Data-Enabled Approaches for Enhancing the Air Force Transformational Capability Pipeline

A key goal for the U.S. Air Force’s Transformational Capabilities Office (TCO) is fostering transformational capabilities across a variety of initiatives. To propose, develop, and select which concepts to advance into the transformational capability pipeline, the TCO must extract information from many data sources. Machine learning and natural language processing can be used to extract information from text sources; however, subject matter expertise must also be applied and leveraged effectively to provide creative insight and make the best use of extracted information. To understand how human-centered, data-enhanced (HCDE) decision processes can be used to determine which concepts to advance into the pipeline, the authors used a multimethod qualitative approach that included a review of the relevant literature on development planning and interviews with senior leaders, technical experts, and subject matter experts from the Air Force and the defense community. The synthesis of their analysis revealed opportunities for the TCO to use data science tools to extract information from vast databases of capability gaps, capability needs, and technology solutions and to use a more diverse set of future-focused decision methods-called foresight methods-to leverage human expertise and creativity. They developed and implemented the proof-of-concept Semantic Clustering Analysis and Thematic Exploration Tool to extract information from free-text descriptions of capability gaps and technologies and combined data extraction with foresight methods as part of an HCDE decision process. The authors demonstrate the data science tool and foresight methods in three case studies.