Hey everyone, my name is Jeru. I'm currently serving as ASAP integration Arctic Manager at PWC. So over past 14 plus years of experience, I have specialized in AI driven integration middleware solutions and enterprise IT transformations. Working with global enterprises on some of the most complex digital inter integration challenges. My career journey spans leading AI power, SAP integration strategies enabling multi-cloud transformations, ensuring regulatory CO across GGPR and H-I-P-A-A and driving cent acquisitions technology integration for Fortune. 500. Firms. So today I'm excited to share with you all a case study that demonstrates how Kubernetes need to AI integration is transforming SAP mergers and, acquisitions. So if we go to the agenda, this is what we cover. What we will cover today is the challenge landscape, the revolutionary approach, the technical architecture business impact strategic implications. So by the end, I hope you will see how AI and Cloud Native Tech. Technologies are fundamentally reshaping enterprise integration. So the traditional mergers and acquisition or integration challenge, so mergers and acquisitions are among the toughest. It challenges. So when two, a large organizations match their SAP landscapes spanning multiple regions customizations and data volumes must integrate. Seamlessly, the traditional approach brings long timelines, sometimes years massive consulting cost business disruptions that risk customer experience. So add to that regulatory complexity, GGPR. So H-I-P-A-A and, need for zero downtime and it creates. The perfect strom of complexity. So these are all the, challenges. And if we go into the complex the integration landscape, let's break this down further, right? So the technicality complex so multiple. Multi different layers of systems like legacy, SCP, some databases, and then legacy migrations and the data volumes in terabyte. So these are all the technical complex. Cities we face on daily basis and regulatory complex, requirements like global compliance frameworks and then business continuity, like keeping the lights on with no disruption. So this is why traditional integration models often fail to deliver efficiently. So if we take about the revolutionary approach Kubernetes native ai. Integration. Instead of treating integration as a slow manual process, we reimagined it as a cloud native workload powered by ai. We used Kubernetes orchestration for scalability. Containerized AI modules like the flexibility in deployment, mission learning automation for mapping analysis and migration continuous learning models that improve over time. So this distributor intelligence means AI could handle multiple integration tasks in parallel cutting time and effort dramatically. So how it works, right? So in, in theory, we know I have explained like these many things like ation the modules, mission learning, continuous learning modules, and the distribution intelligence. But in this is the theory, but in practice, you know how it works, right? So AI models deployed as a micro, if you see the picture here it act, it deploys as a microservices in congen, each specializing in mapping data validation a code analysis, and then transformer networks identified a complex master data patterns and CNNs. Analyzed AAP code for business logic. Graph neural networks optimized SAP dependencies, also Kubernetes orchestration, ensure fault, autoscaling, and high performance because the system process for 500, a hundred thousand records per second across 10 terabyte data sets a scale traditional approach. Couldn't match it all. So that is the, technical architecture we have in in, in, in a practice. And then what is the timeline? The accelerated timeline, if we follow this, the impact was like transformational, like the timelines reduced by 50 to 70%. What once took like 12 to 18 months. Now we took four to six per six months. Also the processing speed if half a million record per second. Also, the automation eliminated manual mapping, like reducing human errors and cost. Parallel processing ensure rapid rapid conflict resolution and continuous operations. He some of the live examples of, the recent examples, what we have had here, we have had like terabytes of data migration, so master data. When it comes to master data, many of we migrate materials, vendors, customers. Vendor banks, profit center, cost center, the PTM related, like the batch traceability metrics. Bill of ma, bill of mapping and the routing, the employee id. So many master data objects we have to migrate during the master data load period where, it has it terabytes of data. So I know or we all know business usually provides the data to the consultants, but what if the data quality is not up to the mark? So we it's very simple to say, Hey, that is the data you have given. So that is how we load it. But instead out of the box, we. Utilize these AI modules, these mission learn, learning tools that will check the data quality as well. So from each functional consultant, we have taken the prerequisites of, the checks, what we need to do. For example, vendor master. These columns are mandatory. Or maybe the delivery date should be, between these time zones. It shouldn't be the past two. It's a very minor example, but. It would make a lot of sense if you get all the world data, if you work, if you go through this workflow, you would get the errors on spot in instead of waiting for longer period after go live and impact the operations. So that is how we have created a workflow based on the inputs that we have received from functional and business. So we will cover that in future also. So here the key is business. IT collaboration is a key for any. Successful project. So with all the inputs we have created a workflow. We have checked the data quality. So it's called like preload sign off. So before loading the data, we got the preload sign off from these AI tools and using the mission linear technologies and then. We started loading the data, which resulted, a hundred percent data quality. Also when it comes to critical operational transactional data, yes, you need open sales order, open patch order. When you are dealing with the ERP customers you need the customer number. Shipped tools, all those details we load as part of the critical operational transactional data with these checks, again, using the AI modules. And when it comes to financial transactional data loads, of course you have to load AP, ar, fixed assets, gl, non GL balances. Stuff and you had to match the trial balance. So that is a key in any of the digital transformation. So we have used these a, AI tools the, using the SAP integration and Kubernetes, to help functional and business it people, to speed up and, to have a smooth digital transformation. So he, this is his like, smallest example, like how we accelerated the integration timeline and then how it impacted the cost and efficiency, right? So beyond speed, the AI automation, it it up, it deliver like massive cost benefits. For example, 78% reduction in integration cost. 85% reduction in manual effort replacing armies of consultants, and 92% reduction in rework cycles. Thanks to AI driven quality validations, like I mentioned, the tools that we have used. So this was not just faster, it was smarter, linear, and more reliable. So this is how we have optimized the cost and also increased the efficiency. And then AI driven data mapping and transformation. So the AI. Could recognize data patterns and automatically suggest mapping and generate transformation logic for conversions, integrity checks and business rules. It provides continuous validation, correcting errors in real time. Also it predicts potential risk and even resolve certain issues automatically. So in fact, 93% of potential issues were caught before impacting the operations. A huge leap from traditional reactive approaches. So what are all the strategic implications, of these? Using these ai kuber net SAP integration tools, right? So this is not just about one project. It's about rethinking mergers and acquisition integrations entirely. So integration becomes innovation, a source of competitive advantage. A company's gain reusable AI framework for ongoing transformations. Also, employees can move faster with greater acquisition flexibility. So in essence, AI transforms integration from a cost center into a growth enabler, so as we look forward, companies must invest in data quality and governance build internal, enterprise in ai, especially in, we have, we all know that there are so many cloud native technologies. So they, there should be a dedicated in general. Ize in AI and cloud native technology. We are, the technologies keep on changing, likewise, we have to brush up our, coming, these new technologies and get up to this. Speed with the as, as we are growing, as we are changing and make sure we have we usually, build internal expertise in ai, cloud Native Tech. So not just by tools, right? So the team should be up. Speed to match with the technology that is rapidly changing. Also, like I mentioned before we encourage business IT collaboration since AI needs real business. Context as a it yes, you know about your tool, how the technology is changing how the ai, is can definitely helpful to to succeed. Yeah. Or to avoid certain issues, right? So as a it, but what issues you need to concentrate. Like you have 1, 2, 3, 4 out of four. What is P one out of four P ones, right? So those important details and all the knowledge has to come from business. Hey, this is what we have. During this digital transformation, these are the complex requirements I have where I need these as a first phase, and maybe out of these three, these two are very business critical where we need to see how ai, this Cuban and SAP integration. Can be useful. So it's all business IT collaboration. Since AI needs a real business context and then approach integration as a long-term capability, not a one-off project. So when you as a integration consultant or as integration architect, when you develop any of the logic for one of the requirements, so you need to think big out of the box. Okay. For simple example, okay, so this is a EDI. PO mapping where we need to map one field to another field. And this is the logic I have. What if. Another customer comes, is this mapping sufficient? Or maybe I need to, develop the mapping a new way that it would be accommodate or easily maintainable when new customer comes in, utilize the similar mapping. So that is how the approach integration as a long term capability, not just for one of the project, so those. Who do this will be a best position to lead the next wave of this digital transformation. So thank you for your time. I hope this session showed you how AI forward Cunet native SAP integration is changing the game for enterprises during mergers and acquisitions. Then I would be very happy to take your questions. Thank you.