AI and Industrial DevOps Teams
Revolutionizing Modern Ways of Working
This groundbreaking paper explores how AI and large language models (LLMs) can enhance Industrial DevOps practices, specifically in the context of cyber-physical systems like manufacturing plants, autonomous vehicles, energy systems, and complex industrial infrastructure. The authors present a comprehensive framework for integrating AI capabilities into daily operations, showing how teams building and maintaining these critical systems can leverage AI as a collaborative partner rather than just a tool.
The paper addresses the unique challenges of applying AI in industrial settings where reliability, safety, and regulatory compliance are paramount. It demonstrates how AI can assist in areas like predictive maintenance, quality control, supply chain optimization, and real-time monitoring of industrial processes while maintaining essential human oversight. The authors provide concrete examples of how industrial teams can use AI to enhance their development and operational practices without compromising the rigorous standards required in industrial environments.
Drawing from extensive research and real-world industrial applications, the paper provides practical guidance for leaders seeking to enhance the value of their cyber-physical systems through emerging technologies. It includes detailed examples of AI implementation across various industrial sectors, alongside patterns and practices for successful adoption that address the specific needs and constraints of industrial systems development and operations.
- Topics
- AI · Software Delivery & Architecture
- Formats
- epub · pdf
- License
- Creative Commons BY-NC-SA