Yes, within our projects we've begun implementing structured readiness protocols to assess and guide the integration of Generative AI, particularly in industrial automation and SCADA systems. Our approach includes:
Data Governance Checklist: We evaluate data quality, labeling accuracy, and compliance (especially with sensitive operational data), ensuring it’s suitable for training or fine-tuning AI models.
Use Case Identification Matrix: We use this to score and prioritize AI use cases based on impact, feasibility, and risk. For instance, anomaly detection in power consumption patterns or predictive maintenance in control systems.
AI Ethics & Security Assessment: We include a checklist to evaluate algorithm bias, explainability, and security concerns (especially in ICS environments), aligned with ISA/IEC 62443 standards.
Infrastructure Readiness Protocol: We assess if our container-based infrastructure (e.g., Node-RED, Anaconda, InfluxDB in Docker Compose) is capable of running lightweight AI models, often tested with simple neural nets for proof-of-concept.
Team Skills & Tools Audit: We review the technical stack and team proficiency with Gen AI tools (e.g., OpenAI APIs, Hugging Face) and platforms like Jupyter, ensuring effective adoption.
Happy to share templates or examples if others are building similar frameworks. Would also love to hear how others are tackling readiness in industrial or data-critical environments.
PMI Team Member
