Most organizations treat AI investment returns as a tooling problem. In practice, returns follow decision architecture quality. The AI ROI Strategy Stack explains how specification, authority boundaries, execution integration, monitoring ownership, and learning loops convert automation into stable economic results instead of scaling hidden risk.