Data Governance, Infrastructure, and AI Project Leadership

Artificial intelligence does not create lasting organisational value through models alone. It depends on governed data, dependable infrastructure, disciplined delivery, responsible practice, and leadership capable of bringing them together.
Data Governance, Infrastructure, and AI Project Leadership presents these elements as one integrated enterprise practice. Written for postgraduate students, researchers, managers, technology professionals, and organisational leaders, the textbook connects the strategic governance of data with the technical foundations of AI and the practical realities of leading AI initiatives.
Across fifteen structured chapters, readers will examine:
- the intellectual foundations and strategic purpose of data governance;
- data operating models and the office of the chief data officer;
- data quality, metadata, master-data management, privacy, and protection;
- enterprise data architecture and the modern AI infrastructure stack;
- cloud, edge, and hybrid deployment decisions;
- MLOps and the operation of production AI systems;
- AI project methods, delivery lifecycles, teams, and stakeholder engagement;
- risk, regulatory compliance, ethics, and responsible AI;
- AI centres of excellence, enterprise scaling, and value measurement.
Composite opening scenarios place each chapter's concepts in realistic organisational settings. Thirty case studies connect the discussion to practice across industries and regions, while figures, tables, synthesis sections, and reflection and application questions support teaching, independent study, and professional development.
Rather than treating governance, infrastructure, project delivery, and responsible AI as separate concerns, this textbook demonstrates why they must be designed and led as a coherent whole.