1st Edition

Decision Intelligence Human–Machine Integration for Decision-Making

By Miriam O'Callaghan Copyright 2023
    280 Pages 36 B/W Illustrations
    by Chapman & Hall

    280 Pages 36 B/W Illustrations
    by Chapman & Hall

    280 Pages 36 B/W Illustrations
    by Chapman & Hall

    Revealing the limitations of human decision-making, this book explores how Artificial Intelligence (AI) can be used to optimize decisions for improved business outcomes and efficiency, as well as looking ahead to the significant contributions Decision Intelligence (DI) can make to society and the ethical challenges it may raise.

    From the theories and concepts used to design autonomous intelligent agents to the technologies that power DI systems and the ways in which companies use decision-making building blocks to build DI solutions that enable businesses to democratize AI, this book presents an impressive framework to integrate artificial and human intelligence for the success of different types of business decisions.

    Replete with case studies on DI applications, as well as wider discussions on the social implications of the technology, Decision Intelligence: Human–Machine Integration for Decision Making appeals to both students of AI and data sciences and businesses considering DI adoption.

    List of Acronyms



    Chapter 1 Decision Intelligence – Introduction and Overview

    Introduction to DI

    Defining Decision Intelligence

    DI Evolution and Landscape

    Why We Need DI

    DI to Optimize Decisions

    DI for Improved Business Outcomes and Efficiency

    How DI Works and How It Looks

    Types of Business Decisions

    Decision Making Process

    DI Forms

    Decision Assistance

    Decision Support

    Decision Augmentation

    Decision Automation

    Infrastructure Design – Data Architecture for DI

    State of DI Adoption

    Factors Affecting DI Adoption Decisions


    Case Study: AI-Powered Recommendation System Delivering Consistent Energy Saving at Google Data Centers

    Questions for Discussion


    Chapter 2 Humans Vs. Machines in Decision-Making

    Humans in Decision-Making

    Behavioral Economics of Decision-Making

    Neuroscience and Neuroeconomics Perspectives

    Computers in Decision-Making

    Basic Programming Methods

    The Evolution of AI-Powered Decision-Making

    Machine Learning

    Supervised Machine Learning

    Unsupervised Machine Learning

    Reinforcement Learning

    Classical Machine Learning

    Neural Networks and Deep Learning

    Human Vs. Computer – Who is Better at Decision-Making?


    Case Study: John Hopkins Manages Patient Flow During Covid-19 With AI Powered Capacity Command Center

    Questions for Discussion


    Chapter 3 Systems and Technologies for Decision-Making

    Organization as a System

    Decision Making System in the Organization

    Decision Making Environments

    Human Agents

    Supporting Technologies for Modern DI Systems


    Computer Vision

    Audio Processing

    NLP (Natural Language Processing)

    Technological Systems for Decision-Making

    Decision Support Systems

    Intelligent Agents

    Recommender Systems


    Case Study: Recommender System for Covid-19 Research – Innovative Deep Neural Network Models

    Questions for Discussion


    Chapter 4 Intelligent Agents – Theoretical Foundations

    Multidisciplinarity of Intelligent Agents

    Agents for Simple Decisions

    Decision Networks

    Agents for Complex Decisions

    Dynamic Decision Networks

    Solving MDPs With Value Iteration and Policy Iteration

    Value Iteration

    Policy Iteration

    Monte Carlo Methods

    Multiagent Decision-Making

    Pure Strategy and Saddle Point Equilibrium

    Mixed Strategy and Nash Equilibrium

    Dominant Strategy Equilibrium

    Pareto Optimal Outcome


    Case Study: Designing Agent for Complex Environment – Multiagent Path Planning With Nonlinear Model Predictive Control

    Questions for Discussion


    Chapter 5 Decision-Making Building Blocks, Tools and Techniques

    Data for Decision-Making

    Decision Analysis

    Decision Tables

    Decision Trees

    Decision Modeling

    Predictive Modeling

    Regression Models

    Classification Models

    Time Series Models

    Outliers Models

    Clustering Models

    Prescriptive Modeling

    Heuristic Models

    Optimization Models

    Simulation Models

    Text Analytics Techniques for Decision Making


    Case Study: Detecting Anomalies and Preventing Equipment Failures in Steel With Noodle.ai Asset Flow

    Questions for Discussion


    Chapter 6 Decision Intelligence Market – Vendors and Solutions

    DI Solutions

    DI Vendors





    Aera Technology




    Case Study: Sisu Helps Samsung Jumpstart a $1 Billion Product Launch

    Questions for Discussion


    Chapter 7 Decision Intelligence Framework for Organizational Decision-Making

    Why We Need a Framework for Decision-Making

    Deciding How to Decide

    DI Framework

    Preparation and Planning

    The 7-Step Process

    Step 1: Setting key goals

    Step 2: Defining the decision

    Step 3: Rating the decision on importance and complexity levels

    Step 4: Prioritizing and classifying decisions to determine the PI-AI mix

    Step 5: Formulating decision implementation strategy

    Step 6: Implementing the strategy

    Step 7: Evaluating the strategy


    Case Study: Dräger Improves Customer Service With Starmind - Less Time Searching, More Time for Customers

    Questions for Discussion


    Chapter 8 Recommendations for DI Implementation and Ethics

    Recommendations for DI Implementation

    DI Readiness Assessment

    Strategic and Leadership Readiness

    Infrastructural and Operational Readiness

    Talent and Cultural Readiness

    DI Readiness Audit

    Ethics for DI

    Biased Algorithms

    Data Privacy and Protection

    Accuracy of Data and Information

    Job Loss

    Initiatives of Large Corporations to Promote AI Ethics

    Conclusion and the Future of DI

    Case Study: AI for Greater Good – Stanford Medicine Uses Google Glass to Help Kids With Autism Socialize

    Questions for Discussion



    Dr. Miriam O'Callaghan, Associate Professor of Management, William Woods University

    "Organizational decision making has been a very challenging area in the last decade. The availability of new technologies, such as AI, has contributed to this. Organizations are looking for guidance on understanding these new technologies and advice on how to incorporate them into their system. The content seems just right for a course covering a non-technical subject area as a textbook. The students, especially those with a business background, will benefit from this textbook greatly. Dr. O’Callaghan’s book covers just the right areas to guide the students seeking to understand and implement these decision-making processes in organizations…This book will serve as an excellent resource for big companies and startups where wise business decisions are needed the most. If new businesses are guided through good decision-making, they will have a successful future. Dr. O’Callaghan’s book does just that and where it is needed most."

    -- Dr. Gulsebnem Bishop, Campbellsville University, Kentucky, UK

    "Decision Intelligence is vital for any enterprise looking to reach new heights of growth because we can no longer deal with the number and speed of daily decisions we must make. Decision Intelligence will be a significant driver for operationalizing AI at scale within the enterprise. Today, AI is capable of understanding the business, recommending a decision, acting to execute it, and learning to improve the quality of future decisions. We will look back at this era as the inflection point in how companies make decisions and how teams strategize and collaborate. Miriam O’Callaghan’s book is an excellent summary and guide to understanding the emerging category of Decision Intelligence. It’s an instrumental book for a wide variety of audiences who wants to grasp the science behind this new technology and harness its potential."

    Shariq Mansoor, Chief Technology Officer and Founder of Aera Technology