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Across industry sectors, both management and leaders see a yawning gap between the promised and delivered impact of data science projects and wonder why the discrepancy exists. It's simple, really. Companies rely on highly skilled and expensive data scientists to help them build predictive capabilities into their products and workflows, but they often think the data science team alone can lead the change.

This report examines issues from several conversations the authors held with data science teams across industries, as well as those issues they've witnessed in their own experience as builders and leaders. Among their findings, the authors agreed that to shorten the production process, lower overhead, and reduce risk, organizations need a comprehensive understanding of how to build AI in a repeatable fashion.

Technologists John J. Thomas, Paco Nathan, and William Roberts show data scientists how an organization and its technology work together to support their mission. Leaders of data science teams will examine how their organizations can transparently and seamlessly facilitate the delivery of data products. And business leaders will learn the value, both realized and potential, of introducing data science expertise in their organizations.

Table of Contents

  1. Foreword
  2. Preface
    1. Acknowledgments
  3. 1. Current View and Challenges of AI Adoption
    1. Challenges for Business Stakeholders
    2. Staffing
    3. Collaboration
    4. Executive Support
    5. Challenges for Technical Stakeholders
    6. People: Training and Mentoring
    7. Process: Unique Platforms and Processes
    8. Platform: Collaborative Data Infrastructure
    9. Other Challenges and Balancing Time-to-Market
    10. A Case Study in Navigating Challenges
    11. The Challenge of Trusted AI
    12. Bias and Fairness
    13. Drift
    14. Robustness
    15. Explainability
    16. Regulations and Roles
  4. 2. Personas and Effective Communication Among Them
    1. Leadership
    2. Data Analyst
    3. Data Scientist
    4. Data Engineer
    5. Data Steward
    6. Product Manager
    7. Risk Manager
    8. Software Engineers
    9. Toward Design Thinking
  5. 3. Design Thinking
    1. Reflect
    2. Explore
    3. Model
    4. Simplification as an Ethos
  6. 4. Stages of the AI Life Cycle
    1. Scope
    2. Understand
    3. Build
    4. Deploy
    5. Manage and Trust
  7. 5. AI Center of Excellence
    1. The AI Factory Concept
    2. People
    3. Process
    4. Platform
    5. Toward an AI Center of Excellence
  8. 6. Case Studies in Operationalizing AI
    1. Red Bull: Sandcastle—Log Cabin—Castle
    2. A Nonbinary Approach
    3. Transition in Practice
    4. Capital One: Model as a Service for Real-Time Decisioning
    5. Wunderman Thompson: AI Factory Brings Solutions to Clients at Scale
    6. Challenge and Opportunity
    7. Journey to AI at Scale
    8. COVID-19 RRR
    9. Expanding to Other Operating Companies
  9. 7. Conclusion
    1. Summary of Key Points
    2. Looking Forward
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