AI-FIRST INDUSTRIAL AUTOMATION

Escape Pilot Purgatory. Build the Embodied Enterprise.

Autonomous manufacturing is no longer a concept-it is an operational requirement. While the industrial AI market is projected to hit $155 billion by 2030, the reality on the factory floor is grim: most AI pilots fail to scale.

The reason? Most manufacturers treat artificial intelligence as a "bolt-on" analytics dashboard. They are decorating legacy problems with expensive software rather than re-engineering their control systems from the ground up.

AI-FIRST INDUSTRIAL AUTOMATION is the definitive practitioner's guide to moving beyond "AI-added" experiments. It provides the blueprint for the Embodied Enterprise-a factory that doesn't just analyze data, but senses, decides, and acts as a fully integrated, closed-loop system.

This book functions as both a strategic roadmap for leadership and a technical reference for the engineering floor. You will learn to bridge the gap between deterministic control theory and probabilistic machine learning to create resilient, self-optimizing operations.

Inside this guide, you will master:

• The AI-First Reference Architecture: Implementation of the five-layer stack and the Unified Namespace (UNS) to finally collapse the cultural and technical divide between IT and OT.

• The Intelligence Layers: How to augment classical PID and Model Predictive Control (MPC) with Reinforcement Learning, Industrial Foundation Models, and Small Language Models (SLMs) at the edge.

• Actionable 90-Day Blueprints: Step-by-step playbooks for high-ROI applications: predictive maintenance, computer vision quality inspection, and digital twin deployments.

• The Rise of Physical AI: Navigating the shift from rigidly programmed robots to learning systems powered by Vision-Language-Action (VLA) models and sim-to-real transfer.

• Governance & Security: Building CFO-ready business cases using OEE-linked ROI and securing cyber-physical systems against novel AI threat vectors.

• The Executive & Plant Manager: Focus on ROI, OEE, and navigating the maturity curve from optimization to full autonomy.

• The Control Systems Engineer: Learn to integrate AI safely with physical constraints, safety systems (SIS), and classic control loops.

• The Data Scientist & IT Leader: Transition into industrial domains by mastering strict deterministic latency, edge computing, and time-series intelligence.

Whether you manage discrete manufacturing, continuous chemical processing, or complex logistics, this book equips you with the architecture to transform your facility into a self-optimizing engine.