Technology Landscape · Federal AI Intelligence

Technology landscape

How reported AI classifications spread across agencies, missions, lifecycle stages and sourcing. Each record carries one reported classification, so categories do not overlap. A classification describes the technique, not a specific commercial model.

Lifecycle by technology

Share of each technology's records

DeployedPilotPre-deploymentRetiredNot reported

Classical / predictive ML

Classical / predictive ML
Deployed: 305 (29.4%)Classical / predictive ML
Pilot: 151 (14.5%)Classical / predictive ML
Pre-deployment: 581 (56.0%)Classical / predictive ML
Retired: 1 (0.1%)

1,038

Sourcing by technology

Where reported

Developed in-houseContract and in-housePurchased from vendorNot reported

Classical / predictive ML

Classical / predictive ML
Developed in-house: 269 (25.9%)Classical / predictive ML
Contract and in-house: 104 (10.0%)Classical / predictive ML
Purchased from vendor: 107 (10.3%)Classical / predictive ML
Not reported: 558 (53.8%)

1,038

Technology adoption by agency

Row share: each agency's records by classification · 16 largest portfolios in the filter

Agency by AI classification
	Classical / predictive ML	n
NASA	100%	282
DOI	100%	170
DOE	100%	93
HHS	100%	87
USDA	100%	77
DOJ	100%	76
VA	100%	76
DHS	100%	45
FRB	100%	16
TREAS	100%	16
FDIC	100%	15
SEC	100%	14
DOL	100%	14
EPA	100%	10
FHFA	100%	8
GSA	100%	6

Technology by topic area

Row share within each topic

Topic by AI classification
	Classical / predictive ML	n
Science	100%	531
Administrative Functions	100%	75
Law Enforcement	100%	68
Information Technology	100%	65
Other	100%	63
Health and Medical	100%	61
Energy and the Environment	100%	45
Service Delivery	100%	29
Procurement and Financial Management	100%	27
Cybersecurity	100%	24
Emergency Management	100%	16
Transportation	100%	15
Government Benefits Processing	100%	9
Human Resources	100%	8

High-impact designation by technology

High-impactPresumed high-impact, determined notNot high-impactNot reported

Classical / predictive ML

Classical / predictive ML
High-impact: 80 (7.7%)Classical / predictive ML
Presumed high-impact, determined not: 49 (4.7%)Classical / predictive ML
Not high-impact: 903 (87.0%)Classical / predictive ML
Not reported: 6 (0.6%)

1,038

Governance disclosure by technology

High-impact records: reported in place / scope

Technology	High-impact	Testing	Impact assess.	Indep. review	Monitoring	Training
Classical / predictive ML	80	0 / 80	0 / 80	0 / 80	0 / 80	0 / 80

Shows counts reported in place against all high-impact records of that technology. The remainder includes in-progress, not applicable and unanswered. Full governance view

Systems, vendors and technology are different things

153 of 1,038 records name a vendor (15%)

Microsoft1010 records Deloitte55 records Esri55 records Google55 records Aretec44 records Guidehouse44 records Leidos44 records Adobe33 records Amazon Web Services33 records Cisco33 records CrowdStrike33 records Splunk33 records LexisNexis22 records Relativity22 records SAIC22 records

Vendor: the supplier the agency names in a free-text field. Matched to a standard list of companies; one record may name several.

System name: the agency's name for the system (reported for 19% of records).

AI classification: the technique category the agency selected. A vendor name does not show which model or technique is used, and this platform does not infer it.