Case Study: Using AI to Design More Effective, Efficient, and Equity Focused Grant Application Review Processes

Grants fuel millions of organizations and advance social innovation across sectors. In the United States, foundations and corporations respectively awarded $103.5B and $36.5B to nonprofits in 2023 alone (Childress, 2024; Lilly Family School of Philanthropy, 2024). By extension, grant applications play a critical role in how grant recipients are identified and selected. While many funders are eager for more streamlined and equitable processes to review grant applications, the process remains primarily a manual and time consuming one (Hewlett Foundation, 2021). These factors leave some funders implementing grant application processes potentially at odds with their impact goals. Examples include restricting who can apply, including only application reviewers with specific credentials, or not screening for inconsistencies among reviewer assessments (Candid., n.d.). In this article, readers will learn how Missouri Foundation for Health and AI PRIORI® used artificial intelligence to inform a more effective, efficient, and equity focused application review process. Readers will gain insights into the experiments, AI tools used, and preparation and evaluation of data sets.