Data and AI Ecosystems

Contemporary organisations do not adopt data and artificial intelligence in isolation. They operate within ecosystems — configurations of cloud platforms, foundation-model providers, data spaces, and counterparties — and the ethical, governance, and strategic decisions they face cannot be separated from one another.

Data and AI Ecosystems is a postgraduate textbook that treats these decisions as a single integrated discipline. Structured around three pillars — ethics, governance, and strategy — it develops the philosophical traditions of consequentialism, deontology, and virtue ethics in their own terms before applying them to real questions of data and AI practice; it examines governance and regulation across the major regulatory environments of the world, including the European Union, the United States, the United Kingdom, and China; and it builds the strategic apparatus through which organisations evaluate options, measure performance, and act under uncertainty.

The book assumes no prior philosophical training and no technical background in machine learning. A continuing case study — a multi-site manufacturer navigating real operational decisions — threads through all twelve chapters, showing how the frameworks work in practice rather than in the abstract. The book draws on examples from developed and emerging economies alike, giving students a genuinely global view of how data and AI practice is being shaped.

Written for MSc and doctoral students, and for executives and practitioners who must make these decisions in operation, Data and AI Ecosystems offers an integrated treatment that bridges philosophy, management, and strategy — and that prepares the reader to engage the data and AI ecosystem with clarity and confidence.