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.
