Federal AI Intelligence
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Snapshot · OMB 2025 inventory · processed Oct 11, 2026
Federal AI landscape

Federal AI at a glance

26 agencies · 214 reported use cases · OMB 2025 Inventory. Portfolio size reflects reporting, not performance.

Executive briefing ↗Explore 214 use cases →
Agency
Stage
High-impact
Topic
AI type
Sourcing
Vendor
Function1
Start year

Only records with a parseable start date match a year filter.

Function: Chatbot or virtual assistanttraining: blank
214 Reported AI use cases26 agencies62 Deployed35 Pilots5 Designated high-impact
Decorative federal AI network visualization; not a representation of agency deployment locations or relationships.
Scale & composition

Agencies behind federal AI

Select an agency to explore
Ring shows composition · node area shows use cases
HHS52TREAS18DOE17VA16DOJ15DHS14
In development/acquisition94Pilot35Deployed62Retired16Not reported7Largest portfolio: 52 · 26 agencies

Conceptual agency landscape · positions do not represent deployment locations. Proximity does not imply a relationship.

AI technology mix

Reported classification · click to cross-filter

64% generative or agentic. Each record carries one classification; not reported is shown hatched.

Mission areas

Topic area as reported

Lifecycle composition

214 records
Explore lifecycle stages and impact
Within each stageHigh-impact · Gen./agentic
Pre-deployment2%70%
Pilot3%80%
Deployed2%71%
Retired6%0%

Impact and technology shares use each stage's records as the denominator.

From development and acquisition to retirement. Select a stage to explore its reported use cases.

Reading the inventory

Evidence, with its limits visible

HHS, TREAS, DOE report 87 of 214 use cases (41%).

Understand the methodology →

Evolution of reported AI use cases

By agency-reported start year · click a year to filter
02040<15'15'16'17'18'19'20'21'22'23'2448'25'26
Generative or agenticOther or unclassified94 of 214 records report a parseable start date

Start dates are agency-reported operational or pilot dates, not inventory submission dates. 2026 values are planned or early-year. Undated records are excluded from this chart only.

Sourcing

Reported for 46% of records

Each square is 1% of filtered records. Where reported, 77% involve in-house development.

High-impact AI

Agency self-designation under M-25-21Governance view
214records
Minimum practices · 5 high-impactin place
Pre-deployment testing0%
AI impact assessment0%
Independent review0%
Ongoing monitoring0%
Operator training0%
Fail-safe0%
Appeal process0%
User and public consultation0%
No answer reported

High-impact is the agency's own classification, not an independent risk rating. No answer reported is missing evidence, not evidence that a practice is absent. 0% of high-impact records answer any practice question.

Analytical observations

Computed from the current filter; reproducible
  • FindingHHS, TREAS, DOE report 87 of 214 use cases (41%).
  • Finding71% of reported deployed use cases (44 of 62) are classified as generative or agentic AI. View records
  • FindingOf 1 deployed high-impact use cases, 0 report a completed AI impact assessment and 1 leave every minimum-practice field blank. View records
  • InterpretationBlank practice fields are missing evidence, not evidence that a safeguard is absent. They mark where follow-up questions to the agency are most useful.
  • FindingSourcing is reported for 46% of use cases. Where reported, 77% involve in-house development.
  • Finding7 use cases (3%) have no reported development stage. View records
AI classification reported89%
Stage not reported7
Sourcing reported46%