USDA Fiscal Year 2025–2026 AI Strategy — first comprehensive USDA AI strategy, Vilsack-era, Alvares CDO/CAIO

NA-US (national federal strategy)

Content

The USDA Fiscal Year 2025–2026 AI Strategy is the first comprehensive USDA AI strategy. Released under the Biden administration in early 2024, signed by Secretary of Agriculture Tom Vilsack and Chief Data & AI Officer Christopher Alvares. The Strategy is publicly available on USDA.gov and remains operationally in effect under the Trump administration as of July 2026, with Alvares continuing to serve as both Chief Data Officer (CDO) and Chief AI Officer (CAIO) in a combined role.

Five goals

1. AI Governance and Leadership — Mature USDA’s AI governance and leadership structures; determine appropriate oversight level for AI use cases; collaborate with governmental, academic, and private sector partners.
2. Workforce Readiness for AI — Foster a culture of innovation; recruit and retain AI-skilled staff; develop training curricula.
3. AI Infrastructure and Toolset — Build technological infrastructure for AI deployment across USDA mission areas.
4. Data Readiness and Access — Improve data quality, accessibility, and integration across USDA’s vast datasets.
5. Ethical, Equitable, and Responsible Use of AI — Design and deploy AI in ways that respect privacy, safeguard data, and minimise biases.

Three operational emphases (per Introduction)

• Generative AI for operational efficiency — drafting summaries, reports, communication materials; AI will not replace USDA workforce but enhance and augment it
• Geospatial data and computer vision — automate analysis of satellite, drone, and ground-level imagery for crop health, forest health, wildfire spread, natural disaster damage, biosecurity threats
• Predictive analytics and ML — agricultural production, food safety, sustainability, crop yield prediction, animal disease outbreak prediction, drought/flood/pest response

Strategic positioning

The Strategy explicitly engages with:

• Executive Order 14110 (Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence) — Biden-era executive order
• OMB M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence)
• OMB M-23-22 (Delivering a Digital-First Public Experience)
• Foundations for Evidence-Based Policymaking Act of 2018 (the “Evidence Act”)
• NIST AI Risk Management Framework
• MITRE AI Maturity Model
• The Strategy builds on the FY24-26 USDA Data Strategy, FY22-26 USDA Strategic Plan, FY23-26 USDA Science and Research Strategy, and FY22-26 USDA IT Strategic Plan.

Process (per Acknowledgments)

• Benchmarking assessment against peer agencies’ AI strategies
• Current state maturity assessment — 110 responses via MITRE AI Maturity Model survey
• Current state assessment and future state visioning workshops — 13 Departmental Administration and Staff Offices, every USDA Mission Area, AI Center of Excellence, Cloud Working Group — over 210 USDA stakeholders
• Taskforce of 57 key AI stakeholders representing USDA Mission Areas

Three substantive dimensions worth knowing

1. Workforce development as substantive priority. Goal 2 (Workforce Readiness) is not boilerplate. The Strategy explicitly identifies recruitment and retention of AI-skilled staff as a major challenge, and the recruitment pipeline includes partnerships with agricultural schools and universities across the country. This is a real institutional commitment with budget implications.

2. Geospatial and remote-sensing infrastructure. USDA has substantial satellite and remote-sensing data (Landsat, USDA NASS Cropland Data Layer, USDA NASS imagery programs). The Strategy’s framing of computer-vision-driven crop monitoring and biosecurity threat detection is substantive — it engages with USDA’s actual data assets, not abstract AI deployment.

3. Responsible-AI framing. Goal 5 (Ethical, Equitable, and Responsible Use of AI) is not boilerplate either. The Strategy commits to “designing and deploying AI strategically and in ways that respect privacy, safeguard data, and minimize biases so that all stakeholders can benefit from the opportunities that AI can provide.” The framing is sincere; the implementation question is separate.

What this unit is doing in the taxonomy

Anchors the US federal AI strategy × USDA × governance × workforce / infrastructure / ethics cell. Distinct from:

• FCC (Canada) (units/fcc-ecosystem-not-technology.md) — Canadian federal Crown corporation perspective on agricultural AI
• USDA FY2024 AI Compliance Plan — distinct but related document
• AI-CLIMATE (units/ai-climate-minnesota-institute.md) — climate-smart ag/forestry AI research; funded by USDA-NIFA per AI-CLIMATE project structure
• Cooperative Extension AI Report (units/extension-foundation-2026-national-ai-report.md) — the practitioner-deployment counterpart; partially funded by USDA-NIFA
• FCC Root AI (units/root-ai.md) — Canadian federal Crown corporation generative AI for extension; structurally distinct from USDA AI Strategy

Why it matters for talks

• The USDA AI Strategy is the canonical US federal institutional response to agrifood AI. It establishes the federal policy framework within which all other US agrifood AI activity operates.
• The Strategy’s three operational emphases (generative AI, geospatial/cv, predictive analytics) are substantive — they reflect USDA’s actual data and operational assets.
• The Strategy is signed by Christopher Alvares (CDO/CAIO) and remains operationally in effect under the Trump administration as of July 2026.

Critical context

• The Strategy is Biden-era. Released under Secretary Vilsack (Biden), not the current Trump administration. It has not been formally rescinded as of July 2026, but the policy environment is uncertain.
• The responsible-AI framing is sincere; implementation is a separate empirical question. Goal 5’s commitments to ethics and bias mitigation are stated; whether they are achieved in operational deployment is a separate matter worth observing.
• The Strategy does not substantively engage Indigenous data sovereignty or smallholder / farmworker concerns. It is centered on the US commodity / large-farm frame. This is a substantive limitation, not an oversight — the US institutional frame prioritises commodity-scale agriculture.
• Trump-administration context (July 2026): Brookings documents that on March 19, 2026, USDA finalised a rule shifting loan approval from USDA to qualified delegated lenders; the broader administration posture is regulatory restructuring, not AI strategy rescission. Worth knowing because the institutional environment is in flux.
• The Strategy does not substantively engage the data-rights question at the level of CARE Principles or IEEE 2890-2025. US federal agrifood AI policy is centered on the data-ethics and bias-mitigation frame, not the data-sovereignty frame. This is a structural difference from Canadian, EU, and IDSov-aligned framings.