Madhukar Gaganam
Physical AI is moving into plants, warehouses, hospitals, ports, and infrastructure. It can raise both the density and the judgment of a live site. The durable gain comes when those systems scale with the people on the floor, not by emptying it.The playbooks that scaled software do not automatically scale a machine that moves. Wear, latency, legacy equipment, and a person in the path are not software bugs. They are the site. Most operating facilities are brownfield. The line cannot stop so the building can be redrawn around the model.This book is a working map for that choice. It is written for leaders who have to put intelligence into shared physical spaces and need a test they can run on a live site.You will get:A clear distinction between software that lives on a screen and a machine that can make contact.The Strategic Decision Filter: scaling with people versus scaling by replacing them.Methods to keep usable authority, capture tacit knowledge before veterans leave, and put a working override in the loop.A shift from one forecast to several prepared paths, and from operator to orchestrator.The gaps digital AI rules do not cover: physical harm, reconstructible action, liability, and energy as a design limit.An Executive Audit at the end of each chapter: five questions on baseline, metrics, knowledge, culture, and the next move.The book is modular. Operations leaders can start with role redesign. Software teams can start with the sim-to-real break. Boards can start with physical liability. If a page does not help you answer the audit on a real site, skip it.This is not a human-free factory story. Closed cells designed around the machine are a different problem. This book is for the shared floor that already runs, where the exception is still the work, and the nearest person still needs the right to stop the unit and change the path. 6
