Ep 302: 5 Laws for Success in the AI Era

Harnessing the Power of AI in Business

The dawn of Artificial Intelligence presents a new frontier for businesses. With AI applications permeating various sectors, the efficiency and productivity of everyday operations have been significantly enhanced. The benefits of AI tools are particularly noteworthy in the creation of design elements for presentations and courses, where the existence of such technology eliminates the need for external graphic designers. This allows business owners to save on costs and resources while expediting the overall design process.

Leveraging Proprietary Data for a Competitive Edge

In addition to enhancing efficiency, AI tools also prove to be instrumental in the optimization of business processes, primarily through the leverage of proprietary data. This proprietary data can be availed to provide a critical competitive edge, particularly in industries that thrive on information such as marketing and sales. However, to effectively handle this data, an understanding of AI policy is pivotal before uploading any vital information onto these platforms.


The Law of Continuously Improving Compromise

As the world becomes increasingly interwoven with AI, it is vital to continuously improve and adapt to new technology in business. Despite the imperfection of AI tools, their potential for incessant advancement highlights the “law of continuously improving compromise”. This encourages businesses to incorporate AI tools into business processes, understanding that perfection is not immediate, but rather, continuous.


Optimizing Processes with Generative AI

Another key to success with AI involves understanding prompt engineering and continuously improving processes. It is also essential to compare and test different AI tools based on specific business use-cases. As a result, productivity and efficiency in marketing, sales, and operational processes within businesses can be significantly enhanced.


Implementing AI for Infinite Scalability

AI tools can be harnessed to eliminate bottlenecks within a business, allowing for increased scalability without the need for additional personnel. For instance, one individual can support three people instead of just one with the help of AI. Beyond enhancing scalability, AI can also identify and execute even minor improvements, leading to increased productivity.


Focus on Quick, Measurable Wins with AI

When it comes to integrating AI, quick, measurable successes should be a major focus. However, the quality of content generated must not be compromised. The deployment of AI should be strategic and employ HR for evaluations that will facilitate the identification and prioritization of feasible improvements.


The Transition of Professions to Skills

As technology continues to evolve, so does the need for skills that can operate and benefit from this change. The notion of transitioning professions to skills is becoming increasingly critical. This trend encourages unlearning of traditional habits, learning from successful use cases, and bringing together different expertise to evaluate processes.


Operating business successfully in the AI era requires constant adaptation and embracing the potential of AI. As the laws explored above illustrate, AI technology is an invaluable tool that can provide competitive advantages, improve efficiency, and stimulate increased productivity when used strategically.

Topics Covered in This Episode

1. Law of Continuously Improving Compromise
2. Importance of Prompt Engineering and AI Tool Testing
3. Scalability and Efficiency with AI
4. Application of AI in Business


Podcast Transcript

Jordan Wilson [00:00:16]:
If only there were simple rules to follow when implementing generative AI, you know, like a step to step guide or, you know, 5 laws for success. Oh, wait. That's today. That's exactly what we're gonna be talking about today on everyday AI, the 5 laws for success in the AI era. So what's going on y'all? My name is Jordan Wilson, and I am the host of Everyday AI. And the show, well, it's for all of us. It's for me. It's for you.

Jordan Wilson [00:00:43]:
It's for anyone looking to grow their company and their career using generative AI. We do this every single day, bringing you a livestream podcast, and free daily newsletter, helping us all grow our companies and grow our careers with generative AI. So if that sounds like you, and if you haven't already, please make sure you go to your everydayai.com. Sign up for the free daily newsletter. We recap our show every single day as well as, go over all of the different AI news and what's happening in the world because there's always a lot. So speaking of that, let's get started with the AI news for today. Well, Toys R Us is kind of back. Well, they're back in the headlines at least and not because of their ongoing bankruptcy battles, but because of their use of AI.

Jordan Wilson [00:01:27]:
So the origin of Toys R Us has been reimagined through an AI generated brand film. And Toys R Us just released an AI generated short film, which was the first at, the Cannes Film Festival. So the brand film toys the the origin of Toys R Us marks the first ever creation using OpenAI's text to video tool, Sora, that debuted at the Cannes Lions Festival. So it was created by Native Foreign, and the film showcased the genesis of Toys R Us in a dream with founder Charles Lazarus and Jeffrey the giraffe. So Soera is obviously an AI text to video tool from OpenAI that has not been publicly made publicly available yet and can produce up to 1 minute video clips without dialogues with a simple text prompt. So, the film is actually catching a lot of flack, online and on Twitter, But I actually don't think it's that bad. Right? I think it's actually a good use case, maybe. I don't know.

Jordan Wilson [00:02:24]:
Toys R Us didn't have the budget to, you know, produce a, super high end film and instead tapped into Sora. So, yeah, I'm I'm curious if any of our livestream viewers caught that and what they think of it, but we'll be sharing it in our newsletter. Alright. More AI news. OpenAI is reportedly shutting down in China. So OpenAI is enforcing a policy to block users in China from accessing its artificial intelligence software starting in July. So Chinese companies like Alibaba and Tencent are encouraging developers to switch to their products. So OpenAI is expanding its existing policy to block Tenney's users from accessing from accessing their AI software.

Jordan Wilson [00:03:04]:
So it was actually screenshots of the memos that was sent to Chinese developers that were posted on social media and kind of set this, news in motion. So critics are, though, saying that this may lead to a concern for the global development and collaboration of AI. And other quick OpenAI news, well, OpenAI's advanced voice mode for ChatGPT, which can understand and respond with emotions and nonverbal cues, has been delayed due to remaining issues and internal checks. However, the video and screen sharing capabilities of the desktop app is still going to be rolled out, and, OpenAI said on Twitter that it needs about 1 more month for the voice technology. And while OpenAI is slowing down, Anthropic and Claude are speeding up. So Anthropic has just unveiled a powerful new feature for its Claude AI platform called projects that aims to enhance teamwork and collaboration with AI by providing a central hub for knowledge and insights. So this is very similar to ChatGPT's custom GPT's feature, but it does have Claude's 200,000 token context window, which allows for more precise and relevant help from Claude. So make sure to check out our newsletter.

Jordan Wilson [00:04:14]:
So go to your everydayai.com for more on those stories and a ton more. Alright. But you didn't tune in for that. You tuned in to hear about the 5 laws for success in the AI area. So, in the AI era. So I'm super excited to talk about this, today because this is something we're always getting questions on and, you know, it's always you're always trying to figure it out. Right? Figure out how can I actually implement generative AI? Now that we all know, there's no denying how powerful it can be for your business, but now we have to talk about how we can implement it. What are the do's and the don'ts? What are the rights and the wrongs? So it's not just me today.

Jordan Wilson [00:04:48]:
I'm very excited to bring on our guest for today, Isar Mehtis, who is the CEO of Multiply. Isar, thank you so much for joining the Everyday AI Show.

Isar Meitis [00:04:58]:
Thank you. I'm I'm really excited to be here.

Jordan Wilson [00:05:00]:
Hey. And if if you've listened before, we've had Isar on once before. He was so good. We had to bring him back. He also has his own great podcast, which he had me on, once or twice before. But, Isar, maybe tell everyone a little bit more about what you do at Multiply.

Isar Meitis [00:05:15]:
Yeah. So at Multiply, we do AI education and consulting. So we teach, courses either private to specific organizations or open to the public, and we do consulting to specific companies on how to actually implement the stuff that we teach at the course. But the goal is to help people through this transformation that are that is going through more or less any industry today, through a lot of free education, kinda like what you do through a podcast and newsletter and all the good stuff, as well as different paid channels depending on what people's needs are.

Jordan Wilson [00:05:45]:
Yeah. Absolutely. And one thing I love about the AI spaCySARS, it's very, like, collaborative. Right?

Isar Meitis [00:05:50]:
Oh, yeah.

Jordan Wilson [00:05:51]:
Like, you know, I like I love that you're coming in, you you know, on here and and sharing some of your great insights. But, you know, let's just let's just start with it. Right? And let's just get straight into these these 5 different laws, Isar, and, you know, let's let's start with number 1. What is the first and I don't know if it's the most important, but what is the first, you know, law, to to kinda get started?

Isar Meitis [00:06:12]:
Let me say one thing before that. Like, these laws continuously evolved. Like, I started with it. I I talk on a lot of stages, so I get to talk to a lot of CEOs and business leaders after and then through the consulting. So I I just meet with a lot of real companies who have real questions, and these kind of evolved through the last year and a half, and it actually was 4 laws until a couple of weeks ago. And they're like, oh my god. This is like so so you you one of the first are getting, law number 5. But but they're they're not in specific order or specific level of importance.

Isar Meitis [00:06:51]:
They're just all insights of lenses through which you need to look at AI in order to help you grow the business. And so based on your request, let's just get started. So the first law is stop thinking efficiency and start thinking outcome. Mhmm. And it's probably the longest one as far as how many words it has in his but in it. But what what does it mean, stop thinking efficiency and start thinking outcome? As business people, we're trained through years to think through processes. Right? So everything we do, we taught in business school or through actual life that there has to be a process to it and it has to be well defined so everybody can follow it, so we can scale. Like, all these amazing books was written about it.

Isar Meitis [00:07:36]:
Right? But so let's take an example. Let's take customer service. How does customer service works? Well, first thing, you have some kind of an intake way. This could be an email. This could be a ticketing platform. This could be a call. This could be a bunter on social media. This could be, like, multiple ways to initiate a customer service process.

Isar Meitis [00:07:58]:
Then somebody has to go through all those intakes and categorize them. Is this a technical question? Is this a conceptual question? Is this a financial question? Is this so and then you have to prioritize them. So after we have them in categories, somebody needs to say, well, this is high priority. It's a big client. They're bringing us $20,000,000 a year. This is just a guy that's asking a general question. So you need to prioritize them, then you need to assign them to the right people. Right? So the the right person, and then these people have to try to solve the problem in whatever platform is relevant, and then you need to document it so in the future it will be easier to solve, and then it starts all over again.

Isar Meitis [00:08:35]:
And when and when you go to people in that field of customer service and tell them, okay, we need to improve our customer service because we're we we don't have enough budget to increase the team, but we gotta do this better. So what do they do? They're gonna say, okay, let's try to improve the intake by not allowing people to call in or we improve the IVR, so the click 1 for this, click 2 for that. We're gonna improve this. We're gonna improve our ticketing system. So it's more specific on how people open tickets. So that's gonna be step 1. And then that's gonna give us a 5% increase in efficiency. And then step 2, we're gonna define a better way on how to prioritize and so on and so forth.

Isar Meitis [00:09:13]:
Right? They go through every step of the process, try to find small efficiencies in the process. The reality is today, right now, as we speak, there are fully capable AI customer service systems that can do the intake, prioritize the same, provide answer 3 165 days a year, 20 47 in multiple languages, connected to your CRM, to your ERP, to your financial system, and they can do everything a customer service agent can do, better, faster, cheaper, including large companies like Klarna that has done this at scale and have cut off 700 jobs, and I'm not suggesting that's what you need to do, but they probably had 20,000 of those people doing customer service. And they were able to provide customer service at 3 minutes average time to close a task versus 13 minutes while, getting the same score for their level of customer service. So, basically, what I'm saying is they've circumvented the entire 7 steps that we talked about. Because the goal, going back to start thinking outcome, the goal of customer service is not a better ticketing system. It's not a better, IVR. It's not a better process. The goal is happy customers.

Isar Meitis [00:10:33]:
If AI can deliver happy customers, faster, better, cheaper, forget about the process. Now, one more thing and then I'll let you actually ask you a question. In some cases, you cannot do the full jump. But in 2 jumps, you can go 70% of the way. So what I tell people is, again, forget about your existing process in literally everything in your business. Think about what is the outcome that you're trying to achieve and then look for AI tools that gets you most of the way there, either in one jump or in a few jumps, that will save you a lot of the steps that you may not need to do anymore. Because if you do it the other way around, you're gonna miss huge savings because you're gonna try to improve little things in your process.

Jordan Wilson [00:11:22]:
The world of communication is changing. That's why you need to pay attention to a sponsor of this podcast, hour 1. Even NVIDIA CEO Jensen Huang used hour 1, and I think you should too. So my name is Jordan Wilson. I'm the host of Everyday AI, and here's the deal with hour 1. You gotta check them out. It's a time saver for communicating at scale in ways you literally couldn't before. So let's say you need to deliver a presentation to global audiences in different languages you don't speak, or maybe you deliver live trainings every single week that are 95% the same, and you're only changing a few things.

Jordan Wilson [00:11:57]:
That's where hour 1 comes in. With a host of features like AI avatars, premade and custom templates, and collaboration tools, hour 1 is an all in one AI video platform to communicate at scale in new ways that weren't possible before. So make sure to check out the show notes or go to your everyday ai.com for info on how you can actually win a free custom AI avatar from Hour 1. Go check it out y'all. Yeah. Isar, I like I think this one is super important and it's worth diving into a little more. You know, one thing that I tell companies that we work with is you have to unlearn good habits. Right? Because we've especially since, you know, the SaaS scene and, you know, all this software has that has become available over the last couple of decades has made us more efficient and has made us more productive.

Jordan Wilson [00:12:51]:
And that's not necessarily now in the age of generative AI. That's not the best way to always do things. Right? Just because they've worked or just because, you know, a certain way right? Like, I find myself personally very efficient and very productive in how I work, but that doesn't necessarily mean going forward that that's the process I should be relying on. Right? If if there are generative AI systems or large language models that can do pieces of it better. How can business leaders, you know, kind of tweak their thinking? Because, yeah, I think for so long, we have been you know, it's been pounded in us, like, hey. If you're efficient, if you're productive, you will be successful. And sometimes I think what I see now is people just being still being very productive, being very efficient, but doing it kind of the quote unquote old way. How can people tweak that way of thinking?

Isar Meitis [00:13:39]:
I think the first thing first thing is mindset. Right? It's exactly these things. Like, look for knowledge on how to do it differently. Look what other people are doing. Follow people like you or me on LinkedIn, TikTok, Instagram, wherever. People who share actual use cases, who can show you what's possible. And the other thing is is start a committee in your in your business. Start a group of people, or it's gonna be your AI committee that can brainstorm and then set these guidelines.

Isar Meitis [00:14:07]:
Okay. These are the rules, like these lenses that I'm talking about right now. This is how we're gonna evaluate everything in our business. And then even if you're not thinking about it, once you have 4 or 5 or 6 or 12 people, depending how big your company is in your committee, somebody will come up with the idea and say, hey. You know, I saw this thing online. I think think we don't have to do these 5 steps. Or I think maybe the 5 reports that we're looking at every week is not the best way to get the information that we need to get to make decisions.

Jordan Wilson [00:14:36]:
Alright. And, hey, this this is great and, very excited to jump into number 2. But just as a reminder, for our livestream audience joining us, if you do have questions, please get them in now. We'll probably have a little bit of time at the end to go over any but, so, Esar, now that we, have the first law, you know, to kind of think more outcome and not necessarily efficiency. What is the second AI law for success?

Isar Meitis [00:14:59]:
So the second law is from profession to skills. And that's a that's a cool one that I really, really like because I'm a I'm a very big example of that. So if you go back, by the way, to 17th century, people had the title of computers because there were very few people who could compute. Now which basically means if there were business cards in the 17th century, which I don't think there were, but if there were, some people had computer written on their business card. I don't know many people who have computer written on their business card right now. Another example is typist. My next door neighbor when I was a kid, still good friends of my family, was a typist. That's what she did for her entire career.

Isar Meitis [00:15:42]:
And, again, I don't know a lot of people who are typists right now. So all of these things were professions that became skills, and they became skills through the implementation of technology. Right? So technology enables us to take something that was a profession that you actually went to school for and made it available to everyone. And this has now with AI been dramatically accelerated. So when I talk about myself as an example, I've never studied graphic design. I've never studied how to write code. I've never written a line of code in my life. I'm a really, really bad accountant and I've never studied that.

Isar Meitis [00:16:18]:
However, now I have amazing tools that allow me to create the designs of everything that I do, whether it's presentations that I'm giving on stages, presentations to clients, courses that I teach, etcetera etcetera. Like, all the graphic design for that, a 100% of it is generated with AI without going to third party graphic designers that I used to spend a lot of time and money in getting their help. And it's not that they became bad, it's just a scale that I was able to acquire using AI. I as I mentioned, I don't have a clue how to write code. I don't understand code, But I don't need to, and yet now I have different codes that are running in different things that are making my work more efficient because I'm using Chachukity and Claude, and I actually use them back and forth to troubleshoot these other issues in the code to get it to work and do the things that I needed to do. Now will that enable me to program the next, FIFA game? No. Probably not. But does it allow me to or at least not in the near future.

Isar Meitis [00:17:15]:
Right? But but it allows me to do a lot of things more efficiently in my business in ways that were just not possible to me before. Now the same thing goes to you talked about Sora. The same thing is gonna go for actors and screenwriters and, filmmakers and, lawyers and paralegals and home automation experts and so on and so forth. Right? Because there's gonna be AI tools that's gonna learn that specific topic and will allow anybody to do this at a professional level.

Jordan Wilson [00:17:49]:
Yeah. And so, our our next rule, Isar, is about how we can win, and everyone wants to win with generative AI. So what is rule number 3, for our AI laws for success?

Isar Meitis [00:18:03]:
Awesome. So rule number 3 is that there's 2 ways to win with an AI in the AI era. 1 is having and leveraging your proprietary data. So if you can train models, and I'll talk about this in a minute because, like, well, I can train. I cannot train models. I'm not Google. But you can, and we're gonna talk about this in a minute. But if you can train models on your proprietary data, you, by definition, can get insights that your competition do not have because they don't have the data, which means you can make better, more data driven educated decisions when your competition doesn't have access to that.

Isar Meitis [00:18:37]:
So there's 2 things that people say. 1 is what I said, like, I don't know how to train models. Well, that concept of training models sounds really complicated, but the reality is with even just what you just said this morning, if you build a GPT and upload 5 documents to it, you just train the model. It's called WAG and it's, doesn't matter what it means because I don't wanna confuse people. But it basically means you just train a model on a specific piece of knowledge from your company. So now going back to, again, your news from this morning, if you have a 200,000 token context window, you can probably upload 20 different documents, which could be your winning proposals into examples of bad proposals. These could be, good ways to do customer service, bad ways to do customer service. This could be HR, like all your HR documents that everybody goes to HR to waste their time to ask them how to get, you know, a PTO or apply for maternity leave or whatever people come to HR for.

Isar Meitis [00:19:36]:
And so you can train models on your propriety data very easily. Like, you don't need any special IT capabilities. Literally, just build a GPT, connect it to things, and, obviously, there's more advanced ways to do that. They're still within the reach of most companies. So proprietary data is 1. By the way, the other thing people ask is, I I don't have proprietary data. Again, I'm not Google. Like, what kind of proprietary data I have? So every company has proposals and customer service and emails back and forth with clients and, success stories of stuff that they've done online that work well from a marketing perspective.

Isar Meitis [00:20:13]:
Like, each and every one of those is a piece of data that you can train models on in order to create more of the successful stuff and avoid the not successful stuff.

Jordan Wilson [00:20:23]:
That's such, like, such great advice. Right? Even as we talk about, you know, GPTs from, you know, chat gpt or the new projects, kind of, feature from Anthropic. Right? And I've said this on the show a lot. I think that the future of large language models is actually small language models or this concept of of working with multiple smaller GPTs or multiple smaller, you know, now projects. I wish I wish these companies had, like, better naming mechanisms for my brain because you'd say projects and GPTs and it's like, what the heck does that mean? But, you know, I think that is the future of how we are going to be working. So I like that we can get a taste of this right now in, you know, these consumer facing tools, you know, in chat gbt and in, anthropic, anthropics Claude. One thing to note there, which, you know, I'm sure, like, Isar and I, talk about this all the time is, like, when it comes to your company's data, always make sure before you run and, you you know, go upload that, make sure you read your company's AI policy handbook first or, you know, talk to someone, you know, make sure, you know, that you have a good game plan when it comes to what files you should or shouldn't upload, you know, depending on your plan, whether you're on a, you know, chat gpt plus or enterprise plan. There's different data sharing, but it's I think it's important to, you know, keep that in mind.

Jordan Wilson [00:21:43]:
Isar, what is our 4th, kind of AI, or law for success in the AI area?

Isar Meitis [00:21:49]:
So, first of all, great advice about the data. Don't upload data to any of these platforms before you know whether you should or you shouldn't do that. So that a great advice. But still in this law, I said there's 2 ways to win. 1 is data proprietary data. The other is optimization. So you can optimize your processes within your business, whether it's marketing, sales, design, product, QA, HR, finance, like, each and every one of those has multiple ways where you can optimize them with AI in order to be a lot more efficient in them. So let's say everything is vanilla and nobody has proprietary data, and you can't have any real edge above your competition in your field, you're in a highly commoditized world, then you can now do things 20, 30 percent more efficient than your competition.

Isar Meitis [00:22:42]:
Meaning, by definition, you can now lower your rates by 15% and still make 15% more money. So your customers are gonna be happier. You'll be able to deliver better work faster, and you will make more money and they will pay you less. So, literally, everybody wins. So proprietary data is 1. Optimization is the other. And, obviously, if you can do both, then you win on both sides of this equation. So that's this law.

Isar Meitis [00:23:11]:
Now to the next one, that's the new law that I told you that I added rule number 5. And that's because a lot of people are disappointed when they start using AI. They're like, oh, this is bullshit. Like, it doesn't work. I can't get consistent answers. Blah blah blah. Like, all that. And and it's true.

Isar Meitis [00:23:26]:
Like, I'm I'm not disqualifying this. So the law is called the law of continuously improving compromise. And what it basically means is that, yes, these systems are not perfect. And but they're good enough for a lot of things once you figure them out. And the problem is that people get into this, and they try 1 or 2 things, and they don't work exactly the way they want it. Or they try the same thing 3 times, and they get 3 different results. I'm like, okay. This is bullshit.

Isar Meitis [00:23:53]:
I can't use this in my business, and they stop. And the reality is if you invest the time and the resources to figure out one use case, 2 use case, 3 use cases, and you end up with 30 use cases, each and every one of these use cases gets you an efficiency in something. And if you don't do that, if you say, well, I'm going to wait till this thing is perfect. I'm not going to compromise. What's gonna happen is, a, everybody else is gonna figure out these small efficiencies along the way and you're gonna left behind. But even at the day, it's perfect. You will still have to figure out how to implement this. You will need to figure out because the the tech is a small aspect of this in a business.

Isar Meitis [00:24:40]:
There's processes. There's training. There is do's and don'ts. There's regulations. Like, there's a lot of stuff beyond, oh, I'm gonna flip the switch and buy everybody the licenses. That's the smallest part of all of this. Figuring out how to use it properly in a business context when there's teams, there's people, there's training, there's, divisions, department. Like, all of this has to come into play when you start implementing these things.

Isar Meitis [00:25:05]:
So if you start now and you're saying, yes. I know I need to compromise. And I call it continuously improving compromise because, as you said, Anthropi came out with Claude 3 3 months ago, and now we had Claude 3.5 that can do a lot more stuff and faster and better and a higher accuracy. So you continuously need to compromise on less things. But if you start now, then you start gaining benefits. You learn how to use these platforms. You find ways to implement them today, the way they are today, and they're good enough today for many, many different things. Not everything, but for many different things.

Isar Meitis [00:25:41]:
And so start today, improve as these platforms improve, and don't wait for it to be bulletproof because then you're gonna be left behind.

Jordan Wilson [00:25:51]:
Yeah. I think that's important. And, you you know, 22 small pieces there, Isar, something that we see all the time, around that is number 1, you have to know the basics of prompt engineering. Right? I'm not saying that you you you have to become an expert, but the future of work is generative AI. You have to understand the basics of how models work and how to get the most out of them. That is prompt engineering. So if you're just trying to, you know, trying to find someone online and trying to find their prompt that they use and, you know, get a copy and paste, make a prompt, that's not how large that's not how models work. That's literally not how they work.

Jordan Wilson [00:26:23]:
So that's important to keep in mind. Another thing is you shouldn't be expecting perfection. Right? Like, I love, Esau, that you said it is continuously improving because, yes, on a week to week basis, you need to be looking at your processes, looking how you're using generative AI, and you have to be continuously improving it. You know, I say, you know, maybe if you do it correctly, you're gonna get 80% of the way there in only 20% of the time. It is not a, you know, copy copy and paste a 100% automation a 100% of the time. So I love the concept of having to continually improve it. Alright. We are near the end.

Jordan Wilson [00:26:57]:
Isar, what is our last, law for AI success?

Isar Meitis [00:27:02]:
Just before I dive into that, I wanna piggyback on the thing you just said on on the tools continuously improving. One of the things that I get asked a lot, and I'm sure you do as well, like, okay. So which tool do I use? Do I use Chachipiti? Do I use Claude? Do I use Gemini? Do I use whatever? And the reality is different tools work in different ways for different use cases. So I use all of them. Every single day, I use Claude. Every single day, I use Perplexity. Every single day, I use Gemini. Every single day, I use Chatgpt.

Isar Meitis [00:27:32]:
Almost every single day, I use, different, image generation tools, whether it's Midjourney or or, or others. And the reason is I use them all the time, and I test them against each other all the time. And I do that to figure out which one is doing this particular use case Better, I'll give a small little tip on what's the easiest way to do that at least on the chat, side. I use a Chrome extension, that that is called, chat hub. So there's a lot of chat hubs if you Google them, but this one's just a Chrome extension. So you go to the Chrome extension store and you and you get it. And I got it early on, so there was, like, a lifetime deal for, like, $30, which I don't think exists anymore. But there's a free version of it as well.

Isar Meitis [00:28:16]:
But what I what it allows me to do is it allows me to run up to 6, I always use 4, large language models in a single chat. So you have all 4 of them on the screen, and you chat on the bottom in, like, your entry, and you see all 4 models running at the same time. And this is an amazing way to compare specific use cases. Okay. So here's my prompt, whether it's short or long or whatever. You put it in, and you see 4 different models running at the same time. And you can then know for this particular use case, if you do it 2 or 3 times, because once is not necessarily gonna give you the right answer, which one does it better. And then I go to the actual tool and use the actual tool.

Isar Meitis [00:28:48]:
So that's just a tip on how to do what you what you mentioned. Last law. So the last law is AI enables infinite scalability. And, like, okay. There's no such thing as infinite scalability. True. There is no such thing as infinite scalability, but it's pretty damn close. So, again, let's think about a business.

Isar Meitis [00:29:08]:
A business every business starts with marketing. So people need to know that you exist in your services and products. And then there's sales. So, okay, people need to engage with your company one way or another to buy your products and services. Then there's some kind of operation where you deliver the goods or the services that you promised. Then there's customer service, and then there's all the back end stuff. Right? There's finance and and and and HR and all the other things. So that's how the business works, and every business has bottlenecks.

Isar Meitis [00:29:38]:
So if we go step by step, like, okay, this is we're limited with the amount of marketing we can do. You hear that with every company you talk to. I work with companies of 5 people and of a 150,000 people. And they all have well, we we're really short with our marketing resources. You gotta understand this, Caesar. Like, I I get it. Everybody doesn't have enough marketing resource. But is it true? Now you can literally 2 people with the right AI tools can generate SEO optimized blog posts probably 30 a week when they could do 2 before.

Isar Meitis [00:30:17]:
They could generate social media content basically as much as they want. So if you don't wanna post more than once a day on on every platform, you can generate optimized content for every platform. So not the same thing, but actually optimized for TikTok versus Instagram versus YouTube versus LinkedIn. Different content with 2 people for every day of the week without running into bandwidth issues. And the same thing for brochures for your next trade show. Like, every one of these things you can generate significantly faster at significantly higher scale. So that solves the marketing thing. So, well, then I don't have enough salespeople.

Isar Meitis [00:30:59]:
I just don't. Like, okay. If I if I now have 30 x the amount of leads, even today, we can't handle the number of leads we have. And so how do I do that? I'm like, okay. So some of the sales process AI can do. It can analyze the initial responses on social media initially or the initial emails for request. It can answer today, right now. I'm not talking about someone in the future.

Isar Meitis [00:31:20]:
That exists right now. Or at least help prioritize them of what people should handle 1st versus second to increase the efficiency dramatically, the scalability. So the same number of salespeople can now handle 3 x, 5 x, 10 x, depending on how you good you become in this, the number of leads. Like, okay. Awesome. But I don't have the people to actually do the thing. Like, whatever so if it's a product, it's not a big deal. Okay.

Isar Meitis [00:31:43]:
Now that you just have more people buying the product. But if the service, then I don't have enough people doing the service. Well, the same thing. The the service can be scaled with using different AI tools. So one person doing the service can now potentially support 3 people instead of 1. So now you can scale without hiring people on the sales team. But there are stuff where you have to hire people. So if you are a lot of my clients are in the home automation industry.

Isar Meitis [00:32:08]:
They need installers. They need people actually going to home, installing speakers and TVs and smart lighting and all the other stuff. So there is no, oh, I I I don't need more people. You need the people. Well, you can hire and train people a lot faster using AI. You can write better, more accurate job description. You can analyze the places where you posted them in a much more accurate way to see where you're getting the better people. You can help it write interview questions and help you during the interview process or evaluating, your resumes that you're getting.

Isar Meitis [00:32:40]:
Like, in literally every step the training for sure, like, with, generation of videos and training and stuff that can be done in minutes instead of hours. So all of these things as far as hiring people and training people becomes a lot better. So literally every aspect of the business, AI enables you a lot more scalability. And the trick is look for your bottlenecks first. Start with the things that are limiting your growth right now and say, okay. These are the three main things that stop us from growing. And look for AI solutions where you can grow 5%. Forget about the 50%, 200%, 500%.

Isar Meitis [00:33:16]:
5%. But if that's your bottleneck, now your company has 5% more throughput. So that's how you need to look through this lens.

Jordan Wilson [00:33:26]:
Yeah. And I love, you you know, starting at the bottleneck. Right? I think that's huge, especially when it comes to, AI implementation or if this is your first. Right? If if if you're listening and this is your company's first big AI initiative. Right? Sometimes people scope out, like, year long projects. It's like, no. Like, you need to find a short quick measurable win and I love starting at where you bottleneck. One other thing, Esar, that I think is important to talk about, you know, when we talk about content generation and infinite scalability.

Jordan Wilson [00:33:57]:
Yes. Absolutely. I think one mistake companies are making, though, is they don't go through the proper processes first. Right? And and they don't, you know, learn the, you know, 101 of of, you know, prompt engineering. They don't learn how to actually create good content, with with generative AI, and then instead they just, you know, have this almost like robotic or lower quality content at scale. So I think it's important. Right? ISAR has fantastic advice here, but you also have to figure out. You have to make sure the quality is, you know, high first before you just put that thing, you know, on scale, you know, all across your organization.

Jordan Wilson [00:34:33]:
So, Isar, we talked about so much in today's episode. Right? So I'm gonna do a quick recap here and then ask you for, your one kind of best piece of advice to put these into practice. So, number 1 is to think outcome. And I'm I'm summarizing here. We'll we'll get them all, you know, as they should be in today's newsletter. But number 1 is to think outcome, not efficiency. Number 2 is kind of, going from professions to skills. Number 3 is there's 2 ways to win both with proprietary data and optimization.

Jordan Wilson [00:35:03]:
4 is this law of continually improving, kind of continually improving with compromise. And then, number 5 is AI enables infinite scalability. So, Isar, so much great information packed into a very short amount of time. How can people actually make use of these, 5 laws, of success? How can they get them, you know, working for them today?

Isar Meitis [00:35:28]:
So the first thing is to actually take action. Right? So a lot of people are afraid of this or too confused or terrified or just not paying attention. Like, there's different excuses. But, start doing stuff. Right? It's and the the only way you learn like, you can I I will add to that? Like, people who are listening to this are already doing the first good step, which is to educate themselves from people who are actually doing this. So if you're listening to this podcast, you're already doing one step in the right direction. So follow the right people to get the information. But step 2 is you gotta start taking action because you can listen to Jordan or myself or a lot of other amazing AI experts out there.

Isar Meitis [00:36:08]:
If you don't actually start, you're not gonna gain any benefits from it even if you become really, really smart. And even the stuff that we say until you start experimenting with your needs, in your environment, with your niche, with your technology, with your knowledge, you won't really gain, the level of understanding you need in order to actually implement this. The second thing, as I mentioned, don't do this alone. Like, find a group of people preferably within your organization, or both. You can have an external support group. But, that can help you brainstorm and implement and test and evaluate different use cases. And you really need to start with 3 different evaluations, and I'm gonna name them very, very quick. We spend a lot of time on this when we actually work with companies.

Isar Meitis [00:36:50]:
But, one is a strategic evaluation. What's gonna change in my industry, in my business, in my niche because of AI? Like, what will my clients not be interested in in 2 years? And what new things will be able to offer them in 2 years? So the strategic evaluation. The second one is, HR evaluation, a skills gap analysis. Like with all these new AI capabilities as I talked about from professional skills, what gaps do we have as far as skills in the company? What do we need to hire for? What do we need to train for? And how do we do both these things? And then the third one is the bottom up approach. So we talked about the top down, the strategic side. The bottom up thing is bottlenecks. Like, what small things, low hanging fruits, we can change in our company today. Like, with a one hour of investment, we can save 1 hour a week.

Isar Meitis [00:37:37]:
And there are dozens of these in every single company. So if you put a group of people, that committee that I talked about before, from every aspect of the company so you have a person from HR, a person from sales, a person from finance, a person from whatever, and they sit together and they look for these things and they prioritize them. And you then look for follow people like Jordan and me and others and that provide solutions and say, okay. This sounds like something that's relevant to our business. Let's start implementing it. And you start testing and evaluating initially small without sharing the wrong data while defining rules and guidelines, like, all the stuff that we talked about before. And then you go to the next one and the next one and the next one.

Jordan Wilson [00:38:17]:
So much great advice. And, you you know, I know sometimes because, you know, having a daily podcast, you know, I hear that advice, like, start every day. And, like like, you have to start somewhere. And I think people might get tired of hearing that, but it's I mean, you have to. Right? Like, you you can't just continually be be learning and brainstorming and, you know, committing your way to this. You actually have to start somewhere, and I love what Esar even just said there. What what one hour onetime investment can then lead to a 1 hour gained a week. Start that small.

Jordan Wilson [00:38:53]:
Right? Let's build something one time that we can win back 1 hour a week. I love it. I love it. So much great, so much great advice there from Isar, the CEO of Multiply. Isar, thank you so much for joining the Everyday AI Show a second time in sharing your insights with us. We really appreciate it.

Isar Meitis [00:39:12]:
Thank you. I'll I'll say something to the audience. If you if you're listening to this on the podcast, open your podcast platform and give Jordan a 5 star and give him a review for the stuff that you're learning. It sounds obvious to do a daily podcast. It's very much not. It's a lot of work. So appreciate what he's doing. Give him a review.

Isar Meitis [00:39:33]:
I'm I'm sure he'll appreciate it too.

Jordan Wilson [00:39:36]:
Gotta love that, you know, fellow fellow podcaster, you know, putting putting all that good information out there. Thanks, Isar. Yeah. And go go we'll we'll, link, to, you know, I was recently on Isar's show. He brought together for his 1 100th episode, a bunch of, you know, some of the best minds in AI. So I'll make sure to link to that in today's newsletter. So make sure to give that a listen as well.

Isar Meitis [00:39:55]:
So Yeah. So I I I will say something about this. Just one last thing before you go because you said earlier that, you know, that the the AI people is a very supportive community. So the the 1 hundredth episode of the generative AI of of the Leveraging AI podcast, I basically sent an email out to people like Jordan and said, hey. Will you be on my show a week from today? I thought, I'll get, you know, 3, 4 people say yes. And, like, 20 people said yes. And it was absolutely, it was a good problem to have, like, having 20 experts instead of 5 show up for your 100th episode. But, thank you and thank, you know, everybody else in this industry that are very open and sharing.

Isar Meitis [00:40:35]:
I appreciate that.

Jordan Wilson [00:40:37]:
Alright. And, hey, You are going to appreciate if you read today's newsletter because Isar dropped so much knowledge. I'm gonna have fun going back to write this one, and, obviously, he's on point. You know? He was talking about chat hub. I'm like, I love chat hub. We've done a couple videos of that. So we're gonna be, you know, recapping every single thing that we talked about. So maybe there's too much good advice in there, you know, whether you're walking your dog around the treadmill, whatever.

Jordan Wilson [00:40:59]:
It's all gonna be on our newsletter. So thanks for joining us. Please go to your everyday ai.com. Get that free daily newsletter, and we'll see you back tomorrow and every day for more everyday AI. Thanks, y'all.

Isar Meitis [00:41:10]:
Thank you.

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