Part III: Operating¶
Running the AI-first company—teams, data, operations, and growth.
Overview¶
With infrastructure and architecture in place, the challenge shifts to day-to-day operations. This section covers how to structure teams for AI-first work, build data strategies that compound, and use AI to scale both internal operations and customer-facing growth.
Chapters in This Part¶
Chapter 8: Teams for AI-First Companies¶
Structuring teams for AI-first success. The org chart changes, hiring shifts, and the 90-day AI fluency program.
Chapter 9: Data Strategy — Flywheels, Moats, and Ethics¶
Building data advantages that compound. Data flywheels, polyglot persistence in practice, and privacy by design.
Chapter 10: AI-Augmented Operations and Go-to-Market¶
Scaling operations and growth with AI. Same patterns for internal and customer-facing functions.
Key Concepts Applied¶
- Build vs Buy Calculus - Team and capability decisions
- Human AI Collaboration - Team structures and workflows
- Data Flywheel - Building self-reinforcing data advantages
- Domain Autonomy - Team ownership patterns
- Operations as APIs - Making internal systems AI-accessible
Reading Guide¶
For people/org leaders: Start with Chapter 8 on teams, with context from Part II on what they'll be building.
For data leaders: Focus on Chapter 9 for flywheel mechanics and ethics.
For operations/growth leaders: Chapter 10 combines both—read it in context of the architecture from Part II.
For startup founders: All three chapters address concurrent challenges—read them together.