Primary Investigator:
Jamie Davis

Project Members
Daniel Lugo Jamie Davis

As AI systems transition from experimental prototypes to mission-critical tools, their dependence on dynamic data, evolving models, and rigorous governance raises pressing questions about whether existing acquisition pathways can keep pace. The U.S. Department of Defanse has modernized its processes through the Adaptive Acquisition Framework, with the Software Acquisition Pathway (SWP) serving as the primary mechanism for acquiring software-intensive capabilities. This paper evaluates whether the SWP, as currently structured, is sufficient to address the unique demands of the AI acquisition lifecycle. In this work, we perform a scenario-based evaluation that traces a notional AI-enabled program through key SWP planning activities to assess how policy translates into program artifacts and decisions. Our findings identify areas of alignment—notably the SWP’s support for iterative delivery—alongside underspecified areas central to AI-enabled systems, including data governance and intellectual property rights, test and evaluation of non-deterministic behavior, and sustainment under model drift. We conclude with recommendations to strengthen the Adaptive Acquisition Framework guidance to better support the responsible and effective acquisition of AI capabilities.