EP 496: How Real Businesses Are Actually Winning with AI
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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. Let's be honest. Business is a game. There's winners. There's losers. It's a nonstop competition. And I think especially with generative AI and large language models, it's a game.

That holds truer than ever. Now all of a sudden you have enterprise companies that are struggling to keep up with everything that AI offers. And then you have smaller businesses that are maybe a little bit more nimble in competing on grander stages that they never thought, all because of

how you can use AI. So today we're going to be going over, you know, not just how you can increase top line revenue and increase bottom line efficiency with AI, but how real businesses are actually winning. That's what it comes down to. Business is a game. And if you want to compete, if you want to win, you have to use AI the right way. And that's exactly what we're going to be talking about

today on Everyday AI. What's going on, y'all? My name is Jordan Wilson, and I'm the host of Everyday AI. This is your daily live stream podcast and free daily newsletter, helping us all not just keep up with AI, but how we can get ahead and use it to grow our companies and our careers. So it starts by learning on our daily podcast and live stream. I have a great guest for you today. I'm going to introduce here in a

but then it actually happens when you leverage it. All right. So, uh, that portion starts by going to our website at your everyday AI.com. While you're there, you can sign up for our free daily newsletter. That's where we're going to be recapping the most valuable insights and more information from today's conversation, as well as giving you the one-on-one, uh,

on everything else happening today in the world of AI. So this is how you can leverage it to get ahead, to grow your company and your career. All right, so I'm excited about that. But before we get started, some days we go over the AI news. Today is one of those days. So let's give you the quick outlook. So first, a new study has shown that AI has passed the Turing test.

Wow. Okay. So a new study by UC San Diego's Language and Cognition Lab found that OpenAI's GPT-4 or 5 convincingly passed the Turing test, being mistaken for human 73% of the time. So the Turing test proposed by Alan Turing in the 1950s evaluates a machine's ability to exhibit human-like intelligence by having a

human interrogator distinguish between a machine and another human in text-based conversations. So GPT 4.5 from OpenAI excelled under persona prompts, where it adapted specific identities like a cultural savvy young person achieving a 73% success rate. Without personas, its win rate dropped to below 50% at 36%.

Other AI models underperformed. Even OpenAI's GPT-4.0 only scored a 21% versus 4.5 that scored that 73. And Meta's Lama, as an example, didn't get to that 30% mark either. So for this test, 300 participants were tested in

eight rounds tasked with identifying which conversation partner was human and which was AI. So yeah, the majority of humans were fooled by GPT 4.5. So yeah, a lot of people are now saying the Turing test has been officially passed. This is one of those things. There's always going to be arguments, right? It's like, oh, when have we achieved AGI? Oh, when will AI pass the Turing test? Well, it looks like at least according to this study, a lot of people are saying it just happened. All right. A couple other bullet points.

point updates for you. There's too much news, right? So rounding this up here in AI product updates. So chat GPT announced that their new mega viral GPT-4-0 image mode now takes longer on purpose to give you better results while generating images. Notebook LM, my favorite AI tool, now has an option to find new sources from the web in addition to grounding it in your own data. Don't forget about that new mind map feature they shipped last week.

pretty mind boggling. Microsoft co-pilot rolled out new voices for its AI voice mode, and we are supposed to be getting a new highly anticipated version seven from mid journey. So if mid journey does drop that V seven, we'll make sure to cover it in today's newsletter. All right. A lot more on those stories and everything else in today's newsletter. So make sure you go check it out at your everyday AI.com. All right.

Let's get to winning. Let's get to winning with AI. It's what businesses, you know, I think for the most part, for three years, they've been trying to figure this out. It's an ongoing battle. But today I have a little help with a great guest. So live stream audience, please help me welcome to the show, Ajay Malik, the CEO of Studio X. Ajay, thank you so much for joining the Everyday AI Show. Oh, thank you for having me. Very excited to be here. All right.

All right. Hey, I'm excited. And I always have to give a special shout out because this is a live stream. It's unscripted, unedited. So, you know, in my time in Chicago, it's 730. But Ajay joining us from San Jose. So 530 AM, got to shout him out there. So real quick, before we get into this topic, Ajay, can you tell us a little bit about what you all do at Studio X? We have an AI platform with multiple applications and we help businesses adopt AI in their business, either to improve their top line or

help with their bottom line. Okay, perfect. And give us an example. A company comes to you or they use the Studio X platform. What's the main problem they have going in and what's that main issue that you all are trying to solve? Very good. So, you know, AI, a lot of hype out there. And, you know, like they are like everybody wants to win with the AI, as you said earlier also, right? Everybody's focused on that, but they really sometimes don't know how to start. Okay.

That is one thing. Second thing is within their company, they have this fear. Some people are like, oh, AI is going to take my job, right? AI is or AI is everybody else is using AI. I have to use it. So, you know, they do not have ability to distinguish between the

ROI versus Hype. That is one of the biggest thing they start with. Like, where do I start? And how do I win? How do I use it? How do I succeed? And that's where they start. And then we start helping them like how and where distinguish them, talk to them, show them the examples. We have customers, Fortune 500, small companies, midsize, all kinds of companies. So we help them, give them examples how others businesses, similar businesses are using to help them decide.

Love it. All right. And hey, as a reminder, live stream audience, thanks for joining us. Get your question in if you have one for Ajay. It can probably take a couple at the end. So everyone joining us on LinkedIn, Kimberly and Jean and Marie and Bronwyn, thanks. And on YouTube, Sandra, Michelle, everyone else, thanks for joining us. If you have a question on how your business can actually win with AI, get it in now. So Ajay, let's start at the end.

How do real companies actually win with AI? Let's give away the answers now and then we can dive in. All right. So you know what?

the real companies focus on real problems real winning comes with real problems real quantifiable you know there is a lot of hype as i said earlier and there is a lot of fluff oh you can change the world and you know what everybody wants to change the world which is good thing i do too right but the key thing is if you how big is your bite can you chew what you are biting right it's like this are you going to solve

If you have to solve one problem, you can say, oh, I will eradicate malaria. Another could be like, hey, I will find a better medicine for malaria rather than eating five days consecutively, you eat two days. And you know what? So you can decide and focus on the smaller problem so that you can execute it, implement it. And that's what the companies who are successful AI doing it. Okay. And they are keeping their focus narrow. Very.

very narrow like i am focusing on improving what and you know by having a very clear focus if like almost like a micro focus or nano focus you know that this is what i want to achieve hey i want to improve my supply uh and demand management i want to change this that you know fix pick a small problem small section of a problem that's what big companies are doing or not big

companies who are successful and you know picking the application let me give you some examples of application right for improving the top line what can you do for top line you can do like hey make your product smarter like have some kind of predictive alerts in the product you know like your car right my car has an engine light when it is on

I am like, oh, I have to go to mechanic right now. Right. You are scared. Can I continue driving? Do I have to rush today? What do I do now? Because the orange light is on. But just imagine if there was a blue light in the car, which was like, hey, you should go to mechanic in next four weeks. That is much more relief. And making your product something like this, adding features which help, that's the idea.

So let's talk about each of those two things. So kind of this top line and bottom line, right? Because I think everyone is always looking at measuring the ROI of AI and my hot take is don't, but everyone needs to, right? So I think bottom line is maybe a little bit easier, but when we talk about top line, right? And increasing revenue, right?

You know, you kind of talked about, you know, the example of predictive analytics, right? Like that's a great one. But maybe can you give us a couple examples or use cases of how businesses should be looking at AI on the top line side, specifically ways that you can add revenue with AI?

Very nice. So top line is all about like adding new line item when you are selling something or charging premium pricing and justifying it. Right. So, you know what everybody's talking about, like for operations, Microsoft Copilot or every application which can help. But just imagine the printer you are selling.

right or a device or product you are selling came with a co-pilot imagine that you know what you are working with a small printer and you are stuck how to deal with it it has some alert small window screen problems but imagine i could just do a qr code go on my phone and now i can talk to the printer and hey what's wrong with you how do i fix this right making the products much more usable and people will like that because it will make the product more usable

right or having something like inspection you know like a vending machine okay so vending machines there is a lot of motors inside it and just imagine if there was some microphone some camera inside it which is listening to the sound and watching and monitoring everything and it can tell oh the machine needs oil change machine the elevator is not working think like this so the AI the part using AI to make your product valuable

The product becoming much more useful, much more reliable for your customer. That is where you add value, right? Whether you can add a voice control interface, you can have embedded inspection in the product, you can have, or even, you know, like simple things like lead management, you know, personalized lead sending. So anything that can help you find more customers, retain more customers, those applications, hundreds of applications, and many are very easy to implement. It's just about focusing on them and thinking how it will help your customer.

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.

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Yeah, a good question here from Monica in the audience. So, you know, you kind of talked about Ajay, kind of this concept of like, hey, with AI, don't focus on eradicating malaria, focus on, you know, the smaller steps, right? So I think that was a good way to talk about starting small. So she's asking, what are some of the smaller problems your clients have solved with AI? Smaller problem. One of the problem, voice control. Like one of our clients,

They make welding positioners. Okay. And the welder is using them. Now the welder is using them. Normally welder was going to a machine and pressing display, connecting the buttons, touching a lot of things. Okay. And welder

He's welding and in the middle of thing they have to change the machine, rotate the part they are welding. It is work and they had to go and gloves, remove gloves, touch the machine, come back. It was operational problem and all they did is added in their helmet they have a Bluetooth and now they can talk to the machine. So that they can say hey machine move up two inches, move down, rotate, tilt, things like this they can see. That's the thing just focus on a very small thing but it immediately solved the problem for the welder. It

It helped it. So now they can sell the product at a higher price. Think like this. So you need to add some small thing like this and or having something like, as I said, the example was real. Actually, the vending machine that is one of our customers. We put a microphone inside the vending machine and it is listening to the sound and it listens like if the machine is making, you know, like your cars make sounds when the brake is going bad. Like you hear that sound and you know, we need to do something. Same thing. Machine is listening to the sounds inside.

And hey, everything is moving smooth. No new sounds. So it means the elevator, the motors, everything is working. That is the thing. Think very, very small, tiny problems inside your product to help or tiny experiences inside your product, for your product.

Like so many of these examples are hitting home for me, Ajay. I'm like squeaky brakes. Yeah, I'm listening to that all the time. And like the printer, it's like, yeah, why aren't printers smarter? Like why, you know, like why do I have to, you know, spend two hours every single time I'm at my mother-in-law's fixing her printer? Why can't the printer fix itself? My gosh. But, you know, maybe let's get back to bottom line.

line, right? And maybe we can ping pong across. But I think this is where a lot of companies start, right? Increasing efficiency, improving outcomes, right? Just making things better with AI for the humans that are spending time, right? I think sometimes these use cases or examples of winning with AI on the bottom line are maybe obvious, but let's talk through, maybe give us two or three examples of what are those bottom

Bottom line wins for companies with AI. All right. So bottom line win is all about where you spend a lot of time or where it is very repetitive. You are doing things like, you know, I do every day this thing. Okay. What are those things? You know, when I was working for Google, my boss said one thing, hey, anything you do more than three times,

should be automated. Why are you repeating it? You know, and that's something very simple. Like, what are those things I keep doing again and again? And am I really applying myself? You know what? There is a fear, genuine fear in operational efficiency. You know, is the operational efficiency going to work on my job? Because, you know, I made things so automated and now they don't need me. It's if you are not adding value,

directly and you were just repeating and doing the work, I think you will get eliminated anyway. That's how it will happen in the world. It's sad, but it is how it will happen. But if you were using AI, you were making your job better so that the company gets more out of you because you are doing things better. You are things people have not done and that's what it is. And in operational efficiency, think about what are the things that take most time for you. They are repetitive. They are boring, right? More prone to human errors.

Look at those things and you can solve them. I give you some examples. OK, knowledge in the company documents are everywhere. Information, contact, all information, product specifications, features, history, so much data. And you know what? Suddenly the sales guy says, hey, hey, did we do this? Have we solved this problem? Have we sold this product? What was the quote we gave last time?

You know what answers are there in the company data, but to finding those answers can take time. You know, there are some studies I saw, 27% sales opportunities are lost because they did not have information at the right time. And it takes time to respond. And when you take time to respond to a customer or prospect, prospect finds another opportunity.

vendor in that time that's the thing or you know what engineer a new engineer has joined he wants to do something she wants to do something they don't know how to do it and they don't want to ask every day hey how do i do this you know what you train them for first two weeks they don't learn everything having that bot having that ai agent assisting within the company providing whether it's demand forecast whether it is things or i give you another example a factory well we we

They have factories, they have a lot of machines going on, right? Hundreds or thousands of machines working. Machines are very good. Everything, like they bought it in 90s. These are not IoT machines. Now,

One machine stops working or is like weaving the fabric wrong way or machine is packing the coffee wrong way or some application like that. How do you know there is a supervisor or there is somebody on the floor checking, constantly looking out, right? Just imagine if the computer camera can do it and alert you, hey, that machine is not working. And that is an operational efficiency achieved inside your business just like that.

Yeah. And I think a lot of great points there, but Ajay, I want to rewind to something you said at the beginning of that answer there. Right. So, you know, you said at your time at Google, your boss said, Hey, what are you doing? You know, more than two to three times a day or, you know, these, these repetitive tasks. Right. And, uh,

I think sometimes there's this narrative that I always like to get in and try to correct, right? Because everyone's talking about upskilling and reskilling and all these buzzwords. And I always say you need to learn to unlearn, right? And because we've been learned and we've been rewarded for doing these manual tasks over and over for decades, right? So I'm curious because it can be hard to give away agency to AI. But going back to that

two or three things, giving it away to AI. What are those two to three things, either you or your team, as a large language model started to surge in capabilities, what are those things that you started to give away?

The key thing is we used to, you know, like I will tell you this two years back, we made a company policy and the company policy was skill is not a differentiator. Okay. Very important point because, you know, we get too hung up on, I know something or he knows something. She knows something. No, you can all find it.

Everybody has an employee named Chad GPT or Claude or whatever you use. Okay. And use it and use that to do better. And the moment you have that, if somebody gives you a problem, you can solve it. Don't think I don't know about it. I have never done this before. No. You know what?

We are no more, you know, like Google search. Google search, I always call the sixth sense. I tell you, we have five senses. We all know that. The sixth sense, because suddenly all the information is accessible to me. I can find the answers. I type something, I get the answers, whether it's nearest pizza or some knowledge or how somebody has done something. But now with AI, AI is the seventh sense.

i actually don't get just the data where i look at the results there is somebody else looking at the data providing a response to me how i can do that right so use that ai to do your augmented ai is your assistant ai is your augmenter and then ai is the skill so all we have to do is how to use ai how

how to apply AI. And so, you know what? The things like, you know what? Social media posts, no more. It can be done by AI, right? And AI creates all the images, the posts and post them. You can do some things without effort, right? You can write coding. Oh, coding is so much better with AI, right? Testing, so many things, test case, every use case really helps the company day by day. And you start using them and certainly people feel more, a lot more productive. I tell you, I feel like

I know it may sound like a big number, but I think we, I am individually 25 times more productive than I was five years back.

and it's just because using AI so much more you know what I tell you I thought AI will make my life easy but my work has increased I am working so much now because I can do so much it's like a non-stop because I have an assistant who thinks a permanent full-time you know the only time I am like oh the cloud is down or chat GPT is down so okay let me take a break okay that's how it works

It's, it's, it's so funny actually, because I was just talking about this with my wife last night. And even for me, it's hard. It's hard because, uh, yes, I'm, I'm more productive than ever. I'm learning things faster than ever, but I'm also forgetting things faster than ever too, because I don't know if the human brain, uh,

is designed in a way that now we can retain and actually use all this knowledge the way that we always had, right? I think we have access to too much great information too quickly, but that's another story for another day. But I wanna get back to what you said. Skill is not a differentiator.

I love that. And I think we have to call it out more. I've been a little more brash with it. And I say your knowledge doesn't matter anymore in the age of AI. Right. So with that and, you know, in your example for your company skill, not being the differentiator. OK, so how do you flip the script and how do you and your team then focus your human experience?

time if skill is not the differentiator and it traditionally has what is then how are you spending that top line in bottom line human time to win ah you know what so this is how it is i give you a formula okay whenever you are doing a task

say it takes 100% it's a task, then the first 15% is done by human. What I'm going to do defining it and using even AI to brainstorm it, but it is about defining it and that is your task. That is the task. We do a good job in that 15% and then let AI do another 70%.

how to write the code about it, how to do whatever you need to be done by AI in that task and it does it. And then last 15% to cross verify and making sure. So you focus on your

your top line and bottom line. And for our company, like the things which we want to achieve, hey, this was my goal and how do I achieve that? That is for each employee's job, right? Each person, think like this and this is for everybody. Use AI for the first 15% to define and making sure you are in the right direction of what you want to achieve, okay? And then let AI do the work and then last 15%,

is AI doing it or not doing it exactly what I want and this way I am more productive because now I am spending like 30% of my time on the task and 70% AI is doing it now I can do lot more my velocity is lot more and

Hopefully I have more time because I'm telling you time is becoming even more shortage because everybody is like it's excitement because now I can do so much. It's like, you know what? Oh my God, I can do this. I can do that. And you are continuously running faster now just because you can do that. That's how I see. Yeah.

So, so, you know, good, good question here from Kimberly. So Kimberly, thanks for this one. So she's asking, have you, you know, you, you kind of mentioned this earlier, right? Like companies trying to bite off more than they can chew when it comes to AI. So she's asking, have you ever seen companies bite off more than they can chew too early? And then how did, or how should they correct this? Okay.

Very common. You know what I say, there are top three problems with AI. When people are trying to implement businesses, they start, the number one problem is starting too big. Second problem is they ignore their data reality. And third is they focus, they chase the trends. Hey, what others are benefiting? These are the three problems. They do that. And I'm glad Kimberly is asking this question.

you have to course correct. Don't be afraid to course correct. Course correction is the most important thing humans can do. Okay. And like you are talking to Claude and asking it to do something and then suddenly it's not working, then just start a new chat. It's okay to start a new chat. In my own personal life, I have done that. You know, like that whole saying that, oh, changing the horse in the middle of the race.

please do please do please do not keep going on that house change it okay if you made a wrong decision you can actually recuperate you can fix it you can do something much better faster people do that mistake and you know whenever you realize oh my god it is like not

milestones, you should, you know, like, as I was telling you earlier, you should have a picture. Okay. This is the way have a picture, you know, like the before and after picture for weight loss. Like, Hey, I was like here, 260 pound. And I'm like now eight pack and I'm like looking fabulous. Okay. But there are, it doesn't happen overnight. There will be like, Hey, somewhere my weight will be lost to another goal. There are milestones in the journey.

Monitor your journey. Are you hitting your milestones? Are you moving towards the goals you wanted to achieve? And if not, immediately course correct. And then immediately say, okay, guys, we are not going to work on the whole thing. Let us see which parts of the real problem we want to solve.

This is the whole big use case which we wanted to solve. We are not going to automate everything and make everything AI. Let's fix. And then person says, oh, you know what? We spent two weeks in finding all the information when customer reports the problem.

Okay, in the whole six week process for solving the problems. Okay, let's just focus on that micro problem. And it's okay. And you know what? You will have wins. And then I tell you, in one year, six months, nine months, two years, I don't know the time frame for your problem. End to end problem, you will solve it. But pick that micro problem. And if you have started big, change the horse, stop it and move to the smaller part, which is measurable. If it is not measurable, if you cannot quantify what you got out of it,

Stop.

But I think sometimes, right, because traditionally the way that, you know, tech innovation has gone, you know, you generally plan things out, you know, multiple quarters or maybe even multiple years in advance. Right. So we've always thought of these games, the game of business being a very long run, but

But with AI, it's hard to do that, right? If you take two weeks off from using AI, it feels like you're from the 70s. It's like, wait, how does this work, right? So how do business leaders, how can they, I guess, find that balance of being present in quote unquote, playing the game and winning with AI, but also being like, yo, is this horse, do I need to swap this one out? How do you do that? Yeah.

All right. I think every business owner already knows what is good for them, what they want to achieve and what is hurting them. They already know that. Actually, you know what? Just have one meeting with your staff or with the team or everybody and all hands. All right. And just discuss, hey, what are the key things which where we are wasting time or what happens again and again? And ask those questions. You have to think like this and then come up with those specific metrics for success that, hey,

it would be nice if we could do this. It would be nice if I could do this in finance. It would be nice if my product did this. It would be nice if my customer support had this because AI is usable in many, many departments. Don't get hung up on where you want to use. Don't start with the idea your neighbor had. Don't start with that idea that you just heard on a podcast. Focus on your real problems. Always focus on that and

Always have a re-evaluation, continuously ongoing re-evaluation of what you are doing. I would say have some kind of AI steering committee or something in your business looking at it. Always looking at it, what we are achieving. And be specific, how much processing time we have saved in last three months by doing AI. How much...

Conversion rate has changed. You know, be so measurable. Be very, very specific. If you are not very specific, you will fail. And if it is not, you know what? No hard feelings. Don't say that, hey, whatever you were doing is wrong. No, it's not like this. It's like, you know, sometimes we get so, oh, it will improve the efficiency. It will improve the revenue. We should do this. It will just change the game for us. No.

Focus on how much change and how will you measure how much game is changed. If you start having that thought process, forcing yourself to write it. And, you know, a lot of coaches and everybody says this. If you cannot measure it, don't do it. And that is the key thing in the AI also. And if you identify, see something, you are making progress, but not perfect.

How I say it in my company, I give you the number, everything, you know, but do not use sentences. I always say that people laugh at me. I say, I don't want a Shakespeare answer. I need numbers. Make sure your answer has numbers, digits, zero to nine. If your answer digits, you are not using digits. It's not moving. The needle is not moving right enough.

Love that. Another piece of great advice. So Ajay, we've covered a lot in today's conversation from different use cases on winning with AI on the top line, different use cases of efficiency and performance gains on the bottom line with AI. But as we wrap up today's conversation, what's your one most important, most actionable piece of advice for business leaders out there who are like, hey,

My team, my company, we've been playing the game, right? And using AI, but we're a little stuck. What's the one takeaway for them to actually start winning with AI today?

I will say the same thing which I said earlier. Start small. Think big. Start small. Scale fast. That's a good way also. Think big. Start small. Always start a small measurable application. Pick a small thing which you can say, oh, we did it and it works. Have those small successful moments given by AI. Do that.

And you know what? And as a leader, it is your responsibility. If you are the business owner, if you are the manager, if you are the person who is driving, it's your responsibility to

lead with AI, force people that, hey, what are we doing today? You know what? Today, when you go to work, have this question, what I am going to do using AI or what I will do AI in my job or how I will use AI in business, not in a big way, just a small. Even if it saves me 10 minutes a day, think of those applications. Even if it gives you accuracy for one small item every day, think those. That is what it is.

Love that. Can we get that on a T-shirt or something? Start small, think big, scale fast. Love to see it. Ajay, great conversation today. So thank you so much for taking time out of your day to join the Everyday AI Show. We really appreciate it. Thank you so much. Really enjoyed.

All right. As a reminder, y'all like that was raining, just raining, uh, great advice on our head, just dropping, uh, you know, moments of truth on us when it comes to leveraging AI. So if you missed anything, don't worry about it. Yeah. You can go rewatch, re-listen, but what you need to do is go to your everyday AI.com sign up for the free daily newsletter. We're going to be recapping, uh, today's conversation and a whole lot more. So make sure you've learned now go leverage with our free daily, uh,

email newsletter. So thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. 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 time.