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

Generative AI

Generative AI
Deployed: 10 (100.0%)

Classical / predictive ML

Classical / predictive ML
Deployed: 33 (100.0%)

Natural language processing

Natural language processing
Deployed: 15 (100.0%)

Computer vision

Computer vision
Deployed: 20 (100.0%)

Agentic AI

Agentic AI
Deployed: 1 (100.0%)

Sourcing by technology

Where reported

Developed in-houseContract and in-housePurchased from vendorNot reported

Generative AI

Generative AI
Contract and in-house: 2 (20.0%)Generative AI
Purchased from vendor: 8 (80.0%)

Classical / predictive ML

Classical / predictive ML
Developed in-house: 1 (3.0%)Classical / predictive ML
Contract and in-house: 1 (3.0%)Classical / predictive ML
Purchased from vendor: 31 (93.9%)

Natural language processing

Natural language processing
Developed in-house: 1 (6.7%)Natural language processing
Contract and in-house: 2 (13.3%)Natural language processing
Purchased from vendor: 12 (80.0%)

Computer vision

Computer vision
Developed in-house: 1 (5.0%)Computer vision
Contract and in-house: 1 (5.0%)Computer vision
Purchased from vendor: 18 (90.0%)

Agentic AI

Agentic AI
Developed in-house: 1 (100.0%)

Technology adoption by agency

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

Agency by AI classification
	Generative AI	Classical / predictive ML	Natural language processing	Computer vision	Agentic AI	n
DOJ	12%	44%	19%	23%	1%	73
DHS	17%	17%	17%	50%		6

Technology by topic area

Row share within each topic

Topic by AI classification
	Generative AI	Classical / predictive ML	Natural language processing	Computer vision	Agentic AI	n
Law Enforcement	13%	36%	21%	28%	1%	67
Health and Medical		86%	14%			7
Administrative Functions		67%		33%		3
Human Resources	100%					1
Information Technology		100%				1

High-impact designation by technology

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

Generative AI

Generative AI
High-impact: 10 (100.0%)

Classical / predictive ML

Classical / predictive ML
High-impact: 33 (100.0%)

Natural language processing

Natural language processing
High-impact: 15 (100.0%)

Computer vision

Computer vision
High-impact: 20 (100.0%)

Agentic AI

Agentic AI
High-impact: 1 (100.0%)

Governance disclosure by technology

High-impact records: reported in place / scope

Technology	High-impact	Testing	Impact assess.	Indep. review	Monitoring	Training
Generative AI	10	0 / 10	0 / 10	0 / 10	0 / 10	0 / 10
Classical / predictive ML	33	0 / 33	0 / 33	0 / 33	0 / 33	0 / 33
Natural language processing	15	0 / 15	0 / 15	0 / 15	0 / 15	0 / 15
Computer vision	20	0 / 20	0 / 20	0 / 20	0 / 20	0 / 20
Agentic AI	1	0 / 1	0 / 1	0 / 1	0 / 1	0 / 1

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

57 of 79 records name a vendor (72%)

Axon55 records SAS44 records Thomson Reuters33 records Veritone33 records LexisNexis22 records Motorola Solutions22 records Palantir22 records Cellebrite11 records Clearview AI11 records NEC11 records TRM Labs11 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 94% 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.