Accelerating Innovation, Performance, and Impact at USAID
Summary
The United States Agency for International Development (USAID) tackles some of the toughest challenges in some of the toughest places on earth, including fighting global pandemics, growing economic prosperity, strengthening democratic institutions, and providing humanitarian relief. USAID plays an important role in addressing global development problems that initially lack effective, scalable, and sustainable solutions. Yet USAID’s activities allow little room for the risk taking or iteration needed to drive significant improvements and encourage disruptive new ideas, with most programs implemented through detailed plans with rigid designs.
Imagine if the pace of progress for global development could match the breakneck pace of advances in the tech sector. The next administration should assess where current USAID interventions are inadequately meeting global need, applying best practices from innovation to improve programs accordingly. This will include shifting to outcomes-based performance metrics, dedicating budget for experimentation, establishing incentives that encourage risk-taking, linking payments with outcomes, and conducting ex-post evaluations of scale and sustainability.
AI’s high-stakes outputs directly shape American economic, industrial, and technological outcomes. They must not be accepted entirely on the basis of institutional faith rather than verifiable evidence.
The clearinghouse will also neutrally mediate the assignment of auditors to audits—strengthening independence, trust, and ethical governance across the AI ecosystem.
Soft law was never meant to be a permanent solution. Treating it as one, and letting the sandcastle stand in for the skyscraper indefinitely, is how we end up with a decade of voluntary commitments and no enforceable accountability to show for it.
Jobseekers, employees at smaller organizations, and local public workers need more than a weekend crash course in AI. They need practice choosing useful tasks, protecting data, testing outputs, and explaining where human judgment remains necessary.