 I welcome all of you to our next panel, it's on technology, transforming enterprises and we have an esteemed set of panelists. Can we have a brief introduction? Sure, hello. Thanks for hosting us for you. So my name is Harish, I'm the Senior VP of Engineering at Echo Technologies. Hi everyone, I'm Aditya, I'm the founder and CEO for Central Ethics, we're a product of this company, so hopefully we'll talk about tech today. Good afternoon, I'm Jagannathan, Associate Vice President from Hirachi Systems, I appreciate you for being here, we are standing between you and me. Alright, I'll begin with a common question to all of you, we are seeing technologies, Blockchain, AI, ML, how these technologies are reshaping traditional business models and becoming a core requirement for businesses' deaches. I'll answer this in two parts, what we are doing internally in tech systems and what we are doing for our customers. When we look at this journey of multiple new technologies right from cloud and all that, obviously right balanced decision has to be taken. So we kind of looked at it and then looked at which are the ones that has to do with cloud. Those that were customer facing was decided to be taken to cloud, it makes sense for the simple fact that scalability was a requirement, reliability was one and availability. And then when it came to internal, again there was kind of study done to find out which are the ones that are across the organization, across users, which are departmental ones, stuff like that. So then whatever was company-wide gain in the cloud, so either SaaS based or a cloud-based product once, and whatever was more departmental, one-to-use, we are still in, are still in rather on-premise. So we were able to achieve a combination of adoption of new technology and the existing ones, thereby whatever is needed for business and from cost we were able to achieve. Now coming to the same side of the other technologies what we are doing for customers, because Hitachi brings in both combination of IT and OT, we have more than a century-old conglomerate, both in IT and OT. So for a state police force what we are doing is helping them to identify suspicious objects and also to predict the protests. Why this becomes important is, especially a protest one, if computer vision is picking up a set of data from metro entrance or exit, that is not a protest. There has to be a better way of really knowing which is a protest. That is something that is built by the AI-MN algorithm. That is something that we are kind of doing. Second stuff when it comes to IOT related because AI-MN is more on vibration analytics. What you are doing there is helping a lot of municipal corporations which run with water and also the garbage trucks. Somewhere we have to find out the optimum level of weight being carried by these. That is something that we are doing when it comes to IOT related, that we liberate on sensors a lot. Those are things that we are doing. I think technology has become a key enabler to scale. I believe if you are running a small setup, I will take the example of a retail store. You do not need a lot of technology per se or you could not have a lot of technology per se because let us say you want to build your own POS system. It is not post-effective. If you want to do your own inventory management, it is not post-effective. You are forced to use non-digital methods from your ledgers to your Excel to your pieces of paper. What happens is when you have a POS being built centrally by a company out there and it is being sold to millions of customers, automatically it becomes much more cost-effective. That same retail store can not only procure that technology and that application at a sustainable cost but he or she can now think about opening three more stores so that the core business is very well digitally enabled. I think technology has become the key enabler behind anything that has to scale. Beyond technology, there is no scale. To me, that is what it has done to modern-day businesses. Harish, I would ask you, do you want to answer this? Partially, yes. I think you have asked a question at three or four parts. One was blockchain, IoT, third was AI, what is the MF party? So as an insurer, again, people read a lot into IoT. It is very device-intensive. People talk about, for example, vehicle analytics, etc. But there is a practical angle to building technology. I think we created to forget that. People generally jump into a technology bandwagon because it is new, because it is shiny. For example, there are a lot of proxies that we have to consider when building a particular technology. Every day it has to make sense to the masses. So we have to follow where the customers are going and not because there is a technology. It is not like you have a nail and you are looking for a wall to put it in. So, for example, we did a lot of data before you can even imagine an ML project. Data is extremely, extremely important. But today we do, for example, dynamic pricing for automobiles or say biking cars. For that, you need another amount of data to understand all the nuances of a particular customer to be able to give them the right price depending on multiple factors. Age of the vehicle, where you are driving, how long has it been? Are you the first owner, second owner? What has been your previous name, etc. etc. This is an enormous amount of data that does go away before you can even attempt it. Similarly, in IoT for example, there are proxies on behavior that can actually tell you without the device itself. So do you live, for example, in an area where there is a high probability of floods? You don't need an IoT device to tell you that. There is demographic data, there is geographical data that actually tell you that. Similarly, blockchain also, for example, you need to know whether that non-demodation kind of a record keeping is really relevant for you or not, whether you can afford the cost of having such a system. Whether that particular record keeping system needs to be decentralized or not. So I believe that in the context of how businesses are shaping up in India, probably AI ML will take a leap because of course there is an option of other technologies too. But here because in India we generated a lot of data, simply because there is enough population which is transacting, interacting, our internet consumer base is enormously high. So I think companies will leverage, have to leverage that amount of data. Of course they need to do good by the customer and not use the data for what it is not intended to do. I think that amount of responsibility has to be with the companies when they collect their particular data. But I think there is enough quantum of data, there is enough threshold where these two technologies I believe will be adopted to much larger scale than where we are today. If we take linguistics itself, for example, you cannot have English as one solve for everybody. There are at least 25 major languages that are spoken in India. So can there be a LLM model that can work, say for example for Kannada, Marathi and Telugu together? It's a very difficult question to ask, so a difficult question to also answer. But I think we generate enough data where for example the grammar for all these languages is almost the same. So investing here and as industries move towards using this, I think India is very pivotal role to play and I believe that companies will invest more into it. For example, Aqua is investing into this. I think more companies will invest. That is the area that people, entrepreneurs especially have to keep an eye on what's happening there. So that means businesses need to adopt these technologies. What's your thought about, you know, do these technologies bring efficiencies and business optimization? Absolutely, right? I mean the paradox is that sometimes people think technology will replace people. I don't think that at all. I think people work with people, for people, alongside people. So I don't see how technology replaces people in any way, but I see them becoming more and more efficient. So if somebody was doing something in a day to day, technology will help us to do it in six hours, then four hours, then maybe one minute. But that doesn't replace the people element at all. So it's all about efficiencies, optimizations, specialization. What technology will force us to do, it will force us to become experts in individual fields, right? It will force us to get very core domain oriented that this is what I know, this is what I do and nobody does this better than I can, right? So that by default creates a lot of other challenges. How do companies, you know, operationally higher roles that may not have, you know, that much capacity planning needed? Or how do you mitigate the risk of these technologies sort of becoming expensive or going overboard as far as their usage is concerned? So there are other risks that come with it for sure, but at its core, all that any technology can do is bring efficiency. It cannot actually replace the outcome. I will take that. It's a very relevant important question, important question to answer right now, right? So there are three parts to this answer. Part number one is technology bringing in efficiency. The answer to that is you have to ask where is it bringing efficiency. Number two does this, allow us to do something which was not possible before. That's the second part to that answer. The third part of this is the human factor to this. So maybe I'll answer them in three parts. So the first part to this course is, yes, it brings in efficiency. Efficiency means using less people to do the same kind of job. It's not just people, it's also less systems. For example, what an AS 400 did 20 years ago, today a mobile phone does it. It means that the mobile phone replaces an AS 400 to a large degree, right? So it's not just people that is getting, that is affected. It's also the system of the kind of systems that we build. And I can give an example, simply because today if you want, if you're a D2C brand, right? Where you're there to consume a brand for insurance, people come directly to us. Our product is the face of, or to the customer. We want our product to build that kind of trust with the customer, right? Because there's no identity between who's selling the product to you. You come to, you look at Acqua and say, hey, here do I trust this insurance company with my money? Will they do good by me whenever the need arises? So building trust in this particular system, how does technology help us? How can we leverage it, right? If you look at, for example, buying a health insurance, how do we ensure that the customer is guided appropriately? The customer is here, the customer probably doesn't understand much on the nuances of buying an insurance. How do you guide the customer in a way that you tell them, hey, do we get the right kind of information? Do we take the customer through the right sales motion where they understand what they need? Do I need a X versus a Y kind of an insurance amount? Hey, what about my dependents? My parents are living somewhere else outside. What is the right kind of a construct for me? Typically, this can only be solved by technology, right? Understanding where you are, your customer context, gathering the right kind of data, and solving for it in real time, right? Somebody could say, hey, am I okay? But hey, I'm diabetic, for example, right? So what happens? Can the system automatically understand the context and switch to a solution that is good for the particular customer? This is bringing efficiency. In the older days, this used to be a 15-day process. Somebody applies, the clerk takes it, does everything else, goes through some medical test, that is about another 10 days, comes back, looks at it and says, oh, this HBA1C, oh, this is very high, goes back again, reproposes the customer, customer comes back again. So this used to be a 15-day process. Today, on our side of the app, this is a 15-minute process. This is bringing efficiency, absolutely. But what is it solving for? It's actually solving for the customer. It's not solving for the office that does it. The office exactly does the same things today. The amount of risk that an insurer carries is the same. That's not changing. What is changing is that the consumer can make that decision right now. If you buy life insurance, for example, which we launched about 15 years ago. So how do I know, how much color do I need? Am I overinsured? Am I paying too much money? Or am I underinsured? You know that the quality of life of my family will change just because I didn't know how much to buy. So how do we ensure that the customer is guided appropriately? There are systems today that are built and technology is the major player here. We use ML knowledge today, for example, to suggest the right kind of cover for the particular customer. That's answering the efficiency question. Coming to the other side of the efficiency question is in the service motion. You always think about sales, sales, sales, right? Efficiency, no. There's a lot of efficiency to be built even in the service motion. Service motion means when a customer comes and comes for claims, for example. Think about a scenario where you're in the hospital, somebody is undergoing a surgery, and then you're at a desk saying, hey, I have insurance, you know, blah. Today that is, there's not a friction in the system. You go to the desk, you give it, you call the doctor, you call the insurer, you'll say he's discovered, not covered, blah. Somebody will ask no questions. You'll say, hey, that report is missing. How do we, for example, do auto-adjudication today? It's a very large problem to solve. A lot of companies are solving it, including ACO. So how do we ensure that the document submitted by the customer on the spot is analyzed by systems today? We use, for example, AI systems to understand, hey, you applied, for example, for rapid disease operation. Is that being done? Is that the treatment? You know, is that right kind of medicines given to you? Can I tell you at the desk, and even on the human desk at some point in time, right? To say, hey, just come tell me what has happened. Here are the five documents. Send it to us, and then we'll tell you what has happened. Today we do instant take management, which is in a matter of minutes. We're at the hospital not trying to work with the insurer. We're at the hospital trying to get somebody to get well soon. So how can we solve for the customer? I think that's the key angle to look at when you talk about all of these technologies. Not for the sake of, okay, it's there. Let's build something shiny on top of it because it solves a particular customer problem. So auto-adjudication is a very big example of what we're working. I think similar systems, not only in insurance, in full type, there's going to be a lot of dispersion, right? It could be something else. How I think companies will invest, have to invest, and solve for the customer, you know, consumer first. The third angle to this is the people angle that we talked about, right? There's a popular saying going on nowadays to say, the calculator didn't replace the accountant. It replaced the accountant who didn't know how to use it, right? So I think we should apply that sort of a preventable thing across the industry today. Anything in your system that will guide people, for example, to do their job better. If you look at, for example, co-pilot, I don't know if there are engineers here in the room and in the news day, what you should take about, say, on an average, three hours to build a service. To build something you don't figure out, oh, this data model, what do we do? Goes to the stack workflow, looks at something else, comes back, you know, go get something else, looks at some more big repo. Today that entire three-hour process has come down to about ten minutes. You have had co-pilot, you know, you're checking your repo. The system understands what you're coding. It'll tell you, hey, this structure, you know, you have to use this. Oh, we're trying to make a service called the boilerplate code is here. So people who know what to do do it very efficiently. So I think it's not going to replace people. I think we need a large retraining philosophy in the industry. And if you look at the 80s where computerization itself, you know, that's a big thing. People move from offline accounting to online accounting. We have this entire, I don't know, the generation that was data operators, right? I don't know how to use a computer, a data operator. I think that generation saw that shift. From there, then we moved to, for example, cloud where people said, oh, I don't have an on-premise. How do I spin up, for example, something else? How do I talk to a land or AWS, for example, right? That's the people shift. The third most important shift I think right now is going to happen is where tools, technologies will get built on top of these very large and powerful, you know, AI models. People have to adapt to it. So retraining organization is needed. You know, the people need to think differently. Management needs to think differently. They need to have a long-term investment to make it happen. I think we have to look at this in three phases of both efficiency and the code, the people angle to it. And in those cases that are getting built on top of it, that is not possible. Jagannath, you want to add something? Primarily what my take is obviously adding to what Harish and the judgment told us, it directly contributes to customer side of things where it could be on our experiences or for a company to get more revenues and profitability. Second side is on operations. Again, more primary focus on internal side of things, bringing in that efficiency. Can I do things faster? Can I automate all of this? I only had about 10% of decision making to a person. A 80% of my system would automate and give that what is needed for the person to take decision. The third element is on the risk. How much can I reduce the risk on certain things? Especially this comes into play on, say, fraud analytics, especially for banks. Is this transaction genuine based on the location, based on the person's profile and all that? I think these are the areas that directly impact the business. That's what I think. All right, we talked about the benefits these technologies bring. What are the challenges you think these technologies have while implementing? I think the biggest challenge that I see is our ability to unlearn and relearn these new technologies. I think that you touched upon it as well. It's about knowing how to use the calculator. I was having this interesting conversation where somebody was saying that, now the new generation might not even know how to do tables. The point is they don't need to. That's the basic difference that comes generation after generation is you may not need to know things because it's completely outdated, completely redundant. One biggest challenge will be how do we keep up with the change and unlearn and relearn how to do the same thing, but just a different way. The second major challenge that we see is obviously the risks that technologies bring. There's a lot of conversation around security. There's a lot of conversation around cost. There's a lot of conversations around performance. At the end of the day, there are always two sides to the same coin. We always have to make sure that the good will be more than the bad out of it. Obviously, it also creates opportunity because if we create some problems, somebody else will come and solve those problems. Certainly, it's going to be a new world. I think the best part is if we add AI plus IoT plus 5G together, I just can't imagine that I feel like this generation will see Iron Man as become a reality. VR is going to become real. AR is going to become real. We just have to be ready to accept it. I think that's the biggest challenge is our own ability to accept that reality and be willing to move into that reality is how I would see it. My take is broadly when the company itself is founded. Second is what is the kind of digital trust that particular organization has? Third element is what is the level of technical data it can? So, combination of these grows up as a challenge. Now, to make it as a simple analogy, we can relate easily with home users. We can categorize them into three. One is a landline user. Maybe our fathers and grandfathers. Second is the basic feature phone era where there are only buttons for that. And third is the smartphone era. If you look at what is happening around us, our kits are for super-duper in the way they are using smart phones, sometimes better than what we are doing. So, that is a better level of adoption. Relating that to a start-up which kind of came in a couple of years back, for less than five years, ten years, they take up these technologies really well. The second set is the basic feature phone people. They are slowly moving to the smartphone adoption. They do what is essential for them in a smartphone, such as organizations that are trying a blend of technology. They still carry some legacy systems and then they kind of modernize some set of applications and then pick up new ones. They are in that journey. And the landline kind of category people, there are still companies that are a legacy system. They struggle because the technical that is so high for them, they are struggling to move to the next level. That would be a hard journey for them. But with the kind of benefits that they see around, they would take a step forward somewhere, break the jinx and take the step forward. When we say challenges, we should look at it as an opportunity. Of course, every opportunity has a challenge. You have to adapt to it. So, if you look at the entire industry, there are companies that are well established. For them, it's a matter of lack of better word survival. There are companies that are already there, not too old. For them, it's a matter of growth. And the third kind of companies are companies that have not been bombed yet. For real, it's an opportunity. So, if you look at it, if you look at the challenge, if you look at it that way, I would use the word opportunity rather than challenge for all these three. For example, today, like I said, if you consider a company that does not adapt to any of these technologies, one or more of them, which is relevant to that particular industry, it becomes a matter of survival for them. Let's say there is a company that still does a lot of manual processing, where you have to go to the customer, come to the customer, do it. They would find a lot of trouble, you know, getting up with companies that don't do it. Today, for example, people use EKRC. I could actually use EKRC. It gets done in less than a minute if you have the documents with you, right? Imagine that versus somebody who says, I'm going to send somebody home, then you have to call him up, then he will come, then I get entry, then he comes in, blah, the entire motion around that is going to be so frictional, right? So companies that have to look at transforming using this technology, it could be AI, it could be IoT, it could be whatever, right? Even cloud companies, a lot of companies are not even there yet, right? So for them, it's a generational shift. So for those companies, the challenges are going to be around how does their core business get protected by shifting to these technologies. They don't do it, it's a matter of survival for them. They will have to, however big or small the company is, they will end up, you know, they will have to do this. Today, there are kiosks, you know, there's one in Bangalore, one in Hyderabad also, where you could just walk into the kiosks, kiosk it by yourself, there is a prick that it does, and it gives you your health report in about half an hour. Imagine that versus a traditional, say, diagnostic centre. They have to go in the morning, fasting, sit there, you know, do something, then go back, eat your idli, come back, sit there again, some doctor will come, whatever. So one day process. So that industry, for example, would be very easily disrupted by this. Why? Because for them, it's a matter of saying, hey, how can I shift to this particular technology slash platforms, slash tools as they come up with. So part one is there is a challenge for companies where it's a matter of survival, right? They have to do this transformation on the shift, there's no other way out for that. The second piece is for companies that need growth. Simple example, today SEO, everybody who does DTC knows what SEO is, right? Essentially, ensuring Google that you're on the top. SEO has always changed drastically with generational layout. Today, companies can spin up, I don't know, enormous amount of SEO content pages in a matter of minutes and you don't need content writers to sit there and try to bring your company on to the top. That's a matter of growth for companies that are doing DTC today, right? So for them, you need to understand and leverage this. Google itself has to make changes to ensure that this, lack of better word, robot-generated content, you know, is not putting companies that really don't do anything on top of it, right? So both the infrastructure players and the content players have to change. So here it's a matter of growth. Why DTC is becoming much larger? Because customers trust these brands right now. So today, I mean, 15 years ago, you couldn't imagine a person saying, some website, pay 1 lakh rupees, they said you're firm home. I don't think anybody in the audience would have done that. But today people buy like an iPhone on Flipkart and Amazon. You trust them to say, OK, I gave 1 lakh rupees. I'm sure today's the time to get the firm. But trust on this infrastructure is moving. Basically infrastructure is clear by technology, right? So that trust, it's a matter of growth for companies that are already there. But the biggest opportunity slash challenge for me is companies that are not being born yet, right? Like nobody thought about e-commerce 20 years ago, right? Nobody thought about, you know, 50 things online. Nobody thought about Zepto, for example, right? I'm sitting at home, OK, I'll do something. I do it by the time I'm doing the Tharka, it's already here. So that quick comes whatever. It's happening everywhere. Of course, insurance industries also, for example. Like for example, in terms of back up today, I know people where you look at the police, or you figure out your insurance is expired, they stop your car by the insurance engender. That would not have been possible 15 to 20 years ago. So these companies will be born who will leverage this particular technology, right? And solve these cases that are not being seen yet. So if you look at it as a challenge, yes, the challenge is to reach to an opportunity and it will involve rescaling of people. It will involve rethinking of systems, right? For example, edge computing with AI will change a lot. Like Apple is doing today. You can do a lot of what you did on the phone for which you needed a Photoshop 10 years ago, right? So that technology has moved from being heavily compute intensive or to being on the edge. So that chip has happened, right? So I think we'll all sort of get there. So we need to look at the challenge, which is the challenge to get to that particular opportunity. I think it will work in all these three things. New entrepreneurs have to make that feeling process. Get it right. Look at where they need to help for survival. Companies will get built there. Where they need to, for example, look at pooling growth and where, for example, to build something new that's not existing so far. All right. We are short of time, so we'll take some questions. One or two audience. Hi. Good afternoon. Thanks for the information that's shared. As you rightly suggested, companies which are not yet born, maybe startups have a definite potential to change whatever is not seen. For example, AWS in India is not well known. Also, EKYC, which Echo has bought. So I am also trying to do similar to EKYC where you flash your Aadhar card using the optical card reader. You can easily do your EKYC. Why there are so much companies not coming forward to these kind of technologies implemented as a start-up? Any suggestions from your end? How we as startups can think over these kind of innovative ideas? I don't know if the question is for me. Yeah, sure. Thank you. I'll let others also opinion on this. See, it's a, I mean, if you're in a regulated environment, like insurance, fintech, banking, EKYC, where you hunt a lot of PIA data, personal data, there is a particular friction to build a start-up, because you need to be compliant on 50 different things. You need to ensure that you store the data right. You need to understand, for example, how to store, especially working with Aadhar, for instance, how to store the data, how to transmit the data securely, how to keep it. You need a amount of depth to build companies that specifically are working in that area very pointedly that you're asking, EKYC, a lot of companies who are doing this, for example, right? So not just KYC, if you look at, say, existence check, as we call it today, how do I ensure that you're living? If you're buying a life policy, I need to be sure that you're living, right, so that you don't fraud me. So I will ask the customer to blink. I'll ask the customer to say a sentence. So companies are getting building there. I think it's a matter of race. It's a matter of somebody committing to this. Startup has to commit to this. You see, I'm going to do this. Study the market. And then look at the technology available to it and build it. It's not really difficult to build it. I'm assuming it's just a matter of commitment that somebody has to put in, right? And if you need a longer conversation, I don't know, you could talk offline. But to sum it up, you need commitment to do this. It's a regulated field, so you need to understand the regulations very well. You need to have the depth to do that. Number three is you're handling very sensitive customer data. You're handling other data, you're handling, for example, name, address, email, phone number, maybe credit card, whatever, et cetera. So you need to really do good by there. Number three is you need to know what technology to leverage. See, tech is not the solve. The solve is just calling for the KYC of the customer. The access somebody says, my other is not visible. The photo is not visible at all. What do you do? So you can't say, oh, sorry, this tech is not working now. I can't do a CR reader. So I'll send somebody home. Then you're using the plot, right? So how do you know how far you can push a technology to make that use case happen? I think that's critical. So I think there's an opportunity. A lot of companies are doing this. They're doing this. Fantastic. I appreciate that. Hello. We're short of time. One question. All right. My name is Virat Shetty. I'm so sorry. My name is Virat Shetty. I'm from prevalent automotive. Wishing everyone a happy woman's day and also happy Shetty. So my question is to Mr. Aditya. Sir, what would be the scope of technological advancement in software like ERP or management software where we can increase the production rates and also monitor on the transportation dispatch and also the production lines? That's a great question. I've actually been thinking about this because internally we have like seven different management softwares from CRM, ERPs, HRMS, ITSM. And one day I was thinking, what the hell I mean all these management softwares are basically there for people to punch data instead of working. And I was actually thinking what would be the solution to this. And I think the answer lies in completely reversing the story. So co-pilots and GPTs are great examples where I don't think these systems will go away but I see some type of an interface that sits on top which is sort of like my partner telling me what am I supposed to do. So imagine as a sales guy instead of me punching in opportunities every day the system is prompting me that, hey, you're supposed to follow up with this customer. Imagine if I'm able to write my notes there and it's recommending to me that why don't you go ahead and follow up with them on these days I'll put an actionable for you on your calendar. So I completely see these management systems completely transforming into partnerships in the process. And I think that's a key area of opportunity that you see because if you look at these companies they're all really, really in the legacy phase as Harish mentioned. Now that can become an opportunity for them and they are investing. Salesforce has put together a fantastic AIT, I know SAP is investing in that direction but it also becomes an opportunity for the not yet gone startups because if somebody can come and disrupt that space completely pivot the way we work, right? Make it not a management system where I'm creating and punching data for my bosses to see but more of an enablement system that actually lets me do my job while creating the data that my management wants. That will completely flip the use case. Today I think the hardest adoption is of these management systems because everybody sees it as an annoyance. I'm not getting any help back. I'm just punching data so that somebody can create a report that can then go to somebody in the process. It doesn't really add value back to me the person who's actually using that system. So that's to me a very key gap that I would love to see a startup come in and actually fulfill.