EP 280: GenAI for Business - A 5-Step Beginner's Guide

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Implementing generative AI in your business today is one of the hottest topics and it's something that just about everyone is trying to figure out. And I thought to myself a couple of weeks ago, wait, I've talked to more than 120 experts, leaders in generative AI from across the globe, working at some of the biggest companies and entrepreneurs, small business owners. And I said, I think we've got a pretty good blueprint here. So that's what we're going to be going over today, the five simple steps to start using Gen AI in your business today. And I think this particular episode is probably going to be one of the most helpful. So thank you for tuning in. If you're joining us, welcome for the first time. Welcome. My name is Jordan Wilson. I'm the host of Everyday AI and this is for you. Everyday AI is your daily livestream, podcast and free daily newsletter to help you demystify generative AI and to teach you how you can actually use it to grow your company in and to grow your career. Let's get to it, y'. All. Let's get to the five simple steps to start using Gen AI in your business today. All right, so I'm going to hit rewind and then I'm just going to quickly tell you those five steps. I'm not going to draw you along for another 20 minutes like I sometimes do when I'm by myself. But let me tell you this. We've been working with companies of all sizes, not just with, you know, GPT and AI consulting and strategy, but, but even previously, before I even started Everyday AI, we essentially did digital strategy for companies of all sizes and we've been able to grow companies of all sizes. So I want you to keep that in mind. Yes, a lot of what we do here@everyday AI also draws on my background as being an investigative journalist and, you know, giving you all the actual news and the actual facts and breaking down how generative AI technology works and giving you actionable steps on how you can actually use it. But we've been growing companies for a while and I noticed something a couple of months ago. I said, I've talked to dozens of experts from, you know, billion and trillion dollar companies, you know, companies like, had multiple guests from Nvidia and Microsoft and IBM and aws, right? So all of these companies building this technology, this generative AI technology, that we're all using. And I'm always talking to people kind of behind the scenes, so people that aren't even on the show. And I realized, wow, I have literally thousands of pages of show notes with so much great information and then drawing on things that we've discovered ourself. And it always gets back to this question people are always asking, okay, this generative AI sounds great, but how do I actually use it? And sometimes I send people three or four shows and I'm like, oh, there is great insights here, great insights here, but this is, I think now going to be potentially the single most helpful podcast we've ever done. Yes. Out of 200 or just about 200, the most helpful. All right, so let's skip ahead right now and tell you those five steps. I'm not going to drag you on here. All right? But we're going to dive deep into each of these five steps so you can understand them. All right? So step one, gather insights from a ground up committee. Step two, create straightforward guidelines with guardrails. Step three, sprint toward your first measurable AI project. Step four, invest heavily in education and training that align with long term business goals. In step five, plan for a future of what happens when AI works. I am excited to dive into each of these a little deeper, but I will let you know this, even my show notes for this show are crazy long. Right? I'm going to try not to make accidentally make this a, you know, an hour long podcast. But go ahead. If this show is helpful for you, please go repost this if you're listening on LinkedIn or maybe, you know, you found this post on Twitter. Just repost this and I will send you literally all of my episode notes. I think I have like 10 or 15 pages. So there's so many specific insights even on just these five steps that I'm not going to get it, that I'm not going to be able to get to. All right? And I'll tell you this, if you go higher, we've had great, even AI consultants on our show. If you hire someone like that, or even hire companies like ourselves, it's oftentimes tens of thousands of dollars or more. So there is so much valuable and practical information literally just in my show notes that I won't be able to get to. So make sure you repost this or retweet this on Twitter or repost it here on LinkedIn and I'll send you everything. All right? And I'm actually curious as I, as I sit here and take a Sip of my lukewarm coffee before we get into it. So everyone joining us here live, like, hey, all these great PPP supporters too, like Harvey and Brian and Ted, what's going on? Ted, another Chicago guy. But let me know right now, I want to know, especially from our live stream audience, is your company actually using generative AI in your day to day? Let me know. You can just type in like one, not yet. You can just type in two. We're using genai a little. Or you can type in 3 and just say we're using gen AI everywhere. I kind of wanted to do an unofficial poll of our live stream audience. And hey, let me know also if you're listening on the podcast, you can always just email me and let me know how even your company is using this because there's a lot of studies out there. But I'm also wondering, hey, our, you know, growing community here of AI enthusiasts, I'm guessing maybe y' all are a little ahead of everyone else, but maybe not. Maybe, you know, you're that loan advocate, you know, in your company really pushing AI. I actually got a great, you know, message. I think it was all my days are blending together. I think it was last week someone said, hey, I got promoted to head of AI in my entire company and they thanked me and the everyday AI crew for being a part of that. Right? So, okay, looking at our unofficial poll here, it looks like most, most of us are in the one or two. So it looks like either most companies haven't yet dove in to generative AI or maybe just using it just a little. We do have a couple advanced people here like, like Justin and in May, Brit and Daniel who are using it everywhere. But it looks like most people are in the one or two. So either not using it yet or using generative AI just a little. And you know, we did talk about this just a little bit yesterday in our episode, very related episode on education and training. So make sure to go check that out. But I'm gonna, I'm gonna recap just two or three stats that I think are speak to exactly what we just talked about. How is your company using generative AI? Because a recent Forbes study said 83 of companies claim that using AI in their business strategy is a top priority. Yet a tech co study found that only 4% of companies have implemented AI AI throughout their org. Let me say that again if you're listening on the podcast because you can't see my screen right now that, you know, I'm showing all of these different studies but 83% of companies say generative AI is a top priority, yet only 4% have implemented it throughout their organization. That is a huge problem. It's a huge problem. We talked about this yesterday. I think so many companies are just throwing money at the problem, but versus rolling up the sleeves, digging in deep and getting this thing figured out. All right, one other stat from erson young ey. So 73% of people are concerned about their organization not offering sufficient training. Right? So not just that, but so many studies just say that, you know, employees, managers, directors don't have full confidence in their leadership to be able to lead them forward in AI implementation. All right, let's start to solve that, shall we? All right, here we go. This is a lot, y', all, but get your, you know, if you do have questions, try to get them in. I might not be able to get to them in real time, but I'll try to grab your questions and your comments. You know, love hearing from you. Sometimes we, we mention our favorites in the newsletter. So let's start with step one. Ready? Again, this is from hundreds of hours of conversations with, you know, the top executives building AI, but also with small business owners, entrepreneurs, startups. Right? And also, hey, this is. We've taught more than 2,000 business leaders proper prompt engineering with our free PPP course. Right? So this is a culmination of literally hundreds of hours on talking about AI implementation. It starts with step one. Need to gather insights from a ground up committee in. Every word that I chose here in these five rules is intentional because AI implementation is not top down, it is bottom up. One of the biggest mistakes if you want AI to work for your company, this isn't a CEO directive, right? Because so many times, sorry, C suite people, so many times, C suite sweepal C suite people are removed from knowledge work. Okay? Generative AI helps you win back time in knowledge work. You can't, you can't make directives from the top of the mountain when everything's happening on the ground level. All right? Also, a ground up committee prioritizes transparency, safety and alignment. So this bottom up approach is actually pretty similar to how even OpenAI and Google have developed their own AI implementation. All right, you can go read about that, but there's plenty of research out there. Another huge benefit in a ground up approach is you get people from all levels of the company, right? You get people who in theory are actually going to be using whatever generative AI systems that you will be deploying. You get a diverse group of perspective, right? You get A diverse perspective. Okay. I tell people who's actually going to be using this generative AI technology the most if and when it's successful because those are the people whose voices that you need to hear at the beginning. Again, this isn't for your leadership team to create something and then, you know, pseudo get feedback from people before it rolls out. No, you build it from the ground up and gather insights from the ground up committee. Okay. Here's another reason why that approach is preferable and it will work better in the long run. Well it because you can then incorporate more data and be more adaptable. Whereas a top down approach, which is what almost everyone does, a top down approach is anecdotal. Right. Someone up there from the top of the mountain with binoculars, they're telling a story, they don't understand it. Top down is anecdotal, rigid and often misplaced. Bottom up. It learns from actual data, from the people who are actually doing it and it becomes adaptable. Let me start with this. You notice how step two is guidelines and guardrails, not step one. Okay. Step one is gathering insights, having open and honest conversations. Even need to talk with people first. And here is the most important thing that you need to talk about in this committee. Why? You need to have a serious and transparent conversation about the why. I can go ahead and in theory answer that for you. Well, here's why. Talked about this once or twice before on the show, But a recent McKinsey & Company research shows that generative AI may automate work activities that absorb up to 70% of employees time. Yeah, it's the future of work is generative AI from beginning of your day to the end of the day. And it's working 247 for your company. Right. That's the thing. Generative AI doesn't sleep, doesn't need to, doesn't need a break, doesn't need vacation, doesn't need pto. But you have to have the conversation. Why? Because one of the biggest disparities between that 83% of companies saying generative AI is the most important initiative and only 4% of companies actually implementing it across their organization is friction. It's friction and a lack of transparency. Because obviously your quote unquote frontline workers, your coordinators, your AI entry level people are probably going to be hesitant toward generative AI because the story of generative AI, and we're going to talk about this more here pretty soon, is that you cut jobs and don't replace them. Or maybe you cut a thousand jobs and you are left with 100 people working with AI, right? That's what's happening. It's already been happening. Widespread scale, even so far in 2024. So you have to have an honest, an honest and open conversation about why AI. Are you just trying to automate all of those tasks or are you trying to clear the mundane for your most important employees and allow them to focus on the meaningful? You know, and part of this, and we'll get into this in part five, but part of what you need to talk about in your ground up committee is talking not just about why, but what happens when it works. Right? So more on that in part five. So you might be asking, okay, this sounds like a pretty big ordeal. We need a ground up committee. You should be bringing in members from just about every organization. This isn't to kick the can, this should be. Yes, you should patiently take in insights from everyone. But you also need to move. This isn't one of those committees that meets quarterly. And you know you're going to kick the can for 18 months, you will probably either go out of business or you are going to be bleeding. Okay, when I talk about a committee, this is a fast committee. You need to be patient in hearing your people out. But you have to be able to implement it with speed. And prioritizing that generative AI implementation is crucial for the success, sustainability of your business, period. Said this yesterday. I've said this a hundred times. Think of how your company, or companies in general had a good 15ish year period to adapt to the Internet. All right? But hey, now if you're not using the Internet, you can't do business, right? If you're a knowledge worker, you can't, period, right? Your, your hi, your, your hr, your, your marketing, your customer service, everything's online, okay? So think of that 15 to 20 year period where companies had this safety net to adapt to the Internet. With generative AI, you have 15 to 20 months and we're already midway through that. You have to act with a sense of urgency in this committee. But you also don't have to start from scratch. Okay? Look at other countries and other organizations for guidance. You know, you can follow the model of a large language model. You can just borrow ideas from those that have come before you and have put in the hard work. I'm not telling you to plagiarize, don't do that. But see what other companies are doing, other companies, other countries, other groups of countries are doing successfully when it comes to AI implementation in the workplace. All right, couple examples. The eu, AI Act. Go read it. See what may apply to your company. The Hiroshima AI process, highly regarded as one of the most respected kind of AI implementation processes out there. It was even cited by the White House Executive order on AI. It's another resource for your company is to look at the White House executive order on AI, but also the White House Select Committee on AI. Go look at their work. A little bit more on AI.gov we're getting some Preach Jordans. All right, so people are feeling this, this is good. Stick around. It's going to get better. Don't worry. All right? And hey, keep, keep your comments coming. Keep your questions coming. I'm going to try to tackle them at the end or as I go along. We'll see. All right, ready? Step two. And notice we don't start with guidelines and guardrails. That is step two. If you are starting with guidelines in guardrails, you are just creating friction that tells your employees, right? Whether you have a team of 100 or 100,000, if the first time your company or your employees hear about AI implementation, your AI, your generative AI implementation strategy is going over the guidelines and guardrails failure. You've already failed. You've lost the battle before it's begun. Don't start there. This is not like any other technical implementation. This isn't like bringing in a new CRM. You make the decision and train someone. This is changing the way we work. And people, understandably so, who don't understand generative AI technology are going to be uneasy. So if you start with step two, you're going to lose people. You're going to lose people. People are immediately going to be scared of their jobs and you're probably going to. Your, your churn is going to go through the roof, right? If you don't get people's buy in and if you just roll this out, you're going to fail, period. That's why 83% say this is the highest priority, but only 4% have successfully implemented generative AI throughout their organization. All right, let's talk about how and why you need to do step two, which is creating straightforward guidelines with guardrails. All right, so creating AI policies and responsible use guidelines is probably one of the most important steps. But like we said, you don't start there. And I get it, it's going to seem daunting, right? Because you think, here is this brand new technology that hardly anyone in the organization even understands. So now we have to create guidelines and guardrails. But guess what? You probably already have a Lot of that in place. Here's a mistake that people make when they're trying to create guidelines, generative AI guidelines and guardrails. Well, see what you already have in place, y', all, okay? Because probably somewhere in your current employee guidelines, in your HR docs and your hardware, software, email, Internet usage policy, etc, you probably already have some basics of how employees should and shouldn't be working with technology. All right? So if you combine what you already have in place for different technology, software, Internet, email, if you combine that with insights you gather from step one in your ground up committee and borrowing from best practices, like I said from the EU AI act, the Hiroshima AI process, the White House Executive Order on AI, the White House Select Committee on AI. When you start to combine those, combine what you already have with what we talked about in step one with the insights that you gained from bringing in ground up employees. It's not as daunting then. All right, so the guidelines make sense, but let's talk a little bit about guardrails. First of all, why do you need guardrails? Well, number one, it's a great business decision, you know, and that's something that can't be overlooked here. All of these steps, they need to work hand in hand with your business goals in setting guardrails is an extremely important business decision. This lets you know, this is essentially when you think of guardrails, I think of them quite literally, right? That's why they're called guardrails. Okay? Think you, you now think of it like this. You're. Whether it's a hundred or a hundred thousand employees are going to be driving, but they're going to be driving new vehicles that they've never used before. They don't know anything about it on a new type of road that they've never driven before. If you don't have guardrails, you can imagine there's going to be a lot of accidents, a lot of cars driving off the cliffs, a lot of insurance claims. Right? You get it. Guardrails are extremely important. That says, here's what's inbounds, here's what's out of bounds in putting different safety measures in place, right? We have to have data security, data protection, and we also have to act ethically. All right? So number one, guardrails are essentially a required business decision because this is the new way that your company is going to be working. All right? And sometimes what this means, when we talk about that, your guidelines and guard rails for generative AI, they need to work hand in hand with your business strategy. And sometimes. And you're not going to like this. You're not going to like this, especially if you're, you know, chief marketing officer or, you know, if you're in growth for your company. Sometimes you have to scale back or adjust your actual business Strategy or your KPIs or your intended business outcomes to better align with a generative AI policy. All right? So it might seem like you are taking a step back, but you are taking a literal step back with your feet in order to get on a jet. So you have to understand that it might take one physical step backwards or a couple of realigning your actual business goals, your intended business outcomes, in order to get the most out of generative AI. Right? Because everyone's talking, oh, like the McKinsey study. How can we use AI to save employees 70% of their time? Right. That's a conservative estimate, by the way, as well, I've talked about. I think it's actually 80 to 85% depending on your role. It's significant savings. So you might have to adjust your line of business a little bit. That might be uncomfortable for you. But you need your guidelines and guardrails to align with your business objectives if they're going in different directions. Right? The old saying, one degree of misdirection. After a while, those two things aren't going to know each other. They're going to be thousands of miles apart. You have to align them closely. All right? Another important thing when we're talking about creating straightforward guidelines and guardrails is making ethical decisions. Okay? So that's not just the, the big picture of like, okay, what happens when we start to replace employees? We're going to talk about that in step five. But also, you need to act in a responsible and ethical way with your data. Right? One of the biggest hangups with people in using large language models is data. You need to exercise caution with sensitive data and reflect that in your guidelines and guardrails. However, give me, give me your company. I guarantee you I will find more data on your company than you think is publicly available. Scrapers. Let me say this. Scrapers are better at finding information than humans, okay? There's a great chance that whether we're talking about OpenAI's GPT bot or perplexity is perplexity bot or Googlebot or whatever, all of these large language models, they've crawled every single page on your site, and there's probably dozens or hundreds or thousands of pages on your website that you maybe didn't know existed with a lot of data out there about your company. So this isn't one size fits all when it comes to data. But here's the thing you probably have, especially if you're a large enterprise company, you have to release so much data about your company anyways. Okay? So a big part of this, and I can't just give you one bullet point of advice on what is the best guardrails to put in place for your company because it depends on what sector that you're you're operating in. It depends on different laws and regulations. It depends on if you're working with Pii phi, right? So there's no one size fits all here. But I will tell you this, your data is probably a lot more public than you think. All right? So again I can't give you bullet point recommendations on what should be in your guardrail, but I will like I did with step number one is see the great resources that are already out there that are great guidelines and guardrails already in place. So already mentioned, you know, a couple multiple times, the EU AI act, the Hiroshima AI process, the White House Executive Order. But another great one specifically for guidelines and guard rails is Uniseco's recommendation on the ethics of AI and also the National Institute of Standards and Technology. They have great guidelines and they talk openly about different guard rails organizations should have in place when it comes to implementing generative AI. Tara says Amen on a Wednesday. Amen. Foreign. Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Gen AI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too. Just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead in Built a straight path to ROI on gen. Let's put up guard Rails comment coming in here. But don't forget to sprinkle in some rewards for those who stay on track. Yes, you have to stay on track. You absolutely have to. All right, speaking of, I should stay on track and talk about step three here, right? All right, let's get to step three, sprint toward your first measurable AI project. Okay? And here's the thing. Number three and number four could be interchangeable. It depends on your company, right? So number four is invest heavily in education and training that align with long term business goals. But step three, sprint toward your first measurable AI project. I think for most companies this will be first. Another big mistake that I think companies are making is they're trying to make a splash when it comes to generative AI. Let me tell you this, 99% of companies do not need their own large language model. I am baffled by the amount of smart people that I talk to and you know, I'll ask them like, oh, hey, what's your company doing? And they're like, well, you know, we're trying to, you know, create our own large language model. I don't know. That's because it's like, oh, it's the cool, sexy thing to do. But like, you probably do not need that, right? So many smart business leaders are over complicating what generative AI even is and what it can do for their companies, right? People just see OpenAI, right? And they see, you know, Google Bard and they see, they, they, they see anthropic and they think, well, yeah, we should have our own large language model that makes sense. And there's obviously people out there hounding big companies and saying, you need this, you need this. Well, I'm letting you know you probably don't. Like I said, 99% of the time, you do not need your own large language model. Right? You should be working with whatever model makes the most sense for you. And then using rag, which is retrieval augmented generation, that's bringing your own data in, your own knowledge base, your own company documents in a way that is much more economical and obviously much more likely to be deployed in less than 50 years. Right? Good luck. If again for the 99% of companies that it doesn't make sense, good luck creating your own large language model. It's not going to work. It's incredibly expensive. You're not going to be able to find the actual developers who are smart enough. I talk about it all the time. This is like finding a good developer right now in, in AI, specifically in generative AI, it's like, you know, the bulls of the 90s. There's so few of them. Good luck. All right, so let's talk about why you should sprint toward a first measurable AI project and not taking on some unrealistic huge generative AI implementation. Well, sometimes before you even get to training and education, you need to focus on quick and measurable wins. Why? Well, focusing on a long term large scale project is risky. It might not even work. You might not even know what you're doing, you know, and you'll probably have to do backflips just to get stakeholder buy in to do something on a large scale, it's risky. All right, so to get company wide implementation, don't do that. You need a low hanging fruit W. You got to get the easy win. You need to not only be able to get an easy win, but you need to be able to say, to tell the story of it, right? To simplify what generative AI AI is and what it can do across your organization, you need to be able to say, hey, look at this generative AI thing, look at how it helped us win and then tell the story. And that's how you get stakeholder buy in for that large project, that large company wide implementation. That's how you do it. You don't start there. Sorry, I think I'm still getting. Because I worked on this a lot on Tuesday. I'm getting some hot take Tuesday takes in in my head. But that's, yeah, that's a recipe for disaster. Don't do it that way. It's not the right way to do it. Smart people say that's the it's not the right way to do it. Use common sense. No one understands generative AI technology. No one. You know, there's a reason why explainability and black box is, is such a big thing right now in generative AI. So when you sprint toward your first small measurable AI project, you need to only focus on areas with a measurable impact. You need to be able to translate that one sprint and tell the story of real world results. Time saved, money saved, etc, project timelines moved, customer success went up by by this percent. You need to have one very specific win that is quantifiable and that you can tell the story. And don't focus on one tool. Don't even focus on a main business goal. Don't do it that way. That's backwards. Find the most quantifiable lowest hanging win. Go after that. Focus on one outcome that is also transferable across departments or locations of your org. If you're Only speaking your, your language. Let's say you're in sales and you do something that is so niche for sales, but it's a low hanging fruit. And then you go try and tell that story. People are going to be like, all right, well that doesn't matter. That's not applicable to our other 15 departments within our organization. Focus on winning back time in a manual, mundane knowledge work area that's transferable, maybe even something that no one likes or is incredibly time consuming. One or a couple examples I always like to give. Document creation, microlearning, data analysis. Right? Those are three areas that are extremely transferable from department to department. Easy wins that you can then replicate and save time, save money across the organization. Right? Document creation, micro learning and data analysis. It's also an easy way to use public data. All right, so you don't even have to jump through 50 hoops and you know, get some complicated implementation techniques. You can use public data in that case to win back time to show that first sprint to show the win. All right. Also one last thing to think about in going about it this way, in going about it as sprinting toward your first small measurable AI project versus a large scale AI implementation is you're also minimizing the AI risk, right? That's the thing that always causes hold up with stakeholders is they don't understand the risks for technology that so few people can understand. Minimize the risk, make it short, make a sprint, tell the story, go do it. That's the blueprint. Step four. We're getting there, y'. All foreign. We're getting there. All right. Tara says bring it. Tara still with us? What are y' all thinking? What's been helpful so far? Let me know. Let's talk about four. We're gonna go fast here because this one is obviously huge, right? And again, step three and step four could be interchangeable in these. Even these, the order of the steps and these exact steps, I understand these are not going to be applicable to every single technology, to every single company. I'm talking to the middle of the road company. All right, but again, number three and number four can be interchangeable. But here's why you have to invest step four, invest heavily in education and training that align with long term business goals. In this, step four is an ongoing iterative process. All right? It is cyclical. You are constantly doing step four, okay? Because this is now where you take the learnings from your short sprint and you start to implement longer term goals and create training and education ongoing around them. And this is another reason why long term projects don't work. Right? You're going to be talking about this for two quarters and then you're going to have a, you know, a year long pilot. No, the technology that you're talking about during the planning phase is going to be antiquated by the time the one year pilot's over. You're wrong. It's not how you do it. All right, so step four, you actually proper. This is crazy. Proper AI implementation, it actually requires unlearning decades of positive business habits. That one's worth repeating. Proper AI implementation requires unlearning decades of positive business habits. We're working in a new way, all right? And yeah, I did do like 40 minutes on this yesterday, so I'm gonna go, I'm gonna go through quicker here. So before implementing any generative AI tool, you need to first deeply understand how it operates. Education so important and emphasize explainability across the board. All right, so if you're not super technical, explainability is such a huge thing in generative AI. Let me illustrate what that means. Think of generative AI as a box, all right? You put things into the box. There's inputs, and let's just say those are prompts, right? So it might be a text prompt or an image or speech. Right? So you have all these text to speech model, photo to video, video to whatever, right? But everything goes into a black box. That is the generative AI model. Okay? So explainability. So everything goes in and then on the back end, it magically turns into something 10, 50 times more impactful. Right? That's what generative AI is. You put in something, you know, a couple bullet points and out comes a 50 page market plan with photos, videos and voiceovers. Right? As an example. All of that happens in a black box. That is the concept of explainability. Because most people have a lack of trust and understanding both in generative AI technologies, but also they don't trust the outputs because they don't understand the magic that's happening inside of that black box. That goes to explainability. Okay? So you need people in your org that can demystify the black box. You need to be able to explain what is actually going on with whatever generative AI or large language model tool that you're actually using. You need to break it down in an elementary way. Right? That's literally what we do in our free prime prom Polish PPP course. We go into explainability. We say this is how large language models work. They don't work how you think they work, right? Because the more that you understand something and the more that you educate and train yourself on what's happening inside of that black box, the more trust that is then within your organization. Which means more people are getting on board, which means you're going to have better and more usable outputs. Because the more that you understand, the more explainability there is, the better the outcomes are. The fewer hallucinations there are, right? So here's another thing. After you can demystify the black box, you really need to emphasize training. Okay? So I tell people this. Call in your vendors. They're busy right now. And unfortunately not even all of you know, these large tech trillionaire companies are training all of their employees top to bottom. But whatever, whatever vendor that you're using for, you know, whether you're using Microsoft Copilot or you know, any suite of Google's AI products or AWS, or you know, Q from Amazon, Azure, Salesforce, etc. Whatever product or vendor that you are using for your generative AI, you need to call on them for education. You need to take whatever you know, most of them have pretty good free courses. I shared a lot of those in our newsletter yesterday. But you need to call on those vendors. You also should be bringing in outside experts that can explain one specific thing. Don't. If, if you don't understand a certain aspect, you need to invest heavily in the education and training. Think if you can actually save 70% of your employees time, right? You should either appoint people in your organization to then carry the torch forward and you need to bring in outside consultants, outside experts to train those people, right? That's one of the things that we do at everyday AI is we, we help people, right? People, you know, hire us to help them demystify chat, GBT or help them learn other generative AI systems. You should be doing that as well. So what's the best way to do these things? What's the best way to properly both invest in training in education, but to also give your employees a safe way to do this, Right? Think of that picture that we painted earlier. A brand new road, a brand new car. No one knows what they're doing. You need to give them set up a playground in a safe place for employees to learn, right? So literally as an example, you talk of OpenAI. OpenAI has a playground. It's a sandbox, right? You can go in there and try different models and break things, right? You should be doing that. You know, had a great, great conversation with someone from Walmart a couple of months ago and they talked about they have an entire custom playground for their Walmart's corporate employees to play with different generative AI tools. All right. It's probably the best way to experiment isn't something that ultimately impacts a live product, a live service, a live offering. You need to have a place to practice first. Just like if you're building a new, you know, NBA team as an example, you got to do a lot of practicing first. You don't just throw them out there on the court. They're going to lose, they're going to get embarrassed. All right, an added tip here. Ready? Your AI abilities aren't just determined by your AI knowledge. Because when we're talking about education and training, I want you to take a step back and I want you to think that doesn't mean you have to become a tech person, a code person. That doesn't mean you need to become a machine learning, deep learning expert. No, because when we're talking about generative AI again, it's simple prompts going into this black box with great outputs coming out of it. You know what one of the biggest skills in skills is if you want to train your employees? Old school skills. Speaking, listening, typing, problem solving, clear communication. Right. I see a huge resurgence in 2024 of going old school. Do you know what prompt engineering is? Y' all like, prompt engineers are out there getting paid, you know, professional athlete salaries, but essentially it's people who can communicate clearly with a large language model. It's not as technical as you might think. If we're talking about basic generative AI models that we can all use right now, you need specificity and clarity in your language. You need to be able to ask questions. One of the reasons why I think personally I get great results out of, you know, large language models and other generative AI systems is my background as a journalist. When I focus, I can ask very clear questions. Right? I can go back and forth with a large language model. That's what you need to be doing. All right, so you also last tidbit here on number four and then we're going to wrap this up with number five. You need to be able to explain AI project, implement, implementations, implications company wide before deploying them. That's another part of education and training. All right. After you get your first big win. Part of step four, it's an iterative ongoing process. It's where you are also integrating whatever your next big picture or medium picture AI implementation is. You need to be able to explain that company wide, but also train everyone on that company wide so it's not just training on a skill set or on a specific tool. It is training on the big picture. It is reinstituting a new way that your company, your organization is going to work. All right, hey, Nancy says more voice in 2024. Yeah. More clarity in your communication, for sure. Love this, Liz. Liz says, embrace a culture of learning. AI Gen. AI. Yes. You need to have a culture of learning. That's why we did an entire episode yesterday on education. All right, here we go. We're going to wrap this up. Step five, you need to plan for a future of what happens when AI works. So we referenced this in step one, because in step one, when you are having that ground up, right, that ground up committee to gather insights with your company, you need to say why we are doing this, but also what happens when it works. So you need to plan for this future, right? And there's probably. When you ask why you're using AI, you know, it's filled with buzzwords, right? Oh, we want to increase automation, reduce overhead, doing more with less, etc. Right? So, okay, it's probably going to work. If you go through this the right way, if you follow steps one through four closely, it's probably going to work. So what happens then? What happens? No one wants to talk about this. I talk about this pretty openly. AI is going to replace more jobs than it will create. All right? I'm not going to end this on a sour note, but you need to have these conversations in step one. But part of step five is you need to plan and work toward that future of what happens when AI works. Right? You need to have a plan in place. As an example, let's look at that 70% statistic again from McKinsey that says generative AI may automate work activities that absorb up to 70% of employees time. So as an example, if you have a hundred people in sales, are you going to lay 70 of them off? I don't know. You have to have that conversation. Are you going to free up some of the more mundane tasks and have your sales people work on something more meaningful to work on, more, you know, more kind of on customer service, customer experience? That's a question you have to have. That's a conversation you have to have. What happens when AI works? Are you just going to downsize? If you're a public company, are you just going to focus on shareholders? You need to be transparent about it from the beginning. That's why it starts in step one and we're wrapping it up with step five. Your goals of AI need to be transparent. Are you going to move to a four day work week? As crazy of this, as crazy as this sounds, literally, I'll, I'll find the study. I read it the other day that AI companies, you know, AI powered or AI first companies, have already started to implement a four day work week. Paying their company, you know, paying their employees the same. They're not paying them 80% of their pay. They're saying, hey, AI has been great for us. We're going to a four day work week. Is that something you're going to do? Are you going to create new roles? Are you going to create new divisions in your company? Is your company going to take on new lines of businesses of business after AI works? How are you going to get more human contact in all steps of your business, whether you're a product business, a service business, etc. One of the downsides of proper gen AI implementation is automation, right? It saves time, but it also takes away a lot of that human contact. How are you going to combat that? How are you going to keep a happy and productive workforce of passionate people who feel purpose in their work after AI works? I don't have the answers, right, because that looks different across different organizations, across different companies, across different parts of the world. But you need to plan for that. You need to plan for that. And that is where ethical AI implementation comes into place. When we talk about ethical AI implementation, it doesn't just mean data and guard rails etc. It means you have to also be ethically, like acting ethically toward the humans that have made your company what it is today, need to envision and work toward a hybrid approach, right? Humans and generative AI systems working hand in hand, not against each other. But you also need to say, what is that more meaningful work? If we can get rid of the mundane, what is the most meaningful work? Something I always suggest people to, to ask a question or to have a discussion around. And this is probably something to talk about in step one, when you're gathering insights from a ground up committee, is, hey, asking everyone in the organization, what would you do if there was two of you? Not hey, I would do more of this, what would you do differently? That's what proper gen AI implementation is, right? The potential to free up 70% of your time, what would you do differently? What would you do more of if there were two of you that you can't do now? You need to plan for that future of what happens when AI works. All right, let's recap, y'. All. If you do have a question, get it in quick because I'm going to wrap this one up. All right, so here's the five simple steps to start using Gen AI in your business today. Ready? Step one, gather insights from a ground up committee. Step two, create straightforward guidelines with guardrails. Step three, sprint toward your first small measurable AI project. Step four, invest heavily in education and training that align with long term business goals. And step five, plan for a future of what happens when AI works. All right, like I said, y', all, I had I don't even know how many pages and notes, but the majority of content that I put together that I spent hours going through dozens of episodes, I have so many notes, right? So if you want access to all of this, if this was helpful, people always ask all the time, hey, Jordan, every day AI helped me get a promotion. Now I'm head of AI. What can I do to help? Share this with people. Share this episode with people. There's so much bad information out there. That's literally why I started Everyday AI people think that generative AI is just using prompts. It's not. This is step by step blueprint to revolutionize and transform the way that companies work. So please, if this was helpful, please repost this. You know, if you're listening on LinkedIn or you know, if you can go find this on our Twitter account, repost this, let me know you reposted it and I'll share all our notes. It's a lot you might look at and be like, oh my gosh, Jordan, this is more than I bargained for. All right, so a couple, couple quick questions that I think I saw here. I might be missing some and sometimes our the stream doesn't show everything but Mauricio thoughts on an internal chief AI officer versus hiring a consultant firm to implement the strategy? The answer is yes, you should be doing both, right? This obviously depends on the company, company size, etc. But I firmly believe that you need to. The same way that I say a ground up approach. When you are starting with step one of gathering insights, you shouldn't just be gathering insights from everyone internally, every department, every layer of your organizational chart internally. But you should be leveraging people from the outside as well. Because here's the thing. So many times internally you have a mindset of just doing things the way they've always been done. You should be hiring someone externally to poke holes and help guide you. Right? Something we do for companies. You can always reach out to us and we can let you know what that looks like. But to answer the question, Mauricio both Rolando says, I would think that in step five, companies need to think about the impact of successful gen AI with their customers. And how do you communicate that? Yes, absolutely. Absolutely. I agree. Know the hope is that as you free up some of this more manual, mundane time of your employees that ultimately leads to better customer experience. Right. We talked about that. In also measuring. Right. How you can get to a, a small measurable AI project. Sometimes it's time, sometimes it's money, sometimes it's increased customer service scores. Right? Yes. You cannot lose fact of the human. Everything should be a hybrid approach, whether it's your own humans internally in your company or the humans who are ultimately buying your product or service. When you free up time, you need to focus more on ways that you can engage with those humans and better serve them. All right, I think there was one more question here. So again from Mauricio saying are there tangible case studies for different departments of what AI solutions tools to implement and return on investment for this? Are there not? Not across many different verticals. So yeah, there are great studies and we shared a lot of them yesterday in our newsletter. There's great studies out there about like oh, in HR or oh in sales. And I'll try to pull a couple more for today as well. All right. But that is it, y'. All. I hope you enjoyed this episode on the five simple steps to start using gen in your business today. Like I talked about, this is one. We've technically been planning this for months. This is the culmination of con, hundreds of hours of, of conversations with, with experts building generative AI technology with, with, you know, leaders in the generative AI space through hundreds of hours of us teaching thousands of others, of, of other people how to leverage generative AI. This is a blueprint, all right? I want you to use this. That's the point. That's why we put in so much work here at Everyday AI. We want to cut through the smoke and mirrors that are out there elsewhere in the generative AI space. We want to simplify this and we want to be the resource that helps your, that helps you leverage Generative AI to grow your company and to grow your career. All right, this newsletter is one you're going to want to sign up for. So make sure to go to your everydayai.com, sign up for that free daily newsletter if you're listening on the podcast. All that information is in the show Notes as well. That's it. I appreciate y' all go to your everydayai.com for more. But I hope you can now understand your blueprint forward to grow your company, to grow your career with Generative AI with these five simple steps. Thanks y'.

All. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next.