Ep 555: Accessible AI – Practical Strategies for Every Business Leader

Topics Covered in This Episode:

1. AI Strategy vs Traditional Business Strategy
2. The Shift to AI Mindset in Business
3. Evolution of Machine Learning Accessibility
4. Impact of Generative AI on Business
5. AI Tools for Small and Large Businesses
6. AI Implementation for Business Efficiency
7. Overcoming Inertia with AI Thinking
8. AI's Role in Productivity Enhancement

Keywords:

AI thinking, Traditional business strategy, AI mindset, AI implementation, Tech adoption, Machine learning, Data science, Generative AI, Large language models, Open source models, AI tools, Text summarization, News text summarizations, Extractive summarization, Abstractive summarization, BERT models, GPT models, AI accessibility, AI usability, AI adoption, Return on investment, AI tools for businesses, Business strategy, AI innovation, Productivity enhancement, Decision making process, Risk assessment, Small business AI, Enterprise companies, Business value proposition, Automation with AI, AI toolkit, Business growth with AI, AI-driven development, AI mindset adoption.

Podcast Transcript

Jordan Wilson [00:00:16]:
If your company is using the exact same business strategy it was using ten or fifteen 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, twenty, thirty 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. Alright. I'm excited to talk about that today and a lot more on everyday AI.

Jordan Wilson [00:01:17]:
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 livestream, but it is actually that's just part one. You have to, for part two, go to our website at youreverydayai.com. There, we're gonna be recapping in our daily newsletter some 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 gonna keep you up to date with everything else that you need that's happening in the world of AI.

Jordan Wilson [00:02:01]:
Alright. So enough from me. Enough chitchat. Please help me welcome my guests. There we go. Aishwarya Srinivasan. Ash, thank you so much for joining the Everyday AI Show.

Aishwarya Srinivasan [00:02:15]:
Thanks, Jordan, for having me here. And I'm very excited to be a guest on your podcast. And as 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.

Jordan Wilson [00:02:30]:
Oh, absolutely. Absolutely. So let's talk a little bit about your background because it's impressive. It's like, you know, looking at your, you know, your LinkedIn profile is like looking at, you know, every single big tech company out there. So right now, you know, 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.

Aishwarya Srinivasan [00:02:50]:
Yeah. Absolutely. I wouldn't say I have had a nontraditional 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 general BI systems, but also the very, very growing economy of users who are, interfacing with it.

Aishwarya Srinivasan [00:03:38]:
So it's just fun that, now my mother is more interested to learn more about what I'm doing. Thanks to ChargeGPD, compared to, like, what it used to be probably a decade ago. So yeah. It's it's good that a lot of people are understanding what AI is, what what the use cases are. So it's definitely a very fascinating time to be.

Jordan Wilson [00:03:59]:
You know, and and even kind of how I started out the show and, you you know, as someone that's been in, machine learning for a while, how do you think it's even personally changed, like, in terms of business priority? Right? 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, 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 changed in the industry as someone that's worked in it since pre-ChatGPT.

Aishwarya Srinivasan [00:04:39]:
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 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 both models and, like, GPT two 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 majority 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 is 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.

Aishwarya Srinivasan [00:06:09]:
So any any any small business owner I was even, like, 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 GPT two model, imagine the level of complexities you have to go through to really put it in production.

Jordan Wilson [00:06:40]:
Mhmm.

Aishwarya Srinivasan [00:06:40]:
So that itself and, like, the the fact about, like, the skill gap that you would have as a small business owner or or a college grad who has a great idea but doesn't know how to, like, really use these models and 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 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, note GPT or all of these tool kits. 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 is 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.

Jordan Wilson [00:07:56]:
So, I mean, one thing that you talked a lot about there, Ash, is just this accessibility. Right? And and not just for consumers, but also for businesses, right, and how easy it is now, right, to build on top of this 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, nontechnical people to kind of do some development work. Right? Yeah. But 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 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?

Aishwarya Srinivasan [00:08:57]:
That's a great question, and that sort of brings me to the point that I was chatting to you about earlier. Right? There is a perception and there is a reality. Right? The perception is that with these AI, GenAI, 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 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.

Aishwarya Srinivasan [00:09:57]:
Right? At the end of the day, small businesses, large businesses, universities, organizations, whoever is planning to use AI tool kits, 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 two two hundred 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. Mhmm. And that itself will drive them towards what AI tools to use. Mhmm.

Aishwarya Srinivasan [00:11:00]:
I think what happens a lot of times is people are trying to approach it in the opposite direction, which is like, hey. I have 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 what people need to understand is that AI is a toolkit. It's the same thing like a hammer or a drill or 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 It cannot solve every single problem.

Aishwarya Srinivasan [00:11:39]:
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 gonna 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.

Jordan Wilson [00:12:00]:
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 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 you know, decision makers, you 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.

Jordan Wilson [00:12:58]:
So where do you kind of, you you know, find the happy medium, between those two things?

Aishwarya Srinivasan [00:13:04]:
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 gonna free up my time? How is it gonna make me better in my decision making process? How is it gonna 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 will 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 lemma model or a model or this or that or a small or a medium model or, like, a 400,000,000,000 parameter model? Well, it depends on what are you trying to solve. So it all goes back to the question of what 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 as giving an example. Right? Something as simple as I want to launch a a company similar to Airbnb.

Aishwarya Srinivasan [00:14:29]:
Historically, if you are speaking, you'll you'll need, like, a team of front end developer, back end developer, product designer, creative person, database manager. All of these all of these individuals whom I need to even get to the first step of starting my company. But now I think with the 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 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.

Aishwarya Srinivasan [00:15:26]:
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.

Jordan Wilson [00:15:53]:
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 decisions 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 Yep. Trying to keep up with the the the hype cycle of 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, you know, people that don't have a decade of experience in machine learning, how can they still have that AI thinking or AI mindset?

Aishwarya Srinivasan [00:16:43]:
I'm gonna give you a nontechnical answer to this.

Jordan Wilson [00:16:46]:
I love it.

Aishwarya Srinivasan [00:16:47]:
And it goes back to a conversation I was having with my friend because I was 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 life style. And I think the 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 I have, like, x y z, dietary restrictions. So how do I go about it? I have to, like, go contact a nutritionist. I have to do this.

Aishwarya Srinivasan [00:18:07]:
I have to do that. It 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 chat deputy and tell it that, hey. This is my goals. This is this is my current weight. This is my current workout regime. This is what I can eat.

Aishwarya Srinivasan [00:18:27]:
I cannot eat. This is how busy I am. So help me develop a day to day eating chart. Mhmm. 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 Chargebee. Right? And I'll give you another example, the way that my mom's life changed because of Chargebee. 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 she's, like, watching YouTube videos.

Aishwarya Srinivasan [00:19:05]:
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.

Aishwarya Srinivasan [00:19:25]:
And or, like, she would get worried about something. And now with, like, tools like ChatGPD 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 to get answers for herself. So now she has moved and she has 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. 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.

Jordan Wilson [00:20:28]:
I love I 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 it's your brain's on autopilot sometimes from Exactly. Not AI ways of thinking. You know, I'm curious.

Jordan Wilson [00:20:50]:
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 you know, business strategy mindset, you you know, having, you know, generative AI even versus, you you know, traditional AI or, ML.

Aishwarya Srinivasan [00:21:11]:
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, four or five years old. I 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 story line together, to put the imagery together, and build it out on Canva and then reproduce it.

Jordan Wilson [00:21:47]:
Mhmm.

Aishwarya Srinivasan [00:21:48]:
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 gonna release another, comic around quantum computing. So it's teaching people about quantum computing using AI comics, and it took me less than thirty minutes to build it up.

Jordan Wilson [00:22:05]:
Wow.

Aishwarya Srinivasan [00:22:06]:
So that's the level of productivity I'm talking about. And I do want to address this because I have heard this from a lot of people that, hey. Why are we trying to, like, use so much of the AI tools and, like, is it, like, a huge threat against people of employment? What I would say is we have had these sort of, like, 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.

Aishwarya Srinivasan [00:22:55]:
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 gonna 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, upscale them, and grow your business.

Jordan Wilson [00:23:16]:
I think that's a great, a great way to put it. Right? And I I I love the example, you know, of of the comics going from eight to nine hours to to six to now just, you know, in minutes. Yeah. Just 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?

Aishwarya Srinivasan [00:23:56]:
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.

Aishwarya Srinivasan [00:24:47]:
So that also helps a lot. Like so just just give these tools a try.

Jordan Wilson [00:24:51]:
Mhmm. 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 day to join us on the Everyday AI Show. We really appreciate it.

Aishwarya Srinivasan [00:25:07]:
Thanks, Jordan. Alright.

Jordan Wilson [00:25:09]:
And as a reminder, we covered a lot there. There's so many quotables, so much good advice. If you missed it, don't worry about it. We're gonna be recapping it all in our daily newsletter. So if you haven't already, please go to your everyday a I Com for more on this show, more on what you need to grow your business, grow your company, and career. Thanks for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks, y'all.