Integration of Artificial Intelligence and Robotic Process Automation Literature Review and Proposal for a Sustainable Model
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-129Keywords:
Artificial Intelligence, Robotic Process Automation, Integration, Sustainable Model, Social Impact, Environmental ImpactAbstract
This paper investigates the convergence of Artificial Intelligence (AI) and Robotic Process Automation (RPA) as a transformative force in enhancing organizational efficiency and operational scalability. By automating repetitive tasks and enabling intelligent decision-making, the integration of AI and RPA offers significant potential for cost reduction, productivity gains, and improved accuracy across various industries. However, alongside these benefits, the rapid deployment of such technologies introduces complex challenges, particularly in terms of workforce displacement, ethical considerations, and environmental sustainability. To address these concerns, the study conducts a systematic literature review of current academic and industry research, identifying key trends, technological frameworks, and socio-environmental impacts associated with AI and RPA implementation. The findings highlight a critical gap in models that holistically integrate technical innovation with sustainability and ethical responsibility. In response, this paper proposes a sustainable adoption model that emphasizes inclusive governance, continuous reskilling initiatives, energy-efficient infrastructure, and stakeholder engagement. Ultimately, the research contributes to a more nuanced understanding of how organizations can leverage AI and RPA not just for operational excellence, but also for long-term social value and ecological balance. The proposed framework serves as a practical guide for policymakers, business leaders, and technologists striving to navigate the evolving digital landscape responsibly
References
[1] Patrício, L., Varela, L., & Silveira, Z. (2024). Integration of Artificial Intelligence and Robotic Process Automation: Literature Review and Proposal for a Sustainable Model. Applied Sciences, 14(21), 9648.
[2] Animesh Kumar, “AI-Driven Innovations in Modern Cloud Computing”, Computer Science and Engineering, 14(6), 129-134, 2024.
[3] Chakraborti, T., Isahagian, V., Khalaf, R., Khazaeni, Y., Muthusamy, V., Rizk, Y., & Unuvar, M. (2020). From Robotic Process Automation to Intelligent Process Automation: Emerging Trends.
[4] Kirti Vasdev. (2019). “GIS in Disaster Management: Real-Time Mapping and Risk Assessment”. International Journal on Science and Technology, 10(1), 1–8. https://doi.org/10.5281/zenodo.14288561
[5] Wewerka, J. & Reichert, M. (2020). Robotic Process Automation — A Systematic Literature Review and Assessment Framework.
[6] C. C. Marella and A. Palakurti, “Harnessing Python for AI and machine learning: Techniques, tools, and green solutions,” In Advances in Environmental Engineering and Green Technologies, IGI Global, 2025, pp. 237–250
[7] Sahil Bucha, “Integrating Cloud-Based E-Commerce Logistics Platforms While Ensuring Data Privacy: A Technical Review,” Journal Of Critical Reviews, Vol 09, Issue 05 2022, Pages1256-1263.
[8] Nelson, J. P., Biddle, J. B., & Shapira, P. (2023). Applications and Societal Implications of Artificial Intelligence in Manufacturing: A Systematic Review.
[9] Palakurti, A., & Kodi, D. (2025). “Building intelligent systems with Python: An AI and ML journey for social good”. In Advancing social equity through accessible green innovation (pp. 1–16). IGI Global.
[10] Pradeep Kumar, A. N., Bogner, J., Funke, M., & Lago, P. (2024). Balancing Progress and Responsibility: A Synthesis of Sustainability Trade-Offs of AI Based Systems.
[11] Attaluri, V., & Aragani, V. M. (2025). “Sustainable Business Models: Role-Based Access Control (RBAC) Enhancing Security and User Management”. In Driving Business Success Through Eco-Friendly Strategies (pp. 341- 356). IGI Global Scientific Publishing.
[12] Ma, S. (2025). A Review of Integration of Robotic Process Automation and Artificial Intelligence: Advancements, Applications and Challenges. Applied and Computational Engineering, 121, 161–165.
[13] Kommineni, M., & Chundru, S. (2025). Sustainable Data Governance Implementing Energy-Efficient Data Lifecycle Management in Enterprise Systems. In Driving Business Success Through Eco-Friendly Strategies (pp. 397-418). IGI Global Scientific Publishing.
[14] S. Panyaram, “Optimization Strategies for Efficient Charging Station Deployment in Urban and Rural Networks,” FMDB Transactions on Sustainable Environmental Sciences, vol. 1, no. 2, pp. 69–80, 2024.
[15] Praveen Kumar Maroju, "Assessing the Impact of AI and Virtual Reality on Strengthening Cybersecurity Resilience Through Data Techniques," Conference: 3rd International conference on Research in Multidisciplinary Studies Volume: 10, 2024.
[16] Daase, C., Pandey, A., Staegemann, D., & Turowski, K. (2023). Sustainability in Robotic Process Automation: Proposing a Universal Implementation Model. In ICINCO 2023, pp. 770–779.
[17] RK Puvvada . “SAP S/4HANA Finance on Cloud: AI-Powered Deployment and Extensibility” - IJSAT-International Journal on Science and …16.1 2025 :1-14.
[18] P. Pulivarthy Enhancing Data Integration in Oracle Databases: Leveraging Machine Learning for Automated Data Cleansing, Transformation, and Enrichment International Journal of Holistic Management Perspectives, 4 (4) (2023), pp. 1-18
[19] Ribeiro, J., Lima, R., Eckhardt, T., & Paiva, S. (2020). Robotic Process Automation and Artificial Intelligence in Industry 4.0—A Literature Review. Procedia Computer Science, 181, 51–58.
[20] Advancing sustainable energy: A systematic review of renewable resources, technologies, and public perceptions, Sree Lakshmi Vineetha Bitragunta, International Journal of Multidisciplinary Research and Growth Evaluation, Volume 4; Issue 2; March-April 2023; Page No. 608-614.
[21] Alshehri, A., Aljarbou, M., Elaraki, Y., & Alsehaimi, A. (2024). Evaluating RPA for Human Centric and Future Oriented Sustainable Regulations in Construction. Nanotechnology Perceptions, 20, 430–449.
[22] Joseph, O. (2023). Sustainable Banking through RPA: What Role Does ESG and Cognitive AI Play? J. Digitovation Inf. Syst., 3, 116–140.
[23] Anumolu, V. R., & Marella, B. C. C. (2025). Maximizing ROI: The Intersection of Productivity, Generative AI, and Social Equity. In Advancing Social Equity Through Accessible Green Innovation (pp. 373-386). IGI Global Scientific Publishing.
[24] Maroju, P. K. (2024). Advancing synergy of computing and artificial intelligence with innovations challenges and future prospects. FMDB Transactions on Sustainable Intelligent Networks, 1(1), 1-14.
[25] Padmaja Pulivarthy, (2024/3/9). Semiconductor Industry Innovations: Database Management in the Era of Wafer Manufacturing. FMDB Transactions on Sustainable Intelligent Networks. 1(1). 15-26. FMDB.
[26] Praveen Kumar Maroju, Venu Madhav Aragani (2025). Predictive Analytics in Education: Early Intervention and Proactive Support With Gen AI Cloud. Igi Global Scientific Publishing 1 (1):317-332.
[27] Pulivarthy, P. (2024). Optimizing Large Scale Distributed Data Systems Using Intelligent Load Balancing Algorithms. AVE Trends in Intelligent Computing Systems, 1(4), 219–230.
[28] Mudunuri, L. N., Hullurappa, M., Vemula, V. R., & Selvakumar, P. (2025). “AI-Powered Leadership: Shaping the Future of Management. In F. Özsungur (Ed.), Navigating Organizational Behavior in the Digital Age With AI” (pp. 127-152). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8442-8.ch006
[29] Panyaram, S., & Kotte, K. R. (2025). Leveraging AI and Data Analytics for Sustainable Robotic Process Automation (RPA) in Media: Driving Innovation in Green Field Business Process. In Driving Business Success Through Eco-Friendly Strategies (pp. 249-262). IGI Global Scientific Publishing.
[30] Intelligent Power Feedback Control for Motor-Generator Pairs: A Machine Learning-Based Approach - Sree Lakshmi Vineetha Bitragunta - IJLRP Volume 5, Issue 12, December 2024, PP-1-9, DOI 10.5281/zenodo.14945799.
[31] Mohanarajesh, Kommineni (2024). Develop New Techniques for Ensuring Fairness in Artificial Intelligence and ML Models to Promote Ethical and Unbiased Decision-Making. International Journal of Innovations in Applied Sciences and Engineering 10 (1):47-59.
[32] Kotte, K. R., & Panyaram, S. (2025). Supply Chain 4.0: Advancing Sustainable Business. Driving Business Success Through Eco-Friendly Strategies, 303.
[33] Puvvada, R. K. (2025). Enterprise Revenue Analytics and Reporting in SAP S/4HANA Cloud. European Journal of Science, Innovation and Technology, 5(3), 25-40.
[34] Pugazhenthi, V. J., Singh, J. K., Visagan, E., Pandy, G., Jeyarajan, B., & Murugan, A. (2025, March). Quantitative Evaluation of User Experience in Digital Voice Assistant Systems: Analyzing Task Completion Time, Success Rate, and User Satisfaction. In SoutheastCon 2025 (pp. 662-668). IEEE.
[35] Patibandla, K. K., Daruvuri, R., & Mannem, P. (2025, April). Enhancing Online Retail Insights: K-Means Clustering and PCA for Customer Segmentation. In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT) (pp. 388-393). IEEE.
[36] Bhagath Chandra Chowdari Marella, “From Silos to Synergy: Delivering Unified Data Insights across Disparate Business Units”, International Journal of Innovative Research in Computer and Communication Engineering, vol.12, no.11, pp. 11993-12003, 2024.
[37] L. Thammareddi, V. R. Anumolu, K. R. Kotte, B. C. Chowdari Marella, K. Arun Kumar and J. Bisht, "Random Security Generators with Enhanced Cryptography for Cybersecurity in Financial Supply Chains," 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), Bhimtal, Nainital, India, 2025, pp. 1173-1178, doi: 10.1109/CE2CT64011.2025.10939785.
[38] Pulivarthy, P. (2024). Gen AI Impact on the Database Industry Innovations. International Journal of Advances in Engineering Research (IJAER), 28(III), 1–10.
[39] R. Daruvuri, K. K. Patibandla, and P. Mannem, “Data Driven Retail Price Optimization Using XGBoost and Predictive Modeling”, in Proc. 2025 International Conference on Intelligent Computing and Control Systems (ICICCS), Chennai, India. 2025, pp. 838–843.
[40] Mohanarajesh, Kommineni (2024). Generative Models with Privacy Guarantees: Enhancing Data Utility while Minimizing Risk of Sensitive Data Exposure. International Journal of Intelligent Systems and Applications in Engineering 12 (23):1036-1044.
[41] Vegineni, Gopi Chand, and Bhagath Chandra Chowdari Marella. "Integrating AI-Powered Dashboards in State Government Programs for Real-Time Decision Support." AI-Enabled Sustainable Innovations in Education and Business, edited by Ali Sorayyaei Azar, et al., IGI Global, 2025, pp. 251-276. https://doi.org/10.4018/979-8-3373-3952-8.ch011
[42] S. Panyaram, “Integrating Artificial Intelligence with Big Data for RealTime Insights and Decision-Making in Complex Systems,” FMDB Transactions on Sustainable Intelligent Networks., vol.1, no.2, pp. 85–95, 2024.
[43] Marella, B. C. C., & Kodi, D. (2025). Fraud Resilience: Innovating Enterprise Models for Risk Mitigation. Journal of Information Systems Engineering and Management, 10, 683– 695. Scopus. https://doi.org/10.52783/jisem.v10i12s.1942
[44] Mr. G. Rajassekaran Padmaja Pulivarthy,Mr. Mohanarajesh Kommineni,Mr. Venu Madhav Aragani, (2025), Real Time Data Pipeline Engineering for Scalable Insights, IGI Global.
[45] Islam Uddin, Salman A. AlQahtani, Sumaiya Noor, Salman Khan. “Deep-m6Am: a deep learning model for identifying N6, 2′-O-Dimethyladenosine (m6Am) sites using hybrid features[J]”. AIMS Bioengineering, 2025, 12(1): 145-161. doi: 10.3934/bioeng.2025006.
[46] Noor, S., Awan, H.H., Hashmi, A.S. et al. “Optimizing performance of parallel computing platforms for large-scale genome data analysis”. Computing 107, 86 (2025). https://doi.org/10.1007/s00607-025-01441-y.
[47] A. Garg, S Mishra, and A Jain, “Leveraging IoT-Driven Customer Intelligence for Adaptive Financial Services”, IJAIDSML, vol. 4, no. 3, pp. 60–71, Oct. 2023, doi: 10.63282/3050-9262.IJAIDSML-V4I3P107
[48] Vootkuri, C. AI-Powered Cloud Security: A Unified Approach to Threat Modeling and Vulnerability Management.
[49] Settibathini, V. S., Virmani, A., Kuppam, M., S., N., Manikandan, S., & C., E. (2024). Shedding Light on Dataset Influence for More Transparent Machine Learning. In P. Paramasivan, S. Rajest, K. Chinnusamy, R. Regin, & F. John Joseph (Eds.), Explainable AI Applications for Human Behavior Analysis (pp. 33-48). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-1355-8.ch003
