Unlocking IIoT Potential: A Systematic Review of AI Applications, Adoption Drivers, and Implementation Barriers

Abstract

Artificial Intelligence (AI) is playing an increasingly vital role in the Industrial Internet of Things (IIoT), enabling predictive analytics, real-time monitoring, and autonomous operations across industries such as manufacturing, logistics, and energy. However, widespread adoption is hindered by technological, organizational, and infrastructural challenges. This paper examines the adoption, application, and challenges of AI–IIoT environments, focusing on implementation domains, adoption drivers, enabling technologies, and key barriers. We conducted a Systematic Literature Review (SLR using PRISMA). Peer-reviewed English-language journal articles published between 2018 and 2025 were sourced from ScienceDirect, Web of Science (WoS), Scopus, IEEE Xplore, Springer, Google Scholar, Elsevier, and Taylor & Francis. After applying inclusion criteria and screening procedures, 46 relevant journal articles were included for analysis. Key AI applications identified include predictive maintenance, anomaly detection, real-time monitoring, autonomous process control, and smart supply chains. Adoption is facilitated by external enablers 5G infrastructure, regulatory support, and internal factors, organizational readiness, and workforce skills. Challenges include data quality issues, cybersecurity risks, legacy system integration, and limited model scalability. Technologies such as edge computing, cloud platforms, and federated learning are instrumental in mitigating these challenges. While adoption is growing, significant barriers remain. AI has the potential to drive operational efficiency and innovation in IIoT, provided these constraints are addressed. This paper offers a comprehensive taxonomy of AI applications and proposes a framework of adoption factors, offering valuable insights for researchers, practitioners, and policymakers involved in AI-driven industrial transformation.