The Enterprise AI Operating System

A CXO Playbook for Strategy, Governance, and Execution
1. Auflage 2027
ISBN: 979-8-8688-3565-0
Verlag: APRESS L.P.

Buch, Englisch, Format (B × H): 178 mm x 254 mm

This book explores what it truly takes to make intelligence operational inside modern enterprises. Rather than focusing solely on AI technology, it examines the organizational, architectural, and operational challenges that determine whether AI initiatives scale successfully or stall after the pilot phase. From workflow-centric design and production architecture to governance, security, and trust boundaries, the book provides a practical roadmap for building AI systems that are reliable, sustainable, and aligned with business goals. It also addresses critical enterprise decisions around assistants and agents, build-vs-buy strategies, operational reliability, cost economics, and long-term scalability.

Drawing from real-world experience within regulated financial services, the book explores why many enterprise AI initiatives fail to reach production scale. It highlights recurring challenges such as fragmented data, unclear ownership, weak adoption models, and poorly understood cost structures. Covering the full enterprise stack—including strategy, governance, security, architecture, operating models, talent, and financial sustainability—the book equips CXOs, Chief AI Officers, senior architects, and transformation leaders with practical frameworks for operationalizing AI responsibly. Grounded in real-world enterprise experience, it serves as a strategic guide for organizations looking to build durable AI capabilities that continue delivering value long after today’s tools and trends evolve.

 You Will:

• Evaluate build-vs-buy decisions, AI operating models, and the real economics of deploying AI at scale
• Implement reliable workflows, agents, and production architectures aligned with enterprise business goals
• Address security, trust boundaries, observability, and risk management for AI in regulated environments
• Lead organizational adoption, talent transformation, and long-term AI strategy across the enterprise

This book is for: CXOs, Chief AI Officers and enterprise architects

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Prakash, Sunil

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Informatik

Inhaltsverzeichnis

1. The Shift: When Intelligence Becomes Operational.- 2. Why Most AI Initiatives Stall.- 3. Data Readiness Is Necessary, But Not Sufficient.- 4. The Workflow Is the Product.- 5. Governance Cannot Sit on Top of the System.- 6. The Layers of a Production AI System.- 7. Assistants, Copilots, Workflows, and Agents.- 8. The Build-Buy-Partner Decision Framework.- 9. When Smarter AI Is Not Worth It.- 10. Trust Boundaries, Identity, and Governed Autonomy.- 11. Security, Red-Teaming, and the Attack Surface You Don't See.- 12. Evaluation, Reliability, and Day-2 Operations.- 13. The Cost of Intelligence: Unit Economics, FinOps, and Staying Solvent.- 14. The Enterprise AI Operating Model.- 15. Talent, Judgment, and the New Leadership Burden.- 16. Change Management and Organizational Adoption.- 17. What Enterprises Actually Buy in the AI Era.- 18. AI in Regulated Industries.- 19. The Principles That Will Still Matter When Today's Tools Are Gone.

Über Autor(innen)

Sunil Prakash is an enterprise AI and platform architecture leader with nearly two decades of experience turning emerging technology into governed operating capability. He leads enterprise AI architecture at a global Tier-1 bank, focused on what it takes to move AI beyond pilots: production systems, governance controls, platform foundations, and operating-model design in regulated environments.

Before this, he was Chief Scientist at an AI startup in Singapore and held senior architecture roles and consulting at BNP Paribas, JPMorgan Chase, and PNC. He researches trust infrastructure for multi-agent AI systems and created the Agent Identity Protocol, which has an IETF Internet-Draft and reference implementations on PyPI. His published research includes four papers on agent identity, delegation, provenance, and collective reasoning. He is the author of and holds degrees from ISB and NUS.