AI, Human Judgment, and the Future of Talent with Sameer Ranjan

You're listening to One Sharp Sword, cutting through to what matters most with Outfluencer Dr. Wayne Pernell. Welcome to One Sharp Sword, cutting through to what matters most. I'm Dr. P, Dr. Wayne Pernell, your host, your guide, and your coach on this journey. My guest today is Sameer Ranjan, the CTO and Director of Data Science at Captonate, a company using artificial intelligence to help people make better decisions about their careers, their growth, and future opportunities. With a background spanning engineering, analytics, healthcare, and AI, Sameer's built his career around understanding how technology can support human potential. It's so important these days, especially. He's also an advocate for education and talent development. And because concepts that center around critical thinking and literacy are dear to my heart, I'm looking forward to exploring his perspective on the future of AI and human decision-making. I can see that we'll be spending some time on decision-making processes and Sorting how humans can use AI to their advantage. So Sameer, welcome.

Thank you so much, Dr. Wayne Pernell, and I'm excited to be here. I think this can be a great conversation. I remember we had an earlier discussion and I love the notion to some exchanges and let's see where this conversation takes us.

That's how this podcast works. People always ask me, oh, can you send me the the questions you have ahead of time. And it's like, I don't have questions ahead of time. I have ideas about what I want to talk about. And usually it's the human path that opens up. And then I did this and it's like, well, why? What happened? And what, right? So here's where we're going to start. We're going to start with what it is you do, Katinet. I always want to put an N at the very beginning, like cantonate. It's not that. It's Katenate, C-A-T-E-N-A-T-E. Yeah. All right. So, so let's talk about what you're doing now, because your focus right now is on education and helping students and people in career transition to make better choices about their future. So let's talk a little bit about that. And then, and And then we'll wind back the clock a little bit and find out how did you get there? Because you've done some serious data analytics and research, and some of the projects you've worked on have been really significant. So I want to dive into that too, because I think our audience could be enlightened a little bit. Sure, sure, sure.

So let's start with Catenate. Catenate, we started in 2021. The name itself came from the word concatenation, which basically means chain reaction. So we thought like, what would be a best nerdy way to write a company's name? And we landed at Catenate. But there's an underlying notion to it, which is, this is the belief of the founders. We are 3 other founders and we believe that Every life decision that we make is usually influenced by data points, and usually the data points are people around us. So for example, I want to go, uh, pursue my higher education at a college. I'll basically ask few people that what should be my major, what I should do, because I will have some, some hypothesis in my head that I can be a good aeronautical engineer, but I'll definitely go to the counselors and will try to validate it. Now same happens about the money matters, which stock portfolio I should go invest. Same thing happens on whom I should go marry, dating world, right? And so we thought like every important life decision is usually around data points and people influencing your decision. Then the notion came, what if we expand the data point to millions of parameters where millions of data points can come in? Tell you like, okay, this is what 60% of you is aligned to XYZ thing, then you can make a much better decision. Uh, with that notion, we started Catenate, and, uh, the first foremost area that we wanted to tackle was education and career, because, uh, that's where everything starts. And, uh, education, since it is very close to my heart, and the career piece of it, because I have been not a formal educator, but trying to educate people or giving guest lectures, taking the professorship at different, different places over the last decade. I believe that education industry per se is very much colluded with confusion because every single person who enters a college on day one, they have a dream. On day two, they have confusion. And by day 3, they don't— they hate the entire education industry and the whole education system.

Do you think that starts with expectations, right? I expect— I'm coming into this institution, they're gonna fill me with knowledge, and then I'll have everything I need to go out and conquer the world. And day 1, you're like, yeah. And day 2, you're like, whoa.

Yeah, I, I think so. I think like, uh, And I think like this is what we learn by spending time in corporate, that there's a huge gap between what do we market and what do we sell. So we market the vision and we sell a product, and the product has its journey to reach to the vision, which is any education system, any, any college will say that, uh, will make you the best engineer who is out there or the best CEO because see the list of CEOs we had who graduated from our college, but they don't market the age because the CEO might have been at the age of 55 years old, and someone who is 21 years old who is entering the education ecosystem thinks that they will become CEO, but they don't understand that there's a gap of 40 years or 30 years that will take them to be that CEO. So you're very right in saying that it's always the market versus what you're selling. That gap is so huge, it creates a lot of pressure on the young minds and the young brains.

So, so you use data to inform. It's like a stochastic process, right? This is all about given what we know, what's the probability of success on any particular pathway?

Yeah.

Yeah.

So, so that's— so we took the concepts we derived So what you're saying is exactly right, but we added some more metrics to it, which is it is very easy to say that if you have taken maths as your major subject in your high school, you should either go for engineering degree, not primarily go for something which is medical because you have not taken biology as a major subject. But then one more element comes into the picture, which is who you are as a person. Right? We discover ourselves in our entire lifespan that we are changing every day. Every experience shapes us into something. So we thought, let's take a principle of soft skill, the ikigai, which is who you want to be, and then merge it with career. So it becomes a little bit more fulfilling and more directional rather than just probably we'll take that yes, this is who you are because you have just taken the maths. I, I know a lot of mathematicians who are great DJs, but my system would have never predicted it if it was just based on the probability of careers.

Yes. So, um, it's interesting that you brought in ikigai because that is— it's a beautiful concept of, uh, the— for those that don't know, it's the concept of purpose. Who, who Who am I?

What do—

what matters so much to me that I would— I just want to do this?

How do you—

like, it's easy to do in my mind. I'm thinking it's easy to get an assessment of, oh, this person's good at math, they're pretty good at English, they, uh, aren't so great with chemistry. Like, it's easy to get an assessment that way. How do you get purpose out of somebody, right?

So, uh, then what we did was we took the concept of behavioral economics. Uh, it's by Dr. Kahneman, who has written the book Noise and, uh, Think Fast, Think Slow. Those books, we basically took out the concept, married it with ikigai. Now, if I'll just take ikigai as a principle, in my head it is very monk-ish, which is you have to be a monk kind of a thing where you have did all the self-attainment and you have understood yourself. But that's not who everyone is. We all tend to work towards earning something, right? For people who are working for the society, their dollar amount that they want to earn might be zero. And for someone, it might be billions and trillions of dollars. But at the end of the day, we are trying to work towards something. So we've married both the concepts. We took the philosophy of Ikigai, which is spirit, purpose, passion, and reward, and then took the concept of behavioral economics, which is in a given situation, how will you act? And when you add these two, now I can put a number metrics over spirit, over profession, over purpose, over reward, but to just an outlook of how you will act in a corporate setting where at the end of the day you will earn something. So I am just trying to narrow it down to a level where I'm saying that, okay, I cannot go and tell whether you can be a priest or a monk or somebody who is the best NGO worker who doesn't want to earn money. I just want to narrow it down to the people who are looking to make a career, a profession, who want to have a living income till they become self-attained monks. So That's what, that's, that's it. That's what we have done.

So are you working with people that are in career transition? Like they got out of college, they've been doing what they've been doing for 10 or 15 years, even at various companies, because there's a lot of job hopping that happens. And then they come to this place of Is this all there is for me? I think I'm more— like, that's my sweet spot. I work with, I work with people in what I would consider the existential dilemma, which is, well, I did all the things I'm supposed to do. I ache for more. I know there's more, right? So it's, it's a really exciting thing because you watch the person who's the person inside this open up and go, oh, I can make a difference in the world, right? They come alive. So are you working with people in transition the same way? Right.

So, so we are working with primarily 3 cohorts. One is any other students who are fresh grads and who want to go into the job market. But the current scenario that we have seen in last, I want to say, 8, 9 months is people who are in transition of their career. Most of the people who got displaced due to AI are the people who were at 12 to 15 years into their career and they just didn't know what to do. Uh, one fine day they got an email that they are no longer required, and they had done everything that was supposed to be done, and they were in IT. And now we are working with those people especially to figure out, okay, uh, what were the gaps, how can you fulfill the gap, and how you can be what you want to be. So we have this notion that soft skills are something which is consistent throughout your life, right? It can change a little bit here and there, but those are learned skill which you can do something about it. And if you have a soft skill, any job skill that is going to be required, you can learn if you have a learning ability. So that's what our notion has been, and that's what our hypothesis is, and That's what we work on. So our sweet spot starts at 21 years old where you're just fresh out of school and you want to get a job, and it goes till like 45, 50 years old where they actually are thinking, what's next? How I can be better at my job or how I can go and launch my venture? Should I go and actually launch my venture? Should I look for promotion? So that's our sweet spot.

That's great. That's great. And what's, um, how do people find you and what's their— how do they know they're done with you? Is it a, is it a one and done? Like, do they do the assessment and you say, look, based on what you've given us, here's what we see, right? Right.

So, so we, we don't sell this whole solution to an individual. We We sell it to organizations.

Okay.

So for example, a university can buy it same way, uh, enterprise or a small company can buy it. And they basically go and try to judge the people who are, who, who they have, like what are their actual skills are like, uh, let's say I am a company of 500 employees and now I want to spin up a new department which will work for some of the newer technology or newer focus area. How I can repurpose my internal resources I have rather than doing the guesswork. This is the strong data signals that we can give. These are the people who are ready for that job role. These are the people who have certain gaps which you can fulfill with the courses. These are the people who can be leader in your organization if you give them XYZ leadership courses by any external provider. We don't provide any courses, we just give the intent and signal. And now this is a cyclic thing, which is after every 6 months, after every 3 months, after every year, you can do this assessment so that you can continuously map and monitor your own people. Be it at college, be it at any company, you need to understand how your people are shifting. Because one thing which, which is very consistent is our soft skill and personality changes based on the experiences that somebody have. If somebody got married recently, they will behave very differently than somebody who got divorced. And it's just that the spectrum of life, because you react to the situation, you act only 5% of the time in your life. 95% is reaction to that 5% of the action. And those reactions actually tend to shape your personality. You can be bitter, you can be bittersweet, you can be sweet. It just depends on what has happened in your life in the past month. So it becomes a very continuous assessment over a period of time. And this is my vision. I don't want anybody to get ever fired from any company. I just want the companies to understand that, okay, we have a method by which we can actually quantify our people. We can give them measurable things that they can do. So that they can be better at the job, produce more revenue. We can spin up new organization and do the job with the same people because every organization is about people. It's never about their processes or technology. So that's exactly what we are doing.

So that's amazing. All right. So yeah, I've talked to a lot of people in tech and the conversation always goes to platform, process, and people. And platform and process don't matter if you don't have people that really care about what they're doing and they understand the platform and they care about the process. So always getting people to grow is an amazing thing. The other— it's kind of a crazy thing, Samir, because I I consult with organizations, and there are times when organizations will share their values with me. And they will be emphatic about, we are a learning organization. I'm like, great, what are your initiatives around learning and growth? Well, we've had to divert those funds to this other project. I'm like, then you're not a learning organization. You've undermined your own values if you do that. Like, that's terrible. So for you to say, okay, here is, here's an assessment. It's a tool that allows you to track and support employees as a group and team members as individuals. And I think that that's really great because you, you know, From my perspective, a manager or anyone in leadership, a supervisor, a, a, you know, anybody who's in leadership all the way to the top, their job is to reduce friction so that each team member feels valued and feels like they can step into that next level of their, of their job. This instrument, the Catenate instrument, allows you to guide that kind of growth, right?

Yeah, yeah, yeah, exactly. Like, uh, our— one of the challenges that we have seen in the industry is L&D is not measurable because everybody thinks learning and development is something that we have to provide. They don't believe that it can be measured also, and something which cannot be measured often at the boardroom seems like a cost. It is, if I'm just looking at the books, it would be a cost line item.

That's why funds are diverted. Well, it's, you know, we're a learning organization, but we'll take this program, we'll use those funds for this project, right? Because it's not measurable the way most people think about this action created this benefit to the bottom line.

Right, right. And if the boardroom doesn't believe in it, right, it just becomes again an item on your wall where you have written, these are the values of the organization, which you never follow. So it's okay. Like everybody wants to chase revenue, but you have to always think long term, right? Can your revenue be sustained? And if there is no learning in the current atmosphere, your revenue can never be sustained. So upskilling is the newer way of running any organization.

So I love that. Yay for upskilling, upskilling, you know, it's like that's if you're not growing, you're actively, if you are not actively growing, you are actively really slowing the organization down. Especially now, especially now. So, uh, that's amazing. Um, this is great, this is great. What else did you want to say? Anything else about that?

Because I have other questions. I think, yeah, more or less that, that's what the point— I think like one thing which, um, we have seen is people dependent or being too much dependent on the courses platform. For these jobs where their job is not to do the— they can track well whether somebody completed the course or not, but they cannot tell somebody whether, uh, they learned anything or not. Uh, doing, pursuing a course and learning from it are the two different things which I think most of the people now have to go and understand that this is how the newer world works, right? So, uh, yeah, that's— that, that was the one thing that I wanted to add.

What do you think— like, you've spent years, years in analytics and more recently in AI. What, what do you think there is like, what is the thing in human behavior that data can't predict? It's like data just can't predict this about, right? It just is in terms of how you're trying to capture all of it and still you have to manage this other part, right?

I think, and, and this is with all honesty, since last 3 years I have been trying to figure out an algorithm with which I can map emotions, right?

Amazing.

And assessment by which I can actually map an emotion of a person. And that's the hardest thing because it has so many variables in it, which is very hard for a machine to understand. And that's why AI can never be human because as somebody who is, who can be very analytical at the same time, very emotional. And how do I define very emotional or least emotion or least emotion person? That spectrum is very hard to define by an assessment or a machine. Unless until you have to be with them in person and you have to put them into a situation, then only you can understand. And I'll give you a very personal anecdote, right? This happened with I was visiting my in-laws and we were at a swimming pool and my father-in-law said that after the retirement, this is the first time we are enjoying the life and he was always a very serious person and now he has started loosening up. Same thing is happening with my own dad also. He was very serious when I was growing up. He always wanted us to focus on career. And now he has started losing up, right? So if I would have created an assessment, an assessment would have said that, oh, uh, the emotion of this guy is very less. But towards what, right? Because he was very empathetic towards his father, right? Uh, his mother, because they were the aging parents and he had the sense that now they are aging, so I have to be more empathetic towards them. But since we were young in the teenage years, where boys being boys kind of a thing, so he couldn't be that empathetic towards us. He has to have that strict borderline things, right? Now, this, this becomes very hard to map it mathematically, emotions, right? So that, that's the one thing I have not been able to figure out in any of my researches. How do I create an algorithm?

It's tricky because there are different— I think there are different ways to read emotions, right? And for some people, they don't know that they are being read. Differently by other people. I worked with a CEO of an organization, and every time I would talk with her, she looked really judgmental. Really, like, she looked really judgmental. Her— she would purse her lips and she would furrow her brow, and I thought she was judging everything I was saying. And at one point I was like, I asked because that's what I do, right? It's like, this is what it looks like to me. And she goes, oh no, that's my listening face. I was listening really intently. And I'm like, well, I'm taking it like you're judging 100% of what I'm saying and that it's showing up as not good enough. And if you do that with your team members, like, they're not going to know whether they're on track with you or not. And so the emotion was what I created based on what I was seeing, and her intent was completely different. I actually taught her how to relax her face and raise her eyebrows when she was listening so that it was a different presentation than what looked like But it can be subtle, which is why you can't measure it, because what's intended as transmission is not always what's received based on what's going on in the receiver as well. Right? Yeah.

Now, I have heard like the best emotion detection machines are at casinos at Las Vegas. That's what I have heard. I have seen a few of the research papers around it which claims that they can predict the emotion of a person and they can see how shrewd they are based on facial expression. But I think like the chances of false negative will be very high in it because it just depends on how you master your face, how poker face you can have. And then Bashid goes suddenly off, right? So it's hard.

I mean, there's so, there's so much that goes into it. Plus, you know, the, the other thing is that you, you talked about being reactive. We believe that emotions— people typically believe that emotions are real and that their thoughts come after their emotions, and You know, what you've hinted at is that actually thought happens first. It's based on how we interpret something, anything in that instant, and it's a micro instant. We interpret it, that's the thought, and then we have an emotion to which we give meaning, right? Oh, I'm feeling this, that must mean this. And then, so we have this thought, we have this emotion, and then we and then we give that emotion meaning. And it, it, it's like a lot of the training that I do with high-performing individuals is, is about, let's look at your thoughts, right? First, what's, what's creating the emotion? So it's very interesting. Emotions in any given situation— you talked about situational as well as life season. Right? So people that are retired will take the same thing that happens and they'll treat it differently.

It's great. That's great.

Let me ask you this. As AI, right? AI is already here. It's not— I used to, when I was doing these interviews early on, I was like, well, as AI comes, it's like AI's here. It's been here. So as AI becomes more capable, then that's the way to frame it because it's here. As AI becomes more capable, where do you think human judgment becomes more important rather than less important? Because a lot of people are abdicating. They're not delegating, they're abdicating their own reasoning, their own judgment. So, you know, you're a data analyst, you're, you're deep into that world, plus you've married that with a little bit of psychology that, you know, it's like the human part of that. So, so talk a little bit about, about the human side of this, right?

So I believe like First, I'll try to say the situation the way I see it about AI, right? AI has become mainstream, right? Which means that when internet came and broadband came, when the laptops came or the mobile came, it was not mainstream because everybody couldn't access it. It was costly and it It was a product which requires a lot of authentication. It requires a lot of authorizations, and then you can access it. And you have to be at a certain class so that you can get these kind of products and tools at your hand. Whereas with AI, now most of the people have AI at their disposal, right? If I go, if I look at any movie right now, the movies that have, that released in past 3 weeks, 4 weeks, everybody had a reference of somewhere of ChatGPT or AI or generation, right? So it means that it has became so mainstream that it has came to our cinema. Now what happens when somebody, something comes to the cinema, the usage doubles, triples, right? And it would be ever growing. And when it grows into the market segment, which is not IT, When internet came, it was first IT, mobile IT, laptop IT. Now these— this AI is not just IT, it is everywhere, right? A plumber also knows how to use ChatGPT as well as a CEO of an IT organization. What it is doing, what I am seeing currently, is something that could have written in like couple of lines becomes 5 paragraphs. Right? Because you can prompt it out, and when you'll prompt it out, you don't know where to stop it. So machine will think like whatever it wants to think and give you the answer, right? What could have been one document become like 10-pager, right? Now you have lot of information at your hand, and this is the, the worst thing about information. If you have access of information which you don't need, it creates lot of hallucination in human brain. Which is now I am overwhelmed with all the information that I didn't need it, right? Apart from that, we are living in a world where social media is everything, right? We are continuously watching content on social media, be it Twitter, be it Instagram, be it Snapchat, be it LinkedIn, anywhere and everywhere. So now your brain is again getting fogged by all the content which is unnecessary to you. So when these two things happen, now your brain is already overworked. Now I want to have a human element in AI. That human element, since it is losing its capability to reason, to think, to actually innovate, it is going to ask AI to judge AI, right? If I'm given a task, whether this should be done or not, the first reference is go to ChatGPT, ask whether it should be done or not, right? So something which was generated by AI, I'm just again asking another LLM or another AI to validate that thing. And at the end of the day, nobody— or I see the people getting much more farther from the reasoning.

Yes.

If there has to be an enforcement from the org level, from the top down, that you cannot use certain things or you have to do certain thing very manually. Right, this has been consistent across 20-people organization to 20,000-people organization. This is something I have seen. People who were supposed to be human in the loop, they are human in the loop with AI, which means that they are actually not in the loop. Uh, but being said that, I think most of the organization will move towards a system where they will have policies for human in the loop. Where if certain XYZ thing— I'll give you a very simple example at banking. Banking is the only industry which deals with fraud at the highest level. Now I can do all the fraud detection with my anti-money laundering algorithms and the KYC algorithms which has been there since last 20 years. Now if I automate that and I put an AI agent to just look at whether it is right or not, The chances of AI agent being wrong will be very high, which is very antithesis to what machines are supposed to do. Machines are supposed to be right every time. They cannot be wrong. But now this machine is colluded with lot of bad information. Nobody is judging what information is going in. So I think the newer human policy will be, uh, I think human will be— they need to be trained on Be creative, right? Which is bring something which is very cross-functional. You do— you are an IT, go learn how to do music, right? Challenge your brain to do something which you're not doing right now. And then use the cross-functional domain and be human in the loop, actual human in the loop, where you're not using actually agent, you're using your own reasoning to understand why it happened and why it should have not happened, where it should go, where it should not go. So I don't see a world where ever the whole AI system will be autonomous. It will always have human, otherwise our macroeconomics industry destroys. And, uh, uh, the human who will be in the loop will be someone who comes not from the traditional IT background, but somebody who comes from, uh, person who knows lot of different industry because I think that's the last way to indulge creativity in you. That's what my vision of future will be.

I love that. So I'm a huge advocate of literacy. So people need to be able to read. And the reason I'm an advocate of literacy and the, the numbers in the US are astounding, just in terms of the reading level of most people. Over, over half of US adults read at or below the 6th grade level, and, uh, it's around 21% are functionally illiterate. And that should be scary, um, and partly because we're asking people to think and to think critically and to make important decisions, including voting. Um, and that's, that's kind of my personal, like, I, I, I, I would love to see more literacy programs that lead to critical thinking programs. So when you say humans should be trained in the creative arts, um, and that allows them to think differently, I'm a huge fan of that. Anything that can get people to think creatively, anything that can get people to think critically. I think we're losing critical thinking skills. I also think the durable skills of, of social engagement are being lost as well. So we do need more of that, just being there where, you know, the, the whole tech and personal growth kind of intertwinement and/or the dissolution of that. How do you get people to use AI as a tool for empowerment versus dependency? Right.

So this starts at the school, university level where I have seen people, especially teachers, basically, they came to me last month and they had this thing like, what should we teach and where should we ask them to stop that they should not use AI? Right. And this is a huge debate and question across the world, like, what should we teach and what should we say that you cannot do it with AI? And the solution that teachers have found and all the solutions that they are doing right now is We'll ask them to write the exam paper. We'll ask them to be in a class and write something, right? My whole idea or the argument, the counterargument was, why don't we change how do we test a person, right? Rather than asking them to not use the technology or be a contained— in contained environment, just use the technology. Let's change the method by which we actually judge whether they passed the course or not. Right, we can change the method of examination because then it would become much more better, uh, in terms of innovation that we can embark there. Now, where does the technology and the human element, or the human, uh, separate, or let's say, uh, or actually dissolve? I won't call them like— it can never be separated. If a kid is born with an iPhone in his hand I cannot assume that they will at 25 or 30, they will think that I don't want to use the phone now because that's not how the learning, the brain works, right? Because it got trained on that technology so much. What I think, what you, the human element can only be added, and this is, I'm a huge advocate for liberal arts, right? I think in the education system, It has to be a mandatory subject where you have to go and learn something in that direction. And same thing, we can take the concept and put it into the workplaces also. When you want to run an L&D program or an upskilling program, add a liberal arts program there. It might be anything. It might be as simple as doing poetry to, uh, pottery, right? You create pots create poem, anything that you are not generating, but you are— and even if you want to generate, you generate something which is original, which makes some sense, right, which speaks your story. Uh, in the current generation where everything is a swipe away, uh, I cannot take technology out of their hand, but I can— I think the one industry that I have seen exploded in the past decade is experimental industry where, I don't know whether you have seen this, where people are waking up at 7 AM to be at a coffee shop, and coffee shop has a complete DJ setting. People are drinking their matchas and enjoying the music, EDM, right? Now, this newer generation is not drinking alcohol that they used to drink. And there was a saying, I think 10 years ago, where you will just go hang out with your co-workers, have a beer, bitch about your boss, and everybody will be super happy. And that's how you, you actually created a collation, you actually did the bonding. But newer generation, they are not doing that, but they are doing something which is way different than that. They'll either go do bungee jumping and they'll do all the experimental things rather than this thing. So we need to invest a lot more in First, teaching the teachers. Second, liberal arts. And third, creating places where people, human, can come and actually experience things, which might be nature walk, which might be Sunday runs, which might be anything which is where they actually have to forcefully go out and do some activity. And then I think when they are married with the technology, things will be still fine, human, because they will at least take out some time to do something which doesn't require mobile or laptop.

Amazing. That's so positive. I love that. That— and, and just to underscore it, bonding happens through experience, and it used to happen as the experience of drinking. And there's, you know, what you described in, in, you know, complaining about the boss. People come together when there's a common enemy, right? So like sporting events, we're gonna beat them and they're gonna, right? So there's this common enemy of, you know, in politics it's the same thing. In the workplace, one hopes that it's not the boss. It could be the workload, it could be whatever, but sometimes it is. And so coming together and creating this common focus. I think through experience, you know, whether it's ropes or walks or whatever— I love that, that it's the common experience is the bonding experience. Uh, it could be the next wave of, of, um, really bringing people together. Yeah, because, you know, so I, I'll just say, when I run retreats I bring people together, uh, it's a small group usually of high performers who are looking for a different way of thinking and a different way of experiencing. And I have sent them out to look for very specific things in the world, right? So like, you walk down a sidewalk, you just walk down a sidewalk, you're thinking about where you're going, you don't think about, wow, somebody actually planted flowers right here, and there's very specific flowers, and they have a very specific shape and texture and even smell. And do you bother to notice? You know, it's— they're small stuff. The architecture— like, as I'm talking to you, I'm, I'm in New York, and, and behind my computer there's— but my windows are open, and I've got New York and bricks and different colors and there's a fire escape that I can see, and there's, you know, a different, a different building with a different color. It's fascinating. And, and we don't usually notice. It's just part of the landscape. So I, I love that, that as much as you're into data, you are really encouraging people to take time to notice. Notice who you are, where you are, who you are in your environment. Yeah, love that.

Curiosity is the key. Like, again, uh, we can build as much technology as we want, right? Uh, we won't change the world with it, but I, I have like every innovation that has been there in the world came from a person who was curious and did something which everybody saw but nobody bothered to do it. So None of the innovations have ever happened by somebody who was— who sat at a laptop and did like 24 hours of coding and actually innovated something. Never happened.

You're saying all the right things, at least to me, um, right? I'm, I'm such a fan of curiosity. It's one of those things where I will sometimes sign off with, stay curious, right? It's It's for me, um, for those that have followed me for some time, they know I keep a Curious George with me wherever I go. I keep a little Curious George monkey, um, as an inspiration, as something that makes me smile through the day. And, um, and the idea that I like to share around curiosity is that if you stay in this place of curiosity, it keeps you out of judgment.

Right?

I wonder is so much different than, oh, that's because, right? And it's like, I wonder. So I love that curiosity is the key, is one of the strongest things. And I'm glad you said it. Thank you. That's so good. So what about you? Like, kind of personally, what decision along the way changed your own trajectory? Right.

So I started as a mining engineer in my career where I actually had to go 1,000 feet below the ground to mine out the zinc and the other minerals that we were mining at that time. And I was super curious because when I'll go and say to somebody that I'm a mining engineer, they can't even comprehend what I'm talking about, that we have like columns, we have different roads inside, like 1,000 feet below the ground, and it's a whole city that lives there, right? Because they go work and it's 24 hours mine, uh, 24/7. So every day somebody's working in it. So it's, it becomes a workplace, right? Then how, when you, when you know, now when you start thinking, okay, people are living there, then there has to be facility by which they can get the air, right? So that's the first level of curiosity. Okay, how will you make the air reach there? So it's a whole different kind of engineering. But one thing that I learned there was, uh, it's a human operation has the highest efficiency at the environment where human can sometimes be very inhumane. Uh, 1,000 feet below the ground, the temperature is around 100, 103 degrees Fahrenheit, which basically is a temperature where you actually get fever. Right? And the air is artificial, water is scarce, like you don't get too much water, and you are working at a very highly noised environment where the machines are drilling, the diesel fumes are around you, and the blasting is happening at the same time because that's how you mine the things. And still at the same time, nobody is killing each other. People are not killing each other. Right, so it's a human efficiency at its peak where everybody is being professional. And I was working with people who were practically illiterate, so they— or they had just the lowest form of education, which might be they just graduated high school, or sometimes didn't even graduated high school, took some diploma, they learned a tool and started working there. Now, and there are people who have like advanced degrees who are going there helping them out. So how do you manage a lot of people at an environment where it should not be managed? And I think that's where my whole brain started thinking like, let me go and collect some data around it by which I can actually figure out why it is happening. And my job was very simple. Mine's manager wanted it to be optimized to the level that they are always in profit, that every shift they are actually generating revenue. And my whole job was very simple. I thought like, if I can figure out that none of the employee or every employee has an alternative employee where if one person doesn't come, next person can go in and fill in the job, it would become the best efficient operation. And I was able to do it. Just, I was able to marry the data with the human psychology. Which is I understand that XYZ won't come today because he has a function at his home and his function is in proximity of like 5 other family. So those family will also not come, but somebody 50 miles down the lane will definitely come because they don't know that person. And if I can rearrange the shift in a manner where now it becomes an operation, just like a Lego box for me, I I can pick something, put there, my minds will always run. And I took the same principle concepts and started building different frameworks. When we envisioned Maya Maya, I took few of the philosophies and the principles, which was very, uh, theory principles, which little bit data to back it. I just started morphing it in here. And then we were, I think I was able to create a system with which we can be much more in human potential understanding. And at the end of the day, I have learned only one thing in my decade-long career, that it doesn't matter what you know, what you don't know, but if you know how to deal with people, you will be, you will be able to survive your life, because at the end of the day, it's people. Be it in job interview, be it for promotion, be it for funding, be it when you are hiring, when you're firing, it is all about people, nothing else.

So great.

That's so great.

Uh, so looking 3 to 5 years out for the organization, and Catenate has become super successful, Right. What would you say is the measurement of, wow, it is successful, it has changed people's lives? Like, how are you, how are you assessing that? What does that look like for you? Right.

So one of the vectors that we are seeing is how many people companies, companies are able to retain, right? How many people are they able to retain? That's one of the metrics. And the second metrics that we usually look is how quickly they can internally mobilize their people. So if there are like— if there is a 500-people organization and they want to shut down the accounts department, what they can do with all the accountants. Did they fire them off, or were they able to upskill them to a role where now the accountants are able to do something in the organization without ever leaving the organization? So those are the two metrics which is very much important for Catenet. One is we are able to reduce the attrition rate. Healthcare has the highest attrition rate right now where like I think 12 or 13% is the attrition rate, which is out of every 100 people, 13 people leave the organization at the end of year one. So are we able to reduce that by helping the organization understand who they are and why they are leaving. Is there any pattern on the people who leave the organization? And the second is internal mobility, that we want to ensure that companies don't have to go and fire rather than internally mobilize. And the one more thing that we are doing and started doing is, can your talent be transferred from one organization to another? That I have a pack of 100 people and I'm going to shut my business, business is shut down. Can I take that whole 100-pack and place it somewhere else where there might be a whole new organization that is being developed into a new business? So the earlier two are the major metrics, but third is the, the wish list that we got. In 5 years, if we are able to do that, we'll call ourselves successful.

Amazing, amazing. So We're coming to the end of our time together. It was a fast hour, wasn't it? Fast time. So here's, here's, here's the question: is, um, was there something that you were hoping I would ask you that we didn't talk about? And so if you have last kind of closing thoughts, and then how would you like people to reach out to you if they wanted to find you?

I think, I think you covered pretty much everything that we wanted to on the spectrum. I would like to just close it on a thought that you have to be very cautious on what you're reading on internet, and you have to develop your own conscience also, which is you have to think on what you're reading. You don't have to believe everything at face value. That's the only way you survive the AI world. Otherwise, uh, you will become replaceable. People often come and debate that, uh, people who know AI or who know, who know how to use AI to do the job will never be replaceable. That's not the truth. Uh, the truth is how much you are able to retrieve the information and actually make sense of the information and figure out the truth. That's what will make you stay at your job level or job role or in the Current AI World. And, uh, I am pretty easy to find on LinkedIn. Go find me on LinkedIn, that's where, uh, you can find me, and I'm pretty active there.

That's great, you are pretty active there. Okay, so for, for those that would love to reach out to Sameer, Sameer, S-A-M-E-E-R, Ranjan, R-A-N-J-A-N, on LinkedIn. He's amazing, and Thank you. I'm so glad we had this, we had this time together. This was really great. Thanks.

Thank you, Dr. Wayne Pernell. Like, it, it was amazing. It was a great conversation. I think I was able to— I, I didn't thought, like, again, as you said, like, I didn't know what the questions will be, but I actually, it was a self-reflecting podcast for me because when I was speaking a lot of things, lot of vision, lot of things that I believe in, it actually reinforcement reinforced the beliefs that I had. So thank you so much for this.

Oh, you're welcome. Thank you for being here. That's— and, and you just basically gave words to my intent, which is, right, when I ask questions, it's not— it's right, it's, it's about you becoming more of a human in your own world and giving thought to it and then sharing that with us. So again, I appreciate it.

Cool.

Alrighty, well, this is One Sharp Sword, cutting through to what matters most. My guest today, Sameer Ranjan, and I am Dr. P, Dr. Wayne Pernell. If you appreciated this podcast, please like it, please share it, and please come on back. We'll see you here next time on One Sharp Sword. Thanks. Keep making your magic. Thanks for listening to One Sharp Sword, cutting through to what matters most with Outfluencer Dr. Wayne Pernell. For more information, please go to onesharpsword.com.