In this episode, Srikanth explains how he promotes innovation at Experian, accounting for its size, role as a trusted brand, and regulated and compliance-oriented processes. He shares his career journey in three parts—banker, strategy executor, and AI and analytics executive—and how the positionalities create a full picture of the issues he’s trying to solve. Srikanth shares how Experian has embraced its rich data history in building analytic and AI ecosystems that have made these resources more affordable to clients beyond the big banks.
As a leader, Srikanth endorses the “crawl, walk, run” method on the boldest visions. He shares how to balance focusing on niche solutions and a wider vision. Srikanth discusses how he encourages his team to move as quickly as possible and how rapid innovation can continue to push boundaries and work symbiotically with approval chains and compliance.
In discussing artificial intelligence, Srikanth shares how he sees the future of AI, specifically gen AI, its rapidly approaching role in all products, an anticipated boom of gen AI agents, and how to embrace the transformative technology in your life and for the next generation.
- (3:30) – Journey to Experian
- (5:45) – Productizing and democratizing data
- (9:26) – Innovation in a large organization
- (12:58) – Engendering trust and confidence
- (15:57) – Having a big vision
- (17:13) – Client profile
- (19:44) – Inspiration
- (21:19) – Acting like a small business
- (24:33) – Team incentives
- (28:28) – Where is gen AI heading?
- (31:30) – Gen AI agents
- (33:32) – What should you do?
About Our Guest
Srikanth Geedipalli is the Senior Vice President of AI Product Management and Commercialization at Experian. Previously, he served as Head of US Strategy at BMO Financial Group, a strategy consultant for McKinsey & Company, and an analytics leader at Capital One. Srikanth earned an MS in Biomedical Engineering from Cornell University and an MBA from the University of Chicago Booth School of Business.
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Podcast episode production by Dante32.
Full Show Transcript
Patrick: Hello fellow innovators, this is Patrick Emmons.
Shelli: And this is Shelli Nelson.
Patrick: Welcome to the Innovation and the Digital Enterprise Podcast, where we interview successful visionaries and leaders and give you an insight into how they drive and support innovation within their organizations. Today we're thrilled to host Srikanth Geedipalli, a seasoned product leader in AI and banking. Srikanth currently serves as the Senior Vice President at Experian, where he has been instrumental in productizing and commercializing analytics and AI on a global scale. His impressive track record includes over 20 years of leadership in banking, FinTech, analytics, product and consulting. As a part of a strategy to build a platform and software business at Experian, Srikanth spearheads initiatives that bridge the gap between artificial intelligence technology and practical business applications. He's tasked with productizing analytics to accelerate market entry, enhance scalability, and boost productivity. Under his leadership, Experian has seen substantial growth in its products and analytics capabilities to pulling them across a global landscape in a modern tech-driven environment.
Srikanth's approach involves leveraging growth hacking techniques to commercialize analytics products, ensuring they not only meet market demands, but also create new revenue opportunities. He actively fosters collaboration amongst cross-functional teams, including data scientists, technology specialists, and product managers. He does this to develop robust platforms that address complex business strategies. Moreover, Srikanth is vital in partnering with major tech companies and Silicon Valley startups, bringing state-of-the-art capabilities to Experian's offerings. This includes integrating cutting-edge technologies and methodologies to enhance the company's product suite, thus providing superior value to clients worldwide. Prior to his current role at Experian, Srikanth was the Head of Strategy at BMO, a strategy consultant at McKinsey and an analytics leader at Capital One. Srikanth is equally distinguished academically. He's earned his master's in bioengineering from Cornell University where he was recognized with the Excellence in Research awards at international conferences. He also holds an MBA from the University of Chicago Booth School of Business, specializing in analytical finance, entrepreneurship and strategy. Excited to have Srikanth share his profound insights on leveraging AI and innovative technologies to drive business growth and efficiency in today's digital age. So welcome to the show.
Srikanth: Thank you, Patrick, and thanks for that introduction. It feels funny to hear everything I've done over 20 years, but thank you. I don't do enough of these, Patrick. I do speak a lot at Experian hosted events and the industry events. This is my second time doing a podcast, but I'm excited about getting a wider audience and being able to speak to folks who listen and share my learnings that I've learned over the 20 years of my career in AI.
Patrick: Well, we're honored to be the second podcast. Obviously, it won't be the last.
Shelli: Yeah. And Srikanth, can you share with our listeners a little bit more about your role at Experian?
Srikanth: Thanks, Shelli. Before I do that, let me talk a little bit of my history and how that got to my current role at Experian. When I look back, I've worked at a few different companies, but it's really been three roles, I would say. One, as a banker. I've worked at two banks, Capital One and BMO, and then really served about 15 to 20 banks when I was a strategy consultant at McKinsey. So I just know the banking world inside out. Everything from consumer banks all the way to capital markets, payments, investment banks, etc. My second role has been as a strategy executer, head of strategy at BMO and then doing strategy consulting for McKinsey clients in banking. And then finally, what really defines me today is an AI and analytics executive. Again, cut my teeth at Capital One in analytics, and then set up McKinsey's risk out exhibition now partly by what I would call design and mostly by fate.
Those three things that I did brought me into my current role here at Experian. Now, as you know, Experian, most people would think of it as a data company, as the credit bureau company, which is what it's known for. But over time, Experian has acquired a lot of other rich data assets, assets and healthcare assets and marketing. My role, I joined Experian about four years ago. I came in with the vision to productize all these data assets and built an analytics and an AI ecosystem around it, which is what I've been doing over four years, played different roles. My current role is head of product for our platforms and software business. Really it is taking and building an AI ecosystem around our data for people to consume our data, to build analytics around it and to build their own products around it.
Patrick: Srikanth, I got a question. That sounds awesome and many people have seen that data is the new gold and it has been. So I know as from a consumer standpoint, what Experian does, what are some of the markets or growth potentials that came out of that investment of productizing the data that you already had then applying AI to it? What kind of impact did that have? Was that what you expected to do, or were there any surprises that occurred on that journey?
Srikanth: The answer is yes and yes.
Patrick: Thank God, because it'd be a pretty boring podcast. If the answer was no and no, it'd be kind of short. Thanks a lot. Bad idea. Don't do it.
Srikanth: Indeed. So the answer yes and yes is yes. We went in with the hypothesis and we proved it right, but during the journey we also learned a bunch of things and we pivoted as we went along and created net new businesses. We did not even anticipate when we were beginning at this, right? So let me get into a couple of specifics there Patrick. One, coming in four years ago, and the experience was already on this journey of modernizing our platforms. We took our data, this is like 20 years of customer data, the credit bureau data, and put it on the cloud on AWS, right? It was on mainframes. That created the right environment for me to come in and democratize Altex. The two areas that I want to call out is circa 2000. We had the big players like Chase and Capital One and Amex investing heavily in Altex, huge data science teams, right?
You're talking like 300, 400 data scientists all over the world, and they were way ahead of what a regional bank, what a credit union and some of the FinTechs could do in their investments, experienceability, right? And for me to come in and to democratize, how can they consume our data, how can they build Altex around this so they could afford credit to their consumers without making a huge upfront investment? My idea was to take that long tail of financial services lenders who would like to bring this modern technology and use it for marketing and underwriting the consumers that has been now been possible. What was only possible for the big large banks now is affordable to a lot more of the clients. There are about, I would say about 5,000 financial institutions in the U.S. That's quite a bit. Only about 50 of these had the capabilities to do this five, 10 years ago.
I believe more than 50% of them, more than 2,500 of them can afford this because of the technology that experience has built. That is one element. So it expands, it democratizes access. This has also created, again, when you think of this ecosystem, right? It is data and Altex in one place. It has made it easier to sustain it. It has made it easier to do this more frequently. Even the big guys would take longer to do this. They will take 12 months to do something that is super cutting edge, and they would then, because it's so long and expensive, they would do it fewer times. Because of the newer technology that Experian has built. They would now be able to do it in a month's time, like 10X faster than before. That has opened up new businesses. So without increasing their spend, they are able to use some of the technology that Experian provides more frequently, enabling higher impact.
Patrick: That is pretty exciting. So quite a few people I know have made a lot of investments in productizing their data, and now AI has upped the voltage even more so in the last 12 to 13 months. But there's still the underlying challenges of quality of data, usability of data, accuracy of data, and then the legal ramifications of that productization. How are you able to achieve so much? Experian's a large organization, you mentioned others that maybe can't move as fast. What are some of the things that you've done to be able to accelerate or advance these things or innovate really in a large organization? What are some of the things that you do to set the platform for you to be able to launch these types of products?
Srikanth: Patrick, again is another great question because those things you listed are real challenges that a lot of clients, not just in our industry but across, like in healthcare, in retail and oil and gas, quality of data, access to data compliance reasons. And as I'm head of product for the platform business, and if I can articulate three value props that I share with my clients on why they should engage with me on my platform. There are three things really. One is connectivity. So my platform not only connects to Experian data, but has connectivity to various data providers that our clients need. We don't own every data set, but if they work on our platform, they get it. Number two, we have the toolkit to solve issues around data quality, data wrangling. This is a constant issue for a lot of clients. They spend months and months of time cleaning up that data. Now, part of my product toolkit is those data wrangling toolkits, data accuracy and data quality.
I create dashboards that give you how good is your data before you start training your models on that. So that ecosystem is embedded in the platform. And then the third part, which directly addresses the compliance thing, right? Financial services lending is a heavily regulated industry. When I build my products, compliance and regulations are embedded into these products. We would tell upfront, Hey, here's the data, here's the use cases you could use it for. And when they do some analytics on it, the analytics IP we provided this has already been pre-approved and pre-validated by internal and external regulators. So that gives confidence to our clients to start using our platform. So the combination of data connectivity, the tools to clean up data, and then the trust and the confidence that they get that compliance is embedded and that just makes it easier for them.
Patrick: So one of the things I'm curious about is building new products, especially for established organizations that have very well established market value. They know what they're doing to get those resources necessary to accomplish the three things that you just identified requires a pretty big commitment without confidence or knowledge. Not that there isn't confidence, I'm sure you did the business research, but it is a bit of a risk for a lot of organizations to even pivot a little bit. So is there things from how to build influence with people, how to create that confidence where it's like we're going to do something new. And then how many customers are using these new products? I think that's the impact I think people would find very interesting is that was this more revenue from existing customers? It sounds like new customers in a different market.
Srikanth: Yeah. A few different questions Patrick you're asking. Let me break those down. I think there's a question on how do you engender trust and confidence for people to come? How do you solve that? It's almost like a cold start problem when you start something new. How do you get people in to adopt and then how do you scale it? The second question is, hey, where is the adoption coming from? Is it more to the existing clients or you're joining, you're getting new clients. And I believe there was a third one in which, how successful have you been? How many clients do you have now? And I think, let me take those in turn. The first one is just, Hey, how do we build trust and how do we get enough. There are a few things that I've had here at Experian that are huge tailwinds for me.
We have been an established brand. We are the custodian of consumers data. We have been a steward and we have relationships with the clients we work with because they buy our data. That is a huge benefit as I go speak to clients. I have been an industry player and I use my own relationships and the fact that I was in their shoes to build that empathy that I know your problem. And that person to person relationship is a huge benefit that I have. And then finally, we are in this age of AI, right? Altex AI and data. So that's a huge tailwind. Clients are cutting budgets across the board.
Patrick: Absolutely
Srikanth: We know that's true. The one they are do investing in selectively is AI products. And so that is a huge advantage for me because there is intent from their leadership, from their CEO and from their vote to answer the question on what are you doing in AI? And it is my role to provide answers to them. Now using those things, now those tailwinds do apply to me Experian, but also all our competitors. So how is Experian differentiated in this? I'll go back to the platform business we are building, Patrick, right? The toolkit we are providing the easy access to not just our data, but other client data, the compliance in there. That is a definite differentiator as I deal with some of our clients and how they hear from their competitors on what their pitch is. The second piece I would say is I run my team as a tech startup or a FinTech startup to be more precise.
There is this piece about I have a big vision. My ambition is to be larger, to provide affordable, fair credit to all consumers and enable our clients to do that. That vision is absolutely big, but I take a very much phased approach, crawl, walk, run, to anything we build. It is similar to, again, I take my inspiration from how big tech companies are built, right? In Silicon Valley, you don't go out and build this big thing, you build the one thing with one functionality that you get a few clients on, and then you take the feedback, you iterate. I want to put products out there for our initial clients to try it out, our friendly clients to give us feedback.
I work with them to co-design these things and then build it out. So being able to run my little shop here at Experian as a FinTech and then grow it out using the tailwinds I have as the big Experian brand and the sales force has been a huge advantage. The question on just who are my clients and how much have I seen in terms of adoption? It is both. So I have a bunch of clients who already buy some of our data and Altex as a consulting service. I have been able to on an approximate, about 50% to 2X increase in revenue per client with those clients.
Patrick: Awesome.
Srikanth: And as I said, there are about 5,000 lending institutions in the us. We have already data relationships with about 3000 of them. Our Altex and AI business used to be much smaller, let's say about 500 to thousand. We have been able to double that over the last four years as we have democratized and made it faster at a lower cost and higher quality outputs to our clients. We have seen our option grow very fast. Definitely benefited from all those tailwinds I mentioned before.
Patrick: That's fantastic. And yeah, the growth within, I think a couple topics that I think are really instrumental for people to take away from what you just said. One, the niche, especially when you're starting, everybody's like, how am I going to boil the ocean? And the goal isn't to win all of it. It's to find a very narrow but very deep niche that only you can own. Like the story about Uber is always whenever I hear people say, oh, we're going to focus on taking everything. Uber started out as a text message service. It didn't even have a user interface and all it did was service, I think it was San Francisco was the only place where it started. And that criticality of really focus on owning this market, this specific need and really becoming the best at that. And then you can see where some of those other things are at.
I think that's tremendous advice as entrepreneurs and entrepreneurs are trying to think about a strategy standpoint. And the crawl, walk, run is something, I don't think there's a day in my life. I don't use that phrase. And Shelly and I met through a group called Project Relo, which is an organization that's committed to changing the narrative on the purpose for hiring military veterans and the leadership skills that they come in. And every time we've been involved with any of their missions, crawl, walk, run. So if there's ever a time where you see a universality between Silicon Valley mindset and the military and it comes into the verge crawl, walk, run, you know there's some value there, right?
Shelli: Yeah.
Srikanth: Absolutely. In fact, I've not lived in Silicon Valley, Patrick, but I do follow a lot of the companies there. I've partnered with some of them. YC has been an inspiration. They in fact have this series and your audience should look, check it out. They have something called the Startup School where various successful entrepreneurs come and talk about how do you build a business and some of these principles around, hey, it's not about the best product or the best UI, you want to throw, you build something quickly, an MVP and get market feedback and then a trade from that. That is kind of the philosophy I use. It becomes challenging in a bigger company.
Patrick: You can only imagine.
Srikanth: Because I have a product portfolio to deal with, I have a lot more stakeholders to do with, but getting that balance right where I can keep, because those Silicon Valley companies out innovate even the bigger size tech companies, the Googles and definitely the of the world, they do out in a way, and I want to see with every other advantage that I have, can I bring those principles in and get that right balance? In fact, the president of my group here, he says, think a small company. That is what I use with my 10. How do we have constant feedback? Small teams move fast and just a high velocity of bringing things to market and updating those. So those are the principles we use and it's worked great so far.
Patrick: Well, and I totally agree with the think like a smaller business, but acting like a smaller business. I'd like to hear how you've been able to actually take the thinking to action. I think how do you create your own, I don't want to call it a subculture, but it is a different culture, right? Experience is about risk. It's a custodian of very important data. It has to have security, it has to have safety. These things are just born into it. We are about risk. And then how I always think about a good organization has the brake and the accelerator. There's the accelerator that gets it to move. They're a little bit less fear of risk, and then you've got the brakes to make sure you're not running off the rails. Is there something you've done to create that capacity or that the availability to act like a smaller business at Experian?
Srikanth: That is such a great question Patrick. Let me start with this, because of what Experian is, which you have articulated well, we're a custodian of consumer data. We have a lot of regulators looking at it. We have built in the processes and the systems to risk manage, right?
Patrick: Gotcha.
Srikanth: Our finance teams, our compliance teams, our security teams, our technology teams have built in things which are foolproof. So it is tough for me or anybody in my team, unless we are totally ignoring things and downloading data in places where it should not, which nobody would do. We cannot do that. We will not be over the line on making these mistakes. So the bias tends to be, do we overdo that given we already have all these systems and checks in place. So my encouragement to my team is, hey, innovate fast and follow every single step of the processes that are needed. Don't break our approval chains. Do not break compliance approvals that are needed. But when it comes to don't impose net new things on yourself, you want to test the boundaries of innovation. If there's two data sets you want to combine to use, do that.
Take that to compliance and get that approval and make that case with them. Do not stop shot with saying, Hey, they're not going to approve this. Do push the boundaries on innovation. And now if I just step back, just move the cultural point of innovation, maybe I would say three things there, Patrick, on how we did this, and even if I start with it is a challenge. I would accept that many companies have tried it, even Google as you know, suffers with this because they see upstarts do better. And this is because as teams get bigger, there are more stakeholders to please, there's more reasons to play it safe because-
Patrick: There's bigger risks. You've grown. It's easy to be brave when there's not a lot of value in the business.
Srikanth: Exactly, right? This is like an option business. And I cannot provide the same incentives to my team that they don't have startup equity. I cannot give them stock. They do get experience equity, but that's not going jump to 15X like somebody at OpenAI would get, right? But my pitch to them is one with hearts and minds and then two with processes and systems, right? With hearts in mind, I tell them like, Hey, your reward here is not going to be in a billion dollar unicorn. That's going to come out of where you have 20% stake. It is going to be the learning you get out of it. You will believe you can build a business when you do this. And then two is when you do things faster, obviously the learning is faster. But then two, tying it back to the why and the vision of what we do.
This is how we get to our vision of fair and affordable credit to everybody in a faster way. So those are two things, but more in terms of tactically what we do. One, I empower my team, right? Hey, you go ahead and make decisions, fast decision making is important. I will cover for you, I will give you that air cover because I will only need to do it a few times. There'll be a few times I'll need to walk back your decisions. But the trade off between the speed at which you move for those 95% of times totally offsets those 5% of times where we'll need to go and fix it. So you have my full trust, go make decisions and I'm there and you will not need to apologize for anything. So that is one, cultural mindset, fast decision making and ownership mindset. The second kind of cultural aspect I kind of keep emphasizing with my team is founder mindset.
You own this business, you're a product leader, but this will win or lose based on what you are going to do here. So take that founder mindset. And it is not just building a great product, it is our option, right? It is our option. So take a very, what you called out in my intro, the growth hacking principle. As soon as you have something, go speak to a client, see if they would subscribe. Once that sells, try and use all the data that we have at Experian to create a targeting list of who else should you be doing this with? Go talk to all our salespeople who serve this. So just take that founder mindset and growth hacking principles to get cash flow in because that is the oxygen you need to get bigger investment and to get the bigger buy-in from all the stakeholders who matter. So getting those early events, super important to get left off.
Patrick: That's awesome. ShellI, I know over at Madison you've got a very distributed organization as well. Is there something that from your experience that you've seen that is fostered to allow that type of innovation to occur as well?
Shelli: Yeah, a lot of things that Srikanth was saying, I was smiling because not only did we use the 80-20 principle focus on what matters, which is exactly what you were talking about earlier, but also failing fast and moving quickly and allowing businesses or teams, in your case, to run autonomously. And like you said, it's a learning environment. So yeah, I appreciate everything that you just shared.
Patrick: Awesome. So curious. I think we just started in the AI world. I know we've been doing AI forever. I've been using Waze for what, seven, eight years? It's not like this is, but the expansion, the awareness. There was a moment after the dot-com bust where e-commerce still wasn't the end-all be-all. Was like DSL had to show up for my mom to buy something from Amazon. I feel like we're kind of at that point, but I'd love to get your perspective. Obviously you're way deeper than I am. So from a gen AI or even AI as a whole, what do you think we should be expecting? I know everybody's got dates in years, like, oh, it's going to be this by whatever, but I'd love to get your perspective on what should our listeners be thinking? What should they be preparing for? What are the threats? What are the opportunities?
Srikanth: Yeah, let me take that and split it into three parts. So where is Gen AI heading? What are some opportunities and what should our listeners personally do right now. On the first part, where is Gen AI heading? I truly believe this is a transformative technology. AI always has had a bit of hype around it. What Gen AI has done in the last whatever 18 months has just made it possible of what we have been talking about. This is truly transformative. People call it on the same level as the internet boom, late nineties of what internet did, and I cannot agree more.
This is going to democratize just a lot of things. It is the gold rush days and everybody and every company is already asking what is their Gen AI play? Every single individual, any young professional should be asking what is that Gen AI kind of professional strategy for themselves? Now, where is the setting? I think of it as two ways. Today, we don't talk about internet as a product drive anymore. It exists and Gen AI will be that. Every single product that we use, we'll have Gen AI embedded in it. You're already seeing this, right? We are on a Zoom call right now in this. Our iPhones are going to have open AI's technology in Siri soon, right? Google has, and Microsoft has Gen AI in all their products.
Patrick: Well, for $10 I can have AI in Slack, I just got offered today. I don't really know what it's going to do for me in Slack, but it's there. To your point of the penetration is ubiquitous.
Srikanth: It is ubiquitous. And you are paying $10 extra today in the future. It's going to be standard. It is there. It is part of the feature. We won't even talk of that. Just the way we won't talk about internet as a channel anymore. Gen AI will be there everywhere embedded in every single product. It's a functionality. It's code in the end. So that I think we should absolutely expect the implication there is for product managers and engineers everywhere. You have to think about not about what my Gen AI strategy is, like can this specific thing, this user story that I'm writing, can this benefit from the Gen AI technology that already exists? That is a question. You need to do it for your clients because your clients are going to ask, and that question might be superficial. They might say, Hey, what Gen AI stuff you have?
But if you look beyond that, it's just a better product with a better experience with Gen AI. Now, this is second part of it, right? Hey, are they going to be net new products or net new businesses that will get chewed up just because we have Gen AI? Absolutely, yes. And the one I'm most excited about is just agents, Gen AI agents, right? I believe not too distant future, I'm thinking 2, 3, 4 years at max, each one of us will have a personal assistant. We have some voice assistants today, right? Alexa and Siri. And when I've tried, it's okay. I do not use them for making decisions for me. I can check on Alexa, what's the weather in Chicago today? I might even check what is the cost of Chicago Bears ticket this Sunday, but I'm not using it to, Hey, buy me a ticket for Bears versus Packers for the Sunday because I do not trust it enough.
I do not know what seats it would get at what price. I think that changes. I think in two years we will be able to give it instruction saying, Hey, buy me three tickets for this event or buy me an airline ticket. Because it your preferences, it knows what kind of seat you want, what airline you fly. You tell it, and you will have the confidence to make that decision. The other place where I use a bit of Gen AI today is write some of my articles, write a document. But what I have felt consistently lacking is it does a good job on content. It does even a good job structuring things, but it does not get my voice. And there's a certain style I write things. I believe in two years, they will be a Srikanth-specific agent where I tell a few things, it'll write things in my voice and will give me suggestions real time as I do that.
And that is the business I'm like, which does not exist today, which I personally am learning about. And that would be a totally net new billion, trillion dollar business coming up. Now, just getting back to, hey, what do our viewers should do? I mean, if you're not using Gen AI, you absolutely should, there is no other. Read all articles you do, watch all YouTubes you do or listen to podcasts. It is not a substitute for getting your hands and feet wet with this, you could build your custom GPTs today with OpenAI, right?
Feed it all the documents and it'll start using that to create your own API on Gen AI, you should be able to use Gen AI to write documents, to validate your code or write net new code to use for your creative pursuits of graphic design or whatnot if you are not using it because there is no other better way of learning and getting and to understand this technology. So start using that both in your personal and professional life. And if you have kids, there's a lot of fun ways to engage your kids, right, with games and with coding and with just even creative writing that you could be doing, and you absolutely should be doing that.
Patrick: Awesome. Thank you so much for taking the time. That's really great stuff. I love wrapping on the end where it's like how to get kids involved, how to get the next generation to help move the ball forward because I do think it's one of the most critical things. It's amazing to me how much opportunity there is for the next generation to get involved in tech. When I think when we were all younger, it was not as openly available. It was you had to have an uncle or aunt or somebody in the business to engage with it. And the proliferation of entry points for people I think is tremendous.
And with AI and the expansion, explosion really of what's possible, it's amazing to think where we'll be in 15, 20 years. That's almost unimaginable. We were before the call joking about some of the bad TV shows from the eighties. And as you were talking about ai, I was thinking about the robot from Lost in Space warning Will Robinson, but that might actually come true. Robots that obviously it wouldn't look like that, but I was thinking as you were talking about having that individual agent, or just think about how many times we'd have a Zoom call and you'll have some kind of drone while you're going for a walk as opposed to, but then that drone can actually send emails in your voice and your tone and things like that. So it's going to be crazy.
Srikanth: It indeed is. And just the pace of innovations, just such a steep slope. This is the worst we will ever be in our Gen AI technology, almost by definition. And the pace of innovation just so fast. What took 20 years for us to reach you is going to happen in the next one. That I'm sure of. And we get to play a role in that, which is exciting.
Patrick: Well, again, thank you so much for taking your time. Maybe in a year we get you back on again and we can talk about, or maybe we'll have your agent on, it won't even be you next time. You'll just send Srikanth number two and we'll have him on.
Srikanth: You know what we should do Patrick? We'll have the agent at me sit next to each other.
Patrick: That's right.
Srikanth: You'll asked the question to the agent and then you asked me if the agent was right, so we could do a real time test of this.
Shelli: I can't wait.
Patrick: I'm wondering, can we make the agent more likable? Because I could use that. It's not so much. I need somebody to answer questions like, no, Pat, don't say that. Pat, don't say that. Don't say that. That's what I need. I need somebody to just sit, don't say that, Pat. That's not good. Awesome. Again, thank you so much. We also want to thank our listeners. We appreciate everyone joining us.
Shelli: And if you'd like to receive new episodes as they're published, you can subscribe by visiting our website at dragonspears.com/podcast or find us on iTunes, Spotify or wherever you get your podcasts.
Patrick: This episode was sponsored by DragonSpears and produced by Dante 32.
