Artificial Intelligence -Robotic Process Automation on Enterprise Architecture in the Telecommunications Industry

Isa Abdulrazaq Imam (Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria)
Ajayi Ore-Ofe Ore-Ofe (Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria)
Abubakar Umar (Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria)
Dako Daniel Emmanuel (Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria)
Dugguh Sylvester Aondonenge (Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria)
Lawal Abdulwahab Olugbenga (Department of Computer Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria)

Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Vol. 1 No. 3 (2024)

This paper explores the strategic impact of Artificial Intelligence (AI)-enhanced Robotic Process Automation (RPA) on Enterprise Architecture (EA) within the telecommunications industry. Traditionally, RPA has been applied to automate repetitive tasks without altering underlying IT infrastructure, focusing primarily on operational efficiency. However, the integration of AI introduces cognitive capabilities to RPA, enabling more dynamic interactions within complex organizational systems. This paper assesses how AI-driven RPA can influence EA by enhancing system efficiency, supporting business-IT alignment, and promoting digital transformation. Through case studies and analyses of various telecommunications operations, the paper investigates the dual role of AI-enhanced RPA in both streamlining enterprise-wide processes and maintaining adaptability to meet industry demands. The findings indicate that, while AI-RPA integration holds significant promise for accelerating operational improvements, it also presents unique challenges related to governance, scalability, and long-term sustainability. This work contributes insights into the adoption of AI-driven RPA as a transformative tool for telecommunications, offering guidance on best practices for aligning automated systems with enterprise strategic goals.Additionally, the study provides a structured framework for integrating AI-driven RPA into EA using ArchiMate and TOGAF modeling methodologies, emphasizing its potential to drive scalability, improve governance, and ensure alignment with strategic business objectives