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Intelligence collections and demand have grown over the past two decades, and intelligence analysts are often performing routine tasks, leaving them unable to conduct larger strategic analyses that are needed to address future threats as outlined by the 2018 National Defense Strategy. The authors provide an in-depth analysis of technologies that could help the Air Force Distributed Common Ground System (AF DCGS) become more effective, efficient, adept at using human capital, and agile. A key point is that artificial intelligence (AI) and machine learning (ML) technologies alone do not solve these intelligence challenges; rather, if they are properly implemented and complemented by human analysts who have the right skills and training, the capabilities can allow the AF DCGS to evolve to better meet warfighter needs.

This is the second volume in a series about how AI/ML technology can help the AF DCGS meet the challenges of a demanding intelligence environment and the complexity of future threats envisioned by the 2018 National Defense Strategy. The authors provide more in-depth discussion of project methodology; a primer on AI/ML technology; case studies of analytic challenges in previous operations; best practices for successfully deploying new technologies; and other topics of interest to specialists, stakeholders, and experts.

Table of Contents

  • Chapter One


  • Chapter Two

    Overview of the AF DCGS Today

  • Chapter Three

    Improving Efficiency, Effectiveness, Human Capital, and Agility: Lessons from Historical Case Studies

  • Chapter Four

    Artificial Intelligence and Machine Learning: A Primer for AF DCGS Analysts

  • Chapter Five

    Improving GEOINT Analysis: Additional Detail

  • Chapter Six

    Rebalancing AF DCGS Competencies and Organization: Additional Detail

  • Chapter Seven

    Building the Right Skills: Additional Detail

  • Chapter Eight

    Fostering Innovation and Successful Implementation: Additional Detail

  • Appendix A

    Defining Technology Readiness Levels for Artificial Intelligence/Machine Learning

Research conducted by

The research described in this report was commissioned by U.S. Air Force/A2 and conducted by the Force Modernization and Employment Program within RAND Project AIR FORCE.

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