This book presents a practical and strategic roadmap for AI adoption through the lens of enterprise architecture. It tackles the urgent challenges organizations face, whether a startup, SMB, or large enterprise, when navigating the complexities of AI and Generative AI implementation on a global scale. Covering critical areas such as architectural readiness, governance, compliance, ethical considerations, integration hurdles, and scalability, the book provides actionable blueprints, decision frameworks, and real-world case studies to enable sustainable AI adoption.
Structured around global trends, core enterprise layers (data, application, infrastructure, and business) and varying organizational maturity levels, it uniquely bridges the gap between high-level executive vision and on-the-ground architectural execution, while keeping the Human Endpoint Principle at its core-ensuring that every system ultimately serves people.
?You Will:
Discover frameworks that align AI with business goals, governance, and compliance while confronting ethical risks.
Understand why many AI projects fail to deliver expected value and how to avoid adoption turning into loss.
Apply enterprise architecture strategies to embed AI across data, applications, and infrastructure with built-in governance, scalability, and human-centered sustainability.
?This book is for: Enterprise Architects, CTOs, as well as students and professionals eager to explore how AI can be adopted responsibly and sustainably across organizations of every scale.
This book presents a practical and strategic roadmap for AI adoption through the lens of enterprise architecture. It tackles the urgent challenges organizations face, whether a startup, SMB, or large enterprise, when navigating the complexities of AI and Generative AI implementation on a global scale. Covering critical areas such as architectural readiness, governance, compliance, ethical considerations, integration hurdles, and scalability, the book provides actionable blueprints, decision frameworks, and real-world case studies to enable sustainable AI adoption.
Structured around global trends, core enterprise layers (data, application, infrastructure, and business) and varying organizational maturity levels, it uniquely bridges the gap between high-level executive vision and on-the-ground architectural execution, while keeping the Human Endpoint Principle at its core-ensuring that every system ultimately serves people.
?You Will:
Discover frameworks that align AI with business goals, governance, and compliance while confronting ethical risks.
Understand why many AI projects fail to deliver expected value and how to avoid adoption turning into loss.
Apply enterprise architecture strategies to embed AI across data, applications, and infrastructure with built-in governance, scalability, and human-centered sustainability.
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