- Introduction
- 1. Setting the Stage
- 2. Day Zero
- 3. The Audit
- 4. Building the Foundation
- 5. Extraction and Testing
- 6. Wrong Turns
- 7. Port and Feature Parity
- 8. Production Readiness
- 9. New Development
- 10. Sustainable Development
- 11. Blind Spots
- 12. Beyond Modernization
- 13. Conclusions
- Appendix
Modernizing Legacy Software with AI
A Technical Leader's Guide to AI-Driven Transformation
This book is 20% completeLast updated on 2026-05-08
Close the gap between "AI that generates code" and "AI-assisted development for production software" with this complete, proven playbook including the six-phase arc from audit to production.
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About the Book
Close the gap between "AI that generates code" and "AI-assisted development for production software".
The methodology inside delivered in roughly 100 developer-hours what two senior engineers would traditionally spend 6–9 months on, without sacrificing test coverage, documentation, or correctness. Not by moving fast and breaking things, but rather by doing the unglamorous work first, namely, infrastructure, oracle tests, and a disciplined division of labor where humans set direction and AI handles volume.
You'll leave with a complete, proven playbook including the six-phase arc from audit to production, session management that keeps AI output trustworthy across a real project, blind spot awareness that prevents the mistakes most teams only recognize in hindsight, and the closed-loop approach that makes the 10x multiplier repeatable and not a one-time result.
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About the Author
Nils Boeffel
Nils Boeffel is a management consultant and technologist who bridges AI strategy and hands-on implementation. He holds a Master's in Computer and Systems Engineering from Rensselaer Polytechnic Institute and an MBA from INSEAD (with an exchange at Wharton), and spent his early career as a consultant at McKinsey's Business Technology Office and as an engineer at Teradyne, building and testing instrumentation systems.
Beyond legacy modernization, he has built and deployed AI-powered systems including a corporate finance automation platform with orchestrated workflows, multi-source data integration, and risk scoring, as well as an AI-driven language learning application using LLM APIs for structured text generation and speech synthesis. These projects, alongside the modernization work informs the practical perspective throughout this guide, not what AI could theoretically do, but what it actually does when you put it into production.
Learn more: https://boeffel.net/ and https://aideci.de/
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