Scaling Business Process Automation: Case Studies from Ride-Hailing and Autonomous Vehicles

Authors: Nagarajan

DOI: https://doi.org/10.5281/zenodo.15084384

Short DOI: https://doi.org/g89sb6

Country: United States

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Abstract: As businesses scale, optimizing operational efficiency through workflow automation has become essential. Large-scale systems, such as ride-hailing platforms and autonomous vehicle networks, rely on process automation to enhance service reliability, reduce manual intervention, and improve customer experience. This paper explores how workflow automation optimizes operations and reduces inefficiencies in complex systems. Specifically, it examines Lyft’s automated dispute resolution system and Zoox’s incident response and fleet monitoring workflows as case studies of business process automation (BPA) at scale. The paper delves into the underlying AI-driven mechanisms, machine learning models, and system architectures that power these automated workflows. Additionally, challenges such as data integration, security concerns, and the balance between human oversight and AI autonomy are discussed. Finally, future trends in large-scale process automation, including predictive analytics and real-time AI-driven optimizations are explored.

Keywords: Business Process Automation (BPA), workflow optimization, ride-hailing automation, fleet monitoring, AI-driven automation, machine learning, process efficiency, dispute resolution, predictive analytics.

Paper Id: 232303

Published On: 2025-01-17

Published In: Volume 13, Issue 1, January-February 2025

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