Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Roadmap for Integrating Automation with Process Optimization for AI-powered Digital Transformation

Version 1 : Received: 14 October 2023 / Approved: 16 October 2023 / Online: 17 October 2023 (09:25:35 CEST)

How to cite: Aldoseri, A.; Al-Khalifa, K.; Hamouda, A. A Roadmap for Integrating Automation with Process Optimization for AI-powered Digital Transformation. Preprints 2023, 2023101055. https://doi.org/10.20944/preprints202310.1055.v1 Aldoseri, A.; Al-Khalifa, K.; Hamouda, A. A Roadmap for Integrating Automation with Process Optimization for AI-powered Digital Transformation. Preprints 2023, 2023101055. https://doi.org/10.20944/preprints202310.1055.v1

Abstract

The integration of automation and process optimization within the context of AI-powered digital transformation has emerged as a pivotal strategy for organizations aiming to enhance efficiency, foster innovation, and competitiveness. This paper is devoted to present innovative contribution by providing a comprehensive structured roadmap that outlines the foundational principles necessary for the successful integration of automation and optimizing processes within the con-text of emerging AI technologies. The paper introduce a cohesive framework consisting of es-sential key pillars: Data-Driven Insights, Seamless Automation, Adaptive Learning and Contin-uous Improvement, Human-Centric Collaboration, Ethical and Responsible AI, Strategic Align-ment, Scalability, and Innovation. These pillars function as guiding principles to navigate the in-tricate landscape of automation-driven initiatives within AI-powered digital transformation. By embracing these pillars, organizations can embark on a transformative journey that maximizes the potential of automation, fosters innovation, and positions them as leaders in the ever-evolving landscape of AI-driven business operations.

Keywords

AI-Powered Digital Transformation; Process Automation; Process Optimization

Subject

Engineering, Industrial and Manufacturing Engineering

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