About this book:

The *Industrial AI Automation Playbook* provides a comprehensive strategic and technical roadmap for integrating AI-driven robotics into modern manufacturing environments. It frames the transition to "Industry 4.0" not merely as a technological upgrade, but as a systemic evolution requiring a unified operating model that bridges the gap between Information Technology (IT) and Operational Technology (OT). By emphasizing a "safety-by-design" philosophy and a phased rollout strategy, the book guides stakeholders through the complexities of selecting sensors for harsh environments, architecting edge-to-cloud data pipelines, and implementing advanced control strategies like Model Predictive Control (MPC).

At the technical core, the playbook details the foundational role of high-quality, contextualized data. It explores how machine vision, tactile sensing, and data fusion empower robots with the perception necessary for complex tasks such as bin picking, precision assembly, and autonomous navigation. The text meticulously breaks down the mechanics of robotics—kinematics, dynamics, and motion planning—while highlighting the rise of collaborative robots (cobots) and autonomous mobile robots (AMRs) that work alongside human operators. Furthermore, it introduces MLOps (Machine Learning Operations) as a vital discipline for managing the lifecycle of AI models, ensuring they remain accurate and reliable through continuous monitoring and automated retraining to combat "model drift."

Beyond the hardware and algorithms, the book addresses the critical organizational pillars of successful automation: governance, cybersecurity, and workforce transformation. It provides detailed frameworks for "build-vs-buy" decisions and ROI calculations that account for both tangible gains, like reduced downtime through predictive maintenance, and intangible benefits like improved worker ergonomics. Cybersecurity is treated as an extension of functional safety, necessitating a "defense-in-depth" approach to protect connected OT systems from digital threats.

The playbook concludes with practical case studies across discrete and process industries, such as automotive, electronics, chemicals, and food and beverage. These real-world examples illustrate how AI can optimize high-stakes environments—from managing exothermic chemical reactions to performing 100% inline quality inspections. Ultimately, the book advocates for a human-centric approach to automation, where intensive upskilling and change management empower the workforce to transition from manual labor to the intelligent oversight of a self-optimizing industrial enterprise.