Decision-Quality AI
From AI Ambition to Operational Success
By Dr. Eva-Marie Muller-Stuler
“AI must earn the right to decide.”
Most organisations have learned how to build increasingly capable AI. Far fewer know when AI deserves decision authority. This book establishes the mathematical, architectural and governance discipline required to answer that question with non-compensatory evidence.
Why this book needed to exist
Every day, enterprises deploy machine learning models, generative agents, and algorithmic workflows deeper into business operations. Yet corporate boards, chief risk officers, and audit committees remain unable to answer a fundamental question: When does an AI system have the standing to make a consequential decision?
The False Promise of Checklists
The industry has responded to AI risk with either abstract ethical principles that sound noble in committee minutes but cannot alter runtime system state, or superficial compliance checklists that create an illusion of safety without verifying operational resilience.
The Laboratory-to-Production Chasm
A high score on an evaluation benchmark does not tell an enterprise whether a model can withstand temporal drift, human review fatigue, or vendor API outages. Technical progress in a laboratory is fundamentally distinct from operational standing in production.
Read Chapter 1: The Illusion of Capability
Discover why state-of-the-art accuracy leaderboards mislead risk committees, and what actually separates a working prototype from a production decision asset.
What readers will learn
The Non-Compensatory Standard
How to audit systems across Feasibility, Admissibility, Deployability, Economics, Governance, and Institutional Viability where no gate can compensate for another.
Concrete Operational Controls
Step-by-step methods for building Decision Receipts, AI Release Cases, evaluation evidence dossiers, and enforceable monitoring contracts.
Bounding Agentic Autonomy
How to calibrate deterministic fallbacks, prevent human review rubber-stamping, and preserve institutional accountability when models drift.
Who this book is for
Establish clear fiduciary boundaries for delegated machine authority and protect institutional balance sheets.
Move machine learning models safely into production with objective engineering bars rather than speculative pilot promises.
Demand structured, auditable evidence packages before signing off on autonomous operational release.
Already preordered on Amazon? Unlock Early Access
Submit your Amazon or retailer order reference to receive the Advance Reading Proof (Chapters 1 & 2), the Six-Gate AI Release Readiness Spreadsheet, and priority enrollment in the 8-Week Cohort Read-Along.
The 15 Chapter Companions
Each chapter features an executive abstract and framing. Full operational release protocols, evidence ledgers, and downloadable case files are reserved for verified book readers and Professional Platform members.
Foundations
A New Reality for AI
From Technical Ambition to Decision Discipline
Which AI Should We Build?
Problem Framing and Non-AI Baselines
The Anatomy of a Decision
Uncertainty, Regimes, and Action Windows
Build
From Design to Evidence
The Nine Data Requirements for Decision-Grade AI
Admissibility in Practice
Provenance, Permissions, and Regime Validity
Evaluation Integrity, Uncertainty and Model Reality
From Benchmark Theatre to Decision-Relevant Evidence
Architecture as Control
Designing Controllable AI Systems
Method Selection Under Constraints
Rules, Trees, Neural Models, and Ensembles
Economic Viability and Operating Burden
The Cost per Governed Task
Release
Operate
Incident Response and the Decision Receipt
Traceability, Forensic Reconstruction, and Containment
Monitoring, Drift, and Permission Renewal
Continued Operation as an Active Permission Decision
Retraining and Model Change
Governing System Transitions and Candidate Replacement
Sustain
Vendor Dependency and Platform Risk
Managing External Foundation Models and Vendor Lock-in
The Decision Quality Playbook
Institutionalizing Decision-Grade AI Across the Enterprise
The 8-Week Decision Quality Read-Along
Author-guided cohort experience for enterprise risk committees auditing live systems chapter by chapter. Includes 8 weekly modules from Foundation to Playbook.
Advance Praise from Industry & Risk Leaders
What Chief AI Officers, Risk Directors, and board advisors are saying about Decision-Quality AI.
“In global financial services, we have dozens of models with 98% benchmark accuracy that cannot pass audit. Dr. Eva provides the missing operational bridge: a non-compensatory evidence standard that risk committees and regulators can actually enforce.”
“The AI industry has spent years drowning in vague ethical charters that change nothing at runtime. Decision Quality AI gives engineering and product leaders concrete Decision Receipts, deterministic fallback contracts, and real release bars.”
“A timely, mathematically grounded antidote to leaderboard vanity. By separating technical progress from operational standing, Dr. Eva provides the playbook corporate boards desperately need before granting machines decision authority.”
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Available for immediate pre-order across major international booksellers and direct from the publisher.
Need to Brief Your Board or Audit Committee?
Request the official 12-Slide AI Steering Committee Presentation Deck. Formatted specifically for Chief Risk Officers, Board Chairs, and CAIOs preparing corporate leadership for consequential AI release.
Worldwide Retailer Availability & Enterprise Orders
Decision-Quality AI by Dr. Eva-Marie Muller-Stuler is available for preorder on Amazon. Hardcover and digital editions will be distributed globally through major booksellers.
Direct via Amazon
Guaranteed preorder price matching and automatic delivery on release date. Hardcover and Kindle editions.
AI Steering Committee Bundle
Equip your governance and audit committee. Includes editable Board Presentation Slide Deck and Six-Gate Readiness Spreadsheets.
Engineering & Risk Team Pack
Equip entire product and risk cohorts. Includes a private 45-minute virtual Case Clinic & Q&A session with Dr. Eva-Marie Muller-Stuler.