AI Textbook for Managers (Paperback)

Ilker Cingillioglu

ISBN 10: 1764811038 / ISBN 13: 9781764811033

Published by Springer Reach, 2026

About this Item

Paperback. Every organisation now has access to powerful AI tools. Very few know how to use them to make better decisions.This book is about the gap between having AI and using it well. It is written for analysts, managers, clinicians, lawyers, engineers, policy professionals, and researchers - anyone whose work involves consequential decisions and who wants to make those decisions more rigorously, more consistently, and more accountably in an age of machine intelligence.Drawing on decision science, cognitive psychology, applied statistics, and ten chapters of worked Python examples, Dr. Ilker Cingillioglu builds a complete framework for AI-augmented decision-making - from the anatomy of a decision and the cognitive biases that corrupt it, to the design of decision pipelines, the governance of automated systems, and the professional skills that remain irreducibly human when machines handle the analysis.Inside this book: Why AI is a decision technology, not just a productivity tool - and what that distinction means for competitive advantageThe anatomy of a decision: structured, semi-structured, and unstructured problems, and where AI helps and where it doesn'tHow to use AI as a genuine reasoning partner - frameworks for Socratic dialogue, pre-mortem analysis, and structured co-thinkingThe four-layer decision stack, from data ingestion to action, with documented failure modes at each layerPython in practice: pandas, NumPy, scikit-learn, and matplotlib applied to real decision workflows, with full code examplesA systematic algorithm comparison exercise - Naive Bayes, Random Forest, SVM, and Gradient Boosting - applied to a customer churn datasetDecision-making across seven domains: business strategy, healthcare, education, finance, public policy, engineering, and lawHow to build AI-augmented workflows that actually reach decision-makers - from automated pipelines to decision engines with feedback loopsThe ethics, accountability, and governance requirements that separate AI deployment that works from AI deployment that harmsThe future professional: the analyst-builder-strategist hybrid who will define the next decade of knowledge workWho this book is forThis book was written for the professional who is serious about AI but not necessarily technical - the manager who needs to govern AI systems they did not build, the analyst who wants to build decision models without a computer science degree, the researcher who wants to use AI as a genuine methodological partner rather than a shortcut. No prior programming experience is required. A willingness to think precisely is. A practical guide for AI-augmented decision-making. Covers decision science, Python modelling, and more with practice data. No coding experience required This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781764811033

Bibliographic Details

• Title: AI Textbook for Managers (Paperback)
• Publisher: Springer Reach
• Publication Date: 2026
• Language: English
• ISBN 10: 1764811038
• ISBN 13: 9781764811033
• Binding: Paperback
• Condition: new

About this title

Synopsis

Every organisation now has access to powerful AI tools. Very few know how to use them to make better decisions.

This book is about the gap between having AI and using it well. It is written for analysts, managers, clinicians, lawyers, engineers, policy professionals, and researchers - anyone whose work involves consequential decisions and who wants to make those decisions more rigorously, more consistently, and more accountably in an age of machine intelligence.

Drawing on decision science, cognitive psychology, applied statistics, and ten chapters of worked Python examples, Dr. Ilker Cingillioglu builds a complete framework for AI-augmented decision-making - from the anatomy of a decision and the cognitive biases that corrupt it, to the design of decision pipelines, the governance of automated systems, and the professional skills that remain irreducibly human when machines handle the analysis.

Inside this book:

• Why AI is a decision technology, not just a productivity tool - and what that distinction means for competitive advantage
• The anatomy of a decision: structured, semi-structured, and unstructured problems, and where AI helps and where it doesn't
• How to use AI as a genuine reasoning partner - frameworks for Socratic dialogue, pre-mortem analysis, and structured co-thinking
• The four-layer decision stack, from data ingestion to action, with documented failure modes at each layer
• Python in practice: pandas, NumPy, scikit-learn, and matplotlib applied to real decision workflows, with full code examples
• A systematic algorithm comparison exercise - Naive Bayes, Random Forest, SVM, and Gradient Boosting - applied to a customer churn dataset
• Decision-making across seven domains: business strategy, healthcare, education, finance, public policy, engineering, and law
• How to build AI-augmented workflows that actually reach decision-makers - from automated pipelines to decision engines with feedback loops
• The ethics, accountability, and governance requirements that separate AI deployment that works from AI deployment that harms
• The future professional: the analyst-builder-strategist hybrid who will define the next decade of knowledge work

Who this book is for

This book was written for the professional who is serious about AI but not necessarily technical - the manager who needs to govern AI systems they did not build, the analyst who wants to build decision models without a computer science degree, the researcher who wants to use AI as a genuine methodological partner rather than a shortcut. No prior programming experience is required. A willingness to think precisely is.