Toward General Industrial Intelligence: A Survey of Large Models as a Service in Industrial IoT

Abstract

Industrial Internet of Things (IIoT) systems are inherently dynamic, deeply embedded in physical environments, and often embodied in autonomous agents. These characteristics demand an AI paradigm that can continuously adapt and generalize across heterogeneous data and tasks. Unlike existing surveys that focus on “IIoT for foundation models” – e.g., how IIoT infrastructure supports data collection or distributed training for large models – this work reverses the perspective by investigating “foundation models for IIoT.” We explore how large pre-trained foundation models (FMs) can be leveraged as a service to empower general industrial intelligence in IIoT. We propose a four-dimensional SCCE framework (Sensing–Computing–Connectivity–Evolution) that systematically examines the deployment of FMs in IIoT along the data processing pipeline and system lifecycle. Within this framework, we survey key IIoT tasks and discuss how state-of-the-art FMs (in vision, language, multimodal learning, etc.) can address challenges in noisy sensor data modeling, edge computing constraints, device connectivity and collaboration, and long-term model evolution. Our survey offers a unified, FM-centric perspective on enabling intelligent IIoT services, highlighting critical open challenges and future directions for integrating foundation model capabilities into industrial applications.