Collective intelligence-based service migration enabling zoom-in functionality within industry 5.0

• Collective Intelligence with AI orchestration enhances Kubernetes for Industry 5.0 • Zoom-In functionality enables dynamic ML upgrades with human-in-the-loop input • LLM-powered intent translation drives context-aware, explainable decisions in IIoT • Proactive service migration reduces downtime via hot/cold state management • Validated in real-world use case, balancing compute efficiency with operational needs The rapid evolution of Industry 5.0 emphasizes the integration of human expertise with machine intelligence to create resilient, adaptive, and human-centric industrial systems. This paper introduces a novel Collective Intelligence (CI)-based service migration framework designed for Industry 5.0 environments, enabling dynamic orchestration of stateful services across heterogeneous edge-to-cloud infrastructures. At its core, the framework leverages Kubernetes (K8s) enhanced with AI-driven decision-making and human-in-the-loop collaboration to address the limitations of traditional orchestration in industrial settings. A key innovation of this work is the Zoom-In functionality, which empowers human operators to escalate anomaly detection and analysis by deploying advanced machine learning models on demand, seamlessly migrating services to resource-rich nodes when deeper investigation is warranted. The proposed framework integrates Large Language Models (LLMs) to translate operator intent into actionable policies, ensuring context-aware and explainable decision-making. Experimental validation in real industrial scenarios demonstrates high anomaly detection accuracy (F1-scores up to 1.0), reliable operator intent translation (over 70% correct JSON generations with lightweight LLMs), and efficient multi-criteria scheduling with millisecond-level decision times. Moreover, the proposed migration mechanism reduces downtime by more than 50% compared to vanilla Kubernetes, ensuring service continuity in mission-critical tasks. This work advances the vision of collaborative intelligence in IoT systems, bridging the gap between human judgment and automated orchestration for Industry 5.0 applications.