How U.S. Federal Artificial Intelligence (AI) policy is shaping agrifood systems: an integrative review

Abstract

Artificial intelligence (AI) is increasingly shaping how agrifood systems function in the United States, yet the role of federal policy in guiding its use, oversight, and broader consequences is still not well defined. This study explores how current U.S. federal AI policies support-or limit-the advancement of agrifood systems by synthesizing evidence from publicly available policy documents. Using a focused search strategy and qualitative content analysis, we reviewed nine federal policy documents released through September 2025 to identify key priorities and overlooked areas relevant to agriculture. Our analysis revealed six recurring themes: environment, precision agriculture, workforce development, governance, technological infrastructure, and partnership. The findings show that federal AI policy places considerable emphasis on building infrastructure, strengthening workforce capacity, and establishing governance frameworks. At the same time, less attention is given to environmental trade-offs, equitable access for small- and mid-scale producers, and the place specific conditions that shape agricultural practice. Notably, tensions emerge between policies that promote rapid expansion of AI infrastructure and those aimed at protecting environmental resources and strengthening climate resilience. Taken together, the results suggest that although agriculture is increasingly recognized within the national AI agenda, the lack of a coordinated, agriculture specific policy framework may lead to uneven adoption and unintended outcomes across the agrifood system. This study offers a synthesized policy foundation to support future research, inform decision making, and engage stakeholders in aligning AI innovation with more sustainable and equitable agrifood systems.