Boards today are operating in the most volatile governance environment in modern history, defined by rapid technological change, AI-driven decision-making systems, geopolitical instability, regulatory scrutiny, and rising stakeholder expectations. This book addresses why traditional governance models fail under pressure and how boards must evolve to remain effective, resilient, and credible.

Drawing on cutting-edge research and two decades of executive and boardroom experience, this book introduces a connected governance model that integrates network science, AI oversight, cognitive diversity, and crisis preparedness. Rather than treating governance as a static compliance function, this book reframes it as a dynamic leadership system that depends on how boards process information, manage interdependencies, and anticipate emerging risks.

Through in-depth case studies of Silicon Valley Bank, Boeing, OpenAI, FTX, Signature Bank, and First Republic, the book reveals common governance breakdowns and extracts actionable lessons for directors and executives to avoid similar failures. Readers are guided through practical tools for board mapping, artificial intelligence (AI) risk oversight, data-driven self-assessment and scenario-based decision-making.

Written for directors, senior executives, and governance professionals, Board Governance Rewired serves as both a diagnostic guide and implementation playbook. It equips readers with immediately usable frameworks to strengthen board effectiveness, enhance resilience, and deliver ethical, future-ready governance in an age of constant disruption.

Rajendra Prasad Gangavarapu is an established executive, board advisor, and academic who brings over two decades of senior leadership experience in financial services, fintech, and regulated industries, having held executive roles at several banks. His work sits at the intersection of board governance, AI oversight, risk management, and executive decision-making. He is an author of Mastering AI Governance published by Springer. He earned his doctorate from Georgia State University. He was recognized among the 100 Most Influential AI Leaders in 2024.