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

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 Agentic AI for Serious Engineers and holds degrees from ISB and NUS.