The AI Systems Field Manual
A production-minded guide to turning messy business workflows into AI systems that can be trusted, measured, governed, and improved after the demo ends.
EUR 97
Price
Online
Format
220 pages
Pages
Production design patterns
Evaluation and reliability model
Workflow-to-system method
Who It Is For
Built for people making business decisions.
Executives, operators, consultants, product owners, and implementation teams responsible for moving AI from prototype to dependable business infrastructure
Use the book as an operating manual for planning AI automation, selecting the right workflows, governing risk, measuring ROI, and moving from isolated pilots to a managed automation portfolio.
Business Outcomes
Convert real workflows into deployable AI systems
Design evaluations before users depend on outputs
Build governance, cost, latency, and adoption into the operating model
Reader Signal
Built for operators who need more than AI hype.
The book is positioned for executives and implementation teams who need a practical path from pilot activity to managed automation capability.
"
The useful part is the operating model. It does not sell AI as a tool purchase; it shows how to make ownership, governance, and ROI clear enough for leadership to act.
COO, mid-market services firm
"
The value map and stage-gate model make the book practical. It gives teams a way to decide what should be automated first and what should be killed before it wastes budget.
Transformation lead, B2B operations
"
This reads like a field manual for executives. The strongest sections are governance, operations, and measurement because they address what usually breaks after the demo.
CIO advisor, enterprise AI programs
Inside the Website Reader
Chapter preview.
Why AI Apps Fail After the Demo
Shows why impressive demos often fail in production, and what must be true before a pilot deserves more investment.
The Workflow Is the Product
Explains how to move from a business process to a working AI-enabled system with clear inputs, owners, and outputs.
Designing the AI Application Stack
Shows how to turn the chapter topic into a clear business decision, operating model, or implementation step.
Context, Memory, and Retrieval
Shows how business knowledge, retrieval, source quality, and context design determine whether AI output can be trusted.
RAG as Business Infrastructure
Shows how business knowledge, retrieval, source quality, and context design determine whether AI output can be trusted.
Prompts as Interface Design
Treats prompting as interface design: objective, context, constraints, examples, output format, and escalation path.
Tools, Agents, and Permission Boundaries
Explains when autonomous or semi-autonomous AI agents are useful, and where tool access, permissions, and approvals must be controlled.
Evaluation Before Deployment
Builds the testing discipline needed to catch weak outputs before users or customers depend on them.
Reliability, Cost, and Latency Tradeoffs
Shows how to turn the chapter topic into a clear business decision, operating model, or implementation step.
Security, Governance, and Auditability
Defines the controls, review points, ownership, and evidence needed to use AI without creating unmanaged exposure.
Adoption, Training, and Human Review
Covers the human side: trust, behavior change, workflow adoption, training, incentives, and resistance.
The 90-Day AI System Rollout
Shows how to turn the chapter topic into a clear business decision, operating model, or implementation step.
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