Interaction patterns between Artificial Intelligence and Digital Twins in the industrial domain

Context: The adoption of Artificial Intelligence (AI) in industrial production systems has raised significant expectations for increased efficiency and innovation. Nevertheless, challenges such as the distributed nature of industrial operations, the heterogeneity of physical devices, and the complexity of real-world processes continue to hinder AI integration. Digital Twins (DTs) have emerged as a promising abstraction to decouple physical complexity from digital representations, facilitating more effective system management. Objective: This work investigates how AI can be systematically integrated with DTs in industrial contexts. The goal is to identify and characterize a set of interaction patterns that leverage the complementary strengths of AI and DTs to enhance industrial intelligence and performance. Methods: Drawing on a structured view of how responsibilities can be shared between AI technologies and DT-enabled shop floors, the paper defines four interaction patterns—AI Observing DTs, AI Advising DTs, AI Controlling DTs, and AI Embedded in DT. Each pattern is analyzed in terms of its roles, data and control flows, and typical application scenarios, and is illustrated on a DT-enabled physical micro-factory that reproduces realistic production conditions. Results: The four patterns show how different placements and responsibilities of AI components with respect to DT layers impact modularity, reuse of AI models, maintainability, and integration with legacy industrial systems. The micro-factory illustration highlights how the patterns can support practical use cases, including root-cause analysis of performance degradation, machine-level health monitoring, and AI-based production scheduling. Conclusion: Structuring AI–DT integration around interaction patterns provides a concrete way to bridge the gap between conceptual opportunities and operational industrial systems. The proposed patterns offer a reusable design vocabulary for positioning AI with respect to DT layers in cyber–physical production systems, and for reasoning about the architectural trade-offs of alternative integration strategies.