The executive briefing
From inventory to insight
A concise reading of federal AI portfolios, deployment and the evidence agencies report.
Read the briefingFederal AI landscape
Federal AI at a glance
10 agencies · 13 reported use cases · OMB 2025 Inventory. Portfolio size reflects reporting, not performance.
Scale & composition
Select an agency to exploreAgencies behind federal AI
Deployed13Largest portfolio: 4 · 10 agencies
Conceptual agency landscape · positions do not represent deployment locations. Proximity does not imply a relationship.
AI technology mix
Reported classification · click to cross-filter46% generative or agentic. Each record carries one classification; not reported is shown hatched.
Mission areas
Topic area as reportedLifecycle composition
13 recordsExplore lifecycle stages and impact
Within each stageHigh-impact · Gen./agentic
Deployed15%46%
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
VA, NSF, HUD report 6 of 13 use cases (46%).
Understand the methodology →Evolution of reported AI use cases
By agency-reported start year · click a year to filterGenerative or agenticOther or unclassified8 of 13 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 85% of recordsEach square is 1% of filtered records. Where reported, 45% involve in-house development.
Minimum practices · 2 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- FindingVA, NSF, HUD report 6 of 13 use cases (46%).
- Finding46% of reported deployed use cases (6 of 13) are classified as generative or agentic AI. View records
- FindingOf 2 deployed high-impact use cases, 0 report a completed AI impact assessment and 2 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 85% of use cases. Where reported, 45% involve in-house development.
AI classification reported92%
Stage not reported0
Sourcing reported85%