Open Access

REVIEW

A Review of AI-Driven Automation Technologies: Latest Taxonomies, Existing Challenges, and Future Prospects

Weiqiang Jin^1,2, Ningwei Wang¹, Lei Zhang³, Xingwu Tian¹, Bohang Shi¹, Biao Zhao^1,*

1 School of Information and Communications Engineering, Xi’an Jiaotong University, iHarbour, Xi’an, 710049, China
2 Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, 999077, China
3 School of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing, 102616, China

* Corresponding Author: Biao Zhao. Email:

(This article belongs to the Special Issue: Intelligent Vehicles and Emerging Automotive Technologies: Integrating AI, IoT, and Computing in Next-Generation in Electric Vehicles)

Computers, Materials & Continua 2025, 84(3), 3961-4018. https://doi.org/10.32604/cmc.2025.067857

Received 14 May 2025; Accepted 10 July 2025; Issue published 30 July 2025

Abstract

With the growing adoption of Artifical Intelligence (AI), AI-driven autonomous techniques and automation systems have seen widespread applications, become pivotal in enhancing operational efficiency and task automation across various aspects of human living. Over the past decade, AI-driven automation has advanced from simple rule-based systems to sophisticated multi-agent hybrid architectures. These technologies not only increase productivity but also enable more scalable and adaptable solutions, proving particularly beneficial in industries such as healthcare, finance, and customer service. However, the absence of a unified review for categorization, benchmarking, and ethical risk assessment hinders the AI-driven automation progress. To bridge this gap, in this survey, we present a comprehensive taxonomy of AI-driven automation methods and analyze recent advancements. We present a comparative analysis of performance metrics between production environments and industrial applications, along with an examination of cutting-edge developments. Specifically, we present a comparative analysis of the performance across various aspects in different industries, offering valuable insights for researchers to select the most suitable approaches for specific applications. Additionally, we also review multiple existing mainstream AI-driven automation applications in detail, highlighting their strengths and limitations. Finally, we outline open research challenges and suggest future directions to address the challenges of AI adoption while maximizing its potential in real-world AI-driven automation applications.

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Keywords

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