Is US Defense Acquisition Ready to Acquire AI-Enabled Capabilities? Assessing the DoD Software Acquisition Pathway Through a Scenario-Based Policy Analysis
For defense acquisition policymakers and program managers, the paper identifies a critical gap in current acquisition guidance for AI systems, though the findings are incremental and scenario-based.
The paper evaluates whether the U.S. Department of Defense's Software Acquisition Pathway (SWP) is sufficient for acquiring AI-enabled capabilities, finding that while the governance stack provides a viable foundation, AI-specific controls remain distributed across supplemental documents, causing an actionability problem. The authors recommend an AI-supporting sub-path and targeted artifact refinements.
As AI systems transition from experimental prototypes to mission-critical tools, their dependence on dynamic data, evolving models, and governance raises questions about whether existing acquisition pathways can keep pace. The U.S. Department of Defense has modernized its acquisition 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 SWP is sufficient to address the unique demands of AI acquisition. 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. We use Policy Scenario Analysis to examine whether the SWP-centered governance stack provides sufficient actionable support for AI acquisition. The governance stack provides a viable foundation for iterative delivery and AI testing. However, we identify a recurring actionability problem in the core guidance. AI-specific controls for data provenance, lifecycle management, and human oversight remain distributed across supplemental documents rather than embedded in the program-facing mechanisms through which SWP is executed. This disconnect leaves program offices reliant on inconsistent local interpretation. We conclude by recommending an AI-supporting sub-path and targeted artifact refinements to better bridge this policy-to-artifact gap.