TOWARDS NEXT-GENERATION AUTOMATION: DATA-DRIVEN SYNERGIES OF AI AND ROBOTICS THROUGH DATA ENGINEERING AND DATA SCIENCE

Abstract

The convergence of artificial intelligence (AI) and robotics is driving a paradigm shift in how automation systems are conceptualized, designed, and deployed across diverse industries. While robotics provides the physical execution and AI offers adaptive intelligence, the success of next-generation intelligent automation depends heavily on the strength of its underlying data ecosystem. This paper argues that the integration of data engineering and data science forms the critical foundation upon which scalable, adaptive, and trustworthy automation can be achieved. Data engineering ensures the creation of robust pipelines for data collection, cleansing, integration, governance, and security, enabling high-quality and real-time data availability. In parallel, data science leverages these curated datasets to generate insights, optimize control strategies, and empower AI models to support robotic decision-making in complex and uncertain environments. This research present a comprehensive framework that illustrates how data engineering and data science synergistically interact to enhance AI- and robotics-driven automation. This framework consists of three interconnected layers: (i) data infrastructure and engineering for real-time ingestion, standardization, and governance of heterogeneous data; (ii) AI and data science modules for predictive modeling, anomaly detection, and reinforcement learning-driven optimization; and (iii) robotic intelligence systems that transform predictive insights into adaptive action, ensuring autonomy, precision, and scalability. Experimental simulations and sector-specific case studies spanning manufacturing assembly lines, healthcare robotics for precision surgery and rehabilitation, and logistics systems for smart supply chains demonstrate the measurable improvements in efficiency, fault tolerance, adaptability, and decision-making enabled by the proposed approach. The findings underscore that the future of automation cannot rely solely on advanced robotics or AI algorithms in isolation. Instead, it requires a tightly integrated, data-driven architecture that unites data engineering and data science to achieve resilience, scalability, compliance with regulatory frameworks, and explainability of system outputs. By articulating this synergy, the study contributes a novel perspective on how next-generation automation systems can be systematically designed to balance technical innovation with real-world operational requirements. Ultimately, this research advances the discourse on intelligent automation by proposing a holistic paradigm that redefines how AI, robotics, and data ecosystems converge to build scalable, trustworthy, and future-ready automation infrastructures.