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AI That Scales

Turn AI pilots into a board-ready enterprise AI platform

This book is 100% completeLast updated on 2026-06-25
Luca Berton

Most enterprise AI programs do not fail because the model is weak. They fail because the organization cannot operate AI: costs are hidden, ownership is vague, governance is late, vendors create dependency, and production risk is not clearly assigned.

This book helps leaders fix the system around the model.

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About

About

About the Book

Enterprise AI rarely fails because a model cannot be built. It fails because the operating system around the model is not ready: priorities are vague, costs are underestimated, governance arrives late, vendors create hidden dependency, and no one owns production risk.

AI That Scales gives senior technology leaders a practical path from scattered AI pilots to a Board-Ready Roadmap and 90-Day Action Plan. The book is vendor-neutral, budget-honest, and written for regulated or governance-heavy enterprises.

Readers build the artifacts that make AI fundable and operable: an AI Platform Scorecard, Use-Case Prioritization Matrix, Budget Model + Cost Guardrails, Vendor Decision Framework, RACI Matrix + Team Plan, Governance Blueprint, GenAI Architecture Decision Tree, Board-Ready Roadmap, and 90-Day Action Plan.

This is practical interpretation for platform leaders, not legal advice.

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Artificial IntelligenceBusiness StrategyLeadershipRisk ManagementDigital Transformation

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Author

About the Author

Luca Berton

Luca Berton is an Ansible Automation Expert who has been working with JPMorgan Chase & Co. and previously worked with the Red Hat Hat Ansible Engineer Team for three years. Published author of the Ansible for VMware by Examples and Ansible for Kubernetes by Examples best-seller of the Ansible By Example(s) practical book series and creator of the Ansible Pilot project. With more than 15 years of experience as a System Administrator, he has strong expertise in Infrastructure Hardening and Automation. Enthusiast of the Open Source supports the community, sharing his knowledge in different events of public access. Geek by nature, Linux by choice, Fedora, of course.

Leanpub Podcast

Episode 280

An Interview with Luca Berton

Contents

Table of Contents

How to Use This Book

  1. Who This Book Is For
  2. What This Book Is Not
  3. What You Will Build

AI That Scales

  1. The Enterprise AI Operating System
  2. Why the Operating System Matters
  3. The Artifact Path
  4. The Board-Ready Standard
  5. How to Read This Book
  6. Chapter Exercise

Why Enterprise AI Fails

  1. Field Case: Compliance Pulls the Plug
  2. The POC Illusion
  3. The Three Failure Gaps
  4. The Five Maturity Levels
  5. The Seven Readiness Dimensions
  6. The Overestimation Trap
  7. Common Failure Patterns
  8. Chapter Exercise

Right-Sized AI Strategy

  1. Field Case: Seven Pilots, Two Funded
  2. The Portfolio Dilution Trap
  3. The Use-Case Prioritization Matrix
  4. Fund, Gate, or Stop
  5. Metrics Beyond Accuracy
  6. The Politics of Killing Pilots
  7. Chapter Exercise

Budget and Economics

  1. Field Case: The Unbounded Pilot Bill
  2. The Production Cost Inversion
  3. The Four Cost Drivers
  4. Scenario Ranges Beat Fake Precision
  5. Cost Guardrails
  6. CFO-Friendly Language
  7. Chapter Exercise

Build vs. Buy

  1. Field Case: Locked In and Paying for It
  2. Partner or Dependency
  3. The Vendor Decision Framework
  4. Why AI Lock-In Is Deeper Than SaaS
  5. Commercial Traps
  6. Build, Buy, or Hybrid
  7. Chapter Exercise

Operating Model

  1. Field Case: Everyone Owns It, So No One Does
  2. Involvement Is Not Accountability
  3. Centralize, Federate, or Jointly Govern
  4. The Golden Path
  5. The RACI Matrix + Team Plan
  6. Team Plan
  7. Chapter Exercise

Governance and Compliance

  1. Field Case: Shadow AI Filled the Vacuum
  2. The Governance Paradox
  3. Minimum Viable Governance
  4. Risk Classification
  5. The Governance Control Matrix
  6. Evidence Is the Product
  7. Built-In vs. Bolted-On Governance
  8. Chapter Exercise

GenAI Architecture Decisions

  1. Field Case: The Demo That Would Not Scale
  2. Why GenAI Is Different
  3. The Easy-Button Trap
  4. The Decision Tree
  5. Hosting
  6. Retrieval
  7. Data Boundaries
  8. IP, Auditability, Security, and Cost
  9. Chapter Exercise

The Roadmap

  1. Field Case: The Board Said No
  2. The Five Board Questions
  3. The Narrative Spine
  4. The Anti-Fluff Test
  5. The Board-Ready Roadmap Structure
  6. The 12-Month View and 90-Day Zoom
  7. The Decision Required
  8. Chapter Exercise

Capstone: Board-Ready AI Platform Roadmap

  1. Field Case: From Pilots to Platform
  2. The Board-Ready Standard
  3. The Executive Summary
  4. The 90-Day Action Plan
  5. The Five-Minute Board Pitch
  6. Objections to Prepare For
  7. Final Definition of Done
  8. Capstone Exercise

Afterword: Make AI Operable

  1. The Operating Cadence
  2. The Final Standard

Appendix A: Artifact Workbook

  1. AI Platform Scorecard
  2. Use-Case Prioritization Matrix
  3. Budget Model + Cost Guardrails
  4. Vendor Decision Framework
  5. RACI Matrix + Team Plan
  6. Governance Blueprint
  7. GenAI Architecture Decision Tree
  8. Board-Ready Roadmap Outline

Appendix B: Boardroom Questions

  1. Current State
  2. Strategy and Prioritization
  3. Economics
  4. Vendor and Architecture
  5. Operating Model
  6. Governance and Compliance
  7. GenAI Architecture
  8. Roadmap and Ask
  9. The Final Test

Appendix C: Case Study Library

  1. How to Use the Cases
  2. Case 01: The Unbounded Pilot Bill
  3. Case 02: Compliance Pulls the Plug
  4. Case 03: Everyone Owns It, So No One Does
  5. Case 04: Locked In and Paying for It
  6. Case 05: The Demo That Would Not Scale
  7. Case 06: The Spike Nobody Forecast
  8. Case 07: Data in the Wrong Place
  9. Case 08: A Thousand Shadow Tools
  10. Case 09: The Model That Drifted
  11. Case 10: No Way Out
  12. Case 11: The Board Said No
  13. Case 12: From Pilots to Platform

Appendix D: Executive Glossary

  1. Strategy and Portfolio
  2. Money
  3. Vendors and Architecture
  4. Operating Model
  5. Governance and Risk
  6. Delivery and Outcomes

About the Author

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