Balancing Success and Failure in Tech with Raja Gangavarapu

AI and Human Integration: Insights from Raja Gangavarapu

Join host Don Finley in The Human Code as he talks with Raja Gangavarapu, an IT executive leader with over 26 years of experience. The discussion explores the integration of AI and machine learning in business, addressing the challenges and opportunities presented by these technologies. Raja shares his professional journey, emphasizing the value of shared experiences and storytelling in learning. Key topics include the role of bias, the importance of data, and the need for clear objectives and investments in technology projects. This episode provides essential insights for organizations looking to responsibly adopt and integrate advanced technologies.


00:00 Introduction to The Human Code

00:50 Guest Introduction: Raja Gangavarapu

01:54 Raja's Journey and Insights

03:54 The Evolution of Learning and Technology

08:05 Addressing Bias in Technology

13:16 The Current State and Future of AI

15:49 Balancing Innovation and Fundamentals

23:54 Caution and Optimism in AI Adoption

32:50 Final Thoughts and Wisdom

34:31 Closing Remarks and Sponsor Message

Sponsored by FINdustries

Hosted by Don Finley

Transcript

Don Finley: 0:00

Welcome to The Human Code, the podcast where technology meets humanity, and the future is shaped by the leaders and innovators of today. I'm your host, Don Finley, inviting you on a journey through the fascinating world of tech, leadership, and personal growth. Here, we delve into the stories of visionary minds, Who are not only driving technological advancement, but also embodying the personal journeys and insights that inspire us all. Each episode, we explore the intersections where human ingenuity meets the cutting edge of technology, unpacking the experiences, challenges, and triumphs that define our era. So, whether you are a tech enthusiast, an inspiring entrepreneur, or simply curious about the human narratives behind the digital revolution, you're in the right place. Welcome to The Human Code. In this episode, we're excited to welcome Raja gon Guevara pu an it executive leader with extensive experience in managing globally distributed teams and execute and complex business mergers. Raja has successfully led teams in migrating critical applications to the cloud, integrating AI and machine learning platforms and delivering digital experiences that drive business growth. Today, Raja, and I will share insights into the evolution and integration of AI in business and how organizations can navigate the challenges of adopting new technologies. The importance of clear objectives and strategic investment in ensuring successful outcomes for it. Projects and mergers. Thoughts on managing bias and learning from failures, particularly in the context of technology development and implementation. Join us as we dive into these compelling topics with Raja. This episode is packed with valuable insights that will inspire you to think critically about how to integrate technology into your business while staying true to your strategic goals. You won't want to miss it. have Raja Gangavarapu on the line, and he's here to Talk about the intersection of humanity and technology today. And so Raja, A, thank you for being on, but B, what got you interested in this topic?

Raja Gangavarapu: 2:09

Yeah, first of all, thanks Don for having me as a guest here today. And, yeah, we had to recreate schedule a couple of times. I appreciate you guys accommodating my schedule. that's part of that life we are in, lead into what I'm interested in sharing that, sharing my background and how I got into it. So I've done plenty of things in my career the last 26, 27 years, and every year going by fast. And I learn few things, as we go along. I learned quite a bit and of course I'm learning, And I always love to share. Because that's one way I learned, when people talk about their experiences more than, sometimes reading a book also, it helps me to listen to somebody quickly, helps me to kind of, what do I do, when that happens, so what I would have done if it happens. and if I pick a new role in my career and how it happens, where's the manager, where's the director, where's the Chief of something means, So I picked up quite a bit, but listening to the people, than reading or, two other self learning mechanisms. so that's what actually motivated me to share my experiences and whoever can learn, listen to, or learn one or two from it, I'll be thrilled. I'd be so glad to do that.

Don Finley: 3:19

Raja, that's absolutely fantastic. that storytelling capability, and I think this is Somewhat profound from where we've also set the stage. we had schedules that were going all over the place. Like I had some scheduling challenges. You also had some opportunities that came up as well. And it's the technology that is allowing us to sit here today to share this conversation. Like we're actually about 665 miles away from each other right now. I've done many trips to Atlanta, and unfortunately know that distance, very well. Now, when it comes to being able to share our stories, I was also thinking about that today. Like one of my personal, passions is mental health and the amount of information that is readily accessible within just five seconds is tremendously different from just even, 20 years ago around 20 years ago when I graduated college, going out, we were on the internet. But like everything I was reading was through books and we didn't have the proliferation of like video that goes along with it. Where do you think we'll end up in the next 20 years of this connection that we have with each other and with the material that we're looking for?

Raja Gangavarapu: 4:36

Yes, It's actually, going to be very profound because the way we learn things is right through interaction, back in the days in 30 years ago, 20 years ago, even 10 years ago, for the matter. classroom learning books and talking to each other, but always we are deprived of this talk to an expert in a shock notice, You had to go to a place to go to listen into our verbal conversation, but seeing a person and listening. is where we are today, We can look into that YouTube and various, like this podcast and various activities. We have this opportunity to see the person and talk and converse and talk about everything, All the books are available. All the knowledge is available. We can't discuss that.'cause when you look at a person and it's a different conversation than not talk, looking at a person un talking. That's where we are. and the knowledge is now widely available to all of us. and in terms of, feature where we are going to go and in that, with the internet bandwidth available, with the devices available now. Until recently, we need to have a computer with a good wifi, need to have the conversation. Now, phones with 5G and six G is coming and the device is getting handy. It can be anywhere in the world. You can have a fantastic conversation with a strong, throughput and then you knowledge getting shared with the least disruption. And the way we're going is knowledge is getting more and more available to our fingertips. Knowledge that is more curated correctly, now we are at the age of that knowledge. It's all over the place, but getting to it and is it right or wrong, we don't know right. That's where we are beginning to now curate in a right way with less bias, I don't know whether we completely remove bias from it. So that's what I foresee in my mind. how this learning process, how beautiful it is getting at. The virtual becomes, Probably a word more than anything else, So you can get to talk to any person, share the knowledge. Get the knowledge, and, that's where I see where we are going. but the key thing is having, make sure that knowledge is available, with the less bias and more real, more factual.

Don Finley: 6:40

I think that's an incredibly good point as far as we need factual information, We've gone through social media, and it has been this thing that he's creating chambers for ourselves. And additionally is creating, division amongst populations of people. And my own personal take is we didn't have this as much of a challenge as we have it today, because. If I went and said something idiotic to my neighbor, they would tell me before, Like they could see that, but I can get on the internet and I can say the worst thing possible, the most factually incorrect. And there's probably three other people that are like, that makes me feel good. And so I'm okay with it. But then there's the idea that. We are in a shared reality. We still have this kind of like culmination together. And we're recording this at probably like the beginning of the, I don't know, when is the election season ever over in the United States? But at the same time, it's ramping And we see pockets of red pockets of blue, but online they're completely separate. Yet those pockets could literally just be down the street. They could be next door. There's that community kind of factor. And I think getting to a shared reality again can be really profound for us to be helping to solve that. And also removing those biases that we all know and feel. But from your standpoint of I know the work you're doing, And like the peripheration of these technologies, how do you see bias playing into really like the healthcare space?

Raja Gangavarapu: 8:17

See, the bias is, I'm not a philosopher, not a claim to be in any kind of a knowledgeable person in that. So I will receive by with that disclaimer. the bias is inherent in our decision making and our approach and everything because we create an opinion once we. Get some information. That's the way the human nature works, and similarly, technology is one thing, because there's something we learn and we apply immediately. That application of what we learn creates that, kind of a decision making process which It comes into its own result, And based on the result, that whatever the opinion that's created will become permanent in our minds. And I move, we move forward with that approach. and in that cycle of learning, applying, and confirming, and then learning, applying, confirming, is where our biases start reducing down or getting eliminated. But we get so much of information going on all around it. And that, that we start intruding with our knowledge. The key thing, at least for me, when I. When I look at my day to day job, it doesn't matter what it is, I have responsibility to that, And as we go in this, IT or technology and the product development and all the stuff we do, how we eliminate bias. The bias for me in my day to day job, at that level where I am playing right now, is how I'm getting that right information. I'm asking questions, I'm getting to that solution applied to it. That's a key thing. And once I apply, I look at the result, What is the result that I began the journey to to achieve and where I ended up being. And the comparison also helped me to remove that kind of a bias or wrong opinion, wrong impressions that I might have about it. And then that's the way, we need to evolve from it. and in this process, there are successes and failures. success is, oh great, I went on the right path. Failures is where it is tricky, Sometimes we learn from it, move on, but a lot of us, get stuck with the failure, get bogged down by it. it's very difficult to learn, move on from it, It's very difficult. And I confess myself that, whenever I fail, it's hard for me to get up and get going immediately. It takes time. And what we learn from it and how that influences our biased opinion so far. So that's another key I learned over several years in the technology world where, technology, this IT, is this software, hardware, and all of that is such a magical world is everything is invisible. It results are also invisible until somebody feels it, You put out a solution out there. You don't know it's really good or not until people start using it. A bank put out a nice website back in several decades ago or a few decades ago. I walked on this first one of the banking sites at the time, online banking site. We put it out there, we learned a lot from it, 30 years today, definitely all the lessons are applied to it, But do we still fail today? That means that evolution in that learning process going on continuously and the bias, which itself is could be incomplete learning, That's how I translate into how I can go after it when a new technology comes in. And that's how also apply that method. That matter to new technologies that come in, We live in this fascinating times, where every three, four years, there's a new technology comes in, Of course, now there's all AI, which I know we go to talk more about it, but the thing is, if you look at last 10, 20 years, or even 30 years with the internet and everything is coming up, It's a new flavor coming in. We go after it, we try to do something, we fall flat, we have some good results, and we learn, we stand up, get going, and we find something new again, That's a very interesting cycle, and that's where I pay a lot of caution as I learn from it. I don't want to be too overwhelmed, too much taken by this. Oh, let's go and do it. It's going to happen, What, how I apply it to my day to day is what I stay within it. What others say, fine, I listen in and absorb it. But what I have done, what I learned is what I get into myself rather than not worried about by all the around things. That's how I deal with my bias in complete information.

Don Finley: 12:21

and I think that you're, you've hit on a couple of points here, Like bias is inherent to our existence, Like we, we bring it, we bring our experiences through, and we also perceive the world through our own experiences. And so it's not an aspect of getting rid of bias completely, but understanding that it's there, it, and being aware enough to say, Hey, I can change my perception because that was where I added a little extra salt to that dish kind of thing.

Raja Gangavarapu: 12:53

that's absolutely right.

Don Finley: 12:54

now. Yeah. And I love the balance between success and failure, success is fantastic, but then also failure is, there's a lot of lessons to be learned in that. And there's sometimes even more lessons in the failure than in the success, because it, it just solidifies my bias anyways, if I'm successful, so yeah, okay. I would love to dive into like where we're at with AI today, We've been on a, an upward trajectory of awesome tools that have been coming out in the generative AI space. I believe you've also been in the ML AI space for many years prior to that as well. And so I'd love to get your take on like what the current state of the technology is and where you see, you the next couple of years playing out for really just the industry, but also for enterprise deployments as well.

Raja Gangavarapu: 13:46

Absolutely. That's where the biggest question, biggest opportunity, biggest challenge in front of us, with the evolution of AI and machine learning and where we are with the advent of in the last couple of years with this LLMs and people are taking this generated AI and bringing more capability to the table, Now, the Enterprise organizations and the technology is like, Hey, I'm going to invest in where I'm going to invest. And we are following the same formula. Oh, here's the latest trend. Oh, this is going to give me 10x benefits or 2x benefits, whatever that like multiplier benefits that are put out there. And we have seen this scene played out every two, three years. And we, cloud came is still cloud is still uncertain, but we almost feel like we adopted it a hundred percent. We behave as if we know everything. And similarly, we are in this AI machine learning world, and some of the concepts are nothing new there, They existed in the past. Now it's coming up more shaped into a proper structure, and more importantly, the way I see today with the way we are with machine learning and AI, With this whole general AI stuff and all coming out is, so far, we present to our customers what we have, We show to them and lure them into kind of buy our products, take our services, stay with us, but that's our ecosystem of it. Always we are going after how I can bring all others learn into my offering and give it to the customers, end of the day, that's how we can keep the customer with us. This is how I see this Generate. AI and all these capabilities that kind of these mega companies like OpenAI are bringing to the table is, they've figured out a way how to bring all the data and for us to go and consume in a way that I can get the world knowledge with my knowledge and create my unique capability and offer to my customers. That's the game now. That is what we are going after. That's an opportunity. That's what we're going after. But the key thing is, just like we always do when a new technology, new concept all comes in, we drop some of the things we are working on and go after this new thing and forget the basics again. The basics are Keep modernizing your systems. Don't leave things behind. Invest wisely because money is limited, always limited. But make sure you complete the work. Longer the arc of this travel, more we leave things behind, So even if you modernize an offer, these things, what we left behind, which we call legacy systems, will start pulling us back. will giving us, this is where that what we talked as beginning as a bias, a wrong result, wrong information creeps in, comes in without us doing anything there. That's the care, we got to be careful and pace is unknown. How fast do you go? Unknown. We don't know that, It's your company, it's a culture, how do you adapt to it? But at least for me, what I have learned, I wouldn't claim as an expert in this space. I'm also a learner. I did a few things for sure, successfully embarking on new things. But one thing I don't want to lose this principle is when something comes, but what you're trying to target and make sure you deliver that while you're delivering it. You other activities that you plan to other initiatives that you plan to other migrations you plan to make sure you keep doing it and this is where that investment and organizations put money in and then only you'll have right result in machine learning you do or AI modeling you develop is going to come into the play top of it when you bring outside models into your model you then fits in correctly. Otherwise, in my opinion, it's difficult. You only achieve results, you end up compromising it. Oh, it's okay. I tried to attempt this and at least I can put something out there. that's a key, and in that whole thing, one ingredient that's extremely common is data, right? And now we know last 20, 30 years. we all talk about, we used to talk about data as a king or a queen, whatever it is, data is information and information got to be correct. Oh, information got to be correct in all the channels, which is a transformation, Every company is trying to do that still. Oh, by the way, let's go into the cloud where information flows easily, easy to go plug in, plug out the infrastructure and components, easy to maneuver. And now with this whole AI coming into the play, all these things need to fall in place. So that's where care got to be. And, bigger the organization, I guess that's where more care needs to be put in place. but that's where I say, don't forget all the good things that you got to do, you plan to do as you embark on this AI machine learning. which itself is very evolving. We're not totally there to fully understand it, but I think we started reaching the peak, Don, as you and I talked at the beginning of the call. I think, I really talked to other experts in this space, but I think that's in my opinion, I think we started seeing that peak and maybe whether we plateau or longer plateau, or we dip again, come back. I don't know. But that's something I'm

Don Finley: 18:56

I saw I saw a chart that was released months ago and the, I feel like I just caught up to it as far as an understanding because Microsoft recently released a report that said that knowledge workers that use AI in their day to day job has increased 50 percent over the last six months. And it's now at 75 percent of knowledge workers are utilizing Gen AI in some capacity in their work. Now. the benefits that they're reporting. And also Let's caveat this conversation with Microsoft is invested a significant amount in open AI. They've invested a significant amount in gen AI in the deployment of it in their tools, co pilot. They have multiple co pilots, if you include GitHub co pilot as well, But like they're pushing this, they want to see it succeed. I believe that they You know, are pointing out some good things because the, it was like 90% of the employees that are using AI report higher job satisfaction, that they use it, it's 85% reported that they use it to focus on more higher priority tasks, and that they use it to help them in their day to day for that purpose. But then you get to it and the actual Productivity benefits that Microsoft was able to highlight like tangible things. These people are looking at 11 percent fewer emails. That was one of the stats and I know it's 11 percent and I know it's fewer emails because it was very shocking to me. That was a culmination. Cause now you're thinking, all right, how much time do I spend on email a day? And let's just assume that it's two hours, right? two hours email. That means 10 minutes is what I saved. That's nominal across. that's not even going to move the radar for anybody. So if you're sitting here as an executive investing into the tool that Microsoft is offering, that's not going to be a positive ROI. from a productivity standpoint, from an employee engagement, from employee satisfaction, it can be a huge lever to lift you up. So you are seeing a benefit. But the other chart that came out a couple of months ago was describing how with zero shot prompting, which is how you're using chat GPT today or the other like GPT LLMs. That only gets the correct answer with GPT 4 around like 65 percent of the time. So you get like a usable response 65 percent of the time. And I think for enterprise deployments, when we look at integrating into infrastructure, that number doesn't cut it. But when you get to using like agenic frameworks on top of an LLM, that can bring your sat, your success greater than 90 percent in these use cases. And so where. I personally see this as we're hitting this hype cycle in which we saw GPT 4, we saw 4 Omni, and we saw the results and all the tests and it's smarter than your average law student like perspective coming out. But when you get down to it, it's not. And it's not hitting those benchmarks, on real world examples or anything that's outside of its training data until you add the additional kind of like software abstraction of Hey, let's do mixture of experts, let's get three LLMs talking together. One's an editor, one's a manager, one's the actual like writer. And they can collaborate to get the document out there. And that's like what we've been doing in our own internal stuff. After seeing that like XeroShot doesn't do the trick to like actually reap those rewards. I personally see that, as we look at the eye of the organization, it's really important that we still have those chat tools that people can do the XeroShot with. It does. improve people's satisfaction. It also helps to break down information barriers. This is another digital transformation that we're going through and digital transformations don't happen In one person's department, they happen across the organization and they happen all throughout. So that piece is necessary, even if it's not a very positive ROI right off the bat, it brings the information, the knowledge and sets the expectation moving forward. But then when we look at the we side of the equation, what's the company doing? We can't base our investment criteria on the chat GPT side of it. We've got to come up with a foundation for Hey, here's the use cases that we're looking to integrate this into. Here's the workflows that we see that we can do an augmented intelligence solution to aid somebody's creativity where point poke holes as much as possible in this. Um, I'd love to get your take on what I just

Raja Gangavarapu: 23:54

It's, these reports, definitely viable, but it is a very planned activity. Microsoft, one of the core intentions, like McGargan and Microsoft, Google's. These are the very few handful of companies going with full end investments. you don't find these kind of companies in every store, every city and all running around. it's not Walmart is pouring in as much as Microsoft into research or, there are few organizations doing and then they got to focus on the ROI and, they share with us, Keeping that in mind, what is our organization, for example, going to invest in? We're still going to invest in the tool or the products which makes us money, which makes, reach our objective, our company goals and objectives, what we set out to do, our mission, In this, that is where, whether these information is going to help or not is the first thing we look at, Employee engagement, employee morale and. This is very important for us. And in this, to your point, the uplift is there, but okay, can I do something else with that kind of, which gives me that kind of uplift? Now there seems to be some opportunities for us rather than going this way. So for us, how much we can take with the amount of brain power, the capacity we all have together, We have going after so much already today. I will wait until this Microsoft's Google's invest and put out this result. There is still, we're watching out, for example, Whether we bring to the table, I don't know, to be honest with you, whether we want to jump in and adopt all of it right away, but definitely whatever they're doing is we are learning. We're bringing the LLMs. Now, while I'm saying that, an idea crossed my mind, Now, basically, the basis of it is, two major components we're bringing together, These LLMs, as we subscribe to or buy, whatever we do, license, however the licensing mechanism works, we're bringing it and bringing it to our ecosystem and delivering the value in form of a new product, new service, new solution to our customers, Just an idea crossed my mind, just what happened in the, recently with the security in Microsoft and, the CrowdStrike situation. The core of it is two different technologies trying to come together, It worked a million times. they're patching going all the time. It's like maybe a billion times, There's a massive amount of, if you just do a percentage, But just one event led to a, you know, we all see what happened there, But if you really look at in the take backs, it is successful so many times, That one event is enough to wipe out all the good stuff it did, or maybe not all, but a good amount of it created such a negative impact to everybody. And we don't really don't know the true impacts across the board. That's the lessons learned. Those are the things always we need to keep in mind when we bring these outside components, however powerful it is. Bring and combine with yours and combine and give it a solution. I was thinking about in the last few days about After. PowerStrike is a fantastic company. There's so many smart people there. Microsoft, of course, And even in spite of all our best efforts and best practices, it happens. So we are in a situation where some people are creating this fantastic knowledge based products, giving it to us through the form of LLMs, we're buying and incorporating our stuff into it. Something goes wrong, We know machine learning and AI are probabilistic, We know that to begin with. It's not one plus one, it's two kind of situation. But right now, how far we go, where is our threshold? And all these questions start to be answered. Coming into my mind, how far we go with this and what is it, such LLMs have is not magically working out the way we wanted to, by the way, We are still learning from it, We are. at the stage. so just to address your point, what you just asked me to poke a hole in that, as such for these organizations, it's really good. I'm glad that's the job. That's what they're trying to do. And, what's coming out of OpenAI and all with all this very smart people trying to do is wonderful. But for us day to day to adapt to it, it's still caution, caution, caution,

Don Finley: 27:56

and I can completely appreciate that sentiment that you need, we need to be cautious as we're letting these things out. we have some AIs that we've, built, were wrapped, like either OpenAI, Mistral, whatever it is, depending on the sensitivity of the data, we've wrapped them. But then we still have the human in the loop, either approving the content that gets generated, approving the messaging that's going to be sent, Because there is still those times when it just really goes off base. And like you're saying, it's a probabilistic machine. We're no longer in the world of deterministic behavior. We're hitting the boundaries, and there's that creativity that we like as part of the solution, but there's also that creativity that can go unchecked. So I think that's also as we were talking about the hype cycle of where we're at. And it looks like, the wave might be cresting. it might not, but also at the same time, I think it's probably cresting for the enterprise we have creative solutions that actually can be somewhat deterministic in the boundaries that they have. So that we can bring things out that are factually true and not being concerned that it's just making things up. because in the end, our companies are liable for what these tools end up

Raja Gangavarapu: 29:12

Absolutely. And see, if you look at these cases as such, we are trying to automate. it's not that we were our objective is to begin to replace humans. that's not no organization is not our organization. We're not doing that. Just improvement productivity, even though sometimes it's not sometimes it comes out as a problem. Yeah, people are going to lose their jobs and all, but in my humblest opinion, again, I'm not an economist either, looked at all of it very closely. It is evolution where we, some jobs go away automatically, gets replaced with the new ones and economy is improving. People are finding new jobs, skill retooling, it's a constant thing in the last 30, 40 years, That's how I see the whole thing, is coming out to be. but really, if you really, look at it with all of the technology is. We're giving a solution to a customer or a product, somebody buying a product from us for them to do the job. All these jobs is a definite outcome. It's not that, Google suddenly says, you can go either right or left is up to you. I mean, nobody uses Google Maps. There's nothing like it's gone, dead, It tells you definitely go right, Yeah, go right. there's some streets so close to it. Sometimes it can, now it's getting more and more accurate, And now we know more, That's what The output should be, even though it's probabilistic, But the technology where we are coming right now is still that human element of, to your point, the mistakes that we do regularly. that's not going away, With all these things and, and that they come into the play. The solution we put our customers may not like it, may give a bad result. We are standing up to provide an answer to it, how to solve that problem, And then when something fails from this, then where's the alternative, We thought this is the one. Oh, it didn't work. what do we do now? The thought process itself is sometimes scary. So that's where, as we go on, our objective got to be clear with this technology. That's one thing, and at least, and I work with my partners and other people, technology business and all, at least I try to bring up the conversation with them and at the end, what's our objective? As all of us trying to do here, we, particularly the business folks, we as technologists, we try to value all this stuff, but the end of the day, you got to be clear, we all got to be, and with the money we have, that's a key aspect still with all of this stuff coming into the play here is that objectivity, clearness, and what solution you put out. Yes. And when you have that clarity. Your probabilistic computing will work as well. Your data, your information, whatever you bring from outside, you can understand, you can put out there. If you don't do it, we may end up under spending money on something to throw away. And today, tomorrow, yesterday, we all know money is important. Investments are precious. that's one thing we always try to control carefully, spend wisely. That still is a game with all the stuff going around. Stay with those fundamentals. And produce the result that really works for our customers, not what you think it is, what works, what your objective is. And that's where that, that guardrails is, and which is same guardrails, For other technologies that came before, that's how I view that whole evolution. Haha.

Don Finley: 32:17

And I think You're grounding this in history, Like we've gone through these transformations before. We did this, 20 years ago when e commerce came about, Like we've had these moments in history that can help us to understand. And at the same time, it's an investment. We're saying, Hey, here's what we're looking to do. Here's what we're looking to get out. And in the end, every business just wants to be able to serve their customers better. So can we do that? And is it actually helping? And that's where we're going to get to the next edge. Raja, I really appreciate you being here with us today. just to wrap this up, is there any other wisdom that you'd like to leave us with?

Raja Gangavarapu: 32:57

Oh, I wouldn't say as a wisdom per se, I wouldn't claim myself as a person who has that wisdom to share, but I definitely can share my experience, lessons learned, in just a summary is. Make sure your objective is clear. You work with your partners. Everybody is agreeing to the solution. Have your tool set correctly aligned with that, with the right investment, right team structure. Make sure your people are there, excited about it, because some people want to do some new technology. It's always the case. Don't discourage that even though the tool you think may not work. But if more people come and say the youngsters particularly want to try something, don't negate that. Understand that. I was once the same boat. I won't try when Java came out mid nineties. I want to go and try Java and beta. My boss said, no, stay in this stuff. I pushed for it and I'm glad he encouraged me and a few others to go and adopt Java at a beta level. And I started at that level and we built a product. production application with a beta Java. It worked fine. I still have that, I'll never forget that and I still try to encourage, anybody who's trying to do it. Technologies always come. They come new, they come repackaged, come in a new evolution. It always happens. We are smart, as smart as we learn from our mistakes and even from a good result that we have. That's where I stay within, all the technologies are just a name, coming your way and trying to help you out. That's what I would leave this conversation with that, with that thought.

Don Finley: 34:27

I love it. Thank you so

Raja Gangavarapu: 34:29

Thank you, Don. Really enjoyed it.

Don Finley: 34:31

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