
2024 / Free online book · The AI-Native Canon
Systems That Ship
The organizational habits behind durable AI products
Access
Free
Chapters
8
Read time
82 min
A practitioner's account of the habits that separate a demo from a durable product, in engineering and in the P&L, because in practice they are the same problem.
This edition is free to read onsite. Each chapter has its own URL, so readers can bookmark, share, and return to the exact section they need.
Table of contents
FM Front Matter: Systems That Ship The organizational habits behind durable AI products 2 min 01 The Demo Lies by Omission The demo worked because the room was arranged for it to work. The dataset was clean. 9 min 02 Scope Is a Trust Mechanism The first version tried to answer every employee question. It failed constantly. 10 min 03 The SHIP-LOOP Operating System The team stopped asking "is the model ready?" and started asking where the workflow was in SHIP-LOOP. Had scope been narrowed? 10 min 04 Harden the Path: Data, Evals, Cost, Latency, Security The assistant failed for five different reasons that looked like one reason to users: it was unreliable. Sometimes the document was stale. 10 min 05 Instrument Behavior and Learn From Contact The product team watched request volume and declared adoption. The support team watched confused escalations and knew something was wrong. 10 min 06 Pace the Rollout and Own the System The beta doubled overnight because the launch email went to the wrong segment. The model held up. 10 min 07 Incidents, Trust Evidence, and Pruning The company fixed the incident and kept the feature. Then the same class of failure happened in a different workflow. 10 min 08 The Cadence That Keeps Shipping The company did not become good at AI because of a hackathon. The hackathon produced ideas. 10 min END Conclusion: Closing Note The AI-native organization is not the organization with the most demos. It is the organization that can turn a promising behavior into a governed, measured, cost-aware, trusted, and owned product surface. 1 min
