 We often get blamed for having too many, just a lot of slideware and not enough content. So I've opted not to have any slides at all and I figured we can just have a great conversation. So good afternoon, everyone. It's always, as one of the speakers said on the earlier panel, it's important to keep things interesting after lunch. And so hopefully I'll try and bring a little bit of a different perspective. I was actually a little worried when I was asked to come and speak here because I said, look, there's going to be a whole bunch of very creative folk here from marketing and advertising and others. And talking about technology could actually become pretty boring. But actually one of the speakers, Amrish, I think, said it really well that technology now opens up the doors and I loved what he said. It was precision targeting at scale, personalization at scale, and experience at scale. So hopefully I'll try and bring that to life for you. Now look, obviously I am in the tech business, IBM is a tech company. And we fundamentally do believe that technology is an enabler and an equalizer. And it has the power to transform lives and societies at large. And we saw this, I think, in full force during the pandemic. I think the pandemic proved that technology, in fact, can be the sutradhar that connects communities, it connects businesses, it connects people, it actually kept things moving. And since then, the whole push around technology as a fundamental enabler for any business, big, small, just became very, very real. Because it wasn't about whether you needed to transform your business through technology. It was, you know, how quickly would you do that and can you afford not to, right? So that clearly becomes extremely important as we think about technology. That is, I think, shaping our lives and businesses in a fundamental way. Now, in my introduction, they talked about hybrid cloud, AI, and so on. And in today's world, I think when people ask me about transformational technologies, I fundamentally think that there are three or four technologies that are going to shape how we see the world, how we will live in the world, how we will work in the world. Clearly, hybrid cloud is a very critical movement that's happening because people are making decisions about, and it all gets down to the large volumes of data that are now available, the kind of computational power that's required to drive better decisions, to drive more automation, and so on. And so infrastructures will need to be decided on whether you have a large machine that actually does a lot of these and runs your systems for you, or you have it as a cloud which is more of a shared infrastructure. And that is a very technical discussion that we have with a lot of companies, but we have a fundamental view that things are moving to a hybrid world. So people will have clouds, people will have on-prem systems, you'll have all kinds of different infrastructure in between, which will need to operate together. What is important, though, as you think about infrastructure and technology infrastructure is whether your data, the data that you need, and I'm going to talk about data in a minute because I think the fundamental paradigm of data is changing, but how that data and applications that actually run the systems and deliver outcomes for you, how they are fungible and they can actually live across these different systems and across this different infrastructure, that becomes a very operative issue. And do that at an economically viable scenario for any business, right? So that's hybrid cloud. Second is AI, and that is something which I want to talk about. And AI, you hear artificial intelligence, people hear about chat GPD and I'm going to try and bring that to life a little bit in terms of how we need to think about it from a business context. And then the third area which becomes extremely important, I think as technology evolves, how we think about security, cyber security becomes extremely important. And I'm not going to dwell on that, but it is something that is a very relevant issue that we do need to think about because as technology becomes smarter, it evolves into advanced areas, the bad actors will become a lot more smarter. And this is where we'd have to think about how you actually counter them and how we are two steps ahead of them. This is where ultimately you may have heard about this whole notion of quantum. We do pride ourselves as IBM in being very advanced in that research space. It's not here and now in terms of what it can do for businesses and practical applications. But quantum technology is coming, which is going to enable us to solve very different kinds of problems in the future. And it will also enable us to find new ways in which we secure our systems to protect from the bad actors. So that is sort of the spectrum of technology and where I see the future of technology going. Within that, I want to hone in and talk about AI because I think it's very relevant for this audience. It's very relevant for this industry as well. One of the other panelists in the last panel talked about data and just think about data. The way it's proliferating around us. So I want to just talk a moment about the data and how the paradigm of data is changing. In the past, we used to be worried about how much data can we store? How much of customer information do we have that we want to store have available that we can use later on? Today, the kind of data that is now available to us, and it was always there. But today, the kind of data that we are able to capture on our systems, then the transient, the nature of that data has changed. So if you think about the temporal aspect of data like weather data, or data that's coming out of someone's cell phone when they're walking around a retail store, that data tends to lose relevance within a few nanoseconds after it's created. But today's technology enables us to capture that data and make sense of it. And there is a lot of that data which needs to come together to enable us to make smarter business decisions. So artificial intelligence clearly is not relevant at all without large volumes of data. That's what makes it relevant. That's what makes it useful and valuable to businesses. And so as you think about that, I like to think about artificial intelligence as more of augmented intelligence. It's not going to replace, it's not going to change the creative aspects of what we do in our businesses, of using judgment calls in terms of how we think about new product design or new campaigns, et cetera. But it does make us a lot more smarter and it enables us to drive better decisions and better campaigns, a lot more targeted campaigns. And as Amrish said, precision targeting at scale, experiences scale and personalization at scale. So and then there is a whole slew of other applications from managing HR workloads, managing customer experiences, modernizing application, even generating games for that matter. Artificial intelligence or augmented intelligence is changing the game. And in many ways, I think we are moving into an AI-first world of sorts. Now, if you look at things today, whether it's placing an order, you request a product exchange or asked about a billing concern, today's customer demands, they require exceptional experience. And the experience is not about if you're a bank, it's not about whether you are as good as another digital bank. It's about are you as good and are you offering an experience that your consumer gets on any other digital platform. So the bar of performance has changed and customers do expect services to be delivered 24-7 across multiple channels irrespective of how they approach the organization. Now, while traditional AI, they do provide customers with quick service, they do have limitations. Because when you think about traditionally what we've referred to as chatbots, which are a form of AI, it's an evolution of AI, they rely on a rule-based system or traditional machine learning or algorithms or models that automate tasks. They look at a set of predefined responses to customer inquiries, common problems and so on, and then they automate those tasks. As marketers, I'm sure a lot of you think about customer satisfaction, how you can make it more productive, how do you improve the metrics. But today, that experience has become central to any business, whether it's a B2C business clearly a lot more relevant, but even in a B2B or B2B2C business, that becomes extremely, extremely important as you think about omnichannel customer experiences. So when you think about AI, AI now has evolved into what we refer to now as generative AI. And let me give you a little bit of a context around generative AI and then I want to share a few examples, which hopefully will bring this to life. What is now making a plus AI to an AI first is what we are seeing with generative AI. And I want to sort of make a little bit of a context and differentiation to chat GPT. What chat GPT does is it is a form of generative AI where there are foundational models that actually return a certain kind of response based on what you ask it to do. And I was having a conversation with someone actually before this talk and they talked about ethics in journalism. As you think about journalism, you can churn out an article using chat GPT. So is that ethical? Isn't it? And I sort of offered my view to that. And I said, look, you know, I actually have manual chat GPTs. My communications team usually gives me some kind of a brief or a talk track that I use. But the ethics behind that is that you never use it as is. I never let anything get published in my name that I haven't read through thoroughly or edited it for content. So as long as you are able to filter it through and add original content and sentiment to anything, right? You are in fact using chat GPT as augmented intelligence for you where you are actually creating a level of productivity where you can probably churn out five articles instead of two, which is okay as long as those five articles and you feel, you know, you have, you can say with integrity that those five articles have your point of view and have original content that you are able to shape as a intelligent professional, right? So that is the differentiation I think we need to think about as we think about AI and even ethics in AI. So to do that, let me give you a very, very interesting example. I'm sure everyone followed or many of you may have, it depends on what kind of support you like, but I'm sure many of you followed Wimbledon, right? So let me give you a very interesting example around Wimbledon because I think I was actually fascinated when we did this. Now, we have been, as IBM, we've been Wimbledon's technology partner for more than three decades and traditionally what we've done is we've been helping Wimbledon grow its digital audience with personalized experiences. I think someone talked about the experiences at scale, so we do personalized experiences through its website called Wimbledon.com and the different apps that are Wimbledon apps that we, you know, build and power and continue to maintain. Now, each year we come together, the Wimbledon team and the IBM team, we come together and ideate and innovate to basically introduce new features, to engage the fans in determining, you know, how do you create more fan engagement? How do you provide insights that enhance the experience? So there's a lot of data analytics and others that go into it. This year, they leveraged generative AI technology from IBM to produce tennis commentary for video highlights and this was its introduction this year as a step towards making the commentary available in an exciting way for matches outside of the Wimbledon's show courts, which already have a live, you know, human commentary, right? Now, to develop this feature, Wimbledon leveraged foundational models from what's next and foundational models essentially are models that are pre-created using large volumes of unlabeled data. So they do take time to create. There is an investment. But it cuts down the cost of building AI models ultimately through the use of those foundational models and in many respects, what ChatGPD does, it leverages a whole slew of these foundational models to do what it does, right? So in this particular instance, Wimbledon leveraged foundational models from what's next, which is our enterprise AI and data platform to train the artificial intelligence in the unique language of tennis. So generative AI, they built these foundational models and it was applied to produce narration with varied sentence structure, vocabulary and others to make the clips informative and engaging. So that was the core work that was done. Personally for me, what was even more exciting was in a first for tennis and a historic moment that I'll remember, we added IBM AI draw and analysis feature which provided a new statistic to define how favorable the path to the final might be for each player in a single, in the single straw. So when you looked at, you know, just the singles draw, what would be the path which would be most favorable for each player in the singles draw. Now, we used over 100,000 data points from every shot that was played across the tournament. It was analyzed by IBM's what's an AI technology on IBM cloud and these digital features were designed to make it easier for fans to understand which players to follow, how they compared to their opponents and who would be most likely to win. Now, everyone sitting here may not believe it and you may not have, I don't think we've published this enough but IBM what's an X AI was actually the only AI model that predicted the outcome of Wimbledon men's singles final and he was the underdog by the way. I think everyone knows that everyone who follows the game knew that he was the underdog but what's an X was the one that actually predicted him as the singles winner and it was absolutely accurate. Now, it was a lot of things around, you know, there was all kinds of analysis and models that went into it but this was, this actually underscored the power of what AI can do through analysis and actually give you a decision ready option if you will around using then your own filter to make decisions. The platform clearly, you know, has been creating unique fan experiences for Wimbledon and it's the same technology. This technology is the same technology that now we are using, you know, for business transformation with clients across a variety of different industries. So let me just, let me give you a couple of quick, very quick examples because I think nothing's better than, you know, real examples that will give you a sense of how this is used. So Asian Paints is one where Asian Paints, you know, has been working with us to deploy automation solutions to simplify and speed up their trade promotion process, for example, for a network of over 20,000 dealers across India and what it has enabled them to do, so the way they use AI and AI models is to enable the sales team to align discount and promotional schemes better as well as propose the mix of products to order that will maximize the dealer's revenue and minimize their costs. Again, something that's been very successful and in all of this it uses a lot of historical data through which AI models are built and it uses AI models to actually make this happen. Another one that which hopefully many of people in this audience can relate to is in the retail industry. So Best Seller, which is actually, they don't go by the Best Seller brand, they have a whole series of brands that they sell as a retailer. They collaborated with us to build something called fabric.ai and it is the, I think it's one of the fashion industries, very first AI project here which was aimed to increase sell through rates and reduce unsold inventory and essentially that would help them predict what products will be successful in the next season. So this tool helped them focus on the supply chain, prioritize the sell through of current products and help ensure that their inventory doesn't keep piling up. So again, this is another business situation that has taken a huge amount of data, used AI models to drive this kind of predictability. Two others that I'll quickly go through. One is Emerald Jewelry. It's one of India's largest jewelry manufacturers that actually do white label jewelry as well. So they worked to develop this thing called TAGE. They called it TAGE, they branded it TAGE. It was the first of its kind AI-enabled mobile app that powered the B2B business for the jewelry business in India. But it connects again with a whole slew of dealers enabling them to search through a growing catalog of over 500,000 jewelry designs, et cetera, and quickly navigate the complex ordering process. Now what they're trying to do is add customer sentiments to how customers are relating to someone in the earlier panel talked about the comment section. So what AI is able to do today is take in all those comments and distill what kind of sentiments people are talking about as they see some of these solutions. Last one I'll say is something hopefully, again, everyone can relate to, is State Bank of India, people who use SBI, there's an application called YONO, which is for every Indian, you only need one and there's a new one now they have YONO for every Indian, which is a comprehensive digital platform. It's something that we've built with them to ultimately create a platform of a digital experience and a digital marketplace of sorts. And as that enhances it actually has intelligence built in that uses advanced analytics to help the bank target customers with more effective and relevant offers. So net of it is, we are living in a world where there's going to be more and more data that's going to be available to us. A lot of that data is not going to be, you're not going to be able to store it, you need to use it instantaneously. But if you can use the power of technologies like AI to bring a lot of those data sets together and make it relevant at that point of decision making, whether it is to enhance a customer experience at scale, whether it is to predict what should be the next best offer you can make to a client, whether it's analyzing sentiments that actually make your creative process of the campaign a lot more effective, whether it is predicting what a path would look like if you engaged in one campaign or the other. Those are the kinds of options and direction that is now available to us. If we can bring all of this data together through technologies like AI through what's next that we use across the platform, the data and the governance of it, that is the promise that technology brings to all of us. And particularly in this industry, hopefully some of these examples highlight what can be the art of possible across businesses in general, but also for your industry. So with that again, thank you very much for the opportunity to be here and have a great conference.