Research on AI Large Model in Industrial Applications

The rapid evolution of artificial intelligence (AI) has ushered in a transformative era for industrial intelligence, driven by the emergence of large-scale AI models. This study investigates the application paradigms, challenges, and future directions of AI large models in industrial settings. By analyzing 99 industrial use cases and comparing the U-shaped and inverted U-shaped distribution trends of large and small models, we identify three core construction modes: pre-training, fine-tuning, and retrieval-augmented generation (RAG). These modes enable large models to enhance efficiency across the entire industrial chain, including R&D design, production, management, and service. Key innovations include the integration of multimodal processing, cross-domain adaptability, and hybrid architectures that synergize large and small models. However, challenges such as data quality, reliability, and high costs hinder widespread adoption. We propose that advancements in model compression, cost reduction, and domain-specific foundational models will accelerate industrial AI deployment. This research provides a systematic framework for leveraging large models to achieve industrial intelligence, offering theoretical and practical insights for academia and industry.