Hello, good morning, everyone. It's a privilege to be here at this conference, sharing insights on how AI in enhanced cloud architecture is revolutionizing enterprise operations. I am Babita Kumari, senior software engineer at Microsoft, where I have been part of groundbreaking innovations that transform how business operate. As the technology evolves, so does its role in business operation. From reducing inefficiencies to predicting future challenges, AI and cloud integration is at the forefront of this transformation. Today, I'll share a comprehensive journey from understanding challenges and seizing opportunities to real world success and future possibilities. Here is what we will cover. Here is what we will cover. The challenges enterprises face. The opportunity in the growing AI cloud market, our innovative solutions, their proven impact, and a glimpse into the future of AI and cloud. Moving forward, moving forward to the challenges in enterprise cloud operations. What are the main pain points? Despite the strides we have made, enterprises still face significant cloud related challenges. Some of those are excessive operational costs, 45 percent overspending due to inefficient resource utilization as well as manual optimizations. Resource management issues that leads to 38 percent of average resource wastage, inconsistent workload distribution, and poor capacity planning. Security vulnerabilities. Over 12, 000 cloud security incidents happens annually. Unfortunately, but that happens. With an average detection of 4. 5 hours. And last but not the least. Limited predictive capabilities. 37 hours of unplanned downtime monthly due to reactive management approaches. So what are the real world impact for this? Such challenges not only inflate operational costs, but also hinder scalability and compromise security. What is the result? The result is huge. Missed opportunities for innovation and growth. So how many of you have experienced these challenges? These aren't just technical problems, they are everyday business problems. Now, going forward to the next slide, that is growing market opportunity. The AI cloud market is projected to reach 236. 4 billion by 2027. Growing at a compound annual rate of 42. 8%. That's huge. Enterprises worldwide are increasing their budgets for cloud AI integration, with 76 percent prioritizing this in the digital transformation strategies. So what is fueling this growth? There are a couple of factors for this. First is digital transformation. 89 percent of enterprises now prioritize cloud AI integration. Operational efficiency demands, which leads to 67 percent are actively seeking automated solutions to optimize operations. Competitive pressure. 91 percent of industry leaders report a 3. 5 percent competitive advantage through AI cloud adoption. So what is the Action we have to take. The question is no longer if, but how we embrace this opportunity to stay ahead in the race of digital transformation. We have to move forward with an action. So what we propose here is our solution, AI enhanced cloud architecture. So in this model, we'll talk about the core features. how it works, and what are the business values for the solution. At Microsoft, we have developed an AI enhanced cloud architecture that addresses these challenges with many, parameters. First is productive resource management. With 91 percent accuracy, machine learning analyzes historical patterns, seasonal variations, and growth trends. Intelligence security framework. We believe in zero trust security. Achieving 99. 7 percent threat detection accuracy. Our frameworks provide real time monitoring, compliance check, and zero trust architecture. Dynamic scaling capabilities, automatic workload, workload balancing, and multi region optimization reduce Over provisioning by 67 percent is that reduce waste is resource wastage and enhance the resource optimization and then multi cloud integration with the possibility of seamlessly connecting platform with unified management interface, ensuring that vendor agnostic deployment happens. And this helps in multi cloud integration talking about their features. But how does it actually works? So we combine both, cloud, public cloud services like, Azure, AWS, cloud, Google Cloud, with private cloud components for sensitive data and edge computing. Optimizing performance across 150 global locations. So that's been a whole lot of business values, and these business values translate into tangible results of a 42 percent reduction in operational cost. 53 percent is increased process efficiency and 89 percent pure security incidence. So we will discuss this with a real, world success stories. So this is something which I have talked about the business values. Let's move on to the real world success stories. I'm going to share with you a fortune 500 manufacturer case study with you. So allow me to share a story about one of our fortune 500 client, a global manufacturing company struggling with operational inefficiencies and high cost. Here is what they achieved with our solution. Cost saving of 4. 2 million saved annually in maintenance. 2. 1 million reduction in emergency repairs and over 1. 3 million in inventory optimization. And talking about operational efficiency, they see a huge improvement that costs around 52 percent of operational improvement in resource allocation. And 47 percent reduction in downtime. Talking about their metrics, processing 850, 000 data points per minute, achieving 99 percent system uptime, and enabling 17. 4 additional production days annually. So what are the key takeaways from this? This case, underscore, how AI cloud integration is not just solving current challenges, but creating scalable, sustainable solutions for the future. That's it. Now let's move on to the next slide. We'll talk about, implementation. We have talked about implementation, how we implement it, what are the competitive advantages. And let's talk about the future innovation and how does the roadmap looks like. More better view. Okay, this looks good. what is the near term innovation in 2024 2025? We are focusing on quantum computing integration, achieving 100 to 500 times workload acceleration, and 256 qubit encryption strength. This advanced cloud orchestration is something we are focusing on, enhancing workload balancing efficiency by 94%, And reducing latency by 67 percent is our midterm enhancements, 2025 to 2026 will be Our goal here is to achieve AI driven automation, 100 percent automated resource management with self healing system in all the resources, and security evolution, introducing quantum safe protocols and advanced threat protection. Vision for 2026 and beyond. So we envision a world of autonomous operation powered by quantum AI integration, delivering zero touch management systems, self healing, based on zero trust architecture and also everything is automated. 100 percent automated resource management with their self filling systems. So now, moving to the conclusion and summarizing the key insights, what we discussed, AI enhanced cloud architecture is just not more than a technology. It's a catalyst for enterprise transformation by addressing pain points, leveraging market opportunities and delivering proven solutions. We are helping business unlock their potential. This is a call to collaborate. At Microsoft, our mission is to empower every organization to achieve more. I encourage you to think about how your organization can leverage these innovations to drive efficiency and innovation. And last but not the least, thank you for your time and attention today. It's been an honor sharing this journey with you. Let's continue the conversation together. We can redefine what is possible and I'm happy to take any questions. You may hear about the challenges or your team face with, any cloud and AI adoption. I'm reachable at LinkedIn and also through this, conference management. So please reach out to me and let me know if you have any questions, concern, or you hear just any challenges your team's faced with, cloud AI adoption. So that's all for today. Thank you so much.