, you know, government agencies and including the Royal Australian Air Force. And you know, this week's topic is really around how AI can help with supply chain and how AI can, can help in, in aeronautics. Ben, thank you for, for coming back this week. Glad to have you.
[00:01:53] Speaker B: Glad to be here. It's good to talk to you again. Had a lot of fun the last time.
[00:01:57] Speaker A: That's great. So, so Ben, we've seen a lot of things in the news about the aerospace industry and you know, different issues that have come to light recently with materials, sourcing materials and those kind of things. From an AI perspective, how do you think AI can really help the aerospace industry and supply chain really overall?
[00:02:27] Speaker B: Well, AI has a tremendous opportunity and it is a theme that AI has the ability to look at very large amounts of unstructured data. So when you're trying to analyze the supply chain, you have an opportunity to look at structured data, shipping and receiving information and cost. But you also have the opportunity to examine newspapers and economic information, economic statements by various governments. So as you're trying to develop and improve your supply chain, the AI can make recommendations and point out all kinds of patterns that the management team can use to make really valuable decisions around their supply chain. And when you look at AI and you look at how it looks at unstructured data, it always reminds me of a famous story. Maybe I don't know if it's true or not, but the CIA figured out that the Soviet Union at the time was developing a very large launching facility for their space program because the analysts happened to read the sports scores of the various Cities that he tracked. He noticed this one city in the Soviet Union dramatically improved their ability to play soccer. He thought, what would be the reason for that? It turns out because they were importing scientists and their well educated families. Those families played sports, so the teams did better. When we look at supply chain and we look at AI, AI has the opportunity to look at very large amounts of data and point out anomalies, point out patterns, really help to develop a comprehensive supply chain strategy, as well as warn of changing patterns that could be occurring that may disrupt that supply chain.
[00:04:21] Speaker A: Yeah, that's, you know, And I agree 100%. And the beauty of that is, you know, it's that anomaly that, you know, can trigger so many other events that take place. You know, we, we saw it during, during COVID when I mean, name a product and it seemed like there was a supply chain issue. You know, you see it when there's a, a disaster like the, the bridge over in Baltimore, you know, and, and how it can disrupt ports and those kind of things. I imagine that you saw it an awful lot too, from an aerospace part perspective, because, you know, you're sourcing so many different parts from so many different places. You know, what kind of impacts, you know, at the time would AI have been able to, to help you with? Was it more around the predictive supply chain piece or was it something that would have been even more, you know, influential for you all?
[00:05:20] Speaker B: Well, for us at our stage of the business, we're not looking at long supply chains. A lot of our work is being done locally. But as we go forward and start developing our product line, building larger aircraft, those sorts of strategies, those sort of information becomes critical. And where we're looking at AI in particular is the idea of creating scenarios. Now in the current, before AI time, if you might do a supply chain analysis once a year because it was really expensive, you might have a team of people looking at your suppliers across the board. You might look at price trends. It might take them months to develop the materials that the group would need to meet, review and then discuss the impacts. With AI, you can do that sort of analysis extremely cost effectively every month. So your cycle times on reviewing your supply chain are much shorter. So you can do monthly reviews, you can re optimize, you can develop alternative suppliers when you think there might be a problem or a cost issue. So there's a lot of great opportunity with the processor power that's available and the large language models that are available to ingest huge amounts of data without any human interaction. If you Will and then presenting the information in digestible forms to the team so the team can make decisions and then look at how those decisions impacted their cost and their delivery and then change those decisions. But now you can do it every four weeks instead of once a year, where the decisions and the measures of how the impact of those decisions are so far apart, but you really can't determine whether your decision had an impact or not.
[00:07:18] Speaker A: Yeah, yeah. And that's what's so exciting as you're diving into the data and as you're looking at those cost impacts. AI is something that now people try to imagine not having it and it seems like it's so far off in the distance, distant past that, you know, we can't, we can't get through some of the data anymore without, you know, that, that sort of, you know, power behind us. You know, one of the other things that I, I continue to think about is fuel. Now you guys are lucky. You don't have to worry about that. That's, you know, fantastic. But some of the other, you know, companies I imagine that you consulted with, fuel is often one of their more expensive, you know, areas. And you know, what sort of advice, when you're looking at whether it's aircraft, whether it's, you know, any sort of, you know, vehicle, you know, supply chain type of activity that's taking place, or if you're managing a fleet, you know, what sort of advice would you give some folks on how you can use AI to help either predict some of the fuel costs or, you know, figure out what the best routes are or those sort of things?
[00:08:33] Speaker B: Well, a lot of the airlines are doing that now. Even the charters do.
Doesn't take AI to analyze commodity, but it can help.
What the airlines do and what most of the large fuel consumers are doing is they arbitrage. If you're doing the mathematical analysis that says you fly from point A to point B, but the fuel is very expensive at point B, you might carry extra fuel into point B so you can pay less or buy less, rather fuel at that location and get you to another location with cheaper fuel. So there is always this idea of arbitraging fuel prices between cities and countries to some extent. With very long air, very long range aircraft, you don't have a lot of opportunity. If you're flying from, you know, Los Angeles to Sydney, you're going to buy fuel in Sydney. I mean, you just don't have much choice and you're going to buy a lot of it. And that's, you know, you're driven by cargo capacity, fuel range. But when you look at AI, there is a whole lot of opportunity, even when you're not alternative fuel driven or perhaps your alternative fuel driven but not electric, to optimize the use of that fuel in flight. And that is a lot harder to do. The current models do a reasonable job of optimizing fuel burn. But when you get to more advanced engines and more advanced engine controls, excuse me, more advanced engine controls, the large language model can look at very, very large quantities of data. They can look at actual instrumentation, so they're looking at weather forecasts, some of which is not structured. They're looking at pilot reports, which sometimes isn't structured or structured yet. They could be looking at live data from the aircraft's navigation system, consolidating all that and re optimizing the altitude you're requesting. It may re optimize the settings on the engine to get a better fuel burn for the flight flight. So there is a lot of opportunity, both in planning and in arbitrage that you can use something like a machine learning or a large learning, large language model to do. But there's also real time interaction with the aircraft systems to optimize the fuel consumption and route. Now, once you get to electric and once you get to non oil based fuels, there's still a huge opportunity to optimize efficiency, to get longer range, to get the less charging cycles on the battery, to get better airspeed or picking an altitude that gives you better over the ground speed. So these models have the ability to, like I said, it's a theme. You pull in huge amounts of information that used to be done by the pilot. And when I learned to fly that we had all these rules of thumb, you just sort of knew this was about the number you picked because you couldn't do all the math. And back in the days of analog, not even digital flight instrumentation, you had to be able to say, well, you know, the wind's from this direction. I'm guessing my correction is going to be this. And then you try it out. You make adjustments so you stay on course. You couldn't do the math. Now, with advanced digital technology and large language models, these systems can not only work out exactly how to keep you on course, but they can forecast the best altitude for you to be at. They can forecast how to set the engine the most accurate. We used to lean out piston engines until they coughed and the engine temperatures would start to go up and then you'd crank in a little extra mixture so you didn't ruin the engine. Now, you know, the digital technology, that sort of thing is precise. They get exactly the peak performance out of the engine that the engine can give.
So it really does provide. There's two things going on. You know, you're getting percentage points of improvement. Now, our particular aircraft, DBT Arrow, has a massive aerodynamic benefit. So we're looking at 30% increase in performance from our aerodynamic benefits. And then we're going to go ahead and milk every piece of efficiency we can get from the propulsion system and the altitude we fly at so that the operators get this extremely long range at a very low price.
So that's the balancing act. And the AI helps with that whole process. It helps with the design, helps with planning the flights, it helps with flying the flights, it helps with the supply chain. It looks at alternative for various versions of engines. It helps us manage and mitigate risk.
And that's the key element when you're looking at supply chain. You want the AI to present you with all the scenarios so you can choose the appropriate risk that you plan for your company and you can then create some mitigating strategies in case it plays against you. You have a way to keep going.
[00:13:55] Speaker A: Yeah, yeah. And that's great. And unfortunately, Ben, we, we've run out of time. There's never enough time when you and I get, get going on these things. We could do this all day. And you know, the great thing is, is Ben's going to be back and, you know, we'll, we'll get to hear some exciting things that they're doing over at DBT Arrow.
Really excited and thankful you for you to be in here this week, Ben. And you know, audience, please stick around for a short commercial break.
[00:14:24] Speaker B: Thank you.
[00:14:49] Speaker C: Foreign.
[00:14:56] Speaker A: Welcome back to AI Today. I'm your host, Dr. Alan Badot. If you're just tuning in, we're deep diving into the world of artificial intelligence, exploring its potential to transform our lives and our businesses and the challenges that we are facing every single day. And we're trying to bring some awareness around that. This week, I'm honored again to have Mr. Ben Nybert from DBT Arrow. He is the chief of staff, recently hired chief of staff. I think he's been there a little less than a month actually. But he's got an extensive background in, you know, aerospace and mechanical engineering and using AI across a lot of different fields of, you know, the aerospace industry especially. You know, he started his work at, you know, Cessna and then it really grew from there becoming a consultant to the, the likes of Boeing and British Aerospace. And the Royal Australian Air Force and you know, Ben, you know, great, great to have you back on the show.
[00:16:00] Speaker B: Great to be back. Love it. Great conversation.
[00:16:04] Speaker A: Yeah. So, so this week is, we're talking about one of the, you know, one of the more interesting topics of where AI is and in some cases a little bit more controversial even. And it's really around AI's involvement in the software development space, whether that is, you know, building the software or managing the software. You know, Ben, from your perspective, what do you think some of the biggest challenges are to having AI do those sort of things in an industry where, you know, a lot of folks were put their lives on the line to, to do, you know, you know, that kind of work and test and even ride on, on some of these airplanes. It scares a lot of people.
[00:16:48] Speaker B: Yeah, I think AI is extremely well suited in the test environment. You can run a very large number of scenarios against the piece of software to determine how the software will respond to those scenarios. And it can do that very quickly. It can even look at the outcomes and generate new scenarios to test weaknesses that it's starting to identify. So it can actually exploit a weakness that it finds to see what the final outcome of the software would be. Developing software can be easier depending on what you're doing.
If it's part of the software where you've got a lot of transparency in the model, you understand how it's writing the code. In a lot of cases it's searching for existing code or similar code and then doing slight modifications. It's looking at what you're coding and helping you to close loops or helping you to manage and track and appropriately apply variables, helping you minimize subroutines. There's a whole bunch of opportunities in the test environment where it's very strong and a little less limiting or a little more limiting in the development opportunities for AI in assisting a human to more efficiently code, especially mission critical software. But the key to the whole thing is transparency. You've got to understand where the model came from and how it's thinking and how it's analyzing your software.
[00:18:21] Speaker A: Yeah, and I think you touched on an awful lot of good points there, Ben. When, you know, we as developers and engineers, you know, we always like to push the boundaries of things and you know, as we're trying to do things better, faster, cheaper, more efficient, you know, of course we want to integrate AI into our software development life cycle because it is, you know, such an accelerator in what we can do from a day to day activity. The challenge, I think that Folks have is that separation between. And your words were spot on, mission critical type software development, which is very different than your normal everyday application, like a word or something like that. So as you're looking at that from that angle, Ben, what are some of your customers, you know, what are some of their thoughts around, how are they going to regulate software, you know, for, for aerospace and, you know, those, those kinds of things and you know, flight for, you know, when, when AI is developing some of that software or how, how are they looking at integrating into their processes the, the certification of, of software that is developed by AI, Right.
[00:19:43] Speaker B: And now, first, I won't pretend to speak for my customers or the regulatory authorities that oversee this work, but experience is the transparency and the security. They're going to be looking at being able to confirm the outcomes of the tests are repeatable, they're reliable, and that there isn't a way, or at least an easy way for someone to interfere with that outcome.
So they want to be sure that the language model you're using or the, or the software that you're coding into these critical systems is secure, that it can't be inappropriately modified, and that any updating process in itself is transparent and secure and controlled, and that they can log it. So if you look at, even going back a long time, when I was a charter pilot and Jefferson would produce these pages for approaches, and every time an approach changed, you would get a revision page, and that page was coded so you knew which page was the most recent, how it's changed, and you would insert it in the right place. The regulatory authority is going to look at exactly the same model with AI. If you're using AI to create the software, they're going to test that software and be sure it's transparent. If you're using AI in the software, if the software is looking at a very large number of variables, if it's learning as it travels, the regulatory authorities and customers are going to want to know how it's doing that.
And certainly recently with the Boeing incidents, there's a lot of information that the manufacturers have that don't always get communicated to the pilots and crew. Sometimes that's very appropriate. Makes perfect sense. The crew doesn't have to know everything, but sometimes it's really important that the crew understand how the system works so that when something happens in flight, they know what corrective action to take. And the regulatory authorities and the customers in general, they're going to want to know what part of this AI model affects the operation of the aircraft. And Understand how it's going to affect the operation of the aircraft.
[00:21:59] Speaker A: Yeah, yeah. And I think, I think that's a huge, huge point because, you know, evidence we've seen with using AI to develop software, at least up to until this point, you oftentimes don't get the same answer when you ask it to do the very same thing. You go in, try to make a correction to it, your software is going to change fundamentally. And, you know, from an AI perspective, that also means that the answer that it's giving is changing for a variety of different reasons. And it's really hard from a repeatability perspective to say the AI is going to say the same thing under each scenario each time. And I think that's definitely a concern that folks have and especially the reason why, you know, we can't, you know, turn, turn these systems over to, to AI and let them just, you know, manage everything, because that's one, it's not safe and two, it's definitely not practical. And so I think there's a lot of conversation that needs to be had around this area. And I know, you know, folks at the FAA and, you know, some other governing bodies are looking at that, but it is a, it is definitely a concern for folks, you know. Ben, from, from your perspective at DBT Arrow, what are you guys looking at using AI for around the software?
[00:23:16] Speaker B: Well, I'm going to go back for just a second real quick. A lot of AI work is done with training modules so they can get repeatable outcomes. So when you're developing the initial versions of the software and you're testing it, you're actually containing it to learning from a known model, so that you're hoping that if the software is working correctly, it will come up with repeatable outcomes because you've severely limited the input. So that's important to get a basic.
[00:23:48] Speaker A: That is. Yes, great point.
[00:23:49] Speaker B: You're right. You know, you're right that once you release it into the world, if you will, and it's picking up all the data from all the sensors available on an aircraft. Yeah, you're going to get different answers because a slight change in variable might end up being very important and the model will account for that, but you want to be sure you're tracking it to understand where a small change in one thing doesn't cause an outsized response from the software. Now, how we're, we're using it, we're early in the game, and so we're looking at it across the board, certainly in helping us develop our software. And don't forget, you know, we're building a, an aerodynamic structure. So we build the fuselage, we build the aircraft, and we have to integrate that with some kind of propulsion unit. And so we're going to be looking at AI to help us to really develop that integration. So the propulsion systems are going to have their software, our software is going to have to integrate with that software to get the right outcomes. And we will be looking at AI to run a very wide range of tests over a very short period of time to make sure that that integration is clean and that the pilots and crew can operate the aircraft safely and understand that integration, because that's pretty important so they'll get the response they expect. If you hit the go around bar, you expect the plane to go to full power, to pitch up, to bring the flaps to a certain condition. Once it gets positive rate of climb, the gear comes up, all kinds of actions take effect. You hit one button, you expect that outcome. If the software doesn't perform, you want to know why?
[00:25:37] Speaker A: Yeah.
[00:25:38] Speaker B: And it has to be clear to you how the AI and whether you need to turn off the autopilot and manually fly out or whether the autopilot is in fact doing the right thing because of the condition you don't understand. So we're looking at it across the gamut. When we look at our critical software, we're definitely looking at it in a test environment. We are definitely looking at it to short, shorten the cycle time in developing the software that makes the aircraft work, which is critical to getting our product into the market. You know, we don't want to spend five years focused on writing software.
[00:26:13] Speaker A: That's right, yeah. And I think I do want to say this. You know, from the discussions that we've had, the things that I've seen that you all are doing, you know, you really are at the forefront of this integration in this boundary and trying to help folks understand that relationship between AI and pilot and system and everything else that is taking place over there. Really. I would say one of the industry leaders by far in this field, and I really commend you all for the amount of detail and work that you're putting into those sort of things. Ben, where can, where can people find you at if they have any questions about this?
[00:26:58] Speaker B: Easiest thing to do is go to our website, which is dbt, Delta, Bravo, Tango, DBT arrow, A E, R O arrow dot arrow, DBT arrow. And from there you can contact us, you can reach out. The website is being updated this week, so if you go at the end of the week early next week, you're going to see some new pages. We're just putting them together now. That's going to allow you to decide what area in particular. Are you a general aviation pilot? Are you interested in drones? Are you an enterprise? Looking at corporate examples like cargo or corporate travel? There's going to be places for you to interact with us. Submit your information and we'll provide you with a reply and provide you with updated information as it becomes available. So we would love to have people come to the website DBT Aero and we'll certainly keep in touch with you through that. That's the best.
[00:27:54] Speaker A: That's fantastic. You know, I appreciate you being here again. You know, Ben, thank you for the conversation around, you know, AI and software. It is a concern for a lot of folks. We got a long way to go before we figure all this out, but I really hope that folks come to you for some additional guidance around that and, you know, for our audience. Stick with us. We are going to continue some of our using AI in the software development space discussions and we'll be back after this short commercial break.
[00:28:27] Speaker B: Thank you.
[00:28:57] Speaker A: Welcome back to AI Today. I'm your host, Dr. Alan Badot. This week we're talking about practical uses of AI and we've, we've just started to dive into how AI can help you build and, you know, build a business plan, manage a business plan and really drive growth from that. You know, it's a fantastic topic and, you know, we're lucky to have back on the show Mr. Evan Xi. He is the CEO of ReGenesis IO. You guys have heard me talk about the, you know, this in the past and we've, we've dug a little bit into regenesis before. But the, the great thing about it is, is how it can, can really help bolster not only your, your ability to market and do those sort of things, but really come up with a strong growth plan, you know, and Evan's going to talk to us about that this week. Evan, thank you for being back on the show.
[00:29:54] Speaker C: Great to be here. Dr. Allen.
[00:29:57] Speaker A: So, you know, Evan, one of the things that, you know, a lot of the viewers have, have asked me to, to show them just really some basics. How do I, how do I grow, you know, how do I use AI to facilitate business relationships and those sort of things, you know, from your perspective, we've talked about how you came up with ReGenesis and how you built it. Why don't you let people know some of the benefits of how they can use it? And how they can apply it and tools like that that they're going to need to really expand.
[00:30:28] Speaker C: Right.
So ReGenesis is really created for brands and people that believe LinkedIn as a platform is important to their brand or business, first of all. And so what we really do is we take LinkedIn and we create LinkedIn or we make a LinkedIn funnel that's a sales and marketing funnel for you. So that's essentially like, you know, your top of funnel efforts, meaning your content, you know, content creation, all of that AI boosted.
And then we have the AI sales automation where the AI comes in and can actually completely create your campaign, generate responses, generate comments, you know, everything.
And so really it's, it's a huge time saver to really come in and automate your LinkedIn and have it work for you. And that's going to be again, from a content perspective and from a sales and marketing perspective. So essentially, you know, having that entire. Building the entire funnel out of LinkedIn.
[00:31:37] Speaker A: Yeah, yeah, that's, that's fantastic. You know, we have talked or, you know, a little earlier about, you know, you only have so much money to spend from a marketing perspective. You only have so much time you can spend on, on marketing, otherwise you wind up not being able to do what you're, you're really trying to do.
From, you know, your, your perspective as people are looking at, you know, where they're going to put their money, how they're going to spend it, what kind of advice do you have for them on how they can, you know, make sure that they're choosing the right space to, to go into, to spend those dollars?
[00:32:15] Speaker C: Well, I mean, first of all, social media is a huge game changer for everyone. I mean, before social media really, like what you're talking about is, you know, paid TV ads or, you know, maybe being on the radio and you can expect a certain type of conversion from that. You know, even today you look at, you know, the cost of super bowl ads, etc, I mean, they're still pretty outrageous.
And then you kind of think about what we're doing. So I mentioned something that we had earlier called the AI Post Booster. I mean, this thing cost 50 bucks to run and we've hit 45 million impressions with one post booster before. So if you think about the value from, you know, a Super bowl commercial to, you know, the value of the post and how many people, how many people, you know, have seen the ads, I mean, there's really like no contest when it comes to value. Social media is really, really kind of like the equalizer for brands, you know, giving you that platform. You know, that being said, you still need to be able to stand out on the platform. Right. Being there is, is usually not enough.
And it takes a long time of really smart, really compelling content to gain a foothold, usually. Right. And so especially like on LinkedIn, where, you know, it's, it's really difficult to go viral, it's the most expensive to advertise on versus all the other social media platforms.
And so that's kind of like why we created this AI post booster really is because, you know, having been an entrepreneur many times over, you know, one of the most important things or one of the things that was always lacking in any new business was always that awareness piece because it was always so expensive to do.
And so now again, with social media, that awareness piece, I mean, that's right in your back pocket and you can really just go for it and, and be creative. And that's how you see a lot of these brands go from you know, one to a hundred super quick, you know, like Liquid Death and, and it's just, you know, they come out with some really cool video, everyone loves it, you know, and that's it. They're a staple on every grocery shelf now. Right. So that's the power of social media.
[00:34:35] Speaker A: Yeah, yeah. And, you know, a lot of businesses, at least from, from my experience in talking to them, have not chosen to dive into LinkedIn very often. And it's a little surprising, you know, because it's really the place where executives, you know, private equity firms, those sort of, you know, high powered individuals will congreg. And a lot of times the response that I get back is, oh, they, they don't even look at it anyway, you know, that's, that's unfortunate. And I think they're missing out on, on an opportunity. And so, you know, if you had to choose or if you had to give, you know, one platform to say, you know, I would, I would definitely go this one versus this one. What, you know, and I know it's going to depend on market, but what sort of comparison would you, would you provide to somebody?
[00:35:29] Speaker C: I mean, look, if you have the time and the money, the best thing to do is be everywhere. Right?
That's, that's, but that's very difficult, very, very difficult to do. It's super time consuming, especially if you're trying to put out really good content and it's just really difficult to do. Right. So you mentioned it earlier, but it really kind of comes down to your brand and who your audience is. You know, if you're a consumer brand or a lifestyle brand, you know, Instagram is your playground. Right. But if you're really trying to, you know, position yourself as a professional or as, you know, a SaaS business or a technologist or a technology business, then you really need to be on LinkedIn. You know, that's where you're definitely going to be able to find not only your customers, but strategic partners, investors, et cetera, et cetera. Right. It's all there.
And so that's why we chose to work on LinkedIn solely in the beginning. We had to focus somewhere and that's why we chose LinkedIn just because again, it's really the most difficult to penetrate and it's the most expensive to actually go and do paid ads on. And that's why AI post boosting, where we can bring virality to any post that is super important to building a brand and growing on the platform.
[00:36:58] Speaker A: Yeah, that's amazing on that AI post boosting. Do you have any before and after? I know you've got some use cases on the website, but can you give us just an idea on what a normal post would, would have been versus boosting it and the return that folks should expect?
[00:37:19] Speaker C: Yeah, so the, the kind of like reach of your post depends on a lot of different things. One of those things is your SSI score, which is something that LinkedIn gives to everyone, and that's based on your activity and kind of like what you're doing on the platform. Right. So LinkedIn assigns a score to everyone. Based on that SSI score, you know, you're able to do more or less of certain things.
And so that's, that's kind of like the, the starting point, but with a post booster before, you know, typically a good post would hit about 20% of your followers. That's a good post, fully optimized. You have a thousand followers, you're hitting about 200 of them. 200 impressions, 200 views. That's roughly about what you're going to get.
So a post booster, for instance, we have a client where we're in the fifth week of doing his, his content for him. You know, he does, he did start with a good SSI score, but we're about at 2.7 million impressions on the week for him.
So, you know, again, if you, if you like think about that compared to like doing paid ads even on LinkedIn. Paid ads on LinkedIn, I mean, that's tens of thousands of dollars right there for just a couple of posts. So that's, that's where AI can really kind of come in and cut out a lot of the fat.
[00:38:46] Speaker A: Yeah, that's amazing. That's absolutely amazing. What, what sort of new things are you going to have in store for us in the coming weeks or months?
[00:38:55] Speaker C: Oh man, there's so many things. There's so many things. I mean, there's probably a bunch of things that I shouldn't be talking about right now. Now that I'm thinking about it.
[00:39:07] Speaker A: I gotta ask.
[00:39:08] Speaker C: We do, but we do have a number of cool tools and stuff that are like on the horizon. I think last time I mentioned on the show that we have something that does AI commenting. So that's actually a really, really powerful tool where you can actually go and auto engage on people's posts. And so if people don't really understand LinkedIn or the power of LinkedIn or the power of that particular action, what that allows you to do is that allows you to have their network see your content. Right. So if I jumped on some big influencers post and I was, you know, doing some thoughtful comments there, that would then get their, their network's eyeballs onto my post and my comments in the coming weeks.
[00:40:00] Speaker A: All right, so very amazing. Yeah, yeah, that's, that's, that's great. Well, Evan, you know, I appreciate it as always. Thank you very much for being here. Folks, you know, pay attention, please go out, check out their website because I'm telling you, it's going to, it's going to be a game changer for you as you are trying to, to grow your business. So, so, you know, Evan, thank you again for being here and I want to take a moment to go to a commercial break. We'll be back and we'll continue talking about how we can use AI to build our business plan and grow. Stay with us.
Foreign welcome back. I'm your host, Dr. Alan Badot and this is AI Today we've had a great show talking about practical applications of ChatGPT and other tools out there like Claude and Cohere and Perplexity and, and some of the other ones. As I've always told you all, don't just use one tool, use multiple tools. It allows you to really, you know, see what one says and compare it to what the other one says. Because what's one of the most important things with AI Trying to get multiple perspectives to understand which one is going to work for you the best. We've talked about different ways that you can use these, these tools to build your business plan as well as you know, try to automate the things in your life that, you know, take some of the most amount of time. You know, as we focused on the business plan, we also talked about how you can really use it for just planning overall. It's, you know, if you think about what you need to do to really generate a high quality presentation on what your business is going to do, what it's going to sell, who it's going to sell to, how it's going to behave, you know, the financials around that, you know, your ability to use, you know, Chat GPT and some of the other ones is, is really, you know, huge. It makes such a difference in how much time it's going to take you to do some of those things. You know, at the, at the stroke of a keyboard, you can ask ChatGPT how long or how many or what's the market demographic for whatever your business is going to be in. And it will give you so many statistics and so much of it that you know, it'll, it'll save you hours and hours and hours of just trying to do all that research on your own. Then you can even start to ask it to make graphs for you, to make charts for you to do a lot of those things that one, those of us that are not big Excel fans, you know, it'll, it'll help facilitate a lot of that work for you. And you know, even if you want to pull in the data on your own and do it in Excel, there are certain things in Excel that you probably don't know. So you can even ask ChatGPT or those other tools how to do it in Excel and it'll explain it to you, you know, that way. And so, you know, I'm encouraging everybody that's watching to again, not be afraid to take that first step. Remember, don't use it like you used to using Google. Think about it as your expert. Think about it as you know that one subject matter expert that you get to spend as much time as you want with them and it will give you some pretty good information that you can use, however you feel that you are going to benefit the most from it. And that's a strong, that's a strong thing. Now again, the reason we use multiple tools is sometimes it'll give you some crazy answers. Sometimes the answers are just wrong. Sometimes the answers are just not what you're, what you're looking for. Then you can turn to another one and you can ask it the same question and you can start to really triangulate on you know, how that information is all going to be pulled together, how you're going to generate a presentation with it or a PowerPoint presentation or a movie or a video or whatever that's going to look like to, you know, to really help market and, and grow what your business is going to look like. I've showed you before how you can use it as a marketing tool to dig into, you know, the, the different sorts of, you know, marketing platforms that are out there and where you can spend those resources and where you should spend that time. You know, we, we talked to Evan and Evan, you know, with ReGenesis IO, that's one example of a tool that can make a huge impact in some types of business. But it's not going to, as even he said, it's not really going to impact everybody. And so you've just got to be smart in how you deploy these things and how you are using them from a practical perspective. But at the end of the day, you've just got to jump in. You can't hurt it. You're not going to hurt its feelings. You can't ask it any question that is going to offend it. You know, the only thing that you can do is ask it, well, how do I, how do I improve the questions that I, you know, want to ask you so that I can get better information? And it's going to help you with that also. So think about it as a relationship. Think about it as you have a personal assistant that is exceptionally smart who is following you around everywhere if you choose to, because I have it on my mobile phone also, and you can ask it at any time, certain, you know, questions and try to get as much information from it as you, as you can. But again, just be smart. Use your own judgment. Make the decision. You're the one that has the power to make the decision, not the tool. Don't be a robot by just listening and doing everything that it says because that's going to get you in trouble. But do it smart. Use it the right way, ask it the right questions, drill down as deep as you need to drill down and then that is going to provide you such a benefit that, you know, I can't wait to see the emails that I get on, on, you know, everybody's, you know, success that they've had using it. Remember, start small too. You know, don't try to boil the ocean and figure out everything at the same time. Start small and everything is going to work out, you know, just fine. And, you know, at the end of the day, though, you've got to take that first step. You got to not be afraid of it. There's nothing to be, you know, afraid of in using it. And pretty soon when you use it and you get really familiar with it, you're going to feel like, you know me and you're going to want to introduce it, you know, when you're giving presentations or demos or those sort of things, because it really does change your relationship with a, you know, with the computer and with, with the AI. And so, you know, I'm. I'm pulling for you. Don't be afraid to send me questions. I love answering questions for folks. I'm not sure how many I get to a week, probably about 200. But I'm more than happy, you know, to do that. And I love reading the emails, so thank you for that. With that, you know, we had a great show and I look forward to speaking with you next week. And we'll have some wonderful guests on the show, some wonderful topics. We'll try to do some demos again. And with that, you know, have a good night and we'll talk to you again soon.
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