AI Readiness for Decision Makers
A practical guide to leading your organization through AI transformation
What This Book Covers
Most organizations know they need an AI strategy. Few know where to start. This book provides a structured framework for assessing your organization's AI readiness and building a practical path forward — covering strategy, technology, people, data, governance, and scale.
Written for executives, program managers, and transformation leaders, it bridges the gap between AI hype and organizational reality. Each chapter includes maturity assessments, economic considerations, and concrete implementation roadmaps.
The book draws on two decades of consulting experience in technology transformation, combined with hands-on AI implementation knowledge. It's the guide the author wished existed when clients started asking "What should we do about AI?"
Who this book is for
- ✓ CIOs, CTOs, and digital transformation leaders
- ✓ Business unit heads evaluating AI for their teams
- ✓ Program managers running AI adoption initiatives
- ✓ Consultants advising organizations on AI strategy
What's Inside
10 chapters across three sections
Introduction & Context
Introduction
How this book came about and why it takes a different approach. Written in collaboration with AI, it practices what it preaches — using AI as a thought partner throughout the writing process.
Chapter 1: Beginning with AI
The state of AI in 2026 — what it is, what it can do, and how we got here. Cuts through the noise to give decision makers a clear picture of AI capabilities, limitations, and the landscape they're navigating.
The AI Readiness Framework
Chapter 2: Understanding AI & Technology
You can't lead what you don't understand. Covers core AI concepts, integration patterns like MCP, and strategies for building the right level of technical literacy across your organization.
Chapter 3: Strategy & Vision
Stop chasing shiny AI toys. Start by asking what business problem you're solving. Covers build vs buy vs partner decisions, measurement frameworks, and how to create an AI strategy that connects to business outcomes.
Chapters 4–5: Adoption & Implementation
Technology only creates value when people use it. Tracks implementation from augmentation to autonomy through five maturity levels, with practical guidance on the Help → Automate → Rethink progression and adoption metrics.
Chapter 6: Data & Infrastructure
You can't build AI on messy data. Covers data privacy including the "Lethal Trifecta," AI security and attack vectors, quality controls, and the infrastructure decisions that enable or block AI adoption.
Chapter 7: People & Skills
AI doesn't replace people — it amplifies them. Addresses skill development, empowerment and democratization, organizational structure for AI, and the change management needed to make adoption stick.
Chapter 8: Governance & Ethics
Set guardrails before things go sideways. Covers human-in-the-loop accountability, responsible AI frameworks, ethical considerations, and how to communicate your AI approach to stakeholders and the public.
Chapter 9: Scale & Flexibility
Today's best solution may be obsolete in six months. Covers building for scale while designing for change — including adaptability, technical operations (MLOps), and strategies for graceful deprecation.
Outlook
Chapter 10: The Future of AI
Looking ahead at how AI will reshape work, organizations, and society. Explores the evolving AI landscape and what it means for leaders preparing their organizations for continued change.
Get Notified When the Book Is Available
Leave your email and we'll let you know when the book is ready. No spam — just one notification.
Thank you!
We'll let you know when the book is available.