Every organization that uses artificial intelligence is now operating inside a regulatory environment that did not exist a decade ago. The EU AI Act, the world's first comprehensive AI law, converted voluntary governance principles into binding obligations with supervisory authorities, conformity assessments, and penalties that match the scale of major data protection regimes. For senior leaders, this is not a legal novelty to be delegated to counsel. It is an enterprise-wide compliance obligation that touches product development, procurement, hiring, credit decisions, healthcare delivery, public-sector contracting, and the foundation models that increasingly sit beneath all of it.
The EU AI Act and Global AI Compliance is a manager's regulatory playbook for this new environment. It is written for the executive, compliance lead, or program owner who needs more than a summary of the Act and less than a law-firm treatise-who needs to understand what the rules actually require, how supervisory authorities will enforce them, and what the organization must build to prove compliance on demand.
Chapter One traces how principles became law-why voluntary AI ethics frameworks failed to close the accountability gap, and why regulators concluded that rights against deploying organizations required statute rather than principle. Chapter Two provides the anatomy of the EU AI Act: the four-tier risk classification, the prohibited practices, the detailed obligations attached to high-risk systems, and the new general-purpose AI category that regulates foundation models distinctly from the applications built on top of them.
Chapter Three widens the lens to the rest of the world. The United States is treated as a fragmented compliance environment in which federal guidance, state laws, and sector-specific regulators interact; Canada's AIDA framework is examined alongside Brazil, China, and emerging regulatory architectures; and the chapter closes with practical approaches to managing jurisdictional complexity at multinational scale.
Chapters Four and Five move from the law to the organization. Chapter Four builds compliance as an organizational discipline-people, process, and technology-closing the implementation gap between policy statements and operational practice. Chapter Five examines the conformity assessment imperative, walking through the architecture of assessment, the evidence that supervisory authorities expect, and the internal controls that produce that evidence reliably.
Chapter Six addresses the distinctive challenge of managing high-risk AI systems under the Act's heightened obligations. Chapter Seven examines the foundation model problem-the regulatory, commercial, and operational implications of general-purpose AI for organizations that build on top of these models rather than train them. Chapter Eight designs the compliance function itself: its structure, staffing, reporting lines, and decision rights inside the broader enterprise.
Chapter Nine addresses governing through change-how to build a compliance program that remains credible as the EU AI Act is refined, as new jurisdictions adopt their own frameworks, and as the underlying AI technology continues to evolve. The closing synthesizes the book's through-lines and offers senior leaders a concrete starting point for the decisions that matter most in the first twelve months of a regulated AI program.
The leaders who will navigate this era successfully are not those who treat AI compliance as paperwork. They are the ones who build governance infrastructure that makes their AI systems defensible on their worst day-because the era of voluntary self-certification is over, and the era in which every consequential AI decision must be documented, justified, and auditable has begun.
