Accountable AI at Scale

Actionable accountability frameworks for deploying AI at enterprise scale

Accountable AI at Scale: Fueling Fast and Effective AI Deployment delivers practical frameworks for building accountability into AI operations without sacrificing competitive advantage. Written by a former Pentagon Chief AI Officer and the Director of Wharton's Accountable AI Lab, the book reframes accountability not as a constraint on innovation but as the foundation for faster deployment, lower risk, and stronger stakeholder trust across enterprise organizations.

Drawing on hands-on experience driving AI transformation across government and Fortune 500 organizations, the authors reveal what actually works in practice, including failures that have cost companies billions and shaped regulatory policy. The book provides data-driven processes, organizational structures, and cultural norms that operationalize AI accountability, moving leaders beyond aspirational ethics discussions toward measurable, implementable governance systems.

Readers will also find:

• Frameworks for balancing AI innovation speed with governance and regulatory compliance across large organizations
• Practical guidance on building organizational structures and cultural norms that address AI risks systematically
• Real-world case studies of AI deployment failures that cost billions and influenced regulatory policy
• Strategies for reframing accountability as a competitive advantage enabling faster, more sustainable AI deployment
• Approaches to operationalizing AI accountability for product managers, engineers, data scientists, and executive leaders

Written for C-suite executives, product managers, engineers, data scientists, and policymakers responsible for AI systems with real consequences, this book provides the operational frameworks needed to deploy AI responsibly at scale while maintaining speed and competitive positioning within evolving regulatory landscapes.