Episode Transcript
[00:00:31] Welcome to AI Today. I'm your host, Dr. Alan Badot. And this week we've got an exciting topic for everyone, something that I think folks are really going to benefit with because I'm getting a lot of questions about how they can use AI to do a lot of different things and make some decisions. Right. And so this week we're going to talk about AI and inserting it into your decision making process. Now, I do want to be clear, right, it doesn't mean abdicating your responsibility to make these decisions. What it is, is really augmenting your capability to make these decisions. Because as you look at organizations that are treating AI as a partner in the decision making process as opposed to, you know, a standalone authority, you know, from my experience, and even, you know, watching what's taking place, you know, outside in the, in the various markets, their success rates are at least five times what they are when you try to leave the AI by itself and really act on its own. You know, and there's a lot of reasons why, because there's workflows, it could be different information. It could be, you know, hallucinations potentially that, you know, can, can really get things, you know, on the bad side of, you know, working properly. It's, it's those sort of things, though, that can cause issues. And it comes from a lot of different reasons. But, you know, that's a, that's a different show. But what we want to do is, you know, we always are, whether, you know, you're a CEO or whether you're, you know, on the senior leadership team or if you're in a different, you know, part of, you know, a business, oftentimes folks will ask you different questions. They'll try to get your perspective on things. And, you know, you can do your best to be an expert in the field or, you know, you can do your best to provide as best information as you can, but oftentimes that's not enough. And, you know, in order to, to really gain a foothold, in order to really make sure that, you know, things are, you know, being decided with the most amount of information, the best possible, you know, perspectives, multiple perspectives that you can get. AI is a perfect, you know, tool to be used for those sort of things. And, you know, that's why it is so important though, for everything else that we've talked about on this show in the past to come into play now. You have heard me say many times, human in the loop or the UDA process or, you know, whatever process, you know, we were, we were trying to talk about at the time. It's exceptionally more important now when you have inserted it into a formal decision making process than it was before. You know, before it was a lot of fun, right? You might ask it certain questions, you might prompt it to say, be, you know, a subject matter expert in some technology field, or you could be using our AI cognitive Personas to really represent the characteristics and traits of a subject matter expert in that field and then that's driving their decision. There's a variety of reasons and tools that you could potentially be using, things that you could be trying, but those are one offs usually. And that doesn't mean that it's really part of your process. It just means you may go out and ask it some questions and think about it. But when you actually are using these tools in a process that drives the decision making, you've got to have a methodology that you stick to, right? You've got to have some sort of capability that says, okay, every time I see something, I'm going to, you know, create an AI that is representative of my customer, of my stakeholders and my competitors, for example. And every time I'm going to ask it these questions and every time I'm going to follow or ask it this type of data, right? There's a big difference between having it part of your decision making processes and just asking it a question every once in a while. And the behavior and the tolerance and the performance of that AI is going to, it's going to change and it's going to be different if you're using it one off as opposed to using it consistent, using it in a way that is really as ethical and transparent as you can. You see, when you use it as a process, you're, you're trying to gain context and you're trying to get it context so it can understand your thought processes, it can understand exactly what you're looking for. So that when it gives you an answer, it is the best answer that it can possibly give. If you are not providing it assumptions or if you're not providing it, you know, oh, I want your perspective from a competitor's point of view, then the AI answers are either going to be too general or they're going to be wrong because it's guessing or it's going to trial to boil the ocean or whatever those, those sort of things are, it's going to be a challenge you to really maximize what impact it can have on your, your process. And that is a challenge. That's a problem. And you really got to make sure that you set something up so it's concrete. So you are really leveraging it consistently. You're leveraging it to maximize your experience, and you're leveraging it to make sure that at the end of the day, it's giving you exactly what you want it to do. And you know, because ultimately, if you think about it, you know, you really want AI to have a good human AI relationship. So you want it to be a partner. You want to create a symbiotic relationship between you and the AI, meaning it understands exactly what you want and you have understand or you have gained an understanding of exactly how to present things to the AI. So it'll give you what it wants. Because if you do that, then that's where your analytical skill sets are going to increase your scalability of not only your own capabilities, but everybody else in the organization that is trying to use these tools and work with them are going to really become almost a superpower for a lot of folks.
[00:07:18] Strategic thinking and driving AI into the process is important. You have to remember that it's still a perspective or multiple perspectives. And, you know, it still, you know, requires some sort of validation, some sort of testing, some sort of way that you have been able to, you know, vet the, the answers that it is providing you. So you've got to make sure your data is clean. You've got to make sure, you've got good processes. You've got to make sure that folks that are using the AI to provide them potentially recommendations are using it the right way. And so it takes a lot of time and a lot of effort to, you know, really hold the, hold the rope and make sure that everybody's using it the same way everybody's using it consistently, everybody's gaining the right perspective. And then how they are presenting that is, is. Is fully open and transparent. Because one of the problems that I've seen is that sometimes folks will pass material, they'll pass decisions, and they won't say, oh, the AI gave this to me. They try to take credit for it on their own, you know, multiple, multiple areas where that has not gone well and it's a problem. And if you're open with everybody and you tell them, this is how I did this, this and this with the AI, this is what it gave me. This is why I think that it is a valid response. And then you present it, then, you know, then folks can use it and improve their own processes and their own decision making.
[00:09:05] And that, at the end of the day, is what you are trying to do.
[00:09:10] If you try to force it, if you try to, you know, not, not do it the right way, you try to skip a whole bunch of steps, then the outcomes that you are going to have are either going to be extraordinarily negative or it's not going to be as positive as it could have been. And that's where we're going to talk about today. That's the information that we're going to dig into. We're going to look at some, some basic processes on how you can, you know, really incorporate it into your everyday life, into your everyday decision making capability, how you can use that to maximize what your team is working on and deciding and using and providing back to you as feedback and then how you can make sure that you are taking full advantage of your investment that you're making because it could be potentially very significant with what, what we could, you know, really do to maximize our, you know, you know, our capability to, to, to drive home profits, to drive home partnering and to, you know, really optimize that, that customer experience that you're trying to do. So stick with us. We'll be back after a short few minutes and you know, for a commercial break.
[00:10:53] Foreign welcome back to AI Today. I'm your host, Dr. Alan Bedot. And this week we're talking about using AI as part of your decision making process. And in the, you know, first segment we introduced some of the concepts just a little bit. I dropped a couple of, of names. But in this segment we're going to talk specifically about the processes themselves, the differences in those processes and how you can maybe, you know, mix and match a little bit depending on where you are in your maturity of deploying AI, you know, just in general. And so, you know, the first one that, you know, is pretty famous, you know, quite, quite honestly, probably the most famous out of all of them, especially if you're in the military. You know, it's a OODA loop, right? And so, you know, the OODA loop itself was developed by Colonel John Boyd when he was at the Pentagon and he was trying to figure out ways that he could, you know, optimize the design of airplanes. And, you know, one of the best books I ever read, and actually I have a copy of it just to show y'all, I'm not sponsored by these guys either. But I just want you to read this because it's a really fantastic book. It's called Boy, the Fighter Pilot who Changed the Art of War. And it's really, you know, a fantastic book written by Robert Coram and If you ever get an opportunity to read it, you should just from a, a thought process perspective. But what it does is, you know, it tries to, to emphasize, you know, the rapid and continuous cycles of observation of, you know, orientation, decision in action. All right, it's really great, especially if you're just getting started to, you know, lay a foundation to give you something that is easy to understand and, you know, you know, easy to apply in a very competitive, you know, or dynamic type environment. Now I want to, I want to, want to, you know, just, just say this though. You can make the OODA loop as complicated as you need to do, right? I mean, it's, it's, it's one of those things where it looks very simple, but applying it in practice can be very, very, you know, difficult. And so there are a whole bunch of things and, you know, different graphics on the Internet that you can go out and you can look at and you're going to see there's really complex OODA loops and there's really, you know, fairly what I would say, you know, simple ways that you can apply it, but you've got to tailor it to what your situation is and how you're going to use it. And so if we take, you know, let's, let's just run through these really quick so I can show you, you know, that, you know, it's very simple concept, but applying it again can be very difficult. So the observe, if you break it down, really what it is, is where you are collecting and you are analyzing data from various sources.
[00:14:17] And in our case, since we want to use it for AI, we're going to get those sources, you know, and that data from our, our AI and how we're deploying it. And you know, a good way to be, you know, to think about it would be to use it for market trends. It could be shopper information in data. But really what you want to do is you just want to make sure that you're looking at whatever the goal that you have set for yourself and how you're going to deploy AI and how you're going to use it to help you that it's helping gather that, that data. Okay, now orient again. It's, it's where you are trying to look at the observed data and figure out and add some sort of context to it or, you know, how you're going to interpret your information.
[00:15:09] And you know, if you think about it, AI can help look at pattern recognition, it can look at scenario analysis. It can do really a lot of things that allow you to understand the, the situation that you have at hand. And so just make sure that you're, you know, getting it from a variety of different sources if you can, but it allows you to really make sure that you're, you're looking at things in, in the right way. Now decide. Pretty simple, right? Seems pretty simple. It's, you're using AI to inform that decision making process. It's built in, right? And so this is what makes it, you know, a fantastic, you know, process, if you're just getting started. Because, you know, you can look at multiple examples, you can look at multiple different types of data and do some situational awareness and some scenario analysis with, with the AI, but it's trying to let you make a decision fairly quickly.
[00:16:14] And then what you do is you act on it. Now act. There's a lot of different definitions. And again, this is, this is where knowing what you're trying to do with the AI and how you want it to help you is going to be important because fundamentally it's, you know, after you have made a decision, you implement it. Okay? And then you use AI to monitor how well it's doing. Because if you can't, if you can't get some sort of results back and you can't interpret those results, that's going to be a challenge for you. Okay? And so what you want to do is you just want to keep going through the loop. Keep going through the loop, Keep going through the loop. Use AI in all of those phases and then use it to monitor and then continuously improve.
[00:17:03] Now similar in some ways, but at the same time very different. You know, the Deming cycle. Right, right. Pdca, Pretty simple again, you know, trying to break it down to its simplest part for, for this, you know, if you think about the purpose of pdca, it's really to, you know, the purpose is to identify a problem or an area of improvement and some sort of strategy to fix it. Okay.
[00:17:33] When you go to purpose, you're trying to think about, okay, so small scales, what's the purpose of it, how do I continue to refine it, how do I continue to test for it, how do I make it more effective?
[00:17:45] And then the check is, you know, you're evaluating the results and your implementation to determine really if the plan is working or not.
[00:17:56] All right? And then act, of course, is the same thing. It's, you know, based on all these factors, what have you decided to do, how are you going to adopt it, how are you going to modify it, and what are the changes that you're going to need to make, to move forward. Now, a lot of folks, you know, if you're not familiar with these processes, you'll see graphics that look pretty similar, but fundamentally they're not the same. Okay, don't treat them the same because if you are in an industry where what you're trying to do is, for instance, you know, optimize something or it's something that is a repeatable cycle, meaning you, you know, go from X to Y, X to Y, X to Y, back and forth and it's just a, you know, you know, one of those processes that continues to repeat. Then that's really an automation trying to figure out how to automate something. And PDCA works fantastic for something like that.
[00:19:02] But if you have to use machine learning, if you want to use more fundamental, you know, AI neural networks, you know, CHAT GPT, some of those things, then the OODA loop is, is going to be more appropriate for you to use. Now that doesn't mean you can't combine them though. There are certain parts that, you know, maybe to get the data and to get it consistently and quickly, you would use some sort of PDCA type process or, you know, then you're ready to use that data for your AI to make a decision. So you would insert that data into the OODA loop and then be able to run and consistently start to make decisions around how the AI is going to, to assist you. Okay, so don't treat them the same. They are not the same.
[00:19:57] They're very simple, very easy to start with for folks. And you know, it really comes down to where you are in the process, what you want to automate, what you want to use machine learning on. Is it appropriate for any of that, what your end goals are and what your business is? Okay, I'm only going to present two. You can go Google some, you're going to find them, you can ask ChatGPT. Chat GPT will tell you a whole bunch of information about them. Heck, you can even use CHAT GPT to implement them. And that's what we're going to do in the next segment. So I want everybody to stick around because we're going to do some, some examples of how you can implement, you know, the OODA loop, PDCA or a combination of both. And that's going to make you successful. So stick with us. And we'll be right back after a few short messages from our sponsors. Thank you.
[00:21:10] Foreign welcome back to AI Today. I'm your host, Dr. Alan Bideau, and this week we are talking about using AI as part of your decision making process.
[00:21:35] Last segment, just to do a quick recap, I talked about two of the pretty standard processes that you can use. One is the OODA loop and the other is pdca. Really the Deming cycle probably is what most folks are used to hearing. So OODA loop, what did it? Quick recap. It really allows you to gather information, make sense of it, choose what you want to do with it and then just do it. Right? Sounds pretty simple. Not, not too complex. I, you know, I hope pdca, another quick recap, right. Lets you identify the problem and develop a plan. You know, how are you going to solve it? Implement it on a small scale, monitor the results and then if it's successful, you apply it. Now the great thing about these two, you know, a lot of times folks will say, oh, you got to pick one or the other. But what I found in, you know, from helping a lot of small businesses is that the, the best results come from when you combine them. Because here's the realities. You're not necessarily going to be ready for one or the other on day one, right? Implementing the full cycle takes time and you know, for both of them. So what you want to do is you want to try to do, align them and do some bits and pieces so that you can, you know, you know, accelerate what your, you know, goals and what you're, what you're trying to do with it. So just a quick example, if you, if you think about it, say for instance, you are, you know, in a banking industry or if you're, let's, let's do something a little bit, actually a little bit more relatable to, to a small business, say that you own, you know, a, a small computer, you know, parts store or something like that. And you know, you get receipts. You want to try to figure out, you know, what sort of behavior are, you know, customers are doing when they're buying things. You know, do you see an uptick? You know, what's the, what's the, the right pattern around that? You know, you're not going to be ready on day one to be able to use AI and you know, machine learning algorithms to support what your goal is. However, you can use some automation to go look at the data, pull the data and clean the data.
[00:24:14] Then you can use AI after that to, you know, start looking at trends, start trying to understand what some of those things are. You know, why is it performing the way it is? Is it repetitive? Is it a rules based, is it something that you've changed? That is Influencing what, what your customers buying habits are. That's, that's, that's key, right? And so that's, you know, a combination of the two at the very early stages.
[00:24:46] So just because you're doing one doesn't mean you can't do the other. And depending on what phase you're in on the, on one doesn't mean you can't be in a different phase for the other. So just because you're in the act phase for one doesn't mean you have to be in the act phase for the other. You may not be ready for it. What's important is that you're tailoring these to what your goals are, what your needs are and how you can, you know, drive that to, to success. Because what you don't want to do is, you know, accelerate one for the sake of acceleration just so you can start to try to do things faster. Because what's going to happen is, is the information that you're going to use to make your decisions is not going to, you know, it's not going to help. And the best example is, you know, you're like I talked about in segment one, you may not have clean data, you may not have the best processes, you may not have an outreach plan for your customers, right? And so how you are using the AI to influence those decisions becomes more very important. And it's driven by these sort of things. But the important thing is, is it allows it to be repeatable. It allows you to start to get in the habit of, okay, you know, if I'm going to, if I'm going to make a decision and I'm going to use AI to help me make that decision, I'm going to make sure that I'm looking at different parts of the data, getting it from the right sources. I'm using it and monitoring it after I make my decision and then I'm improving it as we, as we go along. And if you can do that, then the quality of the decisions that are, or the perspective of, you know, the AI quite honestly is going to improve. And as you continue to do this and continue to do this and continue, then your results and your decisions are only going to get better. And as you start to deploy that to the rest of your team, using across your entire enterprise, the, then not only are you going to see cost savings, right? You're going to see some scale and growth that really can, can accelerate. It's, it's really taking, as we said, it's taking your capabilities, your core sets and elevating them to a different place.
[00:27:16] It's augmenting what your capacity to do work is. And if you think about that, you know, capacity piece, it's, it's important because in today's economy, as we're trying to accelerate the delivery of whatever we're doing, whether it's a product, whether it's a piece of software, whatever that is, you know, being able to make sure that you're maximizing what your output is, is, you know, core to what you're trying to do. So instead of having to hire somebody, you know, you can use the same people and you may be able to get twice as much done. Now, you're not always going to see that though, because every problem is going to be a little bit different, going to be impacted by, you know, different, different types of things. But the important piece is being able to, you know, apply something consistently and then stick with it. So if you think about an example, if you are in a, a customer service type, you know, environment as you're using, you know, the OODA loop, for example, you may look at customer interactions, behaviors on websites and that's what you're observing. That's what your, your pool is. You're orienting it by looking at patterns, understanding what their needs are and understanding what their preferences are. And then the AI can decide what's the best response for or recommendation for, for each customer, and then you act on it. Then you can deliver those personalized services or responses that are, you know, offering your customers something instantly.
[00:28:54] We all have a perfect example of where this is taking place, especially at, you know, this time of year. Because every time you go to Amazon, you probably are seeing, as soon as you log in, recommendations on things you should buy.
[00:29:10] That's not an accident.
[00:29:13] They've taken all that information, they've used the OODA loop or some form of the OODA loop. And then, you know, they're watching what you're buying and then they're trying to get you to, you know, continue those, those behaviors and continue to buy. And that's why they're saying, oh, here's something you might like, or here's something else that you might like based on your previous purchases. Here's something else you might like. You know, that is a great concept. It's also why some small businesses do fantastic on Amazon by selling their stuff, because that's how they get some recommendations, that's how they get some additional visibility and that sort of thing, pulling it together is, is amazing. And then combining that with, you know, PDCA how do you get all that stuff up there? Well, use some automation to, you know, just every time you have a new product come out, it just automatically updates, uploads all that information that you need to, to sell your product on, on that particular platform. And that is, that is the true power of, you know, how you can use these tools together, these processes together to, to really accelerate how you're going to do things in your, in your business. And that's what we're trying to do. You know, we're trying to show you some complimentary things. I don't want you to go out and just, you know, just try to implement every single thing that you have because it's going to, it's going to, you know, help because honestly, it's not okay. You're. Everybody's in different phases, everybody's in different maturity levels. I mean, go take a look at, you know, the AI maturity model and just, just get an understanding or an idea of where you are in that process.
[00:31:02] And depending on where you are, that's going to drive how you're going to use these. If you're, you know, in phase five, but you have no processes, then you're really not in phase five and you're lying to yourself and you're in phase one and you're just getting started.
[00:31:17] Then understanding that, and understanding that having some repeatability in how you do things, how you apply AI and how you look at it is going to help significantly. All right, so just make sure that you look at things that are going to work together, try to balance what your approach is. And then as you're using the AI and making decisions based on those results, just make sure that it's grounded, you're sure it's accurate, and then you know that's going to produce something that it will allow you to accelerate how you're delivering services, how you are delivering products in a much faster and more efficient way. So with that, we're going to go to a short commercial break and we'll be right back after a few messages.
[00:32:41] Welcome back to AI Today. I'm your host, Dr. Alan Badot. And this week we're talking about using the AI to accelerate the decision making process. And we're, we're really talking about frameworks this week.
[00:32:55] You can make them as complicated as you want or as simple as you want, but the important thing is that you are using something that is repeatable. It's something that you have tailored to your business plan. It's something that fits in with your overall strategic goals. And quite honestly, it's something that fits your problem, fits something that has been a pain point in your, your business or it's a goal of yours. And there's a way that you can accelerate that. Because if you're just applying process for the sake of process, then that is, that's bad.
[00:33:35] Fundamentally, it's just bad because then you're putting extra layers of on top of yourself that are unnecessary.
[00:33:45] So we're going to talk really quick about just some basic strategies around how you can deploy these and how you can apply them. I, I, I say you're operationalizing your processes because that's what you're trying to do. You're trying to use them in the real world. You know, I, when I was getting my PMI certification from the Project Management Institute, I said, you know, I always called it Planet pmbok, because in a perfect world you could do absolutely everything on those checklists or in those templates, and that would be fantastic and your results would be perfect every single time. But we don't live on Planet pmbok, do we? You know, there are changes, there's anomalies, there's nuances. And at the end of the day, what PMI really is trying to show you, for example, is that you need to be flexible in how you're applying these. And it's the exact same thing with these OODA loops or the PDCA methodologies. You've got to be able to think how you can combine them and take the best of each one that applies to your situation and use it, document what you're doing and then use it.
[00:35:00] Because, you know, if you try to stick to a specific process, you know, as you're first starting out, and then you don't, you won't use any other process, you won't deviate from it. Then there's going to be some opportunities that you may miss just for the sake of process, and you don't want to do that. Okay? So the first thing that you need to do is you've got to identify what the core problem is that you're trying to solve.
[00:35:29] Take a minute, sit down, write down what you're trying to do, what you want to deploy these processes against, and then, you know, start to integrate bits and pieces of those processes depending on what phase you're in.
[00:35:47] The important thing is really being able to break that core problem down to a whole bunch of smaller problems. Okay? If it's, you know, Accounts Receivable, okay, there are numerous steps in between, you know, sending an invoice and getting paid for it. Right. And you've got to break those pieces down. What I mean is, is you may use a piece of software to do this that has to then get submitted to another system which then sends, you know, another piece of information to another system, which you know which and which. And you can keep going. But what you have to do is, is you would say, okay, you know, I'm going to automate the process from point A to point B. That's it, we're good. That's where we're going to start. It's not the entire accounts receivable process, but it's a tiny bit of the process. Okay. And then once you're successful with that, whether you use PDCA because it's an automation piece or a different part that you, you want to try to figure out something else of the, you know, piece of the account receivable process, then that's what you do.
[00:37:01] When you're successful, then you go to a different piece and then you expand from there. And by the time you know it, you've got an automation that you're using, or a couple of automations, quite honestly, with some AI and machine learning. And you have automated and you're using AI and machine learning on your entire accounts receivable process.
[00:37:22] That's just an example. But you know, when you apply that to any problem, whether it's a complex system of system problem or something like the accounts receivable, then you're going to be able to scale it. Because if it works on accounts receivable, then it'll work on more complex problems.
[00:37:44] Not the algorithm or not the, you know, the, the, the nuances of the process itself. But the methodology that you used to accomplish that will be highly repeatable. You'll be able to refine it, make some changes, make some tweaks. Oh, I did this well. Well, we didn't do this quite as well. The, you know, the results aren't quite as good as we were hoping. You iterate, you continue to do that until it's where you want it to be and you'll be, you know, able to apply that to something else. Because what you're trying to do is you're trying to find something that works best for you, your team, your business, and then just apply it across the board. Now, that doesn't mean if something comes up, that you can't change it. You've got to be willing to do that and you've got to be flexible in order to, you know, really be successful. Because again, not all our problems are the same. They're just not. Sometimes you have to look at them individually and then see what kind of results that you're getting from those different problems and how you're, you're able to use it. That includes tools.
[00:39:00] Not all tools are created equal.
[00:39:04] Not all tools are applicable to every single problem that you have.
[00:39:10] What you want to do as you're going through your OODA loops, as you're going through your deming cycles, as you're doing all that stuff, you're going to have to look at what tool is going to give you the best results specifically for that piece of work that you're trying to, to accomplish or accelerate.
[00:39:28] And then you can integrate them. But that's a different, that's a different show to get into the integration discussion. But what we're trying to do is just making sure that you're not trying to boil the ocean. You're not trying to do it randomly or haphazardly. We're trying to give you a methodology that you can pull from, pick the best that you feel supports your requirements, do them, apply them, combine them, get the results. If they're not what you were hoping for, refine that process, then you'll see. Okay, you know what? I don't need to, I don't need to use AI for this particular thing. I can automate that and then I can accelerate other pieces of my business using AI because now I'm getting the data much faster, much easier, much cleaner. And then I can make, you know, a decision on those, those types of results. That is what you're trying to do.
[00:40:33] Now, depending on what your team looks like, how you're going to do this, some of these things, it can be, you know, pretty complex. And so I don't want you to think that this is, this is all easy in order to do it, because what you're going to find is as you are deploying these, as you're trying, as your business gets bigger, some of the things that you are going to run into is that your processes at a small scale do not necessarily, you know, scale to your growth or, you know, as your, as your business gets larger. And then you're going to have to go back, you got to go through the same process that you, you know, did before. Oh, I picked this one. This is my new strategic goal. I'm going to align the Hula loop piece with this. I'm going to align the PDCA piece with this part and we're going to redefine it because you know, we just, it just isn't able to support what our needs are today.
[00:41:30] That's fine.
[00:41:33] But don't try to do an enterprise process when you're a tiny small business. It doesn't make sense.
[00:41:39] It overloads everything. And there's a lot of folks that are going to see some of these things as they're, you know, if you're a defense contractor and, or you know, you're, you're going to have to go through what's called the CMMC process. And you know, those sort of things are going to put some demands on some small businesses. There's different levels, which is fantastic. Thank goodness for that. But imagine if you had to do all, all of those control checks on all of your IT infrastructure.
[00:42:10] That would be a burden that many small businesses would not be able to handle or absorb. Okay, same thing. If you're trying to sell something, don't try to be Coca Cola today or IBM or whatever the industry is that you're in.
[00:42:29] You're not them.
[00:42:31] So that doesn't mean that the processes that they use, the exact processes that they use are going to apply to you, because if you tried to use those, the overhead alone would kill you. But they use the same methodology. So pick the methodology, tailor it, tweak it, use it for your problem, and then refine it.
[00:42:55] That's how you're going to be successful. That's how you're going to take AI. That's how you're going to make it work for you, help you make better decisions and be able to scale what your business is. That's what we're trying to do. So thank you. I appreciate the, the opportunity this week to, to dive into some processes. I know it's hard. I know you think, oh man, more processes, more processes. It's painful, I know it's painful.
[00:43:24] But a little bit of pain now is going to save you a lot of money later and it's going to make sure that you're able to scale and grow because that's what we're trying to do. So have a great week and we'll see you next week. We'll have a great show again next week and thank you and I appreciate all of you. And you know, with that, good night.
[00:43:48] This has been a NOW Media Networks feature presentation. All rights reserved.
