1st Edition

Why Data Science Projects Fail The Harsh Realities of Implementing AI and Analytics, without the Hype

By Douglas Gray, Evan Shellshear Copyright 2025
    224 Pages 13 B/W Illustrations
    by Chapman & Hall

    214 Pages 13 B/W Illustrations
    by Chapman & Hall

    The field of artificial intelligence, data science and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven.


    This book aims to fix this by countering the AI hype with a dose of realism. Written by two experts in the field, the authors firmly believe in the power of mathematics, computing, and analytics, but if false expectations are set and practitioners and leaders don’t fully understand everything that really goes into data science projects, then a stunning 80% (or more) of analytics projects will continue to fail, costing enterprises and society hundreds of billions of dollars, and leading to non-experts abandoning one of the most important data-driven decision-making capabilities altogether.


    For the first time, business leaders, practitioners, students and interested laypeople will learn what really makes a data science project successful. By illustrating with many personal stories, the authors reveal the harsh realities of implementing AI and analytics.




    The Sepsis Scourge

    An Epic Challenge

    A Focus on Failures: The Purpose Behind Our Literary Venture

    The Epic Battle

    Beyond the Clickbait: When Headlines Just Scratch the Surface

    Data-driven Projects are Complex

    Begin Your Journey to Outsmart Failure

    Critical Thinking: How Not to Fail

    Introduction Bibliography


    The AI Hype

    Mapping the Terrain: Prior Insights

    What Happened to Best Practices?

    What Counts as an ADSAI Failure?

    Our Thesis

    Facing Challenges

    Critical Thinking: How Not to Fail

    Chapter 1 Bibliography


    RetailCo’s Strategic Nightmare

    The Difficult and Critical Role of Strategy7Failing to Build Organizational Need

    Not Understanding the Real Business Problem

    The Problem with Selecting Good Business Problems

    Mike’s Story: AI in the Outback

    Putting the Cart (Technology) Before the Horse (Business)

    The Solution: Put Economics Back in the Driver’s Seat

    Resolving Mike’s AI Investment Challenge

    Solving a Problem That is Not a Business Priority

    WayBlazer: Companies Will Not Always Pay for the Fancier Mousetrap

    Challenges in Aligning Vision, Strategy, and Measuring Success

    Lack of Leadership Buy-in

    Critical Thinking: How Not to Fail

    Chapter 2 Bibliography


    Data Quality and Reliability Issues

    Let the Data Hunt Begin

    (Un)reasonable Expectations

    Houston, We Have a Communication Problem

    Presenting the Message

    Breaking Down Silos

    Starting Small and Simple

    Project Management for ADSAI

    Asking the Right Questions

    Critical Thinking: How Not to Fail

    Chapter 3 Bibliography


    Lacking the Right Resources

    The New Digital Divide

    Analytics (or AI) Translators

    Where Do You Find Analytics Translators?

    Strengthening ADSAI Curricula

    Analytically-driven Leadership

    Change Management

    Justification for Change

    Critical Thinking: How Not to Fail

    Chapter 4 Bibliography


    Model Mishaps

    Misapplying the (Right or Wrong) Model

    Keep it Simple: Overemphasizing the Model, Technique, or Technology

    From Sandbox Model to Production System

    Tools Make Mistakes

    The Final Hurdle: Proper Data and Tool Infrastructure

    Critical Thinking: How Not to Fail

    Chapter 5 Bibliography


    (More) Real-life Failures

    Outside Influences


    Small Stumbles, Solid Outcome

    The Journey to Perfection

    Critical Thinking: How Not to Fail

    Chapter 6 Bibliography


    Continuing the Success






    Final Words

    Critical Thinking: How Not to Fail

    Conclusion Bibliography


    Dr Evan Shellshear is an expert in artificial intelligence with a Ph.D. in Game Theory from the Nobel Prize winning University of Bielefeld in Germany. He has almost two decades of international experience in the development and design of AI tools for a variety of industries having worked with the world's top companies on all aspects of advanced analytical solutions from optimisation to machine learning in applications from HR to oil and gas, and robotics to supply chain. He is also the author of the Amazon best seller, Innovation Tools.


    Douglas A. Gray is a practitioner, leader, and educator with over 30 years of experience leading award-winning teams at industry luminaries in Analytics, including INFORMS Prize-winning American Airlines and Walmart. His teams have delivered advanced game-changing solutions in the airline operations, healthcare, and omnichannel retail supply chain domains which deliver hundreds of millions of dollars in business value and economic impact annually. He teaches Analytics and AI Strategy at Southern Methodist University (SMU) in the Executive MBA, Executive Education, and MS Data Science programs, and has published over a dozen articles on Analytics best practices and applications.