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.

翻译：随着人工智能系统从实验原型向关键任务工具的转变，其对动态数据、演进模型及治理体系的依赖引发质疑：现有采办路径能否跟上这一发展步伐？美国国防部通过适应性采办框架对其采办流程进行了现代化升级，其中软件采办路径（SWP）作为获取软件密集型能力的主要机制。本文评估SWP是否能充分应对AI采办的独特需求。我们通过基于场景的评估方法，跟踪一个假设的AI赋能项目在SWP关键规划活动中的推进过程，以分析政策如何转化为项目工件与决策。采用政策场景分析法，考察以SWP为中心的治理体系是否为AI采办提供了充分的可操作性支持。治理体系为迭代交付与AI测试奠定了可行基础，但我们在核心指南中发现反复出现的可操作性问题。针对数据溯源、生命周期管理及人工监督的AI专项管控措施仍分散于补充性文件中，而非嵌入执行SWP的项目级机制中。这种脱节导致项目办公室依赖不一致的本地化解读。我们建议增设AI支持性子路径并优化目标工件，以弥合政策与工件之间的鸿沟。