How U.S. Department of Agriculture uses AI

USDA is piloting AI at several steps of its environmental review process for permits. At the evaluation stage the model predicts whether an application is a simple categorical exclusion (a project class that needs no detailed environmental assessment) or needs more human attention; AI is also used to process public comments. The aim is faster permit issuance with fewer staff hours before a project can break ground. It is developed in house. The use case was presumed high impact and then determined not to be, because the AI makes recommendations for authorized staff and no final decisions.

No outcome disclosed.

To plan for each fire season, USDA staff in Natural Resources and Environment categorized all of the previous year's incident related purchases by hand, which took a long time and could miss subtle purchasing patterns. An in house classical machine learning model, trained on 2023 item descriptions (such as protective work gloves) and validated on human labeled item categories from the same year, assigns purchases to common purchase categories identified by procurement staff in the pilot. The goal is to find purchasing patterns and cost savings opportunities, which the agency says could lead to quicker resource allocation to incident locations. The inventory lists it as a pilot since December 2024; no outcome figures are published.

No outcome disclosed.

The Forest Service, within USDA's Natural Resources and Environment mission area, runs a generative AI New Hire Experience capability in its Salesforce customer relationship manager. It gives users, new hires by its name, text based self help on human resources, business and finance processes and procedures. The inventory describes it as deployed with an operational date of January 2024, classifies it as not high impact and states that it uses no personal data; no outcome figures are published.

No outcome disclosed.

Since October 2024 USDA has used ProcureSight, a free AI search tool over public SAM.gov and USASpending data, to assist with market research and with responsibility determinations on prospective suppliers. The department expects higher procurement productivity from precise searches over public procurement data. No outcome figures are published.

No outcome disclosed.