EP 507: Why AI Thinking Beats Traditional Business Strategy

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If your company is using the exact same business strategy it was using 10 or 15 years ago, and then you're just trying to insert some AI at the end, might not work out too well for you. You know, I think that, you know, throughout the course of the last, you know, 20, 30 years, you know, enterprise companies could sometimes take their sweet time when it came to tech adoption, when it came to implementing, you know, things like, you know, the web, cloud, PC, mobile phones, you can't really do that with AI. You can't take your old school, traditional business strategy and just sprinkle a little AI on top. That's not how it works anymore. I think you have to have not just an AI thinking, but you have to really have an AI mindset at the core of your business. All right, I'm excited to talk about that today and a lot more on Everyday AI. What's going on, y'? All? My name is Jordan Wilson and I'm the host. Thank you for tuning in to Everyday AI. This is your daily livestream podcast and free daily newsletter, helping us all not just keep up with what's happening in the world of AI, but how we can all actually get ahead to grow our companies and our careers. So it starts by what you learn here on this podcast and live stream. But it is actually, that's just part one. You have to, for part two, go to our website at your everyday AI dot com. There we're going to be recapping in our daily newsletter so of the best insights from today's conversation. And our guest is amazing. I can't wait to bring her on. But also in that same free daily newsletter, we're going to keep you up to date with everything else that you need that's happening in the world of AI. All right, so enough from me. Enough chitchat. Please help me welcome my guests. There we go. Ashvarya Srinivasan Ash, thank you so much for joining the Everyday AI Show.

Thanks, Jordan, for having me here. And I'm very excited to be a guest on your podcast. And as somebody who is listening to your podcast every day, I really enjoy it, and I'm hoping that I'm able to add as much value as your other guests.

Oh, absolutely, absolutely. So let's talk a little bit about your background because it's impressive. It's like looking at your LinkedIn profile is like looking at every single big tech company out there. So right now head of AI developer Relations at Fireworks AI. So tell us a little bit kind of what you do there and a little bit about your background in AI.

Yeah, absolutely. I wouldn't say I have had a non traditional path towards what I'm doing right now. Started off as a software engineer, minored in machine learning, eventually got into doing a master's in data science. So I've been in this machine learning AI space for quite some time, much before like the generative AI large language model hype started. And that's another thing, like I love doing traditional machine learning and I still love continuing to do that. And it's just fun to see that there is a whole new world of not just people building large language model systems and generative AI systems, but also the very, very growing economy of users who are interfacing with it. So it's just fun that now my mother is more interested to learn more about what I'm doing thanks to ChatGPT compared to what it used to be probably a decade ago. So yeah, it's good that a lot of people are understanding what AI is, what the use cases are. So it's definitely a very fascinating time.

To be and even kind of how I started out this show. And as someone that's been in machine learning for a while, how do you think it's even personally changed in terms of business priority? I like, I know for companies that have been data first that, you know, companies that have been, you know, using artificial intelligence and machine learning for many decades, maybe it hasn't changed as much but for every other company, right. It seems like to me at least someone that talks to a lot of people from the outside, it went from ah, what's AI not for us to all of a sudden it's one of the most important things at the core of their business strategy. So talk a little bit just about how it's changing the industry as someone that's worked in IT so since pre chatgpt.

So I would say the biggest difference that has happened three years ago till what what is today is simply put ease of access and ease of use. Three years ago I had delivered a session about text summarization, about how companies do like news text summarizations or like blogs or like larger books. And at that point in time it was BERT models and like GPT2 model and it was still very naive in the sense that it was extractive summarization and abstractive summarization in the very, very simplest form and now in the last three years, it just feels like we have crossed a decade already in just like a short span of three years. And it's just the level of maturity of the technology, of course, but also how easily it's accessible to a lot of people has changed. So earlier, the reason why, say traditional machine learning models or even like the early phases of large language models, or like the BERT models and like the early GPT models, they were, they were very narrowly used in certain research laboratories or certain big tech companies for very specific use cases. But now what has the biggest change, I would say in the last three years that has happened is how easily this technology is accessible to people and how easily they are able to build up on top of it. So any small business owner, I was even speaking with a couple of small business owners and how they can start using AI and it's just how easily it's available to them, right? So if you think about this stack of users and builders and providers, the earlier stack used to be very, very technical, which is like if you want to use a GPT2 model, imagine the level of complexities you have to go through to really put it in production. So that itself, and the fact about the skill gap that you would have as a small business owner or a college grad who has a great idea but doesn't know how to really use these models in practice to build their product has changed now that it's easy to get access to it, thanks to all the open source models. Second, it's just become the use, the usability has increased. So with more and more frameworks and providers and people who have encapsulated it with the terminology of wrappers, so it's just available to the end users in a much easier fashion. And that's why we are seeing a lot more people using it. That's why we're seeing tools like Otter AI, which I'm using for like translation or like, you know, Fireflies, which is like doing your meeting, meeting recordings and summarization or like Node GPT or all of these toolkits, right, like that we are using every day. It's not just, it's not just users from like a technical background, but it is everybody who's trying to like use these technologies to increase their productivity. And at the end of the day it improves their bottom line, which is increased productivity is faster results, better business value.

So I mean, one thing that you talked a lot about there, Ash, is just this accessibility, right? And not just for consumers, but also for businesses. Right. And how easy it is now, Right. To build on top of this amazing, amazing technology. Right. With, you know, between, you know, Google and OpenAI and Microsoft and Claude and everyone else making it so easy for even non technical people to kind of do some development work. Right? Yeah. When like, how does this impact actually what businesses are doing? Right. Like, I think that's something, you know, a lot of business owners are still, you know, struggling with. They're like, okay, does this mean we should be, you know, building on top of it if we don't necessarily have a use case, but because it's easy and maybe we can, you know, create new lines of revenues. Right, but how should, you know, business owners, entrepreneurs, you know, people at big companies, how should they be thinking about their business strategy? Maybe differently. With this accessibility and the usability, the two things you mentioned, Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on gen AI. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and built a straight path to ROI on gen.

That's a great question and that sort of brings me to the point that I was chatting to you about early on, right? There is a perception and there is a reality, right? The perception is that with these AI gen LLM like it's these terms are being used interchangeably with these tools, there are certain possibilities that you can unlock. There are some really cool things that you can do and they come with a bunch of features, right? With features that seems very interesting, seems very fascinating, seems almost impossible if you were talking about it a few years ago. So that's the perception of, you know, like how big of an impact it can have. But when it comes to reality, which is about how businesses are using it. I would say it's the same mindset in the sense that businesses think about their bottom line, right? At the end of the day, small businesses, large businesses, universities, organizations, whoever is planning to use AI toolkits, it's about getting to their bottom line. Now how do you get to that bottom line can be in using different channels, right? So the way that I would recommend people to think about it is right now as simple as, you know, breaking down your everyday tasks, right? If you have 200 employees in your company, what are these 200 employees spending their time on? What are the part that are highly critical, which is really risky and requires critical thinking, which requires human decision making? And what are the parts which do not, which are mundane, which are repeatable, which can have a. For the things which can. Where we can have a risk averse mindset versus not. So those are like some of the internal evaluations any company needs to be doing. And that itself will drive them towards what AI tools to use. I think what happens a lot of times is people are trying to approach it in the opposite direction, which is like, hey, I read about this AI tool, how can I use this in my company? It should be the other way around. What are the things that requires a fix? And then you go about thinking, what's the right tool for you? Because at the end of the day, I think what people need to understand is that AI is a toolkit. It's the same thing like a hammer or a drill or a saw. And every tool has a purpose. It cannot solve every single problem. And that's the exact same thing with AI. It cannot solve every single problem. It has its own challenges. It should not be used in a set of use cases. So if you approach it from the, from the standpoint that, hey, I want to use this tool, where do I fit it? That's going to take you a lot more time to figure it out. Rather than taking the right approach, which is like, here are the problems and what are the right tools for me to fix it?

I think what you just said right there is like a perfect way to hammer home this point. It's something I say a lot. So I'm glad that, you know, someone with your background is saying it as well, you know, because I think sometimes people are always looking too far forward and saying, what can we do now that we couldn't do before? But Ash, what you just said right there in this example is saying, like saying, what are our 200 employees spending their time on now? Right. Because sometimes I think you have to work backwards because, you know, you can't go on the same path you were going if the, the way those 200 employees work has fundamentally changed. Right, exactly. And then another thing you just talked about there is the toolkit. So, you know, how can, you know, decision makers, you know, kind of find the balance between those two things? It's like if they look backwards and say, oh, our 200 employees now, how they're spending their time, it might be a really bad way. Right. Because it's very inefficient. But then you look at this, you know, AI is a toolkit. It's an ever evolving toolkit as well. So where do you kind of, you know, find the happy medium between those two things?

So starting with, you know, like, what are the, what are the things which will give you a faster return on investment? Now, the return on investment could be different things for different businesses. For some it would be, how is it going to free up my time? How is it going to make me better in my decision making process? How is it going to serve my end customers better? So the metric that you use to like define this return on investment could be different for different businesses. But as I said, always start with like, what's the value proposition that you're going behind and how can you, what exactly are you trying to solve? And that'll guide you towards the right toolkit. Because a lot of times people come back to me and ask them, like, should I be using a Llama model or a Quinn model or this or that or a small or a medium model or like a 400 billion parameter model? Well, it depends on what are you trying to solve. So it all goes back to the question of what are you trying to solve? And the best part right now is that the literacy or like the access to information has become so much easier that you can do a lot of experimentations by yourself. Things that would require a team of like five to 10 people, like as giving an example. Right. Something as simple as I want to launch a company similar to Airbnb, historically, if you are speaking, you'll need a team of front end developer, back end developer, product designer, creative person, database manager, all of these individuals whom I need to even get to the first step of starting my company. But now I think with the AI tools which are accessible to us, we can get to the MVP part very quickly. So you can develop what a prototype would look like. And getting to that point is going, has become really, really fast. So for you to like get to market. For you to like build out something and test out something, it's become very easy. So for you to learn a particular tool, there are so many online resources which you can access, learn a particular toolkit and get started. So that's how I would recommend like any company, if you are a 200 person company, first evaluate where exactly is your effort going in. What are the bottlenecks? What, what is the bottom line for you? Revenue is one thing, but then what are the other proxies for those bottom line? And how can you improve that in a better fashion? Like can you save people some time, can you make them work on new products? Can you diversify your business business or can you expand your business? How far can you scale your business? So all of those things will direct you in what sort of tools can help you in achieving those goals.

So you know, when, when I hear you talk I can tell that you have kind of this, you know, AI mindset and AI thinking down, right? But that's because you have a background in machine learning. But you know, not everyone does. So you know, you know, how can they kind of address both, you know, you said, hey, going back, finding the bottlenecks, you know, fast roi, right? Like how can they go back and make those decision or look forward to new opportunities and make those decisions if they don't, you know, have that kind of AI thinking already if they don't have that AI mindset? Because trying to keep up with the hype cycle of AI is, you know, impossible even, you know, for someone like me that does it every single day, you know, so how can those maybe non people that don't have a decade of experience in machine learning, how can they still have that AI thinking or AI mindset?

I'm going to give you a non technical answer to this.

I love it.

And it goes back to a conversation I was having with my friend because talking to my friend who wanted to like go over like a diet plan for, you know, like a healthier diet plan for his day to day eating, eating stuff and like improving his lifestyle. And I think the part like that I'm getting to in this conversation is like how do you break that inertia of thinking in the standard style that you always do, right? I cannot really point back to the time when it started for me, but then now for every single thing I just go back to an AI tool to help me make my decisions better. How can I put out better videos, how can I improve my writing, how can I improve my email styles, how can I improve my product design documentations for every single thing that I try to do. I try to consciously make a decision to not let inertia take me in the standard way, the way I used to work. So giving you a simple example, right? Like if somebody tells you that hey, I want to lose weight or my goal is to gain weight and I am, I have like XYZ dietary restrictions. So how do I go about it? I have to like go contact a nutritionist, I have to do this, I have to do that. It's an elongated process and the way that people think about it is in this particular manner, like, which is how we have been doing things historically. But now with AI tools, a simple change is that I can chat with ChatGPT and tell it that, hey, this is my goals, this is my current weight, this is my current workout regime, this is what I can eat, I cannot eat, this is how busy I am. I. So help me develop a day to day eating chart. The amount of time that it would take for me to find a dietitian and go through the process is now completely solved by this particular prompt that I've sent to ChatGPT. Right? And I'll give you another example. The way that my mom's life changed because of ChatGPT. Again, like this is not a sponsored video or like, you know, I'm not, I'm not really endorsing any particular tool but my, my mom uses a lot like social media in different formats. She's like watching YouTube videos, she's reading news, she's using Facebook. I mean she's probably one of the very few people using Facebook. But she's on all of these platforms, right? And she hears a lot of information. She hears different news narrating certain side of the story and she used to get triggered reading something and she would be like, oh my God, I read this and like, you know, it's really disturbing and or like she would get worried about something. And now with like tools like ChatGPT or Perplexity, you can just go and clarify certain things on the platform. So now every time like my mom reads something, she knows that oh, there is a possibility that this news is fake or there is a possibility that it's only being narrated from one side or like it's not, not really the complete side of the story. And she goes and uses these AI tools and to get answers for herself. So now she has moved and she has her own AI mindset because now she's familiar with all of these tools and she knows how she can use that in her daily life. And that's again, going back to the fundamental way of how humans work. We need to get away from our inertia, get away from the autopilot of how we have been historically doing things and just try out something new. It might be a learning curve, maybe for a few hours, for a few days, but then that learning curve will really set you up.

I love, I love that just, you know, not letting the inertia, you know, take you the standard way. I think that's just a really great way to think about, you know, general, like traditional business strategy, right? It's, it's repetition, it's. It's automation. It's just, you know, auto. It's your brain's on autopilot sometimes from not AI ways of thinking, you know, I'm curious. You already kind of gave a couple, you know, simple examples, you know, through your own life, through your mom's life, you know, but even how, how has it changed, you know, how you work, right? Like, you know, just even your, you know, business strategy mindset, you know, having, you know, generative AI even versus, you know, traditional AI or ML.

So I posted about this a while ago that growing up I wanted to be an artist. I wasn't, wasn't very. I mean, like, I didn't even know about engineering when I was probably like 4 or 5 years old. I used to love art and obviously I have not been in touch with art for like quite some time. And I had this wild idea of teaching people about AI topics using comics. So my first version of comic took me probably eight to nine hours to build out. From ideation to finding the right tools to do it, to put the storyline together, to put the imagery together and build it out on canva and then reproduce it. The second time I did it, it took me around five to six hours, slightly less than the first time. But then now I'm going to release another comic around quantum computing. So it's teaching people about quantum computing using AI comics. And it took me less than 30 minutes to build it up.

Wow.

So that's the level of productivity I'm talking about. And I don't want to address this because I have heard this from a lot of people that, hey, why are we trying to use so much of the AI tools? And is it like a huge threat against people of employment? What I would say is we have had these sort of thoughts about threat during the industrial revolutions historically. But a simple example is what I explained right earlier. I used to do 10 things in a day. But now with AI tools, the time that it takes for me to do those 10 things has drastically reduced. Does that mean I'm unemployed for the rest of the time? No, I figured out more things to do in my life. So that's the exact way I would think about not just humans, but also businesses. If you have a 200 person business, rather than thinking about, hey, I'm going to use AI to cut down on what people are doing and fire 50% of the staff, think about how you can use the rest. 50% of the staff, upskill them and grow your business.

I think that's a great, a great way to put it right. And I love the example, you know, of the comics going from eight to nine hours to six to now, just, you know, in minutes. Yeah, just what's possible is changing so quickly with AI. And, you know, I think that does really impact, you know, everyone's business strategy. So, Ash, we've talked a lot in today's conversation in a short period of time. A lot of super helpful insights. But as we wrap up, what would you say is the one most important piece of advice that you have for business leaders to really just adapt an AI mindset or AI thinking and apply it to their business strategy?

I would say try AI tools overwhelmingly as a, as a person, rather than thinking about, as a, thinking about yourself as a business owner. Start using whatever comes to you, like whatever you read about on LinkedIn, on newspaper, on X, on threads, on Instagram, wherever. Like if you come across a tool and it, if it remotely fits whatever you're doing, try it out. The only way that you can understand the pros, cons, like the features, capabilities of any tool is when you try it out. And as soon as you give it a try, the entire overwhelming feeling goes away. So that's the best way to, like, get started. The more you try, the more you get hooked, the more you understand the value and the more you, the more you also get a idea of how to filter good from bad, how to filter hype from reality. So that also helps a lot. Like, so just, just give these tools a try.

Love it. Great advice. I think for a lot of people that are struggling to make sense and keep up with, you know, all the developments that are happening. Ash, I think this was a great conversation, so thank you so much for taking time out of your to join us on the Everyday AI show. We really appreciate it.

Thanks, Jordan.

All right, and as a reminder, we covered a lot there. There's so many quotable so much good advice. If you missed it, don't worry about it. We're going to be recapping it all in our daily newsletter, so if you haven't already, please go to your everydayai.com for more on this show, more on what you need to grow your business, grow your company and career. Thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks so much.

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