AI Implementation Blind Spots

Part I - The Problem No One Measures

Chapter 1 - The Velocity Trap and AI Brain Fry

Chapter 2 - AI Theater vs. the AI Implementation Studio

Chapter 3 - The Rework Tax: The Silent Killer of Scaling

Chapter 4 - The Pilot Trap and the 40% Failure Pattern

Chapter 5 - Trust, Risk, and the Shadow Ledger

Part II - Redefining AI Strategy

Chapter 6 - The Death of Tool-Based Thinking

Chapter 7 - The AI Strategist as a Decision Architect

Chapter 8 - EBITDA: The Financial Translation Layer

Part III - The Decision Architecture System

Chapter 9 - Diagnose and Map: Extracting Decisions From Workflows

Chapter 10 - Extract and Prioritize: Finding Data, Trust, and ROI Bottlenecks

Chapter 11 - Define: The Human vs. AI Decision Boundary Framework

Chapter 12 - Design: Assist, Recommend, Act, or Escalate

Chapter 13 - Pilot and Prove: Reality Tests, Evidence Packs, and Real ROI

Chapter 14 - Scale or Stop: The Discipline of Implementation

Part IV - Enterprise System Playbooks

Chapter 15 - The Revenue Engine

Chapter 16 - The Operational Core

Chapter 17 - The Financial Shield

Chapter 18 - High-Risk Environments

Part V - Compact Hard-Dollar Case Studies

Case 1 - The MSP That Got Faster but Not Cheaper

Case 2 - The Cybersecurity Pilot That Could Not Automate Response

Case 3 - The Marketing Team That Doubled Output and Lost Trust

Case 4 - The Finance Workflow That Almost Overclaimed ROI

Case 5 - The AI Project That Needed to Stop

Case 6 - The Manufacturing AI Pilot That Could Not Survive the Plant Floor

Case 7 - The Customer Service Chatbot That Reduced Tickets and Increased Frustration

Case 8 - The Healthcare Workflow That Improved Speed but Increased Risk

Case 9 - The Venture Portfolio That Confused AI Activity With Enterprise Value

Case 10 - The Procurement Agent That Saved Time and Created Vendor-Risk Exposure

Part VI - The Decision Architect's Toolkit

Final Thoughts - Discipline Over Hype

Companion Resources

Acknowledgments

About the Author

Source Notes