 Hello and welcome to theCUBE. We're here at Google Next 2023. We're breaking down the news that's going on and really exploring Google's ecosystem. And today I'm really excited to be here with TCS. I have two fantastic guests that are going to help me break this down. I have Nidhi Trivastava and who's the vice president and global head of Google business unit TCS. That's unique, having a global business unit in your company dedicated to Google and everything they do. And then Anupam Sinkhal who's the president of manufacturing at TCS who's with us as well. And we're going to really explore how this ecosystem is changing, how they're working with Google and some of the information that they've been collecting from the thousands and thousands of customers that they work with on a daily basis and help them move to a cloud first and a cloud agile system so that they can get more out of their experience with Google. So welcome both and thank you for joining me. Thank you. It's a pleasure to be here. Great, well, let's dive in. Nidhi, I think everybody knows that data is really making companies go today. It really is about the digital experiences and every company these days really has to understand and derive and act on insights almost instantaneously out of that data. What are you really seeing or some of the pressures that companies are under to get to that data faster? So a very relevant question in today's times because data and AI are the two most spoken about words. And especially when we think of our customers here is what we hear from them that while everybody is poised to write the wave of AI it is getting the organizations ready from a data perspective that is very, very important. And what are customers dealing with? They're dealing with organizational silos. They're dealing with data integration issues. They're dealing with data standardization issues and most importantly, data accuracy. So that's something that the customers are dealing most with and to be able to address that what we bring to the table is our advisory and support on how to make the data more available, more accessible and importantly ethical because that's something that is really top of mind as the data and the AI wave really rides the momentum. Definitely, I think that makes a lot of sense and a lot of the companies that I'm talking with they're trying to figure this out and trying to understand what is not only the ROI from data but how that they can use the data and the ethical and they also don't want it to leak and no leakage as well, that's security of it. So Anupam is manufacturing and ahead of other verticals where does it stand within this in some of those concerns in harmonizing and validating the data? To the way I look at manufacturing is unique because in manufacturing, as you would look at it, it's a lot of operational technology and a lot of information technology. It's marrying the two of the IT and OT which creates a very unique environment where you can connect the shop floor to top floor. So what it means is there are a lot of data which is being created on the shop floor which could be sensor data, it could be a machine data, it could be data coming from all the production line and then you would have information technology where a lot of your sourcing systems, your ERPs, your B2B, B2C systems would be there. The ability to connect both IT and OT and creating that resiliency in the whole supply chain creates a very unique perspective in the manufacturing world. Other sectors also have a lot of data and the problem which many of the sector deal with is siloing of the data which need to be talked about. So it's our ability to connect this data and create that meaningful insight where all the personas in the manufacturing ecosystem is going to create the differentiation. How do they get the information at the right time? In case there is a defect in the production, how do you ensure that the defect is captured right at the beginning of this whole process and the process line stop? Because if that keeps on going, you're going to have much more defect, much more wastage. So from the practicality perspective, manufacturing has a very unique view where is the multitude of data which is internal to the organization which is coming in. And if you can throw in some generative data, some large language model, I think the opportunity in the manufacturing world is going to be very, very different and what perhaps could be done in other segments. No, that totally makes sense. I think that manufacturing really does have edge. I think that in edge cloud and near edge, far edge and if it's down, you're really out of luck. And I think so that, and also talk to companies and they want to do inference out the edge and they have all, like you said, all the sensor data. So it's really interesting. But Nidhi, how is TCS enabling companies to make sense of the growing amounts of data to drive actionable insights? I mean, more at a higher level. I think that this has to be something that's on the tops of their minds all the time. Yeah, and the way we support our clients is through a set of intellectual assets and accelerators that we bring to the table that speeds up the time to value. For example, we have an offering called TCS data that does assessment of the maturity of data and gives actionable insights and a roadmap to drive data modernization. There is another offering of ours. It's a platform offering called DeXam which is the data exchange marketplace which we offer on Google Cloud. And what it allows us to do is build data marketplaces for our clients because we are moving to a world where there will be data products that will be available on marketplaces and people can buy data. There can be commercial, contractual agreement under which you buy and sell data while meeting the needs of security, privacy, regulation. And this is going to be very critical because we are moving to a world of ecosystems. And in ecosystems, the ability to share data and data products will be very critical. So that's the new normal of the digital world. And the wave of AI makes these data marketplaces more and more relevant. So our DeXam offering is also something that's very relevant. And we've done some nation building engagements with that offering. So along with it, we also bring in a lot of talent, a lot of depth of experience in the data and analytics space. Yeah, that makes total sense. I think when you look at it, data clean rooms was kind of all the rage last year when people were talking about it. But it's the monetization and coming up with the business case that helps them actually get ROI back from that data. And sometimes, like you said, even being able to sell the data in the proper way with the security and compliance and all of that around them. Anupam, you kind of referenced that other verticals may have to do some catching up or there's some stuff that manufacturing had to go through that they may be able to apply. What are some of those things that other verticals can learn from manufacturing? I think manufacturing being, many times as you would realize, historically, manufacturing has always been ahead because if you look at the whole outsourcing, if you look at the whole componentization, if you look at the whole globalization, I think everything started with manufacturing. So that way, many times, manufacturing takes a leap. And sometimes, if not all the time, manufacturing does have an impact on human lives directly if there is something wrong. So if you're driving a car, if you're flying in a plane, you want to make sure that they are 100% tested, 100% there. So the importance of data is extremely, extremely high in manufacturing because if you are having a life-saving equipment, if you have your MRI machine, if you have any machine which could be in your hospital, you want to make sure everything as per the specification, as per the testing done. And the problem comes down to how many test scenarios you can check. And I think that's where artificial intelligence, machine learning and on top of it, which already AI, it can create multiple scenario testing, which perhaps you may not be even to physically do and create a product which is safe from all perspective or all possible scenarios, which one can think of a much one or one which one can calculate of. So that way, I think manufacturing takes a little bit more importance as far as you look at the power of AI ML as compared to other, but that doesn't mean that others because in the financial services as well, the whole artificial intelligence looking in the whole cyber threat, I think it's playing extremely important role to keep us safe from financial fraud or your identity theft. So I think AI is the core to anything and everything which we do right now and is there to protect. And I think that's where they play a very important role. Totally makes sense and I think you're right. I think it's funny how different industries have to experience things in different ways based on, especially as you said, safety, you have to be able, you don't want the car to go off the road or have certain failures in that system. And I think that's really clear. And I think part of it is that, you can't do this stuff in the old ways as well. And I think that, Nidhi, why don't you help us understand what you're seeing because like cloud, I mean, you're with Google cloud business unit and you're running that and again, that's unique. But how is it that cloud has been kind of that unifying fabric for people when they're trying, or companies when they're trying to go and deal with data intensive technologies, I mean, again, you have BigQuery, you have a number of other different technologies we're gonna hear about or we've been hearing about here at Google Next around Vertex AI and others that are probably as part of your portfolio. But how do you see companies or how do you help companies when they're looking at the investments that they're making? Are they heavily slanted towards AI ML technologies or how do you counsel them on that? So that's a great question. What we're seeing is that companies are they made the move to the cloud in a very real and significant way post the pandemic. So pre-pandemic, even cloud was living in the world of POCs and some scattered migration of data and people were just looking to be convinced on the security aspect as well. But the pandemic proved the business case of cloud and how and we've seen cloud adoption really pick up across all our sectors from an industry perspective and regionally too. So now what the cloud wave did was that it enabled the move of the data to the cloud and in that, not only did it deliver on the efficiency, the cost efficiency but it also set the path for data modernization. And so what we've seen is that in a study that we did very recently on cloud, it's called the TCS Global Study on Cloud. I'll encourage people to check it out. More than 75% of clients are making increasing investments on AI and they're also continuing to make investments on data modernization. So whether it is in terms of setting up centers of excellence, upskilling, we continue to see our clients make very significant investments and this is going to just continue. I think AI is at that point where there is tremendous amount of fascination with the possibilities of what it can do. There is a lot of testing and experimentation at play right now with respect to AI and that is where AI is actually happening. And it's also because platforms like Vertex AI, they make it easier for organizations to be able to take their data, build the machine learning model and begin to test it, measure, learn from it. So you can go through a series of launch and learn cycles in rapid sprints with platforms like Vertex AI. So in that sense, cloud is indeed the unifying digital fabric. That's super interesting and I think that again, that study sounds super interesting and I'm gonna definitely take a look at it after this session because I think there's so much people can learn from other industries and others that are going through this. In fact, Anupam, why don't we kind of, as we get towards the tail end of this interview, let's let you help people understand how can TCS and its framework really help organizations across industries, not just in manufacturing, I think you see broader than that and TCS definitely does. I mean, what's just thousands and thousands of customers across all different industries, how can they help with kind of that data-centric digital transformation? When they're trying to understand and I think Nidhi kind of said this and you've said this in a couple of different ways, is like they're trying to figure out what is the ROI of this data? How do they make money off this data? Because that's why companies are in business. What are some of the pieces or how do they engage with the framework that so they can get started with that? So Rob, interestingly, data is no more a commodity which is lying somewhere. It is the asset. And what it comes down to, how do you monetize the data which you have in your organization for your own good and maybe sometimes the good for the community as well. And that's where when we look at the manufacturing, we call it neural manufacturing. What does neural manufacturing mean? If you look at the whole lifecycle in the manufacturing, it can start from sourcing, it can start from sourcing of the raw material. Before that, you may have a design. Then you are sourcing, then you are making the production or you're doing the manufacturing. Then you do commercial, you go and sell your product and then you do after service. If you look at the whole cycle and the production or the lifecycle, there are a lot of data which is moving. How do you put each one of them in a thread so that once you're designing a product and once you go through the whole process, in case you find something while manufacturing that the design tolerance which you had given are not, you're not able to produce that how can you go back to your drying board, make those changes. So the ability to put a thread into the neural base or something what we call a neural manufacturing and what it becomes from the AI perspective that anything which you do, it should be able to explain that what you're doing, what you're doing. Because if in AI model, you are building on the basis of the past data, if there's a bias in the data, that bias is gonna be showing in your predictive modeling as well. So how do you create artificial intelligence which is explainable, which is ethical wherein you don't allow people to put any bias, whether it could be a sex, it could be a color, it could be nationality, it could be orientation. So how do you ensure that the whole AI model is ethical and how does it create a product which delivers a complete ease of use? It creates a superior customer experience and if it can be conical, which can take out the waste, I think there's gonna be the four dimension which we believe is gonna be extremely important for artificial intelligence. So if you look at these four dimension, in my view, they are relevant for any sector, whether it's a manufacturing or the life sense or whether it's healthcare, whether it's a banking financials or insurance, I think all of them are gonna create extremely important role. And as I always believe that technology doesn't deliver its full purpose, it cannot improve the life of the common citizen on the road. And I believe AI brings that purpose to all the organization where they can use this data, create artificial intelligence modeling and provide that services to many underserved or unserved at a fraction of price watch, what will they do to their wealthier customers? So I think there's an opportunity which AI brings to most of the firms to go beyond their commercial to become more purpose-led. Totally agree. I think you hit on it, and especially in manufacturing side, I don't want my sensors having bias. But I digress. But from Nidhi, why don't you help bring us home here and help the audience understand how they can really leverage TCS's experience and data and AI, especially on Google Cloud, how do they get involved and really understand and build greater resilience, enhanced customer experience and gain the flexibility that you provide, kind of that open, scalable, democratized ecosystem that Anupam was just talking about how the framework delivers that. So I think as we look ahead, it is very important to prepare for this massive AI tsunami that's going to hit all of us. And we have to prepare, looking at three Ps over here. There is the people bit, the process bit and the platform bit. And it is by looking at this troika that you build the AI nucleus of the organization, which will help you not just reimagine your business, it will very dramatically lead to great customer experience as well as employee experience. It will lead to new ways of working. It will lead to new skills, new job descriptions and it will also unleash the creativity of the human mind. So when we look at AI, sometimes there is a little bit of fear, there is some anxiety about what AI will do to jobs. There will be change. That is to be recognized and accepted, but there will be new, there will be new jobs as well. So and where human creativity can be used very powerfully. So if you look at it in that context, it's an exciting time to be in. And with that in the backdrop of Google Next, this is just the perfect storm. I couldn't agree more. And I think it's, this is a perfect time. And I think like you talked about the three Ps and looking at all the stuff that Anupam talked about with how you have to bring the right framework to bear and be prepared for it. I think that's what companies are trying to figure out is the keys. What do I need to know? How do I put the people in the process and the on the right platform? Because I think not every platform is built for every solution. And I think TCS's experience across all of these different verticals, especially in manufacturing that we've heard about tonight. I think is great. And I really want to thank you for coming on. I definitely learned something. I'm really excited to go and look at TCS's global cloud study that you just completed. Definitely going to take a look at that, you know, very soon after this. And I want to thank you for coming on theCUBE today. And we're going to be back with more from Google Next just in a short moment. So thanks, hang in there and we'll see you soon.