Bridging the Divide: A New Model for Integrating Legacy Systems with Robotic Process Automation (RPA) - Kankipati, Veera Venkata Ramana (Digital Repository of Theses, 2025)
The integration of modern digital technologies with entrenched legacy systems remains a formidable challenge for organizations striving to enhance agility, reduce complexity, and remain competitive in a fast-evolving environment. This thesis investigates the transformative potential of Robotic Process Automation (RPA) as a strategic enabler, proposing a novel integration model grounded in Micro UI architecture and Atomic Design principles to achieve modularity, scalability, and operational efficiency. Utilizing a multidisciplinary methodology—including a comprehensive literature review and simulation-based implementation—this research identifies key limitations of legacy infrastructures, notably their rigidity, lack of interoperability, and resistance to change. The integration framework presented leverages RPA’s rule-based automation to emulate human-system interactions while abstracting legacy system complexity. It incorporates Micro UI components to deliver lightweight, reusable, and upgradeable modules with minimal disruption. A hypothetical case study simulates real-world scenarios, demonstrating up to a 40% improvement in workflow speed, enhanced data consistency, and increased system responsiveness. The study further underscores the role of organizational readiness, stakeholder alignment, and iterative development in successful automation. The theoretical foundation draws from the Technology Acceptance Model (Davis, 1989), the Diffusion of Innovation (Rogers, 1995), and the Resource-Based View (Barney, 1991), anchoring the framework in established academic thought. In conclusion, this research contributes a scalable and future-ready blueprint for legacy system modernization through RPA and micro-frontend strategies, offering both theoretical insight and actionable pathways for digital transformation. Future research may extend this model using AI-driven cognitive automation for complex decisionintensive environments