Stop Implementing AI
Stop Implementing AI
The No-BS Playbook for AI That Works
About the Book
Stop Implementing AI: The No-BS Playbook for AI That Works
Most companies are stuck in "pilot purgatory"—endless AI experiments that never scale. Teams spend weeks sharing clever prompts and one-off scripts, but nothing makes it into production. Budgets evaporate, engineers' enthusiasm curdles into cynicism, and executives start asking "we spent how much on this?"
The problem isn't the AI tools—it's the approach. AI models are inherently non-deterministic (ask the same question twice, get different answers), while software engineering demands deterministic, reliable results. Without structure to bridge this gap, AI adoption fails spectacularly.
This book introduces PIT™ (Plan Implement Test), a proven methodology that transforms unpredictable AI outputs into reliable engineering workflows. Built from thousands of hours of real development work, PIT™ channels AI's capabilities through structured planning, phased implementation, and comprehensive testing—delivering the consistency that production code demands.
What You'll Learn:
- Why "just giving engineers AI" fails without structured guidance
- How to structure AI adoption for maximum business impact—not just productivity toys
- The complete PIT™ method: from loading context to delivering production-ready features
- Real-world case studies showing measurable results: 85% time reduction, 82% cost savings, 3-week projects completed in days
- Practical frameworks for governance, team rollout, and measuring ROI that executives understand
Who This Is For:
Engineering leaders, senior individual contributors, and product-minded managers who want to ship real outcomes—not slide decks. If you're accountable for budgets, release cadence, or cross-functional collaboration, you'll find direct guidance on capturing context, shaping prompts, governing adoption, and measuring ROI.
The Bottom Line:
Every month spent in pilot purgatory wastes time and resources while competitors ship faster. This book shows you how to lead that transformation—not through demos and experiments, but by putting AI on the critical path of work that actually matters. The organizations mastering AI-powered development aren't gambling on AI's capabilities—they're channeling those capabilities through proven engineering discipline that makes speed reliable and quality scalable.
That's what determinism looks like in practice: chaos transformed into structure, experimentation turned into process, and AI's potential finally realized through engineering rigor.
About the Author
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Table of Contents
- Stop Implementing AI
- The No-BS Playbook for AI That Works
- Preface
- 1 The AI Tipping Point in Software Development
- 1.1 The Market Shift: AI as a Competitive Necessity
- 1.2 Why “Just Giving Engineers AI” Fails Without Guidance
- 1.3 The Risk of Waiting While Competitors Adopt Now
- Why This Matters to You
- 2 The High-Return Path to AI Adoption
- 2.1 Choosing High-Impact Use Cases
- 2.2 Aligning AI-Assisted Coding with Business Outcomes
- 2.3 Avoiding Pilot Purgatory
- What to Do Next
- Closing Thought
- 3 Why AI-Powered IDEs Change the Game
- 3.1 The Evolution: From Code Assistants to Context-Aware IDEs
- The Anthropic Story: Engineering at Scale
- The Evolution of AI Coding Practices
- 3.2 What Makes AI-Powered IDEs Different
- 3.3 The Competitive Landscape
- 3.4 The Hidden Dangers of Context Overload
- 3.5 Integrating Into Modern Engineering Workflows
- 3.5 Return on Investment for Leadership: Speed, Quality, Maintainability
- What to Do Next
- Closing Thought
- 4 PIT™: Context to Implementation
- The Healthcare Platform Story
- 4.1 Step 1 (Plan) – Load Context
- 4.2 Step 2 (Plan) – Research
- 4.3 Step 3 (Plan) – Create the Plan
- 4.4 Step 4 (Plan) – Review via “Hats”
- 4.5 Step 5 (Implement) – Build in Phases
- 4.6 Step 6 (Implement) – Validate the Build
- 4.7 Step 7 (Test) – Validate Testing
- 4.8 Step 8 (Test) – Human Validation
- 4.9 Security Considerations: What to Share and What to Protect
- Additional Benefits
- What to Do Next
- Closing Thought
- 5 Scaling PIT™ Across Teams and Organizations
- The Team Adoption Challenge
- 5.1 From One Engineer to a Whole Team
- 5.2 Requirement Bottlenecks in the AI Era
- 5.3 Organizational Scaling Strategies
- What to Do Next
- Closing Thought
- 6 From Skeptics to Raving Fans
- 6.1 My iOS & Android App Projects
- 6.2 Colleague Skepticism
- 6.3 The “Aha” Moments
- 6.4 Diverse Success Examples
- 6.5 The Team Alignment Challenge
- 6.6 Lessons Learned
- What to Do Next
- Closing Thought
- 7 Cookbook: Four PIT™ Scenarios You Can Run Tomorrow
- 7.1 Backend Bug Fix: Stabilize Order Intake
- 7.2 Frontend Unit Tests: Lock In Checkout Quality
- 7.3 Feature Expansion: Add “Save for Later”
- 7.4 Greenfield Build: PTO Tracker MVP
- 7.5 Complex Codebase Navigation: Adding Features to Unfamiliar Systems
- What to Do Next
- Closing Thought
- 8 Advanced: MCP Servers: The USB-C Port for AI Applications
- The Protocol That Changes Everything
- 8.1 What Are MCP Servers?
- 8.2 Real-World MCP Server Examples
- 8.3 Getting Started with MCP Servers
- 8.4 Building Your Own MCP Server
- 8.5 MCP Servers and PIT™ Integration
- 8.6 The Future of MCP
- Closing Thought
- 9 Advanced: The TDD Comeback in AI-Powered Development
- 9.1 Why TDD is Making a Comeback
- 9.2 AI-Powered TDD: Faster, Smarter, More Reliable
- 9.3 Measuring TDD Impact
- Closing Thought
- 10 Final Chapter: Wrapping It All Up
- My Story: From Slogging to Sharing
- 10.1 What I Learned the Hard Way
- 10.2 Your Action Plan (No Excuses)
- 10.3 The Numbers That Actually Matter
- 10.4 Making It Stick (The Hard Part)
- 10.5 Don’t Let It Fall Apart
- 10.6 Scale It Right
- Final Thought
- Acknowledgments
- About the Author
- Resources
- Stop Implementing AI
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