From Operating Intelligence · Brandon Chiazza · Silent Way Press · Summer 2026

The Twelve Laws
of Enterprise AI

Twelve operating principles for enterprises building AI that compounds value rather than capability. Short enough to quote in a board meeting. Substantive enough to hold the program accountable to them.

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Operating Intelligence — book cover

Advance Praise

"Generative AI models are undoubtedly amazing, but this refreshingly honest book puts them in their place. Unless your organization redesigns work around AI capabilities, captures organizational context, upskills your people, and puts the AI system into full production, you won't get economic value. It's all hard work, but the sooner you get started the sooner you'll succeed with AI."

Thomas H. Davenport

Distinguished Professor, Babson College; Fellow, MIT Initiative on the Digital Economy and Stanford Institute for Human-Centered AI

"In Power and Prediction, we argued that the real value of AI emerges not just from better point solutions, but from redesigning systems that properly utilize machine prediction and human judgment. Operating Intelligence is an unusually practical guide to that system-level work, showing executives how to move beyond demos toward governed context, proof of value, and operating-model change that creates and compounds value. For leaders seeking to turn clever neural nets into durable enterprise advantage, Brandon Chiazza has written the operating manual."

Ajay Agrawal

Professor, University of Toronto, and Founder, Intrepid Growth Partners; co-author of Prediction Machines and Power and Prediction

"Cheap prediction leads to the question: which decisions do you redesign? Operating Intelligence provides answers based on Chiazza's real-world experience in AI strategy."

The Laws, in one line each.

Each Law is developed in depth in the book — and put to work in the companion artifacts (Self-Assessment, Charter, Diagnostic).

  1. 01

    Models are the smallest part of enterprise AI advantage.

  2. 02

    The Context Layer is the durable asset. Everything else depreciates.

  3. 03

    A proof of concept shows that the technology can perform. A proof of value shows that the work improves.

  4. 04

    Baseline before you build. A weak baseline is more useful than no baseline.

  5. 05

    Hallucinations are a structural property of next-token prediction, not a bug to be patched.

  6. 06

    A disclaimer is not a control boundary.

  7. 07

    Bounded autonomy: every agent's permissions, reversibility, and escalation are first-class design decisions.

  8. 08

    The evaluation harness is the precondition for everything else.

  9. 09

    Three words for value: projected, realized, banked.

  10. 10

    Every AI system needs a written rule under which it will be taken offline.

  11. 11

    Authority should match accuracy.

  12. 12

    Operating-model change is not the consequence of AI adoption. It is the price.

Read the book at operatingintelligence.org →

Who reads this and why.

Executives

The Laws give you a portable framework for the next AI investment conversation. Short enough to quote in a board meeting, substantive enough to hold the program accountable to them.

Builders

The Laws name the operating disciplines that turn a working demo into a system that ships and survives — the evaluation harness, the context layer, bounded autonomy, the written shutdown rule.

Governance & risk leaders

The Laws define the structural controls that distinguish "we deployed AI" from "we govern AI" — control boundaries, baselines, source authority, the operating-model change that adoption actually requires.

From the book.

Operating Intelligence: How Enterprises Build AI That Lasts is the 650-page argument the Twelve Laws compress. It develops the context layer as enterprise infrastructure, proof of value as the unit of AI investment, and operating-model redesign as the price of getting AI value out of the program.

Available on Amazon in paperback and Kindle. An Executive Edition (~250 pages) carries the strategic spine for time-poor leaders. Pre-order the Kindle edition now; paperback ships Summer 2026.

Pre-order on Kindle → Visit operatingintelligence.org →
Operating Intelligence — cover
Brandon Chiazza

Brandon Chiazza

CEO of Modali Consulting and faculty at the Cornell Brooks School of Public Policy. Former CTO at the New York City Mayor's Office of Contract Services. Holds AI-related patents in cost modeling and procurement. Authored the MyCity, MyAI public-policy case study at Cornell — the public-sector AI deployment that frames the governance argument across Chapters 6, 16, and 19 of the book.

LinkedIn · operatingintelligence.org