Operational Technology 2.0: Integrating AI Co-Pilots, Predictive Analytics, and Live Governance

This workshop aims to equip participants with practical skills to leverage AI co-pilots, predictive analytics, and live governance frameworks in operational technology (OT) systems. Learn how to integrate AI-driven decision-making into industrial operations for enhanced efficiency, safety, and real-time control.

• Understand the concepts of OT 2.0 and AI co-pilot integration.
• Implement predictive analytics for industrial operations.
• Monitor OT systems in real-time using AI dashboards and governance tools.
• Detect and mitigate anomalies in industrial processes.
• Integrate AI workflows for optimization, compliance, and operational safety.

DAY 1: The AI Co-Pilot & Prompt Architecture

• Foundations of LLMs in Operational Technology (OT)
• Hands-on: Querying telemetry data and parsing raw error logs in OpenAI Playground
• Frameworks for role-based and few-shot prompt engineering
• Hands-on: Building a custom troubleshooting bot using Flowise/OpenAI GPTs

Day 2: Module 3: No-Code Machine Learning for Predictive Maintenance

• Supervised vs. unsupervised learning in industrial asset management
• Hands-on: Training a failure classification model using BigML/MindsDB
• Interpreting confusion matrices, model accuracy, and false positives
• Hands-on: Running predictive regressions on energy and supply chain costs via Julius AI
• Simulating production pivots and generating automated trend charts

Day 3: AI Governance, Risk & Stress-Testing, Privacy & The "Black Box" Problem

• Data sovereignty: Public vs. local/hybrid AI deployments
• Hands-on: Prompt injection, red-teaming, and vulnerability testing via Giskard/Vellum
• Techniques for ensuring explainability in automated decision-making
• Designing Human-in-the-Loop (HITL) validation checkpoints
• Hands-on: Constructing an agile AI Risk & Audit Dashboard in Airtable

• Gain hands-on experience integrating AI co-pilots in OT systems.
• Learn to apply predictive analytics and anomaly detection in industrial workflows.
• Understand real-time monitoring and governance frameworks.
• Develop skills for optimization, safety, and compliance in OT 2.0 environments.
• Be prepared for roles in smart manufacturing, industrial AI, and OT digital transformation.

• Engineers, professionals, and students in Industrial Automation, IoT, AI/ML, Data Analytics, Electrical/Mechanical Engineering.
• Professionals working in OT, smart manufacturing, industrial IoT, utilities, or process control.
• Data scientists and AI engineers interested in AI applications in operational systems.
• Individuals seeking to integrate predictive analytics and AI governance into OT environments.