Hello everyone. My name is Ra Gen, having 10 plus years of experience in enterprise system market architecture with the focus on a driven workflow optimization and robotic process automation. Currently, I serve as a senior software engineer where I design and delivers scalable intelligent workflow system that seamlessly integrate real time decision making. Artificial inte and end-to-end automation. My work is centered around solving the complex business challenge and enhancing the operational efficiency. Over the course of my career, I have been fortunate to work with the leading organizations in the healthcare, banking, and finance and insurance industry, driving the innovation and the digital transformation at scale. Today I'm going to present how revolutionizing legacy system migration to cloud architecture. So the AI power zero touch integration, revolutionizing legacy system migration to cloud architecture. Welcome to the presentation on the No approach to enterprise legacy system migration through zero touch AI driven middleware. This. Innovative solution autonomously facilitate the integration of aging enterprise infrastructure with the modern cloud architecture. Our approach leverage advanced mission learning programs and algorithms, natural language processing, and the knowledge graphs Two. Automatically discover, map, and optimize the legacy work environments through the manual intervention. So let's understand the legacy migration challenge. Here are three legacy challenges that we have identified. Legacy system barrier, complex integration, high cost and disruption. Let's discuss legacy system barrier. Despite the proven reliability legacy system now significantly impede the digital transformation initiatives and diminish competitive. Advantage in the today's rapidly evolving business environment. The second one, complex integration. Integrating the legacy system with the modern cloud architecture extend far beyond the technical hurdle, encompassing the critical organizational financial and operational challenge that demand the comprehensive solutions, high cost, and, disruption organizations encounter high migration costs, increasing technical debate due to insufficient integration strategies and considerable business disruption during the transition, creating significant obstacles to modernization. So zero touch AI driven integration. Define, here are four things. Autonomous orchestration, AI port analysis, automated transformation, continuous validation. Let's go one by one. Autonomous orchestration. Complete migration without a human interaction. AI port analysis, intelligence, discovery, and map mapping. Automated transformation, self-optimizing conversion pattern, continuous validation, self-healing, integration. Mechanism, zero touch driven integration leverages artificial inte to autonomously orchestrate the entire migration process from legacy system to cloud native environment. This approach builds upon zero touch networking orchestration principles, extending them to enterprise architecture transformation. So current state of. Enterprise system migration, traditional approach. Current migration methods typically follow one of three established approaches, lift and shift, relocating the application without modification, re-platforming, making target modification to leverage specific cloud capabilities. Refactoring. Substantial code restructuring to fully adapt cloud native principles, key limitations. Manual integration methods dominate despite inherent limitations. Reliance on rare specialist with expertise in both the legacy and target technologies. N dependencies on often outdated or absent documentation. Significant potential are human error during transition process, sequential nature, limiting paralyzation opportunities. The pinpoints of current migration process. Here are the four pinpoints that we have identified. The one is reduction in the legacy knowledge, incomplete system discovery data quality issue testing complexity. Let's discuss one by one reduction in the legacy knowledge original system architects and developers retired or. Transition to other roles, taking the crucial undocumented knowledge with them. Creating the fundamental challenge in understanding the system behavior. Incomplete system discovery, dependencies and integration often emerge only during the migration causing the scope. Expansion and timelines delay as teams encounter unexpected. Connection between the system, data quality issues, data quality and consistency problem often come up during the migration needing a lot of cleaning work that was not planned for initially. Let's discuss test testing capabilities, validation complexity increases. Expansion. Expansion only with system size and integration scope, making comprehensive testing nearly impossible through manual means alone. Critical framework for aid driven integration. Precision layer, intelligence layer, orchestration layer, execution layer, governance layer, optimization layer. Let's discuss one by one. So perception layer. Monitors legacy system behavior through non-invasive methods. And the next one, intelligence layer builds comprehensive model of system functionality. Orchestration layer coordinate transformation activities across components. Next one, execution layer implements migration tasks through centralized and APIs governance layer. Compliance with organizational policy optimization layer continuously refine the system performance and resource allocation. This multi-layered architecture enables autonomously migration from legacy system to cloud environment, drawing inspiration from advance in network automation and service. Management, the Keya Touch technologies, enabling integration. There are five things that we have identified. Natural language processing and symbol methods, machine learning, knowledge graphs, computer revision. Rest discussed one by one. Natural language processing extracts the domain knowledge from unstructured documentation code component, and. User manuals and simple methods combines multiple yay approaches to enhance the migration robustness. Machine learning identifies pattern and system behavior without explicit programming knowledge provides semantic representation of system components and relationship. Computer revision. Analyze elements to map the user interaction in systems, lacking a PA access, so pro to methodology for autonomous migration. Here are the four stages that we have identified. One is S system analysis, workflow discovery, code, transformation, continuous validation. Let's discuss one by one system analysis. Non-invasive collection approach. Monitor legacy system without disruption, creating a comprehensive inventory of components, interaction and dependencies. The next one, workflow discovery process. Mining algorithms, analyze event log to reconstruct actual business process revealing shadow workflow, and. Exception handling pattern. Third one, core transformation. Automated tools transform the legacy code into modern programming languages while preserving the functional semantic and generating equivalent. A PS the last one, continuous validation. Digital. Simulation enables verification of migration plans before implementation with automated testing comparison response between the legacy and migrated components implementation case status. Let's go Each industry wise if you look at the financial industry, legacy environment is for many financial industry. Mainframe COBOL application. Target architecture, microservice architecture. Key challenges, transition, integrity, regulatory compliance. Let's look at the manufacturing industry. The key challenge, legacy, environment firm manufacturing industry is customized on Prime ERP targeted architecture cloud. Now. Native ERP with iot. Key challenges, complex customization, real time requirements. Let's look at the healthcare industry. Departmental patient system, target architecture, unified cloud platform, key challenges, data privacy, system fragmentation. Let's look at the telecom. Industry network management system is the legacy environment, the targeted architecture cloud, native orchestration, key challenge, hardware dependencies, legacy protocol. These diverse case study demonstrate that efficiency of touch, AI driven migration approach across. Different sectors with each implementation showing significantly improvements in migration, timelines, resource utilization, and post-migration performance. Key challenges and feature research directions. So here are the tech technical limitations, highly customized legacy system with. Property technologies often resist standardized analysis approach. System with hardware dependencies are non-deterministic behavior. Present sustainable migration challenge, security and compliance legacy system often implement security through. Security rather than modern principle regulatory requirements impose strict constructions on migration approach, particularly regarding the data residency and access control. Human, a collaboration complex scenarios often emerge that suppress the current a capabilities requiring. Effective human AI collaboration model with balance the advantage of automation with human experts to achieve optimal results. The conclusion and key takeaways, 70% of cost reduction compared to traditional migration approach. 60% of time saving faster implementation of cloud migration, 90% of risk reduction, lower business disruption during transition. The achi, a driven integration of framework represents a significant. Advancement in the legacy enterprise system migration by autonomously mapping the optimization and migrating the legacy workflow organization can overcome traditional challenge of high cost technical debate under business disruptions as a digital transformation initiative accelerator across the industries. This framework often. Organization, variable pathway to modernize their legacy and infrastructure while managing the risk and maximizing the business value. Thank you.