Industrial Artificial Intelligence: A Review of Technologies, Applications, Challenges, and Future Directions

Abstract

Industrial Artificial Intelligence (IAI) has emerged as a transformative paradigm that integrates advanced computational intelligence with industrial systems, enabling unprecedented levels of automation, optimization, and decision support across manufacturing and process industries. This review provides a comprehensive synthesis of IAI technologies, spanning machine learning, deep learning, computer vision, natural language processing, reinforcement learning, digital twins, and explainable AI, and examines their deployment across diverse industrial sectors including automotive, aerospace, energy, logistics, and semiconductor manufacturing. Key application domains, including predictive maintenance, quality control, process optimization, and supply chain management, are critically analyzed in terms of methodology, performance benchmarks, and deployment maturity. Unlike prior reviews that focus primarily on a single technology family or a single application domain, this review integrates technology, application, and challenge perspectives within one evidence-based framework and benchmarks technology maturity against deployment readiness across eight industrial sectors. The review further identifies persistent challenges such as data scarcity, black-box opacity, legacy system integration, cybersecurity vulnerabilities, and workforce readiness, and proposes evidence-based mitigation strategies for each. A forward-looking perspective is offered, highlighting the convergence of large language models, federated learning, and Industry 5.0 human-centric frameworks as defining trajectories for the next decade. Drawing on 40 peer-reviewed journal articles identified through a systematic literature search, this article serves as a structured foundation for researchers, engineers, and policymakers navigating the complex IAI landscape.