Introducing an AI-Assisted Cost–Benefit-ROI Model for US AI Use Cases.

This article examines 1,754 publicly disclosed US federal AI use cases from AI.gov (2024), analyzing their distribution, benefits, costs, and return on investment (ROI). Using advanced AI models from OpenAI and Anthropic, the authors developed the McIntyre and Liew Model (MLM) to quantify and rank AI initiatives across 37 federal agencies. The findings show AI applications are concentrated in healthcare, public safety, and infrastructure agencies (HHS, VA, DHS, DOI, USAID), with mission-enabling initiatives dominating. High-ROI applications frequently leverage commercially available tools, integrating big-tech innovations with public sector goals. Unlike private sector analyses, this article focuses on public interest-driven AI initiatives, transforming textual disclosures into actionable metrics. The scalable MLM framework enables cross-agency comparisons and supports innovative AI applications for unresolved public challenges. Establishing a benchmark for future AI evaluations, this research fosters collaboration and demonstrates AI's transformative potential in advancing mission-critical and public interest objectives. [ABSTRACT FROM AUTHOR]