Everyday AI Podcast – An AI and ChatGPT Podcast - EP 126: Real Business Use Cases for AI

Episode Date: October 19, 2023

One of the most common phrases we hear is how can I actually use AI? What use cases exist for me to put AI to work for my business? Isar Meitis, CEO of Multiplai, joins us to discuss real business use cases for AI. From data analysis to marketing tasks, we cover a wide variety of topics. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Isar and Jordan questions about AIUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTimestamps:[00:01:30] Daily AI news[00:04:00] About Isar and Multiplai[00:06:00] How to implement AI in a business[00:10:00] Roadblocks for companies adopting AI [00:15:25] Using AI for data analysis[00:19:30] Data security guidelines for AI[00:23:30] Using Gen AI for content creation[00:30:12] Ideation with LLMs[00:35:05] Isar's advice to start using AITopics Covered in This Episode:1. Integrating AI into business organizations2. Use cases of AI in data analysis3. Role of the committee in exploring and implementing AI4. Content creation and repurposing with AI tools5. Consulting with experts and utilizing their contentKeywords:financial data, session, accessibility, Google, database, sensitive information, sharing, continuous education, exploration, AI, podcast, consulting, teaching courses, staying updated, business owners, limited time, learning, experimenting, new tools, advancements, Microsoft, Google, Salesforce, HubSpot, integrating AI features, business leaders, committee, framework, boundaries, misuse of AI, company values, salesperson, compensation, guidelines, boundaries, education, training sessions, third-party tools, ChatGPT, advanced data analysis, insights, marketing, scattered data, automation tools, Zapier, Maker, data security issues, Gen AI tools, pain points, video recording, content repurposing, transcription, SEO purposes, books, advisory board, committee, bouncing ideas, multiple perspectives, CEO, experienced professionals, tech startups, AI impact, education, data security, commercial version, immediate results, low hanging fruits, data analysis, finance, marketing, sales, HR, fear of publishing private data, large language models, MidJourney, Niji app, AI image generation, anime style, version 5, image generations, DALL E, ideation, content creation processSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. One of the most common things that I think I hear about generative AI in the workplace is everyone saying, all right, but how?

You know, people hear, oh, you can, you know, use chat GPT for this or you can use Dolly for this or, you know, all of these large language models. But how do they actually apply to my business? How can I actually put them to use and help grow my company? Well, if that's you, then today's show is for you. So welcome. My name's Jordan Wilson. I'm the host of Everyday AI. This is your daily live stream podcast and free daily newsletter,

helping everyday people like me and you make sense of what's going on in the world of AI and not just make sense of it, but practical steps on how we can actually use it. All right. So if you are joining us from the podcast, thank you. Make sure to check out the show notes as always. We always leave a lot of great information. there, a link, you can actually come back and join the conversation and, you know, talk to myself and the guest for today, as well as sign up for the free daily newsletter. You got to make sure you

sign up for the free daily newsletter. We always break down the conversation in insanely detailed way on how you can actually make sense of everything that's going on. But before we get into that, let's take a minute to talk about what's going on in the world of AI news. All right, let's get to it. So Mid Journey's first mobile app is out, kind of. So the popular AI image generator is moving to apps now as well. So Mid Journey, I think by far, they are the leader in the AI image generation space. And they built a thriving community on Discord, but now they've expanded their reach with its own mobile app. So in the mobile app, which is called Niji, people are a little confused because it's a normal app that is a little more, it's a normal style.

Inside Mid Journey, that's normally a little more anime, but you can actually download this app. It's in conjunction with another studio, and you can go into the general standard settings and switch to V5. So a lot of people are confused and they're like, oh, Mid Journey has an app. That's amazing. What are these generations that are coming out? So it's in there. You just got to dig in the settings. All right. Next piece of news, DeepMind is fighting climate change. So the new report shows that DeepMind, Google's AI arm is fighting climate change. And a detailing some specific ways that we really didn't know about before. So really, they're using AI in three major ways to understand what's happening with climate change,

to optimize current systems and infrastructure, and also accelerating breakthrough sciences. But, you know, there's obviously challenges with this, including access to data and collaboration with experts. So if you want to know more about that, we're going to have more in the newsletter, as always. Last news story of the day. A news study shows that the world's biggest AI models are. aren't very transparent. So a study from Stanford's human-centered artificial intelligence committee shows that that these new AI models, or sorry, not the new AI models, but the AI models that we all

use every day kind of ranked to them. And it ranked the 10 most popular AI models based on their transparency. And guess what? All of them received pretty low marks. But kind of the most transparent of the not very transparent large language models. included Metas, Lama 2, Bloomsy, and Open AIs GPT4. Those were the highest three scoring models, but none of them received particularly high marks. Again, that's not all the AI news.

That's just the three that we picked. So every day, we have more on those stories and a lot more. So again, make sure you go to your everyday AI.com, sign it for the free daily newsletter to learn more about those. But you probably didn't come here to talk about the AI news. Maybe you did. But you probably want to know some real business. use cases for AI. So I'm excited to have on our show and please help me welcome to the stage. We have Isar Matis, the CEO of Multiply. Isar, thank you for joining us.

I've been waiting for a while, so I'm excited to be here. Oh, absolutely, absolutely. Issa, first, maybe tell us just a little bit about, you know, what multiply is and what you do. So in my background, I was a tech startup kind of guy. I was a CEO of several different tech startups. Some did better than others. One got to $100 million in size and got sold. Others crashed and burned. So I've seen the good, the bad, and the ugly of running businesses. And in this past year, I've been really obsessed with AI and its impact on business. And really, what multiplies goal is to educate business people on how to leverage AI in the most impactful way, because it's the most transformative cycle of technology that we've ever seen.

and people, most business people are very far behind where they need to be. And so I really, my goal is to help business people, whether for themselves, for their career, as well as people in leadership to transform their businesses in order to be more successful with AI. Yeah. And just as a reminder for those joining us live, Cecilia saying thankful, thoughtful Thursday, Jordan and the everyday AI family can't wait for your insights. Yeah. What do you want to know from Isar?

So he just told you some of his background. He's helping companies leverage AI and has used AI in big ways. So what do you want to know? You know, that's, I like to say that this is the realist show in AI. We go live. So get your questions in so we can pick Esar's brain. But, you know, one thing that I want to know, Isar is it's, you know, I kind of started the show with this is, you know, people, I think always hear about all of these, you know,

new developments and all of these tools. But it's actually, I think, can be a struggle for some companies to actually start using generative AI on a day-to-day basis. How can companies actually do that and get these great tools and techniques actually in their departments, in their company? How can that happen? So the number one thing, and it's true now, but it's going to be true moving forward, is continuous education and exploration. And what that means is if you think about it, and you know, you and I do this day to day, like we, I have my own podcast called leveraging AI. I consult two businesses.

So I sit with them and help with them. I teach courses. I read news. I see the news. I follow stuff. I experiment. That's what you and I do for a living. People who run businesses, well, they run their business.

As much as they want to be interested in, and immersed in the AI stuff, they still have a business to run. whether they're at the top of the pyramid or somewhere within the pyramid, they have other stuff they need to do in their day, meaning they don't have three, four, five, six hours a day to check what's going on, to download new tools, to experiment with them. They just don't have that. So on the other hand, this thing is moving so, so fast,

that every week there are new capabilities and new tools, either that you don't know of that could have helped you, or even features within systems you're using today. So if we look at all the big players, whether it's Microsoft 365, Google, G Suite, Salesforce HubSpot, etc., all of these are bringing AI features into the platforms you're already using. So the question becomes, as a business leader, how do you keep up with all of this as a business, not for individuals within the company? And the way to do this is to put together a committee. And the committee needs to be built or assembled from people from different departments in different levels, preferably somewhat geeks like me that enjoy actually tinkering with these kind of things.

And the committee's role is first and foremost to define the framework, the guardrails, the box within people within the business, are allowed to operate when it comes to AI. Because it's very, very easy to do things with AI that are way beyond what a company wants to accept, whether it's sharing data that should be shared through platforms that use your data, or sharing your customer's data through platforms that use the data, or just things that do not align well with companies' core values. Because I'll give an example. Let's say you're a salesperson, most salespeople gets compensated based on results.

Well, you can do things today that are deep into the gray area with AI, and it can help you get more sales. which means you'll get a higher compensation. Is that something as a company you're willing to accept? Probably not. So defining the guardrails, defining the box within everybody in the company is allowed and even encouraged to use AI is the first step of the committee. But the ongoing role of the committee is to really explore, experiment,

provide education to everybody else in the company. you're like, okay, somebody brought to us, and it could be anybody in the company, this idea for this tool, for this process, for this use case. We've tested it. Here's what we found. Here's how we are going to use this and implement this within business processes. So the old process was this. The new process with AI is that and run education and training sessions to everybody in the company,

A, on the ongoing progress of AI and new ethical issues and so on. and B, on actual use cases that are being implemented, like I said, either through third-party tools that you're not currently using or through the platforms that you're using today that just have new features. Yeah. Issa, what would you say is maybe one of the main reasons? And even in my personal experience, you know, I've talked to hundreds of, you know, professionals who are trying to get AI into their companies, but maybe can't.

What would you say are some of the reasons why, you know, companies haven't done this so far, why they haven't, you know, created these committees and set up these, you know, guardrails and talked about these ethical guidelines. Is it an education piece? Is it a time piece? What would you say is maybe one of the main reasons or the main roadblocks for companies that they haven't been able to do this yet? So I think it's a combination of several different things. You know, the first thing is change. Change is hard. Any change is hard. And the bigger the organization is, the bigger is to drive change. And I've, you know, the largest company I work, worked for was not huge, but it was, you know, 10,000 people, 7 billion euros, so a decent

size company. And it's impossible to change in these organizations. Now, if you're a company of five people, it's a lot easier to change. And the chances you'll be able to enjoy AI very, very quickly are significantly higher. So I think driving change to a large organization is problem number one. Problem number two is, I think, despite everything. you hear and everything in the news and the stuff you and I are involved in daily, it's not common yet.

So you and I live in the AI world for a year now, tinkering with it daily, working with people daily, listening to the news. And we assume, and a lot of people are like that, assume that everybody's already ahead and everybody's doing stuff. The reality is I work with multiple companies. Most of the people are either clueless or just scratching the surface of what these things can do so they don't fully understand the benefits and the risks on the other side that exist if you either implement or don't implement this fast in the right way. Sure. You know,

you kind of talked about how anyone, like in theory, can be on this committee or in this community of people driving Gen A.I. forward within an organization. But should the onus or should the responsibility fall on, you know, a certain group or a certain department. Like as an example, is it ultimately the CEO that should be pushing this or someone from the C-suite? Is it HR, you know, that that should be driving this initiative saying, hey, this is something that falls under a process and procedure? Should it be marketing, right? Like, like, is there maybe, you know, one person or a department out there that should in theory take the lead on this? Because I feel sometimes if it's like, oh, let's all get this and, you know, go together, it might not move

anywhere. So a great question. I think it's a combination of top down and grassroots. And I'll explain what I mean. I think the leadership team, CEO and the C-suite have to be involved. And at least one of them must be on the committee. Why? Because it A, shows that there's interest in the leadership to move this thing. And B, you want the committee to be able to make decisions and make changes happen without having to go through three cycles of approval after they decided on something. So you want somebody, CEO or somebody else in the C-suite to drive this. As far as the different departments that you mentioned, you want people from each department. And the reason is there's different needs, different limitations, different issues with every

department from every department when it comes to implementing AI. They will have different benefits. They will have different needs. They have different issues that AI can solve. So you want one person from each department in the committee helping to define what the company needs, defining priorities between different departments and so on. Oh, sorry. But I mentioned the grassroots part of it.

If you are in any role in the business and you're passionate about AI and you're reading about this and you're playing with this and you see use cases in your business and nobody's actually top down doing anything, raise your voice. come up with an actual business case. Don't come up with a cool idea. Come up with a business case. Here's in my department, we can do one, two, and three, which will achieve A, B, and C. And I want to experiment with this.

Take it to your boss. Take it to the whatever leadership committee there is and say, hey, here's what's happening in the world. Here's what I can see that we can do right now. I want to test it. Here's what I need from resources perspective. Here's what I think the outcome can be, an actual business case.

And you could become the leader of AI in your business overnight by just taking initiative doing that. So I think it works both ways and it works the best when you have both things. When you have people on the bottom passionate about this as well as people on top are saying, yes, we understand the livelihood of our business in the next few years depends on us doing this change. Yeah. Yeah.

That's so true. And I think we sometimes don't want to say that part out loud that if we as business leaders as, you know, small, small business CEOs, you know, marketers, whatever, if we're not using this technology, we are putting the bottom line at risk. And I don't think people fully realize that. But, but actually, Issa, let's just go ahead and fast forward. So let's say we've got a committee together. Things are good to go. Maybe what's one of the first, you know, kind of like real business use cases for once you kind of get the green light or gen AI in an organization. So I think it very much depends on the organization, right?

At the end of the day, you want to go through a process, and that's something I work with all the companies that I work for, is what are the low-hanging fruits? How you can get immediate results without too much effort, without too much risk to prove the point, right? And one of the things that are immediate, or the two things that usually get the most amount of results very, very quickly. One is things that require data analysis,

and preferably that are repetitive. And we do these things all the time in almost every department in the business, whether you're in finance or in marketing or in sales. So let's take example in these three, right? Or in HR, right? So if you're in finance, well, you look at financial reports and you try to analyze the results. Well, today you can take that data and push it to even a tool in chat chitpT. So don't go to anything fancy. And chatypte today has a module called advanced data analysis.

And you can upload any kind of. data into it, whether it's PDF formats or CSVs or Excel files and so on, and it knows how to read them. So you can upload 12 trailing months of financial data and it can give you insights that you yourself cannot find. And it helps you do analysis that normal tools cannot do because it's just like having a business intelligence team in your back pocket for $20 a month. So that's for finance. If you're in marketing, well, all your own. marketing data and one of the huge problems the marketers have is that the data is scattered across 15 different things right so you have data in facebook for your facebook ads and in google for your google

ads and in lincoln for the stuff you do on lincoln and in your marketing email automation tool whatever you're using every one of those is a silo that has some of the data and it's very very hard to connect them together well today you can build automations very very easily using Automational tools like either Zapier or Maker or NA10, one of those tools that push this data into a tool that will then send it to, let's say, again, the same tool I just mentioned, like chat chit, advanced data analysis, and can combine all these things and give you insights that were on the verge of impossible before. So literally in every department, you have historical data proposals that you've written. So take the last 50 proposals that you've written,

load them to Chachipiti and say, okay, these won, these lost. Can you try to help me analyze what's the difference between the winning proposals and the losing proposals? It's really easy to do. And it's stuff that we didn't do before because it's hugely time consuming. And so you need to have three people spending four months to go through what I just said, taking proposals of the last three years and trying to compare them. We're now in Chachupit, you can do it in, I don't know, 20 minutes. And so with one person.

So the ability to analyze data to get real insights that can drive more business or more efficiency within the business is stuff we just never had before. Yeah. And, you know, I do want to follow up on that. But first, just as a reminder, you know, Josh saying, you know, good morning. I had no idea ESAR was going to be on. This is great. Yeah, this is absolutely great.

We do this every day. We bring on guests. Josh was on the show earlier this week. You know, so get your questions in if you want. If you want to know some real business use cases for AI, let's get those questions in and have ESAR tackle them head on. But one question that I have ESR, and this is something, you know, we just, you just mentioned, you know, chat GPT's advanced data analytics, which used to be called code interpreter.

You know, they're always changing the name out there. But, you know, something I see is a very common misconception with people as they say, oh, well, you know, I can't upload any data inside of this because, you know, it's my companies, you know, financial information and people sometimes think that, you know, by uploading this, you're essentially publishing it on the internet, which isn't exactly the case. What is your, you know, kind of best practice guidelines for how people can handle their data, especially if they're not, you know, because, you know, you have the enterprise versions of these coming out, the chat GPT enterprise, the Bing chat enterprise.

But maybe for those that are just using the standard, you know, commercial $20 a month to chat GPT plus, what advice or best practice? this can you give them on data security? Great question. Number one, continuously have somebody review their terms and conditions because they keep on changing it. So whatever I tell you now might not be true tomorrow. So that's number one. Number two, their API, so if you use any of the API tools, presumably, again, assuming you believe them,

does not use the data to train anything it just used to provide responses. Three, specifically advanced data analysis, forget. the data you upload to it at the end of the session. So it's good and bad. It's good because there's no data security issues because it evaporates literally as soon as the session is over. It's bad because if you leave for a meeting that you have and it didn't finish doing what you wanted to finish, you have to start it over again because it forgets the data that you uploaded originally. So that being said, I would still be cautious with uploading really sensitive data. So what I mean by really sensitive data, if you're in any regulated industry,

yeah, big no-no. For those, I say get a local or a cloud version of an open source model like Lama 2 from Facebook meta and run on that. And then you don't have to worry about is chat GPT because you're literally hosting it. You know which data is coming in, which data is coming out. That requires some IT work that requires some additional efforts. But for stuff even like financial data of a company, if it expires at the end of the session, how bad can it be? Or even, even, let's go to the worst case scenario. Let's say they are training on this data.

That doesn't mean that somebody can go and run a search, like in Google and say, oh, I want to see companies ABCs. It's not the way it works. So even if you load your company's financial information, which is like a big secret, right or your list of clients it's not something somebody can search it just becomes a part of a huge database of all the data on the freaking internet as another point of reference for that model to work so my point of view of this is anything that's not crazy sensitive like the secret sauce that your client is using in order to drive all the revenue that you're driving if you're using the

API or if you're using uploading files to advanced data analysis, you should be fine and you're taking a reasonable risk, especially considering the benefits that you're getting on the other end. Oh, absolutely. And even Kevin, thank you for the comment here. So saying from a data analytics standpoint, I think the most significant holdback has been the fear of publishing private data to open AI, hasn't it? Yes.

But, you know, kind of like Isar just said, you know, if he uploads, you know, his, you know, his books essentially, you know, for multiply and puts in all of their financial information. If I then go and say, you know, what is multiplies, you know, top, top line revenue, that's not how it works, right? Yeah, there is no publishing. It is just to train the models. But yeah, you definitely have to, you know, be, be smart about what you do upload and not because, yeah, anything sensitive, confidential, proprietary, probably not the best thing. But that's also why you need to have kind of like what Isar said, a community or a committee to talk about what information should be uploaded to large language models or not. You know, and I think, you know, kind of

once you can bring them local, I think that helps as well. Maybe Isar, let's let's kind of steer away from that because I think what so many people, the easiest, maybe the lowest hanging fruit for so many companies and leveraging Gen. AI is creating content, right? So for your marketing, your advertising, your comms, whatever it is, what are some ways, just very practical ways that have a great return on time invested that people can use Gen AI in those ways? Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience.

Meet Firefly AI Assistant, now live in the Adobe Firefly app, the all-in-one creative AI Studio. Powered by Adobe's creative agent, Firefly AI assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the assistant. The assistant orchestrates multi-step workflows, drawing on 60 plus pro-grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator Premiere, Lightroom Express, and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can

refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.com. Wow. So I'll start with the first thing. So a lot of people, again, fall into the the strap of like, oh, let's create content because it's really good at creative content, which is true. I would say step one, it's incredible in ideation. So if you need ideas on what content to create in order to attract the right audience, I would start there. Help it. Have it help you, have Gen AI tools help you identify the exact pain points of your audience. And then from there, start defining which content pieces in what formats and so on. So

that would be my number one thing. Like, it's incredible in ideation. The second thing is, okay, now that you know what content you want to create, the best way to create a lot of content is what we're doing right now, right, is recording video. Now, it's not for everyone, but the benefit of recording video is that now you can repurpose it for any other kind of content. And the beauty of, and that was true before, but now with Gen AI, you can take this

video and transcribe it, so you have a piece of content that you can put, maybe not very user-friendly, but it still provides you some SEO value if you put it on the back end of something. You can have all these tools and some tools are built exactly for that, like Jasper and like writer and all these tools that know how to take this transcription and turn it into an actual blog post. So now we can have a blog post. You can take and use chat GPT. I use chat GPT and Claude 2 to create the initial draft of my LinkedIn posts for everything that I do. So I take the recording of my podcast and I put it through Claude 2 with pre-existing prompts that I've created. And it gives me ideas down to bullet point level of what I want to publish in the already in the format.

So all I have to go in is go and kind of make it a little more my voice and my own. and I'm ready to publish it. So I can have, from this one podcast that I recorded, 20 pieces of content investing 10 minutes of my time in some editing of stuff that I don't like. It's insane. It's something we never had before. So this is one example of content repurposing.

And think about all the content your business generates on other stuff and how you can repurpose it just using these AI tools, using chat GPT or Claude, like stuff that is free. You don't have to pay for anything fancy. And even the fancy stuff, you know, it's another $20 a month. not a crazy investment. So this is number one is how can you repurpose content that you're already creating using these AI tools, using, and I mention it in a word, but having your prompt library where you know it's already working, now it's like, oh, this, I've perfected how to take

this long form thing and turn it into posts. Use it again and again and again. So now, again, you can build automations around this thing because using the same prompt every time. So this is one thing is repurposing. The other thing that I find, extremely useful is creating images for everything that I need. So whether it's post on social media, presentations, so I do a lot of speaking, like public speaking on different, either in companies that invite me to speak for the company

or on stages and conferences and so on. So I create presentations for those. So you need images. I stopped using the stock photo images stuff. I literally create everything either with Chachipit and now with Dali 3. And I'll say something about Dali that, you know, it's brand new, like it's,

came out last week and it's incredible. I'm absolutely loving it. And the reason I'm absolutely loving it is that it's a chat platform. So if I have to compare Me Journey and Ali, Me Journey still gives me better quality images when it comes to photorealistic stuff. Like if I want something to look like an image that I took with my camera, I go to Mid Journey. But if I want graphics for anything that I'm doing, I find that right now, Dali gives me better results faster because I explain to it in simple terms what I'm trying to do.

And I'll give you an example that I'm doing right now. I'm going into a conference on Monday. And I'm finalizing the thing. And the last thing I do is create the graphics for the presentation. And I literally share with it, this is the kind of conference I'm going to be. This is the audience. These are the things I'm going to talk about. On slide one, I'm talking about this thing.

Give me three ideas for what should be the graphics on the same. slide and it comes up with amazing ideas. And they're like, okay, I really like the second one. Can you create that image? And it creates an image. You're like, oh, I like it, but I would like to change this thing in it. And then you continue the conversation. And in either the first, the second or the third iteration, you'll get something that

is incredible, that is tailored to a specific slide for a specific audience. And this true, again, I now talk about presentation, but you can use this for a sales presentation, you can use this for your next blog post, you can use this in your next social media post, you can just like literally anything on your website. Any content you need from a graphics perspective, it's an incredible process. And again, the reason I love it is because it's iterative and because it really understands just like Chachypiti does what you're trying to achieve. So in Mid Journey, you have to be really good at prompting Me Journey to get what you want because with very limited words, you have to explain your idea. In Dali, you can go five pages explaining your

idea in order to get exactly what you want. Yeah. It's and, you know, one thing, speaking of Dali, one thing that I like and that it does, I think very, very well, better than Mid Journey is when you do give it a prompt, you can give it something very basic, two, three, four words. And it'll give you four variations and it'll expand on that prompt. It'll, you know, turn your simple, straightforward prompt into something that is very intricate, actually. You know, I do want to unpack something there because right in that one answer right there. We just got a whole like history lesson of AI content creation, right? Like dropped a couple names of tools.

We talked about ideation, content creation, preparing for presentations, all of those things. But I want to actually start at the beginning because, you know, Mike here had a question like, hey, what method is used to create that aha moment? And I think for so many people, that aha moment can be ideation. And it's something that we skip over so frequently. I even go back to think, you know, I used to be all the time, I would work on large partnerships and activations with Nike and Jordan brand. And we would have, you know, 10, 12, 15 people in a room for hours talking and coming up with ideas and strategizing and ideating. Is that maybe the ideation

aspect of large language models in Gen AI? Is that being overlooked just because the content thing is, you know, so tangible, right? It's like, oh, I need 10 blog posts and 10 this. It's tangible, but maybe we're not measuring the amount of time that we're, you know, brainstorming, ideating, strategizing. Like, what's your thought? Is that the aha moment here? It's a big aha. Like it's, I think the ability to ask the brightest people on the planet, any question you want based on content that they have already shared with the world, either on Twitter or in books that they've written. So you can build, and that's something, again, I share with people that I work with.

You can build a committee, an advisory board based on specific people that you follow that you think are the most brilliant on a specific topic. Or based on a specific book and say, based on this book or based on this person or based on these five people with these five books, I would like to create a new X. Marketing plan, HR plan, training plan, like whatever, whatever thing that you're trying to do, you can consult not with AI. You can consult with specific people based on specific content that they have shared that you think is the way you want to go.

This, think about it. You can pick the five leading people in the world on a topic that have written 20 books. books each that are best selling, that speak on stages, that probably charge $50,000 an hour. And you can ask them questions about the plan you're trying to put together. That's insane. That's something that never, and is it as good as talking to the person?

I don't know, but it's way better than just me talking to myself. And so bouncing ideas against or through the lens of other people because they're well known and they've shared a lot of their stuff is an incredible capability. So it's all about figuring out not how I'm going to use chat chipit. How am I going to use person X that have written books one, two, and three that I really, really like. And I want to know what that person through the content of these books would give me as advice for the thing that I'm trying to tackle. And you can do this with several different people. And now you have a committee. And now that each and every one of those quote unquote people gives you an answer,

you can combine it all together and say, okay, now you are a CEO of a company. These are the people that are your chief of marketing, chief of this, chief of that, which are really known people. This is what they said.

What do you think is the best way to combine all of these together? Like the combination of things you can do with it in order to get incredible insights for things you're trying to do are unparalleled with anything we ever had before, and it's free. So it's like it's insane. It's really, really amazing. Isar, we've gone over so much.

We've gone over, you know, how to actually get Gen. AI up and going with a committee or a community in your company. We've talked a little bit on the time savings of data analysis and summarizing. And then we talked about how you can use AI to, you know, ideate and to create content. But maybe if someone is a little more excited than they were before, you know, listening to this show. What is the one step kind of kind of as we wrap? What's that one step that you would recommend people take to actually get

that real business use case going for AI in their company? Wow. Overcome fear. Like just try stuff. Like take chaty p.t. It's free or pay the freaking $20. It's worth every cent. And try stuff and follow people like you, right?

Or like me. like listen to podcasts and get ideas from either blogs that people share or newsletters that people share or podcasts or live shows, get ideas and say, oh, this could work for me and just try. Just try stuff out because people are like, oh, I don't know what's going to happen. Like I'm going to use, like I'm going to take this data and put it out there. I'm like, what's the worst that can happen? So again, start with data that is not sensitive, that you feel that is like nothing. Even if it goes on the front page of CNN tomorrow, nothing bad will happen.

happen and and just try it out and see because the results that you'll get, if you'll follow a process that somebody has already charted and again, it's out there. Like people like you and me share that stuff. I don't hide anything that I do. I immediately when I learn something new, I put it out there. And there's thousands of people like you and me. Yeah. And like, oh, this could work for me.

Just try it out. That will be my number one tip. I love that. Just try it. So many people on the fence, you get analysis, paralysis. you have, you know, 500 prompts saved, you know, 300 new tools. Just take Esar's advice. Go out there and try it.

Isar, thank you again so much for sharing your insights. So many good practical steps for people looking to get AI in their business. Thank you for joining the Everyday AI show. Thank you. This was awesome. I really enjoyed myself. All right. And hey, just as a reminder, there was a lot going on there.

Like so much good information. If you miss it all, don't worry. We're going to break it all down for you. So go to your everyday AI.com, sign it for that free daily newsletter. And we'll have so much of what Ezar was talking about and a lot more. So thank you for joining us. And we hope to see you back for another episode soon. Thanks.

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